Check shared exposure
A correlation matrix reveals strategies that move together. Effective bets help distinguish the number of strategies from the amount of independent exposure.
Portfolio construction
The AI portfolio agent reviews candidates in the context of the existing book. Portfolio analytics compare return relationships, allocation choices and risk contributions using the available overlapping history.

Explore how this stage contributes to the AI trading strategy factory.
A correlation matrix reveals strategies that move together. Effective bets help distinguish the number of strategies from the amount of independent exposure.
Explore equal weighting, inverse-volatility weighting and risk parity. Each changes how the historical portfolio distributes capital and risk.
Read portfolio Sharpe, drawdown, diversification and risk contributions together. Promotion also considers existing strategies and available paper capacity.
Portfolio analysis relies on enough overlapping returns. Missing evidence should stay visible instead of turning into a reassuring diversification score.
Compare strategies over a shared observation window.
Inspect correlations and effective independent bets.
Study allocation and risk contributions together.
Combine the evidence with AI portfolio review and release checks.
Read the relationships before interpreting a combined return. Correlation and risk contribution describe different parts of the portfolio.
A and B in this illustration share much of their return behavior. Adding C changes the mix of exposures, but the weights and individual volatilities still determine how much risk each contributes.
The portfolio selector changes an analytical comparison. Choosing a scheme does not submit exchange orders or authorize live capital.
Scroll horizontally to see all columns.
| Scheme | How it allocates | Interpretation |
|---|---|---|
| Equal weight | Gives each strategy the same capital weight. | Simple capital balance does not imply equal risk. |
| Inverse volatility | Gives lower-volatility strategies more weight. | Uses individual volatility; correlations still affect combined risk. |
| Risk parity | Seeks to balance contributions to portfolio variance. | Depends on estimated covariance and the available history. |
The Portfolio section in the signed-in workspace analyzes your graduated strategies. It needs sufficient overlapping return history to calculate meaningful portfolio statistics.
No. It recalculates the historical portfolio analysis. It does not change an exchange account, submit orders or authorize a live allocation.
Effective bets summarize how much independent variation is present in the correlation matrix. Several highly correlated strategies can behave much more like one bet than several independent bets.
Volatility and correlations determine how each strategy contributes to portfolio variance. A small capital allocation can still contribute a large share of risk.
No. It evaluates the available evidence. Correlations and losses can change in stressed conditions, so historical diversification is not a guarantee of future protection.
Signal books combine forecasts within a strategy. Portfolio analysis combines the returns of multiple strategies. They address different levels of exposure and should be reviewed together.
See how AI agents, testing engines and execution controls work together across the strategy factory.
Stratmill is an AI trading strategy platform, not financial advice or a broker. Backtest and paper results are hypothetical. Trading involves risk of loss.