Scoring Multi-Strategy Portfolios with Correlation and Trading Coverage

Summary

This article describes an MQL5 tool for assessing how multiple Expert Advisors interact as a portfolio. It reads daily profit-and-loss series and trading-time metadata from CSV files, calculates pairwise Pearson correlations, and examines activity by hour and weekday. The proposed composite score combines inter-strategy correlation with temporal coverage and asset-class diversity, aiming to expose overlapping exposures and periods when the portfolio is inactive.

The examples explain why strategies on different currency pairs may still share common risk and suggest favoring additions that improve the portfolio as a whole. The article provides implementation detail and illustrative thresholds, but the supplied text does not show a full empirical validation of the composite score or demonstrate that it predicts future robustness. Pearson correlation captures linear co-movement over the sampled period; it can miss changing relationships and tail dependence. The CSV inputs and chosen scoring weights also shape the output, so the score is a screening aid rather than a guarantee of diversification.

Key ideas

• The scorer compares daily P&L series across strategies using a Pearson correlation matrix.
• It supplements return correlation with hourly and weekday trading-activity coverage.
• The proposed portfolio score also considers diversification across asset classes.
• A strategy’s contribution should be judged by its effect on the portfolio, not only by its standalone performance.
• Correlation and composite scores depend on the sample and scoring choices and do not guarantee future diversification.

Tags

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