Correlations

The correlation matrix shows how your strategies move together — the basis of real diversification. It is computed as the Pearson correlation of daily P/L across the visible strategies.

Options

Period

The window to measure over: 1M, 3M, 6M, 1Y, 2Y, the full range (MAX), YTD, or a custom range.

Weekly resample

Aggregate to weekly P/L before correlating, to cut daily noise.

Weekday filters

Active weekday filters are respected, so the matrix matches your filtered portfolio.

Days where a strategy has no trade are treated as zero P/L so series align over the chosen window.

Reading the matrix

Correlation runs from −1 to +1:

Range	Meaning
≥ +0.4	strategies move together → concentration risk
+0.15 to +0.4	partial overlap
−0.15 to +0.15	largely independent
−0.4 to −0.15	offset each other → diversifying
≤ −0.4	strong hedge

A diversification score summarises each strategy's average absolute correlation to the others — low means it genuinely diversifies the book; high means it largely duplicates exposure you already have.

Diversification is a portfolio decision

Two strong strategies that are highly correlated add risk without much smoothing. A modest strategy with low or negative correlation can improve the portfolio's drawdown profile more than another copy of your best performer.

Pair detail

Click any off-diagonal cell to open the detail panel for that pair. Everything in it is computed on the same window, weekly setting, and weekday filters as the matrix, so the headline ρ matches the cell exactly.

ρ daily / weekly

The correlation on both timescales, side by side. A pair can be uncorrelated day to day but correlated week to week — divergence means the relationship is horizon dependent.

Spearman

Rank correlation, which ignores outliers. If it is far from Pearson, a few extreme periods are driving the number.

Rolling correlation

ρ over a moving window (30 daily or 13 weekly observations) — shows whether the correlation is stable or shifts between regimes.

Scatter and beta

Each period's P/L of one strategy against the other, with the regression line. Beta is its slope; R² is the share of variance explained.

Co-movement

Periods both up / both down / opposite, plus the share where both lose and their average combined loss — the tail risk a single ρ hides.

Risk reduction

Volatility saved by holding both legs at equal risk versus apart. It grows as ρ falls.

Combined Sharpe

Sharpe of the equal-risk blend versus each leg alone, alongside each strategy's volatility, total P/L and the common date range.

Drawdown overlay

Underwater curves of each leg and the equal-dollar combined book. When the two draw down at different times the combined curve is shallower than the sum — the offset percentage quantifies how much they cushion each other.

Lead and lag

Cross-correlation across shifted lags: a peak to the right means the first strategy leads the second, to the left the reverse. Usually weak on P/L — a probe for a knock-on effect, not a headline metric.

Equal-volatility weighting

The combined metrics (risk reduction, combined Sharpe) normalise each leg to the same volatility before blending, so they describe the intrinsic relationship of the pair — independent of the weights you give the strategies in the rest of the book.