queue: pre-registered experiment backlog to prove better-trading-performance hypotheses

Mined from book/ on the 'book' branch (HEAD 436692a). 11 queued runs,
each = hypothesis + one-variable change vs the exp-26 reference + acceptance
metric, per the ch.02 isolation/falsification discipline.

- workflows/: 5 runnable config-only YAMLs (Q01 M2 repro, Q02 seed10, Q03 topk20,
  Q04 label10d, Q05 label22d) byte-derived from the exp-26 reference
- designs/: 6 design docs needing custom strategy modules or tool-only A/B
  (Q06 Kelly, Q07 weekly rebalance, Q08 risk-limit A/B, Q09 long-short,
   Q10 HMM overlay, Q11 standalone reversal)

queue/README.md

@@ -0,0 +1,78 @@
+# TradeAC Experiment Queue — hypotheses that would prove "better trading performance"
+
+**Purpose.** A staging queue of experiment runs, each designed to PROVE (or
+REFUTE) one hypothesis about how to achieve better trading performance on the
+TradeAC stack. Every item is pre-registered: hypothesis, change-vs-reference,
+and acceptance metric are fixed BEFORE the run (book ch.02 isolation + falsification
+discipline). Nothing here is executed yet — each entry carries its execution
+command and can be run by tracing first (`rd_trace_start` → `rd_run_workflow` /
+`rd_risk_calibrate` → `rd_trace_finish`).
+
+**Source.** Mined from the `book` branch of this repo (`book/CLAIMS.md`,
+`book/EVIDENCE.md`, `book/chapters/*`, `book/references/chat-ideas.md`). Only
+clean-lake (exp 21+) facts are cited as reference numbers; pre-clean-lake claims
+are idea material that the queue is designed to test.
+
+## Reference / control (MUST reproduce first)
+
+The exp-26 reference — the campaign's best clean-lake result (EVIDENCE#015, run
+`21afc6af…`, mlflow exp 25, branch `exp/26-test-whether-reducing-topkdropout-daily`):
+
+| Config element | Reference value |
+|---|---|
+| Universe | 50-ETF panel (same `UNIVERSE` list as exp-24/26) |
+| Features | compact stochastic: `$open,$high,$low,$close,$vwap,$volume,sp_ret,sp_jump_ratio,sp_jump_flag,sp_jump_tail,sp_max_move,sp_rv1,sp_rv5,sp_rv22,sp_vol_ratio_5_22,sp_vol_ratio_1_22,sp_trend_slope_5,sp_trend_slope_20,sp_trend_slope_60,sp_logp,sp_hurst_exponent,sp_sig_level1_lead,sp_sig_level1_lag,sp_sig_level2_lead_lag,sp_sig_level2_lag_lead` |
+| Label | `Ref($close,-6)/Ref($close,-1)-1` (5d) |
+| Model | `RankICEnsembleLGBModel` (tac_qlib.contrib.model.rank_ensemble), seeds `42,7,2026,99,123`, lr 0.02, num_leaves 31, 3000 rounds, early_stop 200, min_data_in_leaf 20, lambda_l2 0.5, colsample/subsample 0.8 |
+| Train / valid / test | 2016-01-04..2025-09-01 / 2025-09-03..2026-01-03 / 2026-01-04..2026-08-10 |
+| Strategy | TopkDropout, topk 10, n_drop 1, risk_degree 0.95 |
+| Costs / benchmark | open 0.0005 / close 0.0015 / min $5, deal $close, SPY, $1M |
+
+Reference metrics to beat (EVIDENCE#015): **net_ann_return +2.13%, net_IR 0.21,
+gross +7.02%, net_max_drawdown −7.69%, RankIC 0.0663, RankICIR 0.2545, L/S Sharpe 4.54.**
+
+## The queue (ordered by value × feasibility)
+
+| ID | Title / hypothesis | Change vs reference (ONE var) | Acceptance | Config | Ready? |
+|----|--------------------|-------------------------------|------------|--------|--------|
+| Q01 | **M2 Sharpe-drift reproduction** — adding `sp_sharpe_22` (risk-adjusted 22d drift) improves net perf (exp 30: +6.53% IR 0.62, unreproduced → promote HYPOTHESIS) | +`sp_sharpe_22` to features | net_IR > 0.21, net_ann > +2.13% | `workflows/q01_m2_sharpe22_repro.yaml` | ✅ |
+| Q02 | **Seed count 10 vs 5** — more seeds → higher ICIR/net; tests whether averaging saturates (exp 28 proved 5>2) | seeds → 10 | net_IR ≥ 0.21, ICIR/RankICIR ≥ ref | `workflows/q02_seed10.yaml` | ✅ |
+| Q03 | **topk 20 diversification** — effective book is ~4 independent names; wider book cuts drawdown without hurting weak signal | topk 10→20 | net_IR > 0.21, MDD < 7.69%, net_ann ≥ +2.13% | `workflows/q03_topk20.yaml` | ✅ |
+| Q04 | **10-day non-overlapping label** — longer horizon captures trend/reversal 5d blurs, lowers churn | label → 10d | net_IR > 0.21, net_ann > +2.13% | `workflows/q04_label10d.yaml` | ✅ |
+| Q05 | **22-day label** — true trend-following; 5d can't see 1–12m drift (submartingale) | label → 22d | net_IR > 0.21, net_ann > +2.13%, cost ≤ ref | `workflows/q05_label22d.yaml` | ✅ |
+| Q06 | **Fractional-Kelly sizing** (re-run exp 15 on clean lake) — sizing by edge magnitude beats equal-weight net of costs | custom strategy (sizing) | net_IR > 0.21, net_ann > +2.13%, cost ≤ ref | `designs/q06_kelly_sizing.md` | ⚠️ needs `kelly_dropout.py` |
+| Q07 | **Weekly rebalance** — next turnover lever after n_drop 1; cut forced churn at same signal | custom strategy (weekly) | cost/turnover ↓ AND net_IR > 0.21, net_ann > +2.13% | `designs/q07_weekly_rebalance.md` | ⚠️ needs `weekly_rebalance.py` |
+| Q08 | **Risk-limit re-validation** — $5M liquidity floor improves net IR / cuts DD on post-reset signal (exp 18 pre-clean-lake) | `rd_risk_calibrate` A/B on exp-26 pred | net_IR > 0.21, MDD < 7.69% vs no-limit | `designs/q08_risk_limit_ab.md` | ✅ tool-only |
+| Q09 | **Long-short construction** — the L/S edge (Sharpe 4.54) realizes more net of costs than long-only | custom strategy (top+bottom) | net_IR > 0.21, net_ann > +2.13%, cost ≤ 2× ref | `designs/q09_long_short.md` | ⚠️ needs `top_bottom.py` |
+| Q10 | **HMM regime overlay** — regime as overlay (not feature) cuts drawdown; exp 25 proved features fail, overlay untested | custom strategy (regime gate) | MDD < 7.69%, net_IR ≥ 0.21 | `designs/q10_hmm_regime_overlay.md` | ⚠️ needs `regime_gate.py` + `get_lake_sp` |
+| Q11 | **Standalone 5-day reversal** — reversal (β −0.53, t −24) tradable net of 20bp round-trip; unisolated | single-feature model/backtest | net_ann > 0 standalone | `designs/q11_standalone_reversal.md` | ⚠️ partial |
+
+### Deferred (methodology / infra, P3)
+- **Q12 Purged / walk-forward CV** on the exp-26 reference (book ch.02 open
+  question) — methodology improvement, not a direct alpha lever.
+- **Q13 Out-of-universe validation** — non-ETF universe for the compact
+  stochastic feature set (book README open question; needs new lake symbols).
+
+## Execution protocol (per queued run)
+
+1. **Validate the lake first** (`validate_lake_dataset` + `rd_status`) — the
+   clean-lake lesson: silent NaN-drops and hollow coverage invalidate a run.
+2. **Trace before running** (`rd_trace_start` with the hypothesis as `rational`,
+   `evolved_from=auto` for lineage → it will fork from the closest prior
+   experiment). Use a FRESH experiment name per run, e.g. `tac-rd-q01-m2-...`.
+3. **Run** `rd_run_workflow config_path=<abs path to the queue YAML>
+   experiment_name=<fresh name>` — use `wait=false`, poll `rd_exp_get_run` until
+   `FINISHED` (4-year trains outlive the MCP call).
+4. **Verify against acceptance** via `rd_exp_result` (headline + backtest risk).
+5. **Finish the trace** (`rd_trace_finish` with `metrics` + `evaluation`),
+   snapshot any new custom modules (`rd_trace_snapshot`).
+6. **Report to the book** — on PROVE, update `book/CLAIMS.md`/`EVIDENCE.md`;
+   on REFUTE, record the negative (falsification is the output).
+
+Sequential execution only (concurrent runs hang — chat-ideas.md ops lesson).
+
+## Provenance
+
+Mined 2026-08-19 from `book/` on the `book` branch (HEAD `436692a`). Reference
+config reproduced byte-for-byte from the exp-26 run artifact config
+(`/home/data/lake/mlruns/25/21afc6afdb674a399b59dd76c97628ce/artifacts/config`).

queue/designs/q06_kelly_sizing.md

@@ -0,0 +1,33 @@
+# QUEUE-06 — Fractional-Kelly sizing vs equal-weight top-k (re-run exp 15 on clean lake)
+
+**Status:** QUEUED · **Priority:** P1 · **Effort:** custom strategy module + run
+
+## Hypothesis (prove)
+Fractional-Kelly sizing — sizing each name by the edge magnitude of its score
+instead of equal-weight × risk_degree — is a sizing rule (not a strategy) that
+throws away less edge and beats equal-weight top-k **net of costs** on the clean
+lake. Source: `book/README.md` open questions (exp 15 run never finished),
+`book/references/chat-ideas.md` ("Kelly sizing is a sizing rule, not a strategy").
+
+## Change vs exp-26 reference (ONE variable)
+- **Strategy**: equal-weight `TopkDropoutStrategy` (topk 10, n_drop 1) →
+  custom `FractionalKellyDropoutStrategy` (same topk/n_drop selection, sizing ∝
+  score magnitude, capped at a fraction f of the equal-weight notional; f as a
+  parameter, e.g. 0.5).
+- All signal/config unchanged (compact stochastic features, 5-seed RankIC
+  ensemble, 5d label, train/valid/test, SPY benchmark, 5bp/15bp/$5 costs).
+
+## Acceptance
+- `net_IR > 0.21` AND `net_ann_return > +2.13%` (exp-26 reference), with
+  `total_cost` not higher than the reference book.
+- If sizing flattens the book (over-concentration) and net degrades → REFUTED
+  (recorded negative; equal-weight stays canonical).
+
+## Execution prerequisites
+1. New contrib module `tac_qlib/contrib/strategy/kelly_dropout.py`
+   (`FractionalKellyDropoutStrategy` subclassing
+   `qlib.contrib.strategy.signal_strategy.TopkDropoutStrategy`), copy to the
+   venv site-packages copy (`/opt/venv/lib/python3.12/site-packages/tac_qlib/...`).
+2. Workflow YAML with `strategy.class=FractionalKellyDropoutStrategy`,
+   `module_path=tac_qlib.contrib.strategy.kelly_dropout`.
+3. Trace (rd_trace_start → run → rd_trace_finish), snapshot the new module.

queue/designs/q07_weekly_rebalance.md

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+# QUEUE-07 — Turnover relief: weekly rebalance vs daily (next cost lever after n_drop 1)
+
+**Status:** QUEUED · **Priority:** P1 · **Effort:** custom strategy module + run
+
+## Hypothesis (prove)
+n_drop 2→1 proved the cost/turnover frontier is the binding constraint
+(EVIDENCE#015, ch.03/ch.09: identical IC/RankIC, net flips −3.21% → +2.13%).
+The next lever in the same direction: rebalance the TopkDropout book only
+**weekly** (e.g. on Mondays) instead of daily — cutting forced churn further
+should lift net performance at the same signal quality.
+
+Source: `book/references/chat-ideas.md` ("weekly rebalance" among the turnover
+reduction ideas), ch.09 claim inventory.
+
+## Change vs exp-26 reference (ONE variable)
+- **Strategy**: daily TopkDropout (topk 10, n_drop 1) → custom
+  `WeeklyRebalanceDropoutStrategy` that recomputes the target book once per
+  week and otherwise holds (no-trade buffer band for small deltas).
+- All signal/config unchanged.
+
+## Acceptance
+- `total_cost`/turnover strictly below the reference AND `net_IR > 0.21` AND
+  `net_ann_return > +2.13%`.
+- Reference numbers to beat: turnover ~0.74 (round-3 live), est. ~20% daily
+  book turnover at topk10/n_drop2 (pre-clean-lake estimate).
+
+## Execution prerequisites
+1. New contrib module `tac_qlib/contrib/strategy/weekly_rebalance.py`
+   (`WeeklyRebalanceDropoutStrategy` subclassing `TopkDropoutStrategy`, trade
+   only when the trade calendar day is the week's first trading day), copy to
+   the venv site-packages copy.
+2. Workflow YAML wiring the strategy.
+3. Trace + run + snapshot.
+
+## Sibling (deferred)
+No-trade buffer band and notional-vs-qty order sizing are variants of the same
+cost lever; queue them only if Q07 reproduces positively.

queue/designs/q08_risk_limit_ab.md

@@ -0,0 +1,34 @@
+# QUEUE-08 — Risk-limit A/B re-validation: $5M liquidity floor on the exp-26 reference
+
+**Status:** QUEUED · **Priority:** P1 · **Effort:** tool-only (no new code)
+
+## Hypothesis (prove)
+The $5M liquidity floor improves net IR and cuts drawdown on the **post-reset**
+reference signal (pre-reset exp 18, EVIDENCE#008: net IR 0.81→0.98, cumDD
+7.93%→5.44%), while size/concentration caps hurt by cutting deployed capital.
+Needs re-validation on the exp-26 lineage because exp 18 is pre-clean-lake and
+not comparable (EVIDENCE#009/010). Source: `book/CLAIMS.md` open question +
+`book/README.md` `TODO(evidence-needed: reconciliation of exp 18 risk-limit spec
+on the post-reset reference signal)`.
+
+## Change vs exp-26 reference (ONE variable)
+- Reference: the saved exp-26 prediction (run `21afc6af…`, mlflow exp 25).
+- A/B via `rd_risk_calibrate` (runs limit-vs-no-limit A/B + sensitivity grid
+  over size_cap_pct, concentration_cap_pct, liquidity_floor_adv) and/or
+  `rd_backtest` with `risk_limits` on the SAME saved `pred.pkl`:
+  - baseline: no limits (this must reproduce the exp-26 net +2.13% / IR 0.21);
+  - candidate: `{"liquidity_floor_adv": 5000000, "size_cap_pct": 0.12,
+    "concentration_cap_pct": 0.95, "drawdown_pause_pct": 0.10}` (round-3 spec).
+- Pick the spec (B2 calibration) that keeps live ≈ backtest.
+
+## Acceptance
+- Candidate spec: `net_IR > 0.21` AND `net_max_drawdown < 7.69%` vs no-limit on
+  the same pred. Size/concentration caps expected to REDUCE deployed capital
+  (record the direction as confirmation of exp 18).
+- If the floor is a no-op (gates don't bind at this signal) → report that gates
+  are no-ops when the signal is the bottleneck (exp 20 pattern) as a PROVEN
+  clean-lake result.
+
+## Execution prerequisites
+- None (uses saved pred + `rd_risk_calibrate`/`rd_backtest`). Trace the A/B as
+  an experiment; record the spec chosen for the next live round.

queue/designs/q09_long_short.md

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+# QUEUE-09 — Long-short construction: capture the long-short edge net of costs
+
+**Status:** QUEUED · **Priority:** P2 · **Effort:** custom strategy module + run
+
+## Hypothesis (prove)
+The compact stochastic signal's long-short spread is the real edge (L/S ann
+Sharpe 4.54, exp 24; "edge is long-short, not long-only" — chat-ideas.md), but
+all canonical constructions are long-only (TopkDropout buys topk, drops, holds).
+A market-neutral book (long topk, short bottom topk) should realize more of the
+spread net of costs than the long-only book, IF short-side financing + doubled
+turnover cost stays below the added spread capture.
+
+## Change vs exp-26 reference (ONE variable)
+- **Strategy**: long-only TopkDropout (topk 10, n_drop 1) → custom
+  `TopBottomDropoutStrategy` (long topk by rank, short bottom topk, equal
+  weight per side, same risk_degree), realized in a workflow with a cost model
+  that includes both sides (open/close cost symmetric).
+- All signal/config unchanged.
+
+## Acceptance
+- `net_IR > 0.21` AND `net_ann_return > +2.13%` AND `total_cost` within ~2× the
+  reference (doubled side count is the structural cost of this construction).
+- Watch: benchmark neutrality (SPY beta ≈ 0) as a secondary sanity metric.
+
+## Execution prerequisites
+1. New contrib module `tac_qlib/contrib/strategy/top_bottom.py`
+   (`TopBottomDropoutStrategy` subclassing `BaseSignalStrategy`), copy to the
+   venv site-packages copy.
+2. Workflow YAML wiring the strategy; PortAnaRecord benchmark SPY.
+3. Trace + run + snapshot.

queue/designs/q10_hmm_regime_overlay.md

@@ -0,0 +1,35 @@
+# QUEUE-10 — HMM regime overlay on the exp-26 book (overlay, not feature)
+
+**Status:** QUEUED · **Priority:** P2 · **Effort:** custom strategy + feature compute + run
+
+## Hypothesis (prove)
+Regime flags failed as model **features** (exp 9 idea, exp 25 clean-lake
+confirmation that model-specific families regress), but the surviving use is as
+an **overlay**: a long-only/regime-gate that holds names only in the favourable
+HMM state should cut drawdown / improve net IR on the same signal. Source:
+`book/chapters/01` regime section + `chat-ideas.md`
+(`TODO(evidence-needed: HMM regime gate as overlay on exp-26 book)`).
+
+## Change vs exp-26 reference (ONE variable)
+- **Strategy**: plain TopkDropout (topk 10, n_drop 1) → custom
+  `RegimeGateDropoutStrategy`: identical selection, but when the per-symbol
+  HMM posterior (`sp_hmm_p_regime1`) is below a calibrated threshold the name
+  is held in cash instead of bought (entry gate); no new features enter the
+  model — `sp_hmm_p_regime1` is computed for gating only, fit on the train
+  window (no lookahead), via `get_lake_sp` with `fit_end=<train end>`.
+- All signal/config unchanged.
+
+## Acceptance
+- `net_max_drawdown < 7.69%` (reference) AND `net_IR >= 0.21`. If the gate
+  never binds at a sensible threshold → the gate is a no-op on this signal
+  (exp 20 pattern) → recorded REFUTED/neutral, not a failure.
+- Calibrate the threshold on the valid window only (avoid the exp 13/14
+  threshold-overfit trap).
+
+## Execution prerequisites
+1. Persist `sp_hmm_p_regime1` for the universe (get_lake_sp, fit_end =
+   2025-09-01) WITHOUT adding it to `feature_fields` of the model.
+2. New contrib module `tac_qlib/contrib/strategy/regime_gate.py`, copy to the
+   venv site-packages copy.
+3. Workflow YAML wiring the strategy.
+4. Trace + run + snapshot.

queue/designs/q11_standalone_reversal.md

@@ -0,0 +1,32 @@
+# QUEUE-11 — Standalone 5-day reversal signal net of costs (unisolated)
+
+**Status:** QUEUED · **Priority:** P2 · **Effort:** dataset study + backtest
+
+## Hypothesis (prove)
+5-day momentum strongly reverses on this panel (pooled regression:
+`sp_trend_slope_5` β = −0.53, t = −24; VR < 1 at 5–20d for ~32/72 assets —
+chat-derived, pre-clean-lake idea material). The reversal has never been tested
+as a **standalone tradable strategy net of costs**. If it clears the 20bp
+round-trip cost, it is an independent alpha source that can be blended with (or
+replace) the model book.
+Source: `book/ch01` "Timeline" + `chat-ideas.md`
+(`TODO(evidence-needed: standalone 5d-reversal strategy net of costs)`).
+
+## Change vs exp-26 reference
+- This is NOT a model-construction variant — it isolates a SINGLE-FEATURE
+  signal: a model trained on `sp_trend_slope_5` (plus raw OHLCV) alone, or a
+  mechanical reversal book (rank by −`sp_trend_slope_5`, buy the most-reverted
+  topk), backtested net of costs over the exp-26 window.
+- Control: exp-26 compact reference on the same window.
+
+## Acceptance
+- Standalone reversal `net_ann_return > 0` (clears 20bp round-trip) — proves
+  the claim "reversal is tradable net of costs". Secondary: excess vs the
+  model book is the blend decision for a future round.
+
+## Execution prerequisites
+1. `rd_train`/workflow with `feature_fields = $open,$high,$low,$close,$vwap,$volume,sp_trend_slope_5`
+   (single feature) OR a mechanical rank backtest via `rd_backtest` on a
+   hand-built pred (pred = −rank(sp_trend_slope_5)).
+2. Trace + run + record as a standalone study (dataset-study status, not
+   necessarily a traced model experiment).

queue/workflows/q01_m2_sharpe22_repro.yaml

@@ -0,0 +1,140 @@
+# -----------------------------------------------------------------------------
+# QUEUE-01 — M2 reproduction: risk-adjusted 22d Sharpe drift (sp_sharpe_22).
+#
+# Hypothesis (book ch.01/ch.07, EVIDENCE#018 -> exp 30): adding the
+# risk-adjusted 22d Sharpe drift feature (sp_sharpe_22) to the compact
+# stochastic reference IMPROVES net portfolio performance (exp 30: net +6.53%
+# IR 0.62 vs reference +2.13% IR 0.21) while rank metrics dip (RankIC 0.0576 vs
+# 0.0663). exp 30 is a SINGLE clean-lake run, unreproduced -> HYPOTHESIS.
+#
+# Change vs exp-26 reference (EVIDENCE#015, run 21afc6af...): ONE feature added,
+# feature_fields = compact set + sp_sharpe_22. Everything else byte-identical.
+#
+# Acceptance: net_ann_return > +2.13% AND net_IR > 0.21 (else HYPOTHESIS -> REFUTED).
+# Run: rd_run_workflow config_path=<repo>/experiments/queue/workflows/q01_m2_sharpe22_repro.yaml \
+#        experiment_name=tac-rd-q01-m2-sharpe22-repro
+# -----------------------------------------------------------------------------
+{%- set LAKE = TAC_LAKE_DIR %}
+{%- set UNIVERSE = "SPY,QQQ,DIA,IWM,MDY,VTI,VOO,VEA,VWO,VT,EFA,EEM,TLT,IEF,SHY,AGG,BND,LQD,HYG,JNK,EMB,GLD,SLV,USO,UNG,DBA,DBC,XLK,XLF,XLE,XLV,XLI,XLY,XLP,XLU,XLB,XLRE,ARKK,SMH,SOXX,IBB,XBI,ITA,XAR,ICLN,TAN,FDN,IGV,ESPO,REM" %}
+{%- set FEATURES = "$open,$high,$low,$close,$vwap,$volume,sp_ret,sp_jump_ratio,sp_jump_flag,sp_jump_tail,sp_max_move,sp_rv1,sp_rv5,sp_rv22,sp_vol_ratio_5_22,sp_vol_ratio_1_22,sp_trend_slope_5,sp_trend_slope_20,sp_trend_slope_60,sp_logp,sp_hurst_exponent,sp_sig_level1_lead,sp_sig_level1_lag,sp_sig_level2_lead_lag,sp_sig_level2_lag_lead,sp_sharpe_22" %}
+
+qlib_init:
+    provider_uri: "{{ LAKE }}"
+    region: us
+    expression_cache: null
+    dataset_cache: null
+
+    calendar_provider:
+        class: tac_qlib.data.providers.LakeCalendarProvider
+        kwargs:
+            lake_root: "{{ LAKE }}"
+            market: US
+    instrument_provider:
+        class: tac_qlib.data.providers.LakeInstrumentProvider
+        kwargs:
+            lake_root: "{{ LAKE }}"
+            market: US
+            markets: {}
+    feature_provider:
+        class: tac_qlib.data.providers.LakeFeatureProvider
+        kwargs:
+            lake_root: "{{ LAKE }}"
+            market: US
+
+    exp_manager:
+        class: MLflowExpManager
+        module_path: qlib.workflow.expm
+        kwargs:
+            uri: "sqlite:///{{ LAKE }}/mlruns.db"
+            default_exp_name: "tac-rd-q01-m2-sharpe22-repro"
+
+task:
+    model:
+        class: RankICEnsembleLGBModel
+        module_path: tac_qlib.contrib.model.rank_ensemble
+        kwargs:
+            loss: mse
+            learning_rate: 0.02
+            num_leaves: 31
+            n_estimators: 3000
+            num_boost_round: 3000
+            early_stopping_rounds: 200
+            min_data_in_leaf: 20
+            lambda_l2: 0.5
+            colsample_bytree: 0.8
+            subsample: 0.8
+            subsample_freq: 1
+            reg_alpha: 0.1
+            reg_lambda: 1.0
+            seeds: "42,7,2026,99,123"
+            parallel: 5
+
+    dataset:
+        class: DatasetH
+        module_path: qlib.data.dataset
+        kwargs:
+            handler:
+                class: TACHandler
+                module_path: tac_qlib.contrib.data.handler
+                kwargs:
+                    instruments: "{{ UNIVERSE }}"
+                    start_time: 2015-01-03
+                    end_time: 2026-08-10
+                    fit_start_time: 2016-01-04
+                    fit_end_time: 2025-09-01
+                    freq: day
+                    lake_root: "{{ LAKE }}"
+                    market: US
+                    label: "Ref($close,-6)/Ref($close,-1)-1"
+                    feature_fields: "{{ FEATURES }}"
+                    infer_processors:
+                        - class: DropAllNaN
+                          kwargs: {}
+                        - class: ProcessInf
+                          kwargs: {}
+                        - class: CSRankNorm
+                          kwargs: {}
+                        - class: ZScoreNorm
+                          kwargs: {}
+                        - class: Fillna
+                          kwargs: {}
+            segments:
+                train: [2016-01-04, 2025-09-01]
+                valid: [2025-09-03, 2026-01-03]
+                test: [2026-01-04, 2026-08-10]
+
+    record:
+        - class: SignalRecord
+          module_path: qlib.workflow.record_temp
+          kwargs: {}
+        - class: SigAnaRecord
+          module_path: qlib.workflow.record_temp
+          kwargs:
+              ana_long_short: true
+              ann_scaler: 252
+        - class: PortAnaRecord
+          module_path: qlib.workflow.record_temp
+          kwargs:
+              config:
+                  strategy:
+                      class: TopkDropoutStrategy
+                      module_path: qlib.contrib.strategy
+                      kwargs:
+                          signal: "<PRED>"
+                          topk: 10
+                          n_drop: 1
+                          only_tradable: true
+                          risk_degree: 0.95
+                  backtest:
+                      start_time: 2026-01-04
+                      end_time: 2026-08-10
+                      account: 1000000
+                      benchmark: SPY
+                      exchange_kwargs:
+                          codes: "{{ UNIVERSE }}"
+                          deal_price: $close
+                          freq: day
+                          open_cost: 0.0005
+                          close_cost: 0.0015
+                          min_cost: 5.0
+              risk_analysis_freq: 1d

queue/workflows/q02_seed10.yaml

@@ -0,0 +1,140 @@
+# -----------------------------------------------------------------------------
+# QUEUE-02 — Seed-count 10 vs 5 on the compact reference.
+#
+# Hypothesis (book ch.05, EVIDENCE#016 -> exp 28): seed count is load-bearing
+# (2 seeds lose to 5). Extending the same direction, does 10 seeds further
+# raise ICIR and net performance? Tests whether averaging benefit saturates.
+#
+# Change vs exp-26 reference: ONE variable — seeds "42,7,2026,99,123" ->
+# "42,7,2026,99,123,17,3,2020,88,55" (parallel: 10). Everything else identical.
+#
+# Acceptance: net_IR >= 0.21 AND ICIR/RankICIR >= reference (0.235 / 0.243);
+#   if seed count saturates, expect flat ICIR — that result also settles the
+#   mechanism question (variance reduction, not family diversification).
+# Run: rd_run_workflow config_path=<repo>/experiments/queue/workflows/q02_seed10.yaml \
+#        experiment_name=tac-rd-q02-seed10
+# -----------------------------------------------------------------------------
+{%- set LAKE = TAC_LAKE_DIR %}
+{%- set UNIVERSE = "SPY,QQQ,DIA,IWM,MDY,VTI,VOO,VEA,VWO,VT,EFA,EEM,TLT,IEF,SHY,AGG,BND,LQD,HYG,JNK,EMB,GLD,SLV,USO,UNG,DBA,DBC,XLK,XLF,XLE,XLV,XLI,XLY,XLP,XLU,XLB,XLRE,ARKK,SMH,SOXX,IBB,XBI,ITA,XAR,ICLN,TAN,FDN,IGV,ESPO,REM" %}
+{%- set FEATURES = "$open,$high,$low,$close,$vwap,$volume,sp_ret,sp_jump_ratio,sp_jump_flag,sp_jump_tail,sp_max_move,sp_rv1,sp_rv5,sp_rv22,sp_vol_ratio_5_22,sp_vol_ratio_1_22,sp_trend_slope_5,sp_trend_slope_20,sp_trend_slope_60,sp_logp,sp_hurst_exponent,sp_sig_level1_lead,sp_sig_level1_lag,sp_sig_level2_lead_lag,sp_sig_level2_lag_lead" %}
+
+qlib_init:
+    provider_uri: "{{ LAKE }}"
+    region: us
+    expression_cache: null
+    dataset_cache: null
+
+    calendar_provider:
+        class: tac_qlib.data.providers.LakeCalendarProvider
+        kwargs:
+            lake_root: "{{ LAKE }}"
+            market: US
+    instrument_provider:
+        class: tac_qlib.data.providers.LakeInstrumentProvider
+        kwargs:
+            lake_root: "{{ LAKE }}"
+            market: US
+            markets: {}
+    feature_provider:
+        class: tac_qlib.data.providers.LakeFeatureProvider
+        kwargs:
+            lake_root: "{{ LAKE }}"
+            market: US
+
+    exp_manager:
+        class: MLflowExpManager
+        module_path: qlib.workflow.expm
+        kwargs:
+            uri: "sqlite:///{{ LAKE }}/mlruns.db"
+            default_exp_name: "tac-rd-q02-seed10"
+
+task:
+    model:
+        class: RankICEnsembleLGBModel
+        module_path: tac_qlib.contrib.model.rank_ensemble
+        kwargs:
+            loss: mse
+            learning_rate: 0.02
+            num_leaves: 31
+            n_estimators: 3000
+            num_boost_round: 3000
+            early_stopping_rounds: 200
+            min_data_in_leaf: 20
+            lambda_l2: 0.5
+            colsample_bytree: 0.8
+            subsample: 0.8
+            subsample_freq: 1
+            reg_alpha: 0.1
+            reg_lambda: 1.0
+            seeds: "42,7,2026,99,123,17,3,2020,88,55"
+            parallel: 10
+
+    dataset:
+        class: DatasetH
+        module_path: qlib.data.dataset
+        kwargs:
+            handler:
+                class: TACHandler
+                module_path: tac_qlib.contrib.data.handler
+                kwargs:
+                    instruments: "{{ UNIVERSE }}"
+                    start_time: 2015-01-03
+                    end_time: 2026-08-10
+                    fit_start_time: 2016-01-04
+                    fit_end_time: 2025-09-01
+                    freq: day
+                    lake_root: "{{ LAKE }}"
+                    market: US
+                    label: "Ref($close,-6)/Ref($close,-1)-1"
+                    feature_fields: "{{ FEATURES }}"
+                    infer_processors:
+                        - class: DropAllNaN
+                          kwargs: {}
+                        - class: ProcessInf
+                          kwargs: {}
+                        - class: CSRankNorm
+                          kwargs: {}
+                        - class: ZScoreNorm
+                          kwargs: {}
+                        - class: Fillna
+                          kwargs: {}
+            segments:
+                train: [2016-01-04, 2025-09-01]
+                valid: [2025-09-03, 2026-01-03]
+                test: [2026-01-04, 2026-08-10]
+
+    record:
+        - class: SignalRecord
+          module_path: qlib.workflow.record_temp
+          kwargs: {}
+        - class: SigAnaRecord
+          module_path: qlib.workflow.record_temp
+          kwargs:
+              ana_long_short: true
+              ann_scaler: 252
+        - class: PortAnaRecord
+          module_path: qlib.workflow.record_temp
+          kwargs:
+              config:
+                  strategy:
+                      class: TopkDropoutStrategy
+                      module_path: qlib.contrib.strategy
+                      kwargs:
+                          signal: "<PRED>"
+                          topk: 10
+                          n_drop: 1
+                          only_tradable: true
+                          risk_degree: 0.95
+                  backtest:
+                      start_time: 2026-01-04
+                      end_time: 2026-08-10
+                      account: 1000000
+                      benchmark: SPY
+                      exchange_kwargs:
+                          codes: "{{ UNIVERSE }}"
+                          deal_price: $close
+                          freq: day
+                          open_cost: 0.0005
+                          close_cost: 0.0015
+                          min_cost: 5.0
+              risk_analysis_freq: 1d

queue/workflows/q03_topk20.yaml

@@ -0,0 +1,140 @@
+# -----------------------------------------------------------------------------
+# QUEUE-03 — topk 20 vs 10 diversification on the compact reference.
+#
+# Hypothesis (book ch.05/chat-ideas): the effective independent names in the
+# 50-ETF book is small (~4, chat-derived eigenvalue analysis); raising topk
+# diversifies the book and should cut drawdown / raise net IR without hurting
+# the (weak) rank signal — cost relief by spreading the book wider.
+#
+# Change vs exp-26 reference: ONE variable — strategy topk 10 -> 20 (n_drop 1).
+# Everything else identical.
+#
+# Acceptance: net_IR > 0.21 AND net_max_drawdown < 7.69% AND net_ann_return >=
+#   +2.13%; watch total_cost — more names held must not raise turnover/cost.
+# Run: rd_run_workflow config_path=<repo>/experiments/queue/workflows/q03_topk20.yaml \
+#        experiment_name=tac-rd-q03-topk20
+# -----------------------------------------------------------------------------
+{%- set LAKE = TAC_LAKE_DIR %}
+{%- set UNIVERSE = "SPY,QQQ,DIA,IWM,MDY,VTI,VOO,VEA,VWO,VT,EFA,EEM,TLT,IEF,SHY,AGG,BND,LQD,HYG,JNK,EMB,GLD,SLV,USO,UNG,DBA,DBC,XLK,XLF,XLE,XLV,XLI,XLY,XLP,XLU,XLB,XLRE,ARKK,SMH,SOXX,IBB,XBI,ITA,XAR,ICLN,TAN,FDN,IGV,ESPO,REM" %}
+{%- set FEATURES = "$open,$high,$low,$close,$vwap,$volume,sp_ret,sp_jump_ratio,sp_jump_flag,sp_jump_tail,sp_max_move,sp_rv1,sp_rv5,sp_rv22,sp_vol_ratio_5_22,sp_vol_ratio_1_22,sp_trend_slope_5,sp_trend_slope_20,sp_trend_slope_60,sp_logp,sp_hurst_exponent,sp_sig_level1_lead,sp_sig_level1_lag,sp_sig_level2_lead_lag,sp_sig_level2_lag_lead" %}
+
+qlib_init:
+    provider_uri: "{{ LAKE }}"
+    region: us
+    expression_cache: null
+    dataset_cache: null
+
+    calendar_provider:
+        class: tac_qlib.data.providers.LakeCalendarProvider
+        kwargs:
+            lake_root: "{{ LAKE }}"
+            market: US
+    instrument_provider:
+        class: tac_qlib.data.providers.LakeInstrumentProvider
+        kwargs:
+            lake_root: "{{ LAKE }}"
+            market: US
+            markets: {}
+    feature_provider:
+        class: tac_qlib.data.providers.LakeFeatureProvider
+        kwargs:
+            lake_root: "{{ LAKE }}"
+            market: US
+
+    exp_manager:
+        class: MLflowExpManager
+        module_path: qlib.workflow.expm
+        kwargs:
+            uri: "sqlite:///{{ LAKE }}/mlruns.db"
+            default_exp_name: "tac-rd-q03-topk20"
+
+task:
+    model:
+        class: RankICEnsembleLGBModel
+        module_path: tac_qlib.contrib.model.rank_ensemble
+        kwargs:
+            loss: mse
+            learning_rate: 0.02
+            num_leaves: 31
+            n_estimators: 3000
+            num_boost_round: 3000
+            early_stopping_rounds: 200
+            min_data_in_leaf: 20
+            lambda_l2: 0.5
+            colsample_bytree: 0.8
+            subsample: 0.8
+            subsample_freq: 1
+            reg_alpha: 0.1
+            reg_lambda: 1.0
+            seeds: "42,7,2026,99,123"
+            parallel: 5
+
+    dataset:
+        class: DatasetH
+        module_path: qlib.data.dataset
+        kwargs:
+            handler:
+                class: TACHandler
+                module_path: tac_qlib.contrib.data.handler
+                kwargs:
+                    instruments: "{{ UNIVERSE }}"
+                    start_time: 2015-01-03
+                    end_time: 2026-08-10
+                    fit_start_time: 2016-01-04
+                    fit_end_time: 2025-09-01
+                    freq: day
+                    lake_root: "{{ LAKE }}"
+                    market: US
+                    label: "Ref($close,-6)/Ref($close,-1)-1"
+                    feature_fields: "{{ FEATURES }}"
+                    infer_processors:
+                        - class: DropAllNaN
+                          kwargs: {}
+                        - class: ProcessInf
+                          kwargs: {}
+                        - class: CSRankNorm
+                          kwargs: {}
+                        - class: ZScoreNorm
+                          kwargs: {}
+                        - class: Fillna
+                          kwargs: {}
+            segments:
+                train: [2016-01-04, 2025-09-01]
+                valid: [2025-09-03, 2026-01-03]
+                test: [2026-01-04, 2026-08-10]
+
+    record:
+        - class: SignalRecord
+          module_path: qlib.workflow.record_temp
+          kwargs: {}
+        - class: SigAnaRecord
+          module_path: qlib.workflow.record_temp
+          kwargs:
+              ana_long_short: true
+              ann_scaler: 252
+        - class: PortAnaRecord
+          module_path: qlib.workflow.record_temp
+          kwargs:
+              config:
+                  strategy:
+                      class: TopkDropoutStrategy
+                      module_path: qlib.contrib.strategy
+                      kwargs:
+                          signal: "<PRED>"
+                          topk: 20
+                          n_drop: 1
+                          only_tradable: true
+                          risk_degree: 0.95
+                  backtest:
+                      start_time: 2026-01-04
+                      end_time: 2026-08-10
+                      account: 1000000
+                      benchmark: SPY
+                      exchange_kwargs:
+                          codes: "{{ UNIVERSE }}"
+                          deal_price: $close
+                          freq: day
+                          open_cost: 0.0005
+                          close_cost: 0.0015
+                          min_cost: 5.0
+              risk_analysis_freq: 1d

queue/workflows/q04_label10d.yaml

@@ -0,0 +1,141 @@
+# -----------------------------------------------------------------------------
+# QUEUE-04 — Non-overlapping 10-day label horizon.
+#
+# Hypothesis (book ch.01/chat-ideas): the 5d label is the campaign's best IC
+# lever but sees short-horizon reversal only; a non-overlapping 10d label
+# (`Ref($close,-11)/Ref($close,-1)-1`) tests whether a longer, cleaner horizon
+# captures trend/reversal better and survives cost (lower effective turnover).
+#
+# Change vs exp-26 reference: ONE variable — label 5d -> 10d. Everything else
+# identical (features, model, strategy).
+#
+# Acceptance: net_IR > 0.21 AND net_ann_return > +2.13%; secondary: ICIR and
+#   L/S Sharpe >= reference. A flat-but-not-worse result still settles the
+#   horizon-decomposition question (TODO: 5d can't see 1-12m drift).
+# Run: rd_run_workflow config_path=<repo>/experiments/queue/workflows/q04_label10d.yaml \
+#        experiment_name=tac-rd-q04-label10d
+# -----------------------------------------------------------------------------
+{%- set LAKE = TAC_LAKE_DIR %}
+{%- set UNIVERSE = "SPY,QQQ,DIA,IWM,MDY,VTI,VOO,VEA,VWO,VT,EFA,EEM,TLT,IEF,SHY,AGG,BND,LQD,HYG,JNK,EMB,GLD,SLV,USO,UNG,DBA,DBC,XLK,XLF,XLE,XLV,XLI,XLY,XLP,XLU,XLB,XLRE,ARKK,SMH,SOXX,IBB,XBI,ITA,XAR,ICLN,TAN,FDN,IGV,ESPO,REM" %}
+{%- set FEATURES = "$open,$high,$low,$close,$vwap,$volume,sp_ret,sp_jump_ratio,sp_jump_flag,sp_jump_tail,sp_max_move,sp_rv1,sp_rv5,sp_rv22,sp_vol_ratio_5_22,sp_vol_ratio_1_22,sp_trend_slope_5,sp_trend_slope_20,sp_trend_slope_60,sp_logp,sp_hurst_exponent,sp_sig_level1_lead,sp_sig_level1_lag,sp_sig_level2_lead_lag,sp_sig_level2_lag_lead" %}
+
+qlib_init:
+    provider_uri: "{{ LAKE }}"
+    region: us
+    expression_cache: null
+    dataset_cache: null
+
+    calendar_provider:
+        class: tac_qlib.data.providers.LakeCalendarProvider
+        kwargs:
+            lake_root: "{{ LAKE }}"
+            market: US
+    instrument_provider:
+        class: tac_qlib.data.providers.LakeInstrumentProvider
+        kwargs:
+            lake_root: "{{ LAKE }}"
+            market: US
+            markets: {}
+    feature_provider:
+        class: tac_qlib.data.providers.LakeFeatureProvider
+        kwargs:
+            lake_root: "{{ LAKE }}"
+            market: US
+
+    exp_manager:
+        class: MLflowExpManager
+        module_path: qlib.workflow.expm
+        kwargs:
+            uri: "sqlite:///{{ LAKE }}/mlruns.db"
+            default_exp_name: "tac-rd-q04-label10d"
+
+task:
+    model:
+        class: RankICEnsembleLGBModel
+        module_path: tac_qlib.contrib.model.rank_ensemble
+        kwargs:
+            loss: mse
+            learning_rate: 0.02
+            num_leaves: 31
+            n_estimators: 3000
+            num_boost_round: 3000
+            early_stopping_rounds: 200
+            min_data_in_leaf: 20
+            lambda_l2: 0.5
+            colsample_bytree: 0.8
+            subsample: 0.8
+            subsample_freq: 1
+            reg_alpha: 0.1
+            reg_lambda: 1.0
+            seeds: "42,7,2026,99,123"
+            parallel: 5
+
+    dataset:
+        class: DatasetH
+        module_path: qlib.data.dataset
+        kwargs:
+            handler:
+                class: TACHandler
+                module_path: tac_qlib.contrib.data.handler
+                kwargs:
+                    instruments: "{{ UNIVERSE }}"
+                    start_time: 2015-01-03
+                    end_time: 2026-08-10
+                    fit_start_time: 2016-01-04
+                    fit_end_time: 2025-09-01
+                    freq: day
+                    lake_root: "{{ LAKE }}"
+                    market: US
+                    label: "Ref($close,-11)/Ref($close,-1)-1"
+                    feature_fields: "{{ FEATURES }}"
+                    infer_processors:
+                        - class: DropAllNaN
+                          kwargs: {}
+                        - class: ProcessInf
+                          kwargs: {}
+                        - class: CSRankNorm
+                          kwargs: {}
+                        - class: ZScoreNorm
+                          kwargs: {}
+                        - class: Fillna
+                          kwargs: {}
+            segments:
+                train: [2016-01-04, 2025-09-01]
+                valid: [2025-09-03, 2026-01-03]
+                test: [2026-01-04, 2026-08-10]
+
+    record:
+        - class: SignalRecord
+          module_path: qlib.workflow.record_temp
+          kwargs: {}
+        - class: SigAnaRecord
+          module_path: qlib.workflow.record_temp
+          kwargs:
+              ana_long_short: true
+              ann_scaler: 252
+        - class: PortAnaRecord
+          module_path: qlib.workflow.record_temp
+          kwargs:
+              config:
+                  strategy:
+                      class: TopkDropoutStrategy
+                      module_path: qlib.contrib.strategy
+                      kwargs:
+                          signal: "<PRED>"
+                          topk: 10
+                          n_drop: 1
+                          only_tradable: true
+                          risk_degree: 0.95
+                  backtest:
+                      start_time: 2026-01-04
+                      end_time: 2026-08-10
+                      account: 1000000
+                      benchmark: SPY
+                      exchange_kwargs:
+                          codes: "{{ UNIVERSE }}"
+                          deal_price: $close
+                          freq: day
+                          open_cost: 0.0005
+                          close_cost: 0.0015
+                          min_cost: 5.0
+              risk_analysis_freq: 1d

queue/workflows/q05_label22d.yaml

@@ -0,0 +1,140 @@
+# -----------------------------------------------------------------------------
+# QUEUE-05 — Non-overlapping 22-day label horizon.
+#
+# Hypothesis (book ch.01/chat-ideas): a 22d (~monthly) non-overlapping label
+# tests true trend-following — the 5d label can't distinguish a 1-12m drift
+# (submartingale) from short-horizon reversal. Long-horizon labels also cut
+# the rebalance-implied turnover, attacking the cost constraint directly.
+#
+# Change vs exp-26 reference: ONE variable — label 5d -> 22d
+#   (`Ref($close,-23)/Ref($close,-1)-1`). Everything else identical.
+#
+# Acceptance: net_IR > 0.21 AND net_ann_return > +2.13%; secondary: does the
+#   long-horizon signal survive cost with LOWER total_cost than the 5d book?
+# Run: rd_run_workflow config_path=<repo>/experiments/queue/workflows/q05_label22d.yaml \
+#        experiment_name=tac-rd-q05-label22d
+# -----------------------------------------------------------------------------
+{%- set LAKE = TAC_LAKE_DIR %}
+{%- set UNIVERSE = "SPY,QQQ,DIA,IWM,MDY,VTI,VOO,VEA,VWO,VT,EFA,EEM,TLT,IEF,SHY,AGG,BND,LQD,HYG,JNK,EMB,GLD,SLV,USO,UNG,DBA,DBC,XLK,XLF,XLE,XLV,XLI,XLY,XLP,XLU,XLB,XLRE,ARKK,SMH,SOXX,IBB,XBI,ITA,XAR,ICLN,TAN,FDN,IGV,ESPO,REM" %}
+{%- set FEATURES = "$open,$high,$low,$close,$vwap,$volume,sp_ret,sp_jump_ratio,sp_jump_flag,sp_jump_tail,sp_max_move,sp_rv1,sp_rv5,sp_rv22,sp_vol_ratio_5_22,sp_vol_ratio_1_22,sp_trend_slope_5,sp_trend_slope_20,sp_trend_slope_60,sp_logp,sp_hurst_exponent,sp_sig_level1_lead,sp_sig_level1_lag,sp_sig_level2_lead_lag,sp_sig_level2_lag_lead" %}
+
+qlib_init:
+    provider_uri: "{{ LAKE }}"
+    region: us
+    expression_cache: null
+    dataset_cache: null
+
+    calendar_provider:
+        class: tac_qlib.data.providers.LakeCalendarProvider
+        kwargs:
+            lake_root: "{{ LAKE }}"
+            market: US
+    instrument_provider:
+        class: tac_qlib.data.providers.LakeInstrumentProvider
+        kwargs:
+            lake_root: "{{ LAKE }}"
+            market: US
+            markets: {}
+    feature_provider:
+        class: tac_qlib.data.providers.LakeFeatureProvider
+        kwargs:
+            lake_root: "{{ LAKE }}"
+            market: US
+
+    exp_manager:
+        class: MLflowExpManager
+        module_path: qlib.workflow.expm
+        kwargs:
+            uri: "sqlite:///{{ LAKE }}/mlruns.db"
+            default_exp_name: "tac-rd-q05-label22d"
+
+task:
+    model:
+        class: RankICEnsembleLGBModel
+        module_path: tac_qlib.contrib.model.rank_ensemble
+        kwargs:
+            loss: mse
+            learning_rate: 0.02
+            num_leaves: 31
+            n_estimators: 3000
+            num_boost_round: 3000
+            early_stopping_rounds: 200
+            min_data_in_leaf: 20
+            lambda_l2: 0.5
+            colsample_bytree: 0.8
+            subsample: 0.8
+            subsample_freq: 1
+            reg_alpha: 0.1
+            reg_lambda: 1.0
+            seeds: "42,7,2026,99,123"
+            parallel: 5
+
+    dataset:
+        class: DatasetH
+        module_path: qlib.data.dataset
+        kwargs:
+            handler:
+                class: TACHandler
+                module_path: tac_qlib.contrib.data.handler
+                kwargs:
+                    instruments: "{{ UNIVERSE }}"
+                    start_time: 2015-01-03
+                    end_time: 2026-08-10
+                    fit_start_time: 2016-01-04
+                    fit_end_time: 2025-09-01
+                    freq: day
+                    lake_root: "{{ LAKE }}"
+                    market: US
+                    label: "Ref($close,-23)/Ref($close,-1)-1"
+                    feature_fields: "{{ FEATURES }}"
+                    infer_processors:
+                        - class: DropAllNaN
+                          kwargs: {}
+                        - class: ProcessInf
+                          kwargs: {}
+                        - class: CSRankNorm
+                          kwargs: {}
+                        - class: ZScoreNorm
+                          kwargs: {}
+                        - class: Fillna
+                          kwargs: {}
+            segments:
+                train: [2016-01-04, 2025-09-01]
+                valid: [2025-09-03, 2026-01-03]
+                test: [2026-01-04, 2026-08-10]
+
+    record:
+        - class: SignalRecord
+          module_path: qlib.workflow.record_temp
+          kwargs: {}
+        - class: SigAnaRecord
+          module_path: qlib.workflow.record_temp
+          kwargs:
+              ana_long_short: true
+              ann_scaler: 252
+        - class: PortAnaRecord
+          module_path: qlib.workflow.record_temp
+          kwargs:
+              config:
+                  strategy:
+                      class: TopkDropoutStrategy
+                      module_path: qlib.contrib.strategy
+                      kwargs:
+                          signal: "<PRED>"
+                          topk: 10
+                          n_drop: 1
+                          only_tradable: true
+                          risk_degree: 0.95
+                  backtest:
+                      start_time: 2026-01-04
+                      end_time: 2026-08-10
+                      account: 1000000
+                      benchmark: SPY
+                      exchange_kwargs:
+                          codes: "{{ UNIVERSE }}"
+                          deal_price: $close
+                          freq: day
+                          open_cost: 0.0005
+                          close_cost: 0.0015
+                          min_cost: 5.0
+              risk_analysis_freq: 1d