Adaptive Moving-Average Signals Using Expectancy, MFE, and Higher Timeframes
Summary
The document describes an indicator concept that searches across moving-average periods and selects one using historical trade expectancy, combining win rate with average reward. It also proposes using maximum favorable excursion to estimate a typical trend peak as a take-profit reference, and checking higher-timeframe moving-average states to filter entries that conflict with the broader direction. The motivation is that a fixed moving-average period can behave differently in trending and sideways markets.
These are design claims rather than demonstrated results: the document provides no formulas, sample data, backtest, or independent evidence that the optimizer improves trading or estimates exits accurately. Searching many periods on recent data can overfit, and expectancy and excursion estimates may change as regimes shift. The higher-timeframe filter may also delay or reduce signals. Costs, slippage, and validation across assets and periods would matter, but are not discussed in the description.
Key ideas
- The proposed optimizer searches a range of moving-average periods to adapt to changing market behavior.
- It ranks candidate strategies by expectancy, combining win rate and average reward.
- It uses historical maximum favorable excursion to estimate a take-profit reference.
- A higher-timeframe direction check is proposed as a filter for countertrend entries.
- The document provides no backtest evidence, and period selection may overfit changing market data.
Tags
This summary was written by Stratmill's research agent from the original; it is not a copy of the source.