## 2026-04-07

- Added `dragon_excel_export.py` to export v2 strategy results into an Excel workbook styled off the original `龙泉回测20260109.data.xlsx`.
- Export output file: `dragon_v2_compare_export.xlsx`.
- Export workbook keeps the original sheets and adds three v2 comparison sheets:
- `龙泉_v2_workbook_preserving`
- `龙泉_v2_alpha`
- `龙泉_v2_RC1`
- Data source uses freshly fetched `399673` daily history through the latest available market bar and recomputes indicators plus branch events locally.
- Export sheet layout intentionally stays close to the original workbook:
- original header rows retained
- KDJ / QL signal columns retained
- real / auxiliary strategy operations written into the same operation-direction column
- in-position floating PnL rows and yearly summary rows are also generated
- Current generated workbook run used latest available bar `2026-04-03`.

- User then requested an overfitting-risk inspection on dragon v2.
- Current judgment:
- not a trivial black-box overfit, because walk-forward / local sensitivity / execution-latency stress all remain directionally supportive
- but still carries medium-to-high overfitting risk because the strategy tree is complex, the parameter surface is wide, and multiple weak families remain sample-dependent
- key hard risk buckets:
- `deep_oversold_*` family
- `predictive_b1_break_*` bridge logic
- broad historical iterative tuning path dependence around workbook alignment

- serious code-based overfitting review completed on 2026-04-07.
- verdict: not a fake strategy, but still medium-to-high overfitting risk due to high rule-tree complexity, 102-field config surface, and several low-sample specialized families.
- strongest stable core: glued_buy and its refined selective filter family.
- main fragile families: deep_oversold_* and predictive_b1_break_* bridge logic; also one-off reentry/exit rules should be treated as provisional rather than core alpha.

- Added `dragon_overfit_trade_map.py` and generated `dragon_overfit_trade_map.html`.
- This new HTML visualizes full historical trades against the price curve, supports branch switching, and explicitly highlights trades judged as medium/high overfitting risk.
- Current first-pass counts from the generated report:
- `RC1`: total trades `94`, high-risk `10`, medium-risk `12`
- `alpha_first_selective_veto`: total trades `105`, high-risk `10`, medium-risk `14`
- `workbook_preserving`: total trades `109`, high-risk `8`, medium-risk `20`

- Built an external consultation bundle for `dragon/v2`:
- folder: `dragon_v2_consult_2026-04-07/`
- zip: `dragon_v2_consult_2026-04-07.zip`
- bundle contents:
- Chinese bottleneck/risk memo
- suggested ChatGPT Pro prompt
- core source files
- branch / RC1 parameter snapshots
- key evidence reports (`rule_ablation`, `threshold_perturbation`, `walk_forward`, `deep_oversold`, `predictive_break`)
- minimal supporting data and workbook exports
- Main consulting judgment written into the bundle:
- strongest stable core remains `glued_buy`
- biggest architecture bottleneck is the large order-sensitive rule tree in `dragon_strategy.py`
- biggest quant fragility still concentrates in `deep_oversold_*` and `predictive_b1_break_*`