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DiTus 1a95fe1caa Clean up unused files, organize structure, update docs

- Delete obsolete files: data_fetcher_old.py, market_old.py, base_strategy.py (root),
  strategy_sma_cross.py, and old architecture remnants (address_monitor.py,
  position_monitor.py, trade_log.py, wallet_data.py, whale_tracker.py)
- Delete zero-byte Docker artifacts and runtime files (clp_hedger.log,
  clp_hedger/hedge_status.json)
- Move one-off utility scripts to scripts/ directory
- Move example/template files to .temp/ directory
- Update .gitignore: add entries for clp_hedger.log, clp_hedger/hedge_status.json,
  Docker layer hash files, Using, Running, and backups/
- Update .dockerignore: add clp_hedger.log, clp_hedger/hedge_status.json, backups/
- Create example config files: _data/strategies.json.example,
  _data/backtesting_conf.json.example, _data/coin_precision.json.example
- Update GEMINI.md: remove outdated session summaries and duplicate review section
- Update review.md: add cleanup status section, update remaining recommendations
- Update MIGRATION_PLAN.md: mark completed phases, update file references
- Update DOCKER_MIGRATION_GUIDE.md: update import_csv.py path reference

2026-08-05 09:50:36 +02:00

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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

Create and activate a virtual environment:

# For Windows
python -m venv .venv
.\.venv\Scripts\activate

# For macOS/Linux
python3 -m venv .venv
source .venv/bin/activate

Install dependencies:
```
pip install -r requirements.txt
```
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.
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:

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.