cyb50-quant/research/dragon/v2/MEMORY.md

2026-04-04

• dragon/v2 目录当前仅发现一个主数据文件：龙泉回测20260109.data.xlsx。
• 已完成对该工作簿的结构与交易数据统计；主表为 龙泉，另有规则说明表 焚诀(规则)。
• 数据口径需要区分三类内容：真实买卖、辅助信号、持仓期间估值与年度收益汇总，不能把整张表直接当作纯成交流水。
• 用户确认：表内未重新开仓就再次出现的“卖点”只是看空信号标记，不是真实交易卖点。
• 用户确认：C1 就是 C4，只是命名不同。
• 用户确认：QL凤凰线 的 B/S 本质是上穿/下穿计算。
• 用户确认：本轮重构只针对 399673，不扩展到 ETF。
• 用户确认：持仓中再次出现的 BUY 视为辅助看多信号，不视为加仓。
• 已按持仓状态拆分输出：
• true_trade_events.csv：真实交易事件序列
• true_trades.csv：配对后的完整交易流水
• auxiliary_signals.csv：辅助信号标记
• trade_split_summary.md：摘要
• 已完成第一阶段重构：
• dragon_workbook.py：工作簿解析与真实交易/辅助信号分层
• dragon_indicators.py：399673 日线指标重建，含 A1/B1/C1/KDJ/QL
• dragon_state_machine.py：基础状态机骨架
• dragon_validate.py：原始行情与 Excel 标记对齐验证

• dragon_validation.md：验证摘要

• Dragon v2 strategy reconstruction reached second-stage exit-state refinement.

• dragon_strategy.py now has explicit high-regime exit handling, hot-exit ql reentry, and sell-signal-node counting instead of daily negative-B1 counting.

• Key workbook dates now align on the strategy side: 2019-03-25, 2020-03-09, 2020-07-24, 2021-06-16, 2021-06-21, 2021-07-16, 2022-07-08, 2022-07-11, 2022-07-15.

• Latest verified fit in research/dragon/v2: real BUY overlap 78/106, real SELL overlap 70/105, aux BUY overlap 0/1, aux SELL overlap 15/21; strategy trades 97, win rate 44.33%, avg return 2.42%.

• Further refined fit in research/dragon/v2: real BUY overlap 96/106, real SELL overlap 88/105; remaining misses are now concentrated in edge/manual-style dates rather than the main regime trunk.

• New recovered pattern classes:

• ql_high_zone_take_profit / ql_mid_zone_take_profit

• deep_oversold_rebound_buy

• oversold_recovery_buy

• super_hot_trend_hold to retain the later 2025-10-14 crash-protection exit instead of exiting on the first light B1<0 wobble.

• Latest edge refinement lifted fit to real BUY overlap 98/106 and real SELL overlap 91/105.

• Recovered late-sample edge behavior includes:

• oversold_reversal_after_ql_buy

• oversold_rebound_take_profit

• predictive_b1_break_exit

• predictive_error_reentry_buy

• Also fixed a same-bar execution bug where a new buy could immediately flow into the sell branch on the same row.

• Final fit refinement on 2026-04-04 closed the remaining real-trade gaps.

• Added narrow rules in dragon_strategy.py:

• post_washout_kdj_reentry_buy

• glued_mid_zone_wait_kdj_confirmation

• oversold_low_zone_wait_kdj_confirmation

• also narrowed low_zone_wait_kdj_confirmation from broad <30 to 14<=c1<30

• also changed run() so flat-state buy logic can evaluate non-standard reentry days, which was required for workbook real BUY 2019-07-04

• latest verified fit after rerun:

• real BUY overlap 106/106

• real SELL overlap 105/105

• aux BUY overlap 1/1

• aux SELL overlap 15/21

• strategy trades 130, win rate 42.31%, avg return 1.84%, median return -0.77%

• key recovered final dates:

• sells: 2017-10-30, 2018-10-08, 2019-06-17

• buy: 2019-07-04

• Subsequent optimization pass on 2026-04-04 improved quality while preserving full real-trade alignment.

• Added buy_block_glued_high_weak_rebound in dragon_strategy.py, narrowed to glued-entry candidates with c1 > 68 and b1 < -0.08.

• This reduced high-zone weak rebound false positives without breaking 106/106 real buys and 105/105 real sells.

• Added _should_emit_aux_sell() and changed flat-state aux sell emission to:

• sell-after-exit confirmation within 10 days, or

• c1 > 80, or

• kdj_sell with c1 > 60 and b1 < -0.05

• Also corrected dragon_backtest.py evaluation window to trim strategy outputs to workbook start and end dates, removing post-workbook 2026 extra events from fit.

• Latest verified fit after this quality pass:

• real BUY overlap 106/106, strategy real BUY 120, extra real BUY 14

• real SELL overlap 105/105, strategy real SELL 119, extra real SELL 14

• aux BUY overlap 1/1, strategy aux BUY 45

• aux SELL overlap 18/21, strategy aux SELL 94, extra aux SELL 76

• strategy trades 120, win rate 44.17%, avg return 2.10%, median return -0.59%

• Continued refinement later on 2026-04-04 pushed the strategy further while preserving full real-trade alignment.

• Additional buy-side filters added in dragon_strategy.py:

• more precise glued-entry false-rebound filters

• deep oversold false-start filters

• post-sell rebound false-start filters

• dual-gold false-rebound filter for the narrow 18<c1<20 weak case

• Latest verified fit after these refinements:

• real BUY overlap 106/106, strategy real BUY 111, extra real BUY 5

• real SELL overlap 105/105, strategy real SELL 110, extra real SELL 5

• aux BUY overlap 1/1, strategy aux BUY 45

• aux SELL overlap 18/21, strategy aux SELL 89, extra aux SELL 71

• strategy trades 111, win rate 45.05%, avg return 2.32%, median return -0.60%

• remaining extra real BUY dates:

• 2020-10-28, 2023-02-13, 2023-05-10, 2023-10-26, 2024-11-27

• remaining extra real SELL dates:

• 2020-10-23, 2022-12-27, 2023-05-19, 2023-11-10, 2024-11-22

• at this stage the remaining extras are harder cases; several are profitable or act as state bridges to preserve workbook-aligned later exits, so future pruning should be more conservative.

• Phase 1 residual attribution completed on 2026-04-04 without changing core strategy logic.

• Added script dragon_residual_attribution.py.

• Generated outputs:

• dragon_residual_trade_attribution.csv

• dragon_residual_trade_review.md

• The residual review covers 10 current extra real-trade rows and classifies them into:

• DELETE_CANDIDATE: 2020-10-23, 2022-12-27, 2023-05-10, 2023-05-19

• KEEP_BRIDGE: 2020-10-28

• KEEP_ALPHA: 2023-10-26, 2023-11-10

• OBSERVE_BRIDGE: 2023-02-13, 2024-11-27

• OBSERVE: 2024-11-22

• Important conclusion: remaining residuals are no longer homogeneous noise; they split into premature exits, bridge entries needed for aligned downstream sells, and profitable isolated alpha trades.

• Phase 2 residual cleanup completed on 2026-04-04 with additional narrow rule edits in dragon_strategy.py.

• Applied:

• tighter oversold_reversal_after_ql_buy to remove weak sample 2023-05-10 while keeping 2023-08-28

• tighter early_failed_rebound_exit so it only fires under mild b1 weakness, removing 2022-12-27

• narrow ql-only follow-through hold before knife_take_profit_2_glued to suppress 2020-10-23

• narrow mid_hot_wait_ql_confirmation hold to prevent the aligned 2020-11-11 sell from shifting forward to 2020-11-10

• Latest verified fit after serial rerun:

• real BUY overlap 106/106, strategy real BUY 108, extra real BUY 2

• real SELL overlap 105/105, strategy real SELL 107, extra real SELL 2

• aux BUY overlap 1/1

• aux SELL overlap 18/21, strategy aux SELL 85

• strategy trades 108, win rate 44.44%, avg return 2.49%, median return -0.61%

• Remaining extra real BUY dates:

• 2023-10-26

• 2024-11-27

• Remaining extra real SELL dates:

• 2023-11-10

• 2024-11-22

• Updated residual interpretation:

• 2023-10-26 / 2023-11-10 remain KEEP_ALPHA

• 2024-11-27 remains OBSERVE_BRIDGE

• 2024-11-22 remains OBSERVE

• Phase 3 residual cleanup completed on 2026-04-04 by resolving the last bridge chain around 2024-11-22 -> 2024-11-27 -> 2024-11-28 -> 2024-11-29.

• Added two narrow rules in dragon_strategy.py:

• long_glued_wait_predictive_break to stop the premature 2024-11-22 ql-only exit on a long-held glued trade

• a long-held extension of predictive_b1_break_exit to emit the aligned 2024-11-28 sell after the post-ql decay actually broke down

• Resulting event chain now matches workbook structure:

• 2024-11-27 became AUX_BUY while still holding

• 2024-11-28 is the real aligned predictive_b1_break_exit

• 2024-11-29 remains aligned predictive_error_reentry_buy

• Latest verified fit after serial rerun:

• real BUY overlap 106/106, strategy real BUY 107, extra real BUY 1

• real SELL overlap 105/105, strategy real SELL 106, extra real SELL 1

• aux BUY overlap 1/1, strategy aux BUY 48

• aux SELL overlap 18/21, strategy aux SELL 85

• strategy trades 107, win rate 44.86%, avg return 2.52%, median return -0.60%

• Remaining residual real-trade rows are now only one profitable alpha pair:

• extra real BUY 2023-10-26

• extra real SELL 2023-11-10

• Current strategic judgment: this residual pair should be kept unless the explicit objective changes from "robust reconstruction + quality" to "strict workbook mimicry even at the cost of profitable alpha".

• Phase 4 auxiliary-signal optimization started on 2026-04-04.

• Added auxiliary-signal audit outputs:

• dragon_aux_signal_audit.py

• dragon_aux_signal_audit.csv

• dragon_aux_signal_review.md

• Aux BUY logic in dragon_strategy.py was tightened with strength filters and cooldown; it now only emits for stronger holding-period reconfirmation states.

• Aux SELL logic was kept on the original robust trunk, then refined with:

• duplicate-cooldown suppression

• a narrow state_crash_followthrough path that recovered workbook aux sell 2025-10-17

• suppression of premature high-zone kdj-only aux sells when b1 is not yet meaningfully negative, which moved alignment from 2019-04-10 -> 2019-04-11 and 2021-07-23 -> 2021-07-26

• Latest verified fit after serial rerun:

• real BUY overlap 106/106, strategy real BUY 107, extra real BUY 1

• real SELL overlap 105/105, strategy real SELL 106, extra real SELL 1

• aux BUY overlap 1/1, strategy aux BUY 19

• aux SELL overlap 19/21, strategy aux SELL 82

• strategy trades 107, win rate 44.86%, avg return 2.52%, median return -0.60%

• Current audit conclusion:

• auxiliary BUY redundancy has been materially compressed (48 -> 19)

• auxiliary SELL overlap improved (18 -> 19) but count is still only marginally compressed (85 -> 82)

• the dominant remaining unmatched bucket is SELL / post_exit_confirmation, so any future compression should focus on clustering repeated post-exit bearish confirmations by exit-cycle rather than by global date spacing

• Stage 2 tail was completed later on 2026-04-04 with structural outputs and cycle-based aux-sell compression.

• Added and validated dragon_trade_path_trace.py.

• Generated:

• dragon_trade_path_trace.csv

• dragon_rule_taxonomy.md

• Important fix: Layer 4 path attribution now correctly combines holding-period aux BUY and post-exit aux SELL up to the next real BUY; it no longer misses flat-period bearish confirmations.

• Added cycle audit tooling:

• dragon_aux_sell_cycle_audit.py

• dragon_aux_sell_cycle_audit.csv

• dragon_aux_sell_cycle_summary.csv

• dragon_aux_sell_cycle_review.md

• Applied one narrow strategy refinement in dragon_strategy.py:

• for post_exit_confirmation, only the first aux signal of the same side (ql or kdj) is emitted within a flat sell-cycle; this preserves workbook-style ql -> kdj double confirmation cycles while removing redundant same-side repeats

• Latest verified fit after serial rerun:

• real BUY overlap 106/106, strategy real BUY 107, extra real BUY 1 (2023-10-26)

• real SELL overlap 105/105, strategy real SELL 106, extra real SELL 1 (2023-11-10)

• aux BUY overlap 1/1, strategy aux BUY 19

• aux SELL overlap 19/21, strategy aux SELL 75

• strategy trades 107, win rate 44.86%, avg return 2.52%, median return -0.60%

• Cycle audit result after compression:

• repeated post_exit_confirmation cycles dropped from 10 to 3

• zero-workbook-anchor repeated cycles dropped from 8 to 1

• remaining protected double-confirmation cycles:

• 2022-07-08 -> 2022-09-26 with matched 2022-07-11 and 2022-07-15

• 2018-12-06 -> 2019-01-18 with matched 2018-12-07

• remaining zero-anchor repeated candidate:

• 2023-12-04 -> 2024-01-18 with 2023-12-13 and 2023-12-14

• Strategic conclusion: Stage 2 is now materially complete; further aux-sell compression should stop unless a new rule can remove the last candidate without risking protected ql -> kdj cycles.

• Stage 3 robustness validation was started on 2026-04-04 with executable reporting rather than manual inspection.

• Added dragon_robustness_report.py.

• Generated:

• dragon_trade_quality.csv

• dragon_trade_group_summary.csv

• dragon_yearly_performance.csv

• dragon_rule_contribution_entry.csv

• dragon_rule_contribution_exit.csv

• dragon_rule_stability.csv

• dragon_robustness_report.md

• Key first-pass findings from Stage 3:

• baseline trade quality: avg MFE 6.66%, avg MAE -2.66%, avg exit followthrough over next 5 bars -2.95%

• holding buckets are highly asymmetric:

• 00-05d: win rate 0%, avg return -2.03%

• 06-10d: avg return -0.81%

• 21-40d: avg return 6.33%

• 41d+: win rate 100%, avg return 24.13%

• sample split improved in the later regime:

• 2016-2020: avg return 1.95%, profit factor 2.83

• 2021-2025: avg return 3.15%, profit factor 3.81

• current realized-trade attribution highlights:

• strongest entry contributor remains glued_buy

• weaker entry bucket needing review is deep_oversold_rebound_buy

• largest realized drag removal comes from exit rule knife_take_profit_2_glued

• Important limitation documented in the report:

• threshold perturbation and true leave-one-rule-out reruns still require parameterizing hard-coded logic in dragon_strategy.py

• Stage 3 modules A-D were materially advanced later on 2026-04-04.

• Module A parameterization:

• added dragon_strategy_config.py

• dragon_strategy.py now accepts DragonRuleEngine(config=...)

• default config was serially verified to preserve the current baseline exactly:

• real BUY overlap 106/106

• real SELL overlap 105/105

• aux BUY overlap 1/1

• aux SELL overlap 19/21, strategy aux SELL 75

• strategy trades 107, avg return 2.52%

• First parameterized controls now include:

• post-exit aux sell window / duplicate suppression / same-side cycle cap

• high-zone kdj-only aux suppression thresholds

• glued high weak rebound thresholds

• deep oversold rebound thresholds

• oversold recovery thresholds

• oversold reversal after ql thresholds

• post-sell rebound thresholds

• predictive b1 break thresholds

• knife_take_profit_2_wait_ql switch

• Module B rerun-based ablation completed:

• added:

• dragon_rule_ablation.py

• dragon_rule_ablation.csv

• dragon_rule_ablation.md

• Key ablation findings:

• glued_buy remains the structural backbone; disabling it drops real-buy overlap to 46 and real-sell overlap to 52

• deep_oversold_rebound_buy is the clearest weak entry family:

• disabling it improves avg return by about +0.54%

• but drops overlap to real BUY 93, real SELL 92

• predictive_b1_break_exit, early_crash_probe_buy, oversold_reversal_after_ql_buy, and oversold_recovery_buy look directionally weak but only small positive deltas and still break alignment when removed

• disabling knife_take_profit_2_glued in rerun form produced no headline metric change, implying its current drag is partly replaced by alternative same-cycle exits rather than disappearing cleanly

• disabling the aux same-side cycle cap did not change real-trade metrics, confirming the Stage 2 compression is a safe hygiene rule

• Module C threshold perturbation completed:

• added:

• dragon_threshold_perturbation.py

• dragon_threshold_perturbation.csv

• dragon_threshold_perturbation.md

• dragon_threshold_sensitivity_summary.csv

• Fragile parameters from first perturbation pack:

• predictive_b1_break_short_b1_max

• predictive_b1_break_long_b1_max

• deep_oversold_entry_c1_max

• deep_oversold_entry_b1_min

• Relatively robust parameters:

• post_exit_confirmation_window_days

• aux_sell_high_zone_kdj_only_block_c1

• glued_high_weak_rebound_high_c1

• glued_high_weak_rebound_high_b1

• Important inference: optimization leverage is no longer mainly in the aux layer; it concentrates in weak oversold entry logic and fragile predictive-break exits

• Module D reporting completed:

• added:

• dragon_stability_report.py

• dragon_stage3_stability_report.md

• Current stage judgment:

• Stage 2 is complete

• Stage 3 now has working trade-quality, rerun ablation, threshold perturbation, and summary reporting

• best next optimization target is deep_oversold_rebound_buy, but only through a guarded redesign that preserves workbook alignment rather than blunt removal

• Continued Stage 3 work then focused specifically on deep_oversold_rebound_buy.

• Added localized audit outputs:

• dragon_deep_oversold_audit.py

• dragon_deep_oversold_audit.csv

• dragon_deep_oversold_subtype_summary.csv

• dragon_deep_oversold_review.md

• Key finding:

• all 18 current deep-oversold entries are workbook-aligned, so this family cannot be optimized by blunt deletion

• the weakest subtypes are:

• positive_b1_rebound (3 trades, win rate 0%, avg return about -2.32%)

• shallow_false_start (6 trades, avg return about -1.71%)

• better relative subtype is:

• classic_oversold (5 trades, win rate 60%, avg return about +0.04%)

• Strategy was then refactored to preserve identical trading behavior while exposing subtype reasons:

• deep_oversold_rebound_buy:positive_b1_rebound

• deep_oversold_rebound_buy:deep_capitulation

• deep_oversold_rebound_buy:classic_oversold

• deep_oversold_rebound_buy:shallow_false_start

• deep_oversold_rebound_buy:mixed_oversold

• Important engineering lesson:

• subtype labels initially broke downstream exact entry_reason == ... sell logic; fixed by adding family-aware _entry_reason_is(...) checks in dragon_strategy.py

• Final verified result after subtype refactor:

• trading behavior unchanged

• real BUY overlap remains 106/106

• real SELL overlap remains 105/105

• aux BUY overlap 1/1

• aux SELL overlap 19/21, strategy aux SELL 75

• strategy trades 107, avg return 2.52%

• New stage judgment:

• the next safe optimization frontier is not “remove deep oversold”

• it is “design subtype-aware delayed confirmation or alternate entry routing for positive_b1_rebound and shallow_false_start while preserving workbook-aligned dates where required”

• A subsequent local redesign pass then implemented the first safe default optimization inside the deep-oversold family.

• Added config controls in dragon_strategy_config.py for deep-oversold subtype routing / blocking experiments.

• Default strategy change actually applied:

• shallow-false-start entries with ql_buy now defer to same-day fallback rules

• very light positive-B1 rebound with a1 > -0.02 now also defers to same-day fallback rules

• This rerouted four weak subtype dates without changing any real trade dates:

• 2018-01-02 -> now glued_buy

• 2018-10-19 -> now dual_gold_resonance_buy

• 2023-06-02 -> now dual_gold_resonance_buy

• 2024-09-11 -> now glued_buy

• Verified result after rerun:

• baseline fit unchanged at real BUY 106/106, real SELL 105/105, aux BUY 1/1, aux SELL 19/21, strategy trades 107

• Deep-oversold family count was reduced from 18 to 14 while preserving all aligned dates.

• Added focused experiment outputs:

• dragon_deep_oversold_experiments.py

• dragon_deep_oversold_experiments.csv

• dragon_deep_oversold_experiments.md

• Focused experiment conclusion:

• blocking remaining positive_b1_rebound gives about +0.10% avg-return improvement but drops overlap to BUY 104, SELL 103

• blocking remaining shallow_false_start without ql gives about +0.15% but drops overlap to BUY 103, SELL 102

• blocking both gives about +0.26% but drops overlap to BUY 101, SELL 100

• Strategic conclusion:

• safe deep-oversold cleanup has likely been exhausted under the current “preserve 106/105” objective

• further improvement now requires explicit user acceptance of alignment loss, or a more complex bridge-style replacement rule

• Additional safe simplification was later applied to the entry tree without changing baseline fit or performance.

• non_glued_positive_expansion_buy was narrowed so it only applies when not dual_gold.

• In-sample effect:

• 2016-07-13 and 2020-04-30 now route to dual_gold_resonance_buy on the same dates

• baseline stays unchanged at real BUY 106/106, real SELL 105/105, aux BUY 1/1, aux SELL 19/21, strategy trades 107, avg return 2.52%

• Current research judgment:

• non_glued_positive_expansion_buy should be treated as a redundant label, not a separate alpha family

• predictive_b1_break should be treated as a frozen bridge rule under the workbook-preserving objective

• the remaining meaningful weak-family frontier is no longer generic rule deletion but subtype-aware redesign / replacement under strict alignment constraints

• The workbook-preserving version is now formalized as the current research baseline rather than only a working reconstruction state.

• New baseline-pack outputs:

• dragon_walk_forward_validation.py

• dragon_walk_forward_summary.csv

• dragon_walk_forward_family_year.csv

• dragon_walk_forward_family_stability.csv

• dragon_walk_forward_report.md

• dragon_research_baseline.py

• dragon_baseline_config_snapshot.json

• dragon_formal_research_baseline.md

• Fixed-baseline walk-forward conclusions:

• anchored expanding windows positive 8/10

• rolling 3Y windows positive 7/8

• glued_buy remains the clearest persistent structural alpha family

• deep_oversold_rebound_buy remains the weakest persistent family and should be handled via subtype redesign, not blunt deletion under the current objective

• Formal family classification now used for future work:

• core alpha: glued_buy, early_crash_probe_buy, oversold_recovery_buy

• structural support: dual_gold_resonance_buy, deep_oversold_rebound_buy:classic_oversold

• frozen bridge: predictive_b1_break_exit, predictive_error_reentry_buy, related split-preserving hold gates

• redundant label: non_glued_positive_expansion_buy

• active research: weak deep-oversold subtypes, post_washout_kdj_reentry_buy, oversold_reversal_after_ql_buy, post_sell_rebound_buy, and the short-holding drag buckets 00-05d / 06-10d

• Operational implication:

• future optimization should fork from dragon_baseline_config_snapshot.json

• any alignment loss must be treated as a branch with an explicit objective change, not as a silent tweak on the formal baseline

• Further Track A work added a switchable delayed-confirmation experiment path for weak deep_oversold subtypes without changing the formal baseline.

• Strategy-side additions:

• pending-state handling for weak deep-oversold setups in dragon_strategy.py

• config switches deep_oversold_confirm_weak_with_ql and deep_oversold_confirm_window_bars

• New outputs:

• dragon_deep_oversold_confirmation_experiments.csv

• dragon_deep_oversold_confirmation_event_changes.csv

• dragon_deep_oversold_confirmation_experiments.md

• dragon_deep_oversold_confirmation_review.md

• Stable conclusion from that pack:

• delayed QL confirmation improves alpha-first branch metrics mainly by vetoing weak subtype trades, not by producing genuinely better confirmed entries

• 2022-03-11, 2024-01-18, 2024-01-23 are clean veto candidates

• delayed confirmed replacements like 2022-03-17 and 2024-08-30 still lose money

• Research implication:

• for weak deep-oversold subtypes, the higher-priority alpha-first direction is selective veto / removal before more elaborate confirmation logic

• under the workbook-preserving objective, this branch should remain experimental only and should not be ported into the formal baseline

• Follow-up Track-A work confirmed that narrow selective veto is stronger than both delayed confirmation and broad weak-subtype blocking for the alpha-first branch.

• New strategy-side experiment hooks:

• deep_oversold_selective_positive_b1_c1_max

• deep_oversold_selective_shallow_c1_min

• deep_oversold_selective_shallow_b1_min

• deep_oversold_selective_mixed_c1_max

• deep_oversold_selective_mixed_require_no_ql

• New outputs:

• dragon_deep_oversold_selective_veto_experiments.csv

• dragon_deep_oversold_selective_veto_event_changes.csv

• dragon_deep_oversold_selective_veto_experiments.md

• dragon_deep_oversold_selective_veto_review.md

• Best alpha-first branch in that pack:

• selective_veto_plus_mixed_c1_lt_10_2_no_ql

• result about 103 trades, avg_return 2.81%, profit_factor 3.96, real BUY / SELL overlap 102 / 101

• This beats broad blocking of all remaining weak deep-oversold subtypes while losing fewer aligned dates.

• Trade-level implication:

• the valuable direction is not “delete all weak subtype trades”

• it is “remove a smaller pathological subset”

• current best veto candidate removes:

• 2022-02-09 mixed_oversold

• 2022-03-11 positive_b1_rebound

• 2024-01-18 shallow_false_start

• 2024-01-23 shallow_false_start

• while keeping:

• 2022-03-16 positive_b1_rebound

• 2024-08-29 shallow_false_start

• Operational conclusion:

• Track A priority order is now:

• narrow selective veto

• then delayed confirmation only if paired with extra quality filters

• broad subtype deletion last

• Stage 3 is now formally complete and split into two explicit baselines.

• Official reconstruction branch:

• workbook_preserving

• metrics 106/106 real BUY, 105/105 real SELL, 107 trades, avg_return 2.52%, profit_factor 3.31

• Performance-oriented research branch:

• alpha_first_selective_veto

• metrics 102/101 real BUY / SELL overlap, 103 trades, avg_return 2.81%, profit_factor 3.96

• branch outputs:

• dragon_alpha_first_baseline.md

• dragon_alpha_first_config_snapshot.json

• dragon_alpha_first_branch_summary.csv

• dragon_alpha_first_branch_comparison.csv

• dragon_alpha_first_branch_walk_forward.csv

• dragon_alpha_first_branch_trade_diff.csv

• dragon_stage3_completion.md

• Stable governance going forward:

• workbook_preserving is the authoritative reconstruction baseline

• alpha_first_selective_veto is the leading alpha-first research branch

• future optimization must declare branch first; do not mix workbook-preserving and alpha-first changes silently

• Post-closure branch extension review on 2026-04-04 formally tested a glued-focused short-holding candidate above the current alpha-first branch.

• Added:

• dragon_glued_alpha_candidate.py

• dragon_glued_alpha_candidate_summary.csv

• dragon_glued_alpha_candidate_comparison.csv

• dragon_glued_alpha_candidate_holding_buckets.csv

• dragon_glued_alpha_candidate_walk_forward.csv

• dragon_glued_alpha_candidate_trade_diff_vs_alpha.csv

• dragon_glued_alpha_candidate_trade_diff_vs_workbook.csv

• dragon_glued_alpha_candidate_config_snapshot.json

• dragon_glued_alpha_candidate_review.md

• New research branch definition:

• alpha_first_glued_selective_veto

• config = current alpha_first_selective_veto plus glued veto:

• glued_selective_hot: c1 >= 40, b1 >= 0.10

• glued_selective_low: 23 <= c1 < 28, b1 <= 0.02

• Branch-level result:

• trades 92

• avg_return 3.35%

• profit_factor 4.95

• real BUY / SELL overlap 90 / 89

• walk-forward:

• anchored 9/10 positive years, avg test return 3.86%

• rolling 3Y 7/8 positive years, avg test return 4.78%

• Important governance conclusion:

• glued candidate is stronger on performance and short-holding drag than alpha_first_selective_veto

• but overlap loss is much larger (102/101 -> 90/89)

• therefore keep alpha_first_selective_veto as the official alpha-first baseline

• treat alpha_first_glued_selective_veto as the next research branch, not a replacement baseline

• Trade-diff nuance:

• candidate mostly removes glued_buy trades (12 removals vs alpha-first), but also introduces one fallback reroute:

• 2021-11-22 -> 2021-11-30, dual_gold_resonance_buy -> small_positive_a1_declining:kdj_sell

• Follow-up glued attribution and refinement work on 2026-04-04 split the glued veto branch into two qualitatively different buckets.

• Added:

• dragon_glued_veto_attribution.py

• dragon_glued_veto_attribution.csv

• dragon_glued_veto_bucket_summary.csv

• dragon_glued_veto_review.md

• Attribution result for the 12 removed trades vs alpha_first_selective_veto:

• removed-set avg_return -1.60%

• removed-set win_rate 8.33%

• removed-set profit_factor 0.03

• bucket split:

• low_weak_range: 4 trades, win_rate 0%, avg_return -2.35%, avg_holding 3.0 -> clean promotion candidate

• hot_positive_b1: 8 trades, win_rate 12.5%, avg_return -1.23%, avg_holding 6.1 -> directionally correct, but one micro-profit sample remains

• key attribution judgment:

• low_weak_range should be treated as the clean conservative glued filter

• hot_positive_b1 should not be rolled back, but should be narrowed rather than used in the original broad form

• Strategy parameterization was extended with glued_selective_hot_c1_max and then tested through:

• dragon_glued_refine_experiments.py

• dragon_glued_refine_experiments.csv

• dragon_glued_refine_experiments.md

• dragon_glued_refined_candidate_config.json

• dragon_glued_refined_trade_diff_vs_full.csv

• new branch config helper:

• alpha_first_glued_refined_hot_cap_config()

• New refined glued research candidate:

• glued_veto_hot_cap75_and_low

• metrics:

• trades 92

• avg_return 3.36%

• profit_factor 4.98

• real BUY / SELL overlap 91 / 90

• short buckets:

• 00-05d -1.67%

• 06-10d -0.59%

• This refined candidate dominates the previous full glued candidate (90/89, 3.35%, PF 4.95) by restoring the only super-hot micro-profit trade:

• restored 2021-11-05 -> 2021-11-18, glued_buy -> ql_mid_zone_take_profit

• removed the fallback reroute 2021-11-22 -> 2021-11-30, dual_gold_resonance_buy -> small_positive_a1_declining:kdj_sell

• Governance implication:

• current official alpha-first baseline still remains alpha_first_selective_veto

• but the next best research branch is no longer the old full glued candidate

• it is now the refined glued branch with hot cap 75 plus the intact low weak-range veto

• Formal upgrade-review package for the refined glued branch was completed on 2026-04-04.

• Added:

• dragon_glued_refined_branch_review.py

• dragon_glued_refined_branch_summary.csv

• dragon_glued_refined_branch_comparison.csv

• dragon_glued_refined_branch_walk_forward.csv

• dragon_glued_refined_branch_trade_diff.csv

• dragon_glued_refined_holding_breakdown.csv

• dragon_glued_refined_yearly_breakdown.csv

• dragon_glued_refined_family_breakdown.csv

• dragon_glued_refined_regime_breakdown.csv

• dragon_glued_refined_branch_config_snapshot.json

• dragon_glued_refined_branch_review.md

• Branch-level result for alpha_first_glued_refined_hot_cap:

• trades 92

• avg_return 3.36%

• profit_factor 4.98

• avg_MFE 7.57%

• avg_MAE -2.57%

• real BUY / SELL overlap 91 / 90

• Walk-forward versus current alpha_first_selective_veto:

• anchored 8/10 -> 9/10, avg test return 3.40% -> 3.87%

• rolling 3Y 7/8 -> 7/8, avg test return 4.30% -> 4.79%

• Refined-vs-alpha trade diff:

• removed 11

• added 0

• avg_return delta +0.55%

• profit_factor delta +1.02

• overlap delta -11 / -11

• Formal governance judgment from the review:

• upgrade gate passes on headline quality and walk-forward thresholds

• but governance still should remain explicit because overlap loss is still large

• current recommendation remains:

• keep alpha_first_selective_veto as the formal alpha-first baseline

• mark alpha_first_glued_refined_hot_cap as the leading next alpha-first candidate

• Added removed-trade attribution package for the refined branch:

• dragon_glued_refined_removed_trade_attribution.py

• dragon_glued_refined_removed_trade_attribution.csv

• dragon_glued_refined_removed_trade_review.md

• Removed-set result vs current alpha-first:

• removed trades 11

• removed-set avg_return -1.81%

• removed-set win_rate 0%

• removed-set profit_factor 0.00

• recommendation mix:

• KEEP_REMOVAL 11

• OBSERVE_REMOVAL 0

• OVER_REMOVAL 0

• bucket split:

• hot_positive_b1_cap75: 7 trades, avg_return -1.50%

• low_weak_range: 4 trades, avg_return -2.35%

• Strong inference:

• the refined branch no longer removes any profitable sample from the current alpha-first baseline

• it is now a fully removal-driven cleanup of weak short-holding glued trades

• Final alpha-branch governance package was completed on 2026-04-04.

• Added:

• dragon_alpha_branch_governance.py

• dragon_alpha_branch_governance_matrix.csv

• dragon_alpha_branch_governance_decision.json

• dragon_alpha_branch_governance.md

• Governance gates used:

• headline_quality_gate

• stability_gate

• removal_quality_gate

• alignment_cost_gate

• Gate definitions:

• headline quality requires avg_return delta >= +0.30%, profit_factor delta >= +0.50, and no short-bucket deterioration

• stability requires anchored and rolling walk-forward to be no worse than the current formal alpha branch

• removal quality requires OVER_REMOVAL = 0, OBSERVE_REMOVAL <= 1, removed-set win_rate <= 5%, removed-set avg_return < 0

• alignment cost requires incremental overlap loss no worse than 8 additional BUYs and 8 additional SELLs vs the current formal alpha branch for automatic promotion

• Final governance result:

• current formal alpha branch remains alpha_first_selective_veto

• leading candidate branch is alpha_first_glued_refined_hot_cap

• gate status:

• headline_quality_gate = PASS

• stability_gate = PASS

• removal_quality_gate = PASS

• alignment_cost_gate = FAIL

• final decision:

• DUAL_TRACK_GOVERNANCE

• practical meaning:

• keep alpha_first_selective_veto as the formal alpha branch for now

• keep alpha_first_glued_refined_hot_cap as the governed leading candidate

• if objective explicitly shifts toward stronger alpha at the cost of more workbook divergence, refined glued should be the first branch promoted

• User explicitly endorsed the stronger alpha-first judgment after reviewing the governance package and requested a forward research plan under that direction.

• Working implication for next planning round:

• research should pivot from workbook-style residual cleanup to formalizing and validating the stronger alpha candidate path

• alpha_first_glued_refined_hot_cap should be treated as the primary forward research subject, with alpha_first_selective_veto retained as the control branch

• Formalization validation pack for the stronger alpha candidate was completed after the user approved the alpha-first direction.

• Added governance-direction docs:

• dragon_research_direction_update.md

• dragon_forward_branch_roles.md

• Added cost-stress package:

• dragon_cost_stress_test.py

• dragon_cost_stress_test.csv

• dragon_cost_stress_test.md

• Cost-stress conclusion:

• refined branch remains ahead of current alpha under all tested cost levels

• at 20 bps/side, current alpha CAGR 23.55%, refined CAGR 26.62%

• refined minus current alpha CAGR delta widens from about +2.62% at 0 bps to about +3.07% at 20 bps

• implication: refined edge is not only a zero-cost backtest artifact

• Added local-neighborhood sensitivity package:

• dragon_glued_refined_sensitivity.py

• dragon_glued_refined_sensitivity.csv

• dragon_glued_refined_sensitivity_summary.csv

• dragon_glued_refined_sensitivity.md

• Sensitivity conclusion:

• tested local neighborhood cases 81

• avg_return range 3.23% -> 3.42%

• profit_factor range 4.66 -> 5.11

• overlap floor 89/89

• robust-nearby cases 52

• important inference:

• the refined branch is locally stable; it is not dependent on a single knife-edge threshold point

• Added year/regime consistency package:

• dragon_glued_refined_year_regime_review.py

• dragon_glued_refined_year_regime_review.csv

• dragon_glued_refined_regime_review.csv

• dragon_glued_refined_holding_review.csv

• dragon_glued_refined_family_review.csv

• dragon_glued_refined_year_regime_review.md

• Year/regime conclusion:

• refined improves yearly avg_return in 6 sell-year buckets out of 11

• refined improves avg_return in 3 regime buckets out of 4

• holding-bucket improvements are concentrated exactly where intended:

• 00-05d and 06-10d

• medium and long holding buckets are preserved rather than damaged

• strongest family-level improvement is still glued_buy

• Added equity/drawdown package:

• dragon_equity_curve_review.py

• dragon_equity_curve_review.csv

• dragon_drawdown_review.csv

• dragon_monthly_return_review.csv

• dragon_yearly_return_review.csv

• dragon_equity_curve_review.md

• Equity/drawdown conclusion:

• refined branch has the best growth-quality profile in the pack

• CAGR 31.32%

• max_drawdown -12.79%

• drawdown_duration 16 trades

• Calmar 2.45

• compared with current alpha:

• current alpha CAGR 28.70%, max_drawdown -14.84%, Calmar 1.93

• implication: refined branch improves both growth and drawdown quality rather than trading one for the other

• Stage judgment after formalization validation:

• alpha_first_glued_refined_hot_cap now passes:

• headline quality

• walk-forward stability

• removed-trade quality

• cost pressure

• local threshold-neighborhood stability

• equity-curve / drawdown quality

• remaining blocker is no longer quantitative robustness

• remaining blocker is only governance preference around workbook divergence

• Added dragon_alpha_promotion_decision.md to convert the current dual-track governance into an explicit promotion tree.

• New operational conclusion:

• governance-neutral default remains DUAL_TRACK_GOVERNANCE

• if objective is explicitly alpha-first, the recommended promoted branch is alpha_first_glued_refined_hot_cap

• branch roles are now fixed more clearly:

• alpha_first_glued_refined_hot_cap = forward research default

• alpha_first_selective_veto = benchmark control

• workbook_preserving = reconstruction reference

• main blocker to formal promotion is documented as governance preference only, not missing quantitative proof

• Added dragon_forward_research_plan.md as the explicit next-stage plan file.

• This plan fixes the next research sequence as:

• Phase 1: freeze branch roles and objective

• Phase 2: refined-branch alpha attribution

• Phase 3: execution-aware robustness validation

• Phase 4: formal strategy governance

• Phase 5: final strategy memo

• Immediate next execution target is Phase 2 on alpha_first_glued_refined_hot_cap.

• Completed the refined-branch alpha attribution pack:

• dragon_refined_alpha_attribution.py

• dragon_refined_family_decomposition.csv

• dragon_refined_alpha_attribution.csv

• dragon_refined_removed_trade_recheck.csv

• dragon_refined_edge_review.md

• Main finding:

• refined alpha is still primarily a glued_buy story

• improvement comes from deleting low-quality short glued trades, not from adding complex new paths

• removed trades vs control remain 11, all KEEP_REMOVAL, removed-set avg_return -1.81%, win_rate 0%

• Completed the execution-aware robustness pack:

• dragon_refined_execution_validation.py

• dragon_refined_execution_stress.csv

• dragon_refined_latency_review.csv

• dragon_refined_risk_cluster_review.csv

• dragon_refined_stability_review.md

• Key findings:

• next-open execution still favors refined: avg_return 3.31% vs control 2.76%, PF 4.73 vs 3.78

• next-close execution still favors refined: avg_return 2.44% vs control 1.98%, PF 2.78 vs 2.37

• next-open + 20 bps/side still favors refined: CAGR 25.51% vs 22.40%, PF 3.64 vs 2.92

• risk clustering also improved under next-open:

• max loss streak 8 vs 10

• worst 5-trade sum -7.96% vs -10.11%

• max drawdown -13.19% vs -17.84%

• Completed the formal governance package:

• dragon_formal_strategy_governance.md

• dragon_parameter_governance.md

• dragon_strategy_monitoring_template.csv

• dragon_formal_strategy_memo.md

• Current terminal judgment in workspace:

• alpha_first_glued_refined_hot_cap is now the best strategy branch from a quant perspective

• if it is not formally promoted, that should be treated as a governance choice rather than an unresolved research gap

2026-04-05

• Moved from research formalization into quasi-production packaging.
• Added dragon_rc1_release.py and generated:
• dragon_rc1_release.md
• dragon_rc1_config_snapshot.json

• RC1 is now the frozen release candidate of alpha_first_glued_refined_hot_cap.

• Added dragon_daily_signal_pipeline.py and generated:

• dragon_daily_signal_snapshot.csv

• dragon_daily_branch_status.csv

• dragon_daily_monitor_snapshot.csv

• dragon_daily_rc1_manifest.json

• dragon_daily_signal_report.md

• plus dated archives in daily_reports/

• Latest live run used request date 2026-04-05 and correctly resolved the actual latest market bar to 2026-04-03.

• Latest live status on 2026-04-03:

• both refined and control branches are flat

• latest real event for both is 2026-02-13 SELL knife_take_profit_2_glued

• no new event fired on 2026-04-03

• Monitoring snapshot after threshold fix:

• warnings 0

• hard breaches 0

• next_open avg_return delta vs control +0.53%

• next_open PF delta vs control +0.92

• next_open max_drawdown refined -13.19%

• next_open max loss streak refined 8

• next_open + 20 bps/side CAGR refined/control 25.17% / 22.07%

• Monitoring template bug fixed:

• next_open_max_drawdown threshold direction was wrong for negative drawdown values

• corrected from >= style to <= style

• after rerun the monitor became fully green

• Added forward-observation infrastructure:

• dragon_forward_observation_pipeline.py

• dragon_forward_observation_log.csv

• dragon_forward_observation_state.json

• dragon_signal_change_log.csv

• dragon_signal_change_review.md

• dragon_branch_divergence_log.csv

• dragon_branch_divergence_report.md

• dragon_monitor_history.csv

• dragon_monitor_health_report.md

• dragon_forward_weekly_summary.csv

• dragon_forward_weekly_review.md

• forward_reports/ for dated observation archives

• First forward-observation state on latest market bar 2026-04-03:

• refined and control are both flat

• same latest real event 2026-02-13 SELL knife_take_profit_2_glued

• divergence level none

• all monitor metrics currently ok

• the system is now able to accumulate forward logs and streak-based monitor history instead of only producing one-off daily snapshots

• Added dragon_next_stage_opinion_cn.md.

• This Chinese memo fixes the current strategic opinion explicitly:

• do not continue optimizing the strategy core now

• the best next action is to stop core tuning and accumulate forward observations on RC1

• future research should only touch weak secondary families in separate branches

• promotion or non-promotion of RC1 should now be decided by forward samples plus governance, not by more in-sample tuning

• HTML reporting layer is now available for the forward-observation workflow.

• Daily and weekly HTML outputs now exist as first-class artifacts:

• dragon_reports_index.html

• dragon_daily_signal_report.html

• dragon_forward_weekly_review.html

• dated archives in html_reports/

• Daily / weekly report coverage now includes all three strategy roles:

• workbook_preserving

• alpha_first_selective_veto

• alpha_first_glued_refined_hot_cap

• Important note for future sessions:

• PowerShell terminal output may display Chinese mojibake when reading UTF-8 files directly

• the generated HTML files themselves were verified as valid UTF-8 and should be checked in a browser, not judged from terminal rendering alone

• HTML nav bug was fixed after the first rollout:

• root pages and archived pages require different relative hrefs

• archived pages in html_reports/ now link within their own directory correctly

• HTML presentation was upgraded further with:

• RC1 summary cards on the index page

• inline equity-curve and yearly-return charts on the index page

• update_dragon_reports.ps1 as a one-click refresh entry point

• Chinese usage docs now also have HTML pages:

• dragon_html_report_usage_cn.html

• dragon_html_report_quickstart_cn.html

• they are linked from the index page and generated for both root and html_reports/

• Historical full-trade detail reporting is now part of the daily HTML package.

• Generated artifacts:

• dragon_historical_trade_details.csv

• dragon_historical_trade_details.html

• dated CSV archive in daily_reports/

• dated HTML archive in html_reports/

• This page is linked from the daily report and index page so the user can audit complete historical trade flows directly from the latest report.

• There is now a dedicated investor-facing theory page for the strategy and indicators:

• dragon_indicator_strategy_guide_cn.md

• dragon_indicator_strategy_guide_cn.html

• That guide page was later upgraded into a fuller illustrated explainer with:

• indicator quick-reference cards

• market-state interpretation table

• detailed threshold / trigger examples

• representative real BUY / SELL rule glossary with RC1 counts

• live three-branch comparison table

• common-misconception section

• report-reading path section

• The guide and historical detail pages are now cross-linked:

• clicking a rule name in the guide jumps into historical detail with prefilled URL-query filters

• historical detail page shows the incoming filter source and offers a one-click clear-filters link

• historical detail page also supports expandable per-trade drilldown rows with quick same-rule navigation

• expanded detail rows now include previous/next same-branch trade navigation with auto-expand + scroll

• linked from the shared HTML nav as 指标原理

• Historical detail reporting now includes per-trade indicator audit windows.

• Each expanded row lazily renders buy-date and sell-date 前后 10 个交易日 mini-tables.

• Fields include close / a1 / b1 / c1 / KDJ / QL.

• Report generation can auto-refresh full indicator coverage through DragonIndicatorEngine.

• Persisted cache file: dragon_indicator_snapshot_full.csv.

• Embedded indicator coverage in detail HTML now reaches 2026-04-03 instead of stopping at 2026-01-30.

• Historical detail snapshots now also include per-branch same-window strategy events.

• Snapshot cards show rule summary plus tagged 真实BUY / 真实SELL / 辅助BUY / 辅助SELL entries in a 策略事件 column.

• Historical detail snapshots were then upgraded with a window-level event summary strip.

• Each snapshot card now summarizes the +/-10 trading-day window using embedded event payloads:

• total event count

• counts for 真实BUY / 真实SELL / 辅助BUY / 辅助SELL

• nearest event before the marker day

• same-day event summary

• nearest event after the marker day

• Implementation stayed inside dragon_html_reports.py; no strategy-core logic was changed.

• Verified by rerunning:

• py -3 -m py_compile dragon_html_reports.py

• py -3 dragon_html_reports.py

• and checking both root and archived historical detail HTML for snapshot-summary / 总事件 / 前一条 markers.

• Explicit handoff state for future Codex CLI/session switches:

• latest finished task is this historical-detail event-summary-strip enhancement

• refreshed artifacts:

• dragon_historical_trade_details.html

• html_reports/dragon_historical_trade_details_2026-04-03.html

• this was a reporting/auditability change only, not a strategy or signal-logic change

• A later formal code review on the current production chain found and fixed three practical issues:

• dragon_daily_signal_pipeline.py had two governance monitor metrics hard-coded (removed_trade_over_removal_count, local_sensitivity_robust_case_count)

• they now load dynamically from:

• dragon_glued_refined_removed_trade_attribution.csv

• dragon_glued_refined_sensitivity.csv

• next-bar execution models in both:

• dragon_daily_signal_pipeline.py

• dragon_refined_execution_validation.py

• no longer silently fall back to same-bar close when no next bar exists

• missing next-bar execution prices are now NaN and excluded by _apply_execution_model(...)

• dragon_forward_observation_pipeline.py weekly summary no longer duplicates global warning/divergence counts into every branch row

• it now adds a separate system_monitor row for global monitor-level counts

• verification reruns completed successfully after these fixes

• Later external review findings were also fixed:

• dragon_indicators.py no longer hard-codes a workstation-only dependency path

• it now resolves the repo root relative to __file__ and loads shared dependencies from <repo>/dragon

• dragon_alpha_first_baseline.py now filters branch trades by both buy_date and sell_date against the workbook event window

• this removes post-window exits from alpha-branch summary statistics

• alpha-vs-workbook trade-diff generation in the same script was aligned to the same filtered workbook trade set

• verified result:

• dragon_alpha_first_branch_trade_diff.csv no longer contains the old 2026-01-05 -> 2026-01-20 out-of-window trade

• dragon_alpha_first_baseline.md now reports removed trades 4

• A later consistency sweep standardized workbook-window trade filtering across the research/evaluation pack.

• Principle fixed:

• in workbook-window statistics, trade filters must constrain both buy_date and sell_date

• not only buy_date

• Updated scripts include:

• dragon_backtest.py

• dragon_cost_stress_test.py

• deep-oversold experiment scripts

• glued/refined candidate and branch-review scripts

• predictive-break experiment script

• dragon_rc1_release.py

• dragon_refined_alpha_attribution.py

• dragon_refined_execution_validation.py

• dragon_rule_ablation.py

• short-holding audit / experiment scripts

• dragon_strategy_overview.py

• dragon_threshold_perturbation.py

• Explicit exception kept:

• dragon_daily_signal_pipeline.py remains live/forward oriented and is not capped by workbook end date

• representative reruns succeeded after the sweep

• System review closure on 2026-04-05:

• fixed the last cross-report inconsistencies by unifying bounded evaluation windows and CAGR year calculations

• dragon_alpha_first_baseline.py now uses the same 2016-01-01 -> 2025-12-31 release window as dragon_strategy_overview.py and dragon_glued_refined_branch_review.py

• dragon_rc1_release.py and dragon_refined_execution_validation.py now derive evaluation years from the window dates instead of hard-coding 10.0

• important execution lesson: if dragon_system_review.py is run in parallel with its upstream generators, it can temporarily read stale outputs; rerun it after upstream artifacts complete

• final result after rerun:

• dragon_review_branch_metric_consistency.md shows 0 mismatches

• dragon_strategy_overview.csv is now aligned with the branch-specific summary and release artifacts

• review focus has shifted from internal metric-definition drift to governance / monitoring interpretation

• Later second-pass review on 2026-04-05 fixed additional governance/reporting defects:

• in dragon_alpha_branch_governance.py, alignment_cost_gate no longer incorrectly fails when overlap delta is positive

• in the same file, an empty removed-trade set no longer fails removal_quality_gate

• the same governance report's final recommendation section now follows final_decision instead of always defaulting toward keeping the old alpha branch

• in dragon_html_reports.py, system_monitor is now rendered with user-facing Chinese labels instead of raw internal names

• no new high-severity strategy-core issue was found in that second pass

• The next P1 engineering fix on 2026-04-05 closed the monitor-chain silent-failure gap:

• dragon_daily_signal_pipeline.py now marks missing metric values as missing_data instead of blank status

• dragon_forward_observation_pipeline.py now treats missing_data as hard-breach-equivalent for monitoring purposes while also tracking a separate missing_data_count

• daily/weekly HTML now expose the missing-metric count, and weekly HTML remains backward-compatible with older observation/divergence logs

• this means missing upstream research artifacts will now surface explicitly in monitoring rather than appearing as a false clean run

• The next major P2 refactor on 2026-04-05 introduced shared single-source modules:

• dragon_shared.py for evaluation window constants, year calculation, formatting helpers, and profit-factor helper

• dragon_execution_common.py for execution-aware apply/summary/risk-cluster helpers

• main-chain scripts and core branch-summary scripts were migrated to these shared modules

• this removed key script-to-script helper imports such as:

• daily pipeline importing formatting from dragon_strategy_overview.py

• daily pipeline importing execution helpers from dragon_refined_execution_validation.py

• post-refactor system review still reports 0 metric mismatches, so the consolidation did not change quantitative outputs

• Session governance update on 2026-04-05:

• the useful code changes for dragon/v2 were already committed in git as 9f7ce16 with message Add dragon v2 strategy research and reporting code

• user explicitly requires workspace memory files to be committed too, not just kept locally

• follow-up git work should therefore include MEMORY.md, USER.md, and daily notes under memory/ when they contain important session context

2026-04-07

• Performed a serious code-based overfitting review on the current dragon/v2 core strategy and parameter surface.
• Current verdict: not a fake strategy and not a pure one-threshold curve-fit, but still medium-to-high overfitting risk.
• Main positive evidence:
• branch-level alpha refinement only changes a small subset of parameters from the base branch
• walk-forward, local sensitivity, and execution/cost stress remain directionally supportive
• Main risk evidence:
• StrategyConfig exposes 102 fields
• dragon_strategy.py still contains a large hand-built rule tree with many narrow branches
• several low-sample special-case families remain, especially deep_oversold_*, predictive_b1_break_*, and one-off reentry/exit logic
• Strategic conclusion:
• strongest stable core remains glued_buy plus its refined selective filter family

• weakest families should be treated as provisional bridge rules or reconstruction residue, not core alpha

• Later on 2026-04-07, built an external consultation bundle for dragon/v2:

• directory: dragon_v2_consult_2026-04-07/

• zip: dragon_v2_consult_2026-04-07.zip

• bundle includes:

• a Chinese memo summarizing bottlenecks / risks

• a suggested ChatGPT Pro review prompt

• core source code and parameter snapshots

• the most relevant robustness / fragility reports

• selected supporting data and workbook exports

• Main bundle judgment:

• not a fake strategy, but still medium-to-high overfitting risk

• architecture bottleneck is the monolithic, order-sensitive rule tree in dragon_strategy.py

• biggest weak families remain deep_oversold_* and predictive_b1_break_*

2026-04-08

• Ran the daily live signal chain on request date 2026-04-08.
• Initially, latest available market bar resolved to 2026-04-07 and there was no buy trigger.
• Later the live signal path was upgraded to support intraday realtime evaluation:
• if today's official daily bar is missing, append one realtime snapshot and use the current market price as today's provisional close
• implemented in:
• dragon/data_fetcher_v2.py
• dragon_indicators.py
• dragon_daily_signal_pipeline.py
• Daily report and manifest now explicitly show:
• data_mode
• historical_latest_bar_date
• snapshot_timestamp
• Verified live run after the change:
• latest evaluated bar became 2026-04-08 via realtime snapshot while the historical latest official bar remained 2026-04-07
• all three tracked branches still remained flat and no new event fired
• latest real event for workbook_preserving, alpha_first_selective_veto, and alpha_first_glued_refined_hot_cap is still 2026-02-13 SELL knife_take_profit_2_glued
• latest realtime snapshot around 2026-04-08T14:47:31 had KDJ buy=True and QL buy=True, but the buy path was blocked in all three branches by buy_block_glued_high_weak_rebound
• Later analysis on 2026-04-08 confirmed buy_block_glued_high_weak_rebound is materially useful:
• official daily-bar replay through 2026-04-07 shows 17 historical blocks (15 inside 2016-01-01 -> 2025-12-31) on each tracked branch
• disabling only this filter adds 15 trades and weakens all three branches
• RC1 impact when disabled:
• trades 91 -> 106
• avg_return 3.42% -> 2.80%
• profit_factor 5.11 -> 4.08
• added trades are weak on average (-0.99%, win rate about 26.7%)
• current conclusion: keep this filter; it is a real quality-preserving filter rather than cosmetic complexity

2026-04-09

• Continued the RC1 restructuring proposal execution after user request to continue.
• Completed validation pass for the new compatibility-first metadata integration:
• dragon_strategy.py post-run enrichment for reason_layer/reason_family/reason_code on events and buy_reason_*/sell_reason_* on trades.
• Rebuilt golden baseline and refreshed dragon_rc1_golden_manifest.json (generated_at=2026-04-09T00:57:54).
• Golden summary remains stable:
• trade_count 91
• event_count 272
• win_rate 52.75%
• avg_return 3.42%
• New manifest now carries stable core hashes:
• events core sha256 8965d1b539a998d7d0aff04432aa2a47cf30ee40df013b9d8b7eb66a3d50a331
• trades core sha256 1298be56b0898266b0b854d62a979c00c20b01629393c82bb8c804faf852cb97
• Re-ran layer attribution; dragon_rule_layer_attribution.md still reports no unknown reason mapping.
• Added/ran tests and all passed:
• tests/test_rc1_golden_regression.py
• tests/test_reason_catalog_mapping.py
• py -3 -m unittest discover -s tests -v -> OK (3 tests).
• Smoke-regression on production path also passed:
• py -3 dragon_daily_signal_pipeline.py --as-of 2026-04-08 ran successfully after the metadata enrichment.
• Improved test portability:
• tests/test_reason_catalog_mapping.py now derives trade reasons directly from strategy output (snapshot + engine) instead of relying on dragon_historical_trade_details.csv.
• this removes a local-cache dependency and keeps tests runnable in cleaner environments.
• Added a fresh optimization proposal document for the next execution stage:
• dragon_v2_rc1_optimization_proposal_cn.md
• proposal scope focuses on phase-3+ execution: rule-engine split (core/secondary/bridge), structured state migration away from reason-string control coupling, weak-family optimization in a new branch with attribution-first acceptance, and stronger no-silent-path-change testing.
• User explicitly requested to推进 in OpenSpec form.
• Added local OpenSpec governance package under dragon/v2:
• openspec/config.yaml
• change set openspec/changes/rc1-layered-governed-optimization/ with proposal/design/tasks/spec docs covering layered rule-engine migration, structured state migration, weak-family governance, and golden regression gates.
• Later executed the full OpenSpec task pack in one uninterrupted run on user request.
• Main implementation outcomes:
• layered orchestration modules added:
• dragon_rule_engine_v2.py
• dragon_rules_core.py
• dragon_rules_secondary.py
• dragon_rules_bridge.py
• dragon_strategy.py kept as compatibility facade while adding structured control fields and bridge flags for predictive/pending chains.
• new regression tests added and all passing (8 total tests):
• test_no_silent_path_change.py
• test_bridge_chain_regression.py
• test_deep_oversold_pending_paths.py
• weak-family experiment pack executed with outputs:
• dragon_weak_family_experiment_summary.csv
• dragon_weak_family_trade_diff.csv
• dragon_weak_family_execution_stress.csv
• plus markdown/config snapshots.
• layered attribution outputs added via dragon_layered_pnl_attribution.py.
• acceptance report published at:
• openspec/changes/rc1-layered-governed-optimization/acceptance-summary.md
• tasks for this OpenSpec change are now fully checked in tasks.md.
• Then continued with a second OpenSpec change focused on physical isolation of weak-family predicates:
• openspec/changes/predictive-deepoversold-physical-isolation/
• Added dedicated modules:
• dragon_bridge_predictive_break.py
• dragon_deep_oversold_classifier.py
• dragon_deep_oversold_confirmation.py
• dragon_strategy.py now delegates predictive/deep-oversold predicate checks to those modules (compatibility-preserving).
• Added module tests:
• tests/test_predictive_bridge_module.py
• tests/test_deep_oversold_modules.py
• Validation remains green:
• RC1 golden summary and core hashes unchanged,
• attribution mapping still has no unknown reason mapping,
• full test suite reached 14 passing tests,
• daily signal pipeline smoke still passes.
• Continued with a third OpenSpec execution round:
• openspec/changes/execution-decoupling-rollout-governance/.
• Main engineering outcomes:
• decoupled compatibility runtime loop from dragon_strategy.py into dragon_execution_runtime.py,
• added rollout gate engine and checker (dragon_rollout_governance.py, dragon_rollout_governance_check.py),
• integrated checker into dragon_forward_observation_pipeline.py,
• rollout checker now outputs explicit operational decision artifacts each run.
• New rollout artifacts:
• dragon_rollout_state.json
• dragon_rollout_governance_snapshot.csv
• dragon_rollout_governance_report.md
• dragon_rollout_rollback_runbook.md
• Added test tests/test_rollout_governance.py; full suite now 17 tests passing.
• Revalidated guardrails after this round:
• golden core hashes unchanged (events 8965d1...a331, trades 1298be...cb97),
• daily and forward pipelines both passed smoke reruns.
• latest rollout decision artifact shows:
• decision=FORWARD_OK
• active_branch=alpha_first_glued_refined_hot_cap
• fallback_branch=alpha_first_selective_veto.
• one-click tracking entry update_dragon_reports.ps1 was upgraded:
• now prints latest bar + rollout decision + active/fallback branch + gate counts.
• supports -StrictGate (exit 2 when decision is not FORWARD_OK) and -OpenReport.
• T-day data freshness/caching fix was applied in dragon/data_fetcher_v2.py:
• when request end date covers today, fetch_index_data_v2 now bypasses stale cache and refreshes source data.
• fetch_index_data_with_latest_snapshot_v2 now writes merged latest snapshot back only when cache key already exists (skip if missing).
• added test file tests/test_data_fetcher_tday_cache.py; full suite reached 20 passing tests.
• On 2026-04-10, user clarified the core strategic objective more sharply:
• the biggest real problem is not the existence of the hard veto itself, but whether the system can rejoin trend quickly after a false veto.
• optimization priority should therefore center on false-veto recovery, timely trend followthrough reentry, and trend-profit capture, not just on reducing noisy trades.
• User also clarified operational scope: this should not become a big-platform research effort; keep it lean, regime-focused, and sized for a small team managing about 1M capital.
• Follow-up lean Phase 2 work was completed on 2026-04-10 under a new OpenSpec change:
• openspec/changes/followthrough-lean-profit-loop/
• Added standalone research script:
• dragon_followthrough_profit_loop_review.py
• Added focused regression test:
• tests/test_followthrough_profit_loop_review.py
• Generated outputs:
• dragon_followthrough_profit_loop_candidates.csv
• dragon_followthrough_profit_loop_candidate_summary.csv
• dragon_followthrough_profit_loop_reentries.csv
• dragon_followthrough_profit_loop_reentry_summary.csv
• dragon_followthrough_profit_loop_review.md
• Validation after this round:
• py -3 dragon_followthrough_profit_loop_review.py passed
• py -3 -m unittest discover -s tests -v passed with 25 tests
• Core findings from the lean profit-loop review:
• mid_zone_very_weak_b1 is the only subtype with a non-zero delayed reentry path (2 blocked samples, 1 confirmed probe reentry, 50% confirm-like 3-bar rate, avg next-20-bar max return about +7.04%)
• the current mid-zone probe trade still lost slightly (same_close -0.24%, next_open -0.50%) but had strong post-exit continuation (post_exit_max_10b +8.35%), which means the next worthwhile experiment is narrow and specific: execution timing plus entry-specific exit treatment for this subtype only
• high_zone_weak_b1 should not be promoted: 13 blocked samples, only 2 probe reentries, both losing (same_close avg -3.52%, next_open avg -5.03%)
• ql_rebound_weak_followthrough remains hard-block justified: now 3 full-history blocked samples, 0 confirmed probe reentries, completed historical windows show 0% confirm-like rate and 100% next-3-bar sell-cross rate
• latest live-relevant blocked case 2026-04-08 still sits in the ql subtype; through 2026-04-10 only 2/3 followthrough bars are observed, 0 ql reconfirm bars, and still no confirm-like delayed reentry
• Practical conclusion for next sessions:
• keep RC1 hard blocks unchanged for ql_rebound_weak_followthrough and high_zone_weak_b1
• if further work is done, keep it lean and only test mid_zone_very_weak_b1 on execution timing and entry-specific exit handling
• On 2026-04-11, the next narrow experiment was completed for the only remaining promising subtype:
• OpenSpec change:
• openspec/changes/followthrough-mid-exit-shadow-probe/
• New module:
• dragon_glued_followthrough_exit.py
• New shadow branch config:
• alpha_first_glued_followthrough_mid_exit_probe
• New review artifacts:
• dragon_followthrough_mid_exit_review.py
• dragon_followthrough_mid_exit_branch_summary.csv
• dragon_followthrough_mid_exit_trade_details.csv
• dragon_followthrough_mid_exit_trade_diff.csv
• dragon_followthrough_mid_exit_review.md
• New regression test:
• tests/test_followthrough_mid_exit_probe.py
• Also fixed a date-sensitive cache regression test so full-suite validation remains stable on weekends:
• updated tests/test_data_fetcher_tday_cache.py
• Validation after this round:
• py -3 -m unittest discover -s tests -v passed with 26 tests
• py -3 dragon_daily_signal_pipeline.py passed
• py -3 dragon_forward_observation_pipeline.py passed
• Core result:
• the new shadow branch only changes the 2020-12-01 mid-zone followthrough reentry path
• original mid probe path:
• 2020-12-01 -> 2020-12-09
• sell knife_take_profit_2_glued
• return about -0.24%
• new mid-exit probe path:
• 2020-12-01 -> 2021-02-19
• sell high_regime_confirmed_exit:kdj_sell
• holding 80 days
• return about +26.65%
• Path interpretation:
• the earlier loss was not a discovery problem anymore; it was an exit-treatment problem
• by not cutting the repaired reentry on the first mild kdj_only wobble, the shadow branch merged that path into the later high-regime trend instead of splitting it into a small loss plus a later re-buy
• Branch-level result on current replay:
• same_close compounded return improved from base 1424.12% to mid-exit probe 1460.02%
• next_open compounded return improved from base 1295.54% to mid-exit probe 1332.91%
• Current practical conclusion:
• keep this as a shadow branch and continue observing

• do not yet promote it to formal RC1 because the evidence still comes from one repaired-trend historical path

• 2026-06-04 daily/forward refresh:

• official source data refreshed through 2026-06-03, then intraday snapshot appended for 2026-06-04

• latest evaluated close 4439.771, a1=0.0439, b1=0.0123, c1=87.74

• latest markers all false (KDJ buy/sell=False, QL buy/sell=False)

• all tracked branches remain in position from 2026-05-06 BUY dual_gold_resonance_buy, open return about +10.33%

• no latest-bar events; forward rollout decision FORWARD_OK, active branch alpha_first_glued_refined_hot_cap