Combining Momentum, Regression, EMA, and Volatility Signals

Summary

This strategy combines a one-bar rate of change, a 14-bar linear regression, a 50-bar exponential moving average, and a sigmoid transformation of price change. It opens a long position when momentum is positive, regression is above the EMA, and the transformed price change exceeds its midpoint; the opposite conditions close the long. The plotted regression and EMA lines provide visual context for the signals.

The document gives backtest settings for daily BTC/USDT futures from late 2019 to September 2024, but reports no performance results. Its claims about improved accuracy and adaptability are not supported by presented metrics. The logic is simple to describe, but the sigmoid applied directly to raw price change may be scale-sensitive, and the stated volatility adjustment does not calculate volatility in the usual statistical sense. The document itself flags overfitting, indicator lag, parameter sensitivity, and difficulty during sharp volatility or trend changes. It suggests testing dynamic parameters, additional filters, exit rules, and machine-learning methods, without evaluating those proposals.

Key ideas

• The strategy combines rate of change, linear regression, an EMA, and a sigmoid transformation of price change.
• A long signal requires positive momentum, regression above the EMA, and a sigmoid value above its midpoint.
• The EMA is intended to represent longer-term direction, while regression represents shorter-term trend.
• The published backtest settings specify daily BTC/USDT futures data but provide no performance results.
• The document identifies lag, overfitting, parameter sensitivity, and changing market conditions as limitations.

Tags

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