ababber/pyhou-02-17-2026
A companion repo to "Quantitative Trading: A First Look With QuantConnect". This YouTube series is a reproduction of a live PyHou Meetup from February 17, 2026.
This project helps quantitative traders and financial analysts evaluate how different machine learning models, from classical linear models to modern foundation models, can predict financial markets. You provide historical market data, and the project demonstrates strategies that output potential trading signals and their backtested performance metrics like Sharpe ratio and alpha. It's designed for anyone looking to understand the practical application and effectiveness of ML in algorithmic trading.
Use this if you are a quantitative trader, portfolio manager, or financial researcher curious about applying various machine learning techniques to financial time series and assessing their performance through backtesting.
Not ideal if you are looking for a plug-and-play solution for live trading or require highly statistically significant and rigorously vetted models ready for production without further research.
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HTML
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MIT
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Last pushed
Mar 17, 2026
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