zhaoli
c0eb65efa7
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)
78 lines
6.7 KiB
Markdown
78 lines
6.7 KiB
Markdown
# TradeAC Experiment Queue — hypotheses that would prove "better trading performance"
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**Purpose.** A staging queue of experiment runs, each designed to PROVE (or
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REFUTE) one hypothesis about how to achieve better trading performance on the
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TradeAC stack. Every item is pre-registered: hypothesis, change-vs-reference,
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and acceptance metric are fixed BEFORE the run (book ch.02 isolation + falsification
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discipline). Nothing here is executed yet — each entry carries its execution
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command and can be run by tracing first (`rd_trace_start` → `rd_run_workflow` /
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`rd_risk_calibrate` → `rd_trace_finish`).
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**Source.** Mined from the `book` branch of this repo (`book/CLAIMS.md`,
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`book/EVIDENCE.md`, `book/chapters/*`, `book/references/chat-ideas.md`). Only
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clean-lake (exp 21+) facts are cited as reference numbers; pre-clean-lake claims
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are idea material that the queue is designed to test.
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## Reference / control (MUST reproduce first)
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The exp-26 reference — the campaign's best clean-lake result (EVIDENCE#015, run
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`21afc6af…`, mlflow exp 25, branch `exp/26-test-whether-reducing-topkdropout-daily`):
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| Config element | Reference value |
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|---|---|
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| Universe | 50-ETF panel (same `UNIVERSE` list as exp-24/26) |
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| 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` |
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| Label | `Ref($close,-6)/Ref($close,-1)-1` (5d) |
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| 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 |
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| Train / valid / test | 2016-01-04..2025-09-01 / 2025-09-03..2026-01-03 / 2026-01-04..2026-08-10 |
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| Strategy | TopkDropout, topk 10, n_drop 1, risk_degree 0.95 |
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| Costs / benchmark | open 0.0005 / close 0.0015 / min $5, deal $close, SPY, $1M |
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Reference metrics to beat (EVIDENCE#015): **net_ann_return +2.13%, net_IR 0.21,
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gross +7.02%, net_max_drawdown −7.69%, RankIC 0.0663, RankICIR 0.2545, L/S Sharpe 4.54.**
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## The queue (ordered by value × feasibility)
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| ID | Title / hypothesis | Change vs reference (ONE var) | Acceptance | Config | Ready? |
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|----|--------------------|-------------------------------|------------|--------|--------|
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| 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` | ✅ |
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| 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` | ✅ |
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| 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` | ✅ |
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| 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` | ✅ |
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| 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` | ✅ |
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| 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` |
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| 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` |
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| 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 |
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| 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` |
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| 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` |
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| 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 |
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### Deferred (methodology / infra, P3)
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- **Q12 Purged / walk-forward CV** on the exp-26 reference (book ch.02 open
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question) — methodology improvement, not a direct alpha lever.
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- **Q13 Out-of-universe validation** — non-ETF universe for the compact
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stochastic feature set (book README open question; needs new lake symbols).
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## Execution protocol (per queued run)
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1. **Validate the lake first** (`validate_lake_dataset` + `rd_status`) — the
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clean-lake lesson: silent NaN-drops and hollow coverage invalidate a run.
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2. **Trace before running** (`rd_trace_start` with the hypothesis as `rational`,
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`evolved_from=auto` for lineage → it will fork from the closest prior
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experiment). Use a FRESH experiment name per run, e.g. `tac-rd-q01-m2-...`.
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3. **Run** `rd_run_workflow config_path=<abs path to the queue YAML>
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experiment_name=<fresh name>` — use `wait=false`, poll `rd_exp_get_run` until
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`FINISHED` (4-year trains outlive the MCP call).
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4. **Verify against acceptance** via `rd_exp_result` (headline + backtest risk).
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5. **Finish the trace** (`rd_trace_finish` with `metrics` + `evaluation`),
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snapshot any new custom modules (`rd_trace_snapshot`).
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6. **Report to the book** — on PROVE, update `book/CLAIMS.md`/`EVIDENCE.md`;
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on REFUTE, record the negative (falsification is the output).
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Sequential execution only (concurrent runs hang — chat-ideas.md ops lesson).
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## Provenance
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Mined 2026-08-19 from `book/` on the `book` branch (HEAD `436692a`). Reference
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config reproduced byte-for-byte from the exp-26 run artifact config
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(`/home/data/lake/mlruns/25/21afc6afdb674a399b59dd76c97628ce/artifacts/config`). |