# 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`.