★ 20 Ways to Generate Stock Investment Profits with AI - 2. Building a Quantitative Trading Strategy with Machine Learning

 

2. Building a Quantitative Trading Strategy with Machine Learning

Introduction
Quantitative trading uses algorithms to make systematic buy/sell decisions. Machine learning enhances these strategies by adapting to market regime shifts.

Key Elements

• Signal Generation: ML models forecast next‐day returns using features like price momentum, volatility, and macro indicators.

• Position Sizing: Determine allocation via Kelly criterion or risk‐parity weights.

• Execution: Integrate slippage and smart order routing to minimize transaction costs.

Implementation Guide

1. Data Preparation: Acquire OHLCV, volume, macro data (interest rates, CPI) and align on a daily timeframe.

2. Labeling: Define a binary target: +1 if next‐day return >0.3%, else 0.

3. Model Selection: Train a LightGBM classifier; use 5‐year rolling‐window cross‐validation to prevent look‐ahead bias.

4. Metrics Tracking: Evaluate Sharpe ratio, max drawdown, hit rate, and average return per trade.

5. Deployment: Containerize with Docker; schedule daily inference on AWS Lambda, triggering buy/sell orders via broker API (Alpaca, Interactive Brokers).

Case Study
A LightGBM‐based strategy on S&P 500 constituents achieved a backtested Sharpe of 1.7 from 2016–2022, with a 58% accuracy rate.

Conclusion
Machine learning–driven quant strategies offer systematic, data‐driven decision-making, enabling you to capture market inefficiencies while managing risk algorithmically.