# claude_evening daily review

Analyze today's trading result and write `reports/daily/YYYY-MM-DD.md`.

## Steps

1. Collect data:
   ```bash
   python app/ai/evening.py --print
   ```

2. Review:
   - total trades, win rate, net PnL, fees
   - exit reason distribution: TP1 / TP2 / SL / TIME / FORCE
   - overtrading: daily entry count, repeated stop losses, TIME/FORCE concentration
   - AI filter quality: boosted tickers and blacklists
   - execution quality: missing prices, zero-price rows, inconsistent open positions

3. Strategy changes:
   - Do not edit `app/config.py` directly.
   - If a change looks justified, create `reports/proposals/YYYY-MM-DD_strategy_proposal.md`.
   - Include exact proposed values, evidence, sample size, expected benefit, and risk.
   - If fewer than 30 closed trades support the change, clearly mark the evidence as insufficient.
   - `FORCE_EXIT = "14:50"` is not changeable.

4. Live readiness:
   - at least 30 closed trades
   - recent win rate > 48%
   - MDD better than -10%
   - Sharpe > 1.0
   - stop/kill risk events <= 2
   - If all pass, create `reports/live_ready/YYYY-MM-DD_READY.md`.

5. Discord:
   Send a concise result summary. If a proposal file was created, include that manual approval is required.

6. Git commit & push:
   Stage and commit the daily report (and proposal file if created), then push to origin master.
   ```bash
   git add reports/daily/YYYY-MM-DD.md reports/proposals/YYYY-MM-DD_strategy_proposal.md  # only if files exist
   git commit -m "[YYYY-MM-DD] 저녁 결산 — <trades>건 / <wins>승 <losses>패 / <pnl>원 (<brief exit summary>)"
   git push origin master
   ```