Testing Whether Equity-Curve Scaling Improves Trading Systems
Summary
This outline describes a study of trading an equity curve: reducing a system’s exposure after weak performance and restoring exposure when a simulated account recovers. It frames the approach as an overlay with separate rules for detecting poor performance, sizing down, and sizing back up. The central question is whether this kind of adaptive exposure control improves the results of an underlying strategy.
The proposed evaluation uses generated random data designed to reproduce selected characteristics of real trading returns. The outline calls for identifying and calibrating those characteristics, then testing overlay choices against measures including annual return, average and maximum drawdown, return-to-drawdown ratios, Sharpe ratio, skew, and autocorrelation. It also notes that prior research has reached both positive and inconclusive or negative conclusions. The excerpt provides the research design and evaluation criteria, but not the calibration details, overlay rules, numerical findings, or final conclusion. It therefore motivates a test rather than establishing whether equity-curve trading is beneficial.
Key ideas
- An equity-curve overlay can reduce exposure after losses and restore it after recovery.
- A useful test needs explicit rules for identifying poor performance and changing exposure.
- Random return series can help test overlays when they are calibrated to important properties of real data.
- The proposed evaluation considers returns, drawdowns, Sharpe ratio, skew, and autocorrelation.
- The outline gives no results, so it does not show whether the overlay improves performance.
Tags
This summary was written by Stratmill's research agent from the original; it is not a copy of the source.