Merge remote-tracking branch 'origin/web_socket'

.env.example

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+# Example environment variables for the Hyperliquid trading toolkit
+# Copy this file to .env and fill in real values. Do NOT commit your real .env file.
+
+# Main wallet (used only to authorize agents on-chain)
+# Example: MAIN_WALLET_PRIVATE_KEY=0x...
+MAIN_WALLET_PRIVATE_KEY=
+MAIN_WALLET_ADDRESS=
+
+# Agent keys (private keys authorized via create_agent.py)
+# Preferred patterns:
+# - AGENT_PRIVATE_KEY: default agent
+# - <NAME>_AGENT_PK or <NAME>_AGENT_PRIVATE_KEY: per-agent keys (e.g., SCALPER_AGENT_PK)
+# Example: AGENT_PRIVATE_KEY=0x...
+AGENT_PRIVATE_KEY=
+# Example per-agent key:
+# SCALPER_AGENT_PK=
+# SWING_AGENT_PK=
+
+# Optional: CoinGecko API key to reduce rate limits for market cap fetches
+COINGECKO_API_KEY=
+
+# Optional: Set a custom environment for development/testing
+# E.g., DEBUG=true
+DEBUG=

.gitignore

@@ -0,0 +1,41 @@
+# --- Secrets & Environment ---
+# Ignore local environment variables
+.env
+# Ignore virtual environment folders
+.venv/
+venv/
+
+# --- Python ---
+# Ignore cache files
+__pycache__/
+*.py[cod]
+
+# --- Data & Logs ---
+# Ignore all database files (db, write-ahead log, shared memory)
+_data/*.db
+_data/*.db-shm
+_data/*.db-wal
+
+# Ignore all JSON files in the data folder
+_data/*.json
+
+# Ignore all log files
+_logs/
+
+# --- SDK ---
+# Ignore all contents of the sdk directory
+sdk/
+
+# --- Other ---
+# Ignore custom agents directory
+agents/
+
+# Ignore Jekyll files
+.nojekyll
+
+# --- Editor & OS Files ---
+# Ignore VSCode, JetBrains, and macOS/Windows system files
+.vscode/
+.idea/
+.DS_Store
+Thumbs.db

GEMINI.md

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+# Project Overview
+
+This project is a sophisticated, multi-process automated trading bot for the Hyperliquid decentralized exchange. It is written in Python and uses a modular architecture to separate concerns like data fetching, strategy execution, and trade management.
+
+The bot uses a high-performance data pipeline with SQLite for storing market data. Trading strategies are defined and configured in a JSON file, allowing for easy adjustments without code changes. The system supports multiple, independent trading agents for risk segregation and PNL tracking. A live terminal dashboard provides real-time monitoring of market data, strategy signals, and the status of all background processes.
+
+## Building and Running
+
+### 1. Setup
+
+1.  **Create and activate a virtual environment:**
+    ```bash
+    # For Windows
+    python -m venv .venv
+    .\.venv\Scripts\activate
+
+    # For macOS/Linux
+    python3 -m venv .venv
+    source .venv/bin/activate
+    ```
+
+2.  **Install dependencies:**
+    ```bash
+    pip install -r requirements.txt
+    ```
+
+3.  **Configure environment variables:**
+    Create a `.env` file in the root of the project (you can copy `.env.example`) and add your Hyperliquid wallet private key and any agent keys.
+
+4.  **Configure strategies:**
+    Edit `_data/strategies.json` to enable and configure your desired trading strategies.
+
+### 2. Running the Bot
+
+To run the main application, which includes the dashboard and all background processes, execute the following command:
+
+```bash
+python main_app.py
+```
+
+## Development Conventions
+
+*   **Modularity:** The project is divided into several scripts, each with a specific responsibility (e.g., `data_fetcher.py`, `trade_executor.py`).
+*   **Configuration-driven:** Strategies are defined in `_data/strategies.json`, not hardcoded. This allows for easy management of strategies.
+*   **Multi-processing:** The application uses the `multiprocessing` module to run different components in parallel for performance and stability.
+*   **Strategies:** Custom strategies should inherit from the `BaseStrategy` class (defined in `strategies/base_strategy.py`) and implement the `calculate_signals` method.
+*   **Documentation:** The `WIKI/` directory contains detailed documentation for the project. Start with `WIKI/SUMMARY.md`.
+
+# Project Review and Recommendations
+
+This review provides an analysis of the current state of the automated trading bot project, proposes specific code improvements, and identifies files that appear to be unused or are one-off utilities that could be reorganized.
+
+The project is a well-structured, multi-process Python application for crypto trading. It has a clear separation of concerns between data fetching, strategy execution, and trade management. The use of `multiprocessing` and a centralized `main_app.py` orchestrator is a solid architectural choice.
+
+The following sections detail recommendations for improving configuration management, code structure, and robustness, along with a list of files recommended for cleanup.
+
+---
+
+## Proposed Code Changes
+
+### 1. Centralize Configuration
+
+- **Issue:** Key configuration variables like `WATCHED_COINS` and `required_timeframes` are hardcoded in `main_app.py`. This makes them difficult to change without modifying the source code.
+- **Proposal:**
+    - Create a central configuration file, e.g., `_data/config.json`.
+    - Move `WATCHED_COINS` and `required_timeframes` into this new file.
+    - Load this configuration in `main_app.py` at startup.
+- **Benefit:** Decouples configuration from code, making the application more flexible and easier to manage.
+
+### 2. Refactor `main_app.py` for Clarity
+
+- **Issue:** `main_app.py` is long and handles multiple responsibilities: process orchestration, dashboard rendering, and data reading.
+- **Proposal:**
+    - **Abstract Process Management:** The functions for running subprocesses (e.g., `run_live_candle_fetcher`, `run_resampler_job`) contain repetitive logic for logging, shutdown handling, and process looping. This could be abstracted into a generic `ProcessRunner` class.
+    - **Create a Dashboard Class:** The complex dashboard rendering logic could be moved into a separate `Dashboard` class to improve separation of concerns and make the main application loop cleaner.
+- **Benefit:** Improves code readability, reduces duplication, and makes the application easier to maintain and extend.
+
+### 3. Improve Project Structure
+
+- **Issue:** The root directory is cluttered with numerous Python scripts, making it difficult to distinguish between core application files, utility scripts, and old/example files.
+- **Proposal:**
+    - Create a `scripts/` directory and move all one-off utility and maintenance scripts into it.
+    - Consider creating a `src/` or `app/` directory to house the core application source code (`main_app.py`, `trade_executor.py`, etc.), separating it clearly from configuration, data, and documentation.
+- **Benefit:** A cleaner, more organized project structure that is easier for new developers to understand.
+
+### 4. Enhance Robustness and Error Handling
+
+- **Issue:** The agent loading in `trade_executor.py` relies on discovering environment variables by a naming convention (`_AGENT_PK`). This is clever but can be brittle if environment variables are named incorrectly.
+- **Proposal:**
+    - Explicitly define the agent names and their corresponding environment variable keys in the proposed `_data/config.json` file. The `trade_executor` would then load only the agents specified in the configuration.
+- **Benefit:** Makes agent configuration more explicit and less prone to errors from stray environment variables.
+
+---
+
+## Identified Unused/Utility Files
+
+The following files were identified as likely being unused by the core application, being obsolete, or serving as one-off utilities. It is recommended to **move them to a `scripts/` directory** or **delete them** if they are obsolete.
+
+### Obsolete / Old Versions:
+- `data_fetcher_old.py`
+- `market_old.py`
+- `base_strategy.py` (The one in the root directory; the one in `strategies/` is used).
+
+### One-Off Utility Scripts (Recommend moving to `scripts/`):
+- `!migrate_to_sqlite.py`
+- `import_csv.py`
+- `del_market_cap_tables.py`
+- `fix_timestamps.py`
+- `list_coins.py`
+- `create_agent.py`
+
+### Examples / Unused Code:
+- `basic_ws.py` (Appears to be an example file).
+- `backtester.py`
+- `strategy_sma_cross.py` (A strategy file in the root, not in the `strategies` folder).
+- `strategy_template.py`
+
+### Standalone / Potentially Unused Core Files:
+The following files seem to have their logic already integrated into the main multi-process application. They might be remnants of a previous architecture and may not be needed as standalone scripts.
+- `address_monitor.py`
+- `position_monitor.py`
+- `trade_log.py`
+- `wallet_data.py`
+- `whale_tracker.py`
+
+### Data / Log Files (Recommend archiving or deleting):
+- `hyperliquid_wallet_data_*.json` (These appear to be backups or logs).

IMPROVEMENT_ROADMAP.md

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+# Improvement Roadmap - Hyperliquid Trading Bot
+
+## Overview
+This document outlines the detailed implementation plan for transforming the trading bot into a production-ready system.
+
+## Phase 1: Foundation (Weeks 1-2)
+
+### Week 1: Security & Stability
+
+#### Day 1-2: Critical Security Fixes
+- [ ] **Implement Encrypted Key Storage**
+  - Create `security/key_manager.py`
+  - Replace environment variable key access
+  - Add key rotation mechanism
+  - **Files**: `trade_executor.py`, `create_agent.py`
+
+- [ ] **Add Input Validation Framework**
+  - Create `validation/trading_validator.py`
+  - Validate all trading parameters
+  - Add sanitization for user inputs
+  - **Files**: `position_manager.py`, `trade_executor.py`
+
+#### Day 3-4: Risk Management
+- [ ] **Implement Circuit Breakers**
+  - Create `risk/circuit_breaker.py`
+  - Add trading halt conditions
+  - Implement automatic recovery
+  - **Files**: `trade_executor.py`, `position_manager.py`
+
+- [ ] **Fix Import Resolution Issues**
+  - Update relative imports
+  - Add `__init__.py` files where missing
+  - Test all module imports
+  - **Files**: `main_app.py`, all strategy files
+
+#### Day 5-7: Code Quality
+- [ ] **Refactor Dashboard Display**
+  - Extract `DashboardRenderer` class
+  - Split into market/strategy/position components
+  - Add configuration for display options
+  - **Files**: `main_app.py`
+
+### Week 2: Configuration & Error Handling
+
+#### Day 8-9: Configuration Management
+- [ ] **Create Centralized Configuration**
+  - Create `config/settings.py`
+  - Move all magic numbers to config
+  - Add environment-specific configs
+  - **Files**: All Python files
+
+- [ ] **Standardize Error Handling**
+  - Create `utils/error_handlers.py`
+  - Implement retry decorators
+  - Add structured exception classes
+  - **Files**: All core modules
+
+#### Day 10-12: Database Improvements
+- [ ] **Implement Connection Pool**
+  - Create `database/connection_pool.py`
+  - Replace direct SQLite connections
+  - Add connection health monitoring
+  - **Files**: `base_strategy.py`, all data access files
+
+- [ ] **Add Database Migrations**
+  - Create `database/migrations/`
+  - Version control schema changes
+  - Add rollback capabilities
+  - **Files**: Database schema files
+
+#### Day 13-14: Basic Testing
+- [ ] **Create Test Framework**
+  - Set up `tests/` directory structure
+  - Add pytest configuration
+  - Create test fixtures and mocks
+  - **Files**: New test files
+
+## Phase 2: Performance & Testing (Weeks 3-4)
+
+### Week 3: Performance Optimization
+
+#### Day 15-17: Caching Layer
+- [ ] **Implement Redis/Memory Cache**
+  - Create `cache/cache_manager.py`
+  - Cache frequently accessed data
+  - Add cache invalidation logic
+  - **Files**: `data_fetcher.py`, `base_strategy.py`
+
+#### Day 18-19: Async Operations
+- [ ] **Convert to Async/Await**
+  - Identify blocking operations
+  - Convert to async patterns
+  - Add async context managers
+  - **Files**: `live_market_utils.py`, API calls
+
+#### Day 20-21: Batch Processing
+- [ ] **Implement Batch Operations**
+  - Batch database writes
+  - Bulk API requests
+  - Optimize data processing
+  - **Files**: Data processing modules
+
+### Week 4: Testing Framework
+
+#### Day 22-24: Unit Tests
+- [ ] **Comprehensive Unit Test Suite**
+  - Test all core classes
+  - Mock external dependencies
+  - Achieve >80% coverage
+  - **Files**: `tests/unit/`
+
+#### Day 25-26: Integration Tests
+- [ ] **End-to-End Testing**
+  - Test complete workflows
+  - Mock Hyperliquid API
+  - Test process communication
+  - **Files**: `tests/integration/`
+
+#### Day 27-28: Paper Trading
+- [ ] **Paper Trading Mode**
+  - Create simulation environment
+  - Mock trade execution
+  - Add performance tracking
+  - **Files**: `trade_executor.py`, new simulation files
+
+## Phase 3: Monitoring & Observability (Weeks 5-6)
+
+### Week 5: Metrics & Monitoring
+
+#### Day 29-31: Metrics Collection
+- [ ] **Add Prometheus Metrics**
+  - Create `monitoring/metrics.py`
+  - Track key performance indicators
+  - Add custom business metrics
+  - **Files**: All core modules
+
+#### Day 32-33: Health Checks
+- [ ] **Health Check System**
+  - Create `monitoring/health_check.py`
+  - Monitor all system components
+  - Add dependency checks
+  - **Files**: `main_app.py`, all processes
+
+#### Day 34-35: Alerting
+- [ ] **Alerting System**
+  - Create `monitoring/alerts.py`
+  - Configure alert rules
+  - Add notification channels
+  - **Files**: New alerting files
+
+### Week 6: Documentation & Developer Experience
+
+#### Day 36-38: API Documentation
+- [ ] **Auto-Generated Docs**
+  - Set up Sphinx/ MkDocs
+  - Document all public APIs
+  - Add code examples
+  - **Files**: `docs/` directory
+
+#### Day 39-40: Setup Improvements
+- [ ] **Interactive Setup**
+  - Create setup wizard
+  - Validate configuration
+  - Add guided configuration
+  - **Files**: `setup.py`, new setup files
+
+#### Day 41-42: Examples & Guides
+- [ ] **Strategy Examples**
+  - Create example strategies
+  - Add development tutorials
+  - Document best practices
+  - **Files**: `examples/`, `WIKI/`
+
+## Phase 4: Advanced Features (Weeks 7-8)
+
+### Week 7: Advanced Risk Management
+
+#### Day 43-45: Position Sizing
+- [ ] **Dynamic Position Sizing**
+  - Volatility-based sizing
+  - Portfolio risk metrics
+  - Kelly criterion implementation
+  - **Files**: `position_manager.py`, new risk modules
+
+#### Day 46-47: Advanced Orders
+- [ ] **Advanced Order Types**
+  - Stop-loss orders
+  - Take-profit orders
+  - Conditional orders
+  - **Files**: `trade_executor.py`
+
+#### Day 48-49: Portfolio Management
+- [ ] **Portfolio Optimization**
+  - Correlation analysis
+  - Risk parity allocation
+  - Rebalancing logic
+  - **Files**: New portfolio modules
+
+### Week 8: Production Readiness
+
+#### Day 50-52: Deployment
+- [ ] **Production Deployment**
+  - Docker containerization
+  - Kubernetes manifests
+  - CI/CD pipeline
+  - **Files**: `docker/`, `.github/workflows/`
+
+#### Day 53-54: Performance Profiling
+- [ ] **Profiling Tools**
+  - Performance monitoring
+  - Memory usage tracking
+  - Bottleneck identification
+  - **Files**: New profiling modules
+
+#### Day 55-56: Final Polish
+- [ ] **Production Hardening**
+  - Security audit
+  - Load testing
+  - Documentation review
+  - **Files**: All files
+
+## Implementation Guidelines
+
+### Daily Workflow
+1. **Morning Standup**: Review progress, identify blockers
+2. **Development**: Focus on assigned tasks
+3. **Testing**: Write tests alongside code
+4. **Code Review**: Peer review all changes
+5. **Documentation**: Update docs with changes
+
+### Quality Gates
+- All code must pass linting and formatting
+- New features require unit tests
+- Integration tests for critical paths
+- Security review for sensitive changes
+
+### Risk Mitigation
+- Feature flags for new functionality
+- Gradual rollout with monitoring
+- Rollback procedures for each change
+- Regular backup and recovery testing
+
+## Success Criteria
+
+### Phase 1 Success
+- [ ] All security vulnerabilities fixed
+- [ ] Import resolution issues resolved
+- [ ] Basic test framework in place
+- [ ] Configuration management implemented
+
+### Phase 2 Success
+- [ ] Performance improvements measured
+- [ ] Test coverage >80%
+- [ ] Paper trading mode functional
+- [ ] Async operations implemented
+
+### Phase 3 Success
+- [ ] Monitoring dashboard operational
+- [ ] Alerting system functional
+- [ ] Documentation complete
+- [ ] Developer experience improved
+
+### Phase 4 Success
+- [ ] Production deployment ready
+- [ ] Advanced features working
+- [ ] Performance benchmarks met
+- [ ] Security audit passed
+
+## Resource Requirements
+
+### Development Team
+- **Senior Python Developer**: Lead architecture and security
+- **Backend Developer**: Performance and database optimization
+- **DevOps Engineer**: Deployment and monitoring
+- **QA Engineer**: Testing framework and automation
+
+### Tools & Services
+- **Development**: PyCharm/VSCode, Git, Docker
+- **Testing**: Pytest, Mock, Coverage tools
+- **Monitoring**: Prometheus, Grafana, AlertManager
+- **CI/CD**: GitHub Actions, Docker Hub
+- **Documentation**: Sphinx/MkDocs, ReadTheDocs
+
+### Infrastructure
+- **Development**: Local development environment
+- **Testing**: Staging environment with test data
+- **Production**: Cloud deployment with monitoring
+- **Backup**: Automated backup and recovery system
+
+## Timeline Summary
+
+| Phase | Duration | Key Deliverables |
+|-------|----------|------------------|
+| Phase 1 | 2 weeks | Security fixes, basic testing, configuration |
+| Phase 2 | 2 weeks | Performance optimization, comprehensive testing |
+| Phase 3 | 2 weeks | Monitoring, documentation, developer tools |
+| Phase 4 | 2 weeks | Advanced features, production deployment |
+| **Total** | **8 weeks** | **Production-ready trading system** |
+
+This roadmap provides a structured approach to transforming the trading bot into a robust, scalable, and maintainable system suitable for production use.

PROJECT_REVIEW_AND_PROPOSALS.md

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+"# Comprehensive Project Review and Improvement Proposals" 

README.md

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+# Automated Crypto Trading Bot
+
+This project is a sophisticated, multi-process automated trading bot designed to interact with the Hyperliquid decentralized exchange. It features a robust data pipeline, a flexible strategy engine, multi-agent trade execution, and a live terminal dashboard for real-time monitoring.
+
+<!-- It's a good idea to take a screenshot of your dashboard and upload it to a service like Imgur to include here -->
+
+## Features
+
+* **Multi-Process Architecture**: Core components (data fetching, trading, strategies) run in parallel processes for maximum performance and stability.
+* **Comprehensive Data Pipeline**:
+  * Live price feeds for all assets.
+  * Historical candle data collection for any coin and timeframe.
+  * Historical market cap data fetching from the CoinGecko API.
+* **High-Performance Database**: Uses SQLite with pandas for fast, indexed storage and retrieval of all market data.
+* **Configuration-Driven Strategies**: Trading strategies are defined and managed in a simple JSON file (`_data/strategies.json`), allowing for easy configuration without code changes.
+* **Multi-Agent Trading**: Supports multiple, independent trading agents for advanced risk segregation and PNL tracking.
+* **Live Terminal Dashboard**: A real-time, flicker-free dashboard to monitor live prices, market caps, strategy signals, and the status of all background processes.
+* **Secure Key Management**: Uses a `.env` file to securely manage all private keys and API keys, keeping them separate from the codebase.
+
+## Project Structure
+
+The project is composed of several key scripts that work together:
+
+* **`main_app.py`**: The central orchestrator. It launches all background processes and displays the main monitoring dashboard.
+* **`trade_executor.py`**: The trading "brain." It reads signals from all active strategies and executes trades using the appropriate agent.
+* **`data_fetcher.py`**: A background service that collects 1-minute historical candle data and saves it to the SQLite database.
+* **`resampler.py`**: A background service that reads the 1-minute data and generates all other required timeframes (e.g., 5m, 1h, 1d).
+* **`market_cap_fetcher.py`**: A scheduled service to download daily market cap data.
+* **`strategy_*.py`**: Individual files containing the logic for different types of trading strategies (e.g., SMA Crossover).
+* **`_data/strategies.json`**: The configuration file for defining and enabling/disabling your trading strategies.
+* **`.env`**: The secure file for storing all your private keys and API keys.
+
+## Installation
+
+1. **Clone the Repository**
+   ```bash
+      git clone [https://github.com/your-username/your-repo-name.git](https://github.com/your-username/your-repo-name.git)
+      cd your-repo-name
+   ```
+2. **Create and Activate a Virtual Environment**
+   ```bash
+      # For Windows
+      python -m venv .venv
+      .\.venv\Scripts\activate
+      # For macOS/Linux
+      python3 -m venv .venv
+      source .venv/bin/activate
+   ```
+3. **Install Dependencies**
+   ```bash
+      pip install -r requirements.txt
+   ```
+## Getting Started: Configuration
+
+Before running the application, you must configure your wallets, agents, and API keys.
+
+1. Create the .env File In the root of the project, create a file named .env. Copy the following content into it and replace the placeholder values with your actual keys.
+
+2. **Activate Your Main Wallet on Hyperliquid**
+   The `trade_executor.py` script will fail if your main wallet is not registered.
+   * Go to the Hyperliquid website, connect your main wallet, and make a small deposit. This is a one-time setup step.
+3. **Create and Authorize Trading Agents**
+   The `trade_executor.py` uses secure "agent" keys that can trade but cannot withdraw. You need to generate these and authorize them with your main wallet.
+   * Run the `create_agent.py` script
+   ```bash
+      python create_agent.py
+   ```
+   The script will output a new Agent Private Key. Copy this key and add it to your .env file (e.g., as SCALPER_AGENT_PK). Repeat this for each agent you want to create.
+4. **Configure**
+   Your Strategies Open the `_data/strategies.json` file to define which strategies you want to run.
+   * Set "enabled": true to activate a strategy.
+   * Assign an "agent" (e.g., "scalper", "swing") to each strategy. The agent name must correspond to a key in your .env file (e.g., SCALPER_AGENT_PK -> "scalper").
+   * Configure the parameters for each strategy, such as the coin, timeframe, and any indicator settings.
+
+##Usage##
+   Once everything is configured, you can run the main application from your terminal:
+   ```bash
+      python main_app.py
+   ```
+
+   ## Documentation
+
+   Detailed project documentation is available in the `WIKI/` directory. Start with the summary page:
+
+   `WIKI/SUMMARY.md`
+
+   This contains links and explanations for `OVERVIEW.md`, `SETUP.md`, `SCRIPTS.md`, and other helpful pages that describe usage, data layout, agent management, development notes, and troubleshooting.
+

WIKI/.gitattributes

@@ -0,0 +1,5 @@
+# Treat markdown files as text with LF normalization
+*.md text eol=lf
+
+# Ensure JSON files are treated as text
+*.json text

WIKI/AGENTS.md

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+Agents and Keys
+
+This project supports running multiple agent identities (private keys) to place orders on Hyperliquid. Agents are lightweight keys authorized on-chain by your main wallet.
+
+Agent storage and environment
+
+- For security, agent private keys should be stored as environment variables and not checked into source control.
+- Supported patterns:
+  - `AGENT_PRIVATE_KEY` (single default agent)
+  - `<NAME>_AGENT_PK` or `<NAME>_AGENT_PRIVATE_KEY` (per-agent keys)
+
+Discovering agents
+
+- `trade_executor.py` scans environment variables for agent keys and loads them into `Exchange` objects so each agent can sign orders independently.
+
+Creating and authorizing agents
+
+- Use `create_agent.py` with your `MAIN_WALLET_PRIVATE_KEY` to authorize a new agent name. The script will attempt to call `exchange.approve_agent(agent_name)` and print the returned agent private key.
+
+Security notes
+
+- Never commit private keys to Git. Keep them in a secure secrets store or local `.env` file excluded from version control.
+- Rotate keys if they are ever exposed and re-authorize agents using your main wallet.
+
+Example `.env` snippet
+
+MAIN_WALLET_PRIVATE_KEY=<your-main-wallet-private-key>
+MAIN_WALLET_ADDRESS=<your-main-wallet-address>
+AGENT_PRIVATE_KEY=<agent-private-key>
+EXECUTOR_SCALPER_AGENT_PK=<agent-private-key-for-scalper>
+
+File `agents`
+
+- This repository may contain a local `agents` file used as a quick snapshot; treat it as insecure and remove it from the repo or add it to `.gitignore` if it contains secrets.

WIKI/CONTRIBUTING.md

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+Contributing
+
+Thanks for considering contributing! Please follow these guidelines to make the process smooth.
+
+How to contribute
+
+1. Fork the repository and create a feature branch for your change.
+2. Keep changes focused and add tests where appropriate.
+3. Submit a Pull Request with a clear description and the reason for the change.
+
+Coding standards
+
+- Keep functions small and well-documented.
+- Use the existing logging utilities for consistent output.
+- Prefer safe, incremental changes for financial code.
+
+Security and secrets
+
+- Never commit private keys, API keys, or secrets. Use environment variables or a secrets manager.
+- If you accidentally commit secrets, rotate them immediately.

WIKI/DATA.md

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+Data layout and formats
+
+This section describes the `_data/` directory and the important files used by the scripts.
+
+Important files
+
+- `_data/market_data.db` — SQLite database that stores candle tables. Tables are typically named `<COIN>_<INTERVAL>` (e.g., `BTC_1m`, `ETH_5m`).
+- `_data/coin_precision.json` — Mapping of coin names to their size precision (created by `list_coins.py`).
+- `_data/current_prices.json` — Latest market prices that `market.py` writes.
+- `_data/fetcher_status.json` — Last run metadata from `data_fetcher.py`.
+- `_data/market_cap_data.json` — Market cap summary saved by `market_cap_fetcher.py`.
+- `_data/strategies.json` — Configuration for strategies (enabled flag, parameters).
+- `_data/strategy_status_<name>.json` — Per-strategy runtime status including last signal and price.
+- `_data/executor_managed_positions.json` — Which strategy is currently managing which live position (used by `trade_executor`).
+
+Candle schema
+
+Each candle table contains columns similar to:
+- `timestamp_ms` (INTEGER) — milliseconds since epoch
+- `open`, `high`, `low`, `close` (FLOAT)
+- `volume` (FLOAT)
+- `number_of_trades` (INTEGER)
+
+Trade logs
+
+- Persistent trade history is stored in `_logs/trade_history.csv` with the following columns: `timestamp_utc`, `strategy`, `coin`, `action`, `price`, `size`, `signal`, `pnl`.
+
+Backups and maintenance
+
+- Periodically back up `_data/market_data.db`. The WAL and SHM files are also present when SQLite uses WAL mode.
+- Keep JSON config/state files under version control only if they contain no secrets.

WIKI/DEVELOPMENT.md

@@ -0,0 +1,24 @@
+Development and testing
+
+Code style and conventions
+
+- Python 3.11+ with typing hints where helpful.
+- Use `logging_utils.setup_logging` for consistent logs across scripts.
+
+Running tests
+
+- This repository doesn't currently include a formal test suite. Suggested quick checks:
+  - Run `python list_coins.py` to verify connectivity to Hyperliquid Info.
+  - Run `python -m pyflakes .` or `python -m pylint` if you have linters installed.
+
+Adding a new strategy
+
+1. Create a new script following the pattern in `strategy_template.py`.
+2. Add an entry to `_data/strategies.json` with `enabled: true` and relevant parameters.
+3. Ensure the strategy writes a status JSON file (`_data/strategy_status_<name>.json`) and uses `trade_log.log_trade` to record actions.
+
+Recommended improvements (low-risk)
+
+- Add a lightweight unit test suite (pytest) for core functions like timeframe parsing, SQL helpers, and signal calculation.
+- Add CI (GitHub Actions) to run flake/pylint and unit tests on PRs.
+- Move secrets handling to a `.env.example` and document environment variables in `WIKI/SETUP.md`.

WIKI/OVERVIEW.md

@@ -0,0 +1,29 @@
+Hyperliquid Trading Toolkit
+
+This repository contains a collection of utility scripts, data fetchers, resamplers, trading strategies, and a trade executor for working with Hyperliquid trading APIs and crawled data. It is organized to support data collection, transformation, strategy development, and automated execution via agents.
+
+Key components
+
+- Data fetching and management: `data_fetcher.py`, `market.py`, `resampler.py`, `market_cap_fetcher.py`, `list_coins.py`
+- Strategies: `strategy_sma_cross.py`, `strategy_template.py`, `strategy_sma_125d.py` (if present)
+- Execution: `trade_executor.py`, `create_agent.py`, `agents` helper
+- Utilities: `logging_utils.py`, `trade_log.py`
+- Data storage: SQLite database in `_data/market_data.db` and JSON files in `_data`
+
+Intended audience
+
+- Developers building strategies and automations on Hyperliquid
+- Data engineers collecting and processing market data
+- Operators running the fetchers and executors on a scheduler or as system services
+
+Project goals
+
+- Reliable collection of 1m candles and resampling to common timeframes
+- Clean separation between data, strategies, and execution
+- Lightweight logging and traceable trade records
+
+Where to start
+
+- Read `WIKI/SETUP.md` to prepare your environment
+- Use `WIKI/SCRIPTS.md` for a description of individual scripts and how to run them
+- Inspect `WIKI/AGENTS.md` to understand agent keys and how to manage them

WIKI/SCRIPTS.md

@@ -0,0 +1,47 @@
+Scripts and How to Use Them
+
+This file documents the main scripts in the repository and their purpose, typical runtime parameters, and key notes.
+
+list_coins.py
+- Purpose: Fetches asset metadata from Hyperliquid (name and size/precision) and saves `_data/coin_precision.json`.
+- Usage: `python list_coins.py`
+- Notes: Reads `hyperliquid.info.Info` and writes a JSON file. Useful to run before market feeders.
+
+market.py (MarketDataFeeder)
+- Purpose: Fetches live prices from Hyperliquid and writes `_data/current_prices.json` while printing a live table.
+- Usage: `python market.py --log-level normal`
+- Notes: Expects `_data/coin_precision.json` to exist.
+
+data_fetcher.py (CandleFetcherDB)
+- Purpose: Fetches historical 1m candles and stores them in `_data/market_data.db` using a table-per-coin naming convention.
+- Usage: `python data_fetcher.py --coins BTC ETH --interval 1m --days 7`
+- Notes: Can be run regularly by a scheduler to keep the DB up to date.
+
+resampler.py (Resampler)
+- Purpose: Reads 1m candles from SQLite and resamples to configured timeframes (e.g. 5m, 15m, 1h), appending new candles to tables.
+- Usage: `python resampler.py --coins BTC ETH --timeframes 5m 15m 1h --log-level normal`
+
+market_cap_fetcher.py (MarketCapFetcher)
+- Purpose: Pulls CoinGecko market cap numbers and maintains historical daily tables in the same SQLite DB.
+- Usage: `python market_cap_fetcher.py --coins BTC ETH --log-level normal`
+- Notes: Optional `COINGECKO_API_KEY` in `.env` avoids throttling.
+
+strategy_sma_cross.py (SmaCrossStrategy)
+- Purpose: Run an SMA-based trading strategy. Reads candles from `_data/market_data.db` and writes status to `_data/strategy_status_<name>.json`.
+- Usage: `python strategy_sma_cross.py --name sma_cross_1 --params '{"coin":"BTC","timeframe":"1m","fast":5,"slow":20}' --log-level normal`
+
+trade_executor.py (TradeExecutor)
+- Purpose: Orchestrates agent-based order execution using agent private keys found in environment variables. Uses `_data/strategies.json` to determine active strategies.
+- Usage: `python trade_executor.py --log-level normal`
+- Notes: Requires `MAIN_WALLET_ADDRESS` and agent keys. See `create_agent.py` to authorize agents on-chain.
+
+create_agent.py
+- Purpose: Authorizes a new on-chain agent using your main wallet (requires `MAIN_WALLET_PRIVATE_KEY`).
+- Usage: `python create_agent.py`
+- Notes: Prints the new agent private key to stdout — save it securely.
+
+trade_log.py
+- Purpose: Provides a thread-safe CSV trade history logger. Used by the executor and strategies to record actions.
+
+Other utility scripts
+- import_csv.py, fix_timestamps.py, list_coins.py, etc. — see file headers for details.

WIKI/SETUP.md

@@ -0,0 +1,42 @@
+Setup and Installation
+
+Prerequisites
+
+- Python 3.11+ (project uses modern dependencies)
+- Git (optional)
+- A Hyperliquid account and an activated main wallet if you want to authorize agents and trade
+
+Virtual environment
+
+1. Create a virtual environment:
+
+   python -m venv .venv
+
+2. Activate the virtual environment (PowerShell on Windows):
+
+   .\.venv\Scripts\Activate.ps1
+
+3. Upgrade pip and install dependencies:
+
+   python -m pip install --upgrade pip
+   pip install -r requirements.txt
+
+Configuration
+
+- Copy `.env.example` to `.env` and set the following variables as required:
+  - MAIN_WALLET_PRIVATE_KEY (used by `create_agent.py` to authorize agents)
+  - MAIN_WALLET_ADDRESS (used by `trade_executor.py`)
+  - AGENT_PRIVATE_KEY or per-agent keys like `EXECUTOR_SCALPER_AGENT_PK`
+  - Optional: COINGECKO_API_KEY for `market_cap_fetcher.py` to avoid rate limits
+
+Data directory
+
+- The project writes and reads data from the `_data/` folder. Ensure the directory exists and is writable by the user running the scripts.
+
+Quick test
+
+After installing packages, run `list_coins.py` in a dry run to verify connectivity to the Hyperliquid info API:
+
+   python list_coins.py
+
+If you encounter import errors, ensure the virtual environment is active and the `requirements.txt` dependencies are installed.

WIKI/SUMMARY.md

@@ -0,0 +1,15 @@
+Project Wiki Summary
+
+This directory contains human-friendly documentation for the project. Files:
+
+- `OVERVIEW.md` — High-level overview and where to start
+- `SETUP.md` — Environment setup and quick test steps
+- `SCRIPTS.md` — Per-script documentation and usage examples
+- `AGENTS.md` — How agents work and secure handling of keys
+- `DATA.md` — Data folder layout and schema notes
+- `DEVELOPMENT.md` — Developer guidance and recommended improvements
+- `CONTRIBUTING.md` — How to contribute safely
+- `TROUBLESHOOTING.md` — Common problems and solutions
+
+Notes:
+- These pages were generated from repository source files and common patterns in trading/data projects. Validate any sensitive information (agent keys) and remove them from the repository when sharing.

WIKI/TROUBLESHOOTING.md

@@ -0,0 +1,21 @@
+Troubleshooting common issues
+
+1. Import errors
+   - Ensure the virtual environment is active.
+   - Run `pip install -r requirements.txt`.
+
+2. Agent authorization failures
+   - Ensure your main wallet is activated on Hyperliquid and has funds.
+   - The `create_agent.py` script will print helpful messages if the vault (main wallet) cannot act.
+
+3. SQLite locked errors
+   - Increase the SQLite timeout when opening connections (this project uses a 10s timeout in fetcher). Close other programs that may hold the DB open.
+
+4. Missing coin precision file
+   - Run `python list_coins.py` to regenerate `_data/coin_precision.json`.
+
+5. Rate limits from CoinGecko
+   - Set `COINGECKO_API_KEY` in your `.env` file and ensure the fetcher respects backoff.
+
+6. Agent keys in `agents` file or other local files
+   - Treat any `agents` file with private keys as compromised; rotate keys and remove the file from the repository.

_data/backtesting_conf.json

@@ -0,0 +1,18 @@
+{
+    "sma_cross_eth_5m": {
+        "strategy_name": "sma_cross_1",
+        "script": "strategies.ma_cross_strategy.MaCrossStrategy",
+        "optimization_params": {
+            "fast": {
+                "start": 5,
+                "end": 150,
+                "step": 1
+            },
+            "slow": {
+                "start": 0,
+                "end": 0,
+                "step": 1
+            }
+        }
+    }
+}

_data/candles/hyperliquid-historical.py

@@ -0,0 +1,231 @@
+import boto3
+from botocore import UNSIGNED
+from botocore.config import Config
+from botocore.exceptions import ClientError
+import os
+import argparse
+from datetime import datetime, timedelta
+import asyncio
+import lz4.frame
+from pathlib import Path
+import csv
+import json
+
+
+
+# MUST USE PATHLIB INSTEAD
+DIR_PATH = Path(__file__).parent
+BUCKET = "hyperliquid-archive"
+CSV_HEADER = ["datetime", "timestamp", "level", "price", "size", "number"]
+
+# s3 = boto3.client('s3', config=Config(signature_version=UNSIGNED))
+# s3.download_file('hyperliquid-archive', 'market_data/20230916/9/l2Book/SOL.lz4', f"{dir_path}/SOL.lz4")
+
+# earliest date: 20230415/0/
+
+
+
+def get_args():
+    parser = argparse.ArgumentParser(description="Retrieve historical tick level market data from Hyperliquid exchange")
+    subparser = parser.add_subparsers(dest="tool", required=True, help="tool: download, decompress, to_csv")
+
+    global_parser = subparser.add_parser("global_settings", add_help=False)
+    global_parser.add_argument("t", metavar="Tickers", help="Tickers of assets to be downloaded seperated by spaces. e.g. BTC ETH", nargs="+")
+    global_parser.add_argument("--all", help="Apply action to all available dates and times.", action="store_true", default=False)
+    global_parser.add_argument("--anonymous", help="Use anonymous (unsigned) S3 requests. Defaults to signed requests if not provided.", action="store_true", default=False)
+    global_parser.add_argument("-sd", metavar="Start date", help="Starting date as one unbroken string formatted: YYYYMMDD.  e.g. 20230916")
+    global_parser.add_argument("-sh", metavar="Start hour", help="Hour of the starting day as an integer between 0 and 23. e.g. 9  Default: 0", type=int, default=0)
+    global_parser.add_argument("-ed", metavar="End date", help="Ending date as one unbroken string formatted: YYYYMMDD.  e.g. 20230916")
+    global_parser.add_argument("-eh", metavar="End hour", help="Hour of the ending day as an integer between 0 and 23. e.g. 9  Default: 23", type=int, default=23)
+    
+
+    download_parser = subparser.add_parser("download", help="Download historical market data", parents=[global_parser])
+    decompress_parser = subparser.add_parser("decompress", help="Decompress downloaded lz4 data", parents=[global_parser])
+    to_csv_parser = subparser.add_parser("to_csv", help="Convert decompressed downloads into formatted CSV", parents=[global_parser])
+
+
+    return parser.parse_args()
+
+
+
+
+def make_date_list(start_date, end_date):
+    start_date = datetime.strptime(start_date, '%Y%m%d')
+    end_date = datetime.strptime(end_date, '%Y%m%d')
+    
+    date_list = []
+    
+    current_date = start_date
+    while current_date <= end_date:
+        date_list.append(current_date.strftime('%Y%m%d'))
+        current_date += timedelta(days=1)
+    
+    return date_list
+
+
+
+
+def make_date_hour_list(date_list, start_hour, end_hour, delimiter="/"):
+    date_hour_list = []
+    end_date = date_list[-1]
+    hour = start_hour
+    end = 23
+    for date in date_list:
+        if date == end_date:
+            end = end_hour
+
+        while hour <= end:
+            date_hour = date + delimiter + str(hour)
+            date_hour_list.append(date_hour)
+            hour += 1
+
+        hour = 0
+
+    return date_hour_list
+
+
+
+
+async def download_object(s3, asset, date_hour):
+    date_and_hour = date_hour.split("/")
+    key = f"market_data/{date_hour}/l2Book/{asset}.lz4"
+    dest = f"{DIR_PATH}/downloads/{asset}/{date_and_hour[0]}-{date_and_hour[1]}.lz4"
+    try:
+        s3.download_file(BUCKET, key, dest)
+    except ClientError as e:
+        # Print a concise message and continue. Common errors: 403 Forbidden, 404 Not Found.
+        code = e.response.get('Error', {}).get('Code') if hasattr(e, 'response') else 'Unknown'
+        print(f"Failed to download {key}: {code} - {e}")
+        return
+
+
+
+
+async def download_objects(s3, assets, date_hour_list):
+    print(f"Downloading {len(date_hour_list)} objects...")
+    for asset in assets:
+        await asyncio.gather(*[download_object(s3, asset, date_hour) for date_hour in date_hour_list])
+
+
+
+
+async def decompress_file(asset, date_hour):
+    lz_file_path = DIR_PATH / "downloads" / asset / f"{date_hour}.lz4"
+    file_path = DIR_PATH / "downloads" / asset / date_hour
+
+    if not lz_file_path.is_file():
+        print(f"decompress_file: file not found: {lz_file_path}")
+        return
+
+    with lz4.frame.open(lz_file_path, mode='r') as lzfile: 
+        data = lzfile.read()
+        with open(file_path, "wb") as file:
+            file.write(data)
+
+
+
+
+async def decompress_files(assets, date_hour_list):
+    print(f"Decompressing {len(date_hour_list)} files...")
+    for asset in assets:
+        await asyncio.gather(*[decompress_file(asset, date_hour) for date_hour in date_hour_list])
+
+
+
+
+def write_rows(csv_writer, line):
+    rows = []
+    entry = json.loads(line)
+    date_time = entry["time"]
+    timestamp = str(entry["raw"]["data"]["time"])
+    all_orders = entry["raw"]["data"]["levels"]
+
+    for i, order_level in enumerate(all_orders):
+        level = str(i + 1)
+        for order in order_level:
+            price = order["px"]
+            size = order["sz"]
+            number = str(order["n"])
+            
+            rows.append([date_time, timestamp, level, price, size, number])
+
+    for row in rows:
+        csv_writer.writerow(row)
+
+
+
+
+
+async def convert_file(asset, date_hour):
+    file_path = DIR_PATH / "downloads" / asset / date_hour
+    csv_path = DIR_PATH / "csv" / asset / f"{date_hour}.csv"
+    
+    with open(csv_path, "w", newline='') as csv_file:
+        csv_writer = csv.writer(csv_file, dialect="excel")
+        csv_writer.writerow(CSV_HEADER)
+
+        with open(file_path) as file:
+            for line in file:
+                write_rows(csv_writer, line)
+
+
+
+
+async def files_to_csv(assets, date_hour_list):
+    print(f"Converting {len(date_hour_list)} files to CSV...")
+    for asset in assets:
+        await asyncio.gather(*[convert_file(asset, date_hour) for date_hour in date_hour_list])
+
+
+
+
+
+def main():
+    print(DIR_PATH)
+    args = get_args()
+
+    # Create S3 client according to whether anonymous access was requested.
+    if getattr(args, 'anonymous', False):
+        s3 = boto3.client('s3', config=Config(signature_version=UNSIGNED))
+    else:
+        s3 = boto3.client('s3')
+
+    downloads_path = DIR_PATH / "downloads"
+    downloads_path.mkdir(exist_ok=True)
+
+    csv_path = DIR_PATH / "csv"
+    csv_path.mkdir(exist_ok=True)
+
+    for asset in args.t:
+        downloads_asset_path = downloads_path / asset
+        downloads_asset_path.mkdir(exist_ok=True)
+        csv_asset_path = csv_path / asset
+        csv_asset_path.mkdir(exist_ok=True)
+
+    date_list = make_date_list(args.sd, args.ed)
+    loop = asyncio.new_event_loop()
+    
+    if args.tool == "download":
+        date_hour_list = make_date_hour_list(date_list, args.sh, args.eh)
+        loop.run_until_complete(download_objects(s3, args.t, date_hour_list))
+        loop.close()
+
+    if args.tool == "decompress":
+        date_hour_list = make_date_hour_list(date_list, args.sh, args.eh, delimiter="-")
+        loop.run_until_complete(decompress_files(args.t, date_hour_list))
+        loop.close()
+
+    if args.tool == "to_csv":
+        date_hour_list = make_date_hour_list(date_list, args.sh, args.eh, delimiter="-")
+        loop.run_until_complete(files_to_csv(args.t, date_hour_list))
+        loop.close()
+
+
+    print("Done")
+
+    
+    
+
+
+if __name__ == "__main__":
+    main()

_data/candles/requirements.txt

@@ -0,0 +1,8 @@
+boto3==1.34.131
+botocore==1.34.131
+jmespath==1.0.1
+lz4==4.3.3
+python-dateutil==2.9.0.post0
+s3transfer==0.10.1
+six==1.16.0
+urllib3==2.2.2

_data/coin_id_map.json

@@ -0,0 +1,208 @@
+{
+    "0G": "zero-gravity",
+    "2Z": "doublezero",
+    "AAVE": "aave",
+    "ACE": "endurance",
+    "ADA": "ada-the-dog",
+    "AI": "sleepless-ai",
+    "AI16Z": "ai16z",
+    "AIXBT": "aixbt",
+    "ALGO": "dear-algorithm",
+    "ALT": "altlayer",
+    "ANIME": "anime-token",
+    "APE": "ape-3",
+    "APEX": "apex-token-2",
+    "APT": "aptos",
+    "AR": "arweave",
+    "ARB": "osmosis-allarb",
+    "ARK": "ark-3",
+    "ASTER": "astar",
+    "ATOM": "lost-bitcoin-layer",
+    "AVAX": "binance-peg-avalanche",
+    "AVNT": "avantis",
+    "BABY": "baby-2",
+    "BADGER": "badger-dao",
+    "BANANA": "nforbanana",
+    "BCH": "bitcoin-cash",
+    "BERA": "berachain-bera",
+    "BIGTIME": "big-time",
+    "BIO": "bio-protocol",
+    "BLAST": "blast",
+    "BLUR": "blur",
+    "BLZ": "bluzelle",
+    "BNB": "binancecoin",
+    "BNT": "bancor",
+    "BOME": "book-of-meme",
+    "BRETT": "brett",
+    "BSV": "bitcoin-cash-sv",
+    "BTC": "bitcoin",
+    "CAKE": "pancakeswap-token",
+    "CANTO": "canto",
+    "CATI": "catizen",
+    "CELO": "celo",
+    "CFX": "cosmic-force-token-v2",
+    "CHILLGUY": "just-a-chill-guy",
+    "COMP": "compound-governance-token",
+    "CRV": "curve-dao-token",
+    "CYBER": "cyberconnect",
+    "DOGE": "doge-on-pulsechain",
+    "DOOD": "doodles",
+    "DOT": "xcdot",
+    "DYDX": "dydx-chain",
+    "DYM": "dymension",
+    "EIGEN": "eigenlayer",
+    "ENA": "ethena",
+    "ENS": "ethereum-name-service",
+    "ETC": "ethereum-classic",
+    "ETH": "ethereum",
+    "ETHFI": "ether-fi",
+    "FARTCOIN": "fartcoin-2",
+    "FET": "fetch-ai",
+    "FIL": "filecoin",
+    "FRIEND": "friend-tech",
+    "FTM": "fantom",
+    "FTT": "ftx-token",
+    "GALA": "gala",
+    "GAS": "gas",
+    "GMT": "stepn",
+    "GMX": "gmx",
+    "GOAT": "goat",
+    "GRASS": "grass-3",
+    "GRIFFAIN": "griffain",
+    "HBAR": "hedera-hashgraph",
+    "HEMI": "hemi",
+    "HMSTR": "hamster-kombat",
+    "HYPE": "hyperliquid",
+    "HYPER": "hyper-4",
+    "ILV": "illuvium",
+    "IMX": "immutable-x",
+    "INIT": "initia",
+    "INJ": "injective-protocol",
+    "IO": "io",
+    "IOTA": "iota-2",
+    "IP": "story-2",
+    "JELLY": "jelly-time",
+    "JTO": "jito-governance-token",
+    "JUP": "jupiter-exchange-solana",
+    "KAITO": "kaito",
+    "KAS": "wrapped-kaspa",
+    "LAUNCHCOIN": "ben-pasternak",
+    "LAYER": "unilayer",
+    "LDO": "linea-bridged-ldo-linea",
+    "LINEA": "linea",
+    "LINK": "osmosis-alllink",
+    "LISTA": "lista",
+    "LOOM": "loom",
+    "LTC": "litecoin",
+    "MANTA": "manta-network",
+    "MATIC": "matic-network",
+    "MAV": "maverick-protocol",
+    "MAVIA": "heroes-of-mavia",
+    "ME": "magic-eden",
+    "MEGA": "megaeth",
+    "MELANIA": "melania-meme",
+    "MEME": "mpx6900",
+    "MERL": "merlin-chain",
+    "MET": "metya",
+    "MEW": "cat-in-a-dogs-world",
+    "MINA": "mina-protocol",
+    "MKR": "maker",
+    "MNT": "mynth",
+    "MON": "mon-protocol",
+    "MOODENG": "moo-deng-2",
+    "MORPHO": "morpho",
+    "MOVE": "movement",
+    "MYRO": "myro",
+    "NEAR": "near",
+    "NEO": "neo",
+    "NIL": "nillion",
+    "NOT": "nothing-3",
+    "NTRN": "neutron-3",
+    "NXPC": "nexpace",
+    "OGN": "origin-protocol",
+    "OM": "mantra-dao",
+    "OMNI": "omni-2",
+    "ONDO": "ondo-finance",
+    "OP": "optimism",
+    "ORBS": "orbs",
+    "ORDI": "ordinals",
+    "OX": "ox-fun",
+    "PANDORA": "pandora",
+    "PAXG": "pax-gold",
+    "PENDLE": "pendle",
+    "PENGU": "pudgy-penguins",
+    "PEOPLE": "constitutiondao-wormhole",
+    "PIXEL": "pixel-3",
+    "PNUT": "pnut",
+    "POL": "proof-of-liquidity",
+    "POLYX": "polymesh",
+    "POPCAT": "popcat",
+    "PROMPT": "wayfinder",
+    "PROVE": "succinct",
+    "PUMP": "pump-fun",
+    "PURR": "purr-2",
+    "PYTH": "pyth-network",
+    "RDNT": "radiant-capital",
+    "RENDER": "render-token",
+    "REQ": "request-network",
+    "RESOLV": "resolv",
+    "REZ": "renzo",
+    "RLB": "rollbit-coin",
+    "RSR": "reserve-rights-token",
+    "RUNE": "thorchain",
+    "S": "token-s",
+    "SAGA": "saga-2",
+    "SAND": "the-sandbox-wormhole",
+    "SCR": "scroll",
+    "SEI": "sei-network",
+    "SHIA": "shiba-saga",
+    "SKY": "sky",
+    "SNX": "havven",
+    "SOL": "solana",
+    "SOPH": "sophon",
+    "SPX": "spx6900",
+    "STBL": "stbl",
+    "STG": "stargate-finance",
+    "STRAX": "stratis",
+    "STRK": "starknet",
+    "STX": "stox",
+    "SUI": "sui",
+    "SUPER": "superfarm",
+    "SUSHI": "sushi",
+    "SYRUP": "syrup",
+    "TAO": "the-anthropic-order",
+    "TIA": "tia",
+    "TNSR": "tensorium",
+    "TON": "tontoken",
+    "TRB": "tellor",
+    "TRUMP": "trumpeffect69420",
+    "TRX": "tron-bsc",
+    "TST": "test-3",
+    "TURBO": "turbo",
+    "UMA": "uma",
+    "UNI": "uni",
+    "UNIBOT": "unibot",
+    "USTC": "wrapped-ust",
+    "USUAL": "usual",
+    "VINE": "vine",
+    "VIRTUAL": "virtual-protocol",
+    "VVV": "venice-token",
+    "W": "w",
+    "WCT": "connect-token-wct",
+    "WIF": "wif-secondchance",
+    "WLD": "worldcoin-wld",
+    "WLFI": "world-liberty-financial",
+    "XAI": "xai-blockchain",
+    "XLM": "stellar",
+    "XPL": "pulse-2",
+    "XRP": "ripple",
+    "YGG": "yield-guild-games",
+    "YZY": "yzy",
+    "ZEC": "zcash",
+    "ZEN": "zenith-3",
+    "ZEREBRO": "zerebro",
+    "ZETA": "zeta",
+    "ZK": "zksync",
+    "ZORA": "zora",
+    "ZRO": "layerzero"
+}

_data/coin_precision.json

@@ -101,6 +101,7 @@
     "MAV": 0,
     "MAVIA": 1,
     "ME": 1,
+    "MEGA": 0,
     "MELANIA": 1,
     "MEME": 0,
     "MERL": 0,

_data/opened_positions.json

@@ -0,0 +1,11 @@
+{
+    "copy_trader_eth_ETH": {
+        "strategy": "copy_trader_eth",
+        "coin": "ETH",
+        "side": "long",
+        "open_time_utc": "2025-11-02T20:35:02.988272+00:00",
+        "open_price": 3854.9,
+        "amount": 0.0055,
+        "leverage": 3
+    }
+}

_data/strategies.json

@@ -0,0 +1,51 @@
+{
+    "sma_cross_1": {
+        "enabled": false,
+        "class": "strategies.ma_cross_strategy.MaCrossStrategy",
+        "agent": "scalper_agent",
+        "parameters": {
+            "coin": "ETH",
+            "timeframe": "15m",
+            "short_ma": 7,
+            "long_ma": 44,
+            "size": 0.0055,
+            "leverage_long": 5,
+            "leverage_short": 5
+        }
+    },
+       "sma_44d_btc": {
+        "enabled": false,
+        "class": "strategies.single_sma_strategy.SingleSmaStrategy",
+        "parameters": {
+            "agent": "swing",
+            "coin": "BTC",
+            "timeframe": "1d",
+            "sma_period": 44,
+            "size": 0.0001,
+            "leverage_long": 3,
+            "leverage_short": 1
+        }
+    },
+        "copy_trader_eth": {
+        "enabled": true,
+        "is_event_driven": true,
+        "class": "strategies.copy_trader_strategy.CopyTraderStrategy",
+        "parameters": {
+            "agent": "scalper",
+            "target_address": "0x32885a6adac4375858E6edC092EfDDb0Ef46484C",
+            "coins_to_copy": {
+                "ETH": {
+                    "size": 0.0055,
+                    "leverage_long": 3,
+                    "leverage_short": 3
+                },
+                "BTC": {
+                    "size": 0.0002,
+                    "leverage_long": 1,
+                    "leverage_short": 1
+                }
+            }
+        }
+    }
+}
+

_data/strategy_state_copy_trader_eth.json

@@ -0,0 +1,7 @@
+{
+    "ETH": {
+        "side": "long",
+        "size": 0.018,
+        "entry": 3864.2
+    }
+}

_data/strategy_status_copy_trader_eth.json

@@ -0,0 +1,7 @@
+{
+    "strategy_name": "copy_trader_eth",
+    "current_signal": "WAIT",
+    "last_signal_change_utc": null,
+    "signal_price": null,
+    "last_checked_utc": "2025-11-02T09:55:08.460168+00:00"
+}

_data/wallets_info.json

@@ -0,0 +1,290 @@
+{
+  "Whale 1 (BTC Maxi)": {
+    "address": "0xb83de012dba672c76a7dbbbf3e459cb59d7d6e36",
+    "core_state": {
+      "raw_state": {
+        "marginSummary": {
+          "accountValue": "30018881.1193690002",
+          "totalNtlPos": "182930683.6996490061",
+          "totalRawUsd": "212949564.8190180063",
+          "totalMarginUsed": "22969943.9848450013"
+        },
+        "crossMarginSummary": {
+          "accountValue": "30018881.1193690002",
+          "totalNtlPos": "182930683.6996490061",
+          "totalRawUsd": "212949564.8190180063",
+          "totalMarginUsed": "22969943.9848450013"
+        },
+        "crossMaintenanceMarginUsed": "5420634.4984849999",
+        "withdrawable": "7043396.1885489998",
+        "assetPositions": [
+          {
+            "type": "oneWay",
+            "position": {
+              "coin": "BTC",
+              "szi": "-546.94441",
+              "leverage": {
+                "type": "cross",
+                "value": 10
+              },
+              "entryPx": "115183.2",
+              "positionValue": "62795781.6009199992",
+              "unrealizedPnl": "203045.067519",
+              "returnOnEquity": "0.0322299761",
+              "liquidationPx": "159230.7089577085",
+              "marginUsed": "6279578.1600919999",
+              "maxLeverage": 40,
+              "cumFunding": {
+                "allTime": "-6923407.0911370004",
+                "sinceOpen": "-6923407.0970780002",
+                "sinceChange": "-1574.188052"
+              }
+            }
+          },
+          {
+            "type": "oneWay",
+            "position": {
+              "coin": "ETH",
+              "szi": "-13938.989",
+              "leverage": {
+                "type": "cross",
+                "value": 10
+              },
+              "entryPx": "4106.64",
+              "positionValue": "58064252.5784000009",
+              "unrealizedPnl": "-821803.895073",
+              "returnOnEquity": "-0.1435654683",
+              "liquidationPx": "5895.7059682083",
+              "marginUsed": "5806425.2578400001",
+              "maxLeverage": 25,
+              "cumFunding": {
+                "allTime": "-6610045.8844170002",
+                "sinceOpen": "-6610045.8844170002",
+                "sinceChange": "-730.403023"
+              }
+            }
+          },
+          {
+            "type": "oneWay",
+            "position": {
+              "coin": "SOL",
+              "szi": "-75080.68",
+              "leverage": {
+                "type": "cross",
+                "value": 10
+              },
+              "entryPx": "201.3063",
+              "positionValue": "14975592.4328000005",
+              "unrealizedPnl": "138627.573942",
+              "returnOnEquity": "0.0917199656",
+              "liquidationPx": "519.0933515657",
+              "marginUsed": "1497559.2432800001",
+              "maxLeverage": 20,
+              "cumFunding": {
+                "allTime": "-792893.154387",
+                "sinceOpen": "-922.301401",
+                "sinceChange": "-187.682929"
+              }
+            }
+          },
+          {
+            "type": "oneWay",
+            "position": {
+              "coin": "DOGE",
+              "szi": "-109217.0",
+              "leverage": {
+                "type": "cross",
+                "value": 10
+              },
+              "entryPx": "0.279959",
+              "positionValue": "22081.49306",
+              "unrealizedPnl": "8494.879599",
+              "returnOnEquity": "2.7782496288",
+              "liquidationPx": "213.2654356057",
+              "marginUsed": "2208.149306",
+              "maxLeverage": 10,
+              "cumFunding": {
+                "allTime": "-1875.469799",
+                "sinceOpen": "-1875.469799",
+                "sinceChange": "45.79339"
+              }
+            }
+          },
+          {
+            "type": "oneWay",
+            "position": {
+              "coin": "INJ",
+              "szi": "-18747.2",
+              "leverage": {
+                "type": "cross",
+                "value": 3
+              },
+              "entryPx": "13.01496",
+              "positionValue": "162200.7744",
+              "unrealizedPnl": "81793.4435",
+              "returnOnEquity": "1.005680924",
+              "liquidationPx": "1208.3529290194",
+              "marginUsed": "54066.9248",
+              "maxLeverage": 10,
+              "cumFunding": {
+                "allTime": "-539.133533",
+                "sinceOpen": "-539.133533",
+                "sinceChange": "-7.367325"
+              }
+            }
+          },
+          {
+            "type": "oneWay",
+            "position": {
+              "coin": "SUI",
+              "szi": "-376577.6",
+              "leverage": {
+                "type": "cross",
+                "value": 3
+              },
+              "entryPx": "3.85881",
+              "positionValue": "989495.3017599999",
+              "unrealizedPnl": "463648.956001",
+              "returnOnEquity": "0.9571980625",
+              "liquidationPx": "64.3045458208",
+              "marginUsed": "329831.767253",
+              "maxLeverage": 10,
+              "cumFunding": {
+                "allTime": "-45793.455728",
+                "sinceOpen": "-45793.450891",
+                "sinceChange": "-1233.875821"
+              }
+            }
+          },
+          {
+            "type": "oneWay",
+            "position": {
+              "coin": "XRP",
+              "szi": "-39691.0",
+              "leverage": {
+                "type": "cross",
+                "value": 20
+              },
+              "entryPx": "2.468585",
+              "positionValue": "105486.7707",
+              "unrealizedPnl": "-7506.1484",
+              "returnOnEquity": "-1.5321699789",
+              "liquidationPx": "607.2856858464",
+              "marginUsed": "5274.338535",
+              "maxLeverage": 20,
+              "cumFunding": {
+                "allTime": "-2645.400002",
+                "sinceOpen": "-116.036833",
+                "sinceChange": "-116.036833"
+              }
+            }
+          },
+          {
+            "type": "oneWay",
+            "position": {
+              "coin": "HYPE",
+              "szi": "-750315.16",
+              "leverage": {
+                "type": "cross",
+                "value": 5
+              },
+              "entryPx": "43.3419",
+              "positionValue": "34957933.6195600033",
+              "unrealizedPnl": "-2437823.0249080001",
+              "returnOnEquity": "-0.3748177636",
+              "liquidationPx": "76.3945326684",
+              "marginUsed": "6991586.7239119997",
+              "maxLeverage": 5,
+              "cumFunding": {
+                "allTime": "-1881584.4214250001",
+                "sinceOpen": "-1881584.4214250001",
+                "sinceChange": "-45247.838743"
+              }
+            }
+          },
+          {
+            "type": "oneWay",
+            "position": {
+              "coin": "FARTCOIN",
+              "szi": "-4122236.7999999998",
+              "leverage": {
+                "type": "cross",
+                "value": 10
+              },
+              "entryPx": "0.80127",
+              "positionValue": "1681584.057824",
+              "unrealizedPnl": "1621478.3279619999",
+              "returnOnEquity": "4.9090151459",
+              "liquidationPx": "6.034656163",
+              "marginUsed": "168158.405782",
+              "maxLeverage": 10,
+              "cumFunding": {
+                "allTime": "-72941.395024",
+                "sinceOpen": "-51271.5204",
+                "sinceChange": "-6504.295598"
+              }
+            }
+          },
+          {
+            "type": "oneWay",
+            "position": {
+              "coin": "PUMP",
+              "szi": "-1921732999.0",
+              "leverage": {
+                "type": "cross",
+                "value": 5
+              },
+              "entryPx": "0.005551",
+              "positionValue": "9176275.0702250004",
+              "unrealizedPnl": "1491738.24016",
+              "returnOnEquity": "0.6991640321",
+              "liquidationPx": "0.0166674064",
+              "marginUsed": "1835255.0140450001",
+              "maxLeverage": 10,
+              "cumFunding": {
+                "allTime": "-196004.534539",
+                "sinceOpen": "-196004.534539",
+                "sinceChange": "-9892.654861"
+              }
+            }
+          }
+        ],
+        "time": 1761595358385
+      },
+      "account_value": 30018881.119369,
+      "margin_used": 22969943.984845,
+      "margin_utilization": 0.765183215640378,
+      "available_margin": 7048937.134523999,
+      "total_position_value": 0.0,
+      "portfolio_leverage": 0.0
+    },
+    "open_orders": {
+      "raw_orders": [
+        {
+          "coin": "WLFI",
+          "side": "B",
+          "limitPx": "0.10447",
+          "sz": "2624.0",
+          "oid": 194029229960,
+          "timestamp": 1760131688558,
+          "origSz": "12760.0",
+          "cloid": "0x00000000000000000000001261000016"
+        },
+        {
+          "coin": "@166",
+          "side": "A",
+          "limitPx": "1.01",
+          "sz": "103038.77",
+          "oid": 174787748753,
+          "timestamp": 1758819420037,
+          "origSz": "3000000.0"
+        }
+      ]
+    },
+    "account_metrics": {
+      "cumVlm": "2823125892.6900000572",
+      "nRequestsUsed": 1766294,
+      "nRequestsCap": 2823135892
+    }
+  }
+}

_data/wallets_to_track.json

@@ -0,0 +1,7 @@
+[
+  {
+    "name": "Whale 1 (BTC Maxi)",
+    "address": "0xb83de012dba672c76a7dbbbf3e459cb59d7d6e36",
+    "tags": ["btc", "high_leverage"]
+  }
+]

address_monitor.py

@@ -0,0 +1,221 @@
+import os
+import sys
+import time
+import json
+import argparse
+from datetime import datetime, timezone
+from hyperliquid.info import Info
+from hyperliquid.utils import constants
+from collections import deque
+import logging
+import csv
+
+from logging_utils import setup_logging
+
+# --- Configuration ---
+DEFAULT_ADDRESSES_TO_WATCH = [
+    #"0xd4c1f7e8d876c4749228d515473d36f919583d1d",
+    "0x47930c76790c865217472f2ddb4d14c640ee450a",
+   # "0x4d69495d16fab95c3c27b76978affa50301079d0",
+   # "0x09bc1cf4d9f0b59e1425a8fde4d4b1f7d3c9410d",
+    "0xc6ac58a7a63339898aeda32499a8238a46d88e84",
+    "0xa8ef95dbd3db55911d3307930a84b27d6e969526",
+  #  "0x4129c62faf652fea61375dcd9ca8ce24b2bb8b95",
+    "0x32885a6adac4375858E6edC092EfDDb0Ef46484C",
+]
+MAX_FILLS_TO_DISPLAY = 10
+LOGS_DIR = "_logs"
+recent_fills = {} 
+_lines_printed = 0
+
+TABLE_HEADER = f"{'Time (UTC)':<10} | {'Coin':<6} | {'Side':<5} | {'Size':>15} | {'Price':>15} | {'Value (USD)':>20}"
+TABLE_WIDTH = len(TABLE_HEADER)
+
+def log_fill_to_csv(address: str, fill_data: dict):
+    """Appends a single fill record to the CSV file for a specific address."""
+    log_file_path = os.path.join(LOGS_DIR, f"fills_{address}.csv")
+    file_exists = os.path.exists(log_file_path)
+    
+    # The CSV will store a flattened version of the decoded fill
+    csv_row = {
+        'time_utc': fill_data['time'].isoformat(),
+        'coin': fill_data['coin'],
+        'side': fill_data['side'],
+        'price': fill_data['price'],
+        'size': fill_data['size'],
+        'value_usd': fill_data['value']
+    }
+    
+    try:
+        with open(log_file_path, 'a', newline='', encoding='utf-8') as f:
+            writer = csv.DictWriter(f, fieldnames=csv_row.keys())
+            if not file_exists:
+                writer.writeheader()
+            writer.writerow(csv_row)
+    except IOError as e:
+        logging.error(f"Failed to write to CSV log for {address}: {e}")
+
+def on_message(message):
+    """
+    Callback function to process incoming userEvents from the WebSocket.
+    """
+    try:
+        logging.debug(f"Received message: {message}")
+        channel = message.get("channel")
+        if channel in ("user", "userFills"):
+            data = message.get("data")
+            if not data:
+                return
+
+            user_address = data.get("user", "").lower()
+            fills = data.get("fills", [])
+            
+            if user_address in recent_fills and fills:
+                logging.info(f"Fill detected for user: {user_address}")
+                for fill_data in fills:
+                    decoded_fill = {
+                        "time": datetime.fromtimestamp(fill_data['time'] / 1000, tz=timezone.utc),
+                        "coin": fill_data['coin'],
+                        "side": "BUY" if fill_data['side'] == "B" else "SELL",
+                        "price": float(fill_data['px']),
+                        "size": float(fill_data['sz']),
+                        "value": float(fill_data['px']) * float(fill_data['sz']),
+                    }
+                    recent_fills[user_address].append(decoded_fill)
+                    # --- ADDED: Log every fill to its CSV file ---
+                    log_fill_to_csv(user_address, decoded_fill)
+
+    except (KeyError, TypeError, ValueError) as e:
+        logging.error(f"Error processing message: {e} | Data: {message}")
+
+def build_fills_table(address: str, fills: deque) -> list:
+    """Builds the formatted lines for a single address's fills table."""
+    lines = []
+    short_address = f"{address[:6]}...{address[-4:]}"
+    
+    lines.append(f"--- Fills for {short_address} ---")
+    lines.append(TABLE_HEADER)
+    lines.append("-" * TABLE_WIDTH)
+
+    for fill in list(fills):
+        lines.append(
+            f"{fill['time'].strftime('%H:%M:%S'):<10} | "
+            f"{fill['coin']:<6} | "
+            f"{fill['side']:<5} | "
+            f"{fill['size']:>15.4f} | "
+            f"{fill['price']:>15,.2f} | "
+            f"${fill['value']:>18,.2f}"
+        )
+    
+    padding_needed = MAX_FILLS_TO_DISPLAY - len(fills)
+    for _ in range(padding_needed):
+        lines.append("")
+        
+    return lines
+
+def display_dashboard():
+    """
+    Clears the screen and prints a two-column layout of recent fills tables.
+    """
+    global _lines_printed
+    
+    if _lines_printed > 0:
+        print(f"\x1b[{_lines_printed}A", end="")
+
+    output_lines = ["--- Live Address Fill Monitor ---", ""]
+    
+    addresses_to_display = list(recent_fills.keys())
+    num_addresses = len(addresses_to_display)
+    mid_point = (num_addresses + 1) // 2
+    left_column_addresses = addresses_to_display[:mid_point]
+    right_column_addresses = addresses_to_display[mid_point:]
+    
+    separator = "   |   "
+    
+    for i in range(mid_point):
+        left_address = left_column_addresses[i]
+        left_table_lines = build_fills_table(left_address, recent_fills[left_address])
+        
+        right_table_lines = []
+        if i < len(right_column_addresses):
+            right_address = right_column_addresses[i]
+            right_table_lines = build_fills_table(right_address, recent_fills[right_address])
+        
+        table_height = 3 + MAX_FILLS_TO_DISPLAY
+        for j in range(table_height):
+            left_part = left_table_lines[j] if j < len(left_table_lines) else ""
+            right_part = right_table_lines[j] if j < len(right_table_lines) else ""
+            output_lines.append(f"{left_part:<{TABLE_WIDTH}}{separator}{right_part}")
+        output_lines.append("")
+
+    final_output = "\n".join(output_lines) + "\n\x1b[J"
+    print(final_output, end="")
+    
+    _lines_printed = len(output_lines)
+    sys.stdout.flush()
+
+def main():
+    """
+    Main function to set up the WebSocket and run the display loop.
+    """
+    global recent_fills
+    parser = argparse.ArgumentParser(description="Monitor live fills for specific wallet addresses on Hyperliquid.")
+    parser.add_argument(
+        "--addresses",
+        nargs='+',
+        default=DEFAULT_ADDRESSES_TO_WATCH,
+        help="A space-separated list of Ethereum addresses to monitor."
+    )
+    parser.add_argument(
+        "--log-level",
+        default="normal",
+        choices=['off', 'normal', 'debug'],
+        help="Set the logging level for the script."
+    )
+    args = parser.parse_args()
+    
+    setup_logging(args.log_level, 'AddressMonitor')
+
+    # --- ADDED: Ensure the logs directory exists ---
+    if not os.path.exists(LOGS_DIR):
+        os.makedirs(LOGS_DIR)
+
+    addresses_to_watch = []
+    for addr in args.addresses:
+        clean_addr = addr.strip().lower()
+        if len(clean_addr) == 42 and clean_addr.startswith('0x'):
+            addresses_to_watch.append(clean_addr)
+        else:
+            logging.warning(f"Invalid or malformed address provided: '{addr}'. Skipping.")
+
+    recent_fills = {addr: deque(maxlen=MAX_FILLS_TO_DISPLAY) for addr in addresses_to_watch}
+
+    if not addresses_to_watch:
+        print("No valid addresses configured to watch. Exiting.", file=sys.stderr)
+        return
+
+    info = Info(constants.MAINNET_API_URL, skip_ws=False)
+    
+    for addr in addresses_to_watch:
+        try:
+            info.subscribe({"type": "userFills", "user": addr}, on_message)
+            logging.debug(f"Queued subscribe for userFills: {addr}")
+            time.sleep(0.02)
+        except Exception as e:
+            logging.error(f"Failed to subscribe for {addr}: {e}")
+
+    logging.info(f"Subscribed to userFills for {len(addresses_to_watch)} addresses")
+
+    print("\nDisplaying live fill data... Press Ctrl+C to stop.")
+    try:
+        while True:
+            display_dashboard()
+            time.sleep(0.2)
+    except KeyboardInterrupt:
+        print("\nStopping WebSocket listener...")
+        info.ws_manager.stop()
+        print("Listener stopped.")
+
+if __name__ == "__main__":
+    main()
+

agents

@@ -1,3 +1,19 @@
-agent 001
-wallet: 0x7773833262f020c7979ec8aae38455c17ba4040c
-Private Key: 0x659326d719a4322244d6e7f28e7fa2780f034e9f6a342ef1919664817e6248df
+==================================================  
+SAVE THESE SECURELY. This is what your bot will use.
+   Name: trade_executor
+   (Agent has a default long-term validity)
+🔑 Agent Private Key: 0xabed7379ec33253694eba50af8a392a88ea32b72b5f4f9cddceb0f5879428b69
+🏠 Agent Address: 0xcB262CeAaE5D8A99b713f87a43Dd18E6Be892739
+==================================================
+SAVE THESE SECURELY. This is what your bot will use.
+   Name: executor_scalper
+   (Agent has a default long-term validity)
+🔑 Agent Private Key: 0xe7bd4f3a1e29252ec40edff1bf796beaf13993d23a0c288a75d79c53e3c97812
+🏠 Agent Address: 0xD211ba67162aD4E785cd4894D00A1A7A32843094
+==================================================
+SAVE THESE SECURELY. This is what your bot will use.
+   Name: executor_swing
+   (Agent has a default long-term validity)
+🔑 Agent Private Key: 0xb6811c8b4a928556b3b95ccfaf72eb452b0d89a903f251b86955654672a3b6ab
+🏠 Agent Address: 0xAD27c936672Fa368c2d96a47FDA34e8e3A0f318C
+==================================================

backtester.py

@@ -0,0 +1,368 @@
+import argparse
+import logging
+import os
+import sys
+import sqlite3
+import pandas as pd
+import json
+from datetime import datetime, timedelta
+import itertools
+import multiprocessing
+from functools import partial
+import time
+import importlib
+import signal
+
+from logging_utils import setup_logging
+
+def _run_trade_simulation(df: pd.DataFrame, capital: float, size_pct: float, leverage_long: int, leverage_short: int, taker_fee_pct: float, maker_fee_pct: float) -> tuple[float, list]:
+    """
+    Simulates a trading strategy with portfolio management, including capital,
+    position sizing, leverage, and fees.
+    """
+    df.dropna(inplace=True)
+    if df.empty: return capital, []
+
+    df['position_change'] = df['signal'].diff()
+    trades = []
+    entry_price = 0
+    asset_size = 0
+    current_position = 0 # 0=flat, 1=long, -1=short
+    equity = capital
+
+    for i, row in df.iterrows():
+        # --- Close Positions ---
+        if (current_position == 1 and row['signal'] != 1) or \
+           (current_position == -1 and row['signal'] != -1):
+            
+            exit_value = asset_size * row['close']
+            fee = exit_value * (taker_fee_pct / 100)
+            
+            if current_position == 1: # Closing a long
+                pnl_usd = (row['close'] - entry_price) * asset_size
+                equity += pnl_usd - fee
+                trades.append({'pnl_usd': pnl_usd, 'pnl_pct': (row['close'] - entry_price) / entry_price, 'type': 'long'})
+            
+            elif current_position == -1: # Closing a short
+                pnl_usd = (entry_price - row['close']) * asset_size
+                equity += pnl_usd - fee
+                trades.append({'pnl_usd': pnl_usd, 'pnl_pct': (entry_price - row['close']) / entry_price, 'type': 'short'})
+
+            entry_price = 0
+            asset_size = 0
+            current_position = 0
+
+        # --- Open New Positions ---
+        if current_position == 0:
+            if row['signal'] == 1: # Open Long
+                margin_to_use = equity * (size_pct / 100)
+                trade_value = margin_to_use * leverage_long
+                asset_size = trade_value / row['close']
+                fee = trade_value * (taker_fee_pct / 100)
+                equity -= fee
+                entry_price = row['close']
+                current_position = 1
+            elif row['signal'] == -1: # Open Short
+                margin_to_use = equity * (size_pct / 100)
+                trade_value = margin_to_use * leverage_short
+                asset_size = trade_value / row['close']
+                fee = trade_value * (taker_fee_pct / 100)
+                equity -= fee
+                entry_price = row['close']
+                current_position = -1
+
+    return equity, trades
+
+
+def simulation_worker(params: dict, db_path: str, coin: str, timeframe: str, start_date: str, end_date: str, strategy_class, sim_params: dict) -> tuple[dict, float, list]:
+    """
+    Worker function that loads data, runs the full simulation, and returns results.
+    """
+    df = pd.DataFrame()
+    try:
+        with sqlite3.connect(db_path) as conn:
+            query = f'SELECT datetime_utc, open, high, low, close FROM "{coin}_{timeframe}" WHERE datetime_utc >= ? AND datetime_utc <= ? ORDER BY datetime_utc'
+            df = pd.read_sql(query, conn, params=(start_date, end_date), parse_dates=['datetime_utc'])
+            if not df.empty:
+                df.set_index('datetime_utc', inplace=True)
+    except Exception as e:
+        print(f"Worker error loading data for params {params}: {e}")
+        return (params, sim_params['capital'], [])
+
+    if df.empty:
+        return (params, sim_params['capital'], [])
+    
+    strategy_instance = strategy_class(params)
+    df_with_signals = strategy_instance.calculate_signals(df)
+    
+    final_equity, trades = _run_trade_simulation(df_with_signals, **sim_params)
+    return (params, final_equity, trades)
+
+
+def init_worker():
+    signal.signal(signal.SIGINT, signal.SIG_IGN)
+
+
+class Backtester:
+    def __init__(self, log_level: str, strategy_name_to_test: str, start_date: str, sim_params: dict):
+        setup_logging(log_level, 'Backtester')
+        self.db_path = os.path.join("_data", "market_data.db")
+        self.simulation_params = sim_params
+        
+        self.backtest_config = self._load_backtest_config(strategy_name_to_test)
+        # ... (rest of __init__ is unchanged)
+        self.strategy_name = self.backtest_config.get('strategy_name')
+        self.strategy_config = self._load_strategy_config()
+        self.params = self.strategy_config.get('parameters', {})
+        self.coin = self.params.get('coin')
+        self.timeframe = self.params.get('timeframe')
+        self.pool = None
+        self.full_history_start_date = start_date
+        try:
+            module_path, class_name = self.backtest_config['script'].rsplit('.', 1)
+            module = importlib.import_module(module_path)
+            self.strategy_class = getattr(module, class_name)
+            logging.info(f"Successfully loaded strategy class '{class_name}'.")
+        except (ImportError, AttributeError, KeyError) as e:
+            logging.error(f"Could not load strategy script '{self.backtest_config.get('script')}': {e}")
+            sys.exit(1)
+            
+    def _load_backtest_config(self, name_to_test: str):
+        # ... (unchanged)
+        config_path = os.path.join("_data", "backtesting_conf.json")
+        try:
+            with open(config_path, 'r') as f: return json.load(f).get(name_to_test)
+        except (FileNotFoundError, json.JSONDecodeError) as e:
+            logging.error(f"Could not load backtesting configuration: {e}")
+            return None
+            
+    def _load_strategy_config(self):
+        # ... (unchanged)
+        config_path = os.path.join("_data", "strategies.json")
+        try:
+            with open(config_path, 'r') as f: return json.load(f).get(self.strategy_name)
+        except (FileNotFoundError, json.JSONDecodeError) as e:
+            logging.error(f"Could not load strategy configuration: {e}")
+            return None
+
+    def run_walk_forward_optimization(self, optimization_weeks: int, testing_weeks: int, step_weeks: int):
+        # ... (unchanged, will now use the new simulation logic via the worker)
+        full_df = self.load_data(self.full_history_start_date, datetime.now().strftime("%Y-%m-%d"))
+        if full_df.empty: return
+
+        optimization_delta = timedelta(weeks=optimization_weeks)
+        testing_delta = timedelta(weeks=testing_weeks)
+        step_delta = timedelta(weeks=step_weeks)
+
+        all_out_of_sample_trades = []
+        all_period_summaries = []
+
+        current_date = full_df.index[0]
+        end_date = full_df.index[-1]
+        
+        period_num = 1
+        while current_date + optimization_delta + testing_delta <= end_date:
+            logging.info(f"\n--- Starting Walk-Forward Period {period_num} ---")
+            
+            in_sample_start = current_date
+            in_sample_end = in_sample_start + optimization_delta
+            out_of_sample_end = in_sample_end + testing_delta
+
+            in_sample_df = full_df[in_sample_start:in_sample_end]
+            out_of_sample_df = full_df[in_sample_end:out_of_sample_end]
+
+            if in_sample_df.empty or out_of_sample_df.empty:
+                break
+
+            logging.info(f"In-Sample (Optimization): {in_sample_df.index[0].date()} to {in_sample_df.index[-1].date()}")
+            logging.info(f"Out-of-Sample (Testing):  {out_of_sample_df.index[0].date()} to {out_of_sample_df.index[-1].date()}")
+            
+            best_result = self._find_best_params(in_sample_df)
+            if not best_result:
+                all_period_summaries.append({"period": period_num, "params": "None Found"})
+                current_date += step_delta
+                period_num += 1
+                continue
+            
+            print("\n--- [1] In-Sample Optimization Result ---")
+            print(f"Best Parameters Found: {best_result['params']}")
+            self._generate_report(best_result['final_equity'], best_result['trades_list'], "In-Sample Performance with Best Params")
+
+            logging.info(f"\n--- [2] Forward Testing on Out-of-Sample Data ---")
+            df_with_signals = self.strategy_class(best_result['params']).calculate_signals(out_of_sample_df.copy())
+            final_equity_oos, out_of_sample_trades = _run_trade_simulation(df_with_signals, **self.simulation_params)
+            
+            all_out_of_sample_trades.extend(out_of_sample_trades)
+            oos_summary = self._generate_report(final_equity_oos, out_of_sample_trades, "Out-of-Sample Performance")
+            
+            # Store the summary for the final table
+            summary_to_store = {"period": period_num, "params": best_result['params'], **oos_summary}
+            all_period_summaries.append(summary_to_store)
+
+            current_date += step_delta
+            period_num += 1
+        
+        # ... (Final reports will be generated here, but need to adapt to equity tracking)
+        print("\n" + "="*50)
+        # self._generate_report(all_out_of_sample_trades, "FINAL AGGREGATE WALK-FORWARD PERFORMANCE")
+        print("="*50)
+
+        # --- ADDED: Final summary table of best parameters and performance per period ---
+        print("\n--- Summary of Best Parameters and Performance per Period ---")
+        header = f"{'#':<3} | {'Best Parameters':<30} | {'Trades':>8} | {'Longs':>6} | {'Shorts':>7} | {'Win %':>8} | {'L Win %':>9} | {'S Win %':>9} | {'Return %':>10} | {'Equity':>15}"
+        print(header)
+        print("-" * len(header))
+        for item in all_period_summaries:
+            params_str = str(item.get('params', 'N/A'))
+            trades = item.get('num_trades', 'N/A')
+            longs = item.get('num_longs', 'N/A')
+            shorts = item.get('num_shorts', 'N/A')
+            win_rate = f"{item.get('win_rate', 0):.2f}%" if 'win_rate' in item else 'N/A'
+            long_win_rate = f"{item.get('long_win_rate', 0):.2f}%" if 'long_win_rate' in item else 'N/A'
+            short_win_rate = f"{item.get('short_win_rate', 0):.2f}%" if 'short_win_rate' in item else 'N/A'
+            return_pct = f"{item.get('return_pct', 0):.2f}%" if 'return_pct' in item else 'N/A'
+            equity = f"${item.get('final_equity', 0):,.2f}" if 'final_equity' in item else 'N/A'
+            print(f"{item['period']:<3} | {params_str:<30} | {trades:>8} | {longs:>6} | {shorts:>7} | {win_rate:>8} | {long_win_rate:>9} | {short_win_rate:>9} | {return_pct:>10} | {equity:>15}")
+            
+    def _find_best_params(self, df: pd.DataFrame) -> dict:
+        param_configs = self.backtest_config.get('optimization_params', {})
+        param_names = list(param_configs.keys())
+        param_ranges = [range(p['start'], p['end'] + 1, p['step']) for p in param_configs.values()]
+        
+        all_combinations = list(itertools.product(*param_ranges))
+        param_dicts = [dict(zip(param_names, combo)) for combo in all_combinations]
+        
+        logging.info(f"Optimizing on {len(all_combinations)} combinations...")
+
+        num_cores = 60
+        self.pool = multiprocessing.Pool(processes=num_cores, initializer=init_worker)
+        
+        worker = partial(
+            simulation_worker,
+            db_path=self.db_path, coin=self.coin, timeframe=self.timeframe,
+            start_date=df.index[0].isoformat(), end_date=df.index[-1].isoformat(),
+            strategy_class=self.strategy_class,
+            sim_params=self.simulation_params
+        )
+
+        all_results = self.pool.map(worker, param_dicts)
+
+        self.pool.close()
+        self.pool.join()
+        self.pool = None
+
+        results = [{'params': params, 'final_equity': final_equity, 'trades_list': trades} for params, final_equity, trades in all_results if trades]
+        if not results: return None
+        return max(results, key=lambda x: x['final_equity'])
+        
+    def load_data(self, start_date, end_date):
+        # ... (unchanged)
+        table_name = f"{self.coin}_{self.timeframe}"
+        logging.info(f"Loading full dataset for {table_name}...")
+        try:
+            with sqlite3.connect(self.db_path) as conn:
+                query = f'SELECT * FROM "{table_name}" WHERE datetime_utc >= ? AND datetime_utc <= ? ORDER BY datetime_utc'
+                df = pd.read_sql(query, conn, params=(start_date, end_date), parse_dates=['datetime_utc'])
+                if df.empty: return pd.DataFrame()
+                df.set_index('datetime_utc', inplace=True)
+                return df
+        except Exception as e:
+            logging.error(f"Failed to load data for backtest: {e}")
+            return pd.DataFrame()
+
+    def _generate_report(self, final_equity: float, trades: list, title: str) -> dict:
+        """Calculates, prints, and returns a detailed performance report."""
+        print(f"\n--- {title} ---")
+        
+        initial_capital = self.simulation_params['capital']
+        
+        if not trades:
+            print("No trades were executed during this period.")
+            print(f"Final Equity:           ${initial_capital:,.2f}")
+            return {"num_trades": 0, "num_longs": 0, "num_shorts": 0, "win_rate": 0, "long_win_rate": 0, "short_win_rate": 0, "return_pct": 0, "final_equity": initial_capital}
+
+        num_trades = len(trades)
+        long_trades = [t for t in trades if t.get('type') == 'long']
+        short_trades = [t for t in trades if t.get('type') == 'short']
+        
+        pnls_pct = pd.Series([t['pnl_pct'] for t in trades])
+        
+        wins = pnls_pct[pnls_pct > 0]
+        win_rate = (len(wins) / num_trades) * 100 if num_trades > 0 else 0
+        
+        long_wins = len([t for t in long_trades if t['pnl_pct'] > 0])
+        short_wins = len([t for t in short_trades if t['pnl_pct'] > 0])
+        long_win_rate = (long_wins / len(long_trades)) * 100 if long_trades else 0
+        short_win_rate = (short_wins / len(short_trades)) * 100 if short_trades else 0
+        
+        total_return_pct = ((final_equity - initial_capital) / initial_capital) * 100
+        
+        print(f"Final Equity:           ${final_equity:,.2f}")
+        print(f"Total Return:           {total_return_pct:.2f}%")
+        print(f"Total Trades:           {num_trades} (Longs: {len(long_trades)}, Shorts: {len(short_trades)})")
+        print(f"Win Rate (Overall):     {win_rate:.2f}%")
+        print(f"Win Rate (Longs):       {long_win_rate:.2f}%")
+        print(f"Win Rate (Shorts):      {short_win_rate:.2f}%")
+        
+        # Return a dictionary of the key metrics for the summary table
+        return {
+            "num_trades": num_trades,
+            "num_longs": len(long_trades),
+            "num_shorts": len(short_trades),
+            "win_rate": win_rate,
+            "long_win_rate": long_win_rate,
+            "short_win_rate": short_win_rate,
+            "return_pct": total_return_pct,
+            "final_equity": final_equity
+        }
+
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser(description="Run a Walk-Forward Optimization for a trading strategy.")
+    parser.add_argument("--strategy", required=True, help="The name of the backtest config to run.")
+    parser.add_argument("--start-date", default="2020-08-01", help="The overall start date for historical data.")
+    parser.add_argument("--optimization-weeks", type=int, default=4)
+    parser.add_argument("--testing-weeks", type=int, default=1)
+    parser.add_argument("--step-weeks", type=int, default=1)
+    parser.add_argument("--log-level", default="normal", choices=['off', 'normal', 'debug'])
+    
+    parser.add_argument("--capital", type=float, default=1000)
+    parser.add_argument("--size-pct", type=float, default=50)
+    parser.add_argument("--leverage-long", type=int, default=3)
+    parser.add_argument("--leverage-short", type=int, default=2)
+    parser.add_argument("--taker-fee-pct", type=float, default=0.045)
+    parser.add_argument("--maker-fee-pct", type=float, default=0.015)
+
+    args = parser.parse_args()
+
+    sim_params = {
+        "capital": args.capital,
+        "size_pct": args.size_pct,
+        "leverage_long": args.leverage_long,
+        "leverage_short": args.leverage_short,
+        "taker_fee_pct": args.taker_fee_pct,
+        "maker_fee_pct": args.maker_fee_pct
+    }
+
+    backtester = Backtester(
+        log_level=args.log_level,
+        strategy_name_to_test=args.strategy,
+        start_date=args.start_date,
+        sim_params=sim_params
+    )
+    
+    try:
+        backtester.run_walk_forward_optimization(
+            optimization_weeks=args.optimization_weeks,
+            testing_weeks=args.testing_weeks,
+            step_weeks=args.step_weeks
+        )
+    except KeyboardInterrupt:
+        logging.info("\nBacktest optimization cancelled by user.")
+    finally:
+        if backtester.pool:
+            logging.info("Terminating worker processes...")
+            backtester.pool.terminate()
+            backtester.pool.join()
+            logging.info("Worker processes terminated.")
+

base_strategy.py

@@ -0,0 +1,165 @@
+from abc import ABC, abstractmethod
+import pandas as pd
+import json
+import os
+import logging
+from datetime import datetime, timezone
+import sqlite3
+import multiprocessing
+import time
+
+from logging_utils import setup_logging
+from hyperliquid.info import Info
+from hyperliquid.utils import constants
+
+class BaseStrategy(ABC):
+    """
+    An abstract base class that defines the blueprint for all trading strategies.
+    It provides common functionality like loading data, saving status, and state management.
+    """
+    
+    def __init__(self, strategy_name: str, params: dict, trade_signal_queue: multiprocessing.Queue = None, shared_status: dict = None):
+        self.strategy_name = strategy_name
+        self.params = params
+        self.trade_signal_queue = trade_signal_queue
+        # Optional multiprocessing.Manager().dict() to hold live status (avoids file IO)
+        self.shared_status = shared_status
+        
+        self.coin = params.get("coin", "N/A")
+        self.timeframe = params.get("timeframe", "N/A")
+        self.db_path = os.path.join("_data", "market_data.db")
+        self.status_file_path = os.path.join("_data", f"strategy_status_{self.strategy_name}.json")
+
+        self.current_signal = "INIT"
+        self.last_signal_change_utc = None
+        self.signal_price = None
+
+        # Note: Logging is set up by the run_strategy function
+
+    def load_data(self) -> pd.DataFrame:
+        """Loads historical data for the configured coin and timeframe."""
+        table_name = f"{self.coin}_{self.timeframe}"
+        
+        periods = [v for k, v in self.params.items() if 'period' in k or '_ma' in k or 'slow' in k or 'fast' in k]
+        limit = max(periods) + 50 if periods else 500
+
+        try:
+            with sqlite3.connect(f"file:{self.db_path}?mode=ro", uri=True) as conn:
+                query = f'SELECT * FROM "{table_name}" ORDER BY datetime_utc DESC LIMIT {limit}'
+                df = pd.read_sql(query, conn, parse_dates=['datetime_utc'])
+                if df.empty: return pd.DataFrame()
+                df.set_index('datetime_utc', inplace=True)
+                df.sort_index(inplace=True)
+                return df
+        except Exception as e:
+            logging.error(f"Failed to load data from table '{table_name}': {e}")
+            return pd.DataFrame()
+
+    @abstractmethod
+    def calculate_signals(self, df: pd.DataFrame) -> pd.DataFrame:
+        """The core logic of the strategy. Must be implemented by child classes."""
+        pass
+        
+    def calculate_signals_and_state(self, df: pd.DataFrame) -> bool:
+        """
+        A wrapper that calls the strategy's signal calculation, determines
+        the last signal change, and returns True if the signal has changed.
+        """
+        df_with_signals = self.calculate_signals(df)
+        df_with_signals.dropna(inplace=True)
+        if df_with_signals.empty:
+            return False
+
+        df_with_signals['position_change'] = df_with_signals['signal'].diff()
+        
+        last_signal_int = df_with_signals['signal'].iloc[-1]
+        new_signal_str = "HOLD"
+        if last_signal_int == 1: new_signal_str = "BUY"
+        elif last_signal_int == -1: new_signal_str = "SELL"
+        
+        signal_changed = False
+        if self.current_signal == "INIT":
+            if new_signal_str == "BUY": self.current_signal = "INIT_BUY"
+            elif new_signal_str == "SELL": self.current_signal = "INIT_SELL"
+            else: self.current_signal = "HOLD"
+            signal_changed = True
+        elif new_signal_str != self.current_signal:
+            self.current_signal = new_signal_str
+            signal_changed = True
+        
+        if signal_changed:
+            last_change_series = df_with_signals[df_with_signals['position_change'] != 0]
+            if not last_change_series.empty:
+                last_change_row = last_change_series.iloc[-1]
+                self.last_signal_change_utc = last_change_row.name.tz_localize('UTC').isoformat()
+                self.signal_price = last_change_row['close']
+        
+        return signal_changed
+
+    def _save_status(self):
+        """Saves the current strategy state to its JSON file."""
+        status = {
+            "strategy_name": self.strategy_name,
+            "current_signal": self.current_signal,
+            "last_signal_change_utc": self.last_signal_change_utc,
+            "signal_price": self.signal_price,
+            "last_checked_utc": datetime.now(timezone.utc).isoformat()
+        }
+        # If a shared status dict is provided (Manager.dict()), update it instead of writing files
+        try:
+            if self.shared_status is not None:
+                try:
+                    # store the status under the strategy name for easy lookup
+                    self.shared_status[self.strategy_name] = status
+                except Exception:
+                    # Manager proxies may not accept nested mutable objects consistently; assign a copy
+                    self.shared_status[self.strategy_name] = dict(status)
+            else:
+                with open(self.status_file_path, 'w', encoding='utf-8') as f:
+                    json.dump(status, f, indent=4)
+        except IOError as e:
+            logging.error(f"Failed to write status file for {self.strategy_name}: {e}")
+
+    def run_polling_loop(self):
+        """
+        The default execution loop for polling-based strategies (e.g., SMAs).
+        """
+        while True:
+            df = self.load_data()
+            if df.empty:
+                logging.warning("No data loaded. Waiting 1 minute...")
+                time.sleep(60)
+                continue
+            
+            signal_changed = self.calculate_signals_and_state(df.copy())
+            self._save_status() 
+            
+            if signal_changed or self.current_signal == "INIT_BUY" or self.current_signal == "INIT_SELL":
+                logging.warning(f"New signal detected: {self.current_signal}")
+                self.trade_signal_queue.put({
+                    "strategy_name": self.strategy_name,
+                    "signal": self.current_signal,
+                    "coin": self.coin,
+                    "signal_price": self.signal_price,
+                    "config": {"agent": self.params.get("agent"), "parameters": self.params}
+                })
+                if self.current_signal == "INIT_BUY": self.current_signal = "BUY"
+                if self.current_signal == "INIT_SELL": self.current_signal = "SELL"
+
+            logging.info(f"Current Signal: {self.current_signal}")
+            time.sleep(60)
+
+    def run_event_loop(self):
+        """
+        A placeholder for event-driven (WebSocket) strategies.
+        Child classes must override this.
+        """
+        logging.error("run_event_loop() is not implemented for this strategy.")
+        time.sleep(3600) # Sleep for an hour to prevent rapid error loops
+        
+    def on_fill_message(self, message):
+        """
+        Placeholder for the WebSocket callback.
+        Child classes must override this.
+        """
+        pass

basic_ws.py

@@ -0,0 +1,31 @@
+import os
+import sys
+import time
+import json
+from datetime import datetime, timezone
+from hyperliquid.info import Info
+from hyperliquid.utils import constants
+from collections import deque
+
+def main():
+    address, info, _ = example_utils.setup(constants.MAINNET_API_URL)
+    # An example showing how to subscribe to the different subscription types and prints the returned messages
+    # Some subscriptions do not return snapshots, so you will not receive a message until something happens
+    info.subscribe({"type": "allMids"}, print)
+    info.subscribe({"type": "l2Book", "coin": "ETH"}, print)
+    info.subscribe({"type": "trades", "coin": "PURR/USDC"}, print)
+    info.subscribe({"type": "userEvents", "user": address}, print)
+    info.subscribe({"type": "userFills", "user": address}, print)
+    info.subscribe({"type": "candle", "coin": "ETH", "interval": "1m"}, print)
+    info.subscribe({"type": "orderUpdates", "user": address}, print)
+    info.subscribe({"type": "userFundings", "user": address}, print)
+    info.subscribe({"type": "userNonFundingLedgerUpdates", "user": address}, print)
+    info.subscribe({"type": "webData2", "user": address}, print)
+    info.subscribe({"type": "bbo", "coin": "ETH"}, print)
+    info.subscribe({"type": "activeAssetCtx", "coin": "BTC"}, print)  # Perp
+    info.subscribe({"type": "activeAssetCtx", "coin": "@1"}, print)  # Spot
+    info.subscribe({"type": "activeAssetData", "user": address, "coin": "BTC"}, print)  # Perp only
+
+
+if __name__ == "__main__":
+    main()

coin_id_map.py

@@ -0,0 +1,95 @@
+import os
+import json
+import logging
+import requests
+from hyperliquid.info import Info
+from hyperliquid.utils import constants
+
+from logging_utils import setup_logging
+
+def update_coin_mapping():
+    """
+    Fetches all assets from Hyperliquid and all coins from CoinGecko,
+    then creates and saves a mapping from the Hyperliquid symbol to the
+    CoinGecko ID using a robust matching algorithm.
+    """
+    setup_logging('normal', 'CoinMapUpdater')
+    logging.info("Starting coin mapping update process...")
+    
+    # --- 1. Fetch all assets from Hyperliquid ---
+    try:
+        logging.info("Fetching assets from Hyperliquid...")
+        info = Info(constants.MAINNET_API_URL, skip_ws=True)
+        meta, asset_contexts = info.meta_and_asset_ctxs()
+        hyperliquid_assets = meta['universe']
+        logging.info(f"Found {len(hyperliquid_assets)} assets on Hyperliquid.")
+    except Exception as e:
+        logging.error(f"Failed to fetch assets from Hyperliquid: {e}")
+        return
+
+    # --- 2. Fetch all coins from CoinGecko ---
+    try:
+        logging.info("Fetching coin list from CoinGecko...")
+        response = requests.get("https://api.coingecko.com/api/v3/coins/list")
+        response.raise_for_status()
+        coingecko_coins = response.json()
+        
+        # Create more robust lookup tables
+        cg_symbol_lookup = {coin['symbol'].upper(): coin['id'] for coin in coingecko_coins}
+        cg_name_lookup = {coin['name'].upper(): coin['id'] for coin in coingecko_coins}
+
+        logging.info(f"Found {len(coingecko_coins)} coins on CoinGecko.")
+    except requests.exceptions.RequestException as e:
+        logging.error(f"Failed to fetch coin list from CoinGecko: {e}")
+        return
+
+    # --- 3. Create the mapping ---
+    final_mapping = {}
+    # Use manual overrides for critical coins where symbols are ambiguous
+    manual_overrides = {
+        "BTC": "bitcoin",
+        "ETH": "ethereum",
+        "SOL": "solana",
+        "BNB": "binancecoin",
+        "HYPE": "hyperliquid",
+        "PUMP": "pump-fun",
+        "ASTER": "astar",
+        "ZEC": "zcash",
+        "SUI": "sui",
+        "ACE": "endurance",
+        # Add other important ones you watch here
+    }
+
+    logging.info("Generating symbol-to-id mapping...")
+    for asset in hyperliquid_assets:
+        asset_symbol = asset['name'].upper()
+        asset_name = asset.get('name', '').upper() # Use full name if available
+
+        # Priority 1: Manual Overrides
+        if asset_symbol in manual_overrides:
+            final_mapping[asset_symbol] = manual_overrides[asset_symbol]
+            continue
+        
+        # Priority 2: Exact Name Match
+        if asset_name in cg_name_lookup:
+            final_mapping[asset_symbol] = cg_name_lookup[asset_name]
+            continue
+            
+        # Priority 3: Symbol Match
+        if asset_symbol in cg_symbol_lookup:
+            final_mapping[asset_symbol] = cg_symbol_lookup[asset_symbol]
+        else:
+            logging.warning(f"No match found for '{asset_symbol}' on CoinGecko. It will be excluded.")
+
+    # --- 4. Save the mapping to a file ---
+    map_file_path = os.path.join("_data", "coin_id_map.json")
+    try:
+        with open(map_file_path, 'w', encoding='utf-8') as f:
+            json.dump(final_mapping, f, indent=4, sort_keys=True)
+        logging.info(f"Successfully saved new coin mapping with {len(final_mapping)} entries to '{map_file_path}'.")
+    except IOError as e:
+        logging.error(f"Failed to write coin mapping file: {e}")
+
+if __name__ == "__main__":
+    update_coin_mapping()
+

create_agent.py

@@ -0,0 +1,70 @@
+import os
+from eth_account import Account
+from hyperliquid.exchange import Exchange
+from hyperliquid.utils import constants
+from dotenv import load_dotenv
+from datetime import datetime, timedelta
+import json
+
+# Load environment variables from a .env file if it exists
+load_dotenv()
+
+def create_and_authorize_agent():
+    """
+    Creates and authorizes a new agent key pair using your main wallet,
+    following the correct SDK pattern.
+    """
+    # --- STEP 1: Load your main wallet ---
+    # This is the wallet that holds the funds and has been activated on Hyperliquid.
+    main_wallet_private_key = os.environ.get("MAIN_WALLET_PRIVATE_KEY")
+    if not main_wallet_private_key:
+        main_wallet_private_key = input("Please enter the private key of your MAIN trading wallet: ")
+
+    try:
+        main_account = Account.from_key(main_wallet_private_key)
+        print(f"\n✅ Loaded main wallet: {main_account.address}")
+    except Exception as e:
+        print(f"❌ Error: Invalid main wallet private key provided. Details: {e}")
+        return
+
+    # --- STEP 2: Initialize the Exchange with your MAIN account ---
+    # This object is used to send the authorization transaction.
+    exchange = Exchange(main_account, constants.MAINNET_API_URL, account_address=main_account.address)
+
+    # --- STEP 3: Create and approve the agent with a specific name ---
+    # agent name must be between 1 and 16 characters long
+    agent_name = "executor_swing"
+
+    print(f"\n🔗 Authorizing a new agent named '{agent_name}'...")
+    try:
+        # --- FIX: Pass only the agent name string to the function ---
+        approve_result, agent_private_key = exchange.approve_agent(agent_name)
+        
+        if approve_result.get("status") == "ok":
+            # Derive the agent's public address from the key we received
+            agent_account = Account.from_key(agent_private_key)
+            
+            print("\n🎉 SUCCESS! Agent has been authorized on-chain.")
+            print("="*50)
+            print("SAVE THESE SECURELY. This is what your bot will use.")
+            print(f"   Name: {agent_name}")
+            print(f"   (Agent has a default long-term validity)")
+            print(f"🔑 Agent Private Key: {agent_private_key}")
+            print(f"🏠 Agent Address: {agent_account.address}")
+            print("="*50)
+            print("\nYou can now set this private key as the AGENT_PRIVATE_KEY environment variable.")
+        else:
+            print("\n❌ ERROR: Agent authorization failed.")
+            print("   Response:", approve_result)
+            if "Vault may not perform this action" in str(approve_result):
+                 print("\n   ACTION REQUIRED: This error means your main wallet (vault) has not been activated. "
+                       "Please go to the Hyperliquid website, connect this wallet, and make a deposit to activate it.")
+
+
+    except Exception as e:
+        print(f"\nAn unexpected error occurred during authorization: {e}")
+
+
+if __name__ == "__main__":
+    create_and_authorize_agent()
+

dashboard_data_fetcher.py

@@ -0,0 +1,136 @@
+import logging
+import os
+import sys
+import json
+import time
+import argparse  # <-- THE FIX: Added this import
+from datetime import datetime
+from eth_account import Account
+from hyperliquid.info import Info
+from hyperliquid.utils import constants
+from dotenv import load_dotenv
+
+from logging_utils import setup_logging
+
+# Load .env file
+load_dotenv()
+
+class DashboardDataFetcher:
+    """
+    A dedicated, lightweight process that runs in a loop to fetch and save
+    the account's state (balances, positions) for the main dashboard to display.
+    """
+
+    def __init__(self, log_level: str):
+        setup_logging(log_level, 'DashboardDataFetcher')
+        
+        self.vault_address = os.environ.get("MAIN_WALLET_ADDRESS")
+        if not self.vault_address:
+            logging.error("MAIN_WALLET_ADDRESS not set in .env file. Cannot proceed.")
+            sys.exit(1)
+            
+        self.info = Info(constants.MAINNET_API_URL, skip_ws=True)
+        self.status_file_path = os.path.join("_logs", "trade_executor_status.json")
+        self.managed_positions_path = os.path.join("_data", "executor_managed_positions.json")
+        logging.info(f"Dashboard Data Fetcher initialized for vault: {self.vault_address}")
+
+    def load_managed_positions(self) -> dict:
+        """Loads the state of which strategy manages which position."""
+        if os.path.exists(self.managed_positions_path):
+            try:
+                with open(self.managed_positions_path, 'r') as f:
+                    data = json.load(f)
+                    # Create a reverse map: {coin: strategy_name}
+                    return {v['coin']: k for k, v in data.items()}
+            except (IOError, json.JSONDecodeError):
+                logging.warning("Could not read managed positions file.")
+        return {}
+    
+    def fetch_and_save_status(self):
+        """Fetches all account data and saves it to the JSON status file."""
+        try:
+            perpetuals_state = self.info.user_state(self.vault_address)
+            spot_state = self.info.spot_user_state(self.vault_address)
+            meta, all_market_contexts = self.info.meta_and_asset_ctxs()
+            coin_to_strategy_map = self.load_managed_positions()
+
+            status = {
+                "last_updated_utc": datetime.now().isoformat(),
+                "perpetuals_account": { "balances": {}, "open_positions": [] },
+                "spot_account": { "positions": [] }
+            }
+
+            # 1. Extract Perpetuals Account Data
+            margin_summary = perpetuals_state.get("marginSummary", {})
+            status["perpetuals_account"]["balances"] = {
+                "account_value": margin_summary.get("accountValue"),
+                "total_margin_used": margin_summary.get("totalMarginUsed"),
+                "withdrawable": margin_summary.get("withdrawable")
+            }
+
+            asset_positions = perpetuals_state.get("assetPositions", [])
+            for asset_pos in asset_positions:
+                pos = asset_pos.get('position', {})
+                if float(pos.get('szi', 0)) != 0:
+                    coin = pos.get('coin')
+                    position_value = float(pos.get('positionValue', 0))
+                    margin_used = float(pos.get('marginUsed', 0))
+                    leverage = position_value / margin_used if margin_used > 0 else 0
+                    
+                    position_info = {
+                        "coin": coin,
+                        "strategy": coin_to_strategy_map.get(coin, "Unmanaged"),
+                        "size": pos.get('szi'),
+                        "position_value": pos.get('positionValue'),
+                        "entry_price": pos.get('entryPx'),
+                        "mark_price": pos.get('markPx'),
+                        "pnl": pos.get('unrealizedPnl'),
+                        "liq_price": pos.get('liquidationPx'),
+                        "margin": pos.get('marginUsed'),
+                        "funding": pos.get('fundingRate'),
+                        "leverage": f"{leverage:.1f}x"
+                    }
+                    status["perpetuals_account"]["open_positions"].append(position_info)
+            
+            # 2. Extract Spot Account Data
+            price_map = { asset.get("universe", {}).get("name"): asset.get("markPx") for asset in all_market_contexts if asset.get("universe", {}).get("name") }
+            spot_balances = spot_state.get("balances", [])
+            for bal in spot_balances:
+                total_balance = float(bal.get('total', 0))
+                if total_balance > 0:
+                    coin = bal.get('coin')
+                    mark_price = float(price_map.get(coin, 0))
+                    status["spot_account"]["positions"].append({
+                        "coin": coin, "balance_size": total_balance,
+                        "position_value": total_balance * mark_price, "pnl": "N/A"
+                    })
+
+            # 3. Write to file
+            # Use atomic write to prevent partial reads from main_app
+            temp_file_path = self.status_file_path + ".tmp"
+            with open(temp_file_path, 'w', encoding='utf-8') as f:
+                json.dump(status, f, indent=4)
+            # Rename is atomic
+            os.replace(temp_file_path, self.status_file_path) 
+            
+            logging.debug(f"Successfully updated dashboard status file.")
+
+        except Exception as e:
+            logging.error(f"Failed to fetch or save account status: {e}")
+
+    def run(self):
+        """Main loop to periodically fetch and save data."""
+        while True:
+            self.fetch_and_save_status()
+            time.sleep(5) # Update dashboard data every 5 seconds
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser(description="Run the Dashboard Data Fetcher.")
+    parser.add_argument("--log-level", default="normal", choices=['off', 'normal', 'debug'])
+    args = parser.parse_args()
+    
+    fetcher = DashboardDataFetcher(log_level=args.log_level)
+    try:
+        fetcher.run()
+    except KeyboardInterrupt:
+        logging.info("Dashboard Data Fetcher stopped.")

del_market_cap_tables.py

@@ -0,0 +1,56 @@
+import sqlite3
+import logging
+import os
+
+from logging_utils import setup_logging
+
+def cleanup_market_cap_tables():
+    """
+    Scans the database and drops all tables related to market cap data
+    to allow for a clean refresh.
+    """
+    setup_logging('normal', 'DBCleanup')
+    db_path = os.path.join("_data", "market_data.db")
+
+    if not os.path.exists(db_path):
+        logging.error(f"Database file not found at '{db_path}'. Nothing to clean.")
+        return
+
+    logging.info(f"Connecting to database at '{db_path}'...")
+    try:
+        with sqlite3.connect(db_path) as conn:
+            cursor = conn.cursor()
+            
+            # Find all tables that were created by the market cap fetcher
+            cursor.execute("""
+                SELECT name FROM sqlite_master 
+                WHERE type='table' 
+                AND (name LIKE '%_market_cap' OR name LIKE 'TOTAL_%')
+            """)
+            
+            tables_to_drop = cursor.fetchall()
+            
+            if not tables_to_drop:
+                logging.info("No market cap tables found to clean up. Database is already clean.")
+                return
+
+            logging.warning(f"Found {len(tables_to_drop)} market cap tables to remove...")
+            
+            for table in tables_to_drop:
+                table_name = table[0]
+                try:
+                    logging.info(f"Dropping table: {table_name}...")
+                    conn.execute(f'DROP TABLE IF EXISTS "{table_name}"')
+                except Exception as e:
+                    logging.error(f"Failed to drop table {table_name}: {e}")
+            
+            conn.commit()
+            logging.info("--- Database cleanup complete ---")
+
+    except sqlite3.Error as e:
+        logging.error(f"A database error occurred: {e}")
+    except Exception as e:
+        logging.error(f"An unexpected error occurred: {e}")
+
+if __name__ == "__main__":
+    cleanup_market_cap_tables()

fix_timestamps.py

@@ -0,0 +1,118 @@
+import argparse
+import logging
+import os
+import sys
+import sqlite3
+import pandas as pd
+# script to fix missing millisecond timestamps in the database after import from CSVs (this is already fixed in import_csv.py)
+# Assuming logging_utils.py is in the same directory
+from logging_utils import setup_logging
+
+class DatabaseFixer:
+    """
+    Scans the SQLite database for rows with missing millisecond timestamps
+    and updates them based on the datetime_utc column.
+    """
+
+    def __init__(self, log_level: str, coin: str):
+        setup_logging(log_level, 'TimestampFixer')
+        self.coin = coin
+        self.table_name = f"{self.coin}_1m"
+        self.db_path = os.path.join("_data", "market_data.db")
+
+    def run(self):
+        """Orchestrates the entire database update and verification process."""
+        logging.info(f"Starting timestamp fix process for table '{self.table_name}'...")
+
+        if not os.path.exists(self.db_path):
+            logging.error(f"Database file not found at '{self.db_path}'. Exiting.")
+            sys.exit(1)
+
+        try:
+            with sqlite3.connect(self.db_path) as conn:
+                conn.execute("PRAGMA journal_mode=WAL;")
+                
+                # 1. Check how many rows need fixing
+                rows_to_fix_count = self._count_rows_to_fix(conn)
+                if rows_to_fix_count == 0:
+                    logging.info(f"No rows with missing timestamps found in '{self.table_name}'. No action needed.")
+                    return
+                
+                logging.info(f"Found {rows_to_fix_count:,} rows with missing timestamps to update.")
+
+                # 2. Process the table in chunks to conserve memory
+                updated_count = self._process_in_chunks(conn)
+
+                # 3. Provide a final summary
+                self._summarize_update(rows_to_fix_count, updated_count)
+
+        except Exception as e:
+            logging.error(f"A critical error occurred: {e}")
+
+    def _count_rows_to_fix(self, conn) -> int:
+        """Counts the number of rows where timestamp_ms is NULL."""
+        try:
+            return pd.read_sql(f'SELECT COUNT(*) FROM "{self.table_name}" WHERE timestamp_ms IS NULL', conn).iloc[0, 0]
+        except pd.io.sql.DatabaseError:
+            logging.error(f"Table '{self.table_name}' not found in the database. Cannot fix timestamps.")
+            sys.exit(1)
+
+    def _process_in_chunks(self, conn) -> int:
+        """Reads, calculates, and updates timestamps in manageable chunks."""
+        total_updated = 0
+        chunk_size = 50000  # Process 50,000 rows at a time
+        
+        # We select the special 'rowid' column to uniquely identify each row for updating
+        query = f'SELECT rowid, datetime_utc FROM "{self.table_name}" WHERE timestamp_ms IS NULL'
+        
+        for chunk_df in pd.read_sql_query(query, conn, chunksize=chunk_size):
+            if chunk_df.empty:
+                break
+                
+            logging.info(f"Processing a chunk of {len(chunk_df)} rows...")
+            
+            # Calculate the missing timestamps
+            chunk_df['datetime_utc'] = pd.to_datetime(chunk_df['datetime_utc'])
+            chunk_df['timestamp_ms'] = (chunk_df['datetime_utc'].astype('int64') // 10**6)
+            
+            # Prepare data for the update command: a list of (timestamp, rowid) tuples
+            update_data = list(zip(chunk_df['timestamp_ms'], chunk_df['rowid']))
+            
+            # Use executemany for a fast bulk update
+            cursor = conn.cursor()
+            cursor.executemany(f'UPDATE "{self.table_name}" SET timestamp_ms = ? WHERE rowid = ?', update_data)
+            conn.commit()
+            
+            total_updated += len(chunk_df)
+            logging.info(f"Updated {total_updated} rows so far...")
+            
+        return total_updated
+
+    def _summarize_update(self, expected_count: int, actual_count: int):
+        """Prints a final summary of the update process."""
+        logging.info("--- Timestamp Fix Summary ---")
+        print(f"\n{'Status':<25}: COMPLETE")
+        print("-" * 40)
+        print(f"{'Table Processed':<25}: {self.table_name}")
+        print(f"{'Rows Needing Update':<25}: {expected_count:,}")
+        print(f"{'Rows Successfully Updated':<25}: {actual_count:,}")
+        
+        if expected_count == actual_count:
+            logging.info("Verification successful: All necessary rows have been updated.")
+        else:
+            logging.warning("Verification warning: The number of updated rows does not match the expected count.")
+
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser(description="Fix missing millisecond timestamps in the SQLite database.")
+    parser.add_argument("--coin", default="BTC", help="The coin symbol for the table to fix (e.g., BTC).")
+    parser.add_argument(
+        "--log-level",
+        default="normal",
+        choices=['off', 'normal', 'debug'],
+        help="Set the logging level for the script."
+    )
+    args = parser.parse_args()
+    
+    fixer = DatabaseFixer(log_level=args.log_level, coin=args.coin)
+    fixer.run()

import_csv.py

@@ -0,0 +1,154 @@
+import argparse
+import logging
+import os
+import sys
+import sqlite3
+import pandas as pd
+from datetime import datetime
+
+# Assuming logging_utils.py is in the same directory
+from logging_utils import setup_logging
+
+class CsvImporter:
+    """
+    Imports historical candle data from a large CSV file into the SQLite database,
+    intelligently adding only the missing data.
+    """
+
+    def __init__(self, log_level: str, csv_path: str, coin: str):
+        setup_logging(log_level, 'CsvImporter')
+        if not os.path.exists(csv_path):
+            logging.error(f"CSV file not found at '{csv_path}'. Please check the path.")
+            sys.exit(1)
+            
+        self.csv_path = csv_path
+        self.coin = coin
+        # --- FIX: Corrected the f-string syntax for the table name ---
+        self.table_name = f"{self.coin}_1m"
+        self.db_path = os.path.join("_data", "market_data.db")
+        self.column_mapping = {
+            'Open time': 'datetime_utc',
+            'Open': 'open',
+            'High': 'high',
+            'Low': 'low',
+            'Close': 'close',
+            'Volume': 'volume',
+            'Number of trades': 'number_of_trades'
+        }
+
+    def run(self):
+        """Orchestrates the entire import and verification process."""
+        logging.info(f"Starting import process for '{self.coin}' from '{self.csv_path}'...")
+
+        with sqlite3.connect(self.db_path) as conn:
+            conn.execute("PRAGMA journal_mode=WAL;")
+            
+            # 1. Get the current state of the database
+            db_oldest, db_newest, initial_row_count = self._get_db_state(conn)
+
+            # 2. Read, clean, and filter the CSV data
+            new_data_df = self._process_and_filter_csv(db_oldest, db_newest)
+
+            if new_data_df.empty:
+                logging.info("No new data to import. Database is already up-to-date with the CSV file.")
+                return
+
+            # 3. Append the new data to the database
+            self._append_to_db(new_data_df, conn)
+
+            # 4. Summarize and verify the import
+            self._summarize_import(initial_row_count, len(new_data_df), conn)
+
+    def _get_db_state(self, conn) -> (datetime, datetime, int):
+        """Gets the oldest and newest timestamps and total row count from the DB table."""
+        try:
+            oldest = pd.read_sql(f'SELECT MIN(datetime_utc) FROM "{self.table_name}"', conn).iloc[0, 0]
+            newest = pd.read_sql(f'SELECT MAX(datetime_utc) FROM "{self.table_name}"', conn).iloc[0, 0]
+            count = pd.read_sql(f'SELECT COUNT(*) FROM "{self.table_name}"', conn).iloc[0, 0]
+            
+            oldest_dt = pd.to_datetime(oldest) if oldest else None
+            newest_dt = pd.to_datetime(newest) if newest else None
+            
+            if oldest_dt:
+                logging.info(f"Database contains data from {oldest_dt} to {newest_dt}.")
+            else:
+                logging.info("Database table is empty. A full import will be performed.")
+                
+            return oldest_dt, newest_dt, count
+        except pd.io.sql.DatabaseError:
+            logging.info(f"Table '{self.table_name}' not found. It will be created.")
+            return None, None, 0
+
+    def _process_and_filter_csv(self, db_oldest: datetime, db_newest: datetime) -> pd.DataFrame:
+        """Reads the CSV and returns a DataFrame of only the missing data."""
+        logging.info("Reading and processing CSV file. This may take a moment for large files...")
+        df = pd.read_csv(self.csv_path, usecols=self.column_mapping.keys())
+        
+        # Clean and format the data
+        df.rename(columns=self.column_mapping, inplace=True)
+        df['datetime_utc'] = pd.to_datetime(df['datetime_utc'])
+
+        # --- FIX: Calculate the millisecond timestamp from the datetime column ---
+        # This converts the datetime to nanoseconds and then to milliseconds.
+        df['timestamp_ms'] = (df['datetime_utc'].astype('int64') // 10**6)
+        
+        # Filter the data to find only rows that are outside the range of what's already in the DB
+        if db_oldest and db_newest:
+            # Get data from before the oldest record and after the newest record
+            df_filtered = df[(df['datetime_utc'] < db_oldest) | (df['datetime_utc'] > db_newest)]
+        else:
+            # If the DB is empty, all data is new
+            df_filtered = df
+        
+        logging.info(f"Found {len(df_filtered):,} new rows to import.")
+        return df_filtered
+
+    def _append_to_db(self, df: pd.DataFrame, conn):
+        """Appends the DataFrame to the SQLite table."""
+        logging.info(f"Appending {len(df):,} new rows to the database...")
+        df.to_sql(self.table_name, conn, if_exists='append', index=False)
+        logging.info("Append operation complete.")
+
+    def _summarize_import(self, initial_count: int, added_count: int, conn):
+        """Prints a final summary and verification of the import."""
+        logging.info("--- Import Summary & Verification ---")
+        
+        try:
+            final_count = pd.read_sql(f'SELECT COUNT(*) FROM "{self.table_name}"', conn).iloc[0, 0]
+            new_oldest = pd.read_sql(f'SELECT MIN(datetime_utc) FROM "{self.table_name}"', conn).iloc[0, 0]
+            new_newest = pd.read_sql(f'SELECT MAX(datetime_utc) FROM "{self.table_name}"', conn).iloc[0, 0]
+
+            print(f"\n{'Status':<20}: SUCCESS")
+            print("-" * 40)
+            print(f"{'Initial Row Count':<20}: {initial_count:,}")
+            print(f"{'Rows Added':<20}: {added_count:,}")
+            print(f"{'Final Row Count':<20}: {final_count:,}")
+            print("-" * 40)
+            print(f"{'New Oldest Record':<20}: {new_oldest}")
+            print(f"{'New Newest Record':<20}: {new_newest}")
+            
+            # Verification check
+            if final_count == initial_count + added_count:
+                logging.info("Verification successful: Final row count matches expected count.")
+            else:
+                logging.warning("Verification warning: Final row count does not match expected count.")
+        except Exception as e:
+            logging.error(f"Could not generate summary. Error: {e}")
+
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser(description="Import historical CSV data into the SQLite database.")
+    parser.add_argument("--file", required=True, help="Path to the large CSV file to import.")
+    parser.add_argument("--coin", default="BTC", help="The coin symbol for this data (e.g., BTC).")
+    parser.add_argument(
+        "--log-level",
+        default="normal",
+        choices=['off', 'normal', 'debug'],
+        help="Set the logging level for the script."
+    )
+    args = parser.parse_args()
+    
+    importer = CsvImporter(log_level=args.log_level, csv_path=args.file, coin=args.coin)
+    importer.run()
+
+

live_candle_fetcher.py

@@ -0,0 +1,238 @@
+import argparse
+import logging
+import os
+import sys
+import json
+import time
+from datetime import datetime, timezone
+from hyperliquid.info import Info
+from hyperliquid.utils import constants
+import sqlite3
+from queue import Queue
+from threading import Thread
+
+from logging_utils import setup_logging
+
+class LiveCandleFetcher:
+    """
+    Connects to Hyperliquid to maintain a complete and up-to-date database of
+    1-minute candles using a robust producer-consumer architecture to prevent
+    data corruption and duplication.
+    """
+
+    def __init__(self, log_level: str, coins: list):
+        setup_logging(log_level, 'LiveCandleFetcher')
+        self.db_path = os.path.join("_data", "market_data.db")
+        self.coins_to_watch = set(coins)
+        if not self.coins_to_watch:
+            logging.error("No coins provided to watch. Exiting.")
+            sys.exit(1)
+        
+        self.info = Info(constants.MAINNET_API_URL, skip_ws=False)
+        self.candle_queue = Queue() # Thread-safe queue for candles
+        self._ensure_tables_exist()
+
+    def _ensure_tables_exist(self):
+        """
+        Ensures that all necessary tables are created with the correct schema and PRIMARY KEY.
+        If a table exists with an incorrect schema, it attempts to migrate the data.
+        """
+        with sqlite3.connect(self.db_path) as conn:
+            for coin in self.coins_to_watch:
+                table_name = f"{coin}_1m"
+                cursor = conn.cursor()
+                cursor.execute(f"PRAGMA table_info('{table_name}')")
+                columns = cursor.fetchall()
+                
+                if columns:
+                    pk_found = any(col[1] == 'timestamp_ms' and col[5] == 1 for col in columns)
+                    if not pk_found:
+                        logging.warning(f"Schema migration needed for table '{table_name}': 'timestamp_ms' is not the PRIMARY KEY.")
+                        logging.warning("Attempting to automatically rebuild the table...")
+                        try:
+                            # 1. Rename old table
+                            conn.execute(f'ALTER TABLE "{table_name}" RENAME TO "{table_name}_old"')
+                            logging.info(f"  -> Renamed existing table to '{table_name}_old'.")
+                            
+                            # 2. Create new table with correct schema
+                            self._create_candle_table(conn, table_name)
+                            logging.info(f"  -> Created new '{table_name}' table with correct schema.")
+                            
+                            # 3. Copy unique data from old table to new table
+                            conn.execute(f'''
+                                INSERT OR IGNORE INTO "{table_name}" (datetime_utc, timestamp_ms, open, high, low, close, volume, number_of_trades)
+                                SELECT datetime_utc, timestamp_ms, open, high, low, close, volume, number_of_trades
+                                FROM "{table_name}_old"
+                            ''')
+                            conn.commit()
+                            logging.info("  -> Copied data to new table.")
+
+                            # 4. Drop the old table
+                            conn.execute(f'DROP TABLE "{table_name}_old"')
+                            logging.info(f"  -> Removed old table. Migration for '{table_name}' complete.")
+                        except Exception as e:
+                            logging.error(f"FATAL: Automatic schema migration for '{table_name}' failed: {e}")
+                            logging.error("Please delete the database file '_data/market_data.db' manually and restart.")
+                            sys.exit(1)
+                else:
+                    # If table does not exist, create it
+                    self._create_candle_table(conn, table_name)
+            logging.info("Database tables verified.")
+
+    def _create_candle_table(self, conn, table_name: str):
+        """Creates a new candle table with the correct schema."""
+        conn.execute(f'''
+            CREATE TABLE "{table_name}" (
+                datetime_utc TEXT,
+                timestamp_ms INTEGER PRIMARY KEY,
+                open REAL,
+                high REAL,
+                low REAL,
+                close REAL,
+                volume REAL,
+                number_of_trades INTEGER
+            )
+        ''')
+
+    def on_message(self, message):
+        """
+        Callback function to process incoming candle messages. This is the "Producer".
+        It puts the raw message onto the queue for the DB writer.
+        """
+        try:
+            if message.get("channel") == "candle":
+                candle_data = message.get("data", {})
+                if candle_data:
+                    self.candle_queue.put(candle_data)
+        except Exception as e:
+            logging.error(f"Error in on_message: {e}")
+
+    def _database_writer_thread(self):
+        """
+        This is the "Consumer" thread. It runs forever, pulling candles from the
+        queue and writing them to the database, ensuring all writes are serial.
+        """
+        while True:
+            try:
+                candle = self.candle_queue.get()
+                if candle is None: # A signal to stop the thread
+                    break
+                
+                coin = candle.get('coin')
+                if not coin:
+                    continue
+
+                table_name = f"{coin}_1m"
+                record = (
+                    datetime.fromtimestamp(candle['t'] / 1000, tz=timezone.utc).strftime('%Y-%m-%d %H:%M:%S'),
+                    candle['t'],
+                    candle.get('o'), candle.get('h'), candle.get('l'), candle.get('c'),
+                    candle.get('v'), candle.get('n')
+                )
+                
+                with sqlite3.connect(self.db_path) as conn:
+                    conn.execute(f'''
+                        INSERT OR REPLACE INTO "{table_name}" (datetime_utc, timestamp_ms, open, high, low, close, volume, number_of_trades)
+                        VALUES (?, ?, ?, ?, ?, ?, ?, ?)
+                    ''', record)
+                    conn.commit()
+                logging.debug(f"Upserted candle for {coin} at {record[0]}")
+
+            except Exception as e:
+                logging.error(f"Error in database writer thread: {e}")
+
+    def _get_last_timestamp_from_db(self, coin: str) -> int:
+        """Gets the most recent millisecond timestamp from a coin's 1m table."""
+        table_name = f"{coin}_1m"
+        try:
+            with sqlite3.connect(self.db_path) as conn:
+                result = conn.execute(f'SELECT MAX(timestamp_ms) FROM "{table_name}"').fetchone()
+                return int(result[0]) if result and result[0] is not None else None
+        except Exception as e:
+            logging.error(f"Could not read last timestamp from table '{table_name}': {e}")
+            return None
+
+    def _fetch_historical_candles(self, coin: str, start_ms: int, end_ms: int):
+        """Fetches historical candles and puts them on the queue for the writer."""
+        logging.info(f"Fetching historical data for {coin} from {datetime.fromtimestamp(start_ms/1000)}...")
+        current_start = start_ms
+        
+        while current_start < end_ms:
+            try:
+                http_info = Info(constants.MAINNET_API_URL, skip_ws=True)
+                batch = http_info.candles_snapshot(coin, "1m", current_start, end_ms)
+                if not batch:
+                    break 
+                
+                for candle in batch:
+                    candle['coin'] = coin
+                    self.candle_queue.put(candle)
+                
+                last_ts = batch[-1]['t']
+                if last_ts < current_start:
+                    break
+                current_start = last_ts + 1 
+                time.sleep(0.5) 
+            except Exception as e:
+                logging.error(f"Error fetching historical chunk for {coin}: {e}")
+                break
+        
+        logging.info(f"Historical data fetching for {coin} is complete.")
+
+    def run(self):
+        """
+        Starts the database writer, catches up on historical data, then
+        subscribes to the WebSocket for live updates.
+        """
+        db_writer = Thread(target=self._database_writer_thread, daemon=True)
+        db_writer.start()
+        
+        logging.info("--- Starting Historical Data Catch-Up Phase ---")
+        now_ms = int(time.time() * 1000)
+        for coin in self.coins_to_watch:
+            last_ts = self._get_last_timestamp_from_db(coin)
+            start_ts = last_ts + 60000 if last_ts else now_ms - (7 * 24 * 60 * 60 * 1000)
+            if start_ts < now_ms:
+                self._fetch_historical_candles(coin, start_ts, now_ms)
+        
+        logging.info("--- Historical Catch-Up Complete. Starting Live WebSocket Feed ---")
+        for coin in self.coins_to_watch:
+            # --- FIX: Use a lambda to create a unique callback for each subscription ---
+            # This captures the 'coin' variable and adds it to the message data.
+            callback = lambda msg, c=coin: self.on_message({**msg, 'data': {**msg.get('data',{}), 'coin': c}})
+            subscription = {"type": "candle", "coin": coin, "interval": "1m"}
+            self.info.subscribe(subscription, callback)
+            logging.info(f"Subscribed to 1m candles for {coin}")
+            time.sleep(0.2)
+
+        print("\nListening for live candle data... Press Ctrl+C to stop.")
+        try:
+            while True:
+                time.sleep(1)
+        except KeyboardInterrupt:
+            print("\nStopping WebSocket listener...")
+            self.info.ws_manager.stop()
+            self.candle_queue.put(None) 
+            db_writer.join()
+            print("Listener stopped.")
+
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser(description="A hybrid historical and live candle data fetcher for Hyperliquid.")
+    parser.add_argument(
+        "--coins",
+        nargs='+',
+        required=True,
+        help="List of coin symbols to fetch (e.g., BTC ETH)."
+    )
+    parser.add_argument(
+        "--log-level",
+        default="normal",
+        choices=['off', 'normal', 'debug'],
+        help="Set the logging level for the script."
+    )
+    args = parser.parse_args()
+    
+    fetcher = LiveCandleFetcher(log_level=args.log_level, coins=args.coins)
+    fetcher.run()
+

live_market.py

@@ -0,0 +1,258 @@
+import os
+import sys
+import time
+import json
+import argparse
+from datetime import datetime, timedelta, timezone
+from hyperliquid.info import Info
+from hyperliquid.utils import constants
+from collections import deque, defaultdict
+
+# --- Configuration ---
+MAX_TRADE_HISTORY = 100000 
+all_trades = {
+    "BTC": deque(maxlen=MAX_TRADE_HISTORY),
+    "ETH": deque(maxlen=MAX_TRADE_HISTORY),
+}
+latest_raw_trades = {
+    "BTC": None,
+    "ETH": None,
+}
+decoded_trade_output = []
+_lines_printed = 0
+
+def get_coins_from_strategies() -> set:
+    """
+    Reads the strategies.json file and returns a unique set of coin symbols
+    from all enabled strategies.
+    """
+    coins = set()
+    config_path = os.path.join("_data", "strategies.json")
+    try:
+        with open(config_path, 'r') as f:
+            all_configs = json.load(f)
+            for name, config in all_configs.items():
+                if config.get("enabled", False):
+                    coin = config.get("parameters", {}).get("coin")
+                    if coin:
+                        coins.add(coin)
+        print(f"Found {len(coins)} unique coins to watch from enabled strategies: {list(coins)}")
+        return coins
+    except (FileNotFoundError, json.JSONDecodeError) as e:
+        print(f"ERROR: Could not load or parse '{config_path}': {e}", file=sys.stderr)
+        return set()
+
+def on_message(message):
+    """
+    Callback function to process incoming trades from the WebSocket and store them.
+    """
+    try:
+        if message.get("channel") == "trades":
+            for trade in message["data"]:
+                coin = trade['coin']
+                if coin in all_trades:
+                    latest_raw_trades[coin] = trade
+                    price = float(trade['px'])
+                    size = float(trade['sz'])
+                    decoded_trade = {
+                        "time": datetime.fromtimestamp(trade['time'] / 1000, tz=timezone.utc),
+                        "side": "BUY" if trade['side'] == "B" else "SELL",
+                        "value": price * size,
+                        "users": trade.get('users', [])
+                    }
+                    all_trades[coin].append(decoded_trade)
+    except (KeyError, TypeError, ValueError):
+        pass
+
+def build_top_trades_table(title: str, trades: list) -> list:
+    """Builds the formatted lines for a top-5 trades by value table."""
+    lines = []
+    header = f"{'Time (UTC)':<10} | {'Side':<5} | {'Value (USD)':>20}"
+    lines.append(f"--- {title} ---")
+    lines.append(header)
+    lines.append("-" * len(header))
+    
+    top_trades = sorted(trades, key=lambda x: x['value'], reverse=True)[:5]
+    
+    for trade in top_trades:
+        lines.append(
+            f"{trade['time'].strftime('%H:%M:%S'):<10} | "
+            f"{trade['side']:<5} | "
+            f"${trade['value']:>18,.2f}"
+        )
+    while len(lines) < 8: lines.append(" " * len(header))
+    return lines
+
+def build_top_takers_table(title: str, trades: list) -> list:
+    """Analyzes a list of trades to find the top 5 takers by total volume."""
+    lines = []
+    header = f"{'#':<2} | {'Taker Address':<15} | {'Total Volume (USD)':>20}"
+    lines.append(f"--- {title} ---")
+    lines.append(header)
+    lines.append("-" * len(header))
+    
+    volumes = defaultdict(float)
+    for trade in trades:
+        for user in trade['users']:
+            volumes[user] += trade['value']
+            
+    top_takers = sorted(volumes.items(), key=lambda item: item[1], reverse=True)[:5]
+    
+    for i, (address, volume) in enumerate(top_takers, 1):
+        short_address = f"{address[:6]}...{address[-4:]}"
+        lines.append(f"{i:<2} | {short_address:<15} | ${volume:>18,.2f}")
+        
+    while len(lines) < 8: lines.append(" " * len(header))
+    return lines
+
+def build_top_active_takers_table(title: str, trades: list) -> list:
+    """Analyzes a list of trades to find the top 5 takers by trade count."""
+    lines = []
+    header = f"{'#':<2} | {'Taker Address':<42} | {'Trade Count':>12} | {'Total Volume (USD)':>20}"
+    lines.append(f"--- {title} ---")
+    lines.append(header)
+    lines.append("-" * len(header))
+    
+    taker_data = defaultdict(lambda: {'count': 0, 'volume': 0.0})
+    for trade in trades:
+        for user in trade['users']:
+            taker_data[user]['count'] += 1
+            taker_data[user]['volume'] += trade['value']
+            
+    top_takers = sorted(taker_data.items(), key=lambda item: item[1]['count'], reverse=True)[:5]
+    
+    for i, (address, data) in enumerate(top_takers, 1):
+        lines.append(f"{i:<2} | {address:<42} | {data['count']:>12} | ${data['volume']:>18,.2f}")
+        
+    while len(lines) < 8: lines.append(" " * len(header))
+    return lines
+
+
+def build_decoded_trade_lines(coin: str) -> list:
+    """Builds a formatted, multi-line string for a single decoded trade."""
+    trade = latest_raw_trades[coin]
+    if not trade: return ["No trade data yet..."] * 7
+
+    return [
+        f"Time:      {datetime.fromtimestamp(trade['time'] / 1000, tz=timezone.utc)}",
+        f"Side:      {'BUY' if trade.get('side') == 'B' else 'SELL'}",
+        f"Price:     {trade.get('px', 'N/A')}",
+        f"Size:      {trade.get('sz', 'N/A')}",
+        f"Trade ID:  {trade.get('tid', 'N/A')}",
+        f"Hash:      {trade.get('hash', 'N/A')}",
+        f"Users:     {', '.join(trade.get('users', []))}"
+    ]
+
+def update_decoded_trade_display():
+    """
+    Updates the global variable holding the decoded trade output, but only
+    at the 40-second mark of each minute.
+    """
+    global decoded_trade_output
+    if datetime.now().second == 40:
+        lines = []
+        lines.append("--- Last BTC Trade (Decoded) ---")
+        lines.extend(build_decoded_trade_lines("BTC"))
+        lines.append("")
+        lines.append("--- Last ETH Trade (Decoded) ---")
+        lines.extend(build_decoded_trade_lines("ETH"))
+        decoded_trade_output = lines
+
+def display_dashboard(view: str):
+    """Clears the screen and prints the selected dashboard view."""
+    global _lines_printed
+    if _lines_printed > 0: print(f"\x1b[{_lines_printed}A", end="")
+
+    now_utc = datetime.now(timezone.utc)
+    output_lines = []
+    separator = "   |   "
+
+    time_windows = [
+        ("All Time", None), ("Last 24h", timedelta(hours=24)),
+        ("Last 1h", timedelta(hours=1)), ("Last 5m", timedelta(minutes=5)),
+        ("Last 1m", timedelta(minutes=1)),
+    ]
+
+    btc_trades_copy = list(all_trades["BTC"])
+    eth_trades_copy = list(all_trades["ETH"])
+
+    if view == "trades":
+        output_lines.append("--- Top 5 Trades by Value ---")
+        for title, delta in time_windows:
+            btc_trades = [t for t in btc_trades_copy if not delta or t['time'] > now_utc - delta]
+            eth_trades = [t for t in eth_trades_copy if not delta or t['time'] > now_utc - delta]
+            btc_lines = build_top_trades_table(f"BTC - {title}", btc_trades)
+            eth_lines = build_top_trades_table(f"ETH - {title}", eth_trades)
+            for i in range(len(btc_lines)):
+                output_lines.append(f"{btc_lines[i]:<45}{separator}{eth_lines[i] if i < len(eth_lines) else ''}")
+            output_lines.append("")
+    
+    elif view == "takers":
+        output_lines.append("--- Top 5 Takers by Volume (Rolling Windows) ---")
+        for title, delta in time_windows[1:]:
+            btc_trades = [t for t in btc_trades_copy if t['time'] > now_utc - delta]
+            eth_trades = [t for t in eth_trades_copy if t['time'] > now_utc - delta]
+            btc_lines = build_top_takers_table(f"BTC - {title}", btc_trades)
+            eth_lines = build_top_takers_table(f"ETH - {title}", eth_trades)
+            for i in range(len(btc_lines)):
+                output_lines.append(f"{btc_lines[i]:<45}{separator}{eth_lines[i] if i < len(eth_lines) else ''}")
+            output_lines.append("")
+
+    elif view == "active_takers":
+        output_lines.append("--- Top 5 Active Takers by Trade Count (Rolling Windows) ---")
+        for title, delta in time_windows[1:]:
+            btc_trades = [t for t in btc_trades_copy if t['time'] > now_utc - delta]
+            eth_trades = [t for t in eth_trades_copy if t['time'] > now_utc - delta]
+            btc_lines = build_top_active_takers_table(f"BTC - {title}", btc_trades)
+            eth_lines = build_top_active_takers_table(f"ETH - {title}", eth_trades)
+            header_width = 85
+            for i in range(len(btc_lines)):
+                output_lines.append(f"{btc_lines[i]:<{header_width}}{separator}{eth_lines[i] if i < len(eth_lines) else ''}")
+            output_lines.append("")
+
+    if decoded_trade_output:
+        output_lines.extend(decoded_trade_output)
+    else:
+        for _ in range(17): output_lines.append("")
+    
+    final_output = "\n".join(output_lines) + "\n\x1b[J"
+    print(final_output, end="")
+    
+    _lines_printed = len(output_lines)
+    sys.stdout.flush()
+
+
+def main():
+    """Main function to set up the WebSocket and run the display loop."""
+    parser = argparse.ArgumentParser(description="Live market data dashboard for Hyperliquid.")
+    parser.add_argument("--view", default="trades", choices=['trades', 'takers', 'active_takers'],
+                        help="The data view to display: 'trades' (default), 'takers', or 'active_takers'.")
+    args = parser.parse_args()
+
+    coins_to_watch = get_coins_from_strategies()
+    if not ("BTC" in coins_to_watch and "ETH" in coins_to_watch):
+        print("This script is configured to display BTC and ETH. Please ensure they are in your strategies.", file=sys.stderr)
+        return
+
+    info = Info(constants.MAINNET_API_URL, skip_ws=False)
+    
+    for coin in ["BTC", "ETH"]:
+        trade_subscription = {"type": "trades", "coin": coin}
+        info.subscribe(trade_subscription, on_message)
+        print(f"Subscribed to Trades for {coin}")
+        time.sleep(0.2)
+
+    print(f"\nDisplaying live '{args.view}' summary... Press Ctrl+C to stop.")
+    try:
+        while True:
+            update_decoded_trade_display()
+            display_dashboard(view=args.view)
+            time.sleep(1)
+    except KeyboardInterrupt:
+        print("\nStopping WebSocket listener...")
+        info.ws_manager.stop()
+        print("Listener stopped.")
+
+if __name__ == "__main__":
+    main()
+

live_market_utils.py

@@ -0,0 +1,187 @@
+import logging
+import json
+import time
+import os
+import traceback
+import sys
+from hyperliquid.info import Info
+from hyperliquid.utils import constants
+
+from logging_utils import setup_logging
+
+# --- Configuration for standalone error logging ---
+LOGS_DIR = "_logs"
+ERROR_LOG_FILE = os.path.join(LOGS_DIR, "live_market_errors.log")
+
+def log_error(error_message: str, include_traceback: bool = True):
+    """A simple, robust file logger for any errors."""
+    try:
+        if not os.path.exists(LOGS_DIR):
+            os.makedirs(LOGS_DIR)
+        
+        with open(ERROR_LOG_FILE, 'a') as f:
+            timestamp = time.strftime('%Y-%m-%d %H:%M:%S', time.gmtime())
+            f.write(f"--- ERROR at {timestamp} UTC ---\n")
+            f.write(error_message + "\n")
+            if include_traceback:
+                f.write(traceback.format_exc() + "\n")
+            f.write("="*50 + "\n")
+    except Exception:
+        print(f"CRITICAL: Failed to write to error log file: {error_message}", file=sys.stderr)
+
+
+def on_message(message, shared_prices_dict):
+    """
+    Callback function to process incoming WebSocket messages for 'bbo' and 'trades'
+    and update the shared memory dictionary.
+    """
+    try:
+        logging.debug(f"Received WebSocket message: {message}")
+        channel = message.get("channel")
+
+        # --- Parser 1: Handle Best Bid/Offer messages ---
+        if channel == "bbo":
+            data = message.get("data")
+            if not data:
+                logging.warning("BBO message received with no data.")
+                return
+
+            coin = data.get("coin")
+            if not coin:
+                logging.warning("BBO data received with no coin identifier.")
+                return
+
+            bid_ask_data = data.get("bbo")
+            
+            if not bid_ask_data or not isinstance(bid_ask_data, list) or len(bid_ask_data) < 2:
+                logging.warning(f"[{coin}] Received BBO message with invalid 'bbo' array: {bid_ask_data}")
+                return
+
+            try:
+                bid_price_str = bid_ask_data[0].get('px')
+                ask_price_str = bid_ask_data[1].get('px')
+
+                if not bid_price_str or not ask_price_str:
+                    logging.warning(f"[{coin}] BBO data missing 'px' field.")
+                    return
+
+                bid_price = float(bid_price_str)
+                ask_price = float(ask_price_str)
+                
+                # Update the shared dictionary for Bid and Ask
+                shared_prices_dict[f"{coin}_bid"] = bid_price
+                shared_prices_dict[f"{coin}_ask"] = ask_price
+                
+                logging.info(f"Updated {coin} (BBO): Bid={bid_price:.4f}, Ask={ask_price:.4f}")
+
+            except (ValueError, TypeError, IndexError) as e:
+                logging.error(f"[{coin}] Error parsing BBO data: {e}. Data: {bid_ask_data}")
+
+        # --- Parser 2: Handle Live Trade messages ---
+        elif channel == "trades":
+            trade_list = message.get("data")
+            
+            if not trade_list or not isinstance(trade_list, list) or len(trade_list) == 0:
+                logging.warning(f"Received 'trades' message with invalid data: {trade_list}")
+                return
+
+            # Process all trades in the batch
+            for trade in trade_list:
+                try:
+                    coin = trade.get("coin")
+                    price_str = trade.get("px")
+
+                    if not coin or not price_str:
+                        logging.warning(f"Trade data missing 'coin' or 'px': {trade}")
+                        continue
+                        
+                    price = float(price_str)
+                    
+                    # Update the shared dictionary for the "Live Price" column
+                    shared_prices_dict[coin] = price
+                    
+                    logging.info(f"Updated {coin} (Live Price) to last trade: {price:.4f}")
+
+                except (ValueError, TypeError) as e:
+                    logging.error(f"Error parsing trade data: {e}. Data: {trade}")
+            
+    except Exception as e:
+        log_error(f"Error in WebSocket on_message: {e}")
+
+def start_live_feed(shared_prices_dict, coins_to_watch: list, log_level='off'):
+    """
+    Main function for the WebSocket process.
+    Subscribes to BOTH 'bbo' and 'trades' for all watched coins.
+    """
+    setup_logging(log_level, 'LiveMarketFeed_Combined')
+    
+    info = None
+    callback = lambda msg: on_message(msg, shared_prices_dict)
+
+    def connect_and_subscribe():
+        """Establishes a new WebSocket connection and subscribes to both streams."""
+        try:
+            logging.info("Connecting to Hyperliquid WebSocket...")
+            new_info = Info(constants.MAINNET_API_URL, skip_ws=False)
+            
+            # --- MODIFIED: Subscribe to 'bbo' AND 'trades' for each coin ---
+            for coin in coins_to_watch:
+                # Subscribe to Best Bid/Offer
+                bbo_sub = {"type": "bbo", "coin": coin}
+                new_info.subscribe(bbo_sub, callback)
+                logging.info(f"Subscribed to 'bbo' for {coin}.")
+                
+                # Subscribe to Live Trades
+                trades_sub = {"type": "trades", "coin": coin}
+                new_info.subscribe(trades_sub, callback)
+                logging.info(f"Subscribed to 'trades' for {coin}.")
+
+            logging.info("WebSocket connected and all subscriptions sent.")
+            return new_info
+        except Exception as e:
+            log_error(f"Failed to connect to WebSocket: {e}")
+            return None
+
+    info = connect_and_subscribe()
+
+    if info is None:
+        logging.critical("Initial WebSocket connection failed. Exiting process.")
+        log_error("Initial WebSocket connection failed. Exiting process.", include_traceback=False)
+        time.sleep(10) # Wait before letting the process manager restart it
+        return
+
+    logging.info("Starting Combined (BBO + Trades) live price feed process.")
+    
+    try:
+        while True:
+            # --- Watchdog Logic ---
+            time.sleep(15) # Check the connection every 15 seconds
+            
+            if not info.ws_manager.is_alive():
+                error_msg = "WebSocket connection lost. Attempting to reconnect..."
+                logging.warning(error_msg)
+                log_error(error_msg, include_traceback=False) # Log it to the file
+                
+                try:
+                    info.ws_manager.stop() # Clean up old manager
+                except Exception as e:
+                    log_error(f"Error stopping old ws_manager: {e}")
+                
+                info = connect_and_subscribe()
+                
+                if info is None:
+                    logging.error("Reconnect failed, will retry in 15s.")
+                else:
+                    logging.info("Successfully reconnected to WebSocket.")
+            else:
+                logging.debug("Watchdog check: WebSocket connection is active.")
+
+    except KeyboardInterrupt:
+        logging.info("Stopping WebSocket listener...")
+    except Exception as e:
+        log_error(f"Live Market Feed process crashed: {e}")
+    finally:
+        if info and info.ws_manager:
+            info.ws_manager.stop()
+        logging.info("Combined Listener stopped.")
+

logging_utils.py

@@ -1,5 +1,29 @@
 import logging
 import sys
+from datetime import datetime
+
+class LocalTimeFormatter(logging.Formatter):
+    """
+    Custom formatter to display time with milliseconds and a (UTC+HH) offset.
+    """
+    def formatTime(self, record, datefmt=None):
+        # Convert log record's creation time to a local, timezone-aware datetime object
+        dt = datetime.fromtimestamp(record.created).astimezone()
+        
+        # Format the main time part
+        time_part = dt.strftime('%Y-%m-%d %H:%M:%S')
+        
+        # Get the UTC offset and format it as (UTC+HH)
+        offset = dt.utcoffset()
+        offset_str = ""
+        if offset is not None:
+            offset_hours = int(offset.total_seconds() / 3600)
+            sign = '+' if offset_hours >= 0 else ''
+            offset_str = f" (UTC{sign}{offset_hours})"
+
+        # --- FIX: Cast record.msecs from float to int before formatting ---
+        # Combine time, milliseconds, and the offset string
+        return f"{time_part},{int(record.msecs):03d}{offset_str}"
 
 def setup_logging(log_level: str, process_name: str):
     """
@@ -29,10 +53,9 @@ def setup_logging(log_level: str, process_name: str):
 
     handler = logging.StreamHandler(sys.stdout)
     
-    # --- FIX: Added a date format that includes the timezone name (%Z) ---
-    formatter = logging.Formatter(
-        f'%(asctime)s - {process_name} - %(levelname)s - %(message)s',
-        datefmt='%Y-%m-%d %H:%M:%S %Z' 
+    # This will produce timestamps like: 2025-10-13 14:30:00,123 (UTC+2)
+    formatter = LocalTimeFormatter(
+        f'%(asctime)s - {process_name} - %(levelname)s - %(message)s'
     )
     handler.setFormatter(formatter)
     logger.addHandler(handler)

main_app.py

@@ -9,197 +9,677 @@ import schedule
 import sqlite3
 import pandas as pd
 from datetime import datetime, timezone
+import importlib
+# --- REMOVED: import signal ---
+# --- REMOVED: from queue import Empty ---
 
 from logging_utils import setup_logging
+# --- Using the new high-performance WebSocket utility for live prices ---
+from live_market_utils import start_live_feed
+# --- Import the base class for type hinting (optional but good practice) ---
+from strategies.base_strategy import BaseStrategy
 
 # --- Configuration ---
 WATCHED_COINS = ["BTC", "ETH", "SOL", "BNB", "HYPE", "ASTER", "ZEC", "PUMP", "SUI"]
-COIN_LISTER_SCRIPT = "list_coins.py"
-MARKET_FEEDER_SCRIPT = "market.py"
-DATA_FETCHER_SCRIPT = "data_fetcher.py"
-RESAMPLER_SCRIPT = "resampler.py" # Restored resampler script
-PRICE_DATA_FILE = os.path.join("_data", "current_prices.json")
+LIVE_CANDLE_FETCHER_SCRIPT = "live_candle_fetcher.py"
+RESAMPLER_SCRIPT = "resampler.py" 
+# --- REMOVED: Market Cap Fetcher ---
+# --- REMOVED: trade_executor.py is no longer a script ---
+DASHBOARD_DATA_FETCHER_SCRIPT = "dashboard_data_fetcher.py"
+STRATEGY_CONFIG_FILE = os.path.join("_data", "strategies.json")
 DB_PATH = os.path.join("_data", "market_data.db")
-STATUS_FILE = os.path.join("_data", "fetcher_status.json")
+# --- REMOVED: Market Cap File ---
+LOGS_DIR = "_logs"
+TRADE_EXECUTOR_STATUS_FILE = os.path.join(LOGS_DIR, "trade_executor_status.json")
 
 
-def run_market_feeder():
-    """Target function to run the market.py script in a separate process."""
-    setup_logging('off', 'MarketFeedProcess')
-    logging.info("Market feeder process started.")
+def format_market_cap(mc_value):
+    """Formats a large number into a human-readable market cap string."""
+    if not isinstance(mc_value, (int, float)) or mc_value == 0:
+        return "N/A"
+    if mc_value >= 1_000_000_000_000:
+        return f"${mc_value / 1_000_000_000_000:.2f}T"
+    if mc_value >= 1_000_000_000:
+        return f"${mc_value / 1_000_000_000:.2f}B"
+    if mc_value >= 1_000_000:
+        return f"${mc_value / 1_000_000:.2f}M"
+    return f"${mc_value:,.2f}"
+
+
+def run_live_candle_fetcher():
+    """Target function to run the live_candle_fetcher.py script in a resilient loop."""
+    
+    # --- GRACEFUL SHUTDOWN HANDLER ---
+    import signal
+    shutdown_requested = False
+    
+    def handle_shutdown_signal(signum, frame):
+        nonlocal shutdown_requested
+        # Use print here as logging may not be set up
+        print(f"[CandleFetcher] Shutdown signal ({signum}) received. Will stop after current run.")
+        shutdown_requested = True
+            
+    signal.signal(signal.SIGTERM, handle_shutdown_signal)
+    signal.signal(signal.SIGINT, handle_shutdown_signal)
+    # --- END GRACEFUL SHUTDOWN HANDLER ---
+
+    log_file = os.path.join(LOGS_DIR, "live_candle_fetcher.log")
+    
+    while not shutdown_requested: # <-- MODIFIED
+        process = None
+        try:
+            with open(log_file, 'a') as f:
+                command = [sys.executable, LIVE_CANDLE_FETCHER_SCRIPT, "--coins"] + WATCHED_COINS + ["--log-level", "off"]
+                f.write(f"\n--- Starting {LIVE_CANDLE_FETCHER_SCRIPT} at {datetime.now()} ---\n")
+                
+                # Use Popen instead of run to be non-blocking
+                process = subprocess.Popen(command, stdout=f, stderr=subprocess.STDOUT)
+                
+                # Poll the process and check for shutdown request
+                while process.poll() is None and not shutdown_requested:
+                    time.sleep(0.5) # Poll every 500ms
+                
+                if shutdown_requested and process.poll() is None:
+                    print(f"[CandleFetcher] Terminating subprocess {LIVE_CANDLE_FETCHER_SCRIPT}...")
+                    process.terminate() # Terminate the child script
+                    process.wait() # Wait for it to exit
+                    print(f"[CandleFetcher] Subprocess terminated.")
+
+        except (subprocess.CalledProcessError, Exception) as e:
+            if shutdown_requested:
+                break # Don't restart if we're shutting down
+            with open(log_file, 'a') as f:
+                f.write(f"\n--- PROCESS ERROR at {datetime.now()} ---\n")
+                f.write(f"Live candle fetcher failed: {e}. Restarting...\n")
+            time.sleep(5)
+        
+        if shutdown_requested:
+            break # Exit outer loop
+            
+    print("[CandleFetcher] Live candle fetcher shutting down.")
+
+
+def run_resampler_job(timeframes_to_generate: list):
+    """Defines the job for the resampler, redirecting output to a log file."""
+    log_file = os.path.join(LOGS_DIR, "resampler.log")
     try:
-        # Pass the log level to the script
-        subprocess.run([sys.executable, MARKET_FEEDER_SCRIPT, "--log-level", "off"], check=True)
-    except subprocess.CalledProcessError as e:
-        logging.error(f"Market feeder script failed with error: {e}")
-    except KeyboardInterrupt:
-        logging.info("Market feeder process stopping.")
-
-
-def run_data_fetcher_job():
-    """Defines the job to be run by the scheduler for the data fetcher."""
-    logging.info(f"Scheduler starting data_fetcher.py task for {', '.join(WATCHED_COINS)}...")
-    try:
-        command = [sys.executable, DATA_FETCHER_SCRIPT, "--coins"] + WATCHED_COINS + ["--days", "7", "--log-level", "off"]
-        subprocess.run(command, check=True)
-        logging.info("data_fetcher.py task finished successfully.")
+        command = [sys.executable, RESAMPLER_SCRIPT, "--coins"] + WATCHED_COINS + ["--timeframes"] + timeframes_to_generate + ["--log-level", "normal"]
+        with open(log_file, 'a') as f:
+            f.write(f"\n--- Starting resampler.py job at {datetime.now()} ---\n")
+            subprocess.run(command, check=True, stdout=f, stderr=subprocess.STDOUT)
     except Exception as e:
-        logging.error(f"Failed to run data_fetcher.py job: {e}")
+        with open(log_file, 'a') as f:
+            f.write(f"\n--- SCHEDULER ERROR at {datetime.now()} ---\n")
+            f.write(f"Failed to run resampler.py job: {e}\n")
 
 
-def data_fetcher_scheduler():
-    """Schedules and runs the data_fetcher.py script periodically."""
-    setup_logging('off', 'DataFetcherScheduler')
-    run_data_fetcher_job()
-    schedule.every(1).minutes.do(run_data_fetcher_job)
-    logging.info("Data fetcher scheduled to run every 1 minute.")
-    while True:
-        schedule.run_pending()
-        time.sleep(1)
+def resampler_scheduler(timeframes_to_generate: list):
+    """Schedules the resampler.py script."""
 
-# --- Restored Resampler Functions ---
-def run_resampler_job():
-    """Defines the job to be run by the scheduler for the resampler."""
-    logging.info(f"Scheduler starting resampler.py task for {', '.join(WATCHED_COINS)}...")
-    try:
-        # Uses default timeframes configured within resampler.py
-        command = [sys.executable, RESAMPLER_SCRIPT, "--coins"] + WATCHED_COINS + ["--log-level", "off"]
-        subprocess.run(command, check=True)
-        logging.info("resampler.py task finished successfully.")
-    except Exception as e:
-        logging.error(f"Failed to run resampler.py job: {e}")
+    # --- GRACEFUL SHUTDOWN HANDLER ---
+    import signal
+    shutdown_requested = False
+    
+    def handle_shutdown_signal(signum, frame):
+        nonlocal shutdown_requested
+        try:
+            logging.info(f"Shutdown signal ({signum}) received. Exiting loop...")
+        except NameError:
+            print(f"[ResamplerScheduler] Shutdown signal ({signum}) received. Exiting loop...")
+        shutdown_requested = True
+            
+    signal.signal(signal.SIGTERM, handle_shutdown_signal)
+    signal.signal(signal.SIGINT, handle_shutdown_signal)
+    # --- END GRACEFUL SHUTDOWN HANDLER ---
 
-
-def resampler_scheduler():
-    """Schedules and runs the resampler.py script periodically."""
     setup_logging('off', 'ResamplerScheduler')
-    run_resampler_job()
-    schedule.every(4).minutes.do(run_resampler_job)
-    logging.info("Resampler scheduled to run every 4 minutes.")
-    while True:
+    run_resampler_job(timeframes_to_generate)
+    # Schedule to run every minute at the :01 second mark
+    schedule.every().minute.at(":01").do(run_resampler_job, timeframes_to_generate=timeframes_to_generate)
+    logging.info("Resampler scheduled to run every minute at :01.")
+    
+    while not shutdown_requested: # <-- MODIFIED
         schedule.run_pending()
-        time.sleep(1)
-# --- End of Restored Functions ---
+        time.sleep(0.5) # Check every 500ms to not miss the scheduled time and be responsive
+        
+    logging.info("ResamplerScheduler shutting down.")
+
+
+# --- REMOVED: run_market_cap_fetcher_job function ---
+
+# --- REMOVED: market_cap_fetcher_scheduler function ---
+
+
+def run_trade_executor(order_execution_queue: multiprocessing.Queue):
+    """
+    Target function to run the TradeExecutor class in a resilient loop.
+    It now consumes from the order_execution_queue.
+    """
+
+    # --- GRACEFUL SHUTDOWN HANDLER ---
+    import signal
+    
+    def handle_shutdown_signal(signum, frame):
+        # We can just raise KeyboardInterrupt, as it's handled below
+        logging.info(f"Shutdown signal ({signum}) received. Initiating graceful exit...")
+        raise KeyboardInterrupt
+            
+    signal.signal(signal.SIGTERM, handle_shutdown_signal)
+    # --- END GRACEFUL SHUTDOWN HANDLER ---
+
+    log_file_path = os.path.join(LOGS_DIR, "trade_executor.log")
+    try:
+        sys.stdout = open(log_file_path, 'a', buffering=1)
+        sys.stderr = sys.stdout
+    except Exception as e:
+        print(f"Failed to open log file for TradeExecutor: {e}")
+
+    setup_logging('normal', f"TradeExecutor")
+    logging.info("\n--- Starting Trade Executor process ---")
+    
+    while True:
+        try:
+            from trade_executor import TradeExecutor
+            
+            executor = TradeExecutor(log_level="normal", order_execution_queue=order_execution_queue)
+            
+            # --- REVERTED: Call executor.run() directly ---
+            executor.run() 
+            
+        except KeyboardInterrupt:
+            logging.info("Trade Executor interrupted. Exiting.")
+            return
+        except Exception as e:
+            logging.error(f"Trade Executor failed: {e}. Restarting...\n", exc_info=True)
+            time.sleep(10)
+
+def run_position_manager(trade_signal_queue: multiprocessing.Queue, order_execution_queue: multiprocessing.Queue):
+    """
+    Target function to run the PositionManager class in a resilient loop.
+    Consumes from trade_signal_queue, produces for order_execution_queue.
+    """
+
+    # --- GRACEFUL SHUTDOWN HANDLER ---
+    import signal
+    
+    def handle_shutdown_signal(signum, frame):
+        # Raise KeyboardInterrupt, as it's handled by the loop
+        logging.info(f"Shutdown signal ({signum}) received. Initiating graceful exit...")
+        raise KeyboardInterrupt
+            
+    signal.signal(signal.SIGTERM, handle_shutdown_signal)
+    # --- END GRACEFUL SHUTDOWN HANDLER ---
+
+    log_file_path = os.path.join(LOGS_DIR, "position_manager.log")
+    try:
+        sys.stdout = open(log_file_path, 'a', buffering=1)
+        sys.stderr = sys.stdout
+    except Exception as e:
+        print(f"Failed to open log file for PositionManager: {e}")
+
+    setup_logging('normal', f"PositionManager")
+    logging.info("\n--- Starting Position Manager process ---")
+
+    while True:
+        try:
+            from position_manager import PositionManager
+            
+            manager = PositionManager(
+                log_level="normal", 
+                trade_signal_queue=trade_signal_queue,
+                order_execution_queue=order_execution_queue
+            )
+            
+            # --- REVERTED: Call manager.run() directly ---
+            manager.run()
+
+        except KeyboardInterrupt:
+            logging.info("Position Manager interrupted. Exiting.")
+            return
+        except Exception as e:
+            logging.error(f"Position Manager failed: {e}. Restarting...\n", exc_info=True)
+            time.sleep(10)
+
+
+def run_strategy(strategy_name: str, config: dict, trade_signal_queue: multiprocessing.Queue):
+    """
+    This function BECOMES the strategy runner. It is executed as a separate
+    process and pushes signals to the shared queue.
+    """
+    # These imports only happen in the new, lightweight process
+    import importlib
+    import os
+    import sys
+    import time
+    import logging
+    import signal # <-- ADDED
+    from logging_utils import setup_logging
+    from strategies.base_strategy import BaseStrategy
+
+    # --- GRACEFUL SHUTDOWN HANDLER ---
+    def handle_shutdown_signal(signum, frame):
+        # Raise KeyboardInterrupt, as it's handled by the loop
+        try:
+            logging.info(f"Shutdown signal ({signum}) received. Initiating graceful exit...")
+        except NameError:
+            print(f"[Strategy-{strategy_name}] Shutdown signal ({signum}) received. Initiating graceful exit...")
+        raise KeyboardInterrupt
+            
+    signal.signal(signal.SIGTERM, handle_shutdown_signal)
+    # --- END GRACEFUL SHUTDOWN HANDLER ---
+
+    # --- Setup logging to file for this specific process ---
+    log_file_path = os.path.join(LOGS_DIR, f"strategy_{strategy_name}.log")
+    try:
+        sys.stdout = open(log_file_path, 'a', buffering=1) # 1 = line buffering
+        sys.stderr = sys.stdout
+    except Exception as e:
+        print(f"Failed to open log file for {strategy_name}: {e}")
+
+    setup_logging('normal', f"Strategy-{strategy_name}")
+
+    while True:
+        try:
+            logging.info(f"--- Starting strategy '{strategy_name}' ---")
+            
+            if 'class' not in config:
+                logging.error(f"Strategy config for '{strategy_name}' is missing the 'class' key. Exiting.")
+                return
+            
+            module_path, class_name = config['class'].rsplit('.', 1)
+            module = importlib.import_module(module_path)
+            StrategyClass = getattr(module, class_name)
+            
+            strategy = StrategyClass(strategy_name, config['parameters'], trade_signal_queue)
+
+            if config.get("is_event_driven", False):
+                logging.info(f"Starting EVENT-DRIVEN logic loop...")
+                strategy.run_event_loop() # This is a blocking call
+            else:
+                logging.info(f"Starting POLLING logic loop...")
+                strategy.run_polling_loop() # This is the original blocking call
+
+        # --- REVERTED: Added back simple KeyboardInterrupt handler ---
+        except KeyboardInterrupt:
+            logging.info(f"Strategy {strategy_name} process stopping.")
+            return
+        except Exception as e:
+            # --- REVERTED: Removed specific check for KeyboardInterrupt ---
+            logging.error(f"Strategy '{strategy_name}' failed: {e}", exc_info=True)
+            logging.info("Restarting strategy in 10 seconds...")
+            time.sleep(10)
+
+
+def run_dashboard_data_fetcher():
+    """Target function to run the dashboard_data_fetcher.py script."""
+    
+    # --- GRACEFUL SHUTDOWN HANDLER ---
+    import signal
+    
+    def handle_shutdown_signal(signum, frame):
+        # Raise KeyboardInterrupt, as it's handled by the loop
+        try:
+            logging.info(f"Shutdown signal ({signum}) received. Initiating graceful exit...")
+        except NameError:
+            print(f"[DashboardDataFetcher] Shutdown signal ({signum}) received. Initiating graceful exit...")
+        raise KeyboardInterrupt
+            
+    signal.signal(signal.SIGTERM, handle_shutdown_signal)
+    # --- END GRACEFUL SHUTDOWN HANDLER ---
+    
+    log_file = os.path.join(LOGS_DIR, "dashboard_data_fetcher.log")
+    while True:
+        try:
+            with open(log_file, 'a') as f:
+                f.write(f"\n--- Starting Dashboard Data Fetcher at {datetime.now()} ---\n")
+                subprocess.run([sys.executable, DASHBOARD_DATA_FETCHER_SCRIPT, "--log-level", "normal"], check=True, stdout=f, stderr=subprocess.STDOUT)
+        except KeyboardInterrupt: # --- MODIFIED: Added to catch interrupt ---
+            logging.info("Dashboard Data Fetcher stopping.")
+            break
+        except (subprocess.CalledProcessError, Exception) as e:
+            with open(log_file, 'a') as f:
+                f.write(f"\n--- PROCESS ERROR at {datetime.now()} ---\n")
+                f.write(f"Dashboard Data Fetcher failed: {e}. Restarting...\n")
+            time.sleep(10)
+
 
 class MainApp:
-    def __init__(self, coins_to_watch: list):
+    def __init__(self, coins_to_watch: list, processes: dict, strategy_configs: dict, shared_prices: dict):
         self.watched_coins = coins_to_watch
+        self.shared_prices = shared_prices
         self.prices = {}
-        self.last_db_update_info = "Initializing..."
-        self._lines_printed = 0 # To track how many lines we printed last time
+        # --- REMOVED: self.market_caps ---
+        self.open_positions = {}
+        self.background_processes = processes
+        self.process_status = {}
+        self.strategy_configs = strategy_configs
+        self.strategy_statuses = {}
 
     def read_prices(self):
-        """Reads the latest prices from the JSON file."""
-        if not os.path.exists(PRICE_DATA_FILE):
-            return
+        """Reads the latest prices directly from the shared memory dictionary."""
         try:
-            with open(PRICE_DATA_FILE, 'r', encoding='utf-8') as f:
-                self.prices = json.load(f)
-        except (json.JSONDecodeError, IOError):
-            logging.debug("Could not read price file (might be locked).")
+            # --- FIX: Use .copy() for thread-safe iteration ---
+            self.prices = self.shared_prices.copy()
+        except Exception as e:
+            logging.debug(f"Could not read from shared prices dict: {e}")
 
-    def get_overall_db_status(self):
-        """Reads the fetcher status from the status file."""
-        if not os.path.exists(STATUS_FILE):
-            self.last_db_update_info = "Status file not found."
-            return
+    # --- REMOVED: read_market_caps method ---
+
+    def read_strategy_statuses(self):
+        """Reads the status JSON file for each enabled strategy."""
+        enabled_statuses = {}
+        for name, config in self.strategy_configs.items():
+            if config.get("enabled", False):
+                status_file = os.path.join("_data", f"strategy_status_{name}.json")
+                if os.path.exists(status_file):
+                    try:
+                        with open(status_file, 'r', encoding='utf-8') as f:
+                            enabled_statuses[name] = json.load(f)
+                    except (IOError, json.JSONDecodeError):
+                        enabled_statuses[name] = {"error": "Could not read status file."}
+                else:
+                    enabled_statuses[name] = {"current_signal": "Initializing..."}
+        self.strategy_statuses = enabled_statuses
+
+    def read_executor_status(self):
+        """Reads the live status file from the trade executor."""
+        if os.path.exists(TRADE_EXECUTOR_STATUS_FILE):
+            try:
+                with open(TRADE_EXECUTOR_STATUS_FILE, 'r', encoding='utf-8') as f:
+                    # --- FIX: Read the 'open_positions' key from the file ---
+                    status_data = json.load(f)
+                    self.open_positions = status_data.get('open_positions', {})
+            except (IOError, json.JSONDecodeError):
+                logging.debug("Could not read trade executor status file.")
+        else:
+            self.open_positions = {}
+
+    def check_process_status(self):
+        """Checks if the background processes are still running."""
+        for name, process in self.background_processes.items():
+            self.process_status[name] = "Running" if process.is_alive() else "STOPPED"
+
+    def _format_price(self, price_val, width=10):
+        """Helper function to format prices for the dashboard."""
         try:
-            with open(STATUS_FILE, 'r', encoding='utf-8') as f:
-                status = json.load(f)
-            coin = status.get("last_updated_coin")
-            timestamp_utc_str = status.get("last_run_timestamp_utc")
-            num_candles = status.get("num_updated_candles", 0)
-
-            if timestamp_utc_str:
-                dt_naive = datetime.strptime(timestamp_utc_str, '%Y-%m-%d %H:%M:%S')
-                dt_utc = dt_naive.replace(tzinfo=timezone.utc)
-                dt_local = dt_utc.astimezone(None)
-                timestamp_display = dt_local.strftime('%Y-%m-%d %H:%M:%S %Z')
+            price_float = float(price_val)
+            if price_float < 1:
+                price_str = f"{price_float:>{width}.6f}"
+            elif price_float < 100:
+                price_str = f"{price_float:>{width}.4f}"
             else:
-                timestamp_display = "N/A"
-
-            self.last_db_update_info = f"{coin} at {timestamp_display} ({num_candles} candles)"
-        except (IOError, json.JSONDecodeError) as e:
-            self.last_db_update_info = "Error reading status file."
-            logging.error(f"Could not read status file: {e}")
+                price_str = f"{price_float:>{width}.2f}"
+        except (ValueError, TypeError):
+            price_str = f"{'Loading...':>{width}}"
+        return price_str
 
     def display_dashboard(self):
-        """Displays a formatted table for prices and DB status without blinking."""
-        # Move the cursor up to overwrite the previous output
-        if self._lines_printed > 0:
-            print(f"\x1b[{self._lines_printed}A", end="")
-
-        # Build the output as a single string
-        output_lines = []
-        output_lines.append("--- Market Dashboard ---")
-        table_width = 26
-        output_lines.append("-" * table_width)
-        output_lines.append(f"{'#':<2} | {'Coin':<6} | {'Live Price':>10} |")
-        output_lines.append("-" * table_width)
+        """Displays a formatted dashboard with side-by-side tables."""
+        print("\x1b[H\x1b[J", end="") # Clear screen
+        
+        left_table_lines = ["--- Market Dashboard ---"]
+        # --- MODIFIED: Adjusted width for new columns ---
+        left_table_width = 65
+        left_table_lines.append("-" * left_table_width)
+        # --- MODIFIED: Replaced Market Cap with Gap ---
+        left_table_lines.append(f"{'#':<2} | {'Coin':^6} | {'Best Bid':>10} | {'Live Price':>10} | {'Best Ask':>10} | {'Gap':>10} |")
+        left_table_lines.append("-" * left_table_width)
         for i, coin in enumerate(self.watched_coins, 1):
-            price = self.prices.get(coin, "Loading...")
-            output_lines.append(f"{i:<2} | {coin:<6} | {price:>10} |")
-        output_lines.append("-" * table_width)
-        output_lines.append(f"DB Status: Last coin updated -> {self.last_db_update_info}")
+            # --- MODIFIED: Fetch all three price types ---
+            mid_price = self.prices.get(coin, "Loading...")
+            bid_price = self.prices.get(f"{coin}_bid", "Loading...")
+            ask_price = self.prices.get(f"{coin}_ask", "Loading...")
+
+            # --- MODIFIED: Use the new formatting helper ---
+            formatted_mid = self._format_price(mid_price)
+            formatted_bid = self._format_price(bid_price)
+            formatted_ask = self._format_price(ask_price)
+
+            # --- MODIFIED: Calculate gap ---
+            gap_str = f"{'Loading...':>10}"
+            try:
+                # Calculate the spread
+                gap_val = float(ask_price) - float(bid_price)
+                # Format gap with high precision, similar to price
+                if gap_val < 1:
+                    gap_str = f"{gap_val:>{10}.6f}"
+                else:
+                    gap_str = f"{gap_val:>{10}.4f}"
+            except (ValueError, TypeError):
+                pass # Keep 'Loading...'
+
+            # --- REMOVED: Market Cap logic ---
+            
+            # --- MODIFIED: Print all price columns including gap ---
+            left_table_lines.append(f"{i:<2} | {coin:^6} | {formatted_bid} | {formatted_mid} | {formatted_ask} | {gap_str} |")
+        left_table_lines.append("-" * left_table_width)
+
+        right_table_lines = ["--- Strategy Status ---"]
+        # --- FIX: Adjusted table width after removing parameters ---
+        right_table_width = 105
+        right_table_lines.append("-" * right_table_width)
+        # --- FIX: Removed 'Parameters' from header ---
+        right_table_lines.append(f"{'#':^2} | {'Strategy Name':<25} | {'Coin':^6} | {'Signal':^8} | {'Signal Price':>12} | {'Last Change':>17} | {'TF':^5} | {'Size':^8} |")
+        right_table_lines.append("-" * right_table_width)
+        for i, (name, status) in enumerate(self.strategy_statuses.items(), 1):
+            signal = status.get('current_signal', 'N/A')
+            price = status.get('signal_price')
+            price_display = f"{price:.4f}" if isinstance(price, (int, float)) else "-"
+            last_change = status.get('last_signal_change_utc')
+            last_change_display = 'Never'
+            if last_change:
+                dt_utc = datetime.fromisoformat(last_change.replace('Z', '+00:00')).replace(tzinfo=timezone.utc)
+                dt_local = dt_utc.astimezone(None)
+                last_change_display = dt_local.strftime('%Y-%m-%d %H:%M')
+            
+            config_params = self.strategy_configs.get(name, {}).get('parameters', {})
+            
+            # --- FIX: Read coin/size from status file first, fallback to config ---
+            coin = status.get('coin', config_params.get('coin', 'N/A'))
+            
+            # --- FIX: Handle nested 'coins_to_copy' logic for size ---
+            # --- MODIFIED: Read 'size' from status first, then config, then 'Multi' ---
+            size = status.get('size')
+            if not size:
+                if 'coins_to_copy' in config_params:
+                    size = 'Multi'
+                else:
+                    size = config_params.get('size', 'N/A')
+            
+            timeframe = config_params.get('timeframe', 'N/A')
+            
+            # --- FIX: Removed parameter string logic ---
+            
+            # --- FIX: Removed 'params_str' from the formatted line ---
+                
+            size_display = f"{size:>8}"
+            if isinstance(size, (int, float)):
+                # --- MODIFIED: More flexible size formatting ---
+                if size < 0.0001:
+                    size_display = f"{size:>8.6f}"
+                elif size < 1:
+                    size_display = f"{size:>8.4f}"
+                else:
+                    size_display = f"{size:>8.2f}"
+            # --- END NEW LOGIC ---
+            
+            right_table_lines.append(f"{i:^2} | {name:<25} | {coin:^6} | {signal:^8} | {price_display:>12} | {last_change_display:>17} | {timeframe:^5} | {size_display} |")
+        right_table_lines.append("-" * right_table_width)
         
-        # Join lines and add a code to clear from cursor to end of screen
-        # This prevents artifacts if the new output is shorter than the old one.
-        final_output = "\n".join(output_lines) + "\n\x1b[J"
-        print(final_output, end="")
-        
-        # Store the number of lines printed for the next iteration
-        self._lines_printed = len(output_lines)
+        output_lines = []
+        max_rows = max(len(left_table_lines), len(right_table_lines))
+        separator = "   "
+        indent = " " * 10
+        for i in range(max_rows):
+            left_part = left_table_lines[i] if i < len(left_table_lines) else " " * left_table_width
+            right_part = indent + right_table_lines[i] if i < len(right_table_lines) else ""
+            output_lines.append(f"{left_part}{separator}{right_part}")
         
+        output_lines.append("\n--- Open Positions ---")
+        pos_table_width = 100
+        output_lines.append("-" * pos_table_width)
+        output_lines.append(f"{'Account':<10} | {'Coin':<6} | {'Size':>15} | {'Entry Price':>12} | {'Mark Price':>12} | {'PNL':>15} | {'Leverage':>10} |")
+        output_lines.append("-" * pos_table_width)
+
+        # --- FIX: Correctly read and display open positions ---
+        if not self.open_positions:
+            output_lines.append(f"{'No open positions.':^{pos_table_width}}")
+        else:
+            for account, positions in self.open_positions.items():
+                if not positions:
+                    continue
+                for coin, pos in positions.items():
+                    try:
+                        size_f = float(pos.get('size', 0))
+                        entry_f = float(pos.get('entry_price', 0))
+                        mark_f = float(self.prices.get(coin, 0))
+                        pnl_f = (mark_f - entry_f) * size_f if size_f > 0 else (entry_f - mark_f) * abs(size_f)
+                        lev = pos.get('leverage', 1)
+                        
+                        size_str = f"{size_f:>{15}.5f}"
+                        entry_str = f"{entry_f:>{12}.2f}"
+                        mark_str = f"{mark_f:>{12}.2f}"
+                        pnl_str = f"{pnl_f:>{15}.2f}"
+                        lev_str = f"{lev}x"
+                        
+                        output_lines.append(f"{account:<10} | {coin:<6} | {size_str} | {entry_str} | {mark_str} | {pnl_str} | {lev_str:>10} |")
+                    except (ValueError, TypeError):
+                         output_lines.append(f"{account:<10} | {coin:<6} | {'Error parsing data...':^{pos_table_width-20}} |")
+
+        output_lines.append("-" * pos_table_width)
+            
+        final_output = "\n".join(output_lines)
+        print(final_output)
         sys.stdout.flush()
 
     def run(self):
-        """Main loop to read and display data."""
+        """Main loop to read data, display dashboard, and check processes."""
         while True:
             self.read_prices()
-            self.get_overall_db_status()
+            # --- REMOVED: self.read_market_caps() ---
+            self.read_strategy_statuses()
+            self.read_executor_status()
+            # --- REMOVED: self.check_process_status() ---
             self.display_dashboard()
-            time.sleep(2)
-
+            time.sleep(0.5)
 
 if __name__ == "__main__":
     setup_logging('normal', 'MainApp')
 
-    logging.info(f"Running coin lister: '{COIN_LISTER_SCRIPT}'...")
+    if not os.path.exists(LOGS_DIR):
+        os.makedirs(LOGS_DIR)
+        
+    processes = {}
+    # --- REVERTED: Removed process groups ---
+    
     try:
-        subprocess.run([sys.executable, COIN_LISTER_SCRIPT], check=True, capture_output=True, text=True)
-    except subprocess.CalledProcessError as e:
-        logging.error(f"Failed to run '{COIN_LISTER_SCRIPT}'. Error: {e.stderr}")
+        with open(STRATEGY_CONFIG_FILE, 'r') as f:
+            strategy_configs = json.load(f)
+    except (FileNotFoundError, json.JSONDecodeError) as e:
+        logging.error(f"Could not load strategies from '{STRATEGY_CONFIG_FILE}': {e}")
         sys.exit(1)
+        
+    # --- FIX: Hardcoded timeframes ---
+    required_timeframes = [
+        "3m", "5m", "15m", "30m", "1h", "2h", "4h", "8h", 
+        "12h", "1d", "3d", "1w", "1M", "148m", "37m"
+    ]
+    logging.info(f"Using fixed timeframes for resampler: {required_timeframes}")
+    
+    with multiprocessing.Manager() as manager:
+        shared_prices = manager.dict()
+        # --- FIX: Create TWO queues ---
+        trade_signal_queue = manager.Queue()
+        order_execution_queue = manager.Queue()
 
-    logging.info(f"Starting market feeder ('{MARKET_FEEDER_SCRIPT}')...")
-    market_process = multiprocessing.Process(target=run_market_feeder, daemon=True)
-    market_process.start()
-    
-    logging.info(f"Starting historical data fetcher ('{DATA_FETCHER_SCRIPT}')...")
-    fetcher_process = multiprocessing.Process(target=data_fetcher_scheduler, daemon=True)
-    fetcher_process.start()
-    
-    # --- Restored Resampler Process Start ---
-    logging.info(f"Starting resampler ('{RESAMPLER_SCRIPT}')...")
-    resampler_process = multiprocessing.Process(target=resampler_scheduler, daemon=True)
-    resampler_process.start()
-    # --- End Resampler Process Start ---
-    
-    time.sleep(3)
+        # --- REVERTED: All processes are daemon=True and in one dict ---
+        
+        # --- FIX: Pass WATCHED_COINS to the start_live_feed process ---
+        # --- MODIFICATION: Set log level back to 'off' ---
+        processes["Live Market Feed"] = multiprocessing.Process(
+            target=start_live_feed, 
+            args=(shared_prices, WATCHED_COINS, 'off'), 
+            daemon=True
+        )
+        processes["Live Candle Fetcher"] = multiprocessing.Process(target=run_live_candle_fetcher, daemon=True)
+        processes["Resampler"] = multiprocessing.Process(target=resampler_scheduler, args=(list(required_timeframes),), daemon=True)
+        # --- REMOVED: Market Cap Fetcher Process ---
+        processes["Dashboard Data"] = multiprocessing.Process(target=run_dashboard_data_fetcher, daemon=True)
 
-    app = MainApp(coins_to_watch=WATCHED_COINS)
-    try:
-        app.run()
-    except KeyboardInterrupt:
-        logging.info("Shutting down...")
-        market_process.terminate()
-        fetcher_process.terminate()
-        # --- Restored Resampler Shutdown ---
-        resampler_process.terminate()
-        market_process.join()
-        fetcher_process.join()
-        resampler_process.join()
-        # --- End Resampler Shutdown ---
+        processes["Position Manager"] = multiprocessing.Process(
+            target=run_position_manager, 
+            args=(trade_signal_queue, order_execution_queue),
+            daemon=True
+        )
+        processes["Trade Executor"] = multiprocessing.Process(
+            target=run_trade_executor, 
+            args=(order_execution_queue,),
+            daemon=True
+        )
+        
+        for name, config in strategy_configs.items():
+            if config.get("enabled", False):
+                if 'class' not in config:
+                    logging.error(f"Strategy '{name}' is missing 'class' key. Skipping.")
+                    continue
+                proc = multiprocessing.Process(target=run_strategy, args=(name, config, trade_signal_queue), daemon=True)
+                processes[f"Strategy: {name}"] = proc # Add to strategy group
+        
+        # --- REVERTED: Removed combined dict ---
+
+        for name, proc in processes.items():
+            logging.info(f"Starting process '{name}'...")
+            proc.start()
+        
+        time.sleep(3)
+
+        app = MainApp(coins_to_watch=WATCHED_COINS, processes=processes, strategy_configs=strategy_configs, shared_prices=shared_prices)
+        try:
+            app.run()
+        except KeyboardInterrupt:
+            # --- MODIFIED: Staged shutdown ---
+            logging.info("Shutting down...")
+
+            strategy_procs = {}
+            other_procs = {}
+            for name, proc in processes.items():
+                if name.startswith("Strategy:"):
+                    strategy_procs[name] = proc
+                else:
+                    other_procs[name] = proc
+
+            # --- 1. Terminate strategy processes ---
+            logging.info("Shutting down strategy processes first...")
+            for name, proc in strategy_procs.items():
+                if proc.is_alive(): 
+                    logging.info(f"Terminating process: '{name}'...")
+                    proc.terminate()
+            
+            # --- 2. Wait for 5 seconds ---
+            logging.info("Waiting 5 seconds for strategies to close...")
+            time.sleep(5)
+            
+            # --- 3. Terminate all other processes ---
+            logging.info("Shutting down remaining core processes...")
+            for name, proc in other_procs.items():
+                 if proc.is_alive(): 
+                    logging.info(f"Terminating process: '{name}'...")
+                    proc.terminate()
+            
+            # --- 4. Join all processes (strategies and others) ---
+            logging.info("Waiting for all processes to join...")
+            for name, proc in processes.items(): # Iterate over the original dict to get all
+                if proc.is_alive(): 
+                    logging.info(f"Waiting for process '{name}' to join...")
+                    proc.join(timeout=5) # Wait up to 5 seconds
+                    if proc.is_alive():
+                        # If it's still alive, force kill
+                        logging.warning(f"Process '{name}' did not terminate, forcing kill.")
+                        proc.kill()
+            # --- END MODIFIED ---
+            
         logging.info("Shutdown complete.")
         sys.exit(0)
 
+
+

market_cap_fetcher.py

@@ -0,0 +1,321 @@
+import argparse
+import logging
+import os
+import sys
+import sqlite3
+import pandas as pd
+import requests
+import time
+from datetime import datetime, timezone, timedelta
+import json
+from dotenv import load_dotenv
+
+load_dotenv()
+
+from logging_utils import setup_logging
+
+class MarketCapFetcher:
+    """
+    Fetches historical daily market cap data from the CoinGecko API and
+    intelligently upserts it into the SQLite database for all coins.
+    """
+
+    def __init__(self, log_level: str):
+        setup_logging(log_level, 'MarketCapFetcher')
+        self.db_path = os.path.join("_data", "market_data.db")
+        self.api_base_url = "https://api.coingecko.com/api/v3"
+        self.api_key = os.environ.get("COINGECKO_API_KEY")
+        if not self.api_key:
+            logging.error("CoinGecko API key not found. Please set the COINGECKO_API_KEY environment variable.")
+            sys.exit(1)
+        
+        self.COIN_ID_MAP = self._load_coin_id_map()
+        if not self.COIN_ID_MAP:
+            logging.error("Coin ID map is empty. Run 'update_coin_map.py' to generate it.")
+            sys.exit(1)
+        
+        self.coins_to_fetch = list(self.COIN_ID_MAP.keys())
+            
+        self.STABLECOIN_ID_MAP = {
+            "USDT": "tether", "USDC": "usd-coin", "USDE": "ethena-usde",
+            "DAI": "dai", "PYUSD": "paypal-usd"
+        }
+        
+        self._ensure_tables_exist()
+
+    def _ensure_tables_exist(self):
+        """Ensures all market cap tables exist with timestamp_ms as PRIMARY KEY."""
+        all_tables_to_check = [f"{coin}_market_cap" for coin in self.coins_to_fetch]
+        all_tables_to_check.extend(["STABLECOINS_market_cap", "TOTAL_market_cap_daily"])
+        
+        with sqlite3.connect(self.db_path) as conn:
+            for table_name in all_tables_to_check:
+                cursor = conn.cursor()
+                cursor.execute(f"PRAGMA table_info('{table_name}')")
+                columns = cursor.fetchall()
+                if columns:
+                    pk_found = any(col[1] == 'timestamp_ms' and col[5] == 1 for col in columns)
+                    if not pk_found:
+                        logging.warning(f"Schema for table '{table_name}' is incorrect. Dropping and recreating table.")
+                        try:
+                            conn.execute(f'DROP TABLE "{table_name}"')
+                            self._create_market_cap_table(conn, table_name)
+                            logging.info(f"Successfully recreated schema for '{table_name}'.")
+                        except Exception as e:
+                            logging.error(f"FATAL: Failed to recreate table '{table_name}': {e}. Please delete 'market_data.db' and restart.")
+                            sys.exit(1)
+                else:
+                    self._create_market_cap_table(conn, table_name)
+            logging.info("All market cap table schemas verified.")
+
+    def _create_market_cap_table(self, conn, table_name):
+        """Creates a new market cap table with the correct schema."""
+        conn.execute(f'''
+            CREATE TABLE IF NOT EXISTS "{table_name}" (
+                datetime_utc TEXT,
+                timestamp_ms INTEGER PRIMARY KEY,
+                market_cap REAL
+            )
+        ''')
+
+    def _load_coin_id_map(self) -> dict:
+        """Loads the dynamically generated coin-to-id mapping."""
+        map_file_path = os.path.join("_data", "coin_id_map.json")
+        try:
+            with open(map_file_path, 'r') as f:
+                return json.load(f)
+        except (FileNotFoundError, json.JSONDecodeError) as e:
+            logging.error(f"Could not load '{map_file_path}'. Please run 'update_coin_map.py' first. Error: {e}")
+            return {}
+
+    def _upsert_market_cap_data(self, conn, table_name: str, df: pd.DataFrame):
+        """Upserts a DataFrame of market cap data into the specified table."""
+        if df.empty:
+            return
+            
+        records_to_upsert = []
+        for index, row in df.iterrows():
+            records_to_upsert.append((
+                row['datetime_utc'].strftime('%Y-%m-%d %H:%M:%S'),
+                row['timestamp_ms'],
+                row['market_cap']
+            ))
+        
+        cursor = conn.cursor()
+        cursor.executemany(f'''
+            INSERT OR REPLACE INTO "{table_name}" (datetime_utc, timestamp_ms, market_cap)
+            VALUES (?, ?, ?)
+        ''', records_to_upsert)
+        conn.commit()
+        logging.info(f"Successfully upserted {len(records_to_upsert)} records into '{table_name}'.")
+
+    def run(self):
+        """
+        Main execution function to process all configured coins and update the database.
+        """
+        logging.info("Starting historical market cap fetch process from CoinGecko...")
+        with sqlite3.connect(self.db_path) as conn:
+            conn.execute("PRAGMA journal_mode=WAL;")
+
+            for coin_symbol in self.coins_to_fetch:
+                coin_id = self.COIN_ID_MAP.get(coin_symbol.upper())
+                if not coin_id:
+                    logging.warning(f"No CoinGecko ID found for '{coin_symbol}'. Skipping.")
+                    continue
+                logging.info(f"--- Processing {coin_symbol} ({coin_id}) ---")
+                try:
+                    self._update_market_cap_for_coin(coin_id, coin_symbol, conn)
+                except Exception as e:
+                    logging.error(f"An unexpected error occurred while processing {coin_symbol}: {e}")
+                time.sleep(2)
+            
+            self._update_stablecoin_aggregate(conn)
+            self._update_total_market_cap(conn)
+            self._save_summary(conn)
+        
+        logging.info("--- Market cap fetch process complete ---")
+
+    def _save_summary(self, conn):
+        # ... (This function is unchanged)
+        logging.info("--- Generating Market Cap Summary ---")
+        summary_data = {}
+        summary_file_path = os.path.join("_data", "market_cap_data.json")
+        try:
+            cursor = conn.cursor()
+            cursor.execute("SELECT name FROM sqlite_master WHERE type='table' AND (name LIKE '%_market_cap' OR name LIKE 'TOTAL_%');")
+            tables = [row[0] for row in cursor.fetchall()]
+            for table_name in tables:
+                try:
+                    df_last = pd.read_sql(f'SELECT * FROM "{table_name}" ORDER BY datetime_utc DESC LIMIT 1', conn)
+                    if not df_last.empty:
+                        summary_data[table_name] = df_last.to_dict('records')[0]
+                except Exception as e:
+                    logging.error(f"Could not read last record from table '{table_name}': {e}")
+            if summary_data:
+                summary_data['summary_last_updated_utc'] = datetime.now(timezone.utc).isoformat()
+                with open(summary_file_path, 'w', encoding='utf-8') as f:
+                    json.dump(summary_data, f, indent=4)
+                logging.info(f"Successfully saved market cap summary to '{summary_file_path}'")
+            else:
+                logging.warning("No data found to create a summary.")
+        except Exception as e:
+            logging.error(f"Failed to generate summary: {e}")
+
+    def _update_total_market_cap(self, conn):
+        """Fetches the current total market cap and upserts it for the current date."""
+        logging.info("--- Processing Total Market Cap ---")
+        table_name = "TOTAL_market_cap_daily"
+        try:
+            today_date = datetime.now(timezone.utc).date()
+            today_dt = pd.to_datetime(today_date)
+            today_ts = int(today_dt.timestamp() * 1000)
+
+            logging.info("Fetching current global market data...")
+            url = f"{self.api_base_url}/global"
+            headers = {"x-cg-demo-api-key": self.api_key}
+            response = requests.get(url, headers=headers)
+            response.raise_for_status()
+            global_data = response.json().get('data', {})
+            total_mc = global_data.get('total_market_cap', {}).get('usd')
+
+            if total_mc:
+                df_total = pd.DataFrame([{
+                    'datetime_utc': today_dt,
+                    'timestamp_ms': today_ts,
+                    'market_cap': total_mc
+                }])
+                self._upsert_market_cap_data(conn, table_name, df_total)
+                logging.info(f"Saved total market cap for {today_date}: ${total_mc:,.2f}")
+
+        except requests.exceptions.RequestException as e:
+            logging.error(f"Failed to fetch global market data: {e}")
+        except Exception as e:
+            logging.error(f"An error occurred while updating total market cap: {e}")
+
+    def _update_stablecoin_aggregate(self, conn):
+        """Fetches data for all stablecoins and saves the aggregated market cap."""
+        logging.info("--- Processing aggregated stablecoin market cap ---")
+        all_stablecoin_df = pd.DataFrame()
+
+        for symbol, coin_id in self.STABLECOIN_ID_MAP.items():
+            logging.info(f"Fetching historical data for stablecoin: {symbol}...")
+            df = self._fetch_historical_data(coin_id, days=365)
+            if not df.empty:
+                all_stablecoin_df = pd.concat([all_stablecoin_df, df])
+            time.sleep(2)
+
+        if all_stablecoin_df.empty:
+            logging.warning("No data fetched for any stablecoins. Cannot create aggregate.")
+            return
+
+        aggregated_df = all_stablecoin_df.groupby('timestamp_ms').agg(
+            datetime_utc=('datetime_utc', 'first'),
+            market_cap=('market_cap', 'sum')
+        ).reset_index()
+
+        table_name = "STABLECOINS_market_cap"
+        last_date_in_db = self._get_last_date_from_db(table_name, conn, is_timestamp_ms=True)
+
+        if last_date_in_db:
+            aggregated_df = aggregated_df[aggregated_df['timestamp_ms'] > last_date_in_db]
+        
+        if not aggregated_df.empty:
+            self._upsert_market_cap_data(conn, table_name, aggregated_df)
+        else:
+            logging.info("Aggregated stablecoin data is already up-to-date.")
+
+    def _update_market_cap_for_coin(self, coin_id: str, coin_symbol: str, conn):
+        """Fetches and appends new market cap data for a single coin."""
+        table_name = f"{coin_symbol}_market_cap"
+        last_date_in_db = self._get_last_date_from_db(table_name, conn, is_timestamp_ms=True)
+        
+        days_to_fetch = 365
+        if last_date_in_db:
+            delta_days = (datetime.now(timezone.utc) - datetime.fromtimestamp(last_date_in_db/1000, tz=timezone.utc)).days
+            if delta_days <= 0:
+                logging.info(f"Market cap data for '{coin_symbol}' is already up-to-date.")
+                return
+            days_to_fetch = min(delta_days + 1, 365)
+        else:
+            logging.info(f"No existing data found. Fetching initial {days_to_fetch} days for {coin_symbol}.")
+
+        df = self._fetch_historical_data(coin_id, days=days_to_fetch)
+        
+        if df.empty:
+            logging.warning(f"No market cap data returned from API for {coin_symbol}.")
+            return
+            
+        if last_date_in_db:
+            df = df[df['timestamp_ms'] > last_date_in_db]
+
+        if not df.empty:
+            self._upsert_market_cap_data(conn, table_name, df)
+        else:
+            logging.info(f"Data was fetched, but no new records needed saving for '{coin_symbol}'.")
+
+    def _get_last_date_from_db(self, table_name: str, conn, is_timestamp_ms: bool = False):
+        """Gets the most recent date or timestamp from a market cap table."""
+        try:
+            cursor = conn.cursor()
+            cursor.execute(f"SELECT name FROM sqlite_master WHERE type='table' AND name='{table_name}';")
+            if not cursor.fetchone():
+                return None
+            
+            col_to_query = "timestamp_ms" if is_timestamp_ms else "datetime_utc"
+            last_val = pd.read_sql(f'SELECT MAX({col_to_query}) FROM "{table_name}"', conn).iloc[0, 0]
+            
+            if pd.isna(last_val):
+                return None
+            if is_timestamp_ms:
+                return int(last_val)
+            return pd.to_datetime(last_val)
+
+        except Exception as e:
+            logging.error(f"Could not read last date from table '{table_name}': {e}")
+            return None
+
+    def _fetch_historical_data(self, coin_id: str, days: int) -> pd.DataFrame:
+        """Fetches historical market chart data from CoinGecko for a specified number of days."""
+        url = f"{self.api_base_url}/coins/{coin_id}/market_chart"
+        params = { "vs_currency": "usd", "days": days, "interval": "daily" }
+        headers = {"x-cg-demo-api-key": self.api_key}
+            
+        try:
+            logging.debug(f"Fetching last {days} days for {coin_id}...")
+            response = requests.get(url, headers=headers, params=params)
+            response.raise_for_status()
+            data = response.json()
+
+            market_caps = data.get('market_caps', [])
+            if not market_caps: return pd.DataFrame()
+
+            df = pd.DataFrame(market_caps, columns=['timestamp_ms', 'market_cap'])
+            
+            # --- FIX: Normalize all timestamps to the start of the day (00:00:00 UTC) ---
+            # This prevents duplicate entries for the same day (e.g., a "live" candle vs. the daily one)
+            df['datetime_utc'] = pd.to_datetime(df['timestamp_ms'], unit='ms').dt.normalize()
+            
+            # Recalculate the timestamp_ms to match the normalized 00:00:00 datetime
+            df['timestamp_ms'] = (df['datetime_utc'].astype('int64') // 10**6)
+            
+            df.drop_duplicates(subset=['timestamp_ms'], keep='last', inplace=True)
+            return df[['datetime_utc', 'timestamp_ms', 'market_cap']]
+        
+        except requests.exceptions.RequestException as e:
+            logging.error(f"API request failed for {coin_id}: {e}.")
+            return pd.DataFrame()
+
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser(description="Fetch historical market cap data from CoinGecko.")
+    parser.add_argument(
+        "--log-level",
+        default="normal",
+        choices=['off', 'normal', 'debug'],
+        help="Set the logging level for the script."
+    )
+    args = parser.parse_args()
+    
+    fetcher = MarketCapFetcher(log_level=args.log_level)
+    fetcher.run()
+

position_logic/__init__.py

@@ -0,0 +1,2 @@
+# This file can be empty.
+# It tells Python that 'position_logic' is a directory containing modules.

position_logic/base_logic.py

@@ -0,0 +1,31 @@
+from abc import ABC, abstractmethod
+import logging
+
+class BasePositionLogic(ABC):
+    """
+    Abstract base class for all strategy-specific position logic.
+    Defines the interface for how the PositionManager interacts with logic modules.
+    """
+    def __init__(self, strategy_name: str, send_order_callback, log_trade_callback):
+        self.strategy_name = strategy_name
+        self.send_order = send_order_callback
+        self.log_trade = log_trade_callback
+        logging.info(f"Initialized position logic for '{strategy_name}'")
+
+    @abstractmethod
+    def handle_signal(self, signal_data: dict, current_strategy_positions: dict) -> dict:
+        """
+        The core logic method. This is called by the PositionManager when a
+        new signal arrives for this strategy.
+        
+        Args:
+            signal_data: The full signal dictionary from the strategy.
+            current_strategy_positions: A dict of this strategy's current positions,
+                                         keyed by coin (e.g., {"BTC": {"side": "long", ...}}).
+        
+        Returns:
+            A dictionary representing the new state for the *specific coin* in the
+            signal (e.g., {"side": "long", "size": 0.1}).
+            Return None to indicate the position for this coin should be closed/removed.
+        """
+        pass

position_logic/default_flip_logic.py

@@ -0,0 +1,83 @@
+import logging
+from position_logic.base_logic import BasePositionLogic
+
+class DefaultFlipLogic(BasePositionLogic):
+    """
+    The standard "flip-on-signal" logic used by most simple strategies
+    (SMA, MA Cross, and even the per-coin Copy Trader signals).
+    
+    - BUY signal: Closes any short, opens a long.
+    - SELL signal: Closes any long, opens a short.
+    - FLAT signal: Closes any open position.
+    """
+    def handle_signal(self, signal_data: dict, current_strategy_positions: dict) -> dict:
+        """
+        Processes a BUY, SELL, or FLAT signal and issues the necessary orders
+        to flip or open a position.
+        """
+        name = self.strategy_name
+        params = signal_data['config']['parameters']
+        coin = signal_data['coin']
+        desired_signal = signal_data['signal']
+        signal_price = signal_data.get('signal_price', 0)
+        
+        size = params.get('size')
+        leverage_long = int(params.get('leverage_long', 2))
+        leverage_short = int(params.get('leverage_short', 2))
+        agent_name = signal_data['config'].get("agent", "default").lower()
+
+        # --- This logic now correctly targets a specific coin ---
+        current_position = current_strategy_positions.get(coin)
+        new_position_state = None # Return None to close position
+
+        if desired_signal == "BUY" or desired_signal == "INIT_BUY":
+            new_position_state = {"coin": coin, "side": "long", "size": size}
+            
+            if not current_position:
+                logging.warning(f"[{name}]-[{coin}] ACTION: Setting leverage to {leverage_long}x and opening LONG.")
+                self.send_order(agent_name, "update_leverage", coin, is_buy=True, size=leverage_long)
+                self.send_order(agent_name, "market_open", coin, is_buy=True, size=size)
+                self.log_trade(strategy=name, coin=coin, action="OPEN_LONG", price=signal_price, size=size, signal=desired_signal)
+            
+            elif current_position['side'] == 'short':
+                logging.warning(f"[{name}]-[{coin}] ACTION: Closing SHORT and opening LONG with {leverage_long}x leverage.")
+                self.send_order(agent_name, "update_leverage", coin, is_buy=True, size=leverage_long)
+                self.send_order(agent_name, "market_open", coin, is_buy=True, size=current_position['size'], reduce_only=True)
+                self.log_trade(strategy=name, coin=coin, action="CLOSE_SHORT", price=signal_price, size=current_position['size'], signal=desired_signal)
+                self.send_order(agent_name, "market_open", coin, is_buy=True, size=size)
+                self.log_trade(strategy=name, coin=coin, action="OPEN_LONG", price=signal_price, size=size, signal=desired_signal)
+            
+            else: # Already long, do nothing
+                logging.info(f"[{name}]-[{coin}] INFO: Already LONG, no action taken.")
+                new_position_state = current_position # State is unchanged
+
+        elif desired_signal == "SELL" or desired_signal == "INIT_SELL":
+            new_position_state = {"coin": coin, "side": "short", "size": size}
+
+            if not current_position:
+                logging.warning(f"[{name}]-[{coin}] ACTION: Setting leverage to {leverage_short}x and opening SHORT.")
+                self.send_order(agent_name, "update_leverage", coin, is_buy=False, size=leverage_short)
+                self.send_order(agent_name, "market_open", coin, is_buy=False, size=size)
+                self.log_trade(strategy=name, coin=coin, action="OPEN_SHORT", price=signal_price, size=size, signal=desired_signal)
+            
+            elif current_position['side'] == 'long':
+                logging.warning(f"[{name}]-[{coin}] ACTION: Closing LONG and opening SHORT with {leverage_short}x leverage.")
+                self.send_order(agent_name, "update_leverage", coin, is_buy=False, size=leverage_short)
+                self.send_order(agent_name, "market_open", coin, is_buy=False, size=current_position['size'], reduce_only=True)
+                self.log_trade(strategy=name, coin=coin, action="CLOSE_LONG", price=signal_price, size=current_position['size'], signal=desired_signal)
+                self.send_order(agent_name, "market_open", coin, is_buy=False, size=size)
+                self.log_trade(strategy=name, coin=coin, action="OPEN_SHORT", price=signal_price, size=size, signal=desired_signal)
+
+            else: # Already short, do nothing
+                logging.info(f"[{name}]-[{coin}] INFO: Already SHORT, no action taken.")
+                new_position_state = current_position # State is unchanged
+
+        elif desired_signal == "FLAT":
+            if current_position:
+                logging.warning(f"[{name}]-[{coin}] ACTION: Close {current_position['side']} position.")
+                is_buy = current_position['side'] == 'short' # To close a short, we buy
+                self.send_order(agent_name, "market_open", coin, is_buy=is_buy, size=current_position['size'], reduce_only=True)
+                self.log_trade(strategy=name, coin=coin, action=f"CLOSE_{current_position['side'].upper()}", price=signal_price, size=current_position['size'], signal=desired_signal)
+            # new_position_state is already None, which will remove it
+
+        return new_position_state

position_manager.py

@@ -0,0 +1,170 @@
+import logging
+import os
+import sys
+import json
+import time
+import multiprocessing
+import numpy as np # Import numpy to handle np.float64
+
+from logging_utils import setup_logging
+from trade_log import log_trade
+
+class PositionManager:
+    """
+    (Stateless) Listens for EXPLICIT signals (e.g., "OPEN_LONG") from all
+    strategies and converts them into specific execution orders 
+    (e.g., "market_open") for the TradeExecutor. 
+    
+    It holds NO position state.
+    """
+
+    def __init__(self, log_level: str, trade_signal_queue: multiprocessing.Queue, order_execution_queue: multiprocessing.Queue):
+        # Note: Logging is set up by the run_position_manager function
+        
+        self.trade_signal_queue = trade_signal_queue
+        self.order_execution_queue = order_execution_queue
+            
+        # --- REMOVED: All state management ---
+        
+        logging.info("Position Manager (Stateless) started.")
+
+    # --- REMOVED: _load_managed_positions method ---
+    # --- REMOVED: _save_managed_positions method ---
+    # --- REMOVED: All tick/rounding/meta logic ---
+
+    def send_order(self, agent: str, action: str, coin: str, is_buy: bool, size: float, reduce_only: bool = False, limit_px=None, sl_px=None, tp_px=None):
+        """Helper function to put a standardized order onto the execution queue."""
+        order_data = {
+            "agent": agent,
+            "action": action,
+            "coin": coin,
+            "is_buy": is_buy,
+            "size": size,
+            "reduce_only": reduce_only,
+            "limit_px": limit_px,
+            "sl_px": sl_px,
+            "tp_px": tp_px,
+        }
+        logging.info(f"Sending order to executor: {order_data}")
+        self.order_execution_queue.put(order_data)
+
+    def run(self):
+        """
+        Main execution loop. Blocks and waits for a signal from the queue.
+        Converts explicit strategy signals into execution orders.
+        """
+        logging.info("Position Manager started. Waiting for signals...")
+        while True:
+            try:
+                trade_signal = self.trade_signal_queue.get()
+                if not trade_signal:
+                    continue
+
+                logging.info(f"Received signal: {trade_signal}")
+
+                name = trade_signal['strategy_name']
+                config = trade_signal['config']
+                params = config['parameters']
+                coin = trade_signal['coin'].upper()
+                
+                # --- NEW: The signal is now the explicit action ---
+                desired_signal = trade_signal['signal'] 
+                
+                status = trade_signal 
+
+                signal_price = status.get('signal_price')
+                if isinstance(signal_price, np.float64):
+                    signal_price = float(signal_price)
+                
+                if not signal_price or signal_price <= 0:
+                    logging.warning(f"[{name}] Signal received with invalid or missing price ({signal_price}). Skipping.")
+                    continue
+
+                # --- This logic is still needed for copy_trader's nested config ---
+                # --- But ONLY for finding leverage, not size ---
+                if 'coins_to_copy' in params:
+                    logging.info(f"[{name}] Detected 'coins_to_copy'. Entering copy_trader logic...")
+                    matching_coin_key = None
+                    for key in params['coins_to_copy'].keys():
+                        if key.upper() == coin:
+                            matching_coin_key = key
+                            break
+                    
+                    if matching_coin_key:
+                        coin_specific_config = params['coins_to_copy'][matching_coin_key]
+                    else:
+                        coin_specific_config = {}
+                    
+                    # --- REMOVED: size = coin_specific_config.get('size') ---
+                    
+                    params['leverage_long'] = coin_specific_config.get('leverage_long', 2)
+                    params['leverage_short'] = coin_specific_config.get('leverage_short', 2)
+                
+                # --- FIX: Read the size from the ROOT of the trade signal ---
+                size = trade_signal.get('size')
+                if not size or size <= 0:
+                    logging.error(f"[{name}] Signal received with no 'size' or invalid size ({size}). Skipping trade.")
+                    continue
+                # --- END FIX ---
+                
+                leverage_long = int(params.get('leverage_long', 2))
+                leverage_short = int(params.get('leverage_short', 2))
+                
+                agent_name = (config.get("agent") or "default").lower()
+                
+                logging.info(f"[{name}] Agent set to: {agent_name}")
+                
+                # --- REMOVED: current_position check ---
+                
+                # --- Use pure signal_price directly for the limit_px ---
+                limit_px = signal_price
+                logging.info(f"[{name}] Using pure signal price for limit_px: {limit_px}")
+
+                # --- NEW: Stateless Signal-to-Order Conversion ---
+                
+                if desired_signal == "OPEN_LONG":
+                    logging.warning(f"[{name}] ACTION: Opening LONG for {coin}.")
+                    # --- REMOVED: Leverage update signal ---
+                    self.send_order(agent_name, "market_open", coin, True, size, limit_px=limit_px)
+                    log_trade(strategy=name, coin=coin, action="OPEN_LONG", price=signal_price, size=size, signal=desired_signal)
+                
+                elif desired_signal == "OPEN_SHORT":
+                    logging.warning(f"[{name}] ACTION: Opening SHORT for {coin}.")
+                    # --- REMOVED: Leverage update signal ---
+                    self.send_order(agent_name, "market_open", coin, False, size, limit_px=limit_px)
+                    log_trade(strategy=name, coin=coin, action="OPEN_SHORT", price=signal_price, size=size, signal=desired_signal)
+
+                elif desired_signal == "CLOSE_LONG":
+                    logging.warning(f"[{name}] ACTION: Closing LONG position for {coin}.")
+                    # A "market_close" for a LONG is a SELL order
+                    self.send_order(agent_name, "market_close", coin, False, size, limit_px=limit_px)
+                    log_trade(strategy=name, coin=coin, action="CLOSE_LONG", price=signal_price, size=size, signal=desired_signal)
+
+                elif desired_signal == "CLOSE_SHORT":
+                    logging.warning(f"[{name}] ACTION: Closing SHORT position for {coin}.")
+                    # A "market_close" for a SHORT is a BUY order
+                    self.send_order(agent_name, "market_close", coin, True, size, limit_px=limit_px)
+                    log_trade(strategy=name, coin=coin, action="CLOSE_SHORT", price=signal_price, size=size, signal=desired_signal)
+
+                # --- NEW: Handle leverage update signals ---
+                elif desired_signal == "UPDATE_LEVERAGE_LONG":
+                    logging.warning(f"[{name}] ACTION: Updating LONG leverage for {coin} to {size}x")
+                    # 'size' field holds the leverage value for this signal
+                    self.send_order(agent_name, "update_leverage", coin, True, size)
+
+                elif desired_signal == "UPDATE_LEVERAGE_SHORT":
+                    logging.warning(f"[{name}] ACTION: Updating SHORT leverage for {coin} to {size}x")
+                    # 'size' field holds the leverage value for this signal
+                    self.send_order(agent_name, "update_leverage", coin, False, size)
+
+                else:
+                    logging.warning(f"[{name}] Received unknown signal '{desired_signal}'. No action taken.")
+
+                # --- REMOVED: _save_managed_positions() ---
+
+            except Exception as e:
+                logging.error(f"An error occurred in the position manager loop: {e}", exc_info=True)
+                time.sleep(1)
+
+# This script is no longer run directly, but is called by main_app.py
+

position_monitor.py

@@ -0,0 +1,159 @@
+import os
+import sys
+import time
+import json
+import argparse
+from datetime import datetime, timezone
+from hyperliquid.info import Info
+from hyperliquid.utils import constants
+from dotenv import load_dotenv
+import logging
+
+from logging_utils import setup_logging
+
+# Load .env file
+load_dotenv()
+
+class PositionMonitor:
+    """
+    A standalone, read-only dashboard for monitoring all open perpetuals
+    positions, spot balances, and their associated strategies.
+    """
+    
+    def __init__(self, log_level: str):
+        setup_logging(log_level, 'PositionMonitor')
+        
+        self.wallet_address = os.environ.get("MAIN_WALLET_ADDRESS")
+        if not self.wallet_address:
+            logging.error("MAIN_WALLET_ADDRESS not set in .env file. Cannot proceed.")
+            sys.exit(1)
+            
+        self.info = Info(constants.MAINNET_API_URL, skip_ws=True)
+        self.managed_positions_path = os.path.join("_data", "executor_managed_positions.json")
+        self._lines_printed = 0
+        
+        logging.info(f"Monitoring vault address: {self.wallet_address}")
+
+    def load_managed_positions(self) -> dict:
+        """Loads the state of which strategy manages which position."""
+        if os.path.exists(self.managed_positions_path):
+            try:
+                with open(self.managed_positions_path, 'r') as f:
+                    # Create a reverse map: {coin: strategy_name}
+                    data = json.load(f)
+                    return {v['coin']: k for k, v in data.items()}
+            except (IOError, json.JSONDecodeError):
+                logging.warning("Could not read managed positions file.")
+        return {}
+
+    def run(self):
+        """Main loop to continuously refresh the dashboard."""
+        try:
+            while True:
+                self.display_dashboard()
+                time.sleep(5) # Refresh every 5 seconds
+        except KeyboardInterrupt:
+            logging.info("Position monitor stopped.")
+
+    def display_dashboard(self):
+        """Fetches all data and draws the dashboard without blinking."""
+        if self._lines_printed > 0:
+            print(f"\x1b[{self._lines_printed}A", end="")
+        
+        output_lines = []
+        try:
+            perp_state = self.info.user_state(self.wallet_address)
+            spot_state = self.info.spot_user_state(self.wallet_address)
+            coin_to_strategy_map = self.load_managed_positions()
+            
+            output_lines.append(f"--- Live Position Monitor for {self.wallet_address[:6]}...{self.wallet_address[-4:]} ---")
+            
+            # --- 1. Perpetuals Account Summary ---
+            margin_summary = perp_state.get('marginSummary', {})
+            account_value = float(margin_summary.get('accountValue', 0))
+            margin_used = float(margin_summary.get('totalMarginUsed', 0))
+            utilization = (margin_used / account_value) * 100 if account_value > 0 else 0
+            
+            output_lines.append("\n--- Perpetuals Account Summary ---")
+            output_lines.append(f"  Account Value: ${account_value:,.2f}  |  Margin Used: ${margin_used:,.2f}  |  Utilization: {utilization:.2f}%")
+            
+            # --- 2. Spot Balances Summary ---
+            output_lines.append("\n--- Spot Balances ---")
+            spot_balances = spot_state.get('balances', [])
+            if not spot_balances:
+                output_lines.append("  No spot balances found.")
+            else:
+                balances_str = ", ".join([f"{b.get('coin')}: {float(b.get('total', 0)):,.4f}" for b in spot_balances if float(b.get('total', 0)) > 0])
+                output_lines.append(f"  {balances_str}")
+                
+            # --- 3. Open Positions Table ---
+            output_lines.append("\n--- Open Perpetual Positions ---")
+            positions = perp_state.get('assetPositions', [])
+            open_positions = [p for p in positions if p.get('position') and float(p['position'].get('szi', 0)) != 0]
+
+            if not open_positions:
+                output_lines.append("  No open perpetual positions found.")
+                output_lines.append("") # Add a line for stable refresh
+            else:
+                self.build_positions_table(open_positions, coin_to_strategy_map, output_lines)
+
+        except Exception as e:
+            output_lines = [f"An error occurred: {e}"]
+        
+        final_output = "\n".join(output_lines) + "\n\x1b[J" # \x1b[J clears to end of screen
+        print(final_output, end="")
+        
+        self._lines_printed = len(output_lines)
+        sys.stdout.flush()
+
+    def build_positions_table(self, positions: list, coin_to_strategy_map: dict, output_lines: list):
+        """Builds the text for the positions summary table."""
+        header = f"| {'Strategy':<25} | {'Coin':<6} | {'Side':<5} | {'Size':>15} | {'Entry Price':>12} | {'Mark Price':>12} | {'PNL':>15} | {'Leverage':>10} |"
+        output_lines.append(header)
+        output_lines.append("-" * len(header))
+        
+        for position in positions:
+            pos = position.get('position', {})
+            coin = pos.get('coin', 'Unknown')
+            size = float(pos.get('szi', 0))
+            entry_px = float(pos.get('entryPx', 0))
+            mark_px = float(pos.get('markPx', 0))
+            unrealized_pnl = float(pos.get('unrealizedPnl', 0))
+            
+            # Get leverage
+            position_value = float(pos.get('positionValue', 0))
+            margin_used = float(pos.get('marginUsed', 0))
+            leverage = (position_value / margin_used) if margin_used > 0 else 0
+            
+            side_text = "LONG" if size > 0 else "SHORT"
+            pnl_sign = "+" if unrealized_pnl >= 0 else ""
+            
+            # Find the strategy that owns this coin
+            strategy_name = coin_to_strategy_map.get(coin, "Unmanaged")
+            
+            # Format all values as strings
+            strategy_str = f"{strategy_name:<25}"
+            coin_str = f"{coin:<6}"
+            side_str = f"{side_text:<5}"
+            size_str = f"{size:>15.4f}"
+            entry_str = f"${entry_px:>11,.2f}"
+            mark_str = f"${mark_px:>11,.2f}"
+            pnl_str = f"{pnl_sign}${unrealized_pnl:>14,.2f}"
+            lev_str = f"{leverage:>9.1f}x"
+            
+            output_lines.append(f"| {strategy_str} | {coin_str} | {side_str} | {size_str} | {entry_str} | {mark_str} | {pnl_str} | {lev_str} |")
+        
+        output_lines.append("-" * len(header))
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser(description="Monitor a Hyperliquid wallet's positions in real-time.")
+    parser.add_argument(
+        "--log-level",
+        default="normal",
+        choices=['off', 'normal', 'debug'],
+        help="Set the logging level for the script."
+    )
+    args = parser.parse_args()
+    
+    monitor = PositionMonitor(log_level=args.log_level)
+    monitor.run()

resampler.py

@@ -5,15 +5,16 @@ import sys
 import sqlite3
 import pandas as pd
 import json
-from datetime import datetime, timezone
+from datetime import datetime, timezone, timedelta
 
 # Assuming logging_utils.py is in the same directory
 from logging_utils import setup_logging
 
 class Resampler:
     """
-    Reads 1-minute candle data directly from the SQLite database, resamples
-    it to various timeframes, and stores the results back in the database.
+    Reads new 1-minute candle data from the SQLite database, resamples it to
+    various timeframes, and upserts the new candles to the corresponding tables,
+    preventing data duplication.
     """
 
     def __init__(self, log_level: str, coins: list, timeframes: dict):
@@ -31,13 +32,70 @@ class Resampler:
             'number_of_trades': 'sum'
         }
         self.resampling_status = self._load_existing_status()
+        self.job_start_time = None
+        self._ensure_tables_exist()
+
+    def _ensure_tables_exist(self):
+        """
+        Ensures all resampled tables exist with a PRIMARY KEY on timestamp_ms.
+        Attempts to migrate existing tables if the schema is incorrect.
+        """
+        with sqlite3.connect(self.db_path) as conn:
+            for coin in self.coins_to_process:
+                for tf_name in self.timeframes.keys():
+                    table_name = f"{coin}_{tf_name}"
+                    cursor = conn.cursor()
+                    cursor.execute(f"PRAGMA table_info('{table_name}')")
+                    columns = cursor.fetchall()
+                    if columns:
+                        # --- FIX: Check for the correct PRIMARY KEY on timestamp_ms ---
+                        pk_found = any(col[1] == 'timestamp_ms' and col[5] == 1 for col in columns)
+                        if not pk_found:
+                            logging.warning(f"Schema migration needed for table '{table_name}'.")
+                            try:
+                                conn.execute(f'ALTER TABLE "{table_name}" RENAME TO "{table_name}_old"')
+                                self._create_resampled_table(conn, table_name)
+                                # Copy data, ensuring to create the timestamp_ms
+                                logging.info(f"  -> Migrating data for '{table_name}'...")
+                                old_df = pd.read_sql(f'SELECT * FROM "{table_name}_old"', conn, parse_dates=['datetime_utc'])
+                                if not old_df.empty:
+                                    old_df['timestamp_ms'] = (old_df['datetime_utc'].astype('int64') // 10**6)
+                                    # Keep only unique timestamps, preserving the last entry
+                                    old_df.drop_duplicates(subset=['timestamp_ms'], keep='last', inplace=True)
+                                    old_df.to_sql(table_name, conn, if_exists='append', index=False)
+                                logging.info(f"  -> Data migration complete.")
+                                conn.execute(f'DROP TABLE "{table_name}_old"')
+                                conn.commit()
+                                logging.info(f"Successfully migrated schema for '{table_name}'.")
+                            except Exception as e:
+                                logging.error(f"FATAL: Migration for '{table_name}' failed: {e}. Please delete 'market_data.db' and restart.")
+                                sys.exit(1)
+                    else:
+                        self._create_resampled_table(conn, table_name)
+            logging.info("All resampled table schemas verified.")
+
+    def _create_resampled_table(self, conn, table_name):
+        """Creates a new resampled table with the correct schema."""
+        # --- FIX: Set PRIMARY KEY on timestamp_ms for performance and uniqueness ---
+        conn.execute(f'''
+            CREATE TABLE "{table_name}" (
+                datetime_utc TEXT,
+                timestamp_ms INTEGER PRIMARY KEY,
+                open REAL,
+                high REAL,
+                low REAL,
+                close REAL,
+                volume REAL,
+                number_of_trades INTEGER
+            )
+        ''')
 
     def _load_existing_status(self) -> dict:
         """Loads the existing status file if it exists, otherwise returns an empty dict."""
         if os.path.exists(self.status_file_path):
             try:
                 with open(self.status_file_path, 'r', encoding='utf-8') as f:
-                    logging.info(f"Loading existing status from '{self.status_file_path}'")
+                    logging.debug(f"Loading existing status from '{self.status_file_path}'")
                     return json.load(f)
             except (IOError, json.JSONDecodeError) as e:
                 logging.warning(f"Could not read existing status file. Starting fresh. Error: {e}")
@@ -47,78 +105,141 @@ class Resampler:
         """
         Main execution function to process all configured coins and update the database.
         """
+        self.job_start_time = datetime.now(timezone.utc)
+        logging.info(f"--- Resampling job started at {self.job_start_time.strftime('%Y-%m-%d %H:%M:%S %Z')} ---")
+        
+        if '1m' in self.timeframes:
+            logging.debug("Ignoring '1m' timeframe as it is the source resolution.")
+            del self.timeframes['1m']
+
+        if not self.timeframes:
+            logging.warning("No timeframes to process after filtering. Exiting job.")
+            return
+
         if not os.path.exists(self.db_path):
-            logging.error(f"Database file '{self.db_path}' not found. "
-                          "Please run the data fetcher script first.")
-            sys.exit(1)
+            logging.error(f"Database file '{self.db_path}' not found.")
+            return
 
         with sqlite3.connect(self.db_path) as conn:
             conn.execute("PRAGMA journal_mode=WAL;")
             
-            logging.info(f"Processing {len(self.coins_to_process)} coins: {', '.join(self.coins_to_process)}")
+            logging.debug(f"Processing {len(self.coins_to_process)} coins...")
 
             for coin in self.coins_to_process:
-                source_table_name = f"{coin}_1m"
-                logging.info(f"--- Processing {coin} ---")
+                logging.debug(f"--- Processing {coin} ---")
                 
                 try:
-                    df = pd.read_sql(f'SELECT * FROM "{source_table_name}"', conn)
-                    
-                    if df.empty:
-                        logging.warning(f"Source table '{source_table_name}' is empty or does not exist. Skipping.")
-                        continue
-
-                    df['datetime_utc'] = pd.to_datetime(df['datetime_utc'])
-                    df.set_index('datetime_utc', inplace=True)
-
                     for tf_name, tf_code in self.timeframes.items():
-                        logging.info(f"  Resampling to {tf_name}...")
+                        target_table_name = f"{coin}_{tf_name}"
+                        source_table_name = f"{coin}_1m"
+                        logging.debug(f"  Updating {tf_name} table...")
                         
-                        resampled_df = df.resample(tf_code).agg(self.aggregation_logic)
+                        last_timestamp_ms = self._get_last_timestamp(conn, target_table_name)
+                        
+                        query = f'SELECT * FROM "{source_table_name}"'
+                        params = ()
+                        if last_timestamp_ms:
+                            query += ' WHERE timestamp_ms >= ?'
+                            # Go back one interval to rebuild the last (potentially partial) candle
+                            try:
+                                interval_delta_ms = pd.to_timedelta(tf_code).total_seconds() * 1000
+                            except ValueError:
+                                # Fall back to a safe 32-day lookback for special timeframes
+                                interval_delta_ms = timedelta(days=32).total_seconds() * 1000
+                            
+                            query_start_ms = last_timestamp_ms - interval_delta_ms
+                            params = (query_start_ms,)
+
+                        df_1m = pd.read_sql(query, conn, params=params, parse_dates=['datetime_utc'])
+
+                        if df_1m.empty:
+                            logging.debug(f"    -> No new 1-minute data for {tf_name}. Table is up to date.")
+                            continue
+                        
+                        df_1m.set_index('datetime_utc', inplace=True)
+                        resampled_df = df_1m.resample(tf_code).agg(self.aggregation_logic)
                         resampled_df.dropna(how='all', inplace=True)
 
-                        if coin not in self.resampling_status:
-                            self.resampling_status[coin] = {}
-
                         if not resampled_df.empty:
-                            target_table_name = f"{coin}_{tf_name}"
-                            resampled_df.to_sql(
-                                target_table_name, 
-                                conn, 
-                                if_exists='replace',
-                                index=True
-                            )
-                            
-                            last_timestamp = resampled_df.index[-1].strftime('%Y-%m-%d %H:%M:%S')
-                            num_candles = len(resampled_df)
+                            records_to_upsert = []
+                            for index, row in resampled_df.iterrows():
+                                records_to_upsert.append((
+                                    index.strftime('%Y-%m-%d %H:%M:%S'),
+                                    int(index.timestamp() * 1000), # Generate timestamp_ms
+                                    row['open'], row['high'], row['low'], row['close'],
+                                    row['volume'], row['number_of_trades']
+                                ))
                             
+                            cursor = conn.cursor()
+                            cursor.executemany(f'''
+                                INSERT OR REPLACE INTO "{target_table_name}" (datetime_utc, timestamp_ms, open, high, low, close, volume, number_of_trades)
+                                VALUES (?, ?, ?, ?, ?, ?, ?, ?)
+                            ''', records_to_upsert)
+                            conn.commit()
+
+                            logging.debug(f"    -> Upserted {len(resampled_df)} candles into '{target_table_name}'.")
+
+                            if coin not in self.resampling_status: self.resampling_status[coin] = {}
+                            total_candles = int(self._get_table_count(conn, target_table_name))
                             self.resampling_status[coin][tf_name] = {
-                                "last_candle_utc": last_timestamp,
-                                "total_candles": num_candles
-                            }
-                        else:
-                            logging.info(f"  -> No data to save for '{coin}_{tf_name}'.")
-                            self.resampling_status[coin][tf_name] = {
-                                "last_candle_utc": "N/A",
-                                "total_candles": 0
+                                "last_candle_utc": resampled_df.index[-1].strftime('%Y-%m-%d %H:%M:%S'),
+                                "total_candles": total_candles
                             }
 
-                except pd.io.sql.DatabaseError as e:
-                     logging.warning(f"Could not read source table '{source_table_name}': {e}")
                 except Exception as e:
                     logging.error(f"Failed to process coin '{coin}': {e}")
     
+        self._log_summary()
         self._save_status()
-        logging.info("--- Resampling process complete ---")
+        logging.info(f"--- Resampling job finished at {datetime.now(timezone.utc).strftime('%Y-%m-%d %H:%M:%S %Z')} ---")
+
+    def _log_summary(self):
+        """Logs a summary of the total candles for each timeframe."""
+        logging.info("--- Resampling Job Summary ---")
+        timeframe_totals = {}
+        for coin, tfs in self.resampling_status.items():
+            if not isinstance(tfs, dict): continue
+            for tf_name, tf_data in tfs.items():
+                total = tf_data.get("total_candles", 0)
+                if tf_name not in timeframe_totals:
+                    timeframe_totals[tf_name] = 0
+                timeframe_totals[tf_name] += total
+        
+        if not timeframe_totals:
+            logging.info("No candles were resampled in this run.")
+            return
+
+        logging.info("Total candles per timeframe across all processed coins:")
+        for tf_name, total in sorted(timeframe_totals.items()):
+            logging.info(f"  - {tf_name:<10}: {total:,} candles")
+
+    def _get_last_timestamp(self, conn, table_name):
+        """Gets the millisecond timestamp of the last entry in a table."""
+        try:
+            # --- FIX: Query for the integer timestamp_ms, not the text datetime_utc ---
+            timestamp_ms = pd.read_sql(f'SELECT MAX(timestamp_ms) FROM "{table_name}"', conn).iloc[0, 0]
+            return int(timestamp_ms) if pd.notna(timestamp_ms) else None
+        except (pd.io.sql.DatabaseError, IndexError):
+            return None
+
+    def _get_table_count(self, conn, table_name):
+        """Gets the total row count of a table."""
+        try:
+            return pd.read_sql(f'SELECT COUNT(*) FROM "{table_name}"', conn).iloc[0, 0]
+        except (pd.io.sql.DatabaseError, IndexError):
+            return 0
 
     def _save_status(self):
         """Saves the final resampling status to a JSON file."""
         if not self.resampling_status:
-            logging.warning("No data was resampled, skipping status file creation.")
             return
-
-        self.resampling_status['last_completed_utc'] = datetime.now(timezone.utc).strftime('%Y-%m-%d %H:%M:%S')
+            
+        stop_time = datetime.now(timezone.utc)
+        self.resampling_status['job_start_time_utc'] = self.job_start_time.strftime('%Y-%m-%d %H:%M:%S')
+        self.resampling_status['job_stop_time_utc'] = stop_time.strftime('%Y-%m-%d %H:%M:%S')
         
+        self.resampling_status.pop('last_completed_utc', None)
+
         try:
             with open(self.status_file_path, 'w', encoding='utf-8') as f:
                 json.dump(self.resampling_status, f, indent=4, sort_keys=True)
@@ -132,58 +253,36 @@ def parse_timeframes(tf_strings: list) -> dict:
     tf_map = {}
     for tf_str in tf_strings:
         numeric_part = ''.join(filter(str.isdigit, tf_str))
-        unit = ''.join(filter(str.isalpha, tf_str)).lower()
+        unit = ''.join(filter(str.isalpha, tf_str)) # Keep case for 'M'
         
+        key = tf_str
         code = ''
-        if unit == 'm':
+        if unit == 'm': 
             code = f"{numeric_part}min"
-        elif unit == 'w':
-            # --- FIX: Use uppercase 'W' for weeks to avoid deprecation warning ---
-            code = f"{numeric_part}W"
-        elif unit in ['h', 'd']:
-            code = f"{numeric_part}{unit}"
-        else:
+        elif unit.lower() == 'w':
+            code = f"{numeric_part}W-MON"
+        elif unit == 'M':
+            code = f"{numeric_part}MS"
+            key = f"{numeric_part}month"
+        elif unit.lower() in ['h', 'd']: 
+            code = f"{numeric_part}{unit.lower()}"
+        else: 
             code = tf_str
             logging.warning(f"Unrecognized timeframe unit in '{tf_str}'. Using as-is.")
             
-        tf_map[tf_str] = code
+        tf_map[key] = code
     return tf_map
 
 
 if __name__ == "__main__":
     parser = argparse.ArgumentParser(description="Resample 1-minute candle data from SQLite to other timeframes.")
-    parser.add_argument(
-        "--coins",
-        nargs='+',
-        default=["BTC", "ETH", "SOL", "BNB", "HYPE", "ASTER", "ZEC", "PUMP", "SUI"],
-        help="List of coins to process."
-    )
-    parser.add_argument(
-        "--timeframes",
-        nargs='+',
-        default=['4m', '5m', '15m', '30m', '37m', '148m', '4h', '12h', '1d', '1w'],
-        help="List of timeframes to generate (e.g., 5m 1h 1d)."
-    )
-    parser.add_argument(
-        "--timeframe",
-        dest="timeframes",
-        nargs='+',
-        help=argparse.SUPPRESS
-    )
-    parser.add_argument(
-        "--log-level",
-        default="normal",
-        choices=['off', 'normal', 'debug'],
-        help="Set the logging level for the script."
-    )
+    parser.add_argument("--coins", nargs='+', required=True, help="List of coins to process.")
+    parser.add_argument("--timeframes", nargs='+', required=True, help="List of timeframes to generate.")
+    parser.add_argument("--log-level", default="normal", choices=['off', 'normal', 'debug'])
     args = parser.parse_args()
     
     timeframes_dict = parse_timeframes(args.timeframes)
     
-    resampler = Resampler(
-        log_level=args.log_level,
-        coins=args.coins,
-        timeframes=timeframes_dict
-    )
+    resampler = Resampler(log_level=args.log_level, coins=args.coins, timeframes=timeframes_dict)
     resampler.run()
 

review.md

@@ -0,0 +1,79 @@
+# Project Review and Recommendations
+
+This review provides an analysis of the current state of the automated trading bot project, proposes specific code improvements, and identifies files that appear to be unused or are one-off utilities that could be reorganized.
+
+The project is a well-structured, multi-process Python application for crypto trading. It has a clear separation of concerns between data fetching, strategy execution, and trade management. The use of `multiprocessing` and a centralized `main_app.py` orchestrator is a solid architectural choice.
+
+The following sections detail recommendations for improving configuration management, code structure, and robustness, along with a list of files recommended for cleanup.
+
+---
+
+## Proposed Code Changes
+
+### 1. Centralize Configuration
+
+- **Issue:** Key configuration variables like `WATCHED_COINS` and `required_timeframes` are hardcoded in `main_app.py`. This makes them difficult to change without modifying the source code.
+- **Proposal:**
+    - Create a central configuration file, e.g., `_data/config.json`.
+    - Move `WATCHED_COINS` and `required_timeframes` into this new file.
+    - Load this configuration in `main_app.py` at startup.
+- **Benefit:** Decouples configuration from code, making the application more flexible and easier to manage.
+
+### 2. Refactor `main_app.py` for Clarity
+
+- **Issue:** `main_app.py` is long and handles multiple responsibilities: process orchestration, dashboard rendering, and data reading.
+- **Proposal:**
+    - **Abstract Process Management:** The functions for running subprocesses (e.g., `run_live_candle_fetcher`, `run_resampler_job`) contain repetitive logic for logging, shutdown handling, and process looping. This could be abstracted into a generic `ProcessRunner` class.
+    - **Create a Dashboard Class:** The complex dashboard rendering logic could be moved into a separate `Dashboard` class to improve separation of concerns and make the main application loop cleaner.
+- **Benefit:** Improves code readability, reduces duplication, and makes the application easier to maintain and extend.
+
+### 3. Improve Project Structure
+
+- **Issue:** The root directory is cluttered with numerous Python scripts, making it difficult to distinguish between core application files, utility scripts, and old/example files.
+- **Proposal:**
+    - Create a `scripts/` directory and move all one-off utility and maintenance scripts into it.
+    - Consider creating a `src/` or `app/` directory to house the core application source code (`main_app.py`, `trade_executor.py`, etc.), separating it clearly from configuration, data, and documentation.
+- **Benefit:** A cleaner, more organized project structure that is easier for new developers to understand.
+
+### 4. Enhance Robustness and Error Handling
+
+- **Issue:** The agent loading in `trade_executor.py` relies on discovering environment variables by a naming convention (`_AGENT_PK`). This is clever but can be brittle if environment variables are named incorrectly.
+- **Proposal:**
+    - Explicitly define the agent names and their corresponding environment variable keys in the proposed `_data/config.json` file. The `trade_executor` would then load only the agents specified in the configuration.
+- **Benefit:** Makes agent configuration more explicit and less prone to errors from stray environment variables.
+
+---
+
+## Identified Unused/Utility Files
+
+The following files were identified as likely being unused by the core application, being obsolete, or serving as one-off utilities. It is recommended to **move them to a `scripts/` directory** or **delete them** if they are obsolete.
+
+### Obsolete / Old Versions:
+- `data_fetcher_old.py`
+- `market_old.py`
+- `base_strategy.py` (The one in the root directory; the one in `strategies/` is used).
+
+### One-Off Utility Scripts (Recommend moving to `scripts/`):
+- `!migrate_to_sqlite.py`
+- `import_csv.py`
+- `del_market_cap_tables.py`
+- `fix_timestamps.py`
+- `list_coins.py`
+- `create_agent.py`
+
+### Examples / Unused Code:
+- `basic_ws.py` (Appears to be an example file).
+- `backtester.py`
+- `strategy_sma_cross.py` (A strategy file in the root, not in the `strategies` folder).
+- `strategy_template.py`
+
+### Standalone / Potentially Unused Core Files:
+The following files seem to have their logic already integrated into the main multi-process application. They might be remnants of a previous architecture and may not be needed as standalone scripts.
+- `address_monitor.py`
+- `position_monitor.py`
+- `trade_log.py`
+- `wallet_data.py`
+- `whale_tracker.py`
+
+### Data / Log Files (Recommend archiving or deleting):
+- `hyperliquid_wallet_data_*.json` (These appear to be backups or logs).

strategies/base_strategy.py

@@ -0,0 +1,166 @@
+from abc import ABC, abstractmethod
+import pandas as pd
+import json
+import os
+import logging
+from datetime import datetime, timezone
+import sqlite3
+import multiprocessing
+import time
+
+from logging_utils import setup_logging
+from hyperliquid.info import Info
+from hyperliquid.utils import constants
+
+class BaseStrategy(ABC):
+    """
+    An abstract base class that defines the blueprint for all trading strategies.
+    It provides common functionality like loading data, saving status, and state management.
+    """
+    
+    def __init__(self, strategy_name: str, params: dict, trade_signal_queue: multiprocessing.Queue = None, shared_status: dict = None):
+        self.strategy_name = strategy_name
+        self.params = params
+        self.trade_signal_queue = trade_signal_queue
+        # Optional multiprocessing.Manager().dict() to hold live status (avoids file IO)
+        self.shared_status = shared_status
+        
+        self.coin = params.get("coin", "N/A")
+        self.timeframe = params.get("timeframe", "N/A")
+        self.db_path = os.path.join("_data", "market_data.db")
+        self.status_file_path = os.path.join("_data", f"strategy_status_{self.strategy_name}.json")
+
+        self.current_signal = "INIT"
+        self.last_signal_change_utc = None
+        self.signal_price = None
+
+        # Note: Logging is set up by the run_strategy function
+
+    def load_data(self) -> pd.DataFrame:
+        """Loads historical data for the configured coin and timeframe."""
+        table_name = f"{self.coin}_{self.timeframe}"
+        
+        periods = [v for k, v in self.params.items() if 'period' in k or '_ma' in k or 'slow' in k or 'fast' in k]
+        limit = max(periods) + 50 if periods else 500
+
+        try:
+            with sqlite3.connect(f"file:{self.db_path}?mode=ro", uri=True) as conn:
+                query = f'SELECT * FROM "{table_name}" ORDER BY datetime_utc DESC LIMIT {limit}'
+                df = pd.read_sql(query, conn, parse_dates=['datetime_utc'])
+                if df.empty: return pd.DataFrame()
+                df.set_index('datetime_utc', inplace=True)
+                df.sort_index(inplace=True)
+                return df
+        except Exception as e:
+            logging.error(f"Failed to load data from table '{table_name}': {e}")
+            return pd.DataFrame()
+
+    @abstractmethod
+    def calculate_signals(self, df: pd.DataFrame) -> pd.DataFrame:
+        """The core logic of the strategy. Must be implemented by child classes."""
+        pass
+        
+    def calculate_signals_and_state(self, df: pd.DataFrame) -> bool:
+        """
+        A wrapper that calls the strategy's signal calculation, determines
+        the last signal change, and returns True if the signal has changed.
+        """
+        df_with_signals = self.calculate_signals(df)
+        df_with_signals.dropna(inplace=True)
+        if df_with_signals.empty:
+            return False
+
+        df_with_signals['position_change'] = df_with_signals['signal'].diff()
+        
+        last_signal_int = df_with_signals['signal'].iloc[-1]
+        new_signal_str = "HOLD"
+        if last_signal_int == 1: new_signal_str = "BUY"
+        elif last_signal_int == -1: new_signal_str = "SELL"
+        
+        signal_changed = False
+        if self.current_signal == "INIT":
+            if new_signal_str == "BUY": self.current_signal = "INIT_BUY"
+            elif new_signal_str == "SELL": self.current_signal = "INIT_SELL"
+            else: self.current_signal = "HOLD"
+            signal_changed = True
+        elif new_signal_str != self.current_signal:
+            self.current_signal = new_signal_str
+            signal_changed = True
+        
+        if signal_changed:
+            last_change_series = df_with_signals[df_with_signals['position_change'] != 0]
+            if not last_change_series.empty:
+                last_change_row = last_change_series.iloc[-1]
+                self.last_signal_change_utc = last_change_row.name.tz_localize('UTC').isoformat()
+                self.signal_price = last_change_row['close']
+        
+        return signal_changed
+
+    def _save_status(self):
+        """Saves the current strategy state to its JSON file."""
+        status = {
+            "strategy_name": self.strategy_name,
+            "current_signal": self.current_signal,
+            "last_signal_change_utc": self.last_signal_change_utc,
+            "signal_price": self.signal_price,
+            "last_checked_utc": datetime.now(timezone.utc).isoformat()
+        }
+        # If a shared status dict is provided (Manager.dict()), update it instead of writing files
+        try:
+            if self.shared_status is not None:
+                try:
+                    # store the status under the strategy name for easy lookup
+                    self.shared_status[self.strategy_name] = status
+                except Exception:
+                    # Manager proxies may not accept nested mutable objects consistently; assign a copy
+                    self.shared_status[self.strategy_name] = dict(status)
+            else:
+                with open(self.status_file_path, 'w', encoding='utf-8') as f:
+                    json.dump(status, f, indent=4)
+        except IOError as e:
+            logging.error(f"Failed to write status file for {self.strategy_name}: {e}")
+
+    def run_polling_loop(self):
+        """
+        The default execution loop for polling-based strategies (e.g., SMAs).
+        """
+        while True:
+            df = self.load_data()
+            if df.empty:
+                logging.warning("No data loaded. Waiting 1 minute...")
+                time.sleep(60)
+                continue
+            
+            signal_changed = self.calculate_signals_and_state(df.copy())
+            self._save_status() 
+            
+            if signal_changed or self.current_signal == "INIT_BUY" or self.current_signal == "INIT_SELL":
+                logging.warning(f"New signal detected: {self.current_signal}")
+                self.trade_signal_queue.put({
+                    "strategy_name": self.strategy_name,
+                    "signal": self.current_signal,
+                    "coin": self.coin,
+                    "signal_price": self.signal_price,
+                    "config": {"agent": self.params.get("agent"), "parameters": self.params}
+                })
+                if self.current_signal == "INIT_BUY": self.current_signal = "BUY"
+                if self.current_signal == "INIT_SELL": self.current_signal = "SELL"
+
+            logging.info(f"Current Signal: {self.current_signal}")
+            time.sleep(60)
+
+    def run_event_loop(self):
+        """
+        A placeholder for event-driven (WebSocket) strategies.
+        Child classes must override this.
+        """
+        logging.error("run_event_loop() is not implemented for this strategy.")
+        time.sleep(3600) # Sleep for an hour to prevent rapid error loops
+        
+    def on_fill_message(self, message):
+        """
+        Placeholder for the WebSocket callback.
+        Child classes must override this.
+        """
+        pass
+

strategies/copy_trader_strategy.py

@@ -0,0 +1,353 @@
+import logging
+import time
+import json
+import os
+from datetime import datetime, timezone
+from hyperliquid.info import Info
+from hyperliquid.utils import constants
+
+from strategies.base_strategy import BaseStrategy
+
+class CopyTraderStrategy(BaseStrategy):
+    """
+    An event-driven strategy that monitors a target wallet address and
+    copies its trades for a specific set of allowed coins.
+    
+    This strategy is STATELESS. It translates a target's fill direction
+    (e.g., "Open Long") directly into an explicit signal 
+    (e.g., "OPEN_LONG") for the PositionManager.
+    """
+    def __init__(self, strategy_name: str, params: dict, trade_signal_queue, shared_status: dict = None):
+        # --- MODIFIED: Pass the correct queue to the parent ---
+        # The event-driven copy trader should send orders to the order_execution_queue
+        # We will assume the queue passed in is the correct one (as setup in main_app.py)
+        super().__init__(strategy_name, params, trade_signal_queue, shared_status)
+        
+        self.target_address = self.params.get("target_address", "").lower()
+        self.coins_to_copy = self.params.get("coins_to_copy", {})
+        # Convert all coin keys to uppercase for consistency
+        self.coins_to_copy = {k.upper(): v for k, v in self.coins_to_copy.items()}
+        self.allowed_coins = list(self.coins_to_copy.keys())
+        
+        if not self.target_address:
+            logging.error("No 'target_address' specified in parameters for copy trader.")
+            raise ValueError("target_address is required")
+        if not self.allowed_coins:
+            logging.warning("No 'coins_to_copy' configured. This strategy will not copy any trades.")
+            
+        self.info = None # Will be initialized in the run loop
+        
+        # --- REMOVED: All local state management ---
+        # self.position_state_file = ...
+        # self.current_positions = ...
+        
+        # --- MODIFIED: Check if shared_status is None before using it ---
+        if self.shared_status is None:
+            logging.warning("No shared_status dictionary provided. Initializing a new one.")
+            self.shared_status = {}
+            
+        self.current_signal = self.shared_status.get("current_signal", "WAIT")
+        self.signal_price = self.shared_status.get("signal_price")
+        self.last_signal_change_utc = self.shared_status.get("last_signal_change_utc")
+        
+        self.start_time_utc = datetime.now(timezone.utc)
+        logging.info(f"Strategy initialized. Ignoring all trades before {self.start_time_utc.isoformat()}")
+
+    # --- REMOVED: _load_position_state ---
+    # --- REMOVED: _save_position_state ---
+
+    def calculate_signals(self, df):
+        # This strategy is event-driven, so it does not use polling-based signal calculation.
+        pass
+
+    def send_explicit_signal(self, signal: str, coin: str, price: float, trade_params: dict, size: float):
+        """Helper to send a formatted signal to the PositionManager."""
+        config = {
+            # --- MODIFIED: Ensure agent is read from params ---
+            "agent": self.params.get("agent"),
+            "parameters": trade_params
+        }
+        
+        # --- MODIFIED: Use self.trade_signal_queue (which is the queue passed in) ---
+        self.trade_signal_queue.put({
+            "strategy_name": self.strategy_name,
+            "signal": signal, # e.g., "OPEN_LONG", "CLOSE_SHORT"
+            "coin": coin,
+            "signal_price": price,
+            "config": config,
+            "size": size # Explicitly pass size (or leverage for leverage updates)
+        })
+        logging.info(f"Explicit signal SENT: {signal} {coin} @ {price}, Size: {size}")
+
+    def on_fill_message(self, message):
+        """
+        This is the callback function that gets triggered by the WebSocket
+        every time the monitored address has an event.
+        """
+        try:
+            # --- NEW: Add logging to see ALL messages ---
+            logging.debug(f"Received WebSocket message: {message}")
+            
+            channel = message.get("channel")
+            if channel not in ("user", "userFills", "userEvents"):
+                # --- NEW: Added debug logging ---
+                logging.debug(f"Ignoring message from unhandled channel: {channel}")
+                return
+
+            data = message.get("data")
+            if not data:
+                # --- NEW: Added debug logging ---
+                logging.debug("Message received with no 'data' field. Ignoring.")
+                return
+
+            # --- NEW: Check for user address FIRST ---
+            user_address = data.get("user", "").lower()
+            if not user_address:
+                logging.debug("Received message with 'data' but no 'user'. Ignoring.")
+                return
+
+            # --- MODIFIED: Check for 'fills' vs. other event types ---
+            # This check is still valid for userFills
+            if "fills" not in data or not data.get("fills"):
+                # This is a userEvent, but not a fill (e.g., order placement, cancel, withdrawal)
+                event_type = data.get("type") # e.g., 'order', 'cancel', 'withdrawal'
+                if event_type:
+                    logging.debug(f"Received non-fill user event: '{event_type}'. Ignoring.")
+                else:
+                    logging.debug(f"Received 'data' message with no 'fills'. Ignoring.")
+                return
+            
+            # --- This line is now safe to run ---
+            if user_address != self.target_address:
+                # This shouldn't happen if the subscription is correct, but good to check
+                logging.warning(f"Received fill for wrong user: {user_address}")
+                return
+
+            fills = data.get("fills")
+            logging.debug(f"Received {len(fills)} fill(s) for user {user_address}")
+
+            for fill in fills:
+                # Check if the trade is new or historical
+                trade_time = datetime.fromtimestamp(fill['time'] / 1000, tz=timezone.utc)
+                if trade_time < self.start_time_utc:
+                    logging.info(f"Ignoring stale/historical trade from {trade_time.isoformat()}")
+                    continue
+                    
+                coin = fill.get('coin').upper()
+                
+                if coin in self.allowed_coins:
+                    price = float(fill.get('px'))
+                    
+                    # --- MODIFIED: Use the target's fill size ---
+                    fill_size = float(fill.get('sz')) # Target's size
+                    
+                    if fill_size == 0:
+                        logging.warning(f"Ignoring fill with size 0.")
+                        continue
+                    
+                    # --- NEW: Get the fill direction ---
+                    # "dir": "Open Long", "Close Long", "Open Short", "Close Short"
+                    fill_direction = fill.get("dir")
+                    
+                    # --- NEW: Get startPosition to calculate flip sizes ---
+                    start_pos_size = float(fill.get('startPosition', 0.0))
+                    
+                    if not fill_direction:
+                        logging.warning(f"Fill message missing 'dir'. Ignoring fill: {fill}")
+                        continue
+
+                    # Get our strategy's configured leverage for this coin
+                    coin_config = self.coins_to_copy.get(coin)
+                    
+                    # --- REMOVED: Check for coin_config.get("size") ---
+                    # --- REMOVED: strategy_trade_size = coin_config.get("size") ---
+                        
+                    # Prepare config for the signal
+                    trade_params = self.params.copy()
+                    if coin_config:
+                        trade_params.update(coin_config)
+                    
+                    # --- REMOVED: All stateful logic (current_local_pos, etc.) ---
+
+                    # --- MODIFIED: Expanded logic to handle flip directions ---
+                    signal_sent = False
+                    dashboard_signal = ""
+
+                    if fill_direction == "Open Long":
+                        logging.warning(f"[{coin}] Target action: {fill_direction}. Sending signal: OPEN_LONG")
+                        self.send_explicit_signal("OPEN_LONG", coin, price, trade_params, fill_size)
+                        signal_sent = True
+                        dashboard_signal = "OPEN_LONG"
+
+                    elif fill_direction == "Close Long":
+                        logging.warning(f"[{coin}] Target action: {fill_direction}. Sending signal: CLOSE_LONG")
+                        self.send_explicit_signal("CLOSE_LONG", coin, price, trade_params, fill_size)
+                        signal_sent = True
+                        dashboard_signal = "CLOSE_LONG"
+
+                    elif fill_direction == "Open Short":
+                        logging.warning(f"[{coin}] Target action: {fill_direction}. Sending signal: OPEN_SHORT")
+                        self.send_explicit_signal("OPEN_SHORT", coin, price, trade_params, fill_size)
+                        signal_sent = True
+                        dashboard_signal = "OPEN_SHORT"
+
+                    elif fill_direction == "Close Short":
+                        logging.warning(f"[{coin}] Target action: {fill_direction}. Sending signal: CLOSE_SHORT")
+                        self.send_explicit_signal("CLOSE_SHORT", coin, price, trade_params, fill_size)
+                        signal_sent = True
+                        dashboard_signal = "CLOSE_SHORT"
+                    
+                    elif fill_direction == "Short > Long":
+                        logging.warning(f"[{coin}] Target action: {fill_direction}. Sending CLOSE_SHORT then OPEN_LONG.")
+                        close_size = abs(start_pos_size)
+                        open_size = fill_size - close_size
+                        
+                        if close_size > 0:
+                            self.send_explicit_signal("CLOSE_SHORT", coin, price, trade_params, close_size)
+                        
+                        if open_size > 0:
+                            self.send_explicit_signal("OPEN_LONG", coin, price, trade_params, open_size)
+                        
+                        signal_sent = True
+                        dashboard_signal = "FLIP_TO_LONG"
+
+                    elif fill_direction == "Long > Short":
+                        logging.warning(f"[{coin}] Target action: {fill_direction}. Sending CLOSE_LONG then OPEN_SHORT.")
+                        close_size = abs(start_pos_size)
+                        open_size = fill_size - close_size
+                        
+                        if close_size > 0:
+                            self.send_explicit_signal("CLOSE_LONG", coin, price, trade_params, close_size)
+                        
+                        if open_size > 0:
+                            self.send_explicit_signal("OPEN_SHORT", coin, price, trade_params, open_size)
+                            
+                        signal_sent = True
+                        dashboard_signal = "FLIP_TO_SHORT"
+
+                    
+                    if signal_sent:
+                        # Update dashboard status
+                        self.current_signal = dashboard_signal # Show the action
+                        self.signal_price = price
+                        self.last_signal_change_utc = trade_time.isoformat()
+                        self.coin = coin # Update coin for dashboard
+                        self.size = fill_size # Update size for dashboard
+                        self._save_status() # For dashboard
+                        
+                        logging.info(f"Source trade logged: {json.dumps(fill)}")
+                    else:
+                        logging.info(f"[{coin}] Ignoring unhandled fill direction: {fill_direction}")
+                else:
+                    logging.info(f"Ignoring fill for unmonitored coin: {coin}")
+
+        except Exception as e:
+            logging.error(f"Error in on_fill_message: {e}", exc_info=True)
+
+    def _connect_and_subscribe(self):
+        """
+        Establishes a new WebSocket connection and subscribes to the userFills channel.
+        """
+        try:
+            logging.info("Connecting to Hyperliquid WebSocket...")
+            self.info = Info(constants.MAINNET_API_URL, skip_ws=False)
+            
+            # --- MODIFIED: Reverted to 'userFills' as requested ---
+            subscription = {"type": "userFills", "user": self.target_address}
+            self.info.subscribe(subscription, self.on_fill_message)
+            logging.info(f"Subscribed to 'userFills' for target address: {self.target_address}")
+            
+            return True
+        except Exception as e:
+            logging.error(f"Failed to connect or subscribe: {e}")
+            self.info = None
+            return False
+
+    def run_event_loop(self):
+        """
+        This method overrides the default polling loop. It establishes a
+        persistent WebSocket connection and runs a watchdog to ensure
+        it stays connected.
+        """
+        try:
+            if not self._connect_and_subscribe():
+                 # If connection fails on start, wait 60s before letting the process restart
+                 time.sleep(60)
+                 return
+
+            # --- MODIFIED: Add a small delay to ensure Info object is ready for REST calls ---
+            logging.info("Connection established. Waiting 2 seconds for Info client to be ready...")
+            time.sleep(2) 
+            # --- END MODIFICATION ---
+
+            # --- NEW: Set initial leverage for all monitored coins ---
+            logging.info("Setting initial leverage for all monitored coins...")
+            try:
+                all_mids = self.info.all_mids()
+                for coin_key, coin_config in self.coins_to_copy.items():
+                    coin = coin_key.upper()
+                    # Use a failsafe price of 1.0 if coin not in mids (e.g., new listing)
+                    current_price = float(all_mids.get(coin, 1.0)) 
+                    
+                    leverage_long = coin_config.get('leverage_long', 2)
+                    leverage_short = coin_config.get('leverage_short', 2)
+                    
+                    # Prepare config for the signal
+                    trade_params = self.params.copy()
+                    trade_params.update(coin_config)
+                    
+                    # Send LONG leverage update
+                    # The 'size' param is used to pass the leverage value for this signal type
+                    self.send_explicit_signal("UPDATE_LEVERAGE_LONG", coin, current_price, trade_params, leverage_long)
+                    
+                    # Send SHORT leverage update
+                    self.send_explicit_signal("UPDATE_LEVERAGE_SHORT", coin, current_price, trade_params, leverage_short)
+                    
+                    logging.info(f"Sent initial leverage signals for {coin} (Long: {leverage_long}x, Short: {leverage_short}x)")
+                    
+            except Exception as e:
+                logging.error(f"Failed to set initial leverage: {e}", exc_info=True)
+            # --- END NEW LEVERAGE LOGIC ---
+
+            # Save the initial "WAIT" status
+            self._save_status()
+
+            while True:
+                try:
+                    time.sleep(15) # Check the connection every 15 seconds
+                    
+                    if self.info is None or not self.info.ws_manager.is_alive():
+                        logging.error(f"WebSocket connection lost. Attempting to reconnect...")
+                        
+                        if self.info and self.info.ws_manager:
+                            try:
+                                self.info.ws_manager.stop()
+                            except Exception as e:
+                                logging.error(f"Error stopping old ws_manager: {e}")
+                        
+                        if not self._connect_and_subscribe():
+                            logging.error("Reconnect failed, will retry in 15s.")
+                        else:
+                            logging.info("Successfully reconnected to WebSocket.")
+                            self._save_status()
+                    else:
+                        logging.debug("Watchdog check: WebSocket connection is active.")
+                
+                except Exception as e:
+                    logging.error(f"An error occurred in the watchdog loop: {e}", exc_info=True)
+
+        except KeyboardInterrupt: 
+            # --- MODIFIED: No positions to close, just exit ---
+            logging.warning(f"Shutdown signal received. Exiting strategy '{self.strategy_name}'.")
+            
+        except Exception as e:
+            logging.error(f"An unhandled error occurred in run_event_loop: {e}", exc_info=True)
+        
+        finally:
+            if self.info and self.info.ws_manager and self.info.ws_manager.is_alive():
+                try:
+                    self.info.ws_manager.stop()
+                    logging.info("WebSocket connection stopped.")
+                except Exception as e:
+                    logging.error(f"Error stopping ws_manager on exit: {e}")
+

strategies/ma_cross_strategy.py

@@ -0,0 +1,30 @@
+import pandas as pd
+from strategies.base_strategy import BaseStrategy
+import logging
+
+class MaCrossStrategy(BaseStrategy):
+    """
+    A strategy based on a fast Simple Moving Average (SMA) crossing
+    a slow SMA.
+    """
+    # --- FIX: Changed 3rd argument from log_level to trade_signal_queue ---
+    def __init__(self, strategy_name: str, params: dict, trade_signal_queue):
+        # --- FIX: Passed trade_signal_queue to the parent class ---
+        super().__init__(strategy_name, params, trade_signal_queue)
+        self.fast_ma_period = self.params.get('short_ma') or self.params.get('fast') or 0
+        self.slow_ma_period = self.params.get('long_ma') or self.params.get('slow') or 0
+
+    def calculate_signals(self, df: pd.DataFrame) -> pd.DataFrame:
+        if not self.fast_ma_period or not self.slow_ma_period or len(df) < self.slow_ma_period:
+            logging.warning(f"Not enough data for MA periods.")
+            df['signal'] = 0
+            return df
+            
+        df['fast_sma'] = df['close'].rolling(window=self.fast_ma_period).mean()
+        df['slow_sma'] = df['close'].rolling(window=self.slow_ma_period).mean()
+        
+        df['signal'] = 0
+        df.loc[df['fast_sma'] > df['slow_sma'], 'signal'] = 1
+        df.loc[df['fast_sma'] < df['slow_sma'], 'signal'] = -1
+        
+        return df

strategies/single_sma_strategy.py

@@ -0,0 +1,27 @@
+import pandas as pd
+from strategies.base_strategy import BaseStrategy
+import logging
+
+class SingleSmaStrategy(BaseStrategy):
+    """
+    A strategy based on the price crossing a single Simple Moving Average (SMA).
+    """
+    # --- FIX: Added trade_signal_queue to the constructor ---
+    def __init__(self, strategy_name: str, params: dict, trade_signal_queue):
+        # --- FIX: Passed trade_signal_queue to the parent class ---
+        super().__init__(strategy_name, params, trade_signal_queue)
+        self.sma_period = self.params.get('sma_period', 0)
+
+    def calculate_signals(self, df: pd.DataFrame) -> pd.DataFrame:
+        if not self.sma_period or len(df) < self.sma_period:
+            logging.warning(f"Not enough data for SMA period {self.sma_period}.")
+            df['signal'] = 0
+            return df
+            
+        df['sma'] = df['close'].rolling(window=self.sma_period).mean()
+        
+        df['signal'] = 0
+        df.loc[df['close'] > df['sma'], 'signal'] = 1
+        df.loc[df['close'] < df['sma'], 'signal'] = -1
+        
+        return df

strategy_runner.py

@@ -0,0 +1,85 @@
+import argparse
+import logging
+import sys
+import time
+import pandas as pd
+import sqlite3
+import json
+import os
+from datetime import datetime, timezone
+import importlib
+
+from logging_utils import setup_logging
+from strategies.base_strategy import BaseStrategy
+
+class StrategyRunner:
+    """
+    A generic runner that can execute any strategy that adheres to the
+    BaseStrategy blueprint. It handles the main logic loop, including data
+    loading, signal calculation, status saving, and sleeping.
+    """
+
+    def __init__(self, strategy_name: str, log_level: str):
+        self.strategy_name = strategy_name
+        self.log_level = log_level
+        self.config = self._load_strategy_config()
+        if not self.config:
+            print(f"FATAL: Strategy '{strategy_name}' not found in configuration.")
+            sys.exit(1)
+            
+        # Dynamically import and instantiate the strategy logic class
+        try:
+            module_path, class_name = self.config['class'].rsplit('.', 1)
+            module = importlib.import_module(module_path)
+            StrategyClass = getattr(module, class_name)
+            self.strategy_instance = StrategyClass(strategy_name, self.config['parameters'], self.log_level)
+        except (ImportError, AttributeError, KeyError) as e:
+            print(f"FATAL: Could not load strategy class for '{strategy_name}': {e}")
+            sys.exit(1)
+
+    def _load_strategy_config(self) -> dict:
+        """Loads the configuration for the specified strategy."""
+        config_path = os.path.join("_data", "strategies.json")
+        try:
+            with open(config_path, 'r') as f:
+                all_configs = json.load(f)
+                return all_configs.get(self.strategy_name)
+        except (FileNotFoundError, json.JSONDecodeError) as e:
+            print(f"FATAL: Could not load strategy configuration: {e}")
+            return None
+
+    def run(self):
+        """Main loop: loads data, calculates signals, saves status, and sleeps."""
+        logging.info(f"Starting main logic loop for {self.strategy_instance.coin} on {self.strategy_instance.timeframe}.")
+        while True:
+            df = self.strategy_instance.load_data()
+            if df.empty:
+                logging.warning("No data loaded. Waiting 1 minute before retrying...")
+                time.sleep(60)
+                continue
+            
+            # The strategy instance calculates signals and updates its internal state
+            self.strategy_instance.calculate_signals_and_state(df.copy())
+            self.strategy_instance._save_status() # Save the new state
+
+            logging.info(f"Current Signal: {self.strategy_instance.current_signal}")
+
+            # Simple 1-minute wait for the next cycle
+            # A more precise timing mechanism could be implemented here if needed
+            time.sleep(60)
+
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser(description="A generic runner for trading strategies.")
+    parser.add_argument("--name", required=True, help="The name of the strategy instance from strategies.json.")
+    parser.add_argument("--log-level", default="normal", choices=['off', 'normal', 'debug'])
+    args = parser.parse_args()
+
+    try:
+        runner = StrategyRunner(strategy_name=args.name, log_level=args.log_level)
+        runner.run()
+    except KeyboardInterrupt:
+        logging.info("Strategy runner stopped.")
+    except Exception as e:
+        logging.error(f"A critical error occurred in the strategy runner: {e}")
+        sys.exit(1)

strategy_sma_cross.py

@@ -0,0 +1,219 @@
+import argparse
+import logging
+import sys
+import time
+import pandas as pd
+import sqlite3
+import json
+import os
+from datetime import datetime, timezone, timedelta
+
+from logging_utils import setup_logging
+
+class SmaCrossStrategy:
+    """
+    A flexible strategy that can operate in two modes:
+    1. Fast SMA / Slow SMA Crossover (if both 'fast' and 'slow' params are set)
+    2. Price / Single SMA Crossover (if only one 'fast' or 'slow' param is set)
+    """
+
+    def __init__(self, strategy_name: str, params: dict, log_level: str):
+        self.strategy_name = strategy_name
+        self.params = params
+        self.coin = params.get("coin", "N/A")
+        self.timeframe = params.get("timeframe", "N/A")
+        
+        # Load fast and slow SMA periods, defaulting to 0 if not present
+        self.fast_ma_period = params.get("fast", 0)
+        self.slow_ma_period = params.get("slow", 0)
+        
+        self.db_path = os.path.join("_data", "market_data.db")
+        self.status_file_path = os.path.join("_data", f"strategy_status_{self.strategy_name}.json")
+        
+        # Strategy state variables
+        self.current_signal = "INIT"
+        self.last_signal_change_utc = None
+        self.signal_price = None
+        self.fast_ma_value = None
+        self.slow_ma_value = None
+        
+        setup_logging(log_level, f"Strategy-{self.strategy_name}")
+        logging.info(f"Initializing SMA Crossover strategy with parameters:")
+        for key, value in self.params.items():
+            logging.info(f"  - {key}: {value}")
+
+    def load_data(self) -> pd.DataFrame:
+        """Loads historical data, ensuring enough for the longest SMA calculation."""
+        table_name = f"{self.coin}_{self.timeframe}"
+        
+        # Determine the longest period needed for calculations
+        longest_period = max(self.fast_ma_period or 0, self.slow_ma_period or 0)
+        if longest_period == 0:
+            logging.error("No valid SMA periods ('fast' or 'slow' > 0) are defined in parameters.")
+            return pd.DataFrame()
+            
+        limit = longest_period + 50 
+
+        try:
+            with sqlite3.connect(f"file:{self.db_path}?mode=ro", uri=True) as conn:
+                query = f'SELECT * FROM "{table_name}" ORDER BY datetime_utc DESC LIMIT {limit}'
+                df = pd.read_sql(query, conn)
+                if df.empty: return pd.DataFrame()
+                
+                df['datetime_utc'] = pd.to_datetime(df['datetime_utc'])
+                df.set_index('datetime_utc', inplace=True)
+                df.sort_index(inplace=True)
+                return df
+        except Exception as e:
+            logging.error(f"Failed to load data from table '{table_name}': {e}")
+            return pd.DataFrame()
+
+    def _calculate_signals(self, data: pd.DataFrame):
+        """
+        Analyzes historical data to find the last crossover event based on the
+        configured parameters (either dual or single SMA mode).
+        """
+        # --- DUAL SMA CROSSOVER LOGIC ---
+        if self.fast_ma_period and self.slow_ma_period:
+            if len(data) < self.slow_ma_period + 1:
+                self.current_signal = "INSUFFICIENT DATA"
+                return
+
+            data['fast_sma'] = data['close'].rolling(window=self.fast_ma_period).mean()
+            data['slow_sma'] = data['close'].rolling(window=self.slow_ma_period).mean()
+            self.fast_ma_value = data['fast_sma'].iloc[-1]
+            self.slow_ma_value = data['slow_sma'].iloc[-1]
+            
+            # Position is 1 for Golden Cross (fast > slow), -1 for Death Cross
+            data['position'] = 0
+            data.loc[data['fast_sma'] > data['slow_sma'], 'position'] = 1
+            data.loc[data['fast_sma'] < data['slow_sma'], 'position'] = -1
+
+        # --- SINGLE SMA PRICE CROSS LOGIC ---
+        else:
+            sma_period = self.fast_ma_period or self.slow_ma_period
+            if len(data) < sma_period + 1:
+                self.current_signal = "INSUFFICIENT DATA"
+                return
+
+            data['sma'] = data['close'].rolling(window=sma_period).mean()
+            self.slow_ma_value = data['sma'].iloc[-1] # Use slow_ma_value to store the single SMA
+            self.fast_ma_value = None # Ensure fast is None
+            
+            # Position is 1 when price is above SMA, -1 when below
+            data['position'] = 0
+            data.loc[data['close'] > data['sma'], 'position'] = 1
+            data.loc[data['close'] < data['sma'], 'position'] = -1
+        
+        # --- COMMON LOGIC for determining signal and last change ---
+        data['crossover'] = data['position'].diff()
+        last_position = data['position'].iloc[-1]
+        
+        if last_position == 1: self.current_signal = "BUY"
+        elif last_position == -1: self.current_signal = "SELL"
+        else: self.current_signal = "HOLD"
+
+        last_cross_series = data[data['crossover'] != 0]
+        if not last_cross_series.empty:
+            last_cross_row = last_cross_series.iloc[-1]
+            self.last_signal_change_utc = last_cross_row.name.tz_localize('UTC').isoformat()
+            self.signal_price = last_cross_row['close']
+            if last_cross_row['position'] == 1: self.current_signal = "BUY"
+            elif last_cross_row['position'] == -1: self.current_signal = "SELL"
+        else:
+            self.last_signal_change_utc = data.index[0].tz_localize('UTC').isoformat()
+            self.signal_price = data['close'].iloc[0]
+
+    def _save_status(self):
+        """Saves the current strategy state to its JSON file."""
+        status = {
+            "strategy_name": self.strategy_name,
+            "current_signal": self.current_signal,
+            "last_signal_change_utc": self.last_signal_change_utc,
+            "signal_price": self.signal_price,
+            "last_checked_utc": datetime.now(timezone.utc).isoformat()
+        }
+        try:
+            with open(self.status_file_path, 'w', encoding='utf-8') as f:
+                json.dump(status, f, indent=4)
+        except IOError as e:
+            logging.error(f"Failed to write status file: {e}")
+
+    def get_sleep_duration(self) -> int:
+        """Calculates seconds to sleep until the next full candle closes."""
+        tf_value = int(''.join(filter(str.isdigit, self.timeframe)))
+        tf_unit = ''.join(filter(str.isalpha, self.timeframe))
+        
+        if tf_unit == 'm': interval_seconds = tf_value * 60
+        elif tf_unit == 'h': interval_seconds = tf_value * 3600
+        elif tf_unit == 'd': interval_seconds = tf_value * 86400
+        else: return 60
+
+        now = datetime.now(timezone.utc)
+        timestamp = now.timestamp()
+        
+        next_candle_ts = ((timestamp // interval_seconds) + 1) * interval_seconds
+        sleep_seconds = (next_candle_ts - timestamp) + 5
+        
+        logging.info(f"Next candle closes at {datetime.fromtimestamp(next_candle_ts, tz=timezone.utc)}. "
+                     f"Sleeping for {sleep_seconds:.2f} seconds.")
+        return sleep_seconds
+
+    def run_logic(self):
+        """Main loop: loads data, calculates signals, saves status, and sleeps."""
+        logging.info(f"Starting logic loop for {self.coin} on {self.timeframe} timeframe.")
+        while True:
+            data = self.load_data()
+            if data.empty:
+                logging.warning("No data loaded. Waiting 1 minute before retrying...")
+                self.current_signal = "NO DATA"
+                self._save_status()
+                time.sleep(60)
+                continue
+            
+            self._calculate_signals(data)
+            self._save_status()
+            
+            last_close = data['close'].iloc[-1]
+            
+            # --- Log based on which mode the strategy is running in ---
+            if self.fast_ma_period and self.slow_ma_period:
+                fast_ma_str = f"{self.fast_ma_value:.4f}" if self.fast_ma_value is not None else "N/A"
+                slow_ma_str = f"{self.slow_ma_value:.4f}" if self.slow_ma_value is not None else "N/A"
+                logging.info(
+                    f"Signal: {self.current_signal} | Price: {last_close:.4f} | "
+                    f"Fast SMA({self.fast_ma_period}): {fast_ma_str} | Slow SMA({self.slow_ma_period}): {slow_ma_str}"
+                )
+            else:
+                sma_period = self.fast_ma_period or self.slow_ma_period
+                sma_val_str = f"{self.slow_ma_value:.4f}" if self.slow_ma_value is not None else "N/A"
+                logging.info(
+                    f"Signal: {self.current_signal} | Price: {last_close:.4f} | "
+                    f"SMA({sma_period}): {sma_val_str}"
+                )
+
+            sleep_time = self.get_sleep_duration()
+            time.sleep(sleep_time)
+
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser(description="Run an SMA Crossover trading strategy.")
+    parser.add_argument("--name", required=True, help="The name of the strategy instance from the config.")
+    parser.add_argument("--params", required=True, help="A JSON string of the strategy's parameters.")
+    parser.add_argument("--log-level", default="normal", choices=['off', 'normal', 'debug'])
+    args = parser.parse_args()
+
+    try:
+        strategy_params = json.loads(args.params)
+        strategy = SmaCrossStrategy(
+            strategy_name=args.name, 
+            params=strategy_params,
+            log_level=args.log_level
+        )
+        strategy.run_logic()
+    except KeyboardInterrupt:
+        logging.info("Strategy process stopped.")
+    except Exception as e:
+        logging.error(f"A critical error occurred: {e}")
+        sys.exit(1)
+

strategy_template.py

@@ -0,0 +1,186 @@
+import argparse
+import logging
+import sys
+import time
+import pandas as pd
+import sqlite3
+import json
+import os
+from datetime import datetime, timezone, timedelta
+
+from logging_utils import setup_logging
+
+class TradingStrategy:
+    """
+    A template for a trading strategy that reads data from the SQLite database
+    and executes its logic in a loop, running once per candle.
+    """
+
+    def __init__(self, strategy_name: str, params: dict, log_level: str):
+        self.strategy_name = strategy_name
+        self.params = params
+        self.coin = params.get("coin", "N/A")
+        self.timeframe = params.get("timeframe", "N/A")
+        self.db_path = os.path.join("_data", "market_data.db")
+        self.status_file_path = os.path.join("_data", f"strategy_status_{self.strategy_name}.json")
+        
+        # Strategy state variables
+        self.current_signal = "INIT"
+        self.last_signal_change_utc = None
+        self.signal_price = None
+        self.indicator_value = None
+        
+        # Load strategy-specific parameters from config
+        self.rsi_period = params.get("rsi_period")
+        self.short_ma = params.get("short_ma")
+        self.long_ma = params.get("long_ma")
+        self.sma_period = params.get("sma_period")
+        
+        setup_logging(log_level, f"Strategy-{self.strategy_name}")
+        logging.info(f"Initializing strategy with parameters: {self.params}")
+
+    def load_data(self) -> pd.DataFrame:
+        """Loads historical data, ensuring enough for the longest indicator period."""
+        table_name = f"{self.coin}_{self.timeframe}"
+        limit = 500
+        # Determine required data limit based on the longest configured indicator
+        periods = [p for p in [self.sma_period, self.long_ma, self.rsi_period] if p is not None]
+        if periods:
+            limit = max(periods) + 50
+
+        try:
+            with sqlite3.connect(f"file:{self.db_path}?mode=ro", uri=True) as conn:
+                query = f'SELECT * FROM "{table_name}" ORDER BY datetime_utc DESC LIMIT {limit}'
+                df = pd.read_sql(query, conn)
+                if df.empty: return pd.DataFrame()
+                
+                df['datetime_utc'] = pd.to_datetime(df['datetime_utc'])
+                df.set_index('datetime_utc', inplace=True)
+                df.sort_index(inplace=True)
+                return df
+        except Exception as e:
+            logging.error(f"Failed to load data from table '{table_name}': {e}")
+            return pd.DataFrame()
+
+    def _calculate_signals(self, data: pd.DataFrame):
+        """
+        Analyzes historical data to find the last signal crossover event.
+        This method should be expanded to handle different strategy types.
+        """
+        if self.sma_period:
+            if len(data) < self.sma_period + 1:
+                self.current_signal = "INSUFFICIENT DATA"
+                return
+
+            data['sma'] = data['close'].rolling(window=self.sma_period).mean()
+            self.indicator_value = data['sma'].iloc[-1]
+
+            data['position'] = 0
+            data.loc[data['close'] > data['sma'], 'position'] = 1
+            data.loc[data['close'] < data['sma'], 'position'] = -1
+            data['crossover'] = data['position'].diff()
+
+            last_position = data['position'].iloc[-1]
+            if last_position == 1: self.current_signal = "BUY"
+            elif last_position == -1: self.current_signal = "SELL"
+            else: self.current_signal = "HOLD"
+
+            last_cross_series = data[data['crossover'] != 0]
+            if not last_cross_series.empty:
+                last_cross_row = last_cross_series.iloc[-1]
+                self.last_signal_change_utc = last_cross_row.name.tz_localize('UTC').isoformat()
+                self.signal_price = last_cross_row['close']
+                if last_cross_row['position'] == 1: self.current_signal = "BUY"
+                elif last_cross_row['position'] == -1: self.current_signal = "SELL"
+            else:
+                self.last_signal_change_utc = data.index[0].tz_localize('UTC').isoformat()
+                self.signal_price = data['close'].iloc[0]
+        
+        elif self.rsi_period:
+            logging.info(f"RSI logic not implemented for period {self.rsi_period}.")
+            self.current_signal = "NOT IMPLEMENTED"
+        
+        elif self.short_ma and self.long_ma:
+            logging.info(f"MA Cross logic not implemented for {self.short_ma}/{self.long_ma}.")
+            self.current_signal = "NOT IMPLEMENTED"
+
+    def _save_status(self):
+        """Saves the current strategy state to its JSON file."""
+        status = {
+            "strategy_name": self.strategy_name,
+            "current_signal": self.current_signal,
+            "last_signal_change_utc": self.last_signal_change_utc,
+            "signal_price": self.signal_price,
+            "last_checked_utc": datetime.now(timezone.utc).isoformat()
+        }
+        try:
+            with open(self.status_file_path, 'w', encoding='utf-8') as f:
+                json.dump(status, f, indent=4)
+        except IOError as e:
+            logging.error(f"Failed to write status file: {e}")
+
+    def get_sleep_duration(self) -> int:
+        """Calculates seconds to sleep until the next full candle closes."""
+        if not self.timeframe: return 60
+        tf_value = int(''.join(filter(str.isdigit, self.timeframe)))
+        tf_unit = ''.join(filter(str.isalpha, self.timeframe))
+        
+        if tf_unit == 'm': interval_seconds = tf_value * 60
+        elif tf_unit == 'h': interval_seconds = tf_value * 3600
+        elif tf_unit == 'd': interval_seconds = tf_value * 86400
+        else: return 60
+
+        now = datetime.now(timezone.utc)
+        timestamp = now.timestamp()
+        
+        next_candle_ts = ((timestamp // interval_seconds) + 1) * interval_seconds
+        sleep_seconds = (next_candle_ts - timestamp) + 5
+        
+        logging.info(f"Next candle closes at {datetime.fromtimestamp(next_candle_ts, tz=timezone.utc)}. "
+                     f"Sleeping for {sleep_seconds:.2f} seconds.")
+        return sleep_seconds
+
+    def run_logic(self):
+        """Main loop: loads data, calculates signals, saves status, and sleeps."""
+        logging.info(f"Starting main logic loop for {self.coin} on {self.timeframe} timeframe.")
+        while True:
+            data = self.load_data()
+            if data.empty:
+                logging.warning("No data loaded. Waiting 1 minute before retrying...")
+                self.current_signal = "NO DATA"
+                self._save_status()
+                time.sleep(60)
+                continue
+            
+            self._calculate_signals(data)
+            self._save_status()
+            
+            last_close = data['close'].iloc[-1]
+            indicator_val_str = f"{self.indicator_value:.4f}" if self.indicator_value is not None else "N/A"
+            logging.info(f"Signal: {self.current_signal} | Price: {last_close:.4f} | Indicator: {indicator_val_str}")
+
+            sleep_time = self.get_sleep_duration()
+            time.sleep(sleep_time)
+
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser(description="Run a trading strategy.")
+    parser.add_argument("--name", required=True, help="The name of the strategy instance from the config.")
+    parser.add_argument("--params", required=True, help="A JSON string of the strategy's parameters.")
+    parser.add_argument("--log-level", default="normal", choices=['off', 'normal', 'debug'])
+    args = parser.parse_args()
+
+    try:
+        strategy_params = json.loads(args.params)
+        strategy = TradingStrategy(
+            strategy_name=args.name, 
+            params=strategy_params,
+            log_level=args.log_level
+        )
+        strategy.run_logic()
+    except KeyboardInterrupt:
+        logging.info("Strategy process stopped.")
+    except Exception as e:
+        logging.error(f"A critical error occurred: {e}")
+        sys.exit(1)
+

trade_executor.py

@@ -0,0 +1,193 @@
+import argparse
+import logging
+import os
+import sys
+import json
+import time
+# --- REVERTED: Removed math import ---
+from datetime import datetime
+import multiprocessing
+
+from eth_account import Account
+from hyperliquid.exchange import Exchange
+from hyperliquid.info import Info
+from hyperliquid.utils import constants
+from dotenv import load_dotenv
+
+from logging_utils import setup_logging
+
+load_dotenv()
+
+class TradeExecutor:
+    """
+    Executes orders from a queue and, upon API success,
+    updates the shared 'opened_positions.json' state file.
+    It is the single source of truth for position state.
+    """
+
+    def __init__(self, log_level: str, order_execution_queue: multiprocessing.Queue):
+        # Note: Logging is set up by the run_trade_executor function
+        
+        self.order_execution_queue = order_execution_queue
+        
+        self.vault_address = os.environ.get("MAIN_WALLET_ADDRESS")
+        if not self.vault_address:
+            logging.error("MAIN_WALLET_ADDRESS not set.")
+            sys.exit(1)
+        
+        self.info = Info(constants.MAINNET_API_URL, skip_ws=True)
+        self.exchanges = self._load_agents()
+        if not self.exchanges:
+            logging.error("No trading agents found in .env file.")
+            sys.exit(1)
+
+        # --- REVERTED: Removed asset_meta loading ---
+        # self.asset_meta = self._load_asset_metadata()
+
+        # --- NEW: State management logic ---
+        self.opened_positions_file = os.path.join("_data", "opened_positions.json")
+        self.opened_positions = self._load_opened_positions()
+        
+        logging.info(f"Trade Executor started. Loaded {len(self.opened_positions)} positions.")
+
+
+    def _load_agents(self) -> dict:
+        # ... (omitted for brevity, this logic is correct and unchanged) ...
+        exchanges = {}
+        logging.info("Discovering agents from environment variables...")
+        for env_var, private_key in os.environ.items():
+            agent_name = None
+            if env_var == "AGENT_PRIVATE_KEY":
+                agent_name = "default"
+            elif env_var.endswith("_AGENT_PK"):
+                agent_name = env_var.replace("_AGENT_PK", "").lower()
+
+            if agent_name and private_key:
+                try:
+                    agent_account = Account.from_key(private_key)
+                    exchanges[agent_name] = Exchange(agent_account, constants.MAINNET_API_URL, account_address=self.vault_address)
+                    logging.info(f"Initialized agent '{agent_name}' with address: {agent_account.address}")
+                except Exception as e:
+                    logging.error(f"Failed to initialize agent '{agent_name}': {e}")
+        return exchanges
+        
+    # --- REVERTED: Removed asset metadata loading ---
+    # def _load_asset_metadata(self) -> dict: ...
+
+    # --- NEW: Position state save/load methods ---
+    def _load_opened_positions(self) -> dict:
+        """Loads the state of currently managed positions from a JSON file."""
+        if not os.path.exists(self.opened_positions_file):
+            return {}
+        try:
+            with open(self.opened_positions_file, 'r', encoding='utf-8') as f:
+                return json.load(f)
+        except (json.JSONDecodeError, IOError) as e:
+            logging.error(f"Failed to read '{self.opened_positions_file}': {e}. Starting with empty state.", exc_info=True)
+            return {}
+
+    def _save_opened_positions(self):
+        """Saves the current state of managed positions to a JSON file."""
+        try:
+            with open(self.opened_positions_file, 'w', encoding='utf-8') as f:
+                json.dump(self.opened_positions, f, indent=4)
+            logging.debug(f"Successfully saved {len(self.opened_positions)} positions to '{self.opened_positions_file}'")
+        except IOError as e:
+            logging.error(f"Failed to write to '{self.opened_positions_file}': {e}", exc_info=True)
+
+    # --- REVERTED: Removed tick rounding function ---
+    # def _round_to_tick(self, price, tick_size): ...
+
+    def run(self):
+        """
+        Main execution loop. Waits for an order and updates state on success.
+        """
+        logging.info("Trade Executor started. Waiting for orders...")
+        while True:
+            try:
+                order = self.order_execution_queue.get()
+                if not order:
+                    continue
+
+                logging.info(f"Received order: {order}")
+
+                agent_name = order['agent']
+                action = order['action']
+                coin = order['coin']
+                is_buy = order['is_buy']
+                size = order['size']
+                limit_px = order.get('limit_px')
+                
+                exchange_to_use = self.exchanges.get(agent_name)
+                if not exchange_to_use:
+                    logging.error(f"Agent '{agent_name}' not found. Skipping order.")
+                    continue
+
+                response = None
+                
+                if action == "market_open" or action == "market_close":
+                    reduce_only = (action == "market_close")
+                    log_action = "MARKET CLOSE" if reduce_only else "MARKET OPEN"
+                    logging.warning(f"ACTION: {log_action} {coin} {'BUY' if is_buy else 'SELL'} {size}")
+                    
+                    # --- REVERTED: Removed all slippage and rounding logic ---
+                    # The raw limit_px from the order is now used directly
+                    final_price = limit_px
+                    logging.info(f"[{agent_name}] Using raw price for {coin}: {final_price}")
+
+                    order_type = {"limit": {"tif": "Ioc"}}
+                    # --- REVERTED: Uses final_price (which is just limit_px) ---
+                    response = exchange_to_use.order(coin, is_buy, size, final_price, order_type, reduce_only=reduce_only)
+                    logging.info(f"Market order response: {response}")
+
+                    # --- NEW: STATE UPDATE ON SUCCESS ---
+                    if response.get("status") == "ok":
+                        response_data = response.get("response", {},).get("data", {})
+                        if response_data and "statuses" in response_data:
+                            # Check if the order status contains an error
+                            if "error" not in response_data["statuses"][0]:
+                                position_key = order['position_key']
+                                if action == "market_open":
+                                    # Add to state
+                                    self.opened_positions[position_key] = {
+                                        "strategy": order['strategy'],
+                                        "coin": coin,
+                                        "side": "long" if is_buy else "short",
+                                        "open_time_utc": order['open_time_utc'],
+                                        "open_price": order['open_price'],
+                                        "amount": order['amount'],
+                                        # --- MODIFIED: Read leverage from the order ---
+                                        "leverage": order.get('leverage') 
+                                    }
+                                    logging.info(f"Successfully opened position {position_key}. Saving state.")
+                                elif action == "market_close":
+                                    # Remove from state
+                                    if position_key in self.opened_positions:
+                                        del self.opened_positions[position_key]
+                                        logging.info(f"Successfully closed position {position_key}. Saving state.")
+                                    else:
+                                        logging.warning(f"Received close confirmation for {position_key}, but it was not in state.")
+                                
+                                self._save_opened_positions() # Save state to disk
+                            
+                            else:
+                                logging.error(f"API Error for {action}: {response_data['statuses'][0]['error']}")
+                        else:
+                            logging.error(f"Unexpected API response format: {response}")
+                    else:
+                         logging.error(f"API call failed, status: {response.get('status')}")
+
+
+                elif action == "update_leverage":
+                    leverage = int(size) 
+                    logging.warning(f"ACTION: UPDATE LEVERAGE {coin} to {leverage}x")
+                    response = exchange_to_use.update_leverage(leverage, coin)
+                    logging.info(f"Update leverage response: {response}")
+                
+                else:
+                    logging.warning(f"Received unknown action: {action}")
+
+            except Exception as e:
+                logging.error(f"An error occurred in the main executor loop: {e}", exc_info=True)
+                time.sleep(1)
+

trade_log.py

@@ -0,0 +1,55 @@
+import os
+import csv
+from datetime import datetime, timezone
+import threading
+
+# A lock to prevent race conditions when multiple strategies might log at once in the future
+log_lock = threading.Lock()
+
+def log_trade(strategy: str, coin: str, action: str, price: float, size: float, signal: str, pnl: float = 0.0):
+    """
+    Appends a record of a trade action to a persistent CSV log file.
+
+    Args:
+        strategy (str): The name of the strategy that triggered the action.
+        coin (str): The coin being traded (e.g., 'BTC').
+        action (str): The action taken (e.g., 'OPEN_LONG', 'CLOSE_LONG').
+        price (float): The execution price of the trade.
+        size (float): The size of the trade.
+        signal (str): The signal that triggered the trade (e.g., 'BUY', 'SELL').
+        pnl (float, optional): The realized profit and loss for closing trades. Defaults to 0.0.
+    """
+    log_dir = "_logs"
+    file_path = os.path.join(log_dir, "trade_history.csv")
+    
+    # Ensure the logs directory exists
+    if not os.path.exists(log_dir):
+        os.makedirs(log_dir)
+
+    # Define the headers for the CSV file
+    headers = ["timestamp_utc", "strategy", "coin", "action", "price", "size", "signal", "pnl"]
+    
+    # Check if the file needs a header
+    file_exists = os.path.isfile(file_path)
+
+    with log_lock:
+        try:
+            with open(file_path, 'a', newline='', encoding='utf-8') as f:
+                writer = csv.DictWriter(f, fieldnames=headers)
+                
+                if not file_exists:
+                    writer.writeheader()
+                
+                writer.writerow({
+                    "timestamp_utc": datetime.now(timezone.utc).isoformat(),
+                    "strategy": strategy,
+                    "coin": coin,
+                    "action": action,
+                    "price": price,
+                    "size": size,
+                    "signal": signal,
+                    "pnl": pnl
+                })
+        except IOError as e:
+            # If logging fails, print an error to the main console as a fallback.
+            print(f"CRITICAL: Failed to write to trade log file: {e}")

wallet_data.py

@@ -0,0 +1,652 @@
+#!/usr/bin/env python3
+"""
+Hyperliquid Wallet Data Fetcher - FINAL Perfect Alignment
+==========================================================
+Complete Python script to pull all available data for a Hyperliquid wallet via API.
+
+Requirements:
+    pip install hyperliquid-python-sdk
+
+Usage:
+    python hyperliquid_wallet_data.py <wallet_address>
+    
+Example:
+    python hyperliquid_wallet_data.py 0xcd5051944f780a621ee62e39e493c489668acf4d
+"""
+
+import sys
+import json
+from datetime import datetime, timedelta
+from typing import Optional, Dict, Any
+from hyperliquid.info import Info
+from hyperliquid.utils import constants
+
+
+class HyperliquidWalletAnalyzer:
+    """
+    Comprehensive wallet data analyzer for Hyperliquid exchange.
+    Fetches all available information about a specific wallet address.
+    """
+    
+    def __init__(self, wallet_address: str, use_testnet: bool = False):
+        """
+        Initialize the analyzer with a wallet address.
+        
+        Args:
+            wallet_address: Ethereum-style address (0x...)
+            use_testnet: If True, use testnet instead of mainnet
+        """
+        self.wallet_address = wallet_address
+        api_url = constants.TESTNET_API_URL if use_testnet else constants.MAINNET_API_URL
+        
+        # Initialize Info API (read-only, no private keys needed)
+        self.info = Info(api_url, skip_ws=True)
+        print(f"Initialized Hyperliquid API: {'Testnet' if use_testnet else 'Mainnet'}")
+        print(f"Target wallet: {wallet_address}\n")
+    
+    def print_position_details(self, position: Dict[str, Any], index: int):
+        """
+        Print detailed information about a single position.
+        
+        Args:
+            position: Position data dictionary
+            index: Position number for display
+        """
+        pos = position.get('position', {})
+        
+        # Extract all position details
+        coin = pos.get('coin', 'Unknown')
+        size = float(pos.get('szi', 0))
+        entry_px = float(pos.get('entryPx', 0))
+        position_value = float(pos.get('positionValue', 0))
+        unrealized_pnl = float(pos.get('unrealizedPnl', 0))
+        return_on_equity = float(pos.get('returnOnEquity', 0))
+        
+        # Leverage details
+        leverage = pos.get('leverage', {})
+        leverage_type = leverage.get('type', 'unknown') if isinstance(leverage, dict) else 'cross'
+        leverage_value = leverage.get('value', 0) if isinstance(leverage, dict) else 0
+        
+        # Margin and liquidation
+        margin_used = float(pos.get('marginUsed', 0))
+        liquidation_px = pos.get('liquidationPx')
+        max_trade_szs = pos.get('maxTradeSzs', [0, 0])
+        
+        # Cumulative funding
+        cumulative_funding = float(pos.get('cumFunding', {}).get('allTime', 0))
+        
+        # Determine if long or short
+        side = "LONG 📈" if size > 0 else "SHORT 📉"
+        side_color = "🟢" if size > 0 else "🔴"
+        
+        # PnL color
+        pnl_symbol = "🟢" if unrealized_pnl >= 0 else "🔴"
+        pnl_sign = "+" if unrealized_pnl >= 0 else ""
+        
+        # ROE color
+        roe_symbol = "🟢" if return_on_equity >= 0 else "🔴"
+        roe_sign = "+" if return_on_equity >= 0 else ""
+        
+        print(f"\n{'='*80}")
+        print(f"POSITION #{index}: {coin} {side} {side_color}")
+        print(f"{'='*80}")
+        
+        print(f"\n📊 POSITION DETAILS:")
+        print(f"   Size:                {abs(size):.6f} {coin}")
+        print(f"   Side:                {side}")
+        print(f"   Entry Price:         ${entry_px:,.4f}")
+        print(f"   Position Value:      ${abs(position_value):,.2f}")
+        
+        print(f"\n💰 PROFITABILITY:")
+        print(f"   Unrealized PnL:      {pnl_symbol} {pnl_sign}${unrealized_pnl:,.2f}")
+        print(f"   Return on Equity:    {roe_symbol} {roe_sign}{return_on_equity:.2%}")
+        print(f"   Cumulative Funding:  ${cumulative_funding:,.4f}")
+        
+        print(f"\n⚙️ LEVERAGE & MARGIN:")
+        print(f"   Leverage Type:       {leverage_type.upper()}")
+        print(f"   Leverage:            {leverage_value}x")
+        print(f"   Margin Used:         ${margin_used:,.2f}")
+        
+        print(f"\n⚠️ RISK MANAGEMENT:")
+        if liquidation_px:
+            liquidation_px_float = float(liquidation_px) if liquidation_px else 0
+            print(f"   Liquidation Price:   ${liquidation_px_float:,.4f}")
+            
+            # Calculate distance to liquidation
+            if entry_px > 0 and liquidation_px_float > 0:
+                if size > 0:  # Long position
+                    distance = ((entry_px - liquidation_px_float) / entry_px) * 100
+                else:  # Short position
+                    distance = ((liquidation_px_float - entry_px) / entry_px) * 100
+                
+                distance_symbol = "🟢" if abs(distance) > 20 else "🟡" if abs(distance) > 10 else "🔴"
+                print(f"   Distance to Liq:     {distance_symbol} {abs(distance):.2f}%")
+        else:
+            print(f"   Liquidation Price:   N/A (Cross margin)")
+        
+        if max_trade_szs and len(max_trade_szs) == 2:
+            print(f"   Max Long Trade:      {max_trade_szs[0]}")
+            print(f"   Max Short Trade:     {max_trade_szs[1]}")
+        
+        print(f"\n{'='*80}")
+    
+    def get_user_state(self) -> Dict[str, Any]:
+        """
+        Get complete user state including positions and margin summary.
+        
+        Returns:
+            Dict containing:
+            - assetPositions: List of open perpetual positions
+            - marginSummary: Account value, margin used, withdrawable
+            - crossMarginSummary: Cross margin details
+            - withdrawable: Available balance to withdraw
+        """
+        print("📊 Fetching User State (Perpetuals)...")
+        try:
+            data = self.info.user_state(self.wallet_address)
+            
+            if data:
+                margin_summary = data.get('marginSummary', {})
+                positions = data.get('assetPositions', [])
+                
+                account_value = float(margin_summary.get('accountValue', 0))
+                total_margin_used = float(margin_summary.get('totalMarginUsed', 0))
+                total_ntl_pos = float(margin_summary.get('totalNtlPos', 0))
+                total_raw_usd = float(margin_summary.get('totalRawUsd', 0))
+                withdrawable = float(data.get('withdrawable', 0))
+                
+                print(f"   ✓ Account Value: ${account_value:,.2f}")
+                print(f"   ✓ Total Margin Used: ${total_margin_used:,.2f}")
+                print(f"   ✓ Total Position Value: ${total_ntl_pos:,.2f}")
+                print(f"   ✓ Withdrawable: ${withdrawable:,.2f}")
+                print(f"   ✓ Open Positions: {len(positions)}")
+                
+                # Calculate margin utilization
+                if account_value > 0:
+                    margin_util = (total_margin_used / account_value) * 100
+                    util_symbol = "🟢" if margin_util < 50 else "🟡" if margin_util < 75 else "🔴"
+                    print(f"   ✓ Margin Utilization: {util_symbol} {margin_util:.2f}%")
+                
+                # Print detailed information for each position
+                if positions:
+                    print(f"\n{'='*80}")
+                    print(f"DETAILED POSITION BREAKDOWN ({len(positions)} positions)")
+                    print(f"{'='*80}")
+                    
+                    for idx, position in enumerate(positions, 1):
+                        self.print_position_details(position, idx)
+                    
+                    # Summary table with perfect alignment
+                    self.print_positions_summary_table(positions)
+                
+            else:
+                print("   ⚠ No perpetual positions found")
+            
+            return data
+        except Exception as e:
+            print(f"   ✗ Error: {e}")
+            return {}
+    
+    def print_positions_summary_table(self, positions: list):
+        """
+        Print a summary table of all positions with perfectly aligned columns.
+        NO emojis in data cells - keeps them simple text only for perfect alignment.
+        
+        Args:
+            positions: List of position dictionaries
+        """
+        print(f"\n{'='*130}")
+        print("POSITIONS SUMMARY TABLE")
+        print('='*130)
+        
+        # Print header
+        print("| Asset    | Side  | Size              | Entry Price       | Position Value    | Unrealized PnL    | ROE        | Leverage   |")
+        print("|----------|-------|-------------------|-------------------|-------------------|-------------------|------------|------------|")
+        
+        total_position_value = 0
+        total_pnl = 0
+        
+        for position in positions:
+            pos = position.get('position', {})
+            
+            coin = pos.get('coin', 'Unknown')
+            size = float(pos.get('szi', 0))
+            entry_px = float(pos.get('entryPx', 0))
+            position_value = float(pos.get('positionValue', 0))
+            unrealized_pnl = float(pos.get('unrealizedPnl', 0))
+            return_on_equity = float(pos.get('returnOnEquity', 0))
+            
+            # Get leverage
+            leverage = pos.get('leverage', {})
+            leverage_value = leverage.get('value', 0) if isinstance(leverage, dict) else 0
+            leverage_type = leverage.get('type', 'cross') if isinstance(leverage, dict) else 'cross'
+            
+            # Determine side - NO EMOJIS in data
+            side_text = "LONG" if size > 0 else "SHORT"
+            
+            # Format PnL and ROE with signs
+            pnl_sign = "+" if unrealized_pnl >= 0 else ""
+            roe_sign = "+" if return_on_equity >= 0 else ""
+            
+            # Accumulate totals
+            total_position_value += abs(position_value)
+            total_pnl += unrealized_pnl
+            
+            # Format all values as strings with proper width
+            asset_str = f"{coin[:8]:<8}"
+            side_str = f"{side_text:<5}"
+            size_str = f"{abs(size):>17,.4f}"
+            entry_str = f"${entry_px:>16,.2f}"
+            value_str = f"${abs(position_value):>16,.2f}"
+            pnl_str = f"{pnl_sign}${unrealized_pnl:>15,.2f}"
+            roe_str = f"{roe_sign}{return_on_equity:>9.2%}"
+            lev_str = f"{leverage_value}x {leverage_type[:4]}"
+            
+            # Print row with exact spacing
+            print(f"| {asset_str} | {side_str} | {size_str} | {entry_str} | {value_str} | {pnl_str} | {roe_str} | {lev_str:<10} |")
+        
+        # Separator before totals
+        print("|==========|=======|===================|===================|===================|===================|============|============|")
+        
+        # Total row
+        total_value_str = f"${total_position_value:>16,.2f}"
+        total_pnl_sign = "+" if total_pnl >= 0 else ""
+        total_pnl_str = f"{total_pnl_sign}${total_pnl:>15,.2f}"
+        
+        print(f"| TOTAL    |       |                   |                   | {total_value_str} | {total_pnl_str} |            |            |")
+        print('='*130 + '\n')
+    
+    def get_spot_state(self) -> Dict[str, Any]:
+        """
+        Get spot trading state including token balances.
+        
+        Returns:
+            Dict containing:
+            - balances: List of spot token holdings
+        """
+        print("\n💰 Fetching Spot State...")
+        try:
+            data = self.info.spot_user_state(self.wallet_address)
+            
+            if data and data.get('balances'):
+                print(f"   ✓ Spot Holdings: {len(data['balances'])} tokens")
+                for balance in data['balances'][:5]:  # Show first 5
+                    print(f"     - {balance.get('coin', 'Unknown')}: {balance.get('total', 0)}")
+            else:
+                print("   ⚠ No spot holdings found")
+            
+            return data
+        except Exception as e:
+            print(f"   ✗ Error: {e}")
+            return {}
+    
+    def get_open_orders(self) -> list:
+        """
+        Get all open orders for the user.
+        
+        Returns:
+            List of open orders with details (price, size, side, etc.)
+        """
+        print("\n📋 Fetching Open Orders...")
+        try:
+            data = self.info.open_orders(self.wallet_address)
+            
+            if data:
+                print(f"   ✓ Open Orders: {len(data)}")
+                for order in data[:3]:  # Show first 3
+                    coin = order.get('coin', 'Unknown')
+                    side = order.get('side', 'Unknown')
+                    size = order.get('sz', 0)
+                    price = order.get('limitPx', 0)
+                    print(f"     - {coin} {side}: {size} @ ${price}")
+            else:
+                print("   ⚠ No open orders")
+            
+            return data
+        except Exception as e:
+            print(f"   ✗ Error: {e}")
+            return []
+    
+    def get_user_fills(self, limit: int = 100) -> list:
+        """
+        Get recent trade fills (executions).
+        
+        Args:
+            limit: Maximum number of fills to retrieve (max 2000)
+            
+        Returns:
+            List of fills with execution details, PnL, timestamps
+        """
+        print(f"\n📈 Fetching Recent Fills (last {limit})...")
+        try:
+            data = self.info.user_fills(self.wallet_address)
+            
+            if data:
+                fills = data[:limit]
+                print(f"   ✓ Total Fills Retrieved: {len(fills)}")
+                
+                # Show summary stats
+                total_pnl = sum(float(f.get('closedPnl', 0)) for f in fills if f.get('closedPnl'))
+                print(f"   ✓ Total Closed PnL: ${total_pnl:.2f}")
+                
+                # Show most recent
+                if fills:
+                    recent = fills[0]
+                    print(f"   ✓ Most Recent: {recent.get('coin')} {recent.get('side')} {recent.get('sz')} @ ${recent.get('px')}")
+            else:
+                print("   ⚠ No fills found")
+            
+            return data[:limit] if data else []
+        except Exception as e:
+            print(f"   ✗ Error: {e}")
+            return []
+    
+    def get_user_fills_by_time(self, start_time: Optional[int] = None, 
+                                end_time: Optional[int] = None) -> list:
+        """
+        Get fills within a specific time range.
+        
+        Args:
+            start_time: Start timestamp in milliseconds (default: 7 days ago)
+            end_time: End timestamp in milliseconds (default: now)
+            
+        Returns:
+            List of fills within the time range
+        """
+        if not start_time:
+            start_time = int((datetime.now() - timedelta(days=7)).timestamp() * 1000)
+        if not end_time:
+            end_time = int(datetime.now().timestamp() * 1000)
+        
+        print(f"\n📅 Fetching Fills by Time Range...")
+        print(f"   From: {datetime.fromtimestamp(start_time/1000)}")
+        print(f"   To: {datetime.fromtimestamp(end_time/1000)}")
+        
+        try:
+            data = self.info.user_fills_by_time(self.wallet_address, start_time, end_time)
+            
+            if data:
+                print(f"   ✓ Fills in Range: {len(data)}")
+            else:
+                print("   ⚠ No fills in this time range")
+            
+            return data
+        except Exception as e:
+            print(f"   ✗ Error: {e}")
+            return []
+    
+    def get_user_fees(self) -> Dict[str, Any]:
+        """
+        Get user's fee schedule and trading volume.
+        
+        Returns:
+            Dict containing:
+            - feeSchedule: Fee rates by tier
+            - userCrossRate: User's current cross trading fee rate
+            - userAddRate: User's maker fee rate
+            - userWithdrawRate: Withdrawal fee rate
+            - dailyUserVlm: Daily trading volume
+        """
+        print("\n💳 Fetching Fee Information...")
+        try:
+            data = self.info.user_fees(self.wallet_address)
+            
+            if data:
+                print(f"   ✓ Maker Fee: {data.get('userAddRate', 0)}%")
+                print(f"   ✓ Taker Fee: {data.get('userCrossRate', 0)}%")
+                print(f"   ✓ Daily Volume: ${data.get('dailyUserVlm', [0])[0] if data.get('dailyUserVlm') else 0}")
+            
+            return data
+        except Exception as e:
+            print(f"   ✗ Error: {e}")
+            return {}
+    
+    def get_user_rate_limit(self) -> Dict[str, Any]:
+        """
+        Get API rate limit information.
+        
+        Returns:
+            Dict containing:
+            - cumVlm: Cumulative trading volume
+            - nRequestsUsed: Number of requests used
+            - nRequestsCap: Request capacity
+        """
+        print("\n⏱️ Fetching Rate Limit Info...")
+        try:
+            data = self.info.user_rate_limit(self.wallet_address)
+            
+            if data:
+                used = data.get('nRequestsUsed', 0)
+                cap = data.get('nRequestsCap', 0)
+                print(f"   ✓ API Requests: {used}/{cap}")
+                print(f"   ✓ Cumulative Volume: ${data.get('cumVlm', 0)}")
+            
+            return data
+        except Exception as e:
+            print(f"   ✗ Error: {e}")
+            return {}
+    
+    def get_funding_history(self, coin: str, days: int = 7) -> list:
+        """
+        Get funding rate history for a specific coin.
+        
+        Args:
+            coin: Asset symbol (e.g., 'BTC', 'ETH')
+            days: Number of days of history (default: 7)
+            
+        Returns:
+            List of funding rate entries
+        """
+        end_time = int(datetime.now().timestamp() * 1000)
+        start_time = int((datetime.now() - timedelta(days=days)).timestamp() * 1000)
+        
+        print(f"\n📊 Fetching Funding History for {coin}...")
+        try:
+            data = self.info.funding_history(coin, start_time, end_time)
+            
+            if data:
+                print(f"   ✓ Funding Entries: {len(data)}")
+                if data:
+                    latest = data[-1]
+                    print(f"   ✓ Latest Rate: {latest.get('fundingRate', 0)}")
+            
+            return data
+        except Exception as e:
+            print(f"   ✗ Error: {e}")
+            return []
+    
+    def get_user_funding_history(self, days: int = 7) -> list:
+        """
+        Get user's funding payments history.
+        
+        Args:
+            days: Number of days of history (default: 7)
+            
+        Returns:
+            List of funding payments
+        """
+        end_time = int(datetime.now().timestamp() * 1000)
+        start_time = int((datetime.now() - timedelta(days=days)).timestamp() * 1000)
+        
+        print(f"\n💸 Fetching User Funding Payments (last {days} days)...")
+        try:
+            data = self.info.user_funding_history(self.wallet_address, start_time, end_time)
+            
+            if data:
+                print(f"   ✓ Funding Payments: {len(data)}")
+                total_funding = sum(float(f.get('usdc', 0)) for f in data)
+                print(f"   ✓ Total Funding P&L: ${total_funding:.2f}")
+            else:
+                print("   ⚠ No funding payments found")
+            
+            return data
+        except Exception as e:
+            print(f"   ✗ Error: {e}")
+            return []
+    
+    def get_user_non_funding_ledger_updates(self, days: int = 7) -> list:
+        """
+        Get non-funding ledger updates (deposits, withdrawals, liquidations).
+        
+        Args:
+            days: Number of days of history (default: 7)
+            
+        Returns:
+            List of ledger updates
+        """
+        end_time = int(datetime.now().timestamp() * 1000)
+        start_time = int((datetime.now() - timedelta(days=days)).timestamp() * 1000)
+        
+        print(f"\n📒 Fetching Ledger Updates (last {days} days)...")
+        try:
+            data = self.info.user_non_funding_ledger_updates(self.wallet_address, start_time, end_time)
+            
+            if data:
+                print(f"   ✓ Ledger Updates: {len(data)}")
+                # Categorize updates
+                deposits = [u for u in data if 'deposit' in str(u.get('delta', {})).lower()]
+                withdrawals = [u for u in data if 'withdraw' in str(u.get('delta', {})).lower()]
+                print(f"   ✓ Deposits: {len(deposits)}, Withdrawals: {len(withdrawals)}")
+            else:
+                print("   ⚠ No ledger updates found")
+            
+            return data
+        except Exception as e:
+            print(f"   ✗ Error: {e}")
+            return []
+    
+    def get_referral_state(self) -> Dict[str, Any]:
+        """
+        Get referral program state for the user.
+        
+        Returns:
+            Dict with referral status and earnings
+        """
+        print("\n🎁 Fetching Referral State...")
+        try:
+            data = self.info.query_referral_state(self.wallet_address)
+            
+            if data:
+                print(f"   ✓ Referral Code: {data.get('referralCode', 'N/A')}")
+                print(f"   ✓ Referees: {len(data.get('referees', []))}")
+            
+            return data
+        except Exception as e:
+            print(f"   ✗ Error: {e}")
+            return {}
+    
+    def get_sub_accounts(self) -> list:
+        """
+        Get list of sub-accounts for the user.
+        
+        Returns:
+            List of sub-account addresses
+        """
+        print("\n👥 Fetching Sub-Accounts...")
+        try:
+            data = self.info.query_sub_accounts(self.wallet_address)
+            
+            if data:
+                print(f"   ✓ Sub-Accounts: {len(data)}")
+            else:
+                print("   ⚠ No sub-accounts found")
+            
+            return data
+        except Exception as e:
+            print(f"   ✗ Error: {e}")
+            return []
+    
+    def fetch_all_data(self, save_to_file: bool = True) -> Dict[str, Any]:
+        """
+        Fetch all available data for the wallet.
+        
+        Args:
+            save_to_file: If True, save results to JSON file
+            
+        Returns:
+            Dict containing all fetched data
+        """
+        print("=" * 80)
+        print("HYPERLIQUID WALLET DATA FETCHER")
+        print("=" * 80)
+        
+        all_data = {
+            'wallet_address': self.wallet_address,
+            'timestamp': datetime.now().isoformat(),
+            'data': {}
+        }
+        
+        # Fetch all data sections
+        all_data['data']['user_state'] = self.get_user_state()
+        all_data['data']['spot_state'] = self.get_spot_state()
+        all_data['data']['open_orders'] = self.get_open_orders()
+        all_data['data']['recent_fills'] = self.get_user_fills(limit=50)
+        all_data['data']['fills_last_7_days'] = self.get_user_fills_by_time()
+        all_data['data']['user_fees'] = self.get_user_fees()
+        all_data['data']['rate_limit'] = self.get_user_rate_limit()
+        all_data['data']['funding_payments'] = self.get_user_funding_history(days=7)
+        all_data['data']['ledger_updates'] = self.get_user_non_funding_ledger_updates(days=7)
+        all_data['data']['referral_state'] = self.get_referral_state()
+        all_data['data']['sub_accounts'] = self.get_sub_accounts()
+        
+        # Optional: Fetch funding history for positions
+        user_state = all_data['data']['user_state']
+        if user_state and user_state.get('assetPositions'):
+            all_data['data']['funding_history'] = {}
+            for position in user_state['assetPositions'][:3]:  # First 3 positions
+                coin = position.get('position', {}).get('coin')
+                if coin:
+                    all_data['data']['funding_history'][coin] = self.get_funding_history(coin, days=7)
+        
+        print("\n" + "=" * 80)
+        print("DATA COLLECTION COMPLETE")
+        print("=" * 80)
+        
+        # Save to file
+        if save_to_file:
+            filename = f"hyperliquid_wallet_data_{self.wallet_address[:10]}_{datetime.now().strftime('%Y%m%d_%H%M%S')}.json"
+            with open(filename, 'w') as f:
+                json.dump(all_data, f, indent=2, default=str)
+            print(f"\n💾 Data saved to: {filename}")
+        
+        return all_data
+
+
+def main():
+    """Main execution function."""
+    if len(sys.argv) < 2:
+        print("Usage: python hyperliquid_wallet_data.py <wallet_address> [--testnet]")
+        print("\nExample:")
+        print("  python hyperliquid_wallet_data.py 0xcd5051944f780a621ee62e39e493c489668acf4d")
+        sys.exit(1)
+    
+    wallet_address = sys.argv[1]
+    use_testnet = '--testnet' in sys.argv
+    
+    # Validate wallet address format
+    if not wallet_address.startswith('0x') or len(wallet_address) != 42:
+        print("❌ Error: Invalid wallet address format")
+        print("   Address must be in format: 0x followed by 40 hexadecimal characters")
+        sys.exit(1)
+    
+    try:
+        analyzer = HyperliquidWalletAnalyzer(wallet_address, use_testnet=use_testnet)
+        data = analyzer.fetch_all_data(save_to_file=True)
+        
+        print("\n✅ All data fetched successfully!")
+        print(f"\n📊 Summary:")
+        print(f"   - Account Value: ${data['data']['user_state'].get('marginSummary', {}).get('accountValue', 0)}")
+        print(f"   - Open Positions: {len(data['data']['user_state'].get('assetPositions', []))}")
+        print(f"   - Spot Holdings: {len(data['data']['spot_state'].get('balances', []))}")
+        print(f"   - Open Orders: {len(data['data']['open_orders'])}")
+        print(f"   - Recent Fills: {len(data['data']['recent_fills'])}")
+        
+    except Exception as e:
+        print(f"\n❌ Fatal Error: {e}")
+        import traceback
+        traceback.print_exc()
+        sys.exit(1)
+
+
+if __name__ == "__main__":
+    main()

whale_tracker.py

@@ -0,0 +1,367 @@
+import json
+import os
+import time
+import requests
+import logging
+import argparse
+import sys
+from datetime import datetime, timedelta
+
+# --- Configuration ---
+# !! IMPORTANT: Update this to your actual Hyperliquid API endpoint !!
+API_ENDPOINT = "https://api.hyperliquid.xyz/info" 
+
+INPUT_FILE = os.path.join("_data", "wallets_to_track.json")
+OUTPUT_FILE = os.path.join("_data", "wallets_info.json")
+LOGS_DIR = "_logs"
+LOG_FILE = os.path.join(LOGS_DIR, "whale_tracker.log")
+
+# Polling intervals (in seconds)
+POLL_INTERVALS = {
+    'core_data': 10,       # 5-15s range
+    'open_orders': 20,     # 15-30s range
+    'account_metrics': 180, # 1-5m range
+    'ledger_updates': 600,  # 5-15m range
+    'save_data': 5,        # How often to write to wallets_info.json
+    'reload_wallets': 60   # Check for wallet list changes every 60s
+}
+
+class HyperliquidAPI:
+    """
+    Client to handle POST requests to the Hyperliquid info endpoint.
+    """
+    def __init__(self, base_url):
+        self.base_url = base_url
+        self.session = requests.Session()
+        logging.info(f"API Client initialized for endpoint: {base_url}")
+
+    def post_request(self, payload):
+        """
+        Internal helper to send POST requests and handle errors.
+        """
+        try:
+            response = self.session.post(self.base_url, json=payload, timeout=10)
+            response.raise_for_status() # Raise an exception for bad status codes (4xx or 5xx)
+            return response.json()
+        except requests.exceptions.HTTPError as e:
+            logging.error(f"HTTP Error: {e.response.status_code} for {e.request.url}. Response: {e.response.text}")
+        except requests.exceptions.ConnectionError as e:
+            logging.error(f"Connection Error: {e}")
+        except requests.exceptions.Timeout:
+            logging.error(f"Request timed out for payload: {payload.get('type')}")
+        except json.JSONDecodeError:
+            logging.error(f"Failed to decode JSON response. Response text: {response.text if 'response' in locals() else 'No response text'}")
+        except Exception as e:
+            logging.error(f"An unexpected error occurred in post_request: {e}", exc_info=True)
+        return None
+
+    def get_user_state(self, user_address: str):
+        payload = {"type": "clearinghouseState", "user": user_address}
+        return self.post_request(payload)
+
+    def get_open_orders(self, user_address: str):
+        payload = {"type": "openOrders", "user": user_address}
+        return self.post_request(payload)
+
+    def get_user_rate_limit(self, user_address: str):
+        payload = {"type": "userRateLimit", "user": user_address}
+        return self.post_request(payload)
+
+    def get_user_ledger_updates(self, user_address: str, start_time_ms: int, end_time_ms: int):
+        payload = {
+            "type": "userNonFundingLedgerUpdates",
+            "user": user_address,
+            "startTime": start_time_ms,
+            "endTime": end_time_ms
+        }
+        return self.post_request(payload)
+
+class WalletTracker:
+    """
+    Main class to track wallets, process data, and store results.
+    """
+    def __init__(self, api_client, wallets_to_track):
+        self.api = api_client
+        self.wallets = wallets_to_track # This is the list of dicts
+        self.wallets_by_name = {w['name']: w for w in self.wallets}
+        self.wallets_data = {
+            wallet['name']: {"address": wallet['address']} for wallet in self.wallets
+        }
+        logging.info(f"WalletTracker initialized for {len(self.wallets)} wallets.")
+
+    def reload_wallets(self):
+        """
+        Checks the INPUT_FILE for changes and updates the tracked wallet list.
+        """
+        logging.debug("Reloading wallet list...")
+        try:
+            with open(INPUT_FILE, 'r') as f:
+                new_wallets_list = json.load(f)
+            if not isinstance(new_wallets_list, list):
+                logging.warning(f"Failed to reload '{INPUT_FILE}': content is not a list.")
+                return
+
+            new_wallets_by_name = {w['name']: w for w in new_wallets_list}
+            old_names = set(self.wallets_by_name.keys())
+            new_names = set(new_wallets_by_name.keys())
+
+            added_names = new_names - old_names
+            removed_names = old_names - new_names
+
+            if not added_names and not removed_names:
+                logging.debug("Wallet list is unchanged.")
+                return # No changes
+
+            # Update internal wallet list
+            self.wallets = new_wallets_list
+            self.wallets_by_name = new_wallets_by_name
+
+            # Add new wallets to wallets_data
+            for name in added_names:
+                self.wallets_data[name] = {"address": self.wallets_by_name[name]['address']}
+                logging.info(f"Added new wallet to track: {name}")
+
+            # Remove old wallets from wallets_data
+            for name in removed_names:
+                if name in self.wallets_data:
+                    del self.wallets_data[name]
+                    logging.info(f"Removed wallet from tracking: {name}")
+            
+            logging.info(f"Wallet list reloaded. Tracking {len(self.wallets)} wallets.")
+
+        except (FileNotFoundError, json.JSONDecodeError, ValueError) as e:
+            logging.error(f"Failed to reload and parse '{INPUT_FILE}': {e}")
+        except Exception as e:
+            logging.error(f"Unexpected error during wallet reload: {e}", exc_info=True)
+
+
+    def calculate_core_metrics(self, state_data: dict) -> dict:
+        """
+        Performs calculations based on user_state data.
+        """
+        if not state_data or 'crossMarginSummary' not in state_data:
+            logging.warning("Core state data is missing 'crossMarginSummary'.")
+            return {"raw_state": state_data}
+        
+        summary = state_data['crossMarginSummary']
+        account_value = float(summary.get('accountValue', 0))
+        margin_used = float(summary.get('totalMarginUsed', 0))
+        
+        # Calculations
+        margin_utilization = (margin_used / account_value) if account_value > 0 else 0
+        available_margin = account_value - margin_used
+        
+        total_position_value = 0
+        if 'assetPositions' in state_data:
+            for pos in state_data.get('assetPositions', []):
+                try:
+                    # Use 'value' for position value
+                    pos_value_str = pos.get('position', {}).get('value', '0')
+                    total_position_value += float(pos_value_str)
+                except (ValueError, TypeError):
+                    logging.warning(f"Could not parse position value: {pos.get('position', {}).get('value')}")
+                    continue
+        
+        portfolio_leverage = (total_position_value / account_value) if account_value > 0 else 0
+        
+        # Return calculated metrics alongside raw data
+        return {
+            "raw_state": state_data,
+            "account_value": account_value,
+            "margin_used": margin_used,
+            "margin_utilization": margin_utilization,
+            "available_margin": available_margin,
+            "total_position_value": total_position_value,
+            "portfolio_leverage": portfolio_leverage
+        }
+
+    def poll_core_data(self):
+        logging.debug("Polling Core Data...")
+        # Use self.wallets which is updated by reload_wallets
+        for wallet in self.wallets:
+            name = wallet['name']
+            address = wallet['address']
+            state_data = self.api.get_user_state(address)
+            if state_data:
+                calculated_data = self.calculate_core_metrics(state_data)
+                # Ensure wallet hasn't been removed by a concurrent reload
+                if name in self.wallets_data:
+                    self.wallets_data[name]['core_state'] = calculated_data
+            time.sleep(0.1) # Avoid bursting requests
+
+    def poll_open_orders(self):
+        logging.debug("Polling Open Orders...")
+        for wallet in self.wallets:
+            name = wallet['name']
+            address = wallet['address']
+            orders_data = self.api.get_open_orders(address)
+            if orders_data:
+                # TODO: Add calculations for 'pending_margin_required' if logic is available
+                if name in self.wallets_data:
+                    self.wallets_data[name]['open_orders'] = {"raw_orders": orders_data}
+            time.sleep(0.1)
+
+    def poll_account_metrics(self):
+        logging.debug("Polling Account Metrics...")
+        for wallet in self.wallets:
+            name = wallet['name']
+            address = wallet['address']
+            metrics_data = self.api.get_user_rate_limit(address)
+            if metrics_data:
+                if name in self.wallets_data:
+                    self.wallets_data[name]['account_metrics'] = metrics_data
+            time.sleep(0.1)
+
+    def poll_ledger_updates(self):
+        logging.debug("Polling Ledger Updates...")
+        end_time_ms = int(datetime.now().timestamp() * 1000)
+        start_time_ms = int((datetime.now() - timedelta(minutes=15)).timestamp() * 1000)
+        
+        for wallet in self.wallets:
+            name = wallet['name']
+            address = wallet['address']
+            ledger_data = self.api.get_user_ledger_updates(address, start_time_ms, end_time_ms)
+            if ledger_data:
+                if name in self.wallets_data:
+                    self.wallets_data[name]['ledger_updates'] = ledger_data
+            time.sleep(0.1)
+
+    def save_data_to_json(self):
+        """
+        Atomically writes the current wallet data to the output JSON file.
+        (No longer needs cleaning logic)
+        """
+        logging.debug(f"Saving data to {OUTPUT_FILE}...")
+
+        temp_file = OUTPUT_FILE + ".tmp"
+        try:
+            # Save the data
+            with open(temp_file, 'w', encoding='utf-8') as f:
+                # self.wallets_data is automatically kept clean by reload_wallets
+                json.dump(self.wallets_data, f, indent=2)
+            # Atomic rename (move)
+            os.replace(temp_file, OUTPUT_FILE)
+        except (IOError, json.JSONDecodeError) as e:
+            logging.error(f"Failed to write wallet data to file: {e}")
+        except Exception as e:
+            logging.error(f"An unexpected error occurred during file save: {e}")
+            if os.path.exists(temp_file):
+                os.remove(temp_file)
+
+class WhaleTrackerRunner:
+    """
+    Manages the polling loop using last-run timestamps instead of a complex scheduler.
+    """
+    def __init__(self, api_client, wallets, shared_whale_data_dict=None): # Kept arg for compatibility
+        self.tracker = WalletTracker(api_client, wallets)
+        self.last_poll_times = {key: 0 for key in POLL_INTERVALS}
+        self.poll_intervals = POLL_INTERVALS
+        logging.info("WhaleTrackerRunner initialized to save to JSON file.")
+
+    def update_shared_data(self):
+        """
+        This function is no longer called by the run loop.
+        It's kept here to prevent errors if imported elsewhere, but is now unused.
+        """
+        logging.debug("No shared dict, saving data to JSON file.")
+        self.tracker.save_data_to_json()
+
+
+    def run(self):
+        logging.info("Starting main polling loop...")
+        while True:
+            try:
+                now = time.time()
+                
+                if now - self.last_poll_times['reload_wallets'] > self.poll_intervals['reload_wallets']:
+                    self.tracker.reload_wallets()
+                    self.last_poll_times['reload_wallets'] = now
+                
+                if now - self.last_poll_times['core_data'] > self.poll_intervals['core_data']:
+                    self.tracker.poll_core_data()
+                    self.last_poll_times['core_data'] = now
+                    
+                if now - self.last_poll_times['open_orders'] > self.poll_intervals['open_orders']:
+                    self.tracker.poll_open_orders()
+                    self.last_poll_times['open_orders'] = now
+                    
+                if now - self.last_poll_times['account_metrics'] > self.poll_intervals['account_metrics']:
+                    self.tracker.poll_account_metrics()
+                    self.last_poll_times['account_metrics'] = now
+                    
+                if now - self.last_poll_times['ledger_updates'] > self.poll_intervals['ledger_updates']:
+                    self.tracker.poll_ledger_updates()
+                    self.last_poll_times['ledger_updates'] = now
+                    
+                if now - self.last_poll_times['save_data'] > self.poll_intervals['save_data']:
+                    self.tracker.save_data_to_json() # <-- NEW
+                    self.last_poll_times['save_data'] = now
+                    
+                # Sleep for a short duration to prevent busy-waiting
+                time.sleep(1)
+            
+            except Exception as e:
+                logging.critical(f"Unhandled exception in main loop: {e}", exc_info=True)
+                time.sleep(10)
+
+def setup_logging(log_level_str: str, process_name: str):
+    """Configures logging for the script."""
+    if not os.path.exists(LOGS_DIR):
+        try:
+            os.makedirs(LOGS_DIR)
+        except OSError as e:
+            print(f"Failed to create logs directory {LOGS_DIR}: {e}")
+            return
+    
+    level_map = {
+        'debug': logging.DEBUG,
+        'normal': logging.INFO,
+        'off': logging.NOTSET
+    }
+    log_level = level_map.get(log_level_str.lower(), logging.INFO)
+
+    if log_level == logging.NOTSET:
+        return
+
+    handlers_list = [logging.FileHandler(LOG_FILE, mode='a')]
+    
+    if sys.stdout.isatty():
+        handlers_list.append(logging.StreamHandler(sys.stdout))
+    
+    logging.basicConfig(
+        level=log_level,
+        format=f"%(asctime)s.%(msecs)03d | {process_name:<20} | %(levelname)-8s | %(message)s",
+        datefmt='%Y-%m-%d %H:%M:%S',
+        handlers=handlers_list
+    )
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser(description="Hyperliquid Whale Tracker")
+    parser.add_argument("--log-level", default="normal", choices=['off', 'normal', 'debug'])
+    args = parser.parse_args()
+
+    setup_logging(args.log_level, "WhaleTracker")
+    
+    # Load wallets to track
+    wallets_to_track = []
+    try:
+        with open(INPUT_FILE, 'r') as f:
+            wallets_to_track = json.load(f)
+        if not isinstance(wallets_to_track, list) or not wallets_to_track:
+            raise ValueError(f"'{INPUT_FILE}' is empty or not a list.")
+    except (FileNotFoundError, json.JSONDecodeError, ValueError) as e:
+        logging.critical(f"Failed to load '{INPUT_FILE}': {e}. Exiting.")
+        sys.exit(1)
+
+    # Initialize API client
+    api_client = HyperliquidAPI(base_url=API_ENDPOINT)
+    
+    # Initialize and run the tracker
+    runner = WhaleTrackerRunner(api_client, wallets_to_track, shared_whale_data_dict=None) 
+    
+    try:
+        runner.run()
+    except KeyboardInterrupt:
+        logging.info("Whale Tracker shutting down.")
+        sys.exit(0)
+