Forecast-Driven Allocation · Portfolio optimization

Turning forecast output into risk-aware allocation.

A Sharpe-ratio optimization framework that turns ML forecast output into disciplined, risk-aware portfolio allocation.

The question

Turning forecast output into risk-aware allocation.

A good forecast is not yet a decision. Between the two sits the question of how much exposure a forecast justifies once its uncertainty and the correlation between assets are taken into account.

Approach

How it works

  1. Forecasts in, weights out. Per-asset forecast series are loaded directly as the input to the allocation problem.

  2. Sharpe-ratio optimization. Weights are chosen to maximise the risk-adjusted return implied by the forecasts, keeping allocation disciplined rather than conviction-driven.

Stack & sources

Under the hood

Pythonmean–variance optimization
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