Experimental AI Quant Strategies

Welcome to the Experimental AI Quant Strategies repository. This project serves as a collection of algorithmic trading strategies powered by AI, quantitative models, and technical analysis. Our goal is to explore, test, and refine cutting-edge trading strategies that can be used for backtesting, research, and live trading execution.

📂 Strategy Collection

This repository houses various trading strategies, each contained in its respective subdirectory. Every strategy includes:

• A README explaining its methodology.
• Python scripts for backtesting and live trading.
• Configuration files for broker integration.

Example Strategy: Applied SMA Outfit Trading Strategy

The Applied SMA Outfit Trading Strategy is a technical analysis-based strategy that leverages moving average crossovers to generate trading signals. It includes:

• Basic SMA Strategy (10/50/200 crossovers on a 30-minute chart)
• Advanced SMA Strategy (multi-timeframe analysis, RSI filtering, ATR-based stop-loss, and machine learning enhancements)
• Live Trading Integration with brokers such as Schwab, Robinhood, and TradingView

📂 Folder Structure:

Experimental-AI-Quant-Strategies/
│
├── Applied-SMA-Outfit-Trading-Strategy/
│   ├── README.md  # Documentation for this specific strategy
│   ├── basic_sma_outfits.py  # Basic SMA crossover strategy
│   ├── advanced_sma_outfits.py  # Advanced version with 
│   ├── live_trading.py  # Live trading script with broker API 
│   ├── test_sma_outfits.py # Test script for strategy
│   ├── config.py  # API keys & settings
│   ├── requirements.txt  # Dependencies
│
├── Other-Strategies/ (Coming Soon)
│   ├── AI-Trend-Following/
│   ├── Reinforcement-Learning-Trading/
│   ├── High-Frequency-Mean-Reversion/
│
└── README.md  # Parent repository overview

🚀 Future Strategies

This repository will continue to grow with more AI-powered trading models, such as:

• AI-Powered Trend Following: Using machine learning to identify market regimes.
• Reinforcement Learning for Trading: Agents that learn optimal trading actions over time.
• High-Frequency Mean Reversion: Exploiting short-term price inefficiencies.

🔧 Setup & Installation

Clone this repository and navigate to any strategy folder to get started:

git clone https://github.com/YOUR_USERNAME/Experimental-AI-Quant-Strategies.git
cd Experimental-AI-Quant-Strategies/Applied-SMA-Outfit-Trading-Strategy
pip install -r requirements.txt

📊 Contributing & Research

We welcome contributions from traders, quants, and developers who are interested in refining quantitative strategies. If you have a new strategy, submit a pull request or open an issue to discuss your ideas.

This repository is for educational and research purposes only. Algorithmic trading carries financial risk, and past performance does not guarantee future results. Please use caution when deploying strategies in live trading.

---

🔍 Stay tuned for new strategies!