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Trading Journal & Coach

The newest genuinely-useful category of AI-for-trading is not predicting the market — it is analysing your own behaviour. The Trading Journal turns your history of runs and backtests into honest feedback so you can improve the strategy you already have.

Open AI → Trading Journal (/journal).

What it surfaces​

From your instances (runs and backtests) it computes, deterministically:

  • Win / loss / failure counts and win rate across your backtests;
  • Behavioural insights — the leaks that quietly cost retail traders:
    • Over-concentration — most of your activity is in one symbol;
    • Repeated failures — a high share of runs failed to build or configure;
    • Losing bias — more losing than winning backtests (with a nudge to run the Integrity Lab and check the edge is real);
    • a clean bill of health when none of the above applies.
GET /api/journal

Why it is reliable​

The behavioural analysis is pure, deterministic domain code (Core.Journal) with no infrastructure dependency — unit-tested for over-concentration, repeated failures, losing bias, the balanced case and the empty account. The facts come first; the AI coach (Portfolio Digest) is an optional narrative layer on top, gated on the Anthropic API key, so the journal works fully without AI configured.