Trading Bot

A trading bot built using Python and TensorFlow to automate trading strategies.

Table of Contents

• Introduction
• Features
• Installation
• Usage
• Configuration
• Contributing
• License

Introduction

The trading bot is designed to automate trading strategies using historical data, technical indicators, and machine learning. It connects to the MetaTrader 5 platform, retrieves historical data, calculates indicators, generates trade signals, executes trades, and updates a neural network model based on trade outcomes.

Features

• Retrieval of historical data from MetaTrader 5
• Calculation of technical indicators (RSI, MACD, etc.)
• Detection of double tops and bottoms patterns
• Generation of trade signals based on indicators, patterns, and trend direction
• Execution of trades with risk management
• Update of a TensorFlow neural network model based on trade outcomes
• Visualization of data and trade signals

Installation

1. Clone the repository:

   git clone https://github.com/your-username/trading-bot.git
   

2. Install Docker and Docker Compose on your system.

3. Build the Docker image and start the container:

   cd trading-bot
   docker-compose up -d --build
   

Usage

1. Ensure that the Docker container is running.

2. Access the running container:

   docker exec -it trading-bot_app_1 bash
   

3. Inside the container, run the trading bot:

   python main.py
   

4. The trading bot will start executing the trading strategies based on the predefined logic.

5. Monitor the bot's output and visualizations.

6. To stop the bot, use Ctrl + C in the terminal.

Configuration

The trading bot can be customized and configured by modifying the following files:

• main.py: Contains the main logic for running the trading bot.
• config.py: Defines the configuration parameters such as symbol, timeframe, lot size, stop loss, take profit, etc.
• indicators.py: Defines additional technical indicators and patterns to be used.
• preprocess.py: Handles data preprocessing and feature engineering.
• model.py: Defines the structure and training of the neural network model.
• docker-compose.yml: Configures the Docker container for running the trading bot.

Contributing

Contributions are welcome! If you encounter any issues or have suggestions for improvements, please feel free to submit a pull request or create an issue in the repository.

License

This project is licensed under the MIT Licence.