Self-correcting trading algorithms

What we walked into.

A model that only learns from price is fine until the world does something the price has not priced yet.

Macro events break naive strategies, and every strategy drifts. The question is whether it notices its own drift fast enough to matter.

The approach.

1. Step 1

   Every signal carries its own weight, and those weights are re-evaluated against realised outcomes instead of being set once.

2. Step 2

   Deviation between prediction and result feeds straight back into the model, so the system corrects itself rather than waiting for a human review cycle.

3. Step 3

   Macro events are treated as first-class inputs with their own weighting, not as noise to be smoothed away.

Stack and disciplines.

What it delivered.

Algorithms that stay self-steering through changing market regimes.

A steadily tightening error margin, with sub one percent as the working target.

Lessons that travel.

• Drift is guaranteed. The only question is whether the system notices before a human does.

• Macro events deserve their own weight, not a smoothing filter.

• Error margin tightens fastest when deviation feeds straight back rather than waiting for review.