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