ML trading models

01 / Challenge

The operating constraint

Market models are especially vulnerable to leakage, unstable labels, optimistic costs and experiments that cannot be reproduced once conditions change.

02 / Solution

A system, not a patch

We structure datasets, features and evaluation around temporal integrity. Models advance only through explicit offline and shadow gates, with production routing remaining outside the research environment.

03 / Components

Core building blocks

• 01Point-in-time datasets
• 02Deterministic feature pipelines
• 03Walk-forward evaluation
• 04Promotion and monitoring gates

04 / Business value

What the architecture enables

• 01Comparable experiments
• 02Visible assumptions and costs
• 03Reduced leakage risk
• 04Controlled path from research to operations