Mike
85 lines
2.7 KiB
Markdown
Executable File
85 lines
2.7 KiB
Markdown
Executable File
# Trading Bot
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[](LICENSE)
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A trading bot built using Python and TensorFlow to automate trading strategies.
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## Table of Contents
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- [Introduction](#introduction)
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- [Features](#features)
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- [Installation](#installation)
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- [Usage](#usage)
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- [Configuration](#configuration)
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- [Contributing](#contributing)
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- [License](#license)
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## Introduction
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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.
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## Features
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- Retrieval of historical data from MetaTrader 5
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- Calculation of technical indicators (RSI, MACD, etc.)
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- Detection of double tops and bottoms patterns
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- Generation of trade signals based on indicators, patterns, and trend direction
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- Execution of trades with risk management
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- Update of a TensorFlow neural network model based on trade outcomes
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- Visualization of data and trade signals
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## Installation
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1. Clone the repository:
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```shell
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git clone https://github.com/your-username/trading-bot.git
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```
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1. Install Docker and Docker Compose on your system.
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1. Build the Docker image and start the container:
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```shell
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cd trading-bot
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docker-compose up -d --build
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```
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## Usage
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1. Ensure that the Docker container is running.
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1. Access the running container:
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```shell
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docker exec -it trading-bot_app_1 bash
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```
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1. Inside the container, run the trading bot:
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```shell
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python main.py
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```
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1. The trading bot will start executing the trading strategies based on the predefined logic.
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1. Monitor the bot's output and visualizations.
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1. To stop the bot, use `Ctrl + C` in the terminal.
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## Configuration
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The trading bot can be customized and configured by modifying the following files:
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- `main.py`: Contains the main logic for running the trading bot.
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- `config.py`: Defines the configuration parameters such as symbol, timeframe, lot size, stop loss, take profit, etc.
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- `indicators.py`: Defines additional technical indicators and patterns to be used.
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- `preprocess.py`: Handles data preprocessing and feature engineering.
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- `model.py`: Defines the structure and training of the neural network model.
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- `docker-compose.yml`: Configures the Docker container for running the trading bot.
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## Contributing
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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.
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## License
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This project is licensed under the [MIT Licence](https://opensource.org/license/mit/).
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