Miya-Su/Quantitative-Trading
量化投资
This project helps quantitative traders and researchers evaluate stock-picking strategies. It takes historical stock data (like monthly or weekly prices and fundamental indicators) and applies machine learning to predict stock price movements. The output is a tested trading factor that can be used to inform investment decisions or build automated trading systems. This is ideal for quantitative analysts or portfolio managers who want to develop and validate new alpha factors.
255 stars. No commits in the last 6 months.
Use this if you need a structured approach and code examples to test new quantitative trading factors and predict stock price trends.
Not ideal if you are looking for ready-to-use trading signals or a comprehensive financial theory textbook.
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255
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87
Language
Jupyter Notebook
License
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Category
Last pushed
Mar 11, 2019
Commits (30d)
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