STAR Market Stock Prediction

Machine learning model for predicting next-day returns on China's STAR Market (科创板), achieving 89.9% annualized return over backtesting period (2019–2025).

Results

Metric	Value
Annualized Return	89.9%
Daily Alpha vs STAR 50	+0.54%
Maximum Drawdown	31.8%
Backtest Period	Jul 2019 – Aug 2025

Approach

Data

• 556,000+ datapoints across 589 stocks on the STAR Market
• Daily OHLCV data plus order flow metrics from Wind/Tushare

Features (8 engineered)

• return_1day — Previous day return
• price_amplitude_2days — 2-day price range / close
• high_low_price_amplitude_diff_5days — 5-day high-low spread trend
• trade_count_1day — Number of trades
• amount_per_trade_5days — Average trade size (5-day)
• turnover_adjusted_by_price_amplitude_1day — Volume normalized by volatility
• big_order_opening_5days — Large order flow at open
• small_order_2days — Retail order flow proxy

Model

• Random Forest Regressor predicting next-day returns (used for daily stock ranking)
• Compared against XGBoost baseline
• Chronological train/test split (no lookahead bias)

Evaluation

• Walk-forward backtesting
• Feature importance analysis
• Risk metrics: Sharpe ratio, max drawdown, win rate

Repository Structure

star_market/
├── src/
│   └── ml.ipynb          # Main notebook: EDA, feature engineering, model training, backtesting
├── tests/                # Unit tests
└── README.md

Tech Stack

• Python, pandas, NumPy
• scikit-learn (Random Forest)
• XGBoost (baseline comparison)
• matplotlib, seaborn (visualization)

Usage

# Install dependencies
pip install pandas numpy scikit-learn xgboost matplotlib seaborn

# Run notebook
jupyter notebook src/ml.ipynb

Disclaimer

This project is for educational and research purposes only. Past performance does not guarantee future results. Not financial advice.