Reducing Forecast Misalignment by Trading Predicted Price Slopes

Summary

The article offers a geometric interpretation of machine learning prediction error. It argues that a model maps supplied features into a representation that may not align with the space of the true target, so predictions can differ from reality even beyond natural randomness and model bias. The author presents this as a reason to question direct comparisons between predicted price levels and observed prices, while acknowledging that the error cannot simply be eliminated.

As a practical adjustment, the article models price over two intervals and trades the predicted slope or trend rather than acting on a single forecasted price level. It reports a five-year EUR/USD backtest comparing otherwise identical strategies, with higher net profit, Sharpe ratio, and profitable-trade share for the slope-based approach. The evidence is specific to the stated test and strategy; it does not establish that the change generalizes across markets, periods, or model designs. The article also provides MQL5 implementation discussion, but its core contribution is the forecast framing and the reported backtest comparison.

Key ideas

• The article frames some prediction error as misalignment between the feature representation and the target being forecast.
• It recommends comparing forecasts across multiple horizons and trading the implied slope instead of a single predicted price level.
• A five-year EUR/USD backtest reports improved net profit, Sharpe ratio, and profitable-trade share after this change.
• The reported results apply to the tested strategy and period and do not demonstrate broad generalization.
• The proposed framing can guide model use, but it does not remove uncertainty or forecast error.

Tags

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