PAT0216/paper-trader
Three ML strategies compete head-to-head on S&P 500 stocks. Runs autonomously with daily GitHub Actions execution and live dashboard. Which model wins? Check the dashboard.
This system helps traders and financial analysts evaluate and compare different quantitative investment strategies for S&P 500 stocks. It takes daily market data and outputs performance metrics, trade histories, and portfolio values for three distinct strategies: momentum, XGBoost machine learning, and LSTM deep learning. This is ideal for quants, portfolio managers, or individual investors interested in automated trading system performance without live capital.
Use this if you want to backtest and continuously monitor the performance of multiple algorithmic trading strategies against the S&P 500 benchmark in a paper trading environment.
Not ideal if you're looking for a platform to execute live trades with real capital, as this is designed for performance comparison and strategy development.
Stars
7
Forks
1
Language
Python
License
MIT
Category
Last pushed
Mar 18, 2026
Commits (30d)
0
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