The Warmup Period Problem: Why Your Python Backtest Doesn't Match Live Trading
📰 Dev.to · Ashish Agarwal
Learn how to identify and address the warmup period problem in Python backtesting to improve live trading accuracy
Action Steps
- Run a pandas backtest with a simple moving average (SMA) crossover strategy
- Compare the backtest results with a live-trading simulator to identify discrepancies
- Configure the backtest to account for the warmup period by adding a initial data buffer
- Test the revised backtest with the warmup period adjustment to evaluate its impact on results
- Apply the warmup period adjustment to other backtesting strategies to improve overall accuracy
Who Needs to Know This
Quantitative traders and data scientists can benefit from understanding the warmup period problem to refine their backtesting strategies and improve live trading performance
Key Insight
💡 The warmup period problem can significantly impact the accuracy of Python backtests, and accounting for it can improve live trading performance
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🚨 Don't let the warmup period problem sabotage your live trading results! 🚨
Full Article
I ran the same SMA crossover strategy through a pandas backtest and a live-trading simulator. Same...
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