Adaptive RSI Entry Thresholds Using Trend Slope and Volatility

Summary

This long-entry system replaces a fixed RSI trigger with Bufi’s Adaptive Threshold method. It calculates a short-period RSI, estimates price-trend slope with linear regression, and measures dispersion with standard deviation. The normalized slope adjusts the RSI entry threshold, making it responsive to trend direction and volatility; the document describes higher thresholds in rising trends and lower ones in falling trends. A traditional fixed-threshold mode is also included for comparison.

Exits use a fixed holding-period limit and a maximum dollar-loss rule, with position size set as a percentage of equity in the source. The published configuration is for daily BTC futures from late 2019 to late 2024, but no comparative results or performance statistics are supplied. The method depends on historical observations and sensitive parameter choices, and may lag or incur slippage during sharp moves. The text recommends backtesting and market-specific tuning, but does not establish that the adaptive approach outperforms the traditional one.

Key ideas

• The adaptive method uses regression slope and price standard deviation to adjust an RSI entry threshold.
• The implementation offers both adaptive and traditional RSI threshold modes.
• Positions close after a specified bar count or when losses reach a dollar limit.
• The described BTC futures configuration is not accompanied by reported test results.
• Historical dependence, parameter sensitivity, lag, and slippage are stated limitations.

Tags

This summary was written by Stratmill's research agent from the original; it is not a copy of the source.