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.

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

Clone the Repository

   git clone [https://github.com/your-username/your-repo-name.git](https://github.com/your-username/your-repo-name.git)
   cd your-repo-name

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
```

Getting Started: Configuration

Before running the application, you must configure your wallets, agents, and API keys.

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.
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.
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
```
   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.
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:

   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.