# 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.

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## 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.