## 2026-04-08

- Ran `dragon_daily_signal_pipeline.py` on request date `2026-04-08`.
- Latest available market bar resolved to `2026-04-07`.
- Latest `399673` snapshot on `2026-04-07`:
- close `3325.910`
- `a1 = -0.0182`
- `b1 = -0.1651`
- `c1 = 44.35`
- `KDJ buy/sell = False/False`
- `QL buy/sell = False/False`
- No branch fired any new event on the latest bar.
- `workbook_preserving`, `alpha_first_selective_veto`, and `alpha_first_glued_refined_hot_cap` all remain flat.
- Latest real event for all three branches is still `2026-02-13 SELL knife_take_profit_2_glued`.
- User-facing answer for this run: no buy trigger on the latest available bar.

- Also ran `dragon_forward_observation_pipeline.py` to persist the daily check.
- Forward-observation result on `2026-04-07`:
- signal-change count `0`
- divergence level `none`
- warnings `0`
- hard breaches `0`
- missing data metrics `0`

- Later on `2026-04-08`, upgraded the live-signal path to support intraday realtime triggering.
- Implementation:
- `dragon/data_fetcher_v2.py` now supports realtime snapshot fetch and conditional current-day snapshot append.
- `dragon_indicators.py` now supports `fetch_daily_data(include_intraday_snapshot=True)` and exposes fetch metadata.
- `dragon_daily_signal_pipeline.py` now uses intraday snapshot mode by default for same-day checks and reports:
- `data_mode`
- `historical_latest_bar_date`
- `snapshot_timestamp`
- explicit note that current market price is used as today's provisional `close`

- Verified live run after the upgrade:
- latest evaluated bar became `2026-04-08` via realtime snapshot
- historical latest official bar remained `2026-04-07`
- snapshot timestamp example: `2026-04-08T14:47:31`
- live price snapshot close around `3524.990`
- latest markers on all three branches:
- `KDJ buy=True`
- `QL buy=True`
- but no real `BUY` event fired
- local decision check on the last bar shows all three branches were blocked by:
- `buy_block_glued_high_weak_rebound`

- Verified `dragon_forward_observation_pipeline.py` still runs successfully after the schema change.

- Analyzed `buy_block_glued_high_weak_rebound` on `2026-04-08`.
- Current rule location:
- `dragon_strategy.py` `_buy_filter_glued_high_weak_rebound(...)`
- official daily-bar replay through `2026-04-07` shows the same historical block count on all three tracked branches:
- full history block count `17`
- release-window (`2016-01-01 -> 2025-12-31`) block count `15`
- subtype split in the release window:
- `high_zone_weak_b1`: `11`
- `mid_zone_very_weak_b1`: `2`
- `ql_rebound_weak_followthrough`: `2`
- release-window block dates:
- `2016-04-29`, `2017-09-27`, `2018-04-18`, `2019-04-24`, `2019-10-10`, `2020-08-18`, `2020-08-24`, `2020-11-30`, `2021-07-29`, `2021-12-09`, `2022-08-01`, `2024-06-04`, `2024-06-17`, `2025-10-21`, `2025-11-13`
- including the intraday realtime snapshot on `2026-04-08`, RC1 block count becomes `18` total; the current live block belongs to the `ql_rebound_weak_followthrough` clause.

- Effect check by disabling only this filter while keeping the rest of the strategy unchanged:
- `workbook_preserving`: trades `106 -> 121`, avg_return `2.57% -> 2.13%`, profit_factor `3.37 -> 2.90`
- `alpha_first_selective_veto`: trades `102 -> 117`, avg_return `2.86% -> 2.36%`, profit_factor `4.04 -> 3.38`
- `alpha_first_glued_refined_hot_cap` / RC1: trades `91 -> 106`, avg_return `3.42% -> 2.80%`, profit_factor `5.11 -> 4.08`
- In all three branches the filter removal adds `15` trades and removes `0`.
- Added-trade quality is weak:
- avg_return about `-0.99%`
- win_rate about `26.7%`
- Therefore current judgment: this filter is materially useful and should be treated as a real quality filter, not noise.

- Added detailed blocked-trade artifacts:
- `dragon_glued_high_weak_rebound_block_review.md`
- `dragon_glued_high_weak_rebound_blocked_trades.csv`
- RC1 blocked-trade detail sorted by realized return shows the worst candidates were:
- `2020-08-18 -> 2020-08-19` `-3.91%`
- `2019-04-24 -> 2019-04-25` `-3.42%`
- `2018-04-18 -> 2018-04-20` `-3.10%`
- best completed blocked candidates were:
- `2020-11-30 -> 2020-12-09` `+2.44%`
- `2017-09-27 -> 2017-10-16` `+1.60%`
- `2020-08-24 -> 2020-08-25` `+0.90%`
- one extra blocked date `2025-11-13` is present as a candidate date but its corresponding exit falls outside the release-window trade summary, so it has no completed in-window trade row.

- Checked the original reconstruction branch (`workbook_preserving`) on the intraday snapshot later on `2026-04-08`.
- Snapshot example:
- evaluated timestamp `2026-04-08T15:56:41`
- close `3532.798`
- `a1 = -0.0073`
- `b1 = -0.0777`
- `c1 = 47.86`
- `KDJ buy = True`
- `QL buy = True`
- original branch decision was still:
- `BLOCK buy_block_glued_high_weak_rebound`

- User later requested the 2026 RC1 buy/sell points.
- Extracted real-trade RC1 points for calendar year 2026:
- `2026-01-05 BUY dual_gold_resonance_buy @ 3488.230`
- `2026-01-20 SELL ql_mid_zone_take_profit @ 3419.621`
- `2026-01-27 BUY glued_buy @ 3476.169`
- `2026-02-02 SELL knife_take_profit_2_glued @ 3432.873`
- `2026-02-09 BUY glued_buy @ 3493.591`
- `2026-02-13 SELL knife_take_profit_2_glued @ 3423.045`
- As of the intraday snapshot on `2026-04-08`, RC1 remains flat and no new real event has been added after `2026-02-13`.

- Later analysis of why no intraday buy fired on `2026-04-08`:
- the live snapshot did create raw buy markers (`KDJ buy=True`, `QL buy=True`) and satisfied glued-state entry context (`abs(a1) < 0.02`)
- but the final buy decision was blocked by `buy_block_glued_high_weak_rebound`
- exact subtype was `ql_rebound_weak_followthrough`
- snapshot values around `2026-04-08T14:48:51`:
- `a1 = -0.00779`
- `b1 = -0.08157`
- `c1 = 47.62`
- this matched the veto window:
- `35 < c1 < 55`
- `b1 < -0.06`
- `a1 > -0.013`
- `ql_buy = True`
- the move was therefore interpreted as a fast intraday rebound with still-weak followthrough rather than a confirmed real buy

- Follow-up judgment on whether this may miss a real rally or reflect overfitting:
- yes, any veto rule can miss an individual future rally
- but current evidence does not support calling this specific filter an obvious overfit blocker
- disabling `buy_block_glued_high_weak_rebound` adds `15` release-window trades and weakens all three tracked branches
- RC1 added-trade set stats if allowed:
- win rate about `26.7%`
- avg return about `-0.99%`
- best completed blocked trade only about `+2.44%`
- today's exact subtype `ql_rebound_weak_followthrough` has only two historical daily examples and both were losers (`-2.20%`, `-1.04%`)
- nuanced conclusion:
- strategy-level overfitting risk remains medium-to-high overall because of rule-tree complexity
- but today's specific veto is more likely a useful quality filter than a clear case of overfit signal suppression
- remaining uncertainty is concentrated in the sparse `ql_rebound_weak_followthrough` subclause because its sample is still small

- Direct advisory judgment for the user:
- if forced to decide on the live `2026-04-08` snapshot, the correct call is still `do not buy yet`
- do not weaken the global `buy_block_glued_high_weak_rebound` rule because of this one signal
- safer future research path, if needed, is a subtype-specific second-chance release after stronger followthrough repair, not removal of the veto itself

- Later nuance after the user suggested this may be the start of a big trend:
- that concern is legitimate; broad neighborhood analysis around today's indicator shape is not obviously bearish
- among the 12 nearest historical RC1 buy-shape neighbors to the `2026-04-08` snapshot, avg return was about `+2.09%`, median about `+1.67%`, win rate about `66.7%`, with `3` trades above `+5%` and `1` above `+10%`
- however, the exact vetoed subclause neighborhood (`ql_rebound_weak_followthrough`) still has only 2 historical blocked daily examples and both were losers
- operational interpretation:
- there is a non-trivial chance today is an early stage of a larger move
- but current evidence still does not justify overriding RC1 on this bar alone
- if followthrough continues and either `B1` repairs above the current veto threshold or the setup migrates out of this weak-followthrough window, the bull case should be re-evaluated quickly rather than dismissed

- Later projected tomorrow's `B1` using the latest available intraday snapshot around `2026-04-08T18:25:09` with `close = 3532.798`.
- Using the current `Y2/Y3` recursion and assuming no new 38-day extreme beyond the projected close:
- if tomorrow closes flat near `3532.798`, projected `B1` is about `-0.0229`
- if tomorrow closes `-1%`, projected `B1` is about `-0.0421`
- if tomorrow closes `-2%`, projected `B1` is about `-0.0614`
- approximate threshold for `B1 >= -0.06` is tomorrow close around `3464.7`, about `-1.93%` vs the latest snapshot close
- practical takeaway:
- yes, under the current snapshot baseline, “tomorrow not falling” should place `B1` above `-0.06`
- but that only means the current `ql_rebound_weak_followthrough` veto likely stops applying
- it does not guarantee a real buy tomorrow because `KDJ buy` / `QL buy` are cross-event flags and may not re-fire on the next bar

- Later reviewed the external recommendation file `dragon_v2_rc1_restructuring_recommendations_for_codex_CN.md` and wrote a local implementation proposal `dragon_v2_rc1_restructuring_proposal_cn.md`.
- Main judgment on that external recommendation:
- directionally correct on freezing RC1, splitting core / secondary / bridge, downgrading predictive logic, and adding golden tests
- but too aggressive as a direct big-bang refactor for the current repo
- key verified repo facts used in the proposal:
- `dragon_strategy.py` has `1297` lines
- `StrategyContext` has `50` fields
- `_buy_decision()` about `125` lines
- `_sell_decision()` about `303` lines
- `StrategyConfig` has `102` fields
- current repo still has no `tests/` directory
- `reason` strings are already a de facto interface across strategy, daily pipeline, path trace, and html reporting
- proposal conclusion:
- do not directly rewrite RC1 or remove reason strings
- first add structured decision metadata with legacy reason compatibility
- first add golden baselines and layer attribution
- only then isolate weak families (`deep_oversold`, `predictive`, `post_sell_rebound`) into separate modules on a research path

- Executed the first implementation pass for proposal Phase 0-2 without changing RC1 live behavior.
- Added governance and scaffolding modules:
- `dragon_rc1_frozen_policy.md`
- `dragon_reason_types.py`
- `dragon_decision_types.py`
- `dragon_rule_catalog.py`
- Added executable scripts:
- `dragon_rc1_golden_baseline.py`
- `dragon_rule_layer_attribution.py`
- Generated new baseline artifact:
- `dragon_rc1_golden_manifest.json`
- generated_at `2026-04-08T23:43:07`
- evaluation window `2016-01-01 -> 2025-12-31`
- RC1 trade_count `91`, event_count `272`, win_rate `52.75%`, avg_return `3.42%`
- indicator source used: `dragon_indicator_snapshot_full.csv`
- Generated layer-attribution report:
- `dragon_rule_layer_attribution.md`
- key entry-layer read:
- `core/glued` remains dominant (`50` trades, avg_return `4.92%`, PF `7.15`)
- `secondary/deep_oversold` remains weak (avg_return `-0.19%`, PF `0.80`)
- `bridge/predictive_break` count still low (`1` entry trade in-window)
- key exit-layer read:
- `secondary/post_sell_rebound` exits show weak quality (avg_return `-2.00%`, PF `0.02`)
- `core/high_regime` exits remain strong (avg_return `11.63%`, PF `107.38`)
- mapping audit is now clean in the generated markdown (`no unknown reason mapping`)
- Important repo note:
- generated `.csv` artifacts from these scripts are covered by `.gitignore` (`*.csv`) and are not tracked by default

- Later answered the user's question about what profitable indicator profiles look like in RC1.
- Main conclusion:
- the best-performing trades are not primarily deep-oversold knife catches
- profitable trades are concentrated in `glued_buy`, plus smaller contributions from `early_crash_probe_buy` / `oversold_recovery_buy`
- holding duration matters strongly:
- `00-05d` avg return about `-2.03%`
- `06-10d` avg return about `-0.81%`
- `21-40d` avg return about `+6.33%`
- `41d+` avg return about `+24.13%`
- top-profit trades (`>=10%`) mostly look like:
- `A1` near zero / mild glue rather than deeply negative
- `B1` around zero to mildly positive more often than the full sample
- `C1` mainly in the `20-60` middle zone rather than extreme `<20` oversold
- `KDJ buy` is almost always present
- `QL buy` is helpful but not mandatory
- strongest actionable read:
- large profits mostly come from entries that can transition into long trend holds and exit through regime/trend management (`prewarning_reduction_exit`, `crash_protection_exit`, high-regime exits), not from short rebound scalps