Adaptive Local Linear Regression for Short-Term Trend Following in Growth Stocks

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:

1. Data Preparation:

   • The prepare_data function 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.

2. Slope Estimation:

   • The estimate_slope function 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_bandwidth function dynamically adjusts the bandwidth used in the LLR based on market conditions, which can improve the robustness of the trend detection.

3. Signal Generation:

   • The generate_signals function 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.

4. Backtesting:

   • The backtest function simulates

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