Ict Engine Runtime

ict-engine-runtime：运行时流水线

ICT-Engine 运行时流水线操作员。负责将因子研究产物转化为执行树决策。

Dense reclaim branch notes: references/low-hazard-reclaim-branch-pattern.md. Real-trade replay guardrails: references/real-trade-feedback-replay.md. Diagnostic/sample-trade replay pattern: references/diagnostic-feedback-replay-sample-logging.md. CRWD 5m PDA/MTF exact downstream repair: references/crwd-pda-mtf-exact-downstream-repair.md.

TradingView public strategy absorption: references/tradingview-public-strategy-absorption.md. Board A/B completion audit readback: references/board-ab-completion-audit-readback.md. Provider runtime repair: references/provider-runtime-repair.md.

核心流水线：

因子迭代 → 滤波 → BBN 证据 → CatBoost → 执行树

核心原则

1. 零配置：默认行为可直接运行，无需用户手动配置复杂参数
2. Token 友好：输出简洁，不输出冗余解释
3. 无污染：不修改仓库代码，通过外部脚本和状态目录隔离操作
4. 热插拔：用户可通过配置文件自定义权重和特征

流水线阶段

Stage 1：因子迭代产物导入

# 导入 Auto-Quant 策略库
./target/debug/ict-engine auto-quant-results-import \
  --symbol <SYM> --state-dir /tmp/<state> \
  --library strategy_library.json

# 应用 BBN prior
./target/debug/ict-engine auto-quant-prior-init \
  --symbol <SYM> --state-dir /tmp/<state>

Stage 2：滤波层（regime_filter.rs + market_state/）

当前仓库支持的滤波：

• HMM/波动率状态过滤
• 多周期共振过滤
• 市场状态分类（新增）

市场状态分类器 (src/market_state/)：

use ict_engine::market_state::{MarketStateClassifier, MarketStateConfig};

// 零配置
let classifier = MarketStateClassifier::new();
let snapshot = classifier.classify(&candles);

// 热插拔配置
let config = MarketStateConfig::load(Path::new("market_state_config.json"))?;
let classifier = MarketStateClassifier::with_config(config);

主大类（PrimaryMarketRegime）：

• TrendExpansion — 趋势扩展（高波动+高流动性+强结构）
• RangeConsolidation — 震荡整理（低波动+正常流动性+弱结构）
• ExtremeStress — 极端状态（危机波动 或 流动性枯竭）
• ReversalBrewing — 反转酝酿（行为极端+结构弱化）

维度分类器：

热插拔配置模板：config/market_state_user_weights_template.json

置信度调优参数（v6）：

• 基础置信度：0.20（避免过低综合置信度）
• 结构权重：50%（趋势识别核心）
• 一致性权重：30%（部分匹配得分）
• 趋势阈值：0.50 / 极端阈值：0.75-0.80 / 反转阈值：0.50

验证结果：

• 平均置信度：61%（目标 >50%）
• 高置信比例：4.17%（目标 >30%）
• 可交易比例：79%（目标 >60%）
• VeryLow 比例：0%

主大类分布：

• TrendExpansion: 41.7% / RangeConsolidation: 41.7% / ExtremeStress: 16.7%

验收标准：每个因子必须声明"允许进入的滤波状态"。

参考文档：references/market-state-confidence-tuning.md — 置信度调优迭代历史与参数

高置信 regime 跑法：references/regime-confidence-factor-runs.md — 95% 置信诉求下的运行命令、覆盖口径、OOS sanity check、已知坑

完整闭环跑法：references/auto-quant-bbn-catboost-execution-tree-closure.md — Auto-Quant → BBN prior → path ranker/CatBoost fallback → execution tree 的实跑顺序、验证口径、已知坑

Regime-rooted provider Auto-Quant chain：references/regime-branch-provider-autoquant-chain.md — 当盈利因子必须按 主regime -> 子regime -> 子子regime/盈利因子 -> 盈利因子 分支，并且要亲跑 provider/Auto-Quant/BBN/CatBoost/execution-tree 时使用；含字段、命令、验收与 repo hygiene。

真实 Auto-Quant 闭环样例：references/auto-quant-runtime-closure-real-run-20260509.md — 记录一次完整实跑：bootstrap/prepare、seed archived strategies、uv run --with ta-lib run.py、手工 v3 manifest、n_ok=2、BBN strategies_applied、ranker fallback 注册、workflow-status 显示执行树结果。

v0.4.1 阻塞修复：references/auto-quant-v041-exporter-catboost-blockers.md — 修 Auto-Quant 多 timerange exporter 导致 BBN trade_count=0、CatBoost 安装/NaN label/真实模型优先注册问题

全仓审计跑法：references/repo-audit-and-consumer-review.md — 架构、功能、闭环、测试、实际体验、消费者视角、开源贡献者视角的只读审计流程与已知坑。

因子到实战建议闭环审计：references/repo-audit-factor-to-execution-closure.md — 审计因子迭代、滤波/regime、BBN 证据、CatBoost/path ranker、执行树、recommended_command 是否真实贯通；含本次发现的常见断链清单。

运行时闭环实现笔记：references/runtime-closure-implementation-notes.md — 将审计缺口落成代码时使用；覆盖 market_state 接入 analyze/BBN/trace、pass_to_bbn=false neutral soft evidence、path-ranker lineage、schema 扩字段编译坑、验证命令。

Heuristic Learning 模块收割：references/heuristic-learning-module-harvest.md — 当用户要求把论文/GitHub 公式、算法、模块拆下来拼进 ict-engine 自迭代链路时使用；含 Triple Barrier、DSR/PBO、Qlib/Alpha101、regime calibration、BBN evidence valuation、path ranking、payoff-shape 的优先接入顺序。

高夏普因子收割 + seed library：references/high-sharpe-factor-harvest-seed-library.md — 当继续从论文/开源仓库收割跨市场/期权高夏普候选，并生成零配置 hot-plug factor seed candidate JSON 时使用；含 16 个首批候选、用户 VRP/NQ 可选字段、验证命令和坑。

Heuristic Learning 第一刀：references/heuristic-learning-first-slice.md — 当用户要求先实现 labeling_triple_barrier.py + factor_payoff_shape_report.py，或继续做到零配置 payoff pipeline / DSR guard / payoff-gated path-ranker target export 时使用；含 TDD 路径、CLI、字段、短空 stop 符号坑、thin_density 非硬拒绝规则、热插拔 profile、PSR/DSR 字段、probe/promote 进 path-ranker+BBN、reject 仅进 failure memory。

Heuristic Learning Payoff + Regime Chain：references/heuristic-learning-payoff-chain.md — 当继续 ICT Engine 自迭代/高夏普防幻觉/95% regime confidence 链路时使用；记录已完成的 sidecar 链：Triple Barrier → payoff report → PSR/DSR → Purged CV/PBO → path-ranker target → BBN gate/failure memory → regime confidence report；含用户 VRP/NQ 辅助字段、验证命令、多 agent dirty worktree 处理规则、下一刀 transition evidence aggregator。

Backtest hotplug + auxiliary / Analyze PDA events：references/backtest-hotplug-auxiliary-and-analyze-pda-events.md — 当补齐 factor-backtest 与 analyze 的行为级闭环时使用；覆盖 --auxiliary-evidence、MTF path 透传、FactorHotplugConfig 注册、H4/D1/W1 PDA events 注入 FactorContext、定向测试与实跑验证坑。

Heuristic Learning sidecar slices：references/heuristic-learning-sidecar-slices.md — 当继续 docs/plans/2026-05-09-heuristic-learning-execution-todo.md 或类似自迭代链路时使用；覆盖 BBN evidence value、risk-adjusted utility、formula seed library、paper2code adapters、payoff pipeline sidecar closure 的 TDD/验证/提交/闭环审计模式。

High-Sharpe factor harvest + payoff gates：references/high-sharpe-factor-harvest-and-payoff-gates.md — 当继续 docs/plans/2026-05-09-high-sharpe-factor-harvest-handoff-todo.md 或从论文/开源仓库收割高夏普因子时使用；覆盖 16 个 seed candidate、用户可选字段、R22 seed library、R23 payoff gate、DSR/PBO/CVaR/tail-risk failure tags、license posture。

MTF / FactorContext 小周期 PDA events：references/mtf-factorcontext-small-timeframe-pda-events.md — 当补齐 30m 与 m1/m5/m15/m30/h1 PDA event 透传时使用；覆盖 FactorContext、AnalyzeNativeFrames、research/backtest/debug/lifecycle 调用点、测试签名与 cargo test --bin ict-engine multi_timeframe 验证坑。

Regime ontology manifest slice：references/regime-ontology-manifest-slice.md — 当做高置信 regime classifier 的 R2 / expert-bank ontology manifest 时使用；记录 53 experts 目标形状、Unknown/Neutral abstain 规则、TDD 与验证命令。

Regime feature builder slice：references/regime-feature-builder-slice.md — 当继续高置信 regime classifier 的 R3 / feature-builder sidecar 时使用；记录 OHLCV/aux/MTF 输入、VRP/NQ 字段透传、特征输出、TDD 与汇报坑。

Regime classifier sidecar chain：references/regime-classifier-sidecar-chain.md — 当继续高置信 regime classifier 的 R2-R17 ontology/feature/discovery/expert-trainer/calibration/transition/consumer-bundle/mainline-adapter/read-only BBN evidence sidecar，或要写 handoff TODO 时使用；含文件清单、验证命令、CLI smoke、下一步和提交避污坑。

主线 regime/CatBoost/execution 审计：references/mainline-regime-catboost-execution-audit.md — 当审计 regime -> factor-research -> factor-backtest -> analyze/live -> recommendation 是否真实贯通时使用；含隔离实跑命令、字段覆盖表、已知弱链。

Market-state threading into factor reports：references/market-state-threading-factor-reports.md — 当 P1 缺口是 factor-research / factor-backtest 未报告 primary/secondary market_state 时使用；含 TDD、实现形状、CLI runtime smoke 和 passthrough 坑。

Execution trace consumer fields：references/mainline-execution-trace-consumer-fields.md — 当 P2/P3 缺口是 path-ranker/CatBoost 只存在文本 lineage、execution_tree_trace 缺稳定机器字段、triage 缺短理由摘要时使用；含字段形状、TDD 锚点、commit 避污恢复。

Mainline consumer reason field：references/mainline-consumer-reason-field.md — 当最终消费者需要一个干净短字段合并 market_state + execution branch/gate/bias + ranker source/model/ready 时使用；含 ExecutionTriage.consumer_reason、ExecutionTreeOutput.consumer_reason trace 下沉、normal analyze/analyze-live 报告透传、human 首行和真实 CLI 验证清单。

Regime consumer bundle + mature ranker validation：references/regime-consumer-bundle-and-mature-ranker-validation.md — 当收束 regime consumer bundle 接入、read-only BBN soft evidence 映射、或复验 mature registered-model path-ranker runtime 时使用；含 trace 字段、bundle fixture、python3 smoke、mature state 结论。

Regime bundle BBN soft evidence opt-in：references/regime-bundle-bbn-soft-evidence-opt-in.md — 当继续 regime consumer bundle -> BBN 主线接入时使用；记录默认 read-only、--apply-regime-bundle-bbn-soft-evidence 显式生效、analyze/analyze-live adapter 去重、isolated smoke、只提交本轮文件。

Regime paper-backed feature wiring audit：references/regime-paper-backed-feature-wiring-audit.md — 当用户问论文能给什么 regime 证据、是否还要跑 regime、或要审计 HV/IV/VIX/VVIX/change-point/directional-change/correlation-dispersion 是否进入 sidecar/BBN/CatBoost/execution trace 时使用；核心结论是少加标签，多查证据接线。

Regime consumer auxiliary evidence wiring：references/regime-consumer-aux-wiring.md — 当 Board A 需要把 consumer_hints.user_vrp_nq_context 的 HV/IV/VIX/VVIX/VRP-NQ 辅助字段稳定下沉到 BBN/pre-Bayes、structural path-ranking/CatBoost target、execution tree trace 时使用；含字段名、验证断言和多 agent 边界。

IBKR intraday/options regime + AQ closure：references/ibkr-intraday-options-regime-aq.md — 当 Board A 需要 1m/5m/15m/30m K线、IBKR 期权/HV/IV、或用户追问是否真的用了 Auto-Quant 时使用；含 ib_async runner、ts -> timestamp AQ 归一化、validator/CatBoost/AQ 判读坑。

IBKR options regime-rooted AQ chain：references/ibkr-options-regime-rooted-aq-chain.md — 当盈利因子必须由真实 IBKR option premium/HV/IV 锚定，并带 主regime -> 子regime -> 子子regime/盈利因子 -> 盈利因子 通过 provider portability、AQ、BBN、真实 CatBoost、execution-tree 时使用；含 uv run --with catboost 重试、成熟度 fail-closed 判读。

IBKR options overlap / HTF neutralization：references/ibkr-options-overlap-neutralization.md — 当小周期 IBKR 期权因子都赚钱但可能是同一信号时使用；含 entry overlap、Jaccard/one-way overlap、HTF hard-gate 真实回测判读。

IBKR cross-asset vol/gamma proxy AQ：references/ibkr-crossasset-vol-gamma-proxy-aq.md — 当用户要求用 IBKR 在股指期货、黄金期货、美股上跑波动率/gamma-wall/IV/OI/Greeks 类盈利因子时使用；含真实数据边界、multi-asset Tomac、basket analyze、CatBoost 注册坑与 fail-closed 判读。

IBKR cross-asset offline parity refinement：references/ibkr-crossasset-offline-parity-refine.md — 当本地 IBKR/Freqtrade feather 可用但 Freqtrade 被 CCXT markets reload/联网元数据阻断时使用；用离线 parity evaluator 区分策略质量与 market metadata 故障，并把候选以 provenance + fail-closed flags 写回 strategy_library。 IBKR max-window expansion / opportunity density：references/max-window-expansion-and-opportunity-density.md — 当用户要求“10Y if possible”或想判断样本少是时间窗还是机会稀少时使用；记录 asset/bar-size ceilings、contract quirks、和严格信号密度判读。

IBKR dense K-line practical gates：references/ibkr-dense-kline-practical-gates.md — 当用户要求 IBKR 优先、尽量一个月/一季度上限窗口、从 1min 出发并覆盖 5min/15min/30min/1h，且最终以实战盈利 gate 为准时使用；含 5min 主信号、1min 入场时机、HTF gate 的 fail-closed 判读。

Provider runtime repair：references/provider-runtime-repair.md — 当 provider-status 报 IBKR 依赖缺失、TradingViewRemix/TVR connectivity failed/429，但本机有可用 Python/本地 MCP stdio 时使用；含 zsh login PATH、~/.zprofile、ICT_ENGINE_TRADINGVIEW_MCP_*、provider-status 与实抓验证。

Board A provider matrix fail-closed：references/board-a-provider-matrix-fail-closed.md — 当 Board A 要求跨 yfinance/TVR/IBKR/Kraken 验证 95% regime confidence 时使用；记录 provider-ready 不等于 fetch-success、混合 harness 失败后需单 provider 复测、AQ seed_required 是 Board B 边界。

Dense K-line regime branch chain：references/dense-kline-regime-branch-chain.md — 当用户要求多跑 1m/5m/15m/30m 增样本，并且仍要保留 主regime -> 子regime -> 子子regime/盈利因子 -> 盈利因子 过 Auto-Quant/BBN/CatBoost/执行树时使用；含 timerange、local Auto-Quant checkout、CatBoost train/apply、trace.output 验收坑。

Dense K-line real trade feedback replay: references/dense-kline-real-trade-feedback-replay.md — when dense branch要从 Auto-Quant/Freqtrade 聚合结果进入 structural feedback 时使用；必须先导出真实 trades 行并校验 sum(profit_abs) == profit_total_abs，再回灌 BBN/CatBoost/执行树，禁止用 aggregate summary 合成反馈.

Provider-quartet regime-rooted AQ chain: references/provider-quartet-regime-rooted-aq-chain.md — when a Board B profitability factor must survive yfinance/TradingViewRemix/IBKR/Kraken through AQ, BBN, CatBoost, and execution tree with branch fields intact.

Provider-quartet retry + fail-closed CatBoost handoff: references/provider-quartet-provider-retry-and-failclosed-catboost.md — when provider-status is green but first fetches fail, or CatBoost setup is flaky; retry provider axes with operator-like zsh/env, preserve branch fields, and do not claim CatBoost/promotion when fallback ranker or observe/transition_guardrail remains.

Regime-rooted live-ready factor gate: references/regime-rooted-live-ready-factor-gate.md — when continuing profitability-factor training under the user's strict rooted-branch and live-ready requirements; covers full branch-path shape, 1m/full-ladder attempts, cost/density Gate 1, fail-closed downstream admission, and the CRWD-style mature-row blocker pattern. CRWD 5m PDA/MTF downstream fail-closed pattern: references/crwd-5m-pda-mtf-downstream-failclosed.md — exact branch/evidence snapshot for a cost-positive, hazard/readiness-passing CRWD 5m branch that remains observation-only because mature rows are insufficient; reminds agents to inspect nested execution-tree ranker fields before judging ranker absence.

Dense downstream SIGTERM readback: references/dense-downstream-sigterm-readback.md — when a downstream factor-training runner exits 143/-15, classify the process as incomplete, inspect sibling completed downstream-* runs, and fail-close repeated high-hazard/PDA-mismatch branches instead of rerunning blindly.

Provider portability before promotion: references/provider-portability-before-promotion.md — when an IBKR-specific MTF factor is promising but a YF/TVR/IBKR/Kraken or similar provider-portability AQ rank is mixed/negative; stop before downstream if no provider row is positive, while preserving the original provider-specific branch as incubate-only.

IBKR ORB/RVOL regime-rooted chain session: references/ibkr-orb-rvol-regime-chain-session.md — when turning public ORB/RVOL or similar sourced factors into 1m -> 5m -> 15m -> 30m -> 1h materials; records the IBKR QQQ ORB/RVOL Gate 1 + BBN/CatBoost/execution-tree fail-closed pattern, provider quartet probes, and exact promotion blockers.

External harvest -> offline practical candidates: references/external-harvest-offline-practical-candidates.md — when the user asks to self-source papers/repos/X/open-source strategies and move toward usable factors; reimplement formulas as sidecar candidates, benchmark on existing provider CSVs, import survivors through BBN/CatBoost/execution tree, and fail-closed until mature feedback exists.

BBN / execution-tree stagnation diagnostic: references/bbn-execution-stagnation-diagnostic.md — when factor training shows no breakthrough; separates candidate quality, BBN gating, CatBoost/runtime consumption, mature target rows, entry-model bridge gaps, and execution-tree guardrails.

Bridge gap vs execution admission: references/bridge-gap-vs-execution-admission.md — when bridge_needs_confirmation is cleared but execution_tree_trace.json remains transition_guardrail / observe; separates bridge threshold, transition hazard/PDA disagreement, and execution-readiness gates.

Mature target rows from feedback replay: references/mature-target-rows-from-feedback-replay.md — when analyze exists but target maturity/training-weight rows are zero; use copied state + RealTradeRecord JSONL ingest + re-export, with strict truth labels for sample-trade replay vs live fills.

Stage 3：BBN 证据节点（bbn/evidence.rs）

用户特定特征（VRP V2 相关）：

• qqq_hv_level：QQQ 历史波动率水平
• nq_vs_200d_pct：NQ 相对 200 日均线位置
• vix3m_level：VIX3M 水平
• qqq_hv_pct_rank_252：QQQ HV 252 日百分位
• vvix_over_vix：VVIX/VIX 比率

验收标准：BBN 后验概率更新必须可追溯。

Stage 4：CatBoost 路径排名

外部训练器：scripts/auto_quant_external/pandas_path_ranker_trainer.py

# 导出 target
./target/debug/ict-engine export-structural-path-ranking-target \
  --symbol <SYM> --state-dir /tmp/<state>

# 训练/生成 scores
python scripts/auto_quant_external/pandas_path_ranker_trainer.py \
  --apply --target-csv <target.csv> --output-scores scores.csv

# 应用 scores
./target/debug/ict-engine apply-structural-path-ranking-external-scores \
  --symbol <SYM> --state-dir /tmp/<state> --scores-file scores.csv

# 注册训练器
./target/debug/ict-engine register-structural-path-ranking-trainer-artifact \
  --symbol <SYM> --state-dir /tmp/<state> \
  --artifact-uri file://trainer_artifact.json \
  --model-family catboost --score-column raw_path_score

训练器特性：

• 零配置：默认行为可直接运行
• 热插拔：用户可通过 user_weights.json 自定义权重
• 回退机制：当 VRP V2 特征缺失时，使用 structural_baseline_score 或 current_posterior

Stage 5：执行树（execution_tree.rs）

执行树分支：

• block_crowded：拥挤阻断
• wait_for_reversion：等待回归
• fill_viable：填充可行
• transition_guardrail：转换护栏

验收标准：执行树 trace 必须包含 CatBoost 贡献记录。

Pitfalls

Repo closure audit: defined module is not enough

When auditing factor iteration → practical advice, prove each layer is consumed by the next runtime layer, not merely present in source:

• market_state primary/secondary labels must enter main analyze, BBN evidence, execution tree, or workflow/report output; otherwise they are sidecar code.
• Profit-factor materials must carry regime branch fields (main_regime, sub_regime, sub_sub_regime_or_profit_factor, profit_factor, regime_profit_branch_path) before Auto-Quant dispatch. If rank output loses these fields, stop before BBN/CatBoost.
• BBN pass_to_bbn=false must actually block or explicitly neutralize inference; a recorded flag alone is not closure.
• CatBoost/path-ranker scores must appear in execution_tree_trace.json if the acceptance criterion says execution tree trace contains CatBoost contribution. Workflow-status visibility alone is not enough.
• recommended_command often changes only indirectly through selected path; if the user asks for practical advice evidence, check human next-action text includes ranker/BBN/regime reasons.
• Before validation, run git status --short; dirty unrelated edits can block cargo check. Report that rather than patching unrelated work.
• Multi-agent Board docs are authoritative. Claim a unique lane in the plan/doc first, append Done/Blocked/Handoff evidence after, and do not edit or rerun another agent's active/completed branch.

See references/repo-audit-factor-to-execution-closure.md and references/regime-branch-provider-autoquant-chain.md for the full checklist.

Provider readiness parity: shell matters

When provider-status or provider fetches look unhealthy from Hermes, verify with the operator shell before diagnosing provider failure. On this host, non-login bash may resolve python3 to Anaconda without ccxt/redis/ib_async, while zsh login resolves the provider-ready Python. Use:

zsh -lc 'cd ~/projects-ict-engine/ict-engine && .local-artifacts/cargo-target/debug/ict-engine provider-status --compact'

or the explicit provider Python ~/.venvs/ict-engine-provider-py313/bin/python for fetch scripts. Capture this as a runtime-parity fix, not as provider failure. See references/provider-runtime-repair.md.

IBKR max-window and sparse-signal traps

When the user asks for 10Y/history expansion, do not assume one duration works for every asset or bar size. Probe the largest usable ceiling per asset/bar size, then remeasure strict signal density. Very high win rate with tiny trade count usually means sparse opportunity density, not a durable edge. See references/max-window-expansion-and-opportunity-density.md.

No-breakthrough diagnosis: prove the failing layer

When factor training looks stalled, do not jump straight to new factor ideation. First audit the existing chain with references/bbn-execution-stagnation-diagnostic.md:

• Gate 1 failed, downstream_allowed=false, or strict_training_rows=0 means candidate quality / schema is the blocker; stop before BBN.
• BBN is healthy if strategies_applied, evidence_value_gate_passed, entropy/log-loss deltas, and posterior changes exist.
• CatBoost/path-ranker is healthy if execution tree has output.path_ranker_score_visible_to_execution_tree=true and output.path_ranker_score_used_by_execution_tree=true.
• No promotion despite healthy BBN/ranker usually means mature_rows=0, rows_with_training_weight=0, entry_models[*].matched_rows=0, closed_loop_branch_admission.status=fail_closed, bridge_needs_confirmation, or transition_guardrail.
• If real trades were ingested but target/entry rows stay zero, the missing bridge is feedback ledger -> policy/entry training rows, not another BBN run.

Target CSV 行数不足

症状：target CSV 只有几行，特征列缺失。 原因：VRP V2 等因子特征未在 analyze 阶段注入。 对策：训练器使用 structural_baseline_score 回退，生成可用分数。

CatBoost/XGBoost 未安装

症状：训练器报 ERROR: catboost not installed 或 RuntimeError: catboost requested but not installed。 对策：若验收要求真实 CatBoost，先用 uv run --with pandas --with numpy --with catboost python support/scripts/auto_quant_external/pandas_path_ranker_trainer.py ... 重跑 train/apply；不要只因当前解释器缺包就降级。若任务允许 fallback，再使用加权求和回退模式，基于 structural_baseline_score 生成分数。 重要：若训练器产物 trainer_artifact.json 写的是 model_family=weighted_feature_sum_v1，注册时必须使用同一 --model-family weighted_feature_sum_v1；不要把 fallback 产物硬注册成 catboost。

成熟样本为 0

症状：policy-training-status 显示 mature_rows=0 或 rows_with_training_weight=0，但 analyze snapshot / BBN / path-ranker 分数已经存在。 原因：目标数据缺少可消费的结构化反馈记录，或反馈未桥接到 policy/entry training rows。 对策：优先确认 workflow_snapshot.latest_analyze 存在；若需要验证反馈桥，复制 state 后按 references/mature-target-rows-from-feedback-replay.md 用 auto-quant-ingest-real-trades 回灌 RealTradeRecord JSONL，再 export-structural-path-ranking-target。如果只是 AQ sample_trades 回放，必须标成 diagnostic/sample replay，不可当 live fill。执行树仍 observe/transition_guardrail 时继续 fail-closed。

Dense K-line / Auto-Quant mixed-packet pitfalls

• Auto-Quant material timerange must be a valid YYYYMMDD-YYYYMMDD; descriptive placeholders such as source_artifact_window make Freqtrade fail before ranking.
• pandas_path_ranker_trainer.py training writes the CatBoost model and trainer_artifact.json; run a second --apply --model-dir ... --output-scores ... pass before apply-structural-path-ranking-external-scores.
• execution_tree_trace.json stores ranker evidence under output.*; check nested output.path_ranker_score_visible_to_execution_tree, output.path_ranker_score_used_by_execution_tree, and output.path_ranker_model_family.
• If Auto-Quant bootstrap cannot clone remotely but an existing checkout is present under another run's state/auto-quant/.deps/auto-quant, pass that local path via --repo-url; capture this as a reproducibility workaround, not as a permanent network/tool limitation.
• Dense samples can reveal timeframe split behavior. If 1m/5m are positive but 15m/30m are negative, mark the mixed packet incubate or split branches; do not promote on aggregate enthusiasm.
• Before structural-feedback replay, export real per-trade rows from Freqtrade/Auto-Quant trades; aggregate rank summaries are not acceptable feedback source data. Verify sum(profit_abs) == profit_total_abs and preserve branch path + provider/timeframe provenance.
• For repeated replay sessions on the same symbol, read state/<SYMBOL>/learning_state.json first and skip already-consumed feedback rows instead of replaying from zero. This keeps replay idempotent and avoids inflating history with duplicate rows.
• When a feedback PnL can be negative, pass --pnl=<value> as a single CLI token. Do not split it into --pnl <value> or the leading minus may be parsed as a new flag.
• For repeated replay sessions on the same symbol, read state/<SYMBOL>/learning_state.json first and skip already-consumed feedback rows instead of replaying from zero. This keeps replay idempotent and avoids inflating history with duplicate rows.
• When a feedback PnL can be negative, pass --pnl=<value> as a single CLI token. Do not split it into --pnl <value> or the leading minus may be parsed as a new flag.

市场状态置信度过低

症状：分类器输出高置信比例 < 5%，大量 VeryLow 样本。 原因：

1. 各维度置信度计算过于严格（中间值得分接近 0）
2. 一致性计算采用"全有全无"模式
3. 聚合阈值过高
4. 极端状态检测过于敏感

对策：

1. 添加基础置信度（vol: 0.35, liq: 0.30, overall: 0.20）
2. 一致性采用部分匹配得分（默认 0.2，部分 0.4-0.5）
3. 降低分类阈值（trend: 0.50, reversal: 0.50）
4. 提高结构权重至 50%
5. 收紧极端状态阈值（波动: 0.75, 流动性: 0.80）

参考：references/market-state-confidence-tuning.md

状态验证

# 检查训练状态
./target/debug/ict-engine policy-training-status \
  --symbol <SYM> --state-dir /tmp/<state> --human

# 检查执行树
./target/debug/ict-engine workflow-status \
  --symbol <SYM> --state-dir /tmp/<state> --human

关键指标：

• trainer_artifact=ready — 训练器已注册
• raw_scored_mature >= 30 — 有足够成熟 target rows；注意这是 target-row 口径，不等同于 feedback observation 条数
• calibration=not_fitted — 需要更多校准数据

多维度覆盖要求

每次因子迭代必须满足：

多品种

• 指数期货：NQ, ES, YM, RTY
• ETF 代理：SPY, QQQ, IWM, DIA
• 商品/金属：GC, CL, XAU
• 外汇：EUR, GBP, JPY
• 个股：AAPL, MSFT, NVDA, TSLA
• 加密：BTC/USDT, ETH/USDT, SOL/USDT

多时间周期

• 1m → 5m → 15m → 1h → 4h → 1d → 1w → 1M

多共振

• 低周期触发必须检查高周期共振
• 共振结果：aligned / contradicted / neutral / missing

迭代不理想时

• 去 arXiv 搜索：trading factor + machine learning
• 去 GitHub 搜索：trading strategy + factor library
• 搜索：momentum factor / mean reversion factor / volatility risk premium
• 搜索：ICT trading + smart money concepts + factor

Factor Hot-Plug Architecture

8 Factor Categories (Rust Enum)

Adding a New Factor Family

1. Add variant to FactorCategory enum in src/factor_lab/factor_definition.rs
2. Add fn <variant>() -> FactorDefinition constructor
3. Register in FactorRegistry::default() in src/factors/registry.rs
4. Add compute path: fn evaluate_<variant>(&self, candles) -> Vec<FactorSignal>
5. Add dispatch arm in FactorDefinition::evaluate() match
6. Add allowed_roles() arm for new category
7. Add is_footprint_context_only() variant if applicable
8. Add 3 mutation arms: mutation_parameter_group, mutation_direction_hint, mutation_step_size_hint
9. Update AGENTS.md traceability table
10. Update docs/factor-catalog.md

Why Agents Say "No Factors"

Root causes and fixes:

1. No AGENTS.md → agents had no entry map. Fix: AGENTS.md now exists with full traceability table.
2. Families E/F/H had zero code → grep returns nothing. Fix: enum variants + compute stubs added.
3. Factor code split across factor_lab/ and factors/ → no index. Fix: docs/factor-catalog.md single-page index.
4. 5500-line TODO doc unscannable → agents can't parse 436KB. Fix: catalog + AGENTS.md provide scannable summaries.

Auto-Quant Output Path Isolation

Auto-Quant artifacts MUST NOT pollute the repo root. Path resolution:

ICT_ENGINE_AUTO_QUANT_OUTPUT_DIR env var  (highest priority)
  → if set and non-empty, use that path
  → else: <state-dir>/auto-quant/  (subdirectory, never root)

Implementation: resolve_auto_quant_output_dir() in src/main.rs. All auto-quant shell functions in src/auto_quant_command.rs resolve through aq_state_dir().

Default behavior unchanged: --state-dir /tmp/... still works. But when using default state/, auto-quant now lands in state/auto-quant/ instead of state/.

Agent Entry Map

AGENTS.md at repo root is the first file any AI agent should read. It contains:

• Factor traceability table (Rust enum → family → code location → status)
• Design-level family gaps (what's missing beyond the 8 Rust categories)
• Key source paths (factor_lab, factors, factor_lifecycle, regime, bbn, execution_tree)
• Hot-plug convention (how to add new families)
• Architecture rules (zero-config, token-friendly, no pollution, auto-quant isolation)

Pitfalls (new)

Adding FactorCategory but forgetting mutation surface

Symptom: cargo check fails with non-exhaustive patterns on mutation_parameter_group, mutation_direction_hint, or mutation_step_size_hint. Root cause: These three methods all match on self.category and require arms for every variant. Fix: Add a match arm for the new variant in ALL THREE methods. The minimal pattern is:

FactorCategory::NewVariant => match reason {
    "balanced_accuracy_regressed" | "bull_bear_separation_regressed"
    | "bull_bear_separation_weak" | "worst_market_separation_weak" => vec![...],
    "bridge_gap_regressed" | "bridge_gap_too_small"
    | "worst_market_bridge_gap_too_small" => vec![...],
    "pre_bayes_gate_regressed" | "pre_bayes_gate_observe_only"
    | "pre_bayes_gate_neutralized" => vec![...],
    _ => Vec::new(),  // or BTreeMap::new() for the hint methods
},

Cargo check: non-exhaustive patterns after FactorCategory extension

Symptom: 3 compile errors all pointing to the same new variants. Root cause: Three match blocks in factor_definition.rs all need exhaustive arms. Fix: Add arms in mutation_parameter_group (line ~528), mutation_direction_hint (line ~637), mutation_step_size_hint (line ~769). All three must be covered or cargo check will not pass.

Impl block after #[cfg(test)] mod tests — clippy items-after-test-module

Symptom: cargo clippy -D warnings fails with error: items after a test module. Root cause: Adding compute stubs as a second impl FactorDefinition {} block at the end of factor_definition.rs, after the #[cfg(test)] mod tests block. Clippy forbids items after test modules. Fix: Move ALL impl blocks BEFORE #[cfg(test)] mod tests. The test module must be the last item in the file. If you need to add methods, insert them into the first impl block or place the new impl block before the test module.

FactorHotplugConfig import path differs between lib and bin crate

Symptom: failed to resolve: unresolved import for FactorHotplugConfig. Root cause: src/ files included via mod in main.rs are in the binary crate, not the lib crate. Lib crate files use crate::factors::FactorHotplugConfig. Binary crate files (main.rs, its direct mods like auto_quant_command.rs, factor_research_runtime.rs) must use ict_engine::factors::FactorHotplugConfig. Fix: Check which crate the file belongs to. If src/lib.rs re-exports it → crate::. If it's a direct mod in main.rs → ict_engine::.

Auto-Quant shell functions must resolve output dir through aq_state_dir()

Symptom: Auto-Quant artifacts land in repo root state/<SYM>/ instead of state/auto-quant/<SYM>/. Root cause: Shell functions in src/auto_quant_command.rs passed state_dir directly to underlying commands, bypassing resolve_auto_quant_output_dir(). Fix: Every auto-quant shell function must call aq_state_dir(state_dir) (which calls resolve_auto_quant_output_dir) and pass the resolved path to the command layer. New shell functions MUST NOT pass raw state_dir to auto-quant commands.

experiment state_* dirs polluting repo root

Symptom: 20+ state_autoresearch_*, state_cluster_*, etc. scattered at repo root. Root cause: Auto-research and other commands use --state-dir state_<experiment> which creates directories at the repo root level. Fix: Consolidate experiment state dirs under state_experiments/. Add state_experiments/ to .gitignore. Production state stays in state/ (already gitignored).

联系文档

• 因子迭代 board：docs/plans/2026-05-05-execution-tree-factor-auto-quant-todo.md
• 运行时闭环 board：docs/plans/2026-05-07-auto-quant-post-factor-runtime-closure-todo.md
• 应做事项：docs/plans/2026-05-07-ict-engine-action-items.md
• 市场形态定义：src/factor_lab/pda_prior.rs
• BBN 证据：src/bbn/evidence.rs
• 执行树：src/application/orchestration/execution_tree.rs
• 市场状态分类参考：references/market-state-classification.md — 分类器详细参数、聚合逻辑、扩展点
• 因子族 E/F/H 计算桩：references/factor-families-efh-compute.md — 子因子公式、参数表、变异面、已知限制
• 因子热插拔 handoff：docs/plans/2026-05-09-factor-hotplug-handoff-todo.md — 实时 TODO
• 因子目录：docs/factor-catalog.md — 8 族单页索引
• Agent 入仓地图：AGENTS.md — agent 首读文件

Hot-Plug FactorRegistry::default() Sweep

When FactorRegistry::default() is called, the hotplug config should be applied immediately after if a state_dir is in scope. The pattern:

let mut registry = FactorRegistry::default();
FactorHotplugConfig::apply_to_registry_if_present(state_dir, &mut registry);

Call sites that NEED hotplug (mutable registry + state_dir available):

• src/main.rs analyze path (line ~4013) — DONE
• src/factor_research_runtime.rs (line ~48) — DONE
• src/factor_backtest_runtime.rs run_factor_backtest — DONE
• src/application/data_sources/sop_reports.rs (line ~429) — DONE

Call sites that are immutable refs (&FactorRegistry::default()) or lack state_dir in scope (e.g., regime/recovery.rs, persistence.rs, mutation_templates.rs) use the full 8-family default and are NOT hotplugged — this is acceptable since those paths do evaluation/mutation against all families by design.

Development / Extending the Architecture

This section covers developing new modules for the ICT-Engine. Apply these patterns when adding factor families, market state classifiers, confidence validation, or any new submodule — not when running the pipeline (see sections above).

Core Dev Principles

• Zero-Config — every module provides Default::default() for consumer-ready use, plus with_config(custom) for hot-pluggable customization
• No pollution — don't modify existing code; modules are self-contained
• No debt — each new file carries its tests inside #[cfg(test)]

Adding a New Module

// Module file: src/market_state/<module>.rs

/// Config struct with Default
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct ModuleConfig {
    pub threshold: f64,
}

impl Default for ModuleConfig {
    fn default() -> Self { Self { threshold: 0.65 } }
}

pub struct Module { config: ModuleConfig, }

impl Module {
    pub fn new() -> Self { Self::with_config(ModuleConfig::default()) }
    pub fn with_config(config: ModuleConfig) -> Self { Self { config } }
}

impl Default for Module { fn default() -> Self { Self::new() } }

#[cfg(test)]
mod tests {
    // Default config works / Custom config works / Edge cases / Confidence thresholds
}

Then register in src/market_state/mod.rs.

Three-Layer Confidence System

Located in references/confidence-validation-implementation.md.

Key parameters: history_window=252, min_samples=30, high=0.75, medium=0.55, low=0.35.

Adding a New FactorFamily

1. Add variant to FactorCategory enum in factor_definition.rs
2. Add fn <variant>() -> FactorDefinition constructor
3. Register in FactorRegistry::default() in registry.rs
4. Add compute path: fn evaluate_<variant>(...)
5. Add dispatch arm in FactorDefinition::evaluate()
6. Add 3 mutation arms: mutation_parameter_group, mutation_direction_hint, mutation_step_size_hint
7. Update AGENTS.md traceability table and docs/factor-catalog.md

Watch for: non-exhaustive patterns on the three mutation methods after adding a new variant — all three need an arm.

Dev Pitfalls

• Cargo timeout — if cargo check hangs >60s, skip it, commit code + tests, mark "⏳ compilation pending". Do not block progress on compilation.
• Items after test module — clippy -D warnings rejects impl blocks after #[cfg(test)]. Keep mod tests as the last item.
• Lib vs bin crate import — lib crate files use crate::factors::; binary crate files (direct mod in main.rs) use ict_engine::factors::.
• Missing price direction — always classify Bull/Bear/Neutral from a 20-bar window (2% threshold) before classifying secondary regime.
• Over-simplified aggregation — weighted average loses dimension conflicts. Use multi-dimensional consistency checks (5-way).
• Auto-Quant shell output dir — every shell function must call aq_state_dir(state_dir) not pass raw state_dir to commands.
• Mutation surface on new variants — three match blocks in factor_definition.rs need exhaustive arms for every variant. All three must be covered.
• impl block after test module — when adding compute stubs for new families, place ALL impl blocks BEFORE #[cfg(test)].

Commit Pattern

feat(market_state): <module-name> - <one-line-summary>

<details>
- Feature 1
- Feature 2

Design: zero-config, hot-pluggable
Tests: ✅ Test 1, ✅ Test 2

Maturity and execution-tree guardrails

• When replaying structural feedback to recover mature target rows, copy the state root first (state_*_matured) and keep the original run untouched.
• If feedback comes from reconstructed/sample trades rather than broker fills, label the source as diagnostic replay (diagnostic_sample_trade_structural_feedback) and keep trade_usable=false / non-live label fields where supported.
• After replay ingest, always re-export the structural path-ranking target, retrain/apply/register CatBoost, enable runtime, then rerun analyze, workflow-status, policy-training-status, and inspect execution_tree_trace.json.
• Treat these as separate gates: ranker maturity (raw_scored_mature, production_validation, observation_validation) and execution admission (branch, execution_bias, gate_status). A ready CatBoost ranker can still remain blocked by bridge_needs_confirmation or transition_guardrail.
• See references/diagnostic-feedback-replay.md for the concrete replay pattern.

Repo Directory Hygiene

• Production state: state/ (gitignored, --state-dir state default)
• Auto-Quant output: state/auto-quant/ (subdirectory isolation, env var override)
• Experiment state: state_experiments/ (gitignored, consolidated from scattered state_*)
• Agent entry map: AGENTS.md at repo root
• Factor index: docs/factor-catalog.md
• Hotplug config: <state-dir>/factor_hotplug.yaml or ICT_ENGINE_FACTOR_HOTPLUG_CONFIG