## 2026-04-10

- User asked: `统计下4.8号那天是什么信号过滤了开单`.
- Verified from `daily_reports/dragon_daily_signal_report_2026-04-08.md` and branch status CSV:
- 2026-04-08 had `KDJ buy=True`, `QL buy=True`, `a1=-0.0073`, `b1=-0.0777`, `c1=47.86`.
- No buy event fired (`events_today_count=0`) on all three branches.
- The blocking rule is `buy_block_glued_high_weak_rebound`, exact subclause `ql_rebound_weak_followthrough`.
- Matching condition set:
- `ql_buy=True`
- `35 < c1 < 55` -> `47.86`
- `b1 < -0.06` -> `-0.0777`
- `a1 > -0.013` -> `-0.0073`

- User asked historical return stats for `buy_block_glued_high_weak_rebound`.
- Based on `dragon_glued_high_weak_rebound_blocked_trades.csv` current snapshot:
- total blocked rows `15`, completed added trades `14`, one pending/unclosed row (`2025-11-13`).
- completed sample stats:
- win rate `28.57%` (`4/14`)
- avg return `-0.98%`
- median return `-0.94%`
- best/worst `+2.44% / -3.91%`
- avg holding `6.93` days
- compounded return if all allowed `-13.04%`
- subtype stats:
- `high_zone_weak_b1`: `10` trades, win rate `20%`, avg `-1.38%`
- `mid_zone_very_weak_b1`: `2` trades, win rate `100%`, avg `+1.67%`
- `ql_rebound_weak_followthrough`: `2` trades, win rate `0%`, avg `-1.62%`

- User asked to further split by market regime and list each trade detail.
- Added regime classification at buy date using close/MA20/MA60 from `dragon_indicator_snapshot_full.csv`:
- uptrend: `close >= MA20 >= MA60`
- downtrend: `close <= MA20 <= MA60`
- range: otherwise
- Generated files:
- `dragon_glued_high_weak_rebound_regime_stats.csv`
- `dragon_glued_high_weak_rebound_year_regime_stats.csv`
- `dragon_glued_high_weak_rebound_trade_details_with_regime.csv`
- Completed-trade regime stats (`14` samples):
- downtrend `1` trade, win rate `0%`, avg `-2.20%`
- uptrend `3` trades, win rate `0%`, avg `-0.94%`
- range `10` trades, win rate `40%`, avg `-0.87%`

- User asked to list trades with the same block reason as 2026-04-08 (`ql_rebound_weak_followthrough` under `buy_block_glued_high_weak_rebound`).
- Matching historical blocked trades:
- `2024-06-04 -> 2024-06-06`, return `-1.04%`, hold `2`d, sell reason `early_failed_rebound_exit`.
- `2024-06-17 -> 2024-06-20`, return `-2.20%`, hold `3`d, sell reason `knife_take_profit_2_glued`.
- Summary of this subtype (`2` completed samples): win rate `0%`, avg return `-1.62%`.
- Reconfirmed on user follow-up: historical blocked trades matching 2026-04-08 same reason remain exactly two rows in `dragon_glued_high_weak_rebound_blocked_trades.csv` (2024-06-04 and 2024-06-17).
- Clarified count confusion with user:
- large count refers to top-level rule `buy_block_glued_high_weak_rebound` (historically 15 in release window; 17 full-history on 2026-04-08 analysis; 18 including 2026-04-08 realtime snapshot),
- while 2026-04-08 exact matching subclause `ql_rebound_weak_followthrough` has only 2 historical rows (2024-06-04, 2024-06-17).
- User asked for conceptual difference between the two signals.
- Clarification:
- `buy_block_glued_high_weak_rebound` is the parent veto rule (OR of three clauses).
- `ql_rebound_weak_followthrough` is only one of the three clauses (requires `ql_buy=True`, `35<c1<55`, `b1<-0.06`, `a1>-0.013`), so naturally has much fewer historical hits.
- User asked to list all child veto clauses under the parent rule.
- Confirmed from strategy code: exactly three OR clauses:
- `high_zone_weak_b1`
- `mid_zone_very_weak_b1`
- `ql_rebound_weak_followthrough`
- User requested all interception details for `buy_block_glued_high_weak_rebound`.
- Re-listed full historical blocked rows from `dragon_glued_high_weak_rebound_blocked_trades.csv`:
- total `15`, completed `14`, pending `1` (`2025-11-13`).
- subtype counts: `high_zone_weak_b1=11`, `mid_zone_very_weak_b1=2`, `ql_rebound_weak_followthrough=2`.

- User requested packaging the full strategy for GPT-Pro consultation.
- Created new consultation bundle:
- folder: `dragon_v2_consult_2026-04-10_rc1/`
- zip: `dragon_v2_consult_2026-04-10_rc1.zip`
- Bundle includes:
- updated core source (layered engine, runtime decoupling, governance, daily/forward pipelines)
- parameter snapshots and manifests
- key reports (ablation/sensitivity/stability/rollout/weak-rebound block review)
- recent daily reports (`2026-04-08`, `2026-04-09`)
- blocked-trade detail datasets including regime breakdown
- OpenSpec change packs for recent restructuring/governance work
- Added bundle docs:
- `README_CN.md`
- `consult_context_CN.md`
- `chatgpt_pro_prompt_CN.md`

- User asked to study detailed answers under `gpt-pro/`.
- Reviewed package:
- `gpt-pro/README_CN.md`
- `gpt-pro/CODEX_STRICT_EXECUTION_PROMPT_CN.md`
- `gpt-pro/PATCH_PLAN_FILE_BY_FILE_CN.md`
- `gpt-pro/dragon_glued_followthrough_codex_pack_2026-04-10/IMPLEMENTATION_SPEC_CN.md`
- `.../PHASED_DELIVERY_AND_ACCEPTANCE_CN.md`
- `.../RESEARCH_NOTES_AND_EXPECTED_BEHAVIOR_CN.md`
- `.../preview_results/preview_validation.json`
- Core proposal recognized:
- Keep `buy_block_glued_high_weak_rebound` hard block in default RC1.
- Add shadow branch `alpha_first_glued_followthrough_probe` with pending/confirmation scaffold.
- Only allow subtype `mid_zone_very_weak_b1` into pending in phase-1; keep `ql_rebound_weak_followthrough` hard-blocked.
- Important compatibility note:
- GPT-Pro preview baseline hashes/counts (`events=296`, `trades=98`, hashes `85ca.../7af4...`) do not match this repo's current RC1 golden (`events=272`, `trades=91`, hashes `8965.../1298...`).
- Their patch paths assume `source/` layout, while current repo uses root-level python files.
- Conclusion: strategy direction is reasonable as shadow-research scaffold, but must be adapted to current repo baseline/tests before any landing.

- User approved: execute full GPT-Pro recommendation in OpenSpec mode.
- Implemented new OpenSpec change:
- `openspec/changes/glued-followthrough-shadow-reentry/`
- includes `.openspec.yaml`, `proposal.md`, `design.md`, `tasks.md`, two spec files, and `acceptance-summary.md`.
- Code implementation completed (current repo layout adaptation):
- new module `dragon_glued_followthrough_confirmation.py`
- added glued followthrough config flags in `dragon_strategy_config.py` (default conservative/off)
- added branch config `alpha_first_glued_followthrough_probe_config()` in `dragon_branch_configs.py`
- strategy wiring in `dragon_strategy.py`:
- added glued pending state fields
- queue/clear helpers
- pending lifecycle update + invalidation
- pending confirmation decision path
- block-path queue hook under subtype-gated branch config
- clear pending on real buy/sell
- added rule catalog mapping in `dragon_rule_catalog.py` for `glued_followthrough_reentry_buy:*`
- added shadow branch to daily pipeline in `dragon_daily_signal_pipeline.py`
- Added test:
- `tests/test_glued_followthrough_shadow_reentry.py`
- Validation results:
- `py -3 -m py_compile ...` pass
- `py -3 -m unittest discover -s tests -v` pass (`22` tests)
- `py -3 dragon_daily_signal_pipeline.py` pass
- `py -3 dragon_forward_observation_pipeline.py` pass
- Compatibility outcomes:
- release-window RC1 hashes unchanged (`events 8965d1...a331`, `trades 1298be...cb97`)
- probe branch release-window adds exactly `1` extra trade:
- `2020-12-01 -> 2020-12-09`
- `glued_followthrough_reentry_buy:confirmed_mid_zone_very_weak_b1`
- return `-0.0024350630226196435`

- User asked what GPT-Pro suggestions remain not implemented.
- Cross-check result:
- Phase 1 mandatory scope is implemented.
- Remaining not-done items are GPT-Pro Phase 2 research suggestions:
- add block-after-followthrough observation metrics
- run independent shadow toggle experiment for `high_zone_weak_b1`
- run execution timing study (`same-close` vs `next-open`) for followthrough reentry path
- evaluate entry-specific exit treatment / exemption for followthrough reentry

- User clarified the core target explicitly:
- the strategy is meant to make money by following trends.
- the biggest real problem is how to rejoin the trend quickly after a false veto.
- future optimization ordering should focus on:
- `false-veto detection`
- `timely followthrough reentry`
- `trend profit capture`
- User further clarified execution style preference:
- do not expand this into a large research project.
- keep future work lean, regime-focused, and practical for a small team / ~1M capital setup.

- User pointed out that GPT-Pro had already stated this direction explicitly.
- Reconfirmed from GPT-Pro docs:
- `IMPLEMENTATION_SPEC_CN.md` states block subtype should enter pending and attempt delayed reentry within `1~3` bars after stronger confirmation.
- `RESEARCH_NOTES_AND_EXPECTED_BEHAVIOR_CN.md` prioritizes `execution timing` first and `entry-specific exit treatment` second.
- After re-reading GPT-Pro docs, one implementation detail was found missing and then fixed:
- added full-snapshot local hash pinning to `tests/test_glued_followthrough_shadow_reentry.py`
- full baseline pinned: events `296`, trades `98`, hashes `5636adc7...` / `1d419aaf...`
- User then made the priority explicit again:
- set false-veto recovery as the first priority and continue developing autonomously without mid-task interruptions.

- Implemented a lean Phase 2 OpenSpec change:
- `openspec/changes/followthrough-lean-profit-loop/`
- Added:
- `dragon_followthrough_profit_loop_review.py`
- `tests/test_followthrough_profit_loop_review.py`
- `openspec/changes/followthrough-lean-profit-loop/acceptance-summary.md`
- Ran:
- `py -3 -m py_compile dragon_followthrough_profit_loop_review.py tests/test_followthrough_profit_loop_review.py`
- `py -3 -m unittest tests.test_followthrough_profit_loop_review -v`
- `py -3 dragon_followthrough_profit_loop_review.py`
- `py -3 -m unittest discover -s tests -v`
- Final validation: full suite `25` tests passed.

- Lean Phase 2 findings:
- `mid_zone_very_weak_b1`:
- blocked_full `2`
- shadow_confirmed `1`
- confirm_like_3bar `50%`
- avg_next20_max `+7.04%`
- probe trade `2020-11-30 -> 2020-12-01 -> 2020-12-09`
- same_close `-0.24%`
- next_open `-0.50%`
- post_exit_max_10b `+8.35%`
- interpretation: the only subtype still worth a next-step experiment, and the bottleneck now looks more like execution/exit handling than whether any followthrough exists at all

- `high_zone_weak_b1`:
- blocked_full `13`
- shadow_confirmed `2`
- same_close avg `-3.52%`
- next_open avg `-5.03%`
- interpretation: do not widen this family; followthrough confirmation here still loses

- `ql_rebound_weak_followthrough`:
- blocked_full `3` (includes current full-history latest case)
- shadow_confirmed `0`
- completed historical windows: confirm_like_3bar `0%`, next3_sell_cross_rate `100%`
- interpretation: hard block remains justified

- Latest live-relevant blocked case review:
- `2026-04-08` subtype is still `ql_rebound_weak_followthrough`
- by `2026-04-10` only `2/3` followthrough bars have elapsed
- ql_reconfirm_count `0`
- sell_cross_count `0`
- max_close_return_so_far `+3.56%`
- still no confirm-like delayed reentry signal under current mid-zone/q l confirmation logic