Skip to content

Latest commit

 

History

History
112 lines (90 loc) · 4.67 KB

README.md

File metadata and controls

112 lines (90 loc) · 4.67 KB

Algorithm-Development-Template

Trading Algorithms Template Repository

This repository serves as a template for developing, testing, and deploying trading algorithms. It provides a structured framework that includes components for backtesting, live trading, and a dashboard interface. The core utilities, configurations, and strategies are organized to ensure modularity and ease of maintenance.

Directory Structure


├── README.md
├── backtesting
│   ├── backTesting-Algo.ipynb
│   ├── data
│   └── dataFormaters
├── dashboard
│   └── algoDashboard
│       ├── app.py
│       ├── static
│       │   └── styles.css
│       └── templates
│           └── index.html
├── docs
│   └── setup_guide.md
├── liveEngine
│   ├── config
│   │   └── config.ini
│   ├── data
│   │   ├── memoryData
│   │   └── savedData
│   ├── logs
│   │   └── logs.log
│   └── src
│       ├── core
│       ├── main.py
│       ├── strategy
│       └── utils
└── requirements.txt

Folder Descriptions

  • backtesting: Contains notebooks, scripts, and data for simulating trading strategies using historical data. This helps in evaluating the performance of algorithms before deploying them live.

    • backtesting-algo.ipynb: Jupyter notebook for running backtests and analyzing results.
    • data: Stores historical market data used for backtesting.
    • data-formatters: Scripts or tools to format and preprocess raw data for backtesting.

  • dashboard: Houses the web-based dashboard for visualizing algorithm performance and monitoring live trading activities.

    • algo-dashboard: Main directory for dashboard code.
      • static: Contains static files like CSS, JavaScript, and images.
      • templates: HTML templates for the dashboard's user interface.

  • docs: Documentation for setting up, configuring, and using the algorithms.

    • setup_guide.md: Guide to help users set up the environment and get started with the algorithms.

  • live-engine: Core engine for live trading, including configuration, data management, and execution logic.

    • logs: Directory for storing log files generated during live trading.

      • logs.log: Example log file for tracking trades, errors, and other events.

    • config: Configuration files required for setting up and running the algorithms.

      • config.ini: Sample configuration file with parameters for live trading, such as API keys, trading pairs, risk management settings, etc.

    • data: Folder for storing runtime data, including in-memory data and saved data for later analysis.

      • memory-data: Temporary data stored during live trading.
        • saved-data`: Persistent data storage for logs, historical trade data, and analysis results.

    • src: Source code directory containing all modules required for live trading.

      • core: Core functionalities shared across different strategies, such as order execution and data fetching.
      • strategy: Implementations of specific trading strategies.
      • utils: Utility functions and helper scripts.
      • main.py: Starting point of the script

  • requirements.txt: Lists all Python dependencies required to run the algorithms. Install using pip install -r requirements.txt.

Getting Started

To get started with this repository, follow these steps:

  1. Clone the Repository:

    git clone https://github.com/your-username/trading-algorithms-template.git
cd trading-algorithms-template

  2. Install Dependencies: Make sure you have Python installed. Then, install the required packages using pip:

    pip install -r requirements.txt

  3. Configure Your Environment: Edit the config/config.ini file to set up your API keys, trading pairs, and other parameters according to your trading strategy and environment.

  4. Run Backtests: Use the Jupyter notebook in the backtesting folder to test your trading strategies with historical data. Open the notebook with:

    jupyter notebook backtesting/backtesting-algo.ipynb

  5. Deploy Live Trading: Ensure your environment is properly configured and run the live trading engine. Navigate to the live-engine/src folder and execute your main trading script:

    python live-engine/src/main.py

  6. Monitor Performance: Launch the dashboard to monitor your trading algorithm in real-time. Navigate to the dashboard directory and start the web server:

    python -m http.server

cd dashboard/algoDashboard

how to run dashboard: python app.py