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Quantitative-Trading-Strategy-Based-on-Machine-Learning

Firstly, multiple effective factors are discovered through IC value, IR value, and correlation analysis and back-testing. Then, XGBoost classification model is adopted to predict whether the stock is profitable in the next month, and the positions are adjusted monthly. The idea of mean-variance analysis is adopted for risk control, and the volatility of the statistical benchmark index (HS300 Index) is used as a threshold for risk control. Back-testing results: the annual return rate is 11.54%, and the maximum drawdown is 17.91%.

  1. Factors Extracting

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  1. Single Factor Testing

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  1. XGBOOST Backtesting

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  1. Out-of-sample Performance

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