TradingBot

TradingBot is a flexible and extensible platform for developing, backtesting, and deploying automated trading strategies. It supports real-time, virtual, and historical backtesting environments. All trading activities are recorded for performance analysis. The accompanying dashboard provides a comprehensive view of strategy performance, including portfolio metrics, interactive charts with trade indicators, and detailed order history.

✨ Features

• Strategy Management: Create and manage multiple trading strategies with customizable parameters.
• Multiple Trading Modes:
  • Backtesting: Test strategies against historical market data.
  • Virtual Trading: Paper trade with real-time market data without risking real capital.
  • Live Trading: Deploy profitable strategies to trade with a real brokerage account.
• Performance Dashboard:
  • Visualize portfolio performance over time.
  • Interactive candlestick charts with buy/sell signals and strategy metadata.
  • Detailed transaction logs for each portfolio.
• Extensible Strategy Framework: Easily implement new trading strategies by extending a base strategy class.
• AI-Powered Strategy Improvement: Download portfolio data to get suggestions for strategy improvements from a Large Language Model (LLM).

🏗️ Architecture

The application is composed of a Python backend, a database, and a web-based dashboard.

• Backend (main.py): A Python server that exposes a REST API for the dashboard and handles all core logic, including strategy execution, portfolio management, and data processing.
• Dashboard: A web interface for users to interact with the system. It allows for creating strategies, running backtests, and analyzing performance.
• Database: A persistent store for all user-created strategies, portfolios, and trade orders.

Core Modules

• Strategy: Defines the base interface for all trading strategies. Each strategy implements logic to process market data (on_tick) and determine whether to buy, sell, or hold.
• Portfolio: Manages the state of a trading account, including its value, positions, and historical data. It executes trades based on signals from its assigned strategy.
• Backtest: Orchestrates the process of running a strategy against historical data. It feeds historical ticks to a portfolio and records the resulting performance.
• Orders: Handles the creation and storage of trade orders. It includes logic to place orders with a real brokerage API for live trading.

🚀 Getting Started

(This section is a placeholder. You can add installation and setup instructions here.)

1. Clone the repository:

   git clone <repository-url>
   cd TradingBot

2. Install backend dependencies:

   pip install -r requirements.txt

3. Install frontend dependencies:

   cd dashboard
   npm install

4. Run the application:

   # Run backend
   python main.py
   
   # Run frontend
   cd dashboard
   npm start

📋 API Endpoints

The backend exposes several endpoints to be consumed by the dashboard.

• get_strategy_objects: Returns a list of available strategy classes (e.g., BollingerBand).
• create_strategy_db: Creates a new strategy instance in the database.
• get_strategies_from_db: Retrieves all user-created strategies from the database.
• get_portfolios_by_strategy: Fetches portfolios associated with a specific strategy.
• get_orders_by_portfolio: Gets all orders for a given portfolio.
• backtest_strategy: Initiates a new backtest for a given strategy, creating a new portfolio.
• deploy_strategy: Deploys a portfolio for virtual or live trading.

🗄️ Database Schema

• strategies
  • id: Primary Key
  • name: User-defined name for the strategy instance.
  • strategy_class_name: The name of the strategy class (e.g., "BollingerBand").
  • parameters: JSON object of modified parameters.
• portfolios
  • id: Primary Key
  • name: User-defined name for the portfolio.
  • strategy_id: Foreign Key to strategies.
  • starting_value: The initial capital.
  • current_value: The current market value of the portfolio.
• orders
  • id: Primary Key
  • portfolio_id: Foreign Key to portfolios.
  • stock: The stock ticker.
  • order_type: backtest, virtual, or real.
  • transaction_type: buy or sell.
  • price: Execution price.
  • timestamp: Time of the transaction.
  • portfolio_value: The value of the portfolio at the time of the order.
  • metadata: JSON object for strategy-specific data to display on the chart.

🤝 Contributing

Contributions are welcome! Please feel free to submit a pull request or open an issue for any bugs, feature requests, or improvements.

📄 License

This project is licensed under the MIT License. See the LICENSE file for details.