The code you've provided outlines a systematic approach to implementing and backtesting a trend-following strategy using local linear regression (LLR) for estimating the slope of price movements. Here's a breakdown of what each part does:
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Data Preparation:
- The
prepare_datafunction loads historical stock prices from a CSV file, calculates daily logarithmic returns, and adds a column indicating whether the market is trending up or down based on the sign of the return.
- The
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Slope Estimation:
- The
estimate_slopefunction computes the slope of the price series using local linear regression (LLR). This involves fitting a line to a window of data around each point and calculating the slope at that point. - The
adaptive_bandwidthfunction dynamically adjusts the bandwidth used in the LLR based on market conditions, which can improve the robustness of the trend detection.
- The
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Signal Generation:
- The
generate_signalsfunction converts the estimated slopes into trading signals (-1 for short positions, 0 for flat positions, and +1 for long positions) by comparing them to a threshold value.
- The
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Backtesting:
- The
backtestfunction simulates
- The
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