Summary

This repository provides code examples for applying Artificial Intelligence and Machine Learning techniques to algorithmic trading. It targets traders, researchers, and developers seeking practical, hands-on implementations to build and enhance AI-driven trading strategies, offering a comprehensive AI toolbelt for real-world financial market applications.

How It Works

The project offers a curated collection of Python scripts and Jupyter notebooks, each demonstrating a specific AI/ML approach to financial trading. Core methodologies include time-series forecasting, sentiment analysis, pattern recognition, and portfolio optimization, leveraging libraries like MLFinLab, scikit-learn, TensorFlow/Keras, and LLMs (GPT-4, FinBERT). Examples range from detecting price trends and predicting returns to selecting trading pairs and optimizing execution, designed for integration within the QuantConnect ecosystem.

Quick Start & Requirements

• Installation: Clone the repository. Copy example scripts (main.py, research.ipynb) into a QuantConnect project.
• Execution: Run backtests via the LEAN CLI or QuantConnect Cloud/Local Platform. Notebooks can be opened locally.
• Prerequisites: QuantConnect account/platform (Cloud, Local, or LEAN CLI). Local execution requires specific datasets.
• Documentation: Examples serve as direct guidance; no separate docs link provided.

Highlighted Details

• Demonstrates MLFinLab for trend scanning and Bitcoin timing.
• Features PCA and clustering for statistical arbitrage pair selection.
• Includes LLM (GPT-4) and FinBERT for news sentiment analysis.
• Applies CNNs for pattern detection (e.g., head-and-shoulders) and Amazon Chronos for price path forecasting.
• Covers diverse ML models: SVM, HMM, Random Forests, Decision Trees, Gaussian Naive Bayes, and regression techniques.

Maintenance & Community

No specific details on contributors, community channels, or project roadmap are present in the provided text.

Licensing & Compatibility

The repository's license is not specified in the provided text. Compatibility for commercial use or integration with closed-source systems is undetermined.

Limitations & Caveats

Requires a QuantConnect environment for execution. Local runs necessitate managing specific financial datasets. The repository contains code examples rather than a fully integrated, standalone application.