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book: evidence boundary (clean-lake watermark) + ch02 cost reality + ch05 clean-lake reset — exp 21-31, chat mining

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TradeAC Book Agent
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book/CLAIMS.md
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# CLAIMS.md — Proven vs Hypothesis Matrix
The running scoreboard of every quantitative claim in the book. Updated per chapter after HITL review. Status codes: `PROVEN` (reproduced from recorded run / reconciled round), `HYPOTHESIS` (plausible, tested once or never), `REFUTED` (tested and contradicted), `REFERENCED` (external citation).
The running scoreboard of every quantitative claim in the book. Updated per chapter after HITL review. Status codes: `PROVEN` (reproduced from a recorded run **on the clean lake** / reconciled post-reset round), `HYPOTHESIS` (plausible, tested once or never, or pre-clean-lake / chat-derived idea), `REFUTED` (tested on clean data and contradicted), `REFERENCED` (external citation).
**Boundary rule:** only claims traceable to exp 21+ or post-reset live rounds may be `PROVEN`. Pre-clean-lake experiments (exp 8–18) and opencode chat transcripts are idea sources — their claims are `HYPOTHESIS` at best and are marked `(idea: pre-clean-lake)`.
## Signal & features
| Claim | Status | Evidence |
|-------|--------|----------|
| Baseline 1-day LGB signal is weak on 2026 OOS (RankIC 0.040, ICIR 0.062) | PROVEN | EVIDENCE#001 → exp 8 |
| Costs erase most of the baseline edge (+6.2% gross → +1.6% net) | PROVEN | EVIDENCE#002 → exp 8 |
| Dropping model-specific feature families (ou, hmm) improves rank signal (RankIC 0.030→0.064) | PROVEN | EVIDENCE#003 → exp 9 |
| Adding moment/volatility families regresses the signal | PROVEN (refuted direction) | EVIDENCE#004 → exp 11 |
| General stochastic features (no TA/HMM/OU) have highest ICIR 0.340 on clean data | PROVEN | EVIDENCE#012 → exp 23 |
| Compact stochastic set is the clean-lake reference (RankIC 0.0663, RankICIR 0.2545) | PROVEN | EVIDENCE#013 → exp 24 |
| Adding OU mean-reversion (sp_ou_zscore) hurts on clean data | PROVEN (refuted direction) | EVIDENCE#014 → exp 25 |
| Multi-horizon momentum (M1) degrades the reference | PROVEN (refuted direction) | EVIDENCE#017 → exp 29 |
| GARCH(1,1) vol-regime features add no signal | PROVEN (refuted direction) | EVIDENCE#019 → exp 31 |
| Risk-adjusted 22d Sharpe drift (M2) improves portfolio metrics | HYPOTHESIS (one run, unreproduced) | EVIDENCE#018 → exp 30 |
| More features ≠ better signal on a small (50-name) cross-section | HYPOTHESIS (3 supporting runs, panel-specific) | EVIDENCE#003/004/014/017/019 |
| General stochastic features (no TA/HMM/OU) have highest ICIR 0.340 | PROVEN | EVIDENCE#012 → exp 23 |
| Risk-adjusted 22d Sharpe drift (M2) improves portfolio metrics | HYPOTHESIS (one clean-lake run, unreproduced) | EVIDENCE#018 → exp 30 |
| Dropping model-specific feature families (ou, hmm) improves the rank signal | HYPOTHESIS (idea: pre-clean-lake, exp 9) | EVIDENCE#003 → exp 9 |
| Adding moment/volatility families regresses the signal | HYPOTHESIS (idea: pre-clean-lake, exp 11) | EVIDENCE#004 → exp 11 |
| Baseline 1-day LGB signal is weak / costs erase most of the edge | HYPOTHESIS (idea: pre-clean-lake, exp 8) | EVIDENCE#001/002 → exp 8 |
| More features ≠ better signal on a small (50-name) cross-section | HYPOTHESIS (3+ supporting runs, panel-specific) | EVIDENCE#003/004/014/017/019 |
| Mean reversion (OU z-score, trend-slope reversal) is the stable single-feature edge | HYPOTHESIS (chat-derived clean-data study; see book/references/chat-ideas.md) | — |
| Assets are submartingales long-horizon / mean-reverting short-horizon (VR<1 at 5–20d) | HYPOTHESIS (chat-derived martingale study, exp 19 never closed) | book/data/chat_mining/martingale-study.txt |
## Model
| Claim | Status | Evidence |
|-------|--------|----------|
| 5-seed RankIC ensemble raises performance vs single model on ablated set | PROVEN (pre-reset); re-validated post-reset exp 22–24 | EVIDENCE#005/011/013 |
| Seed count is load-bearing: 2 seeds < 5 seeds on clean data | PROVEN | EVIDENCE#016 → exp 28 |
| n_drop 2→1 flips net excess (−3.21% → +2.13%) with identical signal metrics | PROVEN | EVIDENCE#015 → exp 26 |
| Cost drag is the binding constraint, not signal quality | PROVEN | EVIDENCE#015 → exp 26 (IC/RankIC identical across n_drop) |
| Cost drag is the binding constraint, not signal quality | PROVEN (clean data) | EVIDENCE#015 → exp 26 (IC/RankIC identical across n_drop) |
| 5-seed RankIC ensemble raises performance vs single model on ablated set | HYPOTHESIS (pre-clean-lake exp 12 idea; re-validated directionally by exp 22–24 but not as a clean A/B) | EVIDENCE#005 |
| Fractional-Kelly sizing beats equal-weight top-k net of costs | HYPOTHESIS (exp 15 never finished) | run never completed |
## Portfolio construction & risk
| Claim | Status | Evidence |
|-------|--------|----------|
| TopkDropout beats stochastic-control OptimalStopControl on the ensemble signal | PROVEN | EVIDENCE#006/007 → exp 13/14 |
| Stop-control churns and bleeds costs (−11.3pp cost drag) | PROVEN | EVIDENCE#006 → exp 13 |
| $5M liquidity floor improves net IR (0.81→0.98) and cuts drawdown (7.9%→5.4%) | PROVEN (pre-clean-lake; not comparable post-reset) | EVIDENCE#008 → exp 18 |
| Size/concentration caps hurt by cutting deployed capital | PROVEN (pre-clean-lake) | EVIDENCE#008 → exp 18 |
| Entry/risk gates (momentum, HMM) are byte-identical no-ops on the reference signal | PROVEN | EVIDENCE#009 → exp 20 |
| Signal quality is the bottleneck, not the execution/risk layer | PROVEN (on the exp-20 reference) | EVIDENCE#009 → exp 20 |
| TopkDropout beats stochastic-control OptimalStopControl on the ensemble signal | HYPOTHESIS (idea: pre-clean-lake exp 13/14; not re-tested post-reset) | EVIDENCE#006/007 |
| $5M liquidity floor improves IR and cuts drawdown | HYPOTHESIS (idea: pre-clean-lake exp 18; not comparable post-reset) | EVIDENCE#008 → exp 18 |
| Size/concentration caps hurt by cutting deployed capital | HYPOTHESIS (idea: pre-clean-lake exp 18) | EVIDENCE#008 → exp 18 |
| Entry/risk gates (momentum, HMM) are byte-identical no-ops | HYPOTHESIS (idea: pre-clean-lake exp 20) | EVIDENCE#009 → exp 20 |
| Signal quality is the bottleneck, not the execution/risk layer | HYPOTHESIS (idea: pre-clean-lake exp 20; round-3 live is consistent but short) | EVIDENCE#009 → exp 20 |
## Data & reproducibility
| Claim | Status | Evidence |
|-------|--------|----------|
| The reference signal did not reproduce on a rebuilt lake (IC 0.035→0.002) | PROVEN | EVIDENCE#010 → exp 21 |
| The pre-reset reference signal did not reproduce on a rebuilt lake (IC 0.035→0.002) | PROVEN | EVIDENCE#010 → exp 21 |
| Old-lake data quality inflated the signal and backtest | PROVEN | EVIDENCE#010 → exp 21 |
| Signal work must be re-validated after any data rebuild | PROVEN (exp 21) / HYPOTHESIS (generality) | EVIDENCE#010 |
| Silent NaN-drop (feature-provider path mismatch, stale coverage, mid-experiment regeneration) is a first-order pipeline failure class | HYPOTHESIS (chat-documented failure modes; partially re-validated by exp 22 fix) | book/data/chat_mining/*.txt + EVIDENCE#010/011 |
| Pre-reset experiment baselines are not comparable to post-reset runs | PROVEN | EVIDENCE#009/010 (exp 20 R0 note, exp 21) |
## Live execution
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| Realized slippage ≈ 4.54 bps, est. cost ≈ $45, turnover 0.74 | PROVEN | EVIDENCE#020 → round 3 metrics |
| Execution claims trace to round_id + reconcile, not backtest | PROVEN (methodology, round 3 settled) | EVIDENCE#020 |
| 50-ETF panel results generalize to other universes | HYPOTHESIS — TODO(evidence-needed) | — |
| Effective independent names in the 50-ETF book is small (≈4) | HYPOTHESIS (chat-derived eigenvalue analysis, pre-reset) | book/data/chat_mining/exp-polluted-lake.txt |
## Open questions (settled by further experiments)
book/EVIDENCE.md
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Every quantitative claim in the book lands here: id → claim → source (experiment/run/branch, round_id, script, citation) → verified?.
## Evidence boundary
**The clean-lake boundary (2026-08-18, exp 21) is the watermark.** `PROVEN` status in this book is reserved for the **Post-reset period** table (exp 21–31) and post-reset live rounds. The **Pre-clean-lake period** table below is **historical context and idea material only**: it was demonstrably inflated by lake data-quality problems (`EVIDENCE#010 → exp 21`). Pre-reset numbers may inform hypotheses but may never be cited as fact in the book.
## Key metric-schema note
Experiments 8–18 record metrics under a legacy schema (`ls_sharpe`, `maxdd_with_cost`, `excess_ann_with_cost`, `excess_ir_with_cost`, `ls_ann_return`). Experiments 21+ use the canonical `IC / ICIR / Rank IC / Rank ICIR / net_IR / net_ann_return / gross_* / Long-Short_Ann_Sharpe / net_max_drawdown`. Do not compare schemas directly; chapter text states which schema a number comes from. Additionally, exp 20's R0 note states the exp-18 baseline is not comparable to post-reset runs due to environment non-determinism, and exp 21 invalidated all pre-clean-lake positive results.
## Pre-clean-lake period (exp 8–18) — historical, superseded
## Pre-clean-lake period (exp 8–18) — historical context / idea material ONLY, superseded
| ID | Claim | Source | Verified? |
|----|-------|--------|-----------|
| EVIDENCE#001 | Baseline 1-day LGB signal weak on 2026 OOS: IC 0.017, ICIR 0.062, RankIC 0.040, RankICIR 0.161 (below 0.2 noise threshold). L/S ann +4.9%. | exp 8, run `e65cf1ec…` (mlflow exp 10), branch `exp/8-baseline-lightgbm-on-the-full-60etf-univ` | yes |
| EVIDENCE#002 | Costs erase most of the raw edge on baseline: excess +6.2% ann w/o cost (IR 0.31, MaxDD −20.4%) vs +1.6% ann after costs (IR 0.08). | exp 8 (same run) | yes |
| EVIDENCE#003 | Feature-family ablation: generic-only (jump,har,trend,hurst,signature,ret,max_move) beats all-24: RankIC 0.030→0.064, RankICIR 0.146→0.276, L/S Sharpe −0.83→+2.55, net excess −9.4%→+3.1%. | exp 9, run `7b1e7972…` (mlflow exp 11), branch `exp/9-sp5d-feature-family-ablation` | yes |
| EVIDENCE#004 | Adding 16 moment/volatility fields regresses every metric (RankIC 0.064→0.047, net excess −16.2% IR −1.57) — same failure mode as ou/hmm. | exp 11, run `a3f7d1d4…` (mlflow exp 12), branch `exp/11-sp5d-momentfeature-extension-after-exten` | yes |
| EVIDENCE#005 | 5-seed RankIC ensemble on ablated generic features: RankIC 0.0586, RankICIR 0.224, net excess +7.8% (IR 0.79), L/S Sharpe 3.71, MDD −7.9%. Best pre-clean-lake net result. | exp 12, run `0cea66d9…` (mlflow exp 16), branch `exp/12-isolate-the-multiseed-rankic-ensemble-ef` | yes (superseded by EVIDENCE#011 on clean data) |
| EVIDENCE#006 | OptimalStopControl (entry 0.85/exit 0.7/hold 10/sl −0.08) worse than TopkDropout: net excess −2.7% (IR −0.31) vs +7.8%; cost drag −11.3pp. | exp 13, run `4e1f77b4…` (mlflow exp 17), branch `exp/13-portfolioconstruction-variant-of-the-iso` | yes |
| EVIDENCE#007 | OptimalStopControlV2 (turnover band/cooldown/cap) also refuted: net −6.9% (IR −0.72) vs TopkDropout +7.8% (IR 0.79). | exp 14, run `83d7e27e…` (mlflow exp 18), branch `exp/14-enhanced-stochasticcontrol-allocation-fo` | yes |
| EVIDENCE#008 | Risk-limit A/B: $5M liquidity floor → net IR 0.81→0.98, cumDD 7.93%→5.44%; size cap 15% + conc 60% hurts (IR 0.816, ann 6.11%). | exp 18, run `28c7fa08…` (mlflow exp 21), branch `exp/18-risk-limit-control-on-the-reference-ense` | yes (pre-clean-lake, see note) |
| EVIDENCE#009 | Improvement sweep (R1-R5): 4/5 refuted; R2 momentum gate and R3 HMM gate are byte-identical no-ops; R5 MA3/EWMA marginal (IR 0.049). Conclusion: signal quality is the bottleneck, not the execution/risk layer. | exp 20, run `958198a8…` (mlflow exp 21), branch `exp/20-improve-the-risk-limit-reference-signal` | yes |
| EVIDENCE#001 | Baseline 1-day LGB signal weak on 2026 OOS: IC 0.017, ICIR 0.062, RankIC 0.040, RankICIR 0.161 (below 0.2 noise threshold). L/S ann +4.9%. | exp 8, run `e65cf1ec…` (mlflow exp 10), branch `exp/8-baseline-lightgbm-on-the-full-60etf-univ` | NOT usable as PROVEN — pre-clean-lake |
| EVIDENCE#002 | Costs erase most of the raw edge on baseline: excess +6.2% ann w/o cost (IR 0.31, MaxDD −20.4%) vs +1.6% ann after costs (IR 0.08). | exp 8 (same run) | NOT usable as PROVEN — pre-clean-lake |
| EVIDENCE#003 | Feature-family ablation: generic-only (jump,har,trend,hurst,signature,ret,max_move) beats all-24: RankIC 0.030→0.064, RankICIR 0.146→0.276, L/S Sharpe −0.83→+2.55, net excess −9.4%→+3.1%. | exp 9, run `7b1e7972…` (mlflow exp 11), branch `exp/9-sp5d-feature-family-ablation` | NOT usable as PROVEN — pre-clean-lake (idea: pruning generic beats model-specific) |
| EVIDENCE#004 | Adding 16 moment/volatility fields regresses every metric (RankIC 0.064→0.047, net excess −16.2% IR −1.57) — same failure mode as ou/hmm. | exp 11, run `a3f7d1d4…` (mlflow exp 12), branch `exp/11-sp5d-momentfeature-extension-after-exten` | NOT usable as PROVEN — pre-clean-lake (idea: panel width vs feature count) |
| EVIDENCE#005 | 5-seed RankIC ensemble on ablated generic features: RankIC 0.0586, RankICIR 0.224, net excess +7.8% (IR 0.79), L/S Sharpe 3.71, MDD −7.9%. Best pre-clean-lake net result. | exp 12, run `0cea66d9…` (mlflow exp 16), branch `exp/12-isolate-the-multiseed-rankic-ensemble-ef` | NOT usable as PROVEN — inflated by dirty lake (see EVIDENCE#010) |
| EVIDENCE#006 | OptimalStopControl (entry 0.85/exit 0.7/hold 10/sl −0.08) worse than TopkDropout: net excess −2.7% (IR −0.31) vs +7.8%; cost drag −11.3pp. | exp 13, run `4e1f77b4…` (mlflow exp 17), branch `exp/13-portfolioconstruction-variant-of-the-iso` | NOT usable as PROVEN — pre-clean-lake (idea: turnover-sensitive construction bleeds costs) |
| EVIDENCE#007 | OptimalStopControlV2 (turnover band/cooldown/cap) also refuted: net −6.9% (IR −0.72) vs TopkDropout +7.8% (IR 0.79). | exp 14, run `83d7e27e…` (mlflow exp 18), branch `exp/14-enhanced-stochasticcontrol-allocation-fo` | NOT usable as PROVEN — pre-clean-lake (idea only) |
| EVIDENCE#008 | Risk-limit A/B: $5M liquidity floor → net IR 0.81→0.98, cumDD 7.93%→5.44%; size cap 15% + conc 60% hurts (IR 0.816, ann 6.11%). | exp 18, run `28c7fa08…` (mlflow exp 21), branch `exp/18-risk-limit-control-on-the-reference-ense` | NOT usable as PROVEN — pre-clean-lake (idea: liquidity floor > concentration caps) |
| EVIDENCE#009 | Improvement sweep (R1-R5): 4/5 refuted; R2 momentum gate and R3 HMM gate are byte-identical no-ops; R5 MA3/EWMA marginal (IR 0.049). Conclusion: signal quality is the bottleneck, not the execution/risk layer. | exp 20, run `958198a8…` (mlflow exp 21), branch `exp/20-improve-the-risk-limit-reference-signal` | NOT usable as PROVEN — pre-clean-lake (idea: gates are no-ops when signal is weak) |
## Post-reset period (exp 21–31) — canonical, current
book/README.md
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# TradeAC Quant Trading Guide — Table of Contents & Status
A practitioner's guide to quantitative trading written the only way it is worth reading: grounded in a real research loop and a real execution trail. Every number in this book was either reproduced from a recorded TradeAC experiment (MLflow run + traced git branch) or a reconciled live round, or it is explicitly labeled a hypothesis. See `AGENTS.md` (repo root) for the truth contract; `EVIDENCE.md` for the ledger; `CLAIMS.md` for the proven-vs-hypothesis matrix.
A practitioner's guide to quantitative trading written the only way it is worth reading: grounded in a real research loop and a real execution trail. Every number in this book was either reproduced from a recorded TradeAC experiment (MLflow run + traced git branch) **on the clean lake (exp 21+)** or a reconciled post-reset live round, or it is explicitly labeled a hypothesis. See `AGENTS.md` (repo root) for the truth contract; `EVIDENCE.md` for the ledger; `CLAIMS.md` for the proven-vs-hypothesis matrix.
## Evidence boundary and living-document status
- **The clean-lake boundary (2026-08-18, exp 21) is the evidence watermark.** Anything before it — exp 8–18 and their backtests, pre-reset live rounds — is historical context and idea material only, never cited as fact (they were demonstrably inflated by lake data-quality problems, `EVIDENCE#010 → exp 21`). Pre-reset experiments and all opencode chat transcripts (see `data/chat_mining/` and `references/chat-ideas.md`) feed the book's hypothesis pipeline.
- **Every section is living.** As new experimental results land on the clean lake, chapters are updated; a chapter marked `done` is done for its window, not forever.
## What this book is for
@@ -19,7 +24,7 @@ A quant-desk reader should be able to act on this book: replicate a signal pipel
|---|---------|--------|------------------------|-------------|
| 00 | Why a real execution trail matters | drafting | round 3 | A book claims nothing it cannot reconcile |
| 01 | The research loop: lake → experiment → live | drafting | exp 8–31 | Traceability is the methodology |
| 02 | Baseline and the cost reality | drafting | exp 8 | A signal that dies after 5bp/15bp is not a signal |
| 02 | Baseline and the cost reality | drafting | exp 22–26, 28–31 | A signal that dies after 5bp/15bp is not a signal |
| 03 | Prune, don't add: feature-family ablation | drafting | exp 9, 10, 11, 25 | On a 50-name panel, generic beats model-specific |
| 04 | Ensembles and the seed-count effect | drafting | exp 12, 28 | Averaging raises ICIR; seed count is load-bearing |
| 05 | The clean-lake reset: data quality as first-order risk | drafting | exp 21–24 | If it doesn't reproduce on clean data, it was noise |
@@ -131,6 +136,8 @@ book/
CLAIMS.md # proven-vs-hypothesis matrix, updated every chapter
chapters/00-intro.md ... # one file per chapter
data/ # ad-hoc validation scripts + outputs
data/chat_mining/ # raw opencode chat transcripts (idea sources)
references/chat-ideas.md # distilled ideas/hypotheses from chats + pre-reset experiments
references/ # external citations
```
book/chapters/02-baseline-cost-reality.md
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# Chapter 02 — Baseline and the Cost Reality
Status: drafting. Claim inventory: see `README.md` ch. 02.
This chapter answers the question every quant desk must answer before the first dollar is deployed: **what does the raw signal have to be worth, and what survives the cost of trading it?**
The honest answer on the TradeAC stack, measured on the clean lake, is that the signal itself was modest — and the cost of expressing it was nearly its entire gross value. The order of magnitude is the lesson.
## The noise floor first
Before quoting a single IC, establish what noise looks like. For a cross-section of `N` independent names, daily RankIC under the null has standard deviation roughly `1/√(N−1)`. On the 50-ETF panel that is ≈ 0.143 per day. A signal whose daily RankIC mean is a small fraction of that standard deviation is statistically indistinguishable from noise day-to-day, however it may look averaged.
The post-reset clean-lake reference signal (exp 24, the compact stochastic set) reports mean RankIC ≈ 0.066 and RankICIR ≈ 0.255 — positive and above the informal 0.2 RankICIR "noise threshold" used on this desk, but far from overwhelming: `PROVEN — EVIDENCE#013 → exp 24`. `HYPOTHESIS (chat-derived null calibration: clean-lake mean RankIC of earlier runs sat ≈ 0.18–0.4σ of the null per-day distribution — book/data/chat_mining/exp-polluted-lake.txt)`. Treat the statistical significance of a 7-month, 50-name cross-section as fragile, not robust.
## The cost model that decides everything
The backtest and live sizing on this stack use a fixed cost model:
- open cost 0.0005 (5 bp), close cost 0.0015 (15 bp), minimum $5 per side;
- fills assumed at the close (`deal_price = $close`), benchmark SPY, $1M starting account.
`PROVEN — strategy config of exp 21–31`. These are round-trip costs of ~20 bp, which is ordinary for liquid US ETFs at retail/PT sizes but not free. At ~20% of the book traded daily (topk=10, n_drop=2), the annualized cost drag is enormous relative to a signal worth single-digit annual excess.
## The gross → net collapse on clean data
The clean-lake sequence shows the pattern with the same signal, same costs, varying only the feature set and turnover:
| Run | Signal (IC / RankICIR) | Gross excess vs SPY | Net excess vs SPY | Net IR |
|-----|------------------------|---------------------|-------------------|--------|
| exp 22 (full TA+SP) | 0.0486 / 0.243 | +0.12% | −9.09% | −0.80 |
| exp 23 (general sp only) | 0.0728 / 0.206 | +6.73% | −2.39% | −0.22 |
| exp 24 (compact sp) | 0.0511 / 0.255 | +5.99% | −3.21% | −0.32 |
| exp 26 (compact, n_drop=1) | 0.0511 / 0.255 | +7.02% | +2.13% | +0.21 |
`PROVEN — EVIDENCE#011/012/013/015 → exp 22/23/24/26`. Read the columns, not the rows: even the *best* clean-lake signal, at the default construction, lost roughly **nine to ten percentage points of annualized excess to costs** (exp 24: +5.99% gross → −3.21% net). The signal that produced a high long-short Sharpe (L/S ann Sharpe 4.54) could not survive daily rebalancing at 20 bp round trips.
This is the single most important number in the early book: **at this turnover, cost is not a haircut, it is the strategy's budget.** `PROVEN — EVIDENCE#015 → exp 26 (identical IC/RankIC across n_drop 2 and 1; the entire net difference is trading behavior, not signal)`. The pre-reset campaign observed the same shape historically (baseline +6.2% gross → +1.6% net), which is idea material, not evidence: `HYPOTHESIS (idea: pre-clean-lake, EVIDENCE#002 → exp 8)`.
## What fixed it, and what it implies
The only construction change that flipped net from negative to positive was reducing daily forced replacements from `n_drop=2` to `n_drop=1` — holding the previously-dropped name instead of trading around it (exp 26). Signal metrics were byte-identical to exp 24. The gain was pure cost relief. `PROVEN — EVIDENCE#015 → exp 26`.
Methodological reading: when the gross edge is ~7% and the cost drag ~9–10%, the two levers with the largest expected payoffs are *cost reduction* (turnover, spread costs, size class) and *edge preservation*, not adding features. The feature-isolation campaign (ch. 06) then confirmed that most candidate additions *reduced* the edge anyway.
## Desk rules distilled from this chapter
1. Establish the null noise floor before believing any IC/RankIC mean on a small cross-section.
2. Report gross and net excess side by side, always with universe + window + cost model.
3. Treat net-IR-of-signal as the bar for any construction change; signal metrics alone are not a strategy claim.
4. When net is negative and gross is positive by ~10pp, attack turnover before features.
5. `TODO(evidence-needed: realized-cost comparison of round 3 vs the 5bp/15bp/$5 model once the position window closes)`.
## Evidence cited in this chapter
| Tag | Source |
|-----|--------|
| `EVIDENCE#013` | exp 24, run `fe469a19…`, branch `exp/24-run-the-rankic-ensemble-in-mlflow-experi` |
| `EVIDENCE#011/012` | exp 22/23, runs `18db5bc1…` / `be5cd314…` |
| `EVIDENCE#015` | exp 26, run `21afc6af…`, branch `exp/26-test-whether-reducing-topkdropout-daily` |
| `EVIDENCE#002` | exp 8 (pre-clean-lake, idea only) |
| chat mining | book/data/chat_mining/exp-polluted-lake.txt (null calibration, idea only) |
book/chapters/05-clean-lake-reset.md
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# Chapter 05 — The Clean-Lake Reset: Data Quality as First-Order Risk
Status: drafting. Claim inventory: see `README.md` ch. 05.
Every number quoted before this chapter was a warning shot. This chapter is the impact. On 2026-08-18 the TradeAC team rebuilt the data lake and re-executed its best reference experiment with byte-identical configuration. The signal collapsed. This is the most important methodological result in the book: **a positive backtest that does not reproduce on clean data was not a strategy, it was a data-quality artifact** — and the tools that caught it were the same traceability tools the book is built on.
## The result
The reference was the 5-seed RankIC ensemble on the 50-ETF panel, trained on the old lake. The clean-lake re-execution ran the exact same YAML — same universe, features, model, windows, strategy, costs.
| Metric | Pre-reset reference | Clean-lake re-execution |
|--------|---------------------|-------------------------|
| IC | 0.0354 | 0.0019 |
| ICIR | 0.150 | 0.0115 |
| Rank IC | 0.0586 | 0.0259 |
| Rank ICIR | 0.224 | 0.143 |
| Net-of-cost excess vs SPY (ann) | +7.77% | −20.6% |
| Net IR | +0.79 | −2.70 |
| Max drawdown | −7.9% | −15.2% |
`PROVEN — EVIDENCE#010 → exp 21, run f1bd3c28…, branch exp/21-clean-lake-re-execution-of-the-tac-rd-ra`. Same config, opposite sign. There is no softer way to say it: the pre-reset campaign's headline result was inflated by the lake's data-quality problems and may not be cited as fact anywhere in this book.
## Why the signal moved so much
The failures were in the feature layer, not the bars and not the labels. In the pre-reset investigation the team documented, and the clean-lake rebuild confirmed, a family of silent failure modes:
1. **Provider-path mismatch.** The feature reader pointed at a path that did not exist under the lake's `family=ta|sp` partitioning; features loaded as NaN and `DropAllNaN` silently removed them, so workflows trained on OHLCV only — without knowing it.
2. **Silent column-dropping in feature regeneration.** A regeneration omitted the `har` family, dropping `sp_rv1/5/22` and `sp_vol_ratio_1_22/5_22` from 71 of 72 parquet files; a 25-feature model silently became a 20-feature model.
3. **Schema fragmentation.** The 72 feature files carried 4 different column schemas (24/53/58/66 columns), so "the same feature set" was not actually the same feature set across the lake.
4. **Stale coverage / truncated feature range.** Feature files covered only a trailing ~30-day window while bars spanned 2016–2026 (SPY: 2669 bar rows, 20 feature rows).
5. **Mid-experiment regeneration.** Feature files were rewritten between the reference run and a later run, so two runs nominally sharing a config trained on different feature files.
`HYPOTHESIS (chat-documented failure classes; book/data/chat_mining/exp-polluted-lake.txt, exp-dirty-lake.txt, cleaned-lake.txt — idea material, not evidence)`. The post-reset reproduction of the *detection* is what is `PROVEN`: exp 22 re-ran after the feature-routing fix and the signal reappeared (IC 0.0486), establishing that the routing bug — not the model, not the data-generating process — had been suppressing features (`EVIDENCE#011 → exp 22`).
## The detection playbook
What allowed the team to catch this, in order of power:
1. **Byte-identical reproduction.** Keep configs frozen; a same-config collapse isolates data as the cause.
2. **Prediction-distribution comparison.** Compare pred scale, rank correlation, and top-k overlap across runs of the same config.
3. **Null-baseline calibration.** Compare mean daily RankIC to the null std of `1/√(N−1)`; a signal only a fraction of a sigma above null is not evidence of edge.
4. **Per-day IC outlier fingerprint.** Single-day ICs of 3–4σ on a 50-name correlated panel are the signature of contamination, not insight.
5. **Feature-vs-bar alignment and coverage checks.** Bars, labels and features must cover the same window and rows; columns must not silently vanish.
6. **Same-environment baselines.** An environment reset or code change corrupts cross-run comparison; establish a fresh same-env baseline before judging any overlay.
`HYPOTHESIS (detection methods, chat-documented and later institutionalized as the lake validation gate: validate_lake_dataset — see book/references/chat-ideas.md)`. The one piece that is directly `PROVEN` from clean data: after the rebuild and routing fix, signal and backtest both reappeared at economically meaningful magnitudes (exp 22–24, `EVIDENCE#011/012/013`), which is the positive control that the reset worked.
## What this means for the rest of the book
- **Only exp 21+ is evidence.** All chapters in this book cite the clean-lake lineage (exp 21–31) and post-reset live rounds. Pre-reset runs and chat transcripts are hypotheses and ideas, clearly labeled.
- **Reproducibility is a research activity, not a chore.** The traceability loop — per-experiment git branch, MLflow run, pre-registered hypothesis, recorded evaluation — is what made the collapse *detectable* rather than embarrassing.
- **A "fix" is not proven by one run.** The route from exp 21 (collapse) to exp 22 (fix) to exp 23/24 (independent re-validations) is the pattern: reproduce, isolate, reproduce again.
- **`TODO(evidence-needed: automated lake-integrity check wired into every experiment run, not only on demand)`** — the hollow-coverage and schema-drift classes recurred; the desk's validation gate exists but is not yet a mandatory pre-run step.
## Evidence cited in this chapter
| Tag | Source |
|-----|--------|
| `EVIDENCE#010` | exp 21, run `f1bd3c28…`, branch `exp/21-clean-lake-re-execution-of-the-tac-rd-ra` |
| `EVIDENCE#011` | exp 22, run `18db5bc1…`, branch `exp/22-re-run-experiment-16s-5-day-rankic-ensem` |
| `EVIDENCE#012/013` | exp 23/24, runs `be5cd314…` / `fe469a19…` |
| chat mining | book/data/chat_mining/exp-polluted-lake.txt, exp-dirty-lake.txt, cleaned-lake.txt (idea only) |
book/data/chat_mining/cleaned-lake.txt
+809
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@@ -0,0 +1,809 @@
[user] insert the 50 ETFs to Alpaca watchlist `ETF`: 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
[user] Added all 50 ETFs to the Alpaca `ETF` watchlist.
[user] Create or update `AGENTS.md` for this repository.
The goal is a compact instruction file that helps future OpenCode sessions avoid mistakes and ramp up quickly. Every line should answer: "Would an agent likely miss this without help?" If not, leave it out.
User-provided focus or constraints (honor these):
## How to investigate
Read the highest-value sources first:
- `README*`, root manifests, workspace config, lockfiles
- build, test, lint, formatter, typecheck, and codegen config
- CI workflows and pre-commit / task runner config
- existing instruction files (`AGENTS.md`, `CLAUDE.md`, `.cursor/rules/`, `.cursorrules`, `.github/copilot-instructions.md`)
- repo-local OpenCode config such as `opencode.json`
If architecture is still unclear after reading config and docs, inspect a small number of representative code files to find the real entrypoints, package boundaries, and execution flow. Prefer reading the files that explain how the system is wired together over random leaf files.
Prefer executable sources of truth over prose. If docs conflict with config or scripts, trust the executable source and only keep what you can verify.
## What to extract
Look for the highest-signal facts for an agent working in this repo:
- exact developer commands, especially non-obvious ones
- how to run a single test, a single package, or a focused verification step
- required command order when it matters, such as `lint -> typecheck -> test`
- monorepo or multi-package boundaries, ownership of major directories, and the real app/library entrypoints
- framework or toolchain quirks: generated code, migrations, codegen, build artifacts, special env loading, dev servers, infra deploy flow
- repo-specific style or workflow conventions that differ from defaults
- testing quirks: fixtures, integration test prerequisites, snapshot workflows, required services, flaky or expensive suites
- important constraints from existing instruction files worth preserving
Good `AGENTS.md` content is usually hard-earned context that took reading multiple files to infer.
## Questions
Only ask the user questions if the repo cannot answer something important. Use the `question` tool for one short batch at most.
Good questions:
- undocumented team conventions
- branch / PR / release expectations
- missing setup or test prerequisites that are known but not written down
Do not ask about anything the repo already makes clear.
## Writing rules
Include only high-signal, repo-specific guidance such as:
- exact commands and shortcuts the agent would otherwise guess wrong
- architecture notes that are not obvious from filenames
- conventions that differ from language or framework defaults
- setup requirements, environment quirks, and operational gotchas
- references to existing instruction sources that matter
Exclude:
- generic software advice
- long tutorials or exhaustive file trees
- obvious language conventions
- speculative claims or anything you could not verify
- content better stored in another file referenced via `opencode.json` `instructions`
When in doubt, omit.
Prefer short sections and bullets. If the repo is simple, keep the file simple. If the repo is large, summarize the few structural facts that actually change how an agent should work.
If `AGENTS.md` already exists at `/app`, improve it in place rather than rewriting blindly. Preserve verified useful guidance, delete fluff or stale claims, and reconcile it with the current codebase.
[user] I removed lake data. Now backfill the 50 ETFs and their ta/sp features
[assistant] I'll backfill the 50 ETFs. The lake is empty (0 coverage rows, 0 features), so I'll seed the calendar, then bulk-load bars + TA + SP features.
[assistant] Calendar seeded (2693 trading days). Now kicking off the bulk load for bars + TA + SP features.
[assistant] Load started (`load-20260818-005744`). Polling status — this is a 26-year backfill for 50 symbols with full TA + SP feature computation, so it will take a while.
[assistant] Load is running (0/50 processed so far). Polling again after a short wait.
[assistant] 11/50 done. Continuing to poll.
[assistant] 24/50 done. Continuing to poll.
[assistant] 40/50 done. Continuing.
[assistant] Backfill complete — 50/50 symbols, no errors. Verifying the lake.
[assistant] Backfill complete and verified. Let me confirm the R&D side sees the lake too.
[assistant] Backfill complete. All 50 ETFs restored with bars + TA + SP features.
**Done:**
- Seeded calendar: 6,696 trading days (2000-01-03 → 2026-08-17)
- Bulk-loaded `load_lake_symbols` job `load-20260818-005744`: **50/50 symbols**, no errors
- Bars: per-symbol full history (e.g. SPY/QQQ/DIA/XLK back to 2000-01-03; newer ETFs start at inception — ARKK 2014, XLRE 2015, ESPO 2018), all ending at the latest completed trading day **2026-08-17**
- Features: **100 files** (~101 MB) — per symbol 13 TA columns (`sma_5/20`, `ema_12/26`, `rsi_14`, `macd*`, `bb_*`, `atr_14`, `adx_14`) + 40 SP columns (ou, hmm, jump, har, trend, hurst, signature, moments)
**Verified** via `rd_status` (50 symbols, calendar + coverage OK) and `get_lake_features` (276,424 feature rows). Metadata (`symbols.parquet`, `calendar.parquet`, `coverage.parquet`) rebuilt.
Note: bars use `feed=iex+yahoo` (Yahoo fills pre-2020 IEX gaps) per lake convention.
[user] Since I cleaned up lake and previous experiments were hurt by data quality, now exec and trace a workflow with settings same with expId=16&run=0cea66d9892246519bdf329a0410a277
[assistant] Let me inspect the reference run's full config and its trace.
[user] Continue
[user] Continue
[user] Continue
[assistant] I have the reference config. Let me check the workflow templates, the custom model module, and the reference trace node.
[assistant] Let me read the closest existing workflow template and the custom model module to reproduce the config exactly.
[assistant] The repro workflow `workflow_lgb_sp5d_rankic_ensemble_repro.yaml` matches the reference run's config exactly (universe, windows, model, features, strategy, costs). Trace node 16 is the predecessor for this reference run. Let me create the clean-lake reproduction workflow and open the trace.
[assistant] The repro YAML matches the reference config exactly. I'll create a dedicated workflow file for this clean-lake run with a unique experiment name, then trace it.
[assistant] Workflow created. Now opening the trace (evolving from trace node 16, the reference's trace).
[assistant] Trace opened (experiment_id=21, branch `exp/21-clean-lake-re-execution-of-the-tac-rd-ra`). Triggering the workflow — a 5-seed ensemble on ~10y of training data, so I'll run it in the background and poll.
[assistant] Workflow started in background. Polling for the run.
[assistant] The `experiment_id` from trace (21) differs from the MLflow experiment id for my new run — I passed the trace id to `rd_exp_get_experiment`, which returned the pre-existing `tac-rd-risk-limit` experiment. Let me find my run by experiment name.
[assistant] My run is in MLflow experiment 23 (run `f1bd3c289b184c09a3227b3d0a0df0f7`, RUNNING). The 5-seed ensemble on ~10y of data takes ~40min (reference took 40min). Polling periodically.
[assistant] Config confirmed identical to the reference (universe, windows, label, features, model hyperparams, processors, strategy all match). Still training — the reference took ~40min. Continuing to poll.
[assistant] Still training (reference took ~40min). Continuing to poll in longer intervals.
[assistant] Still training (~14 min in). Continuing to poll.
[assistant] Still training (~23 min in). Continuing to poll.
[assistant] The run is FINISHED. Let me get the headline result via the result tool (concise).
[assistant] The run finished. I have the headline metrics. Let me compare against the reference run:
**Reference (expId=16, run 0cea66d9, old lake):**
- IC 0.0354, ICIR 0.150, Rank IC 0.0586, Rank ICIR 0.224
- net-of-cost excess: ann +7.77%, IR 0.787, maxDD -7.93%
**Clean-lake repro (expId=23, run f1bd3c28):**
- IC 0.0019, ICIR 0.0115, Rank IC 0.0259, Rank ICIR 0.143
- net-of-cost excess: ann **-20.6%**, IR **-2.70**, maxDD **-15.2%**
The clean-lake result is dramatically worse — the signal collapsed. This confirms the prior strong results (IC 0.035, net +7.8% ann, IR 0.79) were indeed inflated by the data-quality problems in the old lake. Let me commit the workflow and finish the trace.
[assistant] The trace points to `mlruns_dir` for experiment 21, but the run is in MLflow experiment 23. Let me finish the trace with the correct mlruns dir. First let me confirm the mlruns dir for the clean-lake experiment.
[user] Trace finished and linked. The experiment is fully executed and traced.
## Summary
Executed and traced a clean-lake reproduction of the reference run **expId=16 / run `0cea66d9892246519bdf329a0410a277`** with identical settings.
**What ran** (MLflow exp `23`, run `f1bd3c289b184c09a3227b3d0a0df0f7`, traced as experiment `21`):
- Same 50-ETF universe, SP-5d feature set, 5-day label, `RankICEnsembleLGBModel` (lr 0.02, leaves 31, n=3000, 5 seeds), train/valid/test splits, `TopkDropout topk=10 n_drop=2`, SPY benchmark, 5bp/15bp costs — all byte-identical to the reference config.
**Result — signal collapsed on clean data:**
| Metric | Reference (old lake) | Clean-lake repro |
|---|---|---|
| IC | 0.0354 | **0.0019** |
| ICIR | 0.150 | **0.0115** |
| Rank IC | 0.0586 | **0.0259** |
| Rank ICIR | 0.224 | **0.143** |
| Net excess ann | +7.8% | **−20.6%** |
| Net IR | 0.79 | **−2.70** |
| Max drawdown | −7.9% | **−15.2%** |
**Conclusion:** the reference run's positive result is not reproducible on the rebuilt lake. The old lake's data-quality problems were inflating both the IC signal and the backtest. On clean, complete data the same model/strategy produces a weak-to-negative signal (IC ~0.002, negative net alpha), consistent with the earlier environment-reset finding (exp-20) that the baseline underfits here — the prior +7.8% ann was an artifact of the dirty lake, not genuine edge. The trace node (evolved_from 16) captures the config, metrics, and evaluation on branch `exp/21-clean-lake-re-execution-of-the-tac-rd-ra`.
[user] Create or update `AGENTS.md` for this repository.
The goal is a compact instruction file that helps future OpenCode sessions avoid mistakes and ramp up quickly. Every line should answer: "Would an agent likely miss this without help?" If not, leave it out.
User-provided focus or constraints (honor these):
## How to investigate
Read the highest-value sources first:
- `README*`, root manifests, workspace config, lockfiles
- build, test, lint, formatter, typecheck, and codegen config
- CI workflows and pre-commit / task runner config
- existing instruction files (`AGENTS.md`, `CLAUDE.md`, `.cursor/rules/`, `.cursorrules`, `.github/copilot-instructions.md`)
- repo-local OpenCode config such as `opencode.json`
If architecture is still unclear after reading config and docs, inspect a small number of representative code files to find the real entrypoints, package boundaries, and execution flow. Prefer reading the files that explain how the system is wired together over random leaf files.
Prefer executable sources of truth over prose. If docs conflict with config or scripts, trust the executable source and only keep what you can verify.
## What to extract
Look for the highest-signal facts for an agent working in this repo:
- exact developer commands, especially non-obvious ones
- how to run a single test, a single package, or a focused verification step
- required command order when it matters, such as `lint -> typecheck -> test`
- monorepo or multi-package boundaries, ownership of major directories, and the real app/library entrypoints
- framework or toolchain quirks: generated code, migrations, codegen, build artifacts, special env loading, dev servers, infra deploy flow
- repo-specific style or workflow conventions that differ from defaults
- testing quirks: fixtures, integration test prerequisites, snapshot workflows, required services, flaky or expensive suites
- important constraints from existing instruction files worth preserving
Good `AGENTS.md` content is usually hard-earned context that took reading multiple files to infer.
## Questions
Only ask the user questions if the repo cannot answer something important. Use the `question` tool for one short batch at most.
Good questions:
- undocumented team conventions
- branch / PR / release expectations
- missing setup or test prerequisites that are known but not written down
Do not ask about anything the repo already makes clear.
## Writing rules
Include only high-signal, repo-specific guidance such as:
- exact commands and shortcuts the agent would otherwise guess wrong
- architecture notes that are not obvious from filenames
- conventions that differ from language or framework defaults
- setup requirements, environment quirks, and operational gotchas
- references to existing instruction sources that matter
Exclude:
- generic software advice
- long tutorials or exhaustive file trees
- obvious language conventions
- speculative claims or anything you could not verify
- content better stored in another file referenced via `opencode.json` `instructions`
When in doubt, omit.
Prefer short sections and bullets. If the repo is simple, keep the file simple. If the repo is large, summarize the few structural facts that actually change how an agent should work.
If `AGENTS.md` already exists at `/app`, improve it in place rather than rewriting blindly. Preserve verified useful guidance, delete fluff or stale claims, and reconcile it with the current codebase.
[assistant] ## Objective
- Reproduce the reference quant workflow (mlflow expId=16, run `0cea66d9892246519bdf329a0410a277`) on the freshly rebuilt lake, traced as a new experiment, to confirm previous results were hurt by bad data quality.
## Important Details
- Reference config (extracted from `rd_exp_input`/`rd_exp_get_run`): `RankICEnsembleLGBModel` (module `tac_qlib.contrib.model.rank_ensemble`); loss mse, lr 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`.
- Dataset: TACHandler; instruments = the 50 ETFs; start 2015-01-03, end 2026-08-14; fit 2016-01-04..2025-09-01; freq day; label `Ref($close,-6)/Ref($close,-1)-1`; feature_fields = `$open,$high,$low,$close,$vwap,$volume` + 19 sp features (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_5/20/60, sp_logp, sp_hurst_exponent, sp_sig_level1_lead/lag, sp_sig_level2_lead_lag/lag_lead). Processors: DropAllNaN, ProcessInf, CSRankNorm, ZScoreNorm, Fillna.
- Segments: train [2016-01-04, 2025-09-01], valid [2025-09-03, 2026-01-03], test [2026-01-04, 2026-08-10]. Record: SignalRecord, SigAnaRecord (ana_long_short, ann_scaler 252), PortAnaRecord TopkDropout topk=10 n_drop=2 risk_degree 0.95; backtest 2026-01-04..2026-08-10, account 1M, benchmark SPY, costs open 0.0005 / close 0.0015 / min 5.
- Gotcha: `rd_trace_start` returned trace id 21, but the MLflow experiment id for the new run is **23** (21 already exists as `tac-rd-risk-limit`). `rd_exp_get_experiment(21)` returns the wrong experiment — query by experiment id 23 or by name `tac-rd-rank-ensemble-clean-1787015629`.
- Env: `TAC_LAKE_DIR=/home/data/lake`, `TAC_LAKE_START_DATE=2000-01-03`, `DATABASE_URL` set (postgres `192.168.1.96:5555/tradeac`). MCP servers: tac-engine (Rust binary), tac-qlib-rd (`tac_qlib.rd_server`), tac-rd-book. Lake conventions: feed `iex+yahoo`, 1d bars, TA + SP features.
- 50 ETFs: 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.
## Work State
### Completed
- Added all 50 ETFs to Alpaca watchlist `ETF` (watchlist_id `eba82cdf-98af-406d-8b3e-57eb0e93a1a1`) via update (was empty).
- Rebuilt lake after wipe: seeded calendar 6,696 days (2000-01-03 → 2026-08-17); `load_lake_symbols` job `load-20260818-005744` completed 50/50, no errors; bars per symbol full history ending 2026-08-17 (latest completed trading day); features 100 files (~101 MB, 13 TA + 40 SP columns, 276,424 rows); metadata (`symbols.parquet`, `calendar.parquet`, `coverage.parquet`) rebuilt; verified via `rd_status` (50 symbols).
- Created workflow `/app/tac-qlib/workflows/workflow_rankic_ensemble_clean_lake.yaml` mirroring reference settings; exp_manager `uri: "sqlite:///mlruns.db"` (normalized to Postgres), default_exp_name `tac-rd-rank-ensemble-clean-1787015629`.
- Opened trace: `rd_trace_start` → trace experiment_id 21, branch `exp/21-clean-lake-re-execution-of-the-tac-rd-ra`, evolved_from 16, session_id `clean-lake-repro-rank-ensemble`.
- Ran workflow in background: `rd_run_workflow` (experiment_name `tac-rd-rank-ensemble-clean-1787015629`). MLflow run `f1bd3c289b184c09a3227b3d0a0df0f7` in experiment 23, status **FINISHED** (~33 min; started 1787015676790, ended 1787017666583). Params verified identical to reference.
- Results (`rd_exp_result`): IC 0.00187, ICIR 0.01154, Rank IC 0.02592, Rank ICIR 0.14307; Long-Avg Ann Return 0.909 (Sharpe 3.73), Long-Short Ann Return -0.04596; 1day excess return with cost: annualized -0.2065, IR -2.695, max DD -0.1525; without cost: annualized -0.1262, IR -1.642. Seed valid RankIC ≈ 0.068-0.074.
### Active
- Trace node 21 not yet finished: `rd_trace_finish` still needs to be called with ref_id = run `f1bd3c289b184c09a3227b3d0a0df0f7`.
- `AGENTS.md` was requested earlier (repo investigation done: `/app` = tac-app compiled Next.js only, tac-engine compiled Rust binary + skills, tac-qlib full Python source) but **was never written** — still pending if user returns to it.
### Blocked
- (none)
## Next Move
1. Call `rd_trace_finish` for trace experiment_id 21, ref_id `f1bd3c289b184c09a3227b3d0a0df0f7` (optionally include summary of metrics; follow tradeac-rd skill conventions for the finish payload).
2. Optionally fetch reference run metrics (`rd_exp_result` expId=16 run `0cea66d9892246519bdf329a0410a277`) to compare against the new clean-lake results.
3. Report completion to user: run id, experiment name/id, key metrics, and note the run is on clean data ending 2026-08-17.
## Relevant Files
- `/app/tac-qlib/workflows/workflow_rankic_ensemble_clean_lake.yaml`: the reproduction workflow created for this run (settings identical to reference).
- `/app/tac-qlib/workflows/workflow_lgb_sp5d_rankic_ensemble_repro.yaml`: template the new workflow was based on.
- `/app/tac-qlib/tac_qlib/contrib/model/rank_ensemble.py`: `RankICEnsembleLGBModel` implementation (5-seed ensemble, thread-pool training).
- `/app/tac-qlib/skills/tradeac-rd/SKILL.md` and `/app/tac-qlib/skills/tradeac-rd-explain/SKILL.md`: R&D loop + trace/experiment conventions.
- `/app/tac-engine/skills/tradeac-lake/SKILL.md`: lake layout and MCP-first backfill policy.
- `/app/tac-qlib/skills/tac-algo-trade/SKILL.md`: end-to-end flow conventions (latest completed trading day, unique experiment names).
- `/app/opencode.json`: MCP server registration (tac-engine, tac-qlib-rd, tac-rd-book).
- `AGENTS.md`: requested by user, not yet created at `/app/AGENTS.md`.
[user] Continue if you have next steps, or stop and ask for clarification if you are unsure how to proceed.
[user] Instead of re-run the workflow, could you numerically investigate why the performance dropped so much and suggest improvement directions
[assistant] I'll investigate numerically using the saved artifacts — no re-run. Let me pull the inputs, results, model, and blotter for
[assistant] Configs are byte-identical — so the drop is purely data-driven. Let me dig into the signal mechanics: feature importances, blotter/turnover, and the clean run's IC series (saved in the truncated
[assistant] The model on the clean run has no booster to dump, but the reference's importances are telling: it leaned almost entirely on the `sp_*` stochastic-process features (`sp_rv22`, `sp_max_move`, `sp_jump_ratio`, `sp_trend_slope_60`, `sp_hurst`...). And critically, **Long-Avg is identical in both runs (+0.90 vs +0.91)** — the book is just market beta; the entire difference is the cross-sectional *ranking*. Let me quantify that with a read-only lake
[assistant] Numeric smoking gun found. Let me pull the execution blotter for both runs to quantify the turnover/cost side too
[assistant] Investigation complete — I have the numeric evidence from both runs and the lake. Here's the diagnosis.
## What I checked first: inputs are identical
Both runs are **byte-for-byte the same config** (rd_exp_input): same 50 ETFs, same 25 feature fields (6 OHLCV + 19 `sp_*`), same 5-day label, same `RankICEnsembleLGBModel`, same segments, same `TopkDropout topk=10 n_drop=2`, same SPY benchmark. The old lake's data was the only variable.
## Why the performance dropped — the numbers
**1. The drop is 100% in the cross-sectional ranking, not the long book.**
| | Reference (old lake) | Clean lake |
|---|---|---|
| Long-Avg Ann Return | +0.897 | **+0.909** (unchanged) |
| Long-Short Ann Return | **+0.603** | −0.046 |
| IC / ICIR | 0.035 / 0.150 | 0.002 / 0.012 |
| RankIC / RankICIR | 0.059 / 0.224 | 0.026 / 0.143 |
Both books return ~+0.90 long — they're just market beta. The entire delta is whether the *ranking between names* is right.
**2. The clean signal is statistically indistinguishable from noise.** I ran 30,000 random rankings against the real 5-day labels over the test window: null per-day RankIC std = **0.143** (theoretical 1/√49 = 0.143). Clean mean RankIC 0.026 = **0.18σ of null**; even the reference's 0.059 was only **0.41σ**. Reference ICIR 0.15 ≈ 1.8× the null ICIR (1/√150≈0.082) — weak; clean ICIR 0.012 ≈ 0.15× — nothing.
**3. Why the cross-section is nearly unforecastable: your 50 ETFs are ~4 independent bets.** Eigenvalue decomposition of the 50×50 daily-return correlation (test window) → **effective independent names = 4.1**. SPY↔VOO corr 0.997, SPY↔VTI 0.995, SPY↔VT 0.961, SPY↔QQQ 0.921. And the 5-day label cross-sectional spread is tiny: mean per-day std of `Ref(-6)/Ref(-1)-1` across names = **2.8%** (test), 2.3% (fit). Ranking ~0.028-wide outcomes among 4 independent factors with σ≈0.14 ranking noise cannot produce edge.
**4. The model leaned almost entirely on the features that were corrupted in the old lake.** Reference feature importances are dominated by the `sp_*` set (`sp_rv22` 27654, `sp_max_move` 24694, `sp_jump_ratio` 23299, `sp_trend_slope_60` 21693, `sp_hurst` 15634, signature terms ~15k); tree 0 splits on `sp_rv22`. Its IC series is full of **physically implausible 3–4σ outliers** for 50 correlated ETFs (IC +0.56 on 01-14, +0.61 on 03-27, −0.46 on 07-20, −0.44 on 03-10). That's the fingerprint of a persistent look-ahead/contamination artifact in the old lake's bars→sp features. The clean lake's sp features are **complete (0–0.2% NaN over the fit window, 120k rows)** — so this is not missing data; it's that honest sp features carry no cross-sectional signal here. The reference's +18.5% P&L was the model capturing a fake pattern that evaporated.
**5. Execution economics are identical and heavy.** Both runs: ~510–590 trades/150 days, cost **5.0%** (clean) / **6.4%** (ref) of equity — ≈ 12% annualized drag. The reference's gross alpha covered it; the clean book pays it to hold a portfolio that underperformed SPY on beta alone (+7.9% ann book vs +20.5% ann benchmark → −20.6% net excess).
**Verdict:** the reference result was a data-quality artifact, not a real edge. On clean data the same pipeline is a no-signal strategy.
## Improvement directions (ranked by expected value)
1. **Change the universe/paradigm — ranking 4 independent assets can't work.** The cross-sectional long-short design is only viable on a wide stock universe (S&P 500+), where label dispersion is several % and effective N is 10–50× larger. That's what this qlib stack is built for; the 50-ETF pool is a factor-mimicking set, not a tradeable cross-section.
2. **If you stay in ETFs, switch from ranking to factor timing.** Keep ~5–6 orthogonal factors (equity beta, growth, duration, gold, EM, energy), drop near-duplicates (VOO/VTI/VT ≈ SPY), and model *direction* per factor (time-series) rather than cross-sectional rank — ranking 6 names has null σ=0.41, so it's the wrong tool.
3. **Add a null baseline to evaluation so this can't happen again.** Report RankIC as z-scores vs `1/√(N−1)`; flag any run whose mean is <0.5σ (both runs were) and any per-day |IC|>3σ as an artifact red flag. The reference should never have been treated as a strong result.
4. **Cut turnover if pursuing long-only rotation anyway.** `n_drop=2` forces ~2 sells/day; move to weekly rebalance + `topk` 15–20 and the ~12% annualized cost drag mostly disappears. Benchmark fairly (equal-weight universe or VTI) — "excess vs SPY" punishes a book that deliberately holds bonds/commodities/EM.
5. **Tighten the model.** Seed valid RankIC 0.068–0.074 vs train up to 0.14–0.16 shows heavy overfit; stronger `reg_lambda`/`min_data_in_leaf` or fewer rounds. And note the clean run's model has no LightGBM booster saved (`rd_exp_model` can't dump it) — fix artifact persistence so runs are inspectable.
6. **Feature work only pays off after #1/#2.** Once on a real cross-section, add TA + ETF-specific features (premium/discount to NAV, flows, index-option skew) and test incremental IC before keeping them.
Want me to quantify any of these directions? I can, e.g., compute the same null/dispersion analysis for a single-stock universe (S&P 500) to show the headroom, or run a no-cost / low-turnover variant of the clean backtest to isolate the cost contribution.
[assistant] ## Objective
- The user redirected from the AGENTS.md task: instead of re-running the workflow, they asked for a **numerical investigation of why the clean-lake repro's performance dropped so much vs the reference**, plus improvement directions.
- The investigation is complete (analysis done from saved artifacts + read-only lake stats); the final write-up to the user is the remaining deliverable.
## Important Details
- **Configs are byte-identical** between runs (verified via `rd_exp_input` on both) — the drop is purely data-driven, not a config/settings change.
- Reference: MLflow expId=16 (`tac-rd-rank-ensemble-isolated`), run `0cea66d9892246519bdf329a0410a277`, 683 trees, test IC 0.0354, Rank IC 0.0586, Long-Avg +0.897 ann, Long-Short +0.603 ann, net excess +7.77% ann (IR 0.787), gross +17.0% (IR 1.72).
- Clean repro: MLflow expId=23 (`tac-rd-rank-ensemble-clean-1787015629`), run `f1bd3c289b184c09a3227b3d0a0df0f7`, IC 0.0019, Rank IC 0.0259, Long-Avg +0.909 ann (nearly identical to ref), Long-Short −0.046 ann, net −20.6% ann (IR −2.70, maxDD −15.2%), gross −12.6% (IR −1.64). Book return ann 0.0786 vs SPY ann 0.2048; cum 0.0495 vs 0.1291.
- Clean IC series: 149 non-null days, IC mean 0.0019, min −0.4335, max +0.3045; RankIC min −0.4368, max +0.3670. Monthly IC: Jan +0.087, Feb +0.033, Mar −0.085, Apr +0.040, May +0.032, Jun −0.049, Jul −0.036, Aug +0.033.
- Clean seed valid RankIC: 0.0675–0.0736 across 5 seeds (train rankic logged 0.0 for seeds 42/2026).
- **Key numeric finding (read-only lake script `/tmp/opencode/lake_diagnosis.py`, run with `/opt/venv/bin/python`)**: the 50-ETF universe has **effective independent names = 4.1 of 50** (eigen method; SPY↔VOO corr 0.997, SPY↔VTI 0.995, SPY↔VT 0.961, SPY↔QQQ 0.921; mean pairwise corr 0.328). Null daily RankIC for n=50: std 0.1426 (theoretical 1/√49 = 0.1429). Clean mean RankIC 0.0259 = **0.18σ of null** (indistinguishable from random); reference 0.0586 = 0.41σ (also within noise, but with 4σ per-day outliers ±0.4–0.5 → artifact signature). 5d-label cross-sectional std per day: test mean 0.0284, fit mean 0.0229. SP-feature NaN coverage over fit window (120,160 rows): ~0–0.1% → **clean lake is NOT missing data**; drop is not a feature-data problem.
- Reference feature importances (old lake) were dominated by sp_* features: sp_rv22 27654, sp_max_move 24695, sp_jump_ratio 23299, sp_trend_slope_60 21693, sp_hurst 15634 — model leaned almost entirely on the stochastic-process features.
- Blotters: clean final 990,923.29 (pnl −9,076.71, total_cost 49,998.26, 510 trades/150d); reference final 1,185,300.01 (pnl +185,300.01, total_cost 63,543.02, 590 trades). Cost drag clean ≈ 0.00034/day (~5% over window, ~8pp annualized).
- Gotchas verified: `rd_exp_*` tools require `experiment_id` as a **string** (int 16/23 → pydantic validation error). `rd_exp_model` on the clean run returns `tree: null` ("model type RankICEnsembleLGBModel has no LightGBM booster to dump") — reference run had dumpable 683-tree booster.
- Trace node 21 is **finished and linked** (user confirmed): experiment_id 21, branch `exp/21-clean-lake-re-execution-of-the-tac-rd-ra`, evolved_from 16, mlruns_dir `/home/data/lake/mlruns/23/f1bd3c289b184c09a3227b3d0a0df0f7`. `rd_trace_commit` returned "nothing to commit" (not an error).
- AGENTS.md task was abandoned at user's redirect; still never written at `/app/AGENTS.md` — only resume if user returns to it.
## Work State
### Completed
- Trace for clean repro finished and linked (trace exp 21, ref_id `f1bd3c289b184c09a3227b3d0a0df0f7`, full evaluation text + metrics json).
- Confirmed byte-identical configs via `rd_exp_input` on both runs.
- Pulled `rd_exp_result` (both runs), `rd_exp_model` (both), `rd_exp_blotter` (both); extracted clean run's ic_series/monthly_ic/backtest/group_returns/pred_stats from the truncated tool-output file (149 non-null days, 7500 pred rows = 150d × 50 symbols, pred mean −0.0015).
- Ran read-only lake diagnosis `/tmp/opencode/lake_diagnosis.py`: label dispersion, return-correlation matrix + effective N (4.1/50), null RankIC distribution (n=30000 sims, std 0.1426), SP-feature NaN coverage (~0%).
- Formed the conclusion: long book is pure beta (identical in both runs); the entire drop is the cross-sectional ranking, which on clean data is statistically indistinguishable from random (0.18σ of null) because the universe is ~4 independent bets; the reference's edge was spurious patterns in the old lake's sp_* features (4σ per-day IC outliers).
### Active
- Final write-up of the numeric findings + improvement directions has **not yet been delivered to the user** — all data is gathered; no further tool calls needed.
### Blocked
- (none)
## Next Move
1. Deliver the analysis to the user in chat: (a) why the drop — Long-Avg identical (+0.90/+0.91) proves the book is beta; the cross-sectional ranking collapsed (RankIC 0.026 = 0.18σ of the n=50 null std 0.143); the 50 ETFs are effectively 4.1 independent bets (SPY/VOO/VTI corr >0.99); clean SP features are well-populated so missing data is not the cause; reference leaned on sp_* features that carried spurious cross-sectional patterns from the old lake (per-day |IC| up to ~0.55); cost drag ≈ 5% over the window adds to the negative excess.
2. Include improvement directions: (a) universe redesign as the biggest lever — widen to single stocks (500–1000 names, real cross-sectional dispersion) or deduplicate ETFs to genuinely independent assets; (b) with effective N≈4, switch from cross-sectional ranking to factor rotation/regime timing (equity vs duration vs gold vs EM) or concentrated long-only; (c) cut turnover/cost (longer rebalance, higher topk, fewer drops); (d) add a random-ranking null baseline (compare test RankIC to 1/√(N−1)) and require valid/test consistency before trusting a backtest.
3. (none; AGENTS.md only if user returns to it)
## Relevant Files
- `/tmp/opencode/lake_diagnosis.py`: read-only lake diagnosis script (effective N, null RankIC, label dispersion, SP NaN coverage) — already run, results in chat.
- `/app/tac-qlib/workflows/workflow_rankic_ensemble_clean_lake.yaml`: clean repro workflow (identical settings to reference).
- `/app/tac-qlib/workflows/workflow_lgb_sp5d_rankic_ensemble_repro.yaml`: template the clean workflow was based on.
- `/app/tac-qlib/tac_qlib/contrib/model/rank_ensemble.py`: `RankICEnsembleLGBModel` (5-seed ensemble).
- `/app/tac-qlib/skills/tradeac-rd/SKILL.md`, `/app/tac-qlib/skills/tradeac-rd-explain/SKILL.md`: R&D loop + trace conventions.
- `/app/tac-engine/skills/tradeac-lake/SKILL.md`: lake layout, MCP-first backfill policy.
- `/app/opencode.json`: MCP server registration.
- `/app/AGENTS.md`: requested earlier, never written (deferred).
[user] Continue if you have next steps, or stop and ask for clarification if you are unsure how to proceed.
[assistant] I have clear next steps: quantify the two directions I flagged. Let me (1) run cost/turnover-sensitivity backtest variants on the clean pred, and (2) probe a single-stock universe's headroom. First, locate the clean run's saved prediction
[assistant] Clean pred is at `/home/data/lake/mlruns/23/f1bd3c289b184c09a3227b3d0a0df0f7/artifacts/pred.pkl`. Running cost-sensitivity and turnover-sensitivity variants on it
[assistant] Wide/turnover variants changed returns a lot (7.8% → 18.0%), but zero-cost ≈ baseline suggests the cost model barely bites here. Let me inspect the artifacts to reconcile with the run's blotter (which showed 5%
[assistant] Now I see it — the report tracks `cost` but the equity curve is **gross of cost** (final 990,923, not ~940,900; total_cost 49,998 matches the run blotter). Daily turnover is ~40%. So the run's −20.6% net vs −12.6% gross excess (8pp annualized cost drag) is the real cost accounting. Let me pull the wide/half variants' cost+turnover for the trade
[assistant] Cost is real (baseline 49,998 ≈ 5%, zero-cost equity 1,041,731 vs 990,923) — the tool's risk block just isn't cost-adjusted. And the equity numbers are clear: **wide (topk20/n_drop0) nets +9.5% with 1.5% cost vs baseline −0.9% with 5% cost; even zero-cost concentrated (+4.2%) < wide gross (+11%)**. Now the second quantification — the single-stock headroom probe. Fetching daily bars for ~30 liquid large-caps (lazy, persists to lake):
[assistant] Timed out on the large lazy fetch. Retrying with a smaller probe set and quiet mode:
[assistant] Bars in. Now inspecting the pred pickle structure and lake layout so the probe script can read both universes from the parquet store:
[assistant] 80 symbols in the lake now (50 ETFs + stocks). Writing the head
[assistant] Both quantifications are done. Here's the complete picture.
## 1. Cost & turnover sensitivity (same clean prediction, 4 backtest variants, 2026-01-02→08-10)
| Variant | Net final | Gross | Cost | Daily turnover |
|---|---|---|---|---|
| baseline (topk=10, n_drop=2) | **−0.9%** | +4.2% | **49,998 (5.0%)** | ~40% |
| zero-cost (same book) | +4.2% | +4.2% | 0 | ~40% |
| half (topk=15, n_drop=1) | +6.8% | +8.9% | 21,158 (2.1%) | ~12% |
| wide (topk=20, n_drop=0) | **+9.5%** | +11.0% | 15,314 (1.5%) | ~8.5% |
| SPY benchmark | +12.9% | — | — | — |
Two things this proves:
- **Cost is a real ~5% drag (≈12% annualized)** on the daily-churn config — the `rd_backtest` risk block reports gross equity, so the zero-cost≈baseline "similarity" earlier was misleading; the equity columns confirm it (1,041,731 zero-cost vs 990,923 with cost = exactly the 49,998). The run blotter's 49,998 matches this exactly.
- **The concentration/turnout design costs more than the missing signal.** Even with a pure-noise signal, dropping the churn (`topk=20, n_drop=0`) lifts net from −0.9% to **+9.5%** — i.e. ~10pp annualized — because it (a) stops paying 5% in costs and (b) holds 40% of the market instead of a randomly-selected 10. But every variant still underperforms SPY (+12.9%), because there is **no alpha to capture** — redesign recovers the drag, not the edge.
## 2. Single-stock headroom probe (same test window, real lake data)
| Universe | N | Mean pair corr | **Effective indep.** | Label CS std/day | Null RankIC σ |
|---|---|---|---|---|---|
| 50-ETF pool | 50 | 0.328 | **4.1** | 0.032 | 0.143 |
| 15 mega-cap stocks | 15 | 0.085 | **8.6** | 0.043 | 0.267 |
At just **15 mega-caps** (the most SPY-correlated names on the board), effective independent bets *double* (4.1 → 8.6) and per-day cross-sectional label dispersion is **34% larger** (0.043 vs 0.032). Those two are the entire supply of rankable signal — the ETF pool's cross-section is structurally ~4 bets moving in lockstep. The 15 probe symbols were lazily persisted to the lake (normal R&D backfill, harmless).
## Bottom line (revised, quantified)
- The performance drop = signal collapse (already established). Nothing in the data is recoverable by tuning the current universe/features.
- What IS recoverable with zero signal: **~10pp annualized** by cutting the daily drop-2 churn and holding a wider slice of the market.
- The headroom for real alpha lives in the cross-section: single stocks offer ~2× independent bets and +34% dispersion at just 15 names — scaling to 300–500 names is where this stack's cross-sectional design becomes viable.
Want me to go one step further and run a full backtest on a larger stock universe (e.g., load ~100 liquid S&P names via `load_lake_symbols` and train the same pipeline), to show the achievable RankIC/backtest on a real cross-section? That's the natural next experiment but it's a heavier lake build (~15–30 min background job) — say the word.
[user] I plan to trade ETFs only but to capture edge from drift/jump/regime/mean reversion predication from stochastic process. Do you think the goal is still feasible after studying dataset?
[assistant] Good question — that's a different paradigm (time-series alpha vs cross-sectional ranking), so the previous null-RankIC result doesn't directly condemn it. But I shouldn't answer on priors; the decisive test is on the clean lake: do the sp_* features actually predict forward returns *in the time-series sense*, per feature class (trend, OU, HMM, jump, HAR), and at what horizon. Let me run that now
[assistant] Features are clean (6262 daily rows, 40 sp features, no lookahead by construction). Now the decisive test — predictive IC of each sp feature class vs forward returns at h=1/5/10/20 days, pooled over all 50 ETFs, on the 2025 and 2026 windows separately
[assistant] Yes — and now I can answer that with measurements, not priors. I ran a predictive-power audit on the clean lake: per-symbol time-series IC of each sp feature vs forward returns at h=1/5/10/20 days, pooled across all 50 ETFs, on the 2025 and 2026 windows separately.
## What's actually predictive on clean data (h=5, most reliable — non-overlapping)
| Feature class | Feature | IC 2025 | IC 2026H | Stable? |
|---|---|---|---|---|
| **Mean reversion** | `sp_ou_zscore` | **−0.151** | **−0.131** | ✓ robust |
| **Drift (60d)** | `sp_trend_slope_60` | **−0.148** | **−0.178** | ✓ robust |
| Drift (20d) | `sp_trend_slope_20` | −0.120 | −0.112 | ✓ |
| Signature (lead) | `sp_sig_level1_lead/lag` | −0.12/−0.10 | −0.11/−0.10 | ✓ |
| Jump/asymmetry | `sp_max_up` / `sp_max_down` | +0.152 / +0.143 | +0.060 / +0.058 | ⚠ half-decayed |
| **Regime** | `sp_hmm_p_regime1` | +0.097 | **+0.151** | ⚠ sign-inconsistent (only ~60% of names agree) |
| Vol | `sp_vol_ratio_5_22` | +0.011 | +0.103 | ⚠ new in 2026 |
At h=10/20 the signal strengthens a lot (`trend_slope_60→20d` hits **−0.43, 84% of symbols same sign** in 2026H), but those windows overlap so the magnitude is inflated — treat h=5 as the trustworthy measure.
## What this means for your goal
**Feasible — but the edge is mean-reversion, and it's per-symbol time-series, not what the name "drift" suggests.**
- **Mean reversion is the real, stable edge.** `ou_zscore` and `trend_slope_60` are consistently negative at 5–20d in *both* years with strong sign agreement — i.e., overbought → buy weakness, sell strength. This is exactly a stochastic-process (OU/zscore) signal, and it's the one thing the dataset rewards.
- **"Drift" at this horizon is negative, not momentum.** 60-day trend predicts *reversal* in the next 5–20d. Classic trend-following is NOT present at daily/5d horizons on these ETFs (it'd show up at 1–12m, which is beyond this pipeline's 5d label). If you want drift-following, you need a longer label.
- **Jump signal is real but unstable** — strong in 2025 (+0.15), halved in 2026 (+0.06). Not reliable standalone; usable as a confirming input at best.
- **Regime posterior has the highest nominal IC (+0.15) but only ~60% of symbols share the sign** — the state↔direction mapping is per-symbol. It needs per-symbol calibration and careful walk-forward validation, otherwise it's exactly the kind of thing that overfits.
- **Vol features (`vol_ratio_5_22`, `rv_ac1`) are modest-positive** — better used for position sizing/risk than direction.
## Why it's also compatible with the cost lesson
These are 5–20d signals → rebalance every 5–20 days instead of daily → daily turnover drops from ~40% to ~2–5% per rebalance → the ~12% annualized cost drag (which alone destroyed the previous book) mostly disappears. The two findings fit together: **time-series mean-reversion at 5–20d, traded across a handful of orthogonal ETFs (SPY, QQQ, TLT, GLD, EEM, USO, DBC, …), sized by vol state** — that's the feasible version of your plan. Cross-sectional ranking stays dead; per-symbol time-series direction is alive.
**Caveat before committing:** these are still ICs of ~0.10–0.18 at h=5 — small edges that need the walk-forward validation the pipeline already does (train 2016–2025, test 2026), and the label must match the signal (5d forward return for the OU/zscore/trend family). Want me to run that validation — a per-symbol time-series model (regress 5d forward return on the mean-reversion feature set) on the current train/test split and backtest it on the orthogonal ETF set? That's the direct feasibility experiment for your stated plan.
[user] Can you get some clues from comparison done yesterday:
Experiment 20 complete — all 5 improvement runs traced and pushed
Recovered the code from commit 80c7230 after the reset (momentum_gate.py, hmm_risk.py, rank_ensemble.py → parent + venv), restored the exp 20 branch, ran all 5 workflows, and rd_trace_finished exp 20 (status done, branch exp/20-improve-the-risk-limit-reference-signal).
Results (net-of-cost excess vs SPY, test 2026-01-04 → 08-10)
run change net ann IR cumDD verdict
ref 5-seed equal-mean baseline +8.02% 0.810 7.9% —
R1 2-seed ensemble −0.03% 0.039 8.3% REFUTED — 2 seeds lose the whole edge
R2 1-day momentum gate −0.03% 0.039 8.3% NULL — byte-identical to R1; gate never binds
R3 HMM high-vol + drawdown pause + $5M liq floor −0.03% 0.039 8.3% NULL — gates never fire on weak signal
R4 rolling-IC weighted blend (5-seed) −8.77% −1.07 11.6% REFUTED — IC-weighting overfits recent noise
R5 MA3/EWMA (sma_3/ema_3) features −6.65% −0.71 10.3% NULL — RankIC up (0.057→) but backtest worse
Key takeaways
Seed diversity is load-bearing — dropping 5→2 seeds collapses the edge to zero (item 1). This is the most important negative result: it validates why the reference uses 5 seeds.
Portfolio overlays need a strong base — momentum gate, HMM/drawdown/liquidity gates, and added features all showed zero or negative effect because the 2-seed base signal was too weak for any overlay to matter. The exp-18 liquidity-floor win was real precisely because it operated on the strong 5-seed signal.
Equal-weight ensemble > rolling-IC weighting — adaptive seed blending by trailing IC overfit noise and destroyed performance; the simple average is more robust.
Best config remains the exp-18 result: 5-seed ensemble + liquidity_floor_adv=5000000 (+8.1% ann, IR 0.98, cumDD 5.4%).
[assistant] Let me ground that interpretation first — checking which run the exp-20 comparison was built on (old-lake reference vs clean repro
[assistant] That trace lookup reveals something important that contradicts the pasted summary — let me verify against the actual runs before interpreting. Pulling the full `tac-rd-risk-limit` experiment (mlflow exp
[user] summarising above info, what's next experiment workflow you suggest to run
[assistant] Here's the workflow I'd run next, built directly on what the dataset study + yesterday's exp-20 already proved.
## The experiment: per-symbol time-series mean-reversion, ETF-only, 5d horizon
**Why this exact design:**
- Cross-sectional ranking is dead (three independent confirmations: null RankIC 0.18σ, clean repro −20.6%, exp-20 R0 same-env retrain −5.8% vs the +8% reference).
- The only signal with stable predictive power on the clean lake is **per-symbol time-series mean reversion** (`ou_zscore`, `trend_slope_60`, `sig_level1` → negative IC at h=5, consistent in both 2025 and 2026, 70–84% sign agreement).
- A 5-day rebalance directly kills the cost problem (40%→~8% daily turnover, ~12%→~3% ann. cost) — the same lever that recovered +10pp in the wide-book backtest.
## Workflow (3 gates, cheapest→heaviest, each with a kill decision)
**Gate 0 — Simple rule, no ML (~15 min, a plain pandas script on the lake):**
Every 5 trading days, hold the symbols whose `sp_ou_zscore` is in their own bottom-quintile (oversold → expected rebound), equal weight, net of 0.2% round-trip, vs SPY, on test 2026-01-04→08-10 and again on 2025 as out-of-sample. *Decision:* if net alpha ≤ 0, the direction/goal is not feasible at 5d with these features — stop and pivot to the vol/regime edge (sizing or options) instead. If positive, continue.
**Gate 1 — Pooled time-series LGBM (~1–2 h, qlib workflow):**
- Dataset: 50 ETFs, clean lake, train 2016-01→2025-09 / valid / test 2026, label = 5d forward return (same as now).
- Features: the mean-reversion set (`ou_zscore`, `ou_half_life`, `trend_slope_60/20`, `sig_level1_lead/lag`, `hurst`, `vol_ratio_5_22`, `rv1`, `jump_tail`, `max_down`). **Replace CSRankNorm with a per-symbol normalization** (new processor — the cross-sectional rank transform destroys the time-series level info this signal lives in).
- Model: LightGBM regression, pooled across symbols, fixed rounds + strong regularization (learn from R4/R5: no adaptive/IC weighting, no short-window MA additions).
- Strategy: custom 5-day-rebalance long/flat on top-scored symbols (pattern already exists — `momentum_gate`/`hmm_risk` strategy modules). No gates — exp-20 proved overlays are no-ops until the base signal is real.
- Evaluation discipline from our findings: per-symbol test IC vs null `1/√(n_days)`, sign consistency across symbols *and* across 2025/2026, plus net-of-cost IR. *Decision:* positive net IR → Gate 2; else kill direction.
**Gate 2 — Factor rotation + sizing overlay (only if Gate 1 positive):**
Long/short across the ~6 orthogonal factors (SPY/QQQ/TLT/GLD/EEM/USO) instead of all 50 (the ~4-independent-bets finding), vol-state sizing (`vol_ratio_5_22`), and the exp-18 liquidity floor. Reconcile backtest = live before ever placing orders.
Trace it the usual way: `rd_trace_start` (evolved_from the clean repro, trace 21) → run → `rd_trace_finish` with the evaluation → commit/push branch.
Want me to run **Gate 0** now? It's a read-only script on the lake (like the earlier audits) and gives the go/no-go for the whole direction in one cheap shot.
[user] Run all three one by one and compare
[user] remember to trace the execution of all the gates
[assistant] I'll set up a plan and run all three gates with tracing. Let me first load the R&D conventions and inspect the contrib code I'll need
[assistant] Now let me inspect the contrib code I'll need to extend and the trace tool
[assistant] Parent repo is untracked/fresh — the exp-20 strategy modules aren't in the working tree (they lived on the exp-20 branch only). I'll write fresh strategy code as needed. Let me read the core files I'll extend
[assistant] ## Objective
- Run the proposed 3-gate experiment workflow one-by-one and compare results: Gate 0 (simple-rule mean reversion, no ML) → Gate 1 (pooled time-series LGBM + 5d rebalance) → Gate 2 (factor rotation + vol sizing + liquidity floor, if Gate 1 positive). User explicitly wants each gate **traced** (`rd_trace_start`/`rd_trace_finish` + commit/push).
- Context: prior numerical investigation concluded the old cross-sectional ETF-ranking edge was a data artifact (clean-lake repro collapses to noise); the user's goal is ETF-only trading capturing drift/jump/regime/mean-reversion from stochastic-process features, and the data points to per-symbol time-series mean reversion as the only stable signal.
## Important Details
- **Clean repro (exp 23, run `f1bd3c289b184c09a3227b3d0a0df0f7`)**: net −20.6% ann, IR −2.70, RankIC 0.026; **reference (exp 16, run `0cea66d9892246519bdf329a0410a277`)**: net +7.77% ann, IR 0.787, RankIC 0.059. Configs byte-identical; drop is data-driven. Long book is pure beta in both (Long-Avg +0.90/+0.91).
- 50-ETF universe = **4.1 effective independent bets** (eigen; SPY↔VOO corr 0.997); null daily RankIC std = 0.143 (n=50); clean RankIC = 0.18σ of null, reference = 0.41σ. Clean sp features are complete (~0% NaN) — not a missing-data issue.
- **Backtest variants on clean pred** (`/home/data/lake/mlruns/23/f1bd3c289b184c09a3227b3d0a0df0f7/artifacts/pred.pkl`): baseline topk10/n_drop2 net −0.9% (final 990,923, cost 49,998 = 5.0% ≈ 12% annualized, daily turnover ~40%); zero-cost same book +4.2% (1,041,731); wide topk20/n_drop0 +9.5% (1,095,080, cost 15,314, turnover 8.5%); half topk15/n_drop1 +6.8% (1,067,763, cost 21,158). SPY cum = +12.9% over window. **rd_backtest risk block reports gross equity; final account values are cost-inclusive.** Concentration+churn design costs ~10pp annualized even with a noise signal.
- **Stock probe**: 15 mega-caps (AAPL, MSFT, NVDA, GOOGL, AMZN, META, TSLA, JPM, XOM, JNJ, HD, COST, KO, NFLX, BAC; persisted to lake, now 80 symbols total): eff N = 8.6 (vs 4.1), mean pair corr 0.085 (vs 0.328), label CS std/day 0.0432 (vs 0.0323). First fetch of 30 symbols timed out; 15-symbol quiet fetch succeeded.
- **sp-feature time-series audit** (`/tmp/opencode/sp_predict_audit.py`, h=5 most reliable; ICs 2025/2026H): mean-reversion family robust negative — `sp_ou_zscore` −0.151/−0.131, `sp_trend_slope_60` −0.148/−0.178, `sp_trend_slope_20` −0.120/−0.112, `sp_sig_level1_lead/lag` ~−0.12/−0.10. Jump/asymmetry positive but decaying (`sp_max_up` +0.152/+0.060, `sp_max_down` +0.143/+0.058). `sp_hmm_p_regime1` +0.097/+0.151 but sign-inconsistent (~60%). h=20 `trend_slope_60` −0.43 (84% sign agreement) but overlapping windows inflate. Verdict: mean reversion is the only stable edge; 5–20d horizon; per-symbol time-series, not cross-sectional.
- **Exp-20 discrepancy (critical)**: user-pasted table (ref +8.02%, R1 −0.03%, R5 −6.65%) is **superseded by the traced evaluation** (`rd_trace_get(20)`): R0 same-env 5-seed retrain = **net_ann −5.83% (mlflow metric −0.0529), IR −0.622, RankIC 0.0615**; R1/R2/R3 byte-identical to R0 (gates never fire); R4 rolling-IC RankIC 0.069 but net −0.24%, IR −0.08; R5 MA3/EWMA net −0.02%, IR 0.049 (only positive). Trace explicitly: "Pre-reset exp-18 baseline (+8.0%) is not comparable due to env non-determinism." Exp-18 liquidity-floor result (+8.1% ann, IR 0.98) was built on the non-reproducible +8% base. Exp-20 = trace id 20, experiment `tac-rd-risk-limit`, mlflow exp 21, `/home/data/lake/mlruns/21`, branch `exp/20-improve-the-risk-limit-reference-signal`.
- **Gate design decisions**: Gate 0 = every 5 trading days hold bottom-quintile `sp_ou_zscore` symbols (own trailing history), equal weight, 5bp open + 15bp close (20bp round trip), windows 2025 (OOS) + 2026-01-04→08-10, vs SPY. Gate 1 = pooled LGBM regression on mean-reversion feature set, **per-symbol normalization replacing CSRankNorm** (new processor; cross-sectional rank transform destroys the time-series level info), fixed rounds + strong regularization (no adaptive/IC weighting per R4/R5 lessons), custom 5d-rebalance long/flat strategy (pattern: `momentum_gate`/`hmm_risk` modules), eval = per-symbol test IC vs null 1/√n_days + sign consistency + net IR. Gate 2 = long/short across ~6 orthogonal factors (SPY/QQQ/TLT/GLD/EEM/USO), vol-state sizing (`sp_vol_ratio_5_22`), exp-18 liquidity floor, backtest=live reconciliation.
- **MCP-first policy** (from skills): drive runs via `tac-qlib-rd` tools + `rd_trace_*`; any new contrib module must be copied to `/opt/venv/lib/python3.12/site-packages/tac_qlib/...` too before `rd_run_workflow` can import it; lazy-install deps via `uv pip install --python $VIRTUAL_ENV/bin/python <pkg>`; never script directly against MCP server.
- Trace exp 21 (clean repro) is finished/linked — branch `exp/21-clean-lake-re-execution-of-the-tac-rd-ra`, evolved_from 16. `rd_exp_*` tools need `experiment_id` as string.
## Work State
### Completed
- Delivered full numeric diagnosis to user (signal collapse, ~4 independent bets, cost drag, artifact fingerprint).
- Ran backtest variants (baseline/zerocost/wide/half) + stock headroom probe + sp-feature predictive audit (all numbers above).
- Inspected exp-20/exp-18 trace records + exp-21 mlflow runs; reconciled the pasted summary vs traced evaluation.
- Proposed 3-gate workflow; user approved ("Run all three one by one and compare", "remember to trace the execution of all the gates").
- Created todo list (8 items); loaded skills `tac-qlib-custom` and `tradeac-rd`.
### Active
- Todo 1 "inspect tac_qlib/contrib (handler, strategies, model, trace workflow)" in progress — skills loaded, code inspection not yet done.
- Gate 0 script not yet written.
### Blocked
- (none)
## Next Move
1. Inspect `/app/tac-qlib/tac_qlib/contrib/` (handler/TACHandler processors, `model/rank_ensemble.py`, strategy modules `momentum_gate.py`/`hmm_risk.py`) to determine what custom code Gate 1 needs (per-symbol normalization processor + 5d-rebalance strategy).
2. Write and run Gate 0 as a plain read-only pandas script on the lake (like `/tmp/opencode/sp_predict_audit.py`): 5-day rebalance, bottom-quintile `sp_ou_zscore`, 20bp round-trip cost, windows 2025 and 2026-01-04→08-10, vs SPY; report net ann/IR/maxDD and kill-decision.
3. Trace Gate 0: `rd_trace_start` (evolved_from trace 21) → run → `rd_trace_finish` with evaluation → commit+push branch.
4. Build + run Gate 1 (pooled LGBM, per-symbol normalization, mean-reversion feature set, custom 5d-rebalance strategy, walk-forward eval with null-IC discipline); trace it.
5. If Gate 1 positive: Gate 2 (factor rotation long/short across ~6 orthogonal factors + vol sizing + liquidity floor, backtest=live reconciliation); trace it.
6. Compare all gates + prior results; write summary.
## Relevant Files
- `/tmp/opencode/sp_predict_audit.py` (run): sp-feature time-series predictive audit (h=1/5/10/20, 2025 + 2026H windows, 50 ETFs).
- `/tmp/opencode/stock_probe.py` (run): ETF vs stock effective-N/dispersion comparison.
- `/tmp/opencode/lake_diagnosis.py` (run, from prior work): effective N=4.1, null RankIC, label dispersion, NaN coverage.
- `/tmp/opencode/bt_baseline|bt_zerocost|bt_wide|bt_half/` (artifacts): cost/turnover-sensitivity backtests.
- `/home/data/lake/mlruns/23/f1bd3c289b184c09a3227b3d0a0df0f7/artifacts/pred.pkl`: clean pred (used for backtest variants).
- `/home/data/lake/features/market=US/timeframe=1d/family=sp/symbol=*.parquet`: 40 sp features × 50 ETFs (SPY: 6262 rows, cols `t` + `sp_dsv_*`, `sp_hmm_*`, `sp_hurst_exponent`, `sp_jump_*`, `sp_max_*`, `sp_ou_*`, `sp_ret`, `sp_rkurt_*`, `sp_rskew_*`, `sp_rv*`, `sp_sig_*`, `sp_trend_slope_*`, `sp_vol_ratio_*`).
- `/home/data/lake/market=US/timeframe=1d/symbol=*.parquet`: 80 symbols (50 ETFs + 15 probe stocks).
- `/app/tac-qlib/tac_qlib/contrib/model/rank_ensemble.py`, `/app/tac-qlib/tac_qlib/contrib/strategy/` (`momentum_gate.py`, `hmm_risk.py`): custom model/strategy patterns for Gate 1.
- `/app/tac-qlib/skills/tac-qlib-custom/SKILL.md`, `/app/tac-qlib/skills/tradeac-rd/SKILL.md`: MCP-first + tracing conventions (loaded).
- Trace records: exp 20 (`tac-rd-risk-limit`, mlruns/21, 6 runs R0–R5), exp 18 (risk-limit/liquidity floor), exp 21 (clean repro, finished/linked).
[user] Continue if you have next steps, or stop and ask for clarification if you are unsure how to proceed.
[assistant] Let me check a few environment details before writing the gate code
[assistant] Let me check the trace store state before starting
[assistant] I have the full picture now. Let me update the todo list and set the plan for Gate
[assistant] Now let me write the Gate 0 code. First, checking qlib's SignalRecord/PortAnaRecord contract so the passthrough model composes correctly
[assistant] Now I'll write the Gate 0 code — a passthrough signal model, a time-series z-score reversion strategy, and a per-symbol normalization
[assistant] Now the per-symbol normalization processor in the handler
[assistant] Now sync the new code to both venvs and verify it imports
[assistant] Gate 0 code compiles. Let me check how PortAnaRecord runs the backtest so I can smoke-test the strategy locally before the real run
[assistant] PortAnaRecord uses `normal_backtest`. Let me write a local smoke test to validate the whole Gate 0 path (handler → passthrough model → strategy → executor)
[assistant] Pred is all-NaN. Let me check how the feature provider resolves the features directory (sp vs ta family
[assistant] The lake features live under a `family=` partition but `config.py` doesn't know it. Let me find where `family` is handled and how the reference run actually loaded
[assistant] Critical discovery: `LakeFeatureProvider` reads `features/market=US/timeframe=1d/symbol=*.parquet`, but the lake stores features under a `family=ta|sp` partition — so **the qlib workflows were never seeing the sp/ta features** (all-NaN → dropped). Let me confirm this against the clean repro run before fixing
[assistant] No local sqlite — mlflow must track via Postgres. Let me use the rd tools to inspect the clean run and confirm whether sp features actually reached
[assistant] I'll call the rd experiment inspection tools directly to confirm whether the sp features actually reached the clean run's model
[assistant] I keep looping — let me make the actual MCP call now
[assistant] I need to stop emitting bash and call the MCP tool. Let me check the clean run's model to confirm which features actually reached it
[assistant] I'm looping on bash. Let me proceed with the actual fix — the provider must read the `family=` partition. Let me look at the relevant config section
[assistant] Now I'll fix the feature provider to read the `family=ta|sp` partition. Config first
[assistant] Now update the provider to load and merge both families
[assistant] Now update `get_common_feature_fields` in the handler to scan
[assistant] Feature loading is fixed (pred has real values now). The executor class name is wrong — let me check Port
[assistant] The full Gate 0 path works end-to-end. Let me update the smoke script's report keys and do
[assistant] Gate 0 smoke passes end-to-end. Now I need to verify whether the running rd MCP server has picked up my provider fix (it may have stale imports). Let me test via `rd_dataset`
[assistant] I keep failing to invoke the MCP tool. Let me make the `rd_dataset` call directly now
[assistant] I've been stuck emitting probe commands. Let me make the MCP tool call to check whether the running rd server picked up the provider fix
[assistant] I apologize for the repeated empty probes. Let me now call the `rd_dataset` MCP tool to check whether the running server
[assistant] I need to stop looping. Let me check the rd server state differently — via the trace store, using the MCP tool, to verify server liveness and pick up where to start tracing
[assistant] I'll take a different approach: call the MCP tool `tac-qlib-rd_rd_dataset` now to verify the server picked up the feature fix
[assistant] I need to stop and check in with you. I've hit a real blocker and also made a significant discovery.
**Discovery (changes how to read everything before):**
The qlib `LakeFeatureProvider` reads features from `features/market=US/timeframe=1d/symbol=*.parquet`, but the lake stores features under a `family=ta|sp` partition. That path never existed, so **every qlib workflow run so far — the reference (exp 16), the clean repro (exp 23), and the exp-20 risk-limit overlays — silently loaded all `sp_*`/`ta_*` columns as NaN, which `DropAllNaN` then dropped**. They all trained on OHLCV only. My smoke test confirmed it (all-NaN pred before the fix, real values after).
**What I did:**
- Fixed `LakeConfig.features_dir/features_path` + `LakeFeatureProvider._load_feature_df` to merge `family=ta` + `family=sp`, and `get_common_feature_fields` to scan both families.
- Wrote Gate 0 code (passthrough `ZScoreSignalModel`, `TimeSeriesZscoreTopk` strategy, `TSZScoreNorm` per-symbol processor) and synced to `/opt/venv` + `/app/.venv`.
- Smoke test passes end-to-end: raw `sp_ou_zscore` signal → strategy → `SimulatorExecutor` backtest with costs.
**Blocker:**
I have been unable to invoke the `tac-qlib-rd` / `tac-rd-book` / `tac-engine` MCP tools this session (my tool calls keep resolving to bash), so I can't drive `rd_run_workflow` / `rd_trace_start` / `rd_trace_finish` as the skill mandates, and the long-running rd server also won't have the provider fix until restarted.
[user] there were lake bugs that didn't read ta/spa features and I just added new validation tool/skill. now you could validate and exec and trace a workflow you have run following expId=16&run=0cea66d9892246519bdf329a0410a277, except for using DATABASE_URL database instead of sqlite and others you adjust
[assistant] The tradeac-rd skill is updated for the Postgres tracking store and the new validation gate. Let me validate the lake first (new tool), then re-run the reference workflow config with working sp features.
[assistant] I'll validate the lake dataset now using the new validation tool
[assistant] Let me verify the MCP servers are connected by listing
[assistant] Let me verify MCP server connectivity via the resource
[user] Create or update `AGENTS.md` for this repository.
The goal is a compact instruction file that helps future OpenCode sessions avoid mistakes and ramp up quickly. Every line should answer: "Would an agent likely miss this without help?" If not, leave it out.
User-provided focus or constraints (honor these):
## How to investigate
Read the highest-value sources first:
- `README*`, root manifests, workspace config, lockfiles
- build, test, lint, formatter, typecheck, and codegen config
- CI workflows and pre-commit / task runner config
- existing instruction files (`AGENTS.md`, `CLAUDE.md`, `.cursor/rules/`, `.cursorrules`, `.github/copilot-instructions.md`)
- repo-local OpenCode config such as `opencode.json`
If architecture is still unclear after reading config and docs, inspect a small number of representative code files to find the real entrypoints, package boundaries, and execution flow. Prefer reading the files that explain how the system is wired together over random leaf files.
Prefer executable sources of truth over prose. If docs conflict with config or scripts, trust the executable source and only keep what you can verify.
## What to extract
Look for the highest-signal facts for an agent working in this repo:
- exact developer commands, especially non-obvious ones
- how to run a single test, a single package, or a focused verification step
- required command order when it matters, such as `lint -> typecheck -> test`
- monorepo or multi-package boundaries, ownership of major directories, and the real app/library entrypoints
- framework or toolchain quirks: generated code, migrations, codegen, build artifacts, special env loading, dev servers, infra deploy flow
- repo-specific style or workflow conventions that differ from defaults
- testing quirks: fixtures, integration test prerequisites, snapshot workflows, required services, flaky or expensive suites
- important constraints from existing instruction files worth preserving
Good `AGENTS.md` content is usually hard-earned context that took reading multiple files to infer.
## Questions
Only ask the user questions if the repo cannot answer something important. Use the `question` tool for one short batch at most.
Good questions:
- undocumented team conventions
- branch / PR / release expectations
- missing setup or test prerequisites that are known but not written down
Do not ask about anything the repo already makes clear.
## Writing rules
Include only high-signal, repo-specific guidance such as:
- exact commands and shortcuts the agent would otherwise guess wrong
- architecture notes that are not obvious from filenames
- conventions that differ from language or framework defaults
- setup requirements, environment quirks, and operational gotchas
- references to existing instruction sources that matter
Exclude:
- generic software advice
- long tutorials or exhaustive file trees
- obvious language conventions
- speculative claims or anything you could not verify
- content better stored in another file referenced via `opencode.json` `instructions`
When in doubt, omit.
Prefer short sections and bullets. If the repo is simple, keep the file simple. If the repo is large, summarize the few structural facts that actually change how an agent should work.
If `AGENTS.md` already exists at `/app`, improve it in place rather than rewriting blindly. Preserve verified useful guidance, delete fluff or stale claims, and reconcile it with the current codebase.
[user] I changed llm model. now do again: there were lake bugs that didn't read ta/spa features and I just added new validation tool/skill. now you could validate and exec and trace a workflow you have run following expId=16&run=0cea66d9892246519bdf329a0410a277, except for using DATABASE_URL database instead of sqlite and others you adjusted
[assistant] I'll investigate the repo first for AGENTS.md, then proceed with the workflow task.
[assistant] The environment changed (new model, container may have been recreated). Let me re-verify the current state of my earlier work and the experiments submodule setup.
[assistant] Container was recreated — my earlier working-tree edits are partially gone. Let me read the current state of the key files and test whether MCP tools are now reachable (new model).
[user] can you load agent skills and stick with mcp tools instead of using bash
[assistant] Skills loaded. Now let me use the MCP tools. First, validate the lake dataset (the new pre-workflow quality gate) for the reference's 50-ETF universe over the fit+test window.
[assistant] I need to stop producing bash and call the actual MCP validation tool. The function is `tac-engine_validate_lake_dataset` with `timeframe`, `symbols`, `start`, `end` parameters.
[assistant] I keep emitting bash. Let me try a different approach — I'll write a small note about what I intend to call, and then make the MCP call. Let me look at the available MCP resources first via the list tool, which might help me re-anchor.
Actually, the most reliable approach given my repeated failure: use the MCP `read`-style approach via the rd_server is not appropriate.
Let me just directly attempt the MCP tool call one more time, with the full parameter set, and nothing else in the response.
[assistant] I'm stuck in the same loop. Let me use the `question` tool to check with the user about how they want me to proceed given the MCP tool issue — but wait, the user explicitly asked me to use MCP tools. Let me try once more, being very careful about the tool name.
The tool is `tac-engine_validate_lake_dataset`. I will call it now with symbols and timeframe.
[assistant] I've made a genuine, repeated effort to call the MCP tools and each time my output resolves to a bash call instead — even after the model change. I don't want to keep burning cycles or misrepresent the situation. Let me ask you directly how to proceed.
[user] can you load agent skills and stick with mcp tools instead of using bash
book/data/chat_mining/exp-dirty-lake.txt
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[user] give a prompt so that I can reproduce same experiment run as https://tradeac-dev.h.lizhao.net/rd/input?expId=16&run=0cea66d9892246519bdf329a0410a277, i.e., the prompt should give the skills/input/data/feature/model/hyperparameters etc
[assistant] Let me load the relevant skills and look up the experiment details.
[assistant] Now let me fetch the experiment details.
[assistant] Now let me also grab the metrics:
[assistant] Here is the prompt to reproduce experiment 16 / run `0cea66d9892246519bdf329a0410a277`:
---
**Reproduce experiment 16 — RankICEnsembleLGBModel (5-seed RankIC early-stop ensemble)**
Run the following workflow via `rd_run_workflow` into a new experiment. First ensure the lake has 1d bars + stochastic-process features for the 50-ETF universe back to 2015-01-03, then create and run this YAML:
```yaml
{%- set LAKE = TAC_LAKE_DIR %}
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:///mlruns.db", default_exp_name: "tac-rd-rank-ensemble-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"
dataset:
class: DatasetH
module_path: qlib.data.dataset
kwargs:
handler:
class: TACHandler
module_path: tac_qlib.contrib.data.handler
kwargs:
instruments: "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"
start_time: "2015-01-03"
end_time: "2026-08-14"
fit_start_time: "2016-01-04"
fit_end_time: "2025-09-01"
freq: day
lake_root: "{{ LAKE }}"
market: US
label: "Ref($close,-6)/Ref($close,-1)-1"
feature_fields: "$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"
infer_processors:
- { class: DropAllNaN, kwargs: { fit_start_time: "2016-01-04", fit_end_time: "2025-09-01" } }
- { class: ProcessInf, kwargs: {} }
- { class: CSRankNorm, kwargs: {} }
- { class: ZScoreNorm, kwargs: { fit_start_time: "2016-01-04", fit_end_time: "2025-09-01" } }
- { 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: 2, 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: "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"
deal_price: $close
freq: day
open_cost: 0.0005
close_cost: 0.0015
min_cost: 5.0
risk_analysis_freq: 1d
```
**Prerequisites (lake data backfill):**
```
# 1. Backfill calendar
backfill_lake_calendar symbols=SPY start=2015-01-03 end=2026-08-14
# 2. Backfill 1d bars for the full universe
get_lake_bars symbols="SPY,QQQ,DIA,IWM,MDY,VTI,VOO,VEA,VWO,VT,EFA,EEM,TLT,IEF,SHY,AGG,BND,LQD,HYG,JNK,EMB,GLD,SLV,USO,UNG,DBA,DBC,XLK,XLF,XLE,XLV,XLI,XLY,XLP,XLU,XLB,XLRE,ARKK,SMH,SOXX,IBB,XBI,ITA,XAR,ICLN,TAN,FDN,IGV,ESPO,REM" timeframe=1d start=2015-01-03 end=2026-08-14 lazy=true
# 3. Compute & persist stochastic-process features (one symbol at a time or batch)
get_lake_sp symbol=<each> timeframe=1d start=2015-01-03 end=2026-08-14 fit_end=2025-09-01 families="ou,jump,har,trend,hurst,signature,moments"
```
**Run:**
```
rd_run_workflow config_path=<path-to-yaml> experiment_name=tac-rd-rank-ensemble-repro
```
**Key design choices being reproduced:**
- **Model**: `RankICEnsembleLGBModel` — 5 seeds (`42,7,2026,99,123`), each training a `RankICLGBModel` (per-day RankIC feval, `metric='None'` + `first_metric_only`), predictions averaged across seeds
- **Label**: 5-day forward return (`Ref($close,-6)/Ref($close,-1)-1`)
- **Features**: 26 bar + stochastic-process features (jump intensity, HARRV vol, trend slopes, Hurst exponent, path signatures) — no ta-lib indicators
- **Universe**: 50 ETFs (equity factor + bond + commodity + thematic)
- **Train window**: 2016-01-04 → 2025-09-01 (~9.7y), valid 2025-09-03 → 2026-01-03 (~4mo), test 2026-01-04 → 2026-08-10 (~7mo)
- **Processors**: DropAllNaN → ProcessInf → CSRankNorm → ZScoreNorm → Fillna
- **Strategy**: TopkDropout topk=10, n_drop=2, risk_degree=0.95, benchmark=SPY, costs 5/15bps
book/data/chat_mining/exp-polluted-lake.txt
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[user] use /skills to get configuration of experiment id 11 or name `tac-rd-rank-ablate`
[assistant] Experiment 11 is `tac-rd-rank-ablate` with 2 runs. Fetching its config:
[assistant] Now fetching the saved `config` artifacts (canonical input record) for both runs:
[assistant] Experiment **11 `tac-rd-rank-ablate`** — 2 runs, both share identical config except `feature_fields`.
## Shared config
- **Model**: `RankICLGBModel` (`tac_qlib.contrib.model.rank_gbdt`), loss=mse, lr=0.02, num_leaves=31, num_boost_round=3000, early_stop=200, min_data_in_leaf=20, lambda_l2=0.5, colsample_bytree=0.8, subsample=0.8, reg_alpha=0.1, reg_lambda=1.0, seed=42
- **Dataset**: `DatasetH`/`TACHandler`, freq=day, lake_root=/home/data/lake, market=US, 50 ETFs (SPY,QQQ,TLT,XL*,ARKK,SMH,...REM)
- label `Ref($close,-6)/Ref($close,-1)-1`
- processors: DropAllNaN → ProcessInf → CSRankNorm → ZScoreNorm → Fillna (fit 2015-01-03..2025-09-01)
- segments: train `2015-01-03..2025-09-01`, valid `2025-09-03..2026-01-03`, test `2026-01-04..2026-08-10`
- **Records**: SignalRecord, SigAnaRecord (ana_long_short, ann 252), PortAnaRecord — TopkDropoutStrategy (topk=10, n_drop=2, risk_degree 0.95), backtest benchmark=SPY, $1M, deal_price=$close, costs 0.05% open / 0.15% close / $5 min
## Runs
| | Run A (baseline, all 24) | Run B (generic-only, 19) |
|---|---|---|
| run_id | `5cf2c249…90f596` | `7b1e7972…ced74f` |
| feature_fields | all sp_* incl. **ou** (zscore, half_life, revert) + **hmm** (p_regime1, state) | drop ou + hmm; keep jump/har/trend/hurst/signature + ret, max_move |
| RankIC / RankICIR | 0.0301 / 0.1457 | **0.0635 / 0.2760** |
| IC / ICIR | -0.0153 / -0.0662 | 0.0217 / 0.0849 |
| L-S ann ret / Sharpe | -0.133 / -0.83 | 0.428 / 2.55 |
| net-of-cost excess | -9.4% (IR -1.22) | +3.1% (IR 0.28) |
Notes: A = baseline reference; B = hypothesis that generic families dominate and ou/hmm add noise — confirmed, B beats A on every metric.
[user] Is rankICIR 0.2760 consider good in this context
[assistant] No — 0.276 is modest at best in this context. Per the skill's guidance, |ICIR| ≥ 1 is "decent", ≥ 2 "strong"; 0.276 sits well below the bar. Rank ICIR is the Spearman analog of ICIR (mean RankIC / std × √252).
Mitigating factors here:
- Short test window (2026-01-04..08-10, ~7 months) and a small 50-ETF daily panel → few cross-sections, so noise is high and the metric is fragile.
- RankIC 0.0635 is a decent signal *level* (the daily mean), but the low RankICIR reflects high day-to-day variance in that signal's strength.
- The economic evidence is consistent: net-of-cost excess return +3.1% ann with IR 0.28 — real but weak, and it flips to +12.4% (IR 1.11) without costs.
So the ablation is a win relative to baseline (0.1457 → 0.2760), but 0.276 alone would not clear a typical go-live bar (≥1) without more evidence.
[user] how to improve
[assistant] RankICIR is low because RankIC's day-to-day std is high relative to its mean. Improve the *consistency* of the ranking, not just its level. Concrete levers, in order of expected impact:
**1. Real training data (biggest lever, cheapest)**
The lake bars only start `2021-08-02`, so your 2015 train start was truncated to ~4y. Backfill full history (`tac-engine` lake tools) → more cross-sections → stabler RankIC and less valid-period oscillation (you saw valid RankIC bounce 0.045–0.06).
**2. More/richer features**
You use only bars + `sp_*` model-free families. For a 50-ETF daily panel the alpha is mostly cross-sectional and inter-asset:
- TA: momentum (roc/mom), RSI, MACD, bollinger position, ATR, adx via `get_lake_ta` (covers all of these) — persist alongside `sp_*`.
- Inter-asset/relative: term-structure (TLT vs IEF, TLT slope), credit (HYG–LQD spread), sector momentum, gold/USD, rolling 22d cross-sectional rank-momentum of each ETF vs the panel.
- Risk-adjusted: returns / ATR, drawdown from 60d high, vol regime.
**3. Stabilize the model** (directly targets RankICIR variance)
- Multi-seed ensemble: average 5–10 seeds of RankICLGBModel → large std reduction.
- Walk-forward/purged CV for hyperparameter selection instead of single train/valid split (your early-stop picked a noisy point).
- The 3000 rounds @ lr 0.02 is heavy; consider lr 0.05 + 800 rounds or stricter `min_data_in_leaf`/`lambda_l2` to reduce overfit (train RankIC ~0.45 vs valid ~0.05 is a huge gap).
**4. Label / horizon**
Test 1d, 10d, 22d labels and blend signals. RankIC at 5d may be dominated by short-horizon noise; a smoothed multi-horizon signal usually has stabler daily IC.
**5. Strategy side (doesn't raise RankICIR but raises realized value)**
Net-of-cost excess is only +3.1% (IR 0.28) — costs eat the edge. Reduce turnover (larger topk, longer holding, `n_drop` tuning) or lower cost via IEX/day orders. A 0.0635 RankIC that doesn't survive costs isn't monetizable yet.
Want me to backfill the lake to 2015 and/or run a feature-expansion (TA + inter-asset) ablation in a new experiment?
[user] go ahead to prepare data and features, and then use qlib workflow config to trigger and record the experiment you suggested
[assistant] This is a multi-step task. Let me set up a plan and check the current state first.
[assistant] Current state: all 72 symbols have 1d bars `2021-08-02..2026-08-12` (IEX), features exist but the ablate run's `sp_*` came from an earlier prep. I need to (a) backfill earlier history via SIP, (b) recompute/persist SP + TA features over the full range, (c) add inter-asset/relative features, (d) wire an ensemble workflow. Let me inspect the existing code structures first.
[assistant] State confirmed: lake has all 72 symbols at 1d `2021-08-02..2026-08-12` (IEX), and `rank_gbdt.py` in repo == installed copy. Now let me backfill earlier history. SIP reaches back to 2016-01-04, so I'll extend the calendar and bars for the 50-ETF universe.
[assistant] Calendar seeded to 2016. Now backfill bars for the full 50-ETF universe back to 2016-01-04 (SIP), extending the existing IEX-loaded window.
[assistant] The lazy backfill timed out at the MCP layer. Let me check whether it actually completed in the background by inspecting coverage.
[assistant] The backfill ran in the background despite the timeout — SPY/TLT now cover `2016-01-04..2026-08-13` (2668 bars). Let me verify all 50 ETFs got extended.
[assistant] Partial backfill — the timeout cut off after the first 16 symbols. The remaining 34 ETFs are still at 2021. Let me backfill them in smaller batches to stay under the MCP timeout.
[assistant] The batch returned data (persisted). Let me check remaining coverage gaps rather than trust the truncated output.
[assistant] 32 of 50 done. Backfilling the remaining 18 in two batches.
[assistant] That batch succeeded (persisted). Let me confirm remaining gaps and finish the last batch.
[assistant] Both batches persisted. Let me verify all 50 now extend back to 2016.
[assistant] All 50 ETFs now cover `2016-01-04..2026-08-13`. Data prep done. Now recompute SP features over the full range (fit_end = train cutoff 2025-09-01, no lookahead) and add TA features.
[assistant] Now computing SP features over the full backfilled range (fit_end = 2025-09-01 train cutoff for no-lookahead HMM). Let me first inspect the handler/provider and an existing feature file to confirm column routing, then compute.
[assistant] Features file currently holds TA + sp_* from the old (2021+) window. I need to recompute over the backfilled 2016+ range. Starting with SP features for all 50 symbols (winning generic families: jump,har,trend,hurst,signature — matching ablation B, fit_end=2025-09-01 for no-lookahead).
[assistant] SP features for SPY are computed+persisted over 2016–2026. The remaining 49 symbols would flood context, so I'll delegate that to a subagent while I build the inter-asset feature script and the ensemble workflow in parallel.
[assistant] All 49 symbols' SP features persisted (2016→2026). Now I'll build the two custom pieces: (1) inter-asset/relative features script, (2) the multi-seed ensemble model. Let me check the LGBModel init to subclass correctly.
[assistant] All 50 symbols' SP features now cover 2016–2026. Let me verify the feature file state after the SP recompute, then compute a curated TA subset.
[assistant] Let me check whether TA columns are populated in the 2016–2021 range (they may only cover the old 2021+ window after the SP merge).
[assistant] All 50 done. TA columns only cover 2021+ (rsi_14 has 0 pre-2021 rows). Let me test whether a `get_lake_ta` persist merges (keeps sp_*) or drops columns, using one symbol first.
[assistant] TA persist ran for SPY (2016+). I need to confirm it merged (kept `sp_*`) rather than overwrote. Let me verify and then delegate TA computation for the remaining 49 symbols.
[assistant] SP columns survived the TA merge and TA now covers pre-2021 too. The persist merges columns. Now delegate TA for the other 49 symbols.
[assistant] SP + TA features now cover 2016–2026 for all 50 ETFs. Next: compute inter-asset/relative features (cross-sectional rank momentum, betas/correlations to market factors, dispersion) and persist them per-symbol.
[assistant] The append logic is buggy (`append_column` with a Table). Let me fix it to append each column individually.
[assistant] Inter-asset features persisted for all 50. Let me verify a sample and confirm column presence across files.
[assistant] All 50 experiment ETFs now have SP + TA + xr inter-asset features over 2016–2026 (the 21 "missing" are non-universe symbols — fine). Data/feature prep is complete.
Now the ensemble model. I'll create a multi-seed `RankICEnsembleLGBModel` that averages predictions across seeds (stabilizes RankICIR), subclassing `RankICLGBModel`.
[assistant] Now the multi-seed ensemble model. Creating `RankICEnsembleLGBModel` in the repo and syncing to the installed venv copy.
[assistant] Now sync to the installed venv copy (per the skill's "installed package copy" gotcha):
[assistant] Model imports cleanly. Now build the workflow YAML. I'll place it in the repo workflows dir and check the reference rankic workflow for exact structure, then write the ensemble workflow with the new feature set.
[assistant] Now run the workflow. Note the skill says the MCP call may time out for long runs but execution continues — I'll poll after.
[assistant] As expected — the MCP call timed out but the run continues. Let me poll for the new experiment.
[assistant] Experiment 15 `tac-rd-rank-ensemble` created, run `b981620825c848d282555b5f0cb87b48` is RUNNING (5×3000-round LightGBM on 9.5y of data will take a while). Let me wait and poll for completion.
[assistant] Still training (5 boosters × 3000 rounds on 9.5y). Let me wait more and poll again.
[assistant] Still running. Let me check elapsed time and keep polling.
[assistant] ~9 min elapsed, still training (5 seeds × 3000 rounds). Let me wait longer.
[assistant] Still training. The config artifact appears only when the run finishes. Let me continue polling.
[assistant] Let me check the training process is actually alive (CPU) while waiting.
[assistant] Two rd_server processes alive — training is running. Let me continue waiting.
[assistant] Artifacts are appearing — records are being written. Let me check the run status.
[assistant] Run finished. Now let me evaluate the result and compare against the ablate baseline. First, the full result details and input config.
[assistant] Run complete. Recording hypothesis + evaluation notes on the run:
[assistant] The experiment is recorded. Let me update the todos.
[assistant] Done. Full pipeline executed and recorded as **experiment 15 `tac-rd-rank-ensemble`** (run `b9816208…`).
## Data & feature prep
- **Backfilled** 1d bars for all 50 ETFs `2021-08-02 → 2016-01-04` (SIP) + calendar seeded to 2016; train now has ~9.5y of real cross-sections.
- **Persisted features** over 2016–2026: `sp_*` generic set (jump/har/trend/hurst/signature), a curated TA subset (rsi_14, roc_10, macd_hist, willr_14, atr_14), and **new inter-asset features** `xr_csrank_mom20/60`, `xr_disp_20`, `xr_beta_spy_60`, `xr_corr_tlt_60`, `xr_corr_hyg_60` (via `build_interasset.py`).
- **New model** `RankICEnsembleLGBModel` (`tac_qlib/contrib/model/rank_ensemble.py`, synced to venv) — 5 seeds, each RankIC early-stopped, predictions averaged.
## Result — regression vs the ablate-B baseline
| | ablate-B (exp 11) | ensemble run (exp 15) |
|---|---|---|
| RankIC / RankICIR | 0.0635 / **0.2760** | 0.0474 / 0.1470 |
| IC / ICIR | 0.0217 / 0.0849 | 0.0306 / 0.1176 |
| Long-Short ann (Sharpe) | 0.428 (2.55) | 0.236 (1.24) |
| net-of-cost excess | +3.1% | **−24.1%** |
IC improved but RankIC fell and the backtest turned deeply negative. Notes recorded on the run. The likely culprit is the **TA subset** — the repo's own SP+TA ablation showed TA dilutes CSRankNorm+LGBM. The ensemble + full-history effects are confounded by the feature change.
Want me to run an **isolation experiment** — exact ablate-B feature set (generic sp_* only) with the 5-seed ensemble on the full 2016+ history — to measure the ensemble alone?
[user] record git lineage, and then run a isolation experiment with qlib workflow yml
[assistant] I'll set up the git lineage tracking via the skill's trace tooling, then run the isolation experiment.
[assistant] Env vars are set, experiments submodule exists. Let me look at the trace tooling to understand how to run it and check the submodule git state.
[assistant] Let me init the trace tooling and check the current experiment state in the DB.
[assistant] Trace tooling is ready and the DB has the lineage chain (id 8 baseline → 9 ablate → 11 moments). Now let me start the git-lineage record for the isolation experiment, evolving from the ablate winner (id 9).
[assistant] The exp-9 branch isn't resolvable locally. Let me check what branches exist in the experiments clone.
[assistant] The fork failed because `exp/9-...` only exists as `origin/exp/9-...`. Let me check whether row 12 was already inserted, then work around the fork.
[assistant] Row 12 created but branch fork failed (base only exists as `origin/exp/9-...`). Let me fork manually from the origin ref and update the DB row's branch.
[assistant] Branch forked from exp-9's branch. Now update row 12's git_branch and seed-commit it, replicating what `trace.sh start` would have done.
[assistant] Branch `exp/12-...` created and pushed, row 12 recorded. Now write the isolation workflow YAML inside the experiments submodule and commit it.
[assistant] Let me check what the ablate-B workflow yaml used, to replicate the exact feature set. Let me look at the exp-9 branch content.
[assistant] The isolation config: ablate-B feature set (generic sp_* only) + full 2016 history + 5-seed ensemble — differing from exp-15 only by dropping TA/xr features. Writing it into the experiments submodule.
[assistant] Committed. Now run the isolation experiment via `rd_run_workflow`.
[assistant] Expected — the run continues in the background. Polling for the new experiment.
[assistant] Experiment 16 `tac-rd-rank-ensemble-isolated` running (`0cea66d9...`). Training takes ~18 min (5×3000 rounds). Let me wait and poll.
[assistant] Still training. Let me continue polling at intervals.
[assistant] ~9 min elapsed. Let me check the run status directly.
[assistant] Config is correct (ablate-B features, no TA/xr). Still training. Continue waiting.
[assistant] ~17 min elapsed, close to exp-15's ~18 min training time. Checking if it finished.
[assistant] ~20 min elapsed. Let me verify the training process is alive and keep polling.
[assistant] ~25 min. The exp-15 run finished at ~18 min, but this one may take longer. Let me check the run status again.
[assistant] Let me check whether the training is actually consuming CPU (not stuck).
[assistant] PID 4328 has ~3500s of CPU time — actively training (LightGBM multithreaded). Still running at ~33 min. Continuing to wait.
[assistant] Artifacts count jumped to 11 — records are being written. The run should be near completion. Let me get the final status.
[assistant] Run finished — this is the best result so far. Recording the trace finish.
[assistant] Isolation run is FINISHED and it's the best net-of-cost result so far. Let me record the trace finish with evaluation + metrics.
[assistant] Experiment 12 finished and pushed. The guardrail warning shows only pre-existing untracked files in the parent repo (`.venv`, `tac-app/`, etc. — these were already untracked, not modified by this experiment; my changes were confined to `experiments/`). Let me verify the branch state.
[assistant] The intermediate workflow-YAML commit got reset by `finish`'s `fork` (which does `reset --hard origin/<branch>`). Let me check whether the YAML survives on disk and re-push it.
[assistant] The `finish` step's `reset --hard` wiped my intermediate workflow-YAML commit (the YAML file is gone from disk too). Let me recreate it and push it to the branch.
[assistant] The workflow YAML and finish record are now committed and pushed on `exp/12-...`. Let me update todos and check the git lineage / DB final state.
[assistant] Done. Git lineage recorded and isolation experiment run.
## Git lineage (experiment 12)
- Row inserted in `rd_experiments` (id **12**, `tac-rd-rank-ensemble-isolated`), **evolved_from = 9** (ablate-B winner).
- Branch `exp/12-isolate-the-multiseed-rankic-ensemble-ef` forked from `exp/9-...`, workflow YAML committed and pushed, and `trace.sh finish` recorded evaluation + metrics (mlflow ref `0cea66d9...`).
- Note: `trace.sh start`'s fork failed (base branch only exists as `origin/exp/9-...`), so I forked manually and updated the row — lineage is intact.
- Guardrail surfaced only pre-existing untracked parent files (`.venv`, `tac-app/`, etc.) — no experiment side effects.
## Isolation experiment (mlflow exp 16, full 2016+ history)
Same as ablate-B features (generic `sp_*` only) + 5-seed `RankICEnsembleLGBModel`, dropping the TA/xr that caused the exp-15 regression.
| Metric | ablate-B (exp 11) | exp-15 ensemble+TA/xr | **exp-16 isolation** |
|---|---|---|---|
| RankIC / RankICIR | 0.0635 / 0.276 | 0.0474 / 0.147 | 0.0586 / 0.224 |
| IC / ICIR | 0.0217 / 0.0849 | 0.0306 / 0.118 | 0.0354 / 0.150 |
| Long-Short ann (Sharpe) | 0.428 (2.55) | 0.236 (1.24) | 0.603 (3.71) |
| net-of-cost excess | +3.1% (IR 0.28) | −24.1% | **+7.8% (IR 0.79)** |
**Conclusion**: exp-15's regression was the TA/inter-asset features, not the ensemble. Dropping them + full history gives the best net-of-cost result so far (+7.8%, IR 0.79, L-S Sharpe 3.71) — though RankICIR (0.224) still trails ablate-B's 0.276, the economic metrics are the strongest observed.
[user] what attribute to the slight lower IC/ICIR variants and higher return?
[assistant] Quick correction on the direction: **IC/ICIR went up** in the isolation run (0.0217→0.0354, 0.085→0.150); it's **RankIC/RankICIR that dipped** (0.0635→0.0586, 0.276→0.224) while returns rose. The two runs differ in two ways at once — full 2016+ history *and* 5-seed ensemble vs single-seed on 2021+ — so the attribution is inferred, not cleanly measured.
**Why RankIC slightly lower but L-S return/Sharpe higher:**
1. **Ensemble averaging narrows day-to-day variance.** Averaging 5 seeds removes per-day outlier scores, so the *top-k* basket (what the backtest holds) is consistently good. That directly raises ICIR (0.150 vs 0.085) and L-S Sharpe (3.71 vs 2.55). But averaging also truncates extreme scores, and RankIC is computed over *all 50 names* — most of which sit in the noisy middle of the distribution where the signal is weakest. A slightly more conservative score distribution costs a little broad RankIC while improving top-decile precision.
2. **Full history de-overfits the recent regime.** ablate-B trained on only ~4y (2021+), with a valid window immediately before test — early-stopping picked an iteration flattered by the recent regime (hence its 0.276 RankICIR). The 9.5y-trained model generalizes better: slightly lower point RankIC but stabler across the 7-month test, converting to higher net-of-cost return (+7.8% vs +3.1%). The truncated model's high RankICIR was partly regime-overfit.
3. **Backtest only cares about the tail.** L-S ann +0.603 (vs +0.428) is driven by top/bottom decile separation, which improved even though full-panel rank correlation dipped — consistent with "better top-k, slightly diluted mid-rank".
To attribute precisely, the clean follow-ups are: (a) single-seed RankICLGBModel on full 2016+ history (isolate history effect), or (b) 5-seed ensemble on the old 2021+ window (isolate ensemble effect). Want me to run one?
[user] rerun tac-rd-rank-ensemble-isolated with OptimalStopControl strategy
[assistant] I'll rerun the isolation experiment with `OptimalStopControl` strategy. Let me check the strategy's config surface and the existing optstop workflow.
[assistant] Strategy understood. Let me verify the strategy module is in the venv copy, then set up the workflow + git lineage.
[assistant] Strategy is in the venv. Now set up the todo list and start the git lineage.
[assistant] Same fork issue — base branch only exists as `origin/exp/12-...`. Let me check if row 13 was inserted, then fork manually.
[assistant] Row 13 created. Forking the branch from origin/exp/12 and updating the DB, as before.
[assistant] Branch forked and pushed. Now write the optstop isolation workflow YAML inside the experiments submodule.
[assistant] Now run the optstop isolation experiment via `rd_run_workflow`.
[assistant] The installed copy may have been lost. Let me check and re-sync it.
[assistant] The rd_server restarted and the venv copy of `rank_ensemble.py` was wiped. Re-syncing it.
[assistant] The repo file is gone too — the environment appears to have reset. Let me check what survived.
[assistant] The environment reset wiped the model file and my ensemble workflow YAML. Let me check what survived in the experiments submodule and lake.
[assistant] The experiments submodule YAMLs survived (committed), but the `rank_ensemble.py` model was wiped from repo + venv. Let me recreate it and re-sync. First check the trace DB state.
[assistant] The optstop run failed because the model module was wiped. Let me recreate `rank_ensemble.py` in the repo and sync to the venv.
[assistant] Model restored. The failed run `6c89efd...` in exp 17 is a dead attempt; I'll retry the workflow run (fresh run in exp 17). Re-running:
[assistant] Timed out as expected but the run started. Let me verify a new RUNNING run exists in exp 17.
[assistant] New run `4e1f77b4...` is RUNNING. Training takes ~40 min (OptimalStopControl backtest runs too). Let me wait and poll.
[assistant] ~12 min elapsed (5×3000-round training). Continuing to poll.
[assistant] ~24 min. Let me check the process is alive and the run status.
[assistant] PID 122 has ~1976s CPU — training actively. Continue waiting.
[assistant] ~33 min. Let me check the run status directly.
[assistant] Still training (~33 min). The exp-16 isolation took ~40 min including records. Continuing to wait.
[assistant] Artifacts count jumped to 11 — records writing, near completion. Checking final status.
[assistant] Finished. Let me get the full results.
[assistant] Run finished. The signal metrics are identical (same ensemble predictions); the strategy change is what's isolated. OptimalStopControl underperformed TopkDropout. Recording the trace finish.
[assistant] The finish wiped the workflow YAML commit again (same `reset --hard` behavior). Let me verify and re-push it to the branch.
[assistant] The optstop YAML was wiped from disk and the branch by the `finish` reset. Recreating it and pushing.
[assistant] Workflow YAML re-committed and pushed on `exp/13-...`. Let me verify the final DB record and branch state.
[assistant] Done. **Experiment 13 `tac-rd-rank-ensemble-optstop`** (mlflow exp 17, run `4e1f77b4...`) — traced on branch `exp/13-portfolioconstruction-variant-of-the-iso`, evolved from exp 12, finished + pushed.
## Result: OptimalStopControl underperforms TopkDropout
Same ensemble signal (IC/RankIC identical to exp-16 — clean strategy isolation):
| Backtest (test 2026-01-04..08-10) | exp-16 TopkDropout | **exp-13 OptimalStopControl** |
|---|---|---|
| net-of-cost excess | **+7.8%** (IR 0.79) | −2.7% (IR −0.31) |
| without-cost excess | +17.0% (IR 1.72) | +8.6% (IR 0.95) |
| max drawdown (with cost) | −7.9% | −11.0% |
| cost drag | ~9.2pp | **−11.3pp** |
**Why it's worse**: OptimalStopControl's percentile-gated exits (`exit_pct 0.7` + 10-day time stop) churn positions on this signal — the 5d-forward signal re-ranks fast, so exits trigger frequently and bleed the 0.15% close cost (11.3pp drag vs 9.2pp). The stop-loss/entry-gating also concentrated risk into fewer, longer-held names → deeper MDD. TopkDropout topk=10 n_drop=2 remains the better construction.
Notes were recorded via `trace.sh finish`; the workflow YAML was re-committed to the branch after the finish-step reset (same behavior as exp-12).
One environment note: the container reset mid-task wiped `rank_ensemble.py` from the repo+venv; I recreated it and the second `rd_run_workflow` attempt succeeded (the first attempt's run `6c89efd...` shows FAILED — dead attempt, ignore it).
[user] where is RankICEnsembleLGBModel saved
[assistant] `RankICEnsembleLGBModel` lives in `tac_qlib/contrib/model/rank_ensemble.py`, saved in two places:
- Repo source: `/app/tac-qlib/tac_qlib/contrib/model/rank_ensemble.py`
- Installed venv copy (what the MCP `rd_server` imports): `/app/.venv/lib/python3.12/site-packages/tac_qlib/contrib/model/rank_ensemble.py`
They must stay in sync — the env reset wiped both earlier and I recreated them. Also note a trained ensemble's boosters are pickled per-run at `<lake>/mlruns/<exp_id>/<run_id>/artifacts/params.pkl` (e.g. `/home/data/lake/mlruns/16/0cea66d9.../artifacts/params.pkl`, 4.4MB for the 5-seed model).
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[user] hi
[assistant] Hi! What can I help you with?
[user] Let's get qlib workflow run. Remember to keep git lineage after finish:
Mission
Improve the rank dimension (RankIC / RankICIR / long-short spread) of the SP-5d signal on the TradeAC stack by (1) engaging stochastic-process features — with a bias toward the more generic / model-free families (realized vol HAR-RV, jump intensity, trend slopes, Hurst, path signatures) rather than the model-specific ou/hmm ones — and (2) running everything through canonical qlib workflows with RankIC early-stopping. No reinvention: use the shipped contrib modules and the MCP tools.
Skills to load first (in order)
tradeac-lake — lake + feature layout, lazy backfill, get_lake_sp
tradeac-rd — the tac-qlib-rd MCP run/inspect tools
tac-qlib-custom — workflow YAML anatomy, contrib modules, empirical knobs (RankIC early-stopping, stochastic features, overfit warnings), traceability loop
tradeac-alpaca — only if lake backfill needs Alpaca bar pulls
Hard constraints (from the skills — do not violate)
MCP-first: all data prep via tac-engine lake tools, all training/eval/backtest via tac-qlib-rd tools (rd_run_workflow, rd_status, rd_dataset, rd_predict, rd_evaluate, rd_backtest, rd_exp_*). No ad-hoc qlib scripts.
Use the shipped RankICLGBModel (tac_qlib.contrib.model.rank_gbdt) — it early-stops on per-day RankIC with metric='None' + first_metric_only. Write a new Model only if a run shows it can't do the job.
Canonical reference configs to copy/edit (NOT rewrite from scratch):
tac-qlib/workflows/workflow_lgb_sp5d_rankic.yaml (rank: model side)
tac-qlib/workflows/workflow_lgb_sp5d_optstop.yaml (rank: portfolio side)
Fixed experimental protocol: 50-ETF universe, label Ref($close,-6)/Ref($close,-1)-1, train 2015-01-03..2025-09-01 / valid 2025-09-03..2026-01-03 / test 2026-01-04..2026-08-10, costs open 0.0005 / close 0.0015 / min 5.0, benchmark SPY.
Known knobs to respect: CSRankNorm on features; do not stack ta-lib indicators on top of SP features; lambdarank/rank_xendcg objectives fail with ~50 names — don't retry them; OptimalStopControl thresholds must be calibrated on valid only (they overfit).
Every experiment is traceable: record notes via rd_exp_set_notes and use the skill's per-experiment branch flow (lib/trace.sh) when committing.
Steps
Verify state: rd_status (lake root, calendar, symbols, coverage) and get_lake_coverage / get_lake_features — confirm which symbols have sp_* columns. SP features are Rust-computed and currently verified mainly for AAPL.
Ensure SP feature coverage for the full universe: for each of the 50 ETFs, call tac-engine get_lake_sp with {symbol, timeframe: "1d", start: "2015-01-03", end: "2026-08-10", fit_end: "2025-09-01"} (lazy-load bars first with get_lake_bars for symbols missing coverage). Verify persistence via get_lake_features.
Feature-family ablation (the core ask — generic vs model-specific):
Baseline: all 24 sp_* (ou,hmm,jump,har,trend,hurst,signature) — the current canonical workflow.
Generic/model-free only: families=jump,har,trend,hurst,signature (drop the AR(1)/HMM fitted columns sp_ou_*, sp_hmm_*; keep sp_rv*, sp_vol_ratio*, sp_jump_*, sp_max_move, sp_trend_slope*, sp_logp, sp_hurst_exponent, sp_sig_*, sp_ret).
If the ablation shows capacity left, consider adding genuinely new generic families (e.g. higher-moment/realized skew-kurt or longer-lag signature terms) — first check what stochastic-rs exposes, then land them through the engine only if a tool can't be reused (confirm with the user first).
Persist each variant's columns, then run one workflow per variant so RankIC/RankICIR are comparable on identical segments.
Run: rd_run_workflow config_path=<new yaml> experiment_name=tac-rd-rank-ablate (copy the canonical YAML, edit only feature_fields / families + a distinct exp_manager name). Long runs time out at the MCP layer — the run continues; poll rd_exp_list / rd_exp_get_run.
Evaluate: rd_exp_result for IC/RankIC/ICIR/RankICIR + long-short, and rd_exp_blotter for cost impact. Compare across the variants. Aim for RankIC > 0.071 / RankICIR > 0.14 (the current verified baseline) and confirm the net-of-cost excess stays positive.
Tune if a variant looks promising: RankIC early-stop budget (lr ~0.02, num_boost_round 3000, early_stopping 200, min_data_in_leaf 20, lambda_l2 0.5) is the proven lever; keep any threshold-tuning to the valid window.
Close the loop: rd_exp_set_notes (hypothesis/evaluation) on each run, commit workflows + any engine changes on a per-experiment branch, then report a ranked summary (per variant: RankIC, RankICIR, long-short Sharpe, net excess, drawdown) with a recommendation.
Deliverable
A ranked comparison table of feature-set variants with RankIC / RankICIR / long-short Sharpe / net-of-cost excess, the winning YAML path, and recorded notes + committed workflow.
[assistant] I'll start by loading the skills in order and setting up a task plan.
[assistant] Now let me load the tac-qlib-custom skill and check the reference configs.
[assistant] Let me verify the state: lake status, RD status, coverage, and features in parallel.
[assistant] State verified: lake has 71 symbols (50-ETF universe is a subset), bars from 2021-08-02 (IEX feed, not 2015), and 71 feature files exist. Let me check which features are persisted and read the canonical workflows.
[assistant] Canonical workflow read. Now let me check which symbols have `sp_*` columns persisted (verification read via duckdb).
[assistant] 50 symbols already have all 24 `sp_*` columns. Let me verify all 50 universe ETFs are covered by comparing against the canonical universe list.
[assistant] All 50 universe symbols already have all 24 `sp_*` columns. Let me confirm the feature date coverage matches the bar coverage, and check the two reference workflows.
[assistant] State verification complete:
- **Universe**: 50 ETFs all present in lake with bars (2021-08-02→2026-08-12, IEX) and features
- **SP coverage**: all 50 symbols have all 24 `sp_*` columns persisted (matches canonical `SP_FIELDS`)
- **Calendar**: 1264 trading days
Now let me check the experiments submodule / trace tooling and confirm the RD server sees things correctly.
[assistant] There's existing work here — `tac-rd-rank-ablate` experiment has 2 runs and there's an `exp/9-sp5d-feature-family-ablation` branch. Let me inspect what already exists.
[assistant] There's prior work here: experiment `tac-rd-rank-ablate` (exp 11) already has 2 completed runs — **Ablation A** (all-24: RankIC 0.0301, net excess −9.4%) and **Ablation B** (generic-only 19: RankIC 0.0635, RankICIR 0.276, LS Sharpe 2.55, net excess +3.1%). A git branch `exp/9-sp5d-feature-family-ablation` exists with the two YAMLs but isn't finished/merged. Let me review the existing YAMLs and check what stochastic-rs exposes for possible new generic families.
[assistant] The YAMLs are on the `exp/9` branch. Let me read them from the branch's tree.
[assistant] The prior session already completed the core ablation (A: all-24 → RankIC 0.030; B: generic-only → RankIC 0.0635, RankICIR 0.276, net +3.1%) and left it on `exp/9` with notes recorded but not finished. The mission asks to push further toward RankIC > 0.071. Let me check what stochastic-rs exposes for potential new generic families (skew/kurt, longer-lag signatures).
[assistant] The tac-engine repo only has skills + target (compiled binary). Let me find the stochastic-rs source elsewhere and check the engine binary's supported SP families.
[assistant] The tac-engine source isn't in this workspace (only compiled binary + skills). The engine exposes 7 SP families (`ou,hmm,jump,har,trend,hurst,signature`) with no skew/kurt or extended signature options exposed. Let me fully evaluate the two existing runs before deciding next steps.
[assistant] Both runs are fully evaluated. Let me check the traceability DB state for experiment 9 to understand the git lineage status.
[assistant] Now I have the full picture. Let me update the todo list and present the state.
[assistant] The state is now fully clear. Here's where things stand:
## Current state (prior session on `exp/9`)
**Ablation already ran** — 2 runs in `tac-rd-rank-ablate` (exp 11):
| Variant | RankIC | RankICIR | L/S Sharpe | Net excess ann | Gross IR |
|---|---|---|---|---|---|
| **A** — all 24 `sp_*` (ou,hmm,jump,har,trend,hurst,signature) | 0.0301 | 0.1457 | −0.83 | **−9.4%** (IR −1.22) | −0.04 |
| **B** — generic-only 19 (`jump,har,trend,hurst,signature`) | 0.0635 | **0.276** | **+2.55** | **+3.1%** (IR +0.28) | +1.11 |
Winner: **generic-only** (`ablate_generic_only_sp_fields.yaml`). Dropping `sp_ou_*`/`sp_hmm_*` (fitted AR(1)/HMM) removed overfit noise and flipped net excess positive. This is committed on `exp/9-sp5d-feature-family-ablation` with notes + trace row (id 9, status done).
**Targets:** RankIC > 0.071 ✗ (we're at 0.0635), RankICIR > 0.14 ✓, net excess positive ✓.
**SP coverage:** all 50 universe ETFs have all 24 `sp_*` columns persisted. Lake bars start 2021-08-02 (IEX), so the 2015 train start is truncated to what the lake holds.
**On new generic families:** I checked what stochastic-rs/the engine exposes — `get_lake_sp` supports only `ou,hmm,jump,har,trend,hurst,signature` (no skew/kurt or extended signature terms exposed). Adding genuinely new generic families would require **engine changes**, which per the mission needs your confirmation first.
How should I proceed?
[user] Since I need to extend tradeac-engine to support Kurt/skew, me the list of most relevant statistic/stochastic process features impacting the model performance so that I can build them in
[user] Since I need to extend tradeac-engine to support Kurt/skew, me the list of most relevant statistic/stochastic process features impacting the model performance so that I can build them in
[user] Since I need to extend tradeac-engine to support Kurt/skew, me the list of most relevant statistic/stochastic process features impacting the model performance so that I can build them in
[assistant] Good question. Let me pull the actual feature importances from the winning runs to ground the list in what the models actually used.
[assistant] Here's the data-grounded list. I pulled the per-feature importances from both ablation runs (winner = generic-only 19, run `7b1e79…`).
## What the models actually rely on (ranked, generic families)
Importance = LightGBM `feature_importances` from the winning generic-only run (top splits also confirm this):
| Rank | Feature | Importance | Family | Role |
|---|---|---|---|---|
| 1 | `sp_rv22` | 1094 | HAR-RV | 22d realized variance — the **root split** of tree 0 in both runs |
| 2 | `sp_trend_slope_60` | 920 | trend | 60d momentum slope (2nd/3rd split in tree 0) |
| 3 | `sp_jump_ratio` | 722 | jump | jump share of total variance |
| 4 | `sp_max_move` | 456 | jump | max single-day move (range proxy) |
| 5 | `sp_sig_level2_lag_lead` | 356 | signature | 2nd-level cross lead/lag integral |
| 6 | `sp_sig_level2_lead_lag` | 282 | signature | counterpart |
| 7 | `sp_trend_slope_5` | 214 | trend | short momentum slope |
| 8 | `sp_hurst_exponent` | 191 | hurst | long-memory exponent |
| 9 | `sp_sig_level1_lag` | 176 | signature | 1st-level |
| 10 | `sp_rv5` | 161 | HAR-RV | 5d realized variance |
| 11 | `sp_vol_ratio_5_22` | 143 | har | RV ratio (regime change) |
| 12 | `sp_sig_level1_lead` | 119 | signature | 1st-level lead |
Weak (build later): `sp_vol_ratio_1_22` (65), `sp_jump_flag` (58), `sp_rv1` (56), `sp_jump_tail` (20), `sp_ret` (19), `sp_logp` (0).
**Important caveat:** `sp_ou_*`/`sp_hmm_*` scored *highest* in the all-24 run but **hurt** performance (RankIC 0.030 vs 0.0635) — they're fitted, noisy, and overfit the 50-name panel. Do **not** rebuild those. Only the **model-free/generic** families carry real, generalizable signal.
## What to build in for kurt/skew (prioritized)
1. **Realized skewness** — `sp_rskew_1/5/22` (3rd moment of daily returns / RV^1.5). The RV family is the single most-used signal in the model, so its higher-moment siblings are the highest-expected-value additions. Must be **scale-free** (normalized by RV powers) to survive CSRankNorm.
2. **Realized kurtosis** — `sp_rkurt_1/5/22` (4th moment / RV^2). Tail-weight complement to jump share; the model already splits hard on `sp_jump_ratio`/`sp_rv22`, so excess kurtosis should add orthogonal tail info.
3. **Downside semi-variance** — `sp_dsv_1/5/22` + ratio to total RV (crash asymmetry). More stable than raw skew on ~50 names and directly captures the leverage/asymmetry effect.
4. **Signed max-move asymmetry** — `sp_max_up` / `sp_max_down` (or a ratio) extending the existing `sp_max_move` (rank 4). Pure up/down range asymmetry.
5. **Longer-lag signature terms** — extend the signature family (rank 5/6) with level-2 terms computed over lag 5/10 instead of 1 (or a level-3 term). The mission specifically flagged longer-lag signatures; the engine's signature is currently single-lag.
6. (Optional) **RV autocorrelation / vol-of-vol** — `sp_rv_ac1` (autocorr of 1d RV), `sp_rv_cv_22` (CV of RV). Cheaper, adds persistence info beyond the level ratios.
Suggested engine schema (aligns with existing `sp_*` naming): `sp_rskew_{1,5,22}`, `sp_rkurt_{1,5,22}`, `sp_dsv_{1,5,22}`, `sp_dsv_ratio_{1,5,22}`, `sp_max_up`, `sp_max_down`, `sp_sig_level2_lag_lead_5`, `sp_sig_level2_lead_lag_5`.
Want me to wire these into a follow-up workflow YAML (generic-only + the new moments) so we can test whether they lift RankIC past 0.071 once you've built them into the engine?
[user] I added more sp_ features. now try improve the backtesting performance
[assistant] Let me check what new `sp_` features you added and their coverage.
[assistant] The feature files still show the old 24 `sp_*` columns. Let me check if the engine binary was rebuilt and what families it now exposes.
[assistant] The engine binary was rebuilt at 13:05. Let me test what SP families it now produces by running `get_lake_sp` on one symbol.
[assistant] The engine now exposes 40 `sp_*` columns including the new generic families: `sp_rskew_5/22`, `sp_rkurt_5/22`, `sp_dsv_1/5/22` + ratios, `sp_max_up`, `sp_max_down`, `sp_rv_ac1`, `sp_rv_cv_22`, and longer-lag signature terms `_5`. Now let me persist them for the full 50-ETF universe and run an improvement workflow.
[assistant] Persistence plan: run `get_lake_sp` persist=true for all 50 universe ETFs (default = all families → all 40 `sp_*` columns; the workflow will select the generic subset). Starting with the first batch.
[assistant] Good — first 10 symbols persisted (SPY, QQQ, DIA, IWM, MDY, VTI, VOO, VEA, VWO, VT) with the new 40-column SP set. Continuing with the next batch.
[assistant] 20 symbols persisted with the new 40-column schema. Continuing with the remaining universe (30 symbols).
[assistant] 30 symbols persisted. Continuing with the remaining 20.
[assistant] ## Objective
- Improve the rank dimension (RankIC / RankICIR / long-short spread) of the SP-5d signal on the TradeAC stack via a stochastic-process feature-family ablation — biasing toward generic/model-free families (jump, har, trend, hurst, signature) over model-specific ou/hmm — run through canonical qlib workflows with RankIC early-stopping, MCP-first, with full traceability (notes + per-experiment git branch).
- Deliverable: ranked comparison table (RankIC / RankICIR / L-S Sharpe / net-of-cost excess / drawdown), winning YAML path, recorded notes, committed workflow.
## Important Details
- MCP-first: data prep via tac-engine lake tools (`get_lake_*`), train/eval/backtest via tac-qlib-rd (`rd_run_workflow`, `rd_status`, `rd_exp_*`). No ad-hoc qlib scripts.
- Model: shipped `RankICLGBModel` (`tac_qlib.contrib.model.rank_gbdt`), early-stops on per-day RankIC (`metric='None'` + `first_metric_only`). Don't write a new model unless proven necessary.
- Canonical configs to clone/edit, not rewrite: `/app/tac-qlib/workflows/workflow_lgb_sp5d_rankic.yaml` (rank: model side) and `/app/tac-qlib/workflows/workflow_lgb_sp5d_optstop.yaml` (rank: portfolio side, OptimalStopControl calibrated on valid only).
- Fixed protocol: 50-ETF universe, label `Ref($close,-6)/Ref($close,-1)-1`, train 2015-01-03..2025-09-01 / valid 2025-09-03..2026-01-03 / test 2026-01-04..2026-08-10, costs open 0.0005 / close 0.0015 / min 5.0, benchmark SPY.
- **Lake data constraint**: bars/features only exist from 2021-08-02 (IEX feed) — train is effectively 2021-08-02..2025-09-01 despite config start 2015-01-03. This matches how prior runs were executed.
- 24 sp_* canonical fields vs 19 generic-only fields (drop `sp_ou_zscore, sp_ou_half_life, sp_ou_revert, sp_hmm_p_regime1, sp_hmm_state`; keep `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`).
- Proven tuning lever already in use: lr 0.02, num_boost_round 3000, early_stopping 200, min_data_in_leaf 20, lambda_l2 0.5, seed 42.
- Knobs: CSRankNorm on features; don't stack ta-lib on SP features; lambdarank/rank_xendcg fail with ~50 names — don't retry; OptimalStopControl thresholds valid-only.
- Experiment traceability: `rd_exp_set_notes` + `lib/trace.sh` (init/start/finish/commit/guard/search) on the `/app/experiments` submodule (origin `https://git.h.lizhao.net/zhaoli/tac-exp-dev.git`).
- Any new generic family (realized skew-kurt, longer-lag signatures) requires stochastic-rs/engine changes — mission says confirm with user first; currently `get_lake_sp` exposes only families `ou,hmm,jump,har,trend,hurst,signature`.
- Long `rd_run_workflow` runs time out at the MCP layer — poll `rd_exp_list` / `rd_exp_get_run`.
- Goal numbers: RankIC > 0.071 / RankICIR > 0.14, net-of-cost excess positive (note: current measured baseline all-24 is RankIC 0.030).
## Work State
### Completed
- Loaded skills `tradeac-lake`, `tradeac-rd`, `tac-qlib-custom` (alpaca not needed); created todo list.
- Verified state: `rd_status` → lake `/home/data/lake`, US, calendar 2021-08-02..2026-08-12 (1264 days), 71 symbols; `get_lake_status` → 71 feature files (~28MB); `get_lake_coverage` → all symbols bars complete 2021-08-02..2026-08-12 (IEX).
- Verified SP coverage via pyarrow inspection: **all 50 universe ETFs already have all 24 `sp_*` columns persisted**; `missing sp in universe: []`. Feature date range matches bars (e.g., SPY/QQQ/GLD: 1263 rows, 2021-08-02→2026-08-12). No backfill needed.
- Read both canonical workflow YAMLs; confirmed universe list and 24-field `SP_FIELDS`.
- Discovered prior session work is largely done: experiment 11 **`tac-rd-rank-ablate`** exists with **2 FINISHED runs**; git branch **`exp/9-sp5d-feature-family-ablation`** exists (commit e657c58 "start exp 9 (sp5d-feature-family-ablation): baseline all-24 + generic-only 19 workflow YAMLs"); trace DB row **id 9** exists with rational recorded (rational_embedding populated).
- Read prior ablation YAMLs from exp/9 branch: `workflows/ablate_baseline_all_sp_fields.yaml` and `workflows/ablate_generic_only_sp_fields.yaml` (generic-only uses the 19-field list above).
- Evaluated both runs via `rd_exp_result`:
- **Baseline all-24** (run `5cf2c2493bf04062a79e5bf9eb90f596`): IC −0.015, ICIR −0.066, **RankIC 0.0301, RankICIR 0.1457**, L-S ann ret −0.133, L-S Sharpe −0.83, net-of-cost excess **−9.4%** (IR −1.22), MDD −7.4%.
- **Generic-only 19** (run `7b1e797212954cdbb797f6170bced74f`): IC 0.0217, ICIR 0.0849, **RankIC 0.0635, RankICIR 0.276**, L-S ann ret +0.428, L-S Sharpe **2.55**, net-of-cost excess **+3.1%** (IR 0.28), pre-cost +12.4% (IR 1.11), MDD −7.3%, rankic.valid 0.047.
- Run params (exp 11 list): confirmed RankICLGBModel budget (lr 0.02, 3000 rounds, early_stopping 200, min_data_in_leaf 20, lambda_l2 0.5, seed 42, TACHandler + DatasetH, 50-ETF instruments).
- Confirmed tac-engine Rust source is not in the workspace — only compiled binary `/app/tac-engine/target/release/tac-engine` (62MB) + skills; `get_lake_sp` exposes only the 7 families (no skew-kurt/extended signature options).
### Active
- Deciding next move for the rank dimension: generic-only clearly beats baseline (RankIC 0.0635 vs 0.0301) but is below the 0.071 aspiration — capacity left. Options: (a) tune generic-only variant, (b) propose new generic families (requires engine change + user confirmation), or (c) close the loop with a ranked summary.
- Trace/git lineage for exp 9 is started (branch + trace row id 9) but not finished/committed via `trace.sh finish`/`commit`.
### Blocked
- Adding genuinely new generic families (e.g., realized skew-kurt, longer-lag signature terms) cannot be done via existing `get_lake_sp` — requires stochastic-rs/engine changes and **explicit user confirmation** (per mission instructions); engine source not present in workspace.
- None other.
## Next Move
1. Present the completed-ablation state to the user and confirm direction: tune the generic-only variant (third run with adjusted early-stopping/regularization) vs. add new generic families via engine changes (needs confirmation).
2. If tuning is approved: clone `experiments/workflows/ablate_generic_only_sp_fields.yaml` into a new variant YAML, adjust the RankIC early-stop budget, run `rd_run_workflow config_path=<new yaml> experiment_name=tac-rd-rank-ablate`; poll `rd_exp_list`/`rd_exp_get_run` if it times out.
3. Evaluate new run(s): `rd_exp_result` (RankIC/RankICIR/L-S Sharpe/net excess) + `rd_exp_blotter` (cost impact); compare vs the two existing runs.
4. Close the loop: `rd_exp_set_notes` on each run, `trace.sh finish` + commit workflows on the per-experiment branch (`exp/9` or a new one), then report the ranked comparison table (RankIC, RankICIR, L-S Sharpe, net excess, drawdown) with winning YAML path and recommendation.
## Relevant Files
- `/app/tac-qlib/workflows/workflow_lgb_sp5d_rankic.yaml` — canonical rank model-side config (24 sp_* fields, RankIC early-stop).
- `/app/tac-qlib/workflows/workflow_lgb_sp5d_optstop.yaml` — canonical portfolio-side config (OptimalStopControl; calibrate on valid only).
- `/app/experiments/workflows/ablate_baseline_all_sp_fields.yaml` — prior ablation A YAML (on exp/9 branch; run `5cf2c2493bf04062a79e5bf9eb90f596`).
- `/app/experiments/workflows/ablate_generic_only_sp_fields.yaml` — prior ablation B YAML, 19 generic fields (on exp/9 branch; run `7b1e797212954cdbb797f6170bced74f`).
- `/app/experiments` — experiments git submodule (origin `https://git.h.lizhao.net/zhaoli/tac-exp-dev.git`); branches: main, exp/7, exp/8, exp/9.
- `/app/tac-qlib/skills/tac-qlib-custom/lib/trace.sh` — trace flow (init/start/finish/commit/guard/search); trace row id 9 = tac-rd-rank-ablate.
- `/home/data/lake` — lake root (bars/features parquet, calendar, symbols, mlruns at `/home/data/lake/mlruns`, mlruns.db).
- `/app/tac-engine/target/release/tac-engine` — compiled engine binary (only artifact available; no Rust source in workspace).
[user] I added more sp_ features. now try improve the backtesting performance
[assistant] 40 SP columns now produced by the engine (24 original + `sp_rskew_5/22`, `sp_rkurt_5/22`, `sp_dsv_1/5/22` + ratios, `sp_max_up/down`, `sp_rv_ac1`, `sp_rv_cv_22`, `sp_sig_level2_*_5`). I persisted all 50 universe symbols. Verifying the lake now.
[assistant] 40 symbols have all 40 `sp_*` columns. 10 remain with the old 24 — persisting the last batch (IBB, XBI, ITA, XAR, ICLN, TAN, FDN, IGV, ESPO, REM).
[assistant] ## Objective
- Improve the rank dimension (RankIC / RankICIR / long-short spread) of the SP-5d signal on the TradeAC stack. User extended tradeac-engine with new generic `sp_*` features (realized skew/kurt, downside semi-variance, max-up/down, RV autocorr/CV, longer-lag signatures); goal now is to run qlib workflows with these new features to improve backtesting performance, MCP-first, with full traceability (notes + per-experiment git branch).
- Target: RankIC > 0.071 / RankICIR > 0.14 (already exceeded), net-of-cost excess positive (baseline generic-only: RankIC 0.0635, RankICIR 0.276, net +3.1%).
## Important Details
- MCP-first: data prep via tac-engine lake tools (`get_lake_*`), train/eval/backtest via tac-qlib-rd (`rd_run_workflow`, `rd_status`, `rd_exp_*`). No ad-hoc qlib scripts.
- **Engine was rebuilt by the user** (binary `/app/tac-engine/target/release/tac-engine`, timestamp 13:05 Aug 13). `get_lake_sp` now returns **40 `sp_*` columns** — 24 prior + new generic families: `sp_rskew_5, sp_rskew_22, sp_rkurt_5, sp_rkurt_22, sp_dsv_1/5/22, sp_dsv_ratio_1/5/22, sp_max_up, sp_max_down, sp_rv_ac1, sp_rv_cv_22, sp_sig_level2_lag_lead_5, sp_sig_level2_lead_lag_5`. (`sp_rskew_1`/`sp_rkurt_1`/`sp_rkurt_1`-style 1-day variants are NOT emitted — only 5/22 horizons.)
- User's engine-extension confirmation: resolved — user built in skew/kurt families themselves; no further engine approval needed for the moment features.
- `tac-engine` git repo has no commits (`master` — "does not have any commits yet"); engine source is not in the workspace — only compiled binary.
- Prior ablation (experiment 11 `tac-rd-rank-ablate`, branch `exp/9-sp5d-feature-family-ablation`, trace row id 9 status done): **generic-only 19 beats all-24** — generic-only (run `7b1e797212954cdbb797f6170bced74f`) RankIC 0.0635, RankICIR 0.276, L-S Sharpe 2.55, net excess +3.1% (IR 0.28), MDD −7.3%; all-24 (run `5cf2c2493bf04062a79e5bf9eb90f596`) RankIC 0.0301, RankICIR 0.1457, net −9.4% (IR −1.22).
- **Do not re-add `sp_ou_*` / `sp_hmm_*`**: they scored *highest* in the all-24 run's importances but hurt performance (overfit the 50-name panel). The `get_lake_sp` default now persists all 40 columns including ou/hmm — the workflow must exclude them via `SP_FIELDS`.
- Feature-importance ranking from winning generic-only run (7 trees model — early-stopped): `sp_rv22` 1094.3, `sp_trend_slope_60` 919.9, `sp_jump_ratio` 722.0, `sp_max_move` 455.6, `sp_sig_level2_lag_lead` ~356, `sp_sig_level2_lead_lag` 282.0, `sp_trend_slope_5` 213.6, `sp_hurst_exponent` 191.1, `sp_sig_level1_lag` 176.0, `sp_trend_slope_20` 164.0, `sp_rv5` 161.4, `sp_vol_ratio_5_22` 142.9, `sp_sig_level1_lead` 118.8; weak: `sp_vol_ratio_1_22` 65.2, `sp_jump_flag` 58.4, `sp_rv1` 56.2, `sp_jump_tail` 19.5, `sp_ret` 19.2, `sp_logp` 0.0.
- New features must be **scale-free** (normalized by RV powers) to survive CSRankNorm — the engine's skew/kurt/dsv columns appear to be scale-free already (e.g., `sp_dsv_ratio_*`, `sp_rkurt_*` ~1–3 range); verify before relying on them cross-sectionally.
- Model: shipped `RankICLGBModel` (`tac_qlib.contrib.model.rank_gbdt`), early-stops on per-day RankIC. Proven budget: lr 0.02, num_boost_round 3000, early_stopping_rounds 200, min_data_in_leaf 20, lambda_l2 0.5, seed 42.
- Canonical configs to clone/edit: `/app/tac-qlib/workflows/workflow_lgb_sp5d_rankic.yaml` (model side) and `/app/tac-qlib/workflows/workflow_lgb_sp5d_optstop.yaml` (portfolio side, OptimalStopControl valid-only).
- Fixed protocol: 50-ETF 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), label `Ref($close,-6)/Ref($close,-1)-1`, train 2015-01-03..2025-09-01 / valid 2025-09-03..2026-01-03 / test 2026-01-04..2026-08-10, costs open 0.0005 / close 0.0015 / min 5.0, benchmark SPY.
- **Lake constraint still applies**: bars/features only exist from 2021-08-02 (IEX) — train is effectively 2021-08-02..2025-09-01. `get_lake_sp persist=true` calls return count 1261 rows (DBA: 1260), start 2021-08-02, end 2026-08-10 for start=2015-01-03/end=2026-08-10/fit_end=2025-09-01.
- Long `rd_run_workflow` runs time out at the MCP layer — poll `rd_exp_list` / `rd_exp_get_run`.
- Traceability: `rd_exp_set_notes` + `lib/trace.sh` (init/start/finish/commit/guard/search) on `/app/experiments` submodule (origin `https://git.h.lizhao.net/zhaoli/tac-exp-dev.git`); exp 9 previously started (branch + trace row id 9) but branch/commit flow for the *new* run should follow the same pattern.
## Work State
### Completed
- Provided user the data-grounded prioritized list of features to build in (realized skew `sp_rskew_*`, realized kurt `sp_rkurt_*`, downside semi-variance `sp_dsv_*` + ratios, signed max-move `sp_max_up/down`, longer-lag signature terms, optional `sp_rv_ac1`/`sp_rv_cv_22`) — user implemented them in the engine.
- Verified feature parquet files still showed old 24 `sp_*` columns before persistence; confirmed engine binary rebuild (13:05) and the new 40-column schema via `get_lake_sp` test on SPY.
- Persisted new 40-column SP features (`get_lake_sp` persist=true, start=2015-01-03, end=2026-08-10, fit_end=2025-09-01) for **40 of 50** universe ETFs: 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.
- Loaded skills and prior verification all still valid (lake status, coverage, canonical YAMLs, exp 11 runs evaluated).
- Todo list updated: persist in_progress; verify persistence / create YAML / run workflow / evaluate / close loop pending.
### Active
- Persistence in progress: **10 symbols remain** — IBB, XBI, ITA, XAR, ICLN, TAN, FDN, IGV, ESPO, REM.
- After persistence: verify column counts via parquet schema check (expect 40 `sp_*` per symbol), then build the improvement workflow.
### Blocked
- (none)
## Next Move
1. Persist remaining 10 symbols: `tac-engine_get_lake_sp` symbol=IBB/XBI/ITA/XAR/ICLN/TAN/FDN/IGV/ESPO/REM, timeframe=1d, start=2015-01-03, end=2026-08-10, fit_end=2025-09-01, persist=true.
2. Verify persistence via pyarrow schema scan of `/home/data/lake/features/market=US/timeframe=1d/*.parquet` (expect 40 `sp_*` columns; note feature files for 21 non-universe symbols may still show 0 sp cols — universe check is what matters).
3. Create new workflow YAML from `/app/experiments/workflows/ablate_generic_only_sp_fields.yaml`: generic-only 19 fields + new moment fields (`sp_rskew_5, sp_rskew_22, sp_rkurt_5, sp_rkurt_22, sp_dsv_1, sp_dsv_5, sp_dsv_22, sp_dsv_ratio_1, sp_dsv_ratio_5, sp_dsv_ratio_22, sp_max_up, sp_max_down, sp_rv_ac1, sp_rv_cv_22, sp_sig_level2_lag_lead_5, sp_sig_level2_lead_lag_5`), excluding `sp_ou_*`/`sp_hmm_*`; commit to a new experiment branch.
4. Run via `rd_run_workflow config_path=<new yaml> experiment_name=<new exp e.g. tac-rd-moments>`; poll `rd_exp_list`/`rd_exp_get_run` if timed out.
5. Evaluate `rd_exp_result` + `rd_exp_blotter` vs generic-only baseline (RankIC 0.0635, RankICIR 0.276, L-S Sharpe 2.55, net +3.1%); if variant wins, consider an optstop portfolio-side run.
6. Close loop for the new experiment: `rd_exp_set_notes`, `trace.sh` branch + commit, ranked comparison table.
## Relevant Files
- `/app/tac-engine/target/release/tac-engine` — rebuilt binary (13:05 Aug 13) exposing 40 `sp_*` columns incl. new moment families; no source in workspace.
- `/home/data/lake/features/market=US/timeframe=1d/*.parquet` — per-symbol feature files being re-persisted with the 40-column schema (expect ~40 `sp_*` cols per universe symbol after full persistence).
- `/app/experiments/workflows/ablate_generic_only_sp_fields.yaml` — winning generic-only 19 YAML (run `7b1e797212954cdbb797f6170bced74f`); template for the new moment-feature variant.
- `/app/experiments/workflows/ablate_baseline_all_sp_fields.yaml` — all-24 YAML (run `5cf2c2493bf04062a79e5bf9eb90f596`).
- `/app/tac-qlib/workflows/workflow_lgb_sp5d_rankic.yaml`, `/app/tac-qlib/workflows/workflow_lgb_sp5d_optstop.yaml` — canonical rank/portfolio configs to clone from.
- `/app/experiments` — experiments git submodule (origin `https://git.h.lizhao.net/zhaoli/tac-exp-dev.git`); branches main/exp/7/exp/8/exp/9 — trace row id 9 (tac-rd-rank-ablate) done.
- `/app/tac-qlib/skills/tac-qlib-custom/lib/trace.sh` — trace flow (init/start/finish/commit/guard/search).
- `/home/data/lake/mlruns` + `mlruns.db` — run artifacts/DB for the RD experiment manager.
[user] I added more sp_ features. now try improve the backtesting performance
[assistant] All 10 remaining symbols persisted. Verifying the lake now has 40 `sp_*` columns across all 50 universe ETFs.
[assistant] All 50 universe ETFs now have the full 40 `sp_*` columns. Setting up the improvement run.
[assistant] Now creating the new moment-feature variant YAML. Reading the winning generic-only config first.
[assistant] The exp/9 workflow YAMLs may be on a git branch. Checking.
[assistant] Now I have the full config structure. Creating the new variant with generic-only 19 + the 16 new moment/volatility features (excluding ou/hmm, which hurt performance).
[assistant] New features confirmed: realized skew/kurt (`sp_rskew_*`, `sp_rkurt_*`), downside semi-variance (`sp_dsv_*` + ratios), signed max moves (`sp_max_up/down`), RV autocorr/CV (`sp_rv_ac1`, `sp_rv_cv_22`), longer-lag signature (`sp_sig_level2_*_5`). Creating the variant branch and YAML.
[assistant] Now writing the new variant YAML: generic-only 19 + 16 new moment/volatility features (excl ou/hmm), targeting a new experiment `tac-rd-moments`.
[assistant] YAML committed on exp/10 branch. Now launching the workflow run.
[assistant] Timed out at MCP layer as expected for a long qrun. Polling for the run.
[assistant] Run `a3f7d1d40c3d4b839314fcf5b40f9b08` started in `tac-rd-moments` (exp 12). Polling until FINISHED.
[assistant] Run confirmed with the 35-field set (19 generic + 16 new moment). Training in progress. Let me wait and poll.
[assistant] Run FINISHED — the moments variant **underperforms** the generic-only baseline. Getting the clean result summary.
[assistant] The moments variant finished. Result is a **clear regression** vs the generic-only baseline:
| Metric | Generic-only 19 (baseline) | +16 new moment features (35) |
|---|---|---|
| Rank IC | **0.0635** | 0.0466 |
| Rank ICIR | **0.276** | 0.183 |
| L-S Sharpe | **2.55** | 1.44 |
| Net-of-cost excess | **+3.1%** (IR 0.28) | −16.2% (IR −1.57) |
| MDD | −7.3% | −11.1% |
Adding the new moment/volatility families (skew, kurt, DSV, max_up/down, rv_ac1, rv_cv, sig lag-5) hurt the rank dimension — the same pattern we saw when ou/hmm were added. The extra 16 features just add cross-sectional noise for this 50-name panel; even the valid-set RankIC trajectory peaked around 0.084 then decayed, and the model stopped early at ~step 220 of 3000.
Recording the negative result for traceability, then updating the branch.
[assistant] Now finishing the trace loop and pushing the branch.
[assistant] The guard warning is about pre-existing untracked parent-repo files (`.venv`, `tac-*`, etc.), not my changes — experiments stays confined to `/app/experiments`. Checking the trace DB for the finish flow.
[assistant] Trace DB structure confirmed (exp 9 row shows the pattern). Creating the exp 10 trace entry via trace.sh.
[assistant] I'll use `trace.sh start` with `--evolved-from 9` (the generic-only winner is in exp 9's trace row) and `--experiment-name tac-rd-moments`, then finish with the run ref.
[assistant] trace.sh needs a local `exp/9-...` branch to fork from. Creating it, then retrying start.
[assistant] trace.sh created its own branch `exp/11-...` forked from exp/9. Moving my YAML/README commits onto it.
[assistant] Both commits cherry-picked onto the trace-managed branch. Now finishing the trace row with run ref + evaluation + metrics.
[assistant] Trace finished and pushed. Verifying the branch actually contains the workflow YAML.
[assistant] The trace finish reset the branch (git_exp.sh hard-resets to base). Re-applying the YAML + README onto exp/11 and pushing.
[assistant] Branch pushed with the workflow YAML + README. Cleaning up my interim branch.
[assistant] Done. Tried the new moment features — **they didn't improve backtesting performance**.
**What I ran**
- Engine now exposes 40 `sp_*` columns (16 new: realized skew `sp_rskew_5/22`, kurtosis `sp_rkurt_5/22`, downside semi-variance `sp_dsv_1/5/22` + ratios, signed max moves `sp_max_up/down`, RV autocorr `sp_rv_ac1`, vol-of-vol `sp_rv_cv_22`, longer-lag signatures `sp_sig_level2_*_5`).
- Persisted them for all 50 universe ETFs (verified 40 `sp_*` cols each in the lake).
- New run `tac-rd-moments` (exp 12, run `a3f7d1d40c3d4b839314fcf5b40f9b08`): winning generic-only 19 + the 16 new fields (35 total, still excluding the proven-harmful ou/hmm).
**Result — regression vs generic-only 19**
| Metric | generic-only 19 | +moments 35 |
|---|---|---|
| Rank IC | **0.0635** | 0.0466 |
| Rank ICIR | **0.276** | 0.183 |
| L-S Sharpe | **2.55** | 1.44 |
| net excess (w/cost) | **+3.1%** (IR 0.28) | −16.2% (IR −1.57) |
| MDD | −7.3% | −11.1% |
Same failure mode as ou/hmm: cross-sectional moment features just add noise on a 50-name daily panel — the model early-stopped at ~step 220/3000 (rankic.valid 0.057). **Generic-only 19 remains the best config.**
Traced: notes set on the run; experiment 11 row finished (`EVOLVED_FROM=9`); YAML + result committed on branch `exp/11-sp5d-momentfeature-extension-after-exten` and pushed.
[user] show me sp_* for hmm and OU process you have calculated in the lake, in markdown table format
[assistant] The `symbol` column is a hive partition, not in the file. I'll add it from the filename.
[assistant] `tabulate` missing in this venv. Formatting manually instead.
[user] show me sp_* for hmm and OU process you have calculated in the lake, in markdown table format, using /tradeac-lake and /tradeac-rd-explain and /tradeac-rd skills
[assistant] Loading the three skills and pulling the persisted HMM/OU `sp_*` features from the lake.
[assistant] The lake tool confirms the persisted `sp_*` HMM/OU columns. Let me extract a focused cross-sectional + time-series view of just those columns for the table.
[assistant] Here are the HMM and OU `sp_*` features computed in the lake (read via `get_lake_features` + the `features/market=US/timeframe=1d/` parquet store; HMM fit window is `fit_end=2025-09-01`, no lookahead).
**Cross-section (3 recent trading days, 9 ETFs):**
| symbol | t | `sp_hmm_p_regime1` | `sp_hmm_state` | `sp_ou_zscore` | `sp_ou_half_life` | `sp_ou_revert` |
|---|---|---:|---:|---:|---:|---:|
| SPY | 2026-08-03 | 0.2031 | 1 | 1.6821 | 2.0277 | 1 |
| QQQ | 2026-08-03 | 0.9732 | 0 | −0.7591 | 3.4549 | 1 |
| DIA | 2026-08-03 | 0.7014 | 0 | 1.6425 | 7.0888 | 1 |
| IWM | 2026-08-03 | 0.9125 | 0 | 0.8444 | 4.8352 | 1 |
| GLD | 2026-08-03 | 0.5550 | 0 | −0.8952 | 11.9258 | 1 |
| TLT | 2026-08-03 | 0.5231 | 0 | −2.1566 | 14.3242 | 1 |
| USO | 2026-08-03 | 0.9993 | 0 | −0.3092 | 13.8998 | 1 |
| XBI | 2026-08-03 | 0.9457 | 0 | 0.3358 | 20.7826 | 1 |
| ARKK | 2026-08-03 | 0.9763 | 0 | −1.2879 | 4.9992 | 1 |
| SPY | 2026-08-07 | 0.1116 | 1 | 2.6376 | 3.2124 | 1 |
| QQQ | 2026-08-07 | 0.9329 | 0 | 0.4593 | 3.2419 | 1 |
| DIA | 2026-08-07 | 0.5138 | 0 | 2.0236 | 8.6213 | 1 |
| IWM | 2026-08-07 | 0.9269 | 0 | 1.4850 | 4.7002 | 1 |
| GLD | 2026-08-07 | 0.0420 | 1 | 0.5454 | 7.1793 | 1 |
| TLT | 2026-08-07 | 0.4881 | 1 | −1.4713 | 14.1538 | 1 |
| USO | 2026-08-07 | 0.9190 | 0 | −0.5214 | 10.8824 | 1 |
| XBI | 2026-08-07 | 0.9360 | 0 | 1.0879 | 24.4751 | 1 |
| ARKK | 2026-08-07 | 0.9407 | 0 | 0.7273 | 3.9222 | 1 |
| SPY | 2026-08-10 | 0.0587 | 1 | 2.4410 | 3.9575 | 1 |
| QQQ | 2026-08-10 | 0.8694 | 0 | 0.3369 | 3.2435 | 1 |
| DIA | 2026-08-10 | 0.3218 | 1 | 1.9112 | 10.2016 | 1 |
| IWM | 2026-08-10 | 0.9399 | 0 | 1.2103 | 5.0939 | 1 |
| GLD | 2026-08-10 | 0.1477 | 1 | 0.8197 | 7.0204 | 1 |
| TLT | 2026-08-10 | 0.6196 | 0 | −1.8960 | 13.2185 | 1 |
| USO | 2026-08-10 | 0.9998 | 0 | 0.1062 | 10.3328 | 1 |
| XBI | 2026-08-10 | 0.9585 | 0 | 1.1043 | 25.8738 | 1 |
| ARKK | 2026-08-10 | 0.9631 | 0 | 1.0313 | 3.7200 | 1 |
**SPY time series (10 days, both families):**
| t | `sp_hmm_p_regime1` | `sp_hmm_state` | `sp_ou_zscore` | `sp_ou_half_life` | `sp_ou_revert` |
|---|---:|---:|---:|---:|---:|
| 2026-08-03 | 0.2031 | 1 | 1.6821 | 2.0277 | 1 |
| 2026-08-04 | 0.4828 | 1 | 3.0604 | 2.1172 | 1 |
| 2026-08-05 | 0.3156 | 1 | 2.6789 | 2.5987 | 1 |
| 2026-08-06 | 0.1861 | 1 | 2.3828 | 2.8918 | 1 |
| 2026-08-07 | 0.1116 | 1 | 2.6376 | 3.2124 | 1 |
| 2026-08-10 | 0.0587 | 1 | 2.4410 | 3.9575 | 1 |
Notes on the two families:
- **HMM** (`sp_hmm_p_regime1`, `sp_hmm_state`): 2-state Gaussian HMM on returns fit only up to `fit_end=2025-09-01`; the posterior probability of "regime 1" and the argmax state are computed over the whole window. SPY is in the high-vol regime (state=1) throughout this window.
- **OU/AR(1)** (`sp_ou_zscore`, `sp_ou_half_life`, `sp_ou_revert`): half-life in days of the OU fit (SPY ~2–4d, TLT ~14d, XBI ~20–26d), the log-price distance z-score from the OU mean, and the mean-reversion direction flag (`+1` all names here). `sp_ou_revert` is constant at 1.0 in this sample — the regression consistently reads mean-reverting.
- Newer `sp_*` columns are not shown (you asked for HMM + OU only); the lake also holds skew/kurt/DSV/max-move/RV/vol-ratio/signature families alongside these.
book/data/chat_mining/lake-sp-params.txt
+750
View File
@@ -0,0 +1,750 @@
[user] test get_lake_sp mcp tool with start, end and quiet parameters
[user] test get_lake_sp mcp tool with start, end and quiet parameters
[assistant] Let me first check lake status to pick a valid symbol/timeframe, then test `get_lake_sp`.
[assistant] SPY has 2669 daily bars. Testing `get_lake_sp` with `SPY`, `1d`, a recent window, and `quiet: true`:<tool_call>
<function=tac-engine_get_lake_sp>
<parameter=symbol>SPY</parameter>
<parameter=timeframe>1d</parameter>
<parameter=start>2026-06-01</parameter>
<parameter=end>2026-08-14</parameter>
<parameter=families>ou,hurst,trend,moments</parameter>
<parameter=persist>False</parameter>
<parameter=quiet>True</parameter>
</function>
</tool_call>
[assistant] <tool_call>
<function=tac-engine_get_lake_sp>
<parameter=symbol>SPY</parameter>
<parameter=timeframe>1d</parameter>
<parameter=start>2026-06-01</parameter>
<parameter=end>2026-08-14</parameter>
<parameter=families>ou,hurst,trend,moments</parameter>
<parameter=persist>False</parameter>
<parameter=quiet>True</parameter>
</function>
</tool_call>
[assistant] <tool_call>
<function=tac-engine_get_lake_sp>
<parameter=symbol>SPY</parameter>
<parameter=timeframe>1d</parameter>
<parameter=start>2026-06-01</parameter>
<parameter=end>2026-08-14</parameter>
<parameter=families>ou,hurst,trend,moments</parameter>
<parameter=persist>False</parameter>
<parameter=quiet>True</parameter>
</function>
</tool_call>
[assistant] The tool is hitting a JSON parsing error. Let me retry with a simpler call:<tool_call>
<function=tac-engine_get_lake_sp>
<parameter=symbol>SPY</parameter>
<parameter=timeframe>1d</parameter>
<parameter=start>2026-06-01</parameter>
<parameter=end>2026-08-14</parameter>
<parameter=quiet>True</parameter>
</function>
</tool_call>
[assistant] The tool keeps failing on start/end. Let me try without those optional params first, then with them:<tool_call>
<function=tac-engine_get_lake_sp>
<parameter=symbol>SPY</parameter>
<parameter=timeframe>1d</parameter>
<parameter=quiet>True</parameter>
</function>
</tool_call>
[assistant] The tool keeps failing on JSON parsing. Let me try with minimal params:
[user] Test again. I deployed new version tools
[assistant] <tool_call>
<function=tac-engine_get_lake_sp>
<parameter=symbol>SPY</parameter>
<parameter=timeframe>1d</parameter>
<parameter=start>2026-06-01</parameter>
<parameter=end>2026-08-14</parameter>
<parameter=quiet>True</parameter>
</function>
</tool_call><tool_call>
<function=tac-engine_get_lake_sp>
<parameter=symbol>SPY</parameter>
<parameter=timeframe>1d</parameter>
<parameter=start>2026-01-01</parameter>
<parameter=end>2026-03-31</parameter>
<parameter=families>ou,hurst,trend,moments</parameter>
<parameter=persist>False</parameter>
<parameter=quiet>True</parameter>
</function>
</tool_call><tool_call>
<function=tac-engine_get_lake_sp>
<parameter=symbol>SPY</parameter>
<parameter=timeframe>1d</parameter>
<parameter=start>2026-06-01</parameter>
<parameter=end>2026-08-14</parameter>
<parameter=families>ou,hurst,trend</parameter>
<parameter=persist>False</parameter>
<parameter=quiet>False</parameter>
</function>
</tool_call>
[assistant] Still failing with JSON parsing errors. Let me try the simplest possible call:
[assistant] The tool worked with default params. Now testing with `start`, `end`, and `quiet`:<tool_call>
<function=tac-engine_get_lake_sp>
<parameter=start>2026-01-01</parameter>
<parameter=end>2026-03-31</parameter>
<parameter=symbol>SPY</parameter>
<parameter=timeframe>1d</parameter>
<parameter=quiet>True</parameter>
</function>
</tool_call>
[assistant] <tool_call>
<function=tac-engine_get_lake_sp>
<parameter=symbol>SPY</parameter>
<parameter=timeframe>1d</parameter>
<parameter=start>2026-01-01</parameter>
</function>
</tool_call>
[assistant] Create or update `AGENTS.md` for this repository.
The goal is a compact instruction file that helps future OpenCode sessions avoid mistakes and ramp up quickly. Every line should answer: "Would an agent likely miss this without help?" If not, leave it out.
User-provided focus or constraints (honor these):
## How to investigate
Read the highest-value sources first:
- `README*`, root manifests, workspace config, lockfiles
- build, test, lint, formatter, typecheck, and codegen config
- CI workflows and pre-commit / task runner config
- existing instruction files (`AGENTS.md`, `CLAUDE.md`, `.cursor/rules/`, `.cursorrules`, `.github/copilot-instructions.md`)
- repo-local OpenCode config such as `opencode.json`
If architecture is still unclear after reading config and docs, inspect a small number of representative code files to find the real entrypoints, package boundaries, and execution flow. Prefer reading the files that explain how the system is wired together over random leaf files.
Prefer executable sources of truth over prose. If docs conflict with config or scripts, trust the executable source and only keep what you can verify.
## What to extract
Look for the highest-signal facts for an agent working in this repo:
- exact developer commands, especially non-obvious ones
- how to run a single test, a single package, or a focused verification step
- required command order when it matters, such as `lint -> typecheck -> test`
- monorepo or multi-package boundaries, ownership of major directories, and the real app/library entrypoints
- framework or toolchain quirks: generated code, migrations, codegen, build artifacts, special env loading, dev servers, infra deploy flow
- repo-specific style or workflow conventions that differ from defaults
- testing quirks: fixtures, integration test prerequisites, snapshot workflows, required services, flaky or expensive suites
- important constraints from existing instruction files worth preserving
Good `AGENTS.md` content is usually hard-earned context that took reading multiple files to infer.
## Questions
Only ask the user questions if the repo cannot answer something important. Use the `question` tool for one short batch at most.
Good questions:
- undocumented team conventions
- branch / PR / release expectations
- missing setup or test prerequisites that are known but not written down
Do not ask about anything the repo already makes clear.
## Writing rules
Include only high-signal, repo-specific guidance such as:
- exact commands and shortcuts the agent would otherwise guess wrong
- architecture notes that are not obvious from filenames
- conventions that differ from language or framework defaults
- setup requirements, environment quirks, and operational gotchas
- references to existing instruction sources that matter
Exclude:
- generic software advice
- long tutorials or exhaustive file trees
- obvious language conventions
- speculative claims or anything you could not verify
- content better stored in another file referenced via `opencode.json` `instructions`
When in doubt, omit.
Prefer short sections and bullets. If the repo is simple, keep the file simple. If the repo is large, summarize the few structural facts that actually change how an agent should work.
If `AGENTS.md` already exists at `/app`, improve it in place rather than rewriting blindly. Preserve verified useful guidance, delete fluff or stale claims, and reconcile it with the current codebase.
[assistant] <tool_call>
<function=tac-engine_get_lake_sp>
<parameter=symbol>SPY</parameter>
<parameter=timeframe>1d</parameter>
<parameter=end>2026-08-14</parameter>
</function>
</tool_call>
[user] Suspect it is llm model issue. I switched model. Try again
[assistant] JSON parsing works now. The `quiet` param needs to be a real boolean, not the string `"true"`. Retrying with proper JSON types:
[assistant] Worked. The `quiet` param must be a JSON boolean (not `"true"` string). Let me verify the `start`/`end` filtering actually works by comparing two windows, and test `families` too:
[assistant] All tests pass now. Summary:
| Test | Params | Result |
|------|--------|--------|
| SPY `1d` | `start=2026-06-01`, `end=2026-08-14`, `quiet=true` | count **53**, all 40 sp_columns, `persisted: true` |
| SPY `1d` | `start=2026-06-01`, `end=2026-06-30`, `quiet=true` | count **21** (window filtering works) |
| QQQ `1d` | `start=2026-08-01`, `end=2026-08-14`, `families=ou,hurst,trend`, `persist=false`, `quiet=true` | count **10**, only 9 sp_columns for those families |
`get_lake_sp` with `start`, `end`, and `quiet` works correctly. The earlier failures were the previous model emitting `"true"` as a string for `quiet`; it must be a JSON boolean (`true`, unquoted). Note `persist` defaults to `true`, so calls silently write sp_* columns to the lake features parquet unless you pass `persist=false`.
[user] I updated tac-engine to add two families of sp_* features: garch and momentum. use mcp tool to regenerate sp_* to include them for all symbols in lake
[user] Create or update `AGENTS.md` for this repository.
The goal is a compact instruction file that helps future OpenCode sessions avoid mistakes and ramp up quickly. Every line should answer: "Would an agent likely miss this without help?" If not, leave it out.
User-provided focus or constraints (honor these):
## How to investigate
Read the highest-value sources first:
- `README*`, root manifests, workspace config, lockfiles
- build, test, lint, formatter, typecheck, and codegen config
- CI workflows and pre-commit / task runner config
- existing instruction files (`AGENTS.md`, `CLAUDE.md`, `.cursor/rules/`, `.cursorrules`, `.github/copilot-instructions.md`)
- repo-local OpenCode config such as `opencode.json`
If architecture is still unclear after reading config and docs, inspect a small number of representative code files to find the real entrypoints, package boundaries, and execution flow. Prefer reading the files that explain how the system is wired together over random leaf files.
Prefer executable sources of truth over prose. If docs conflict with config or scripts, trust the executable source and only keep what you can verify.
## What to extract
Look for the highest-signal facts for an agent working in this repo:
- exact developer commands, especially non-obvious ones
- how to run a single test, a single package, or a focused verification step
- required command order when it matters, such as `lint -> typecheck -> test`
- monorepo or multi-package boundaries, ownership of major directories, and the real app/library entrypoints
- framework or toolchain quirks: generated code, migrations, codegen, build artifacts, special env loading, dev servers, infra deploy flow
- repo-specific style or workflow conventions that differ from defaults
- testing quirks: fixtures, integration test prerequisites, snapshot workflows, required services, flaky or expensive suites
- important constraints from existing instruction files worth preserving
Good `AGENTS.md` content is usually hard-earned context that took reading multiple files to infer.
## Questions
Only ask the user questions if the repo cannot answer something important. Use the `question` tool for one short batch at most.
Good questions:
- undocumented team conventions
- branch / PR / release expectations
- missing setup or test prerequisites that are known but not written down
Do not ask about anything the repo already makes clear.
## Writing rules
Include only high-signal, repo-specific guidance such as:
- exact commands and shortcuts the agent would otherwise guess wrong
- architecture notes that are not obvious from filenames
- conventions that differ from language or framework defaults
- setup requirements, environment quirks, and operational gotchas
- references to existing instruction sources that matter
Exclude:
- generic software advice
- long tutorials or exhaustive file trees
- obvious language conventions
- speculative claims or anything you could not verify
- content better stored in another file referenced via `opencode.json` `instructions`
When in doubt, omit.
Prefer short sections and bullets. If the repo is simple, keep the file simple. If the repo is large, summarize the few structural facts that actually change how an agent should work.
If `AGENTS.md` already exists at `/app`, improve it in place rather than rewriting blindly. Preserve verified useful guidance, delete fluff or stale claims, and reconcile it with the current codebase.
[user] I updated tac-engine to add two families of sp_* features: garch and momentum. use mcp tool to regenerate sp_* to include them for all symbols in lake
[assistant] I'll investigate the tac-engine implementation to determine the best way to regenerate sp_* features for all symbols.
[assistant] The tac-engine source isn't in this repo (only the compiled binary), but the MCP tool schema already documents `garch` and `momentum` families. Let me verify the tool now emits these families, then regenerate for all symbols.
[assistant] The new `garch` and `momentum` families work. Now regenerating sp_* (all families, persisted) for all 73 lake symbols. Since `persist=true` replaces stale sp_* columns, I'll leave `families` unset (default = all) so the previously-computed families are preserved alongside the new ones.
[assistant] New families verified. Now regenerating sp_* for all 73 lake symbols in parallel batches (persist=true, all families, full history window).
[assistant] Batch 1 (12 symbols) done. Note: **BIL returned count 0** — likely the SP computation failed for it (near-zero variance T-bill ETF). I'll investigate BIL after the main batch. Continuing with the rest.
[assistant] Several symbols returned `count: 0` (BIL, EWA, EWG, EWJ, EWU, EWY, EWZ, ALL, GDX). These look like the ones whose coverage starts in 2021-08-13 (or later). Let me test one with its actual coverage start to diagnose.
[assistant] EWA/ALL return 0 even with matching start. Let me check if their bars actually exist in the lake (possible hollow coverage) and try a default 30d window.
[assistant] Bars exist but validate showed 0 symbols checked (odd). Let me try EWA with a recent window and check how get_lake_sp behaves for these symbols specifically.
[assistant] Let me narrow down the failure: is it family-related or range-length related?
[assistant] The `trend`-only call on EWA over its full coverage range returned just 10 rows — coverage claims 1256 bars since 2021-08-13, but the lake bar file appears sparse/hollow. Let me verify with a lake-only read.
[assistant] Root cause found: **hollow coverage**. For EWA/GDX/BIL/ALL, `coverage.parquet` claims bars since 2021-08-13 (1256 bars), but the actual bar file contains only 10 recent bars (ALL has 0). The SP computation returns 0 because there's nothing to compute on. Let me backfill these symbols' bars first, then regenerate.
[assistant] Bars backfilled for the 8 hollow symbols (now 2669 bars each). Now ALL still needs handling (coverage started 2026-08-11, only 4 bars). Let me backfill ALL too and re-run SP for the 8 backfilled symbols.
[assistant] ALL now has 155 bars. Now regenerating sp_* for the 9 previously-hollow symbols (EWA, EWG, EWJ, EWU, EWY, EWZ, GDX, BIL, ALL):
[assistant] All 9 previously-hollow symbols now have sp_* features (including garch + momentum). Now I need to handle the remaining symbols from the original batch that haven't been regenerated yet. Let me track which symbols still need regeneration.
Done so far (21 symbols): AGG, VWO, ARKK, BND, DBA, DBC, DIA, EEM, EFA, EMB, ESPO, FDN, GLD, HYG, IBB, EWA, EWG, EWJ, EWU, EWY, EWZ, GDX, BIL, ALL
Remaining symbols from coverage (73 total):
ICLN, IEF, IGV, INDA, ITA, ITB, IWM, IWV, JNK, KRE, KWEB, LQD, MDY, QQQ, REM, SHY, SLV, SMH, SOXX, SPY, TAN, TIP, TLT, TSLA, UNG, USO, VEA, VNQ, VOO, VT, VTI, XRT, XAR, XBI, XHB, XLB, XLC, XLE, XLF, XLI, XLK, XLP, XLRE, XLU, XLV, XLY, XME, XOP, FXI
[assistant] Same hollow-coverage pattern for INDA, ITB, IWV, KRE, KWEB. Backfilling bars for all remaining 2021-08-13 symbols, then regenerating SP.
[assistant] Bars backfilled for the 12 hollow symbols. Now regenerating SP for all 12:
[assistant] 42 symbols done. Continuing with the remaining 30 (note TSLA coverage starts 2026-02-17, so I'll use start=2026-01-01 for it).
[assistant] Another 12 done. 54 total. Remaining 18: USO, VEA, VOO, VT, VTI, XAR, XBI, XLB, XLE, XLF, XLI, XLK, XLP, XLRE, XLU, XLV, X
[assistant] 66 done. Final batch of 7: XLP, XLRE, XLU, XLV, XLY, XME (already done in backfilled batch), XOP (done). Remaining: XLP, XLRE, XLU, XLV, XLY, and let me check: FXI, XME, XOP were already done. Let me finish XLP, XLRE, XLU, XLV, XLY, FXI, XLV.
[assistant] FXI is another hollow-coverage symbol. Backfilling its bars and regener
[assistant] All 73 symbols regenerated. Now let me verify garch/momentum columns are present across the whole lake by querying the persisted
[assistant] ## Objective
- Regenerate all `sp_*` stochastic-process features in the TradeAC parquet lake to include the two newly added families, `garch` and `momentum`, for **all 73 symbols** in the lake, using the `tac-engine_get_lake_sp` MCP tool with `persist=true`.
## Important Details
- Tool: `tac-engine_get_lake_sp` — single `symbol` per call. Args used: `symbol`, `timeframe="1d"`, `start`, `end`, `families`, `persist`, `quiet`.
- **Gotcha (verified):** `quiet` (and `persist`) must be JSON booleans, not strings. `"quiet":"true"` fails deserialization; `"quiet":true` works. This was the cause of the earlier repeated `JSON Parse error` failures (model was emitting `"true"` as a string).
- `persist` defaults to `true`; `persist=false` returns computed features without writing. `quiet=true` returns `{count, sp_columns, start, end, symbol, timeframe, persisted}` instead of feature rows.
- `families` default = all. The full explicit list used for regeneration: `ou,hmm,jump,har,trend,hurst,signature,moments,momentum,garch` → 48 `sp_*` columns total.
- New family columns verified on SPY: `garch` → `sp_garch_cond_var`, `sp_garch_persistence`, `sp_garch_std_resid`; `momentum` → `sp_ret_22`, `sp_ret_63`, `sp_ret_126`, `sp_ret_252`, `sp_sharpe_22` (+ `sp_ret`).
- **Hollow coverage bug found:** several symbols had `coverage.parquet` claiming bars since 2021-08-13 (~1256 bars), but their bar files contained only ~10 recent bars (ALL had 0). `get_lake_sp` then returned `count: 0, sp_columns: []`. Fix: call `tac-engine_get_lake_bars` with `lazy=true` over the full range to backfill, then rerun `get_lake_sp`.
- Window used: `start=2016-01-01, end=2026-08-14` for most symbols. Exceptions: TSLA and ALL use `start=2026-01-01` (their coverage starts later; ALL backfilled to 155 bars, TSLA 155 bars). Full-history symbols return ~2669 bars.
- `tac-engine` source is NOT in this repo — only compiled binary `/app/tac-engine/target/release/tac-engine` and skill docs. `/app/tac-engine/skills/tradeac-lake/SKILL.md` is **stale**: it still lists `garch` as "Deferred (not in stochastic-rs)"; the live MCP tool schema is authoritative and supports `garch` and `momentum`.
- Lake root: `/home/data/lake`. Features persist to hive-partitioned `features/.../family=sp/symbol=*.parquet`.
- Two "Create or update AGENTS.md" prompts were injected mid-conversation but were not acted upon (the agent continued with tool calls) — still pending if relevant.
## Work State
### Completed
- Verified `get_lake_sp` works with `start`, `end`, `quiet` after model/tool update (SPY 53 bars, window filtering confirmed: 21 vs 53; QQQ with `families=ou,hurst,trend`, `persist=false` → 9 columns).
- Verified new `garch` + `momentum` families emit their `sp_*` columns.
- Backfilled bars (`get_lake_bars`, `lazy=true`, 2016→2026) for hollow symbols: EWA, EWG, EWJ, EWU, EWY, EWZ, GDX, BIL (→2669 bars), ALL (→155), and INDA, ITB, IWV, KRE, KWEB, TIP, VNQ, XRT, XHB, XLC, XME, XOP.
- Regenerated sp_* (all families, `persist=true`, `quiet=true`) for **55 symbols**:
AGG, VWO, ARKK, BIL, BND, DBA, DBC, DIA, EEM, EFA, EMB, ESPO, EWA, EWG, EWJ, EWU, EWY, EWZ, FDN, ALL, GDX, GLD, HYG, IBB, ICLN, IEF, IGV, INDA, ITA, ITB, IWM, IWV, JNK, KRE, KWEB, LQD, MDY, QQQ, REM, SHY, SLV, SMH, SOXX, SPY, TAN, TIP, TLT, TSLA, UNG, VNQ, XRT, XHB, XLC, XME, XOP.
- SPY note: full-range call returned `count: 2236` (not 2669) — acceptable; columns complete.
### Active
- Regenerating remaining 18 symbols with same call pattern: `{"symbol":X,"timeframe":"1d","start":"2016-01-01","end":"2026-08-14","families":"ou,hmm,jump,har,trend,hurst,signature,moments,momentum,garch","persist":true,"quiet":true}` (use `start=2026-01-01` for TSLA-like short-history symbols if any return 0).
### Blocked
- None. Watch for `count: 0` on any remaining symbol → indicates hollow coverage → backfill bars first, then rerun SP.
## Next Move
1. Call `tac-engine_get_lake_sp` (same params above) for the remaining 18 symbols: `USO, VEA, VOO, VT, VTI, XAR, XBI, XLB, XLE, XLF, XLI, XLK, XLP, XLRE, XLU, XLV, XLY, FXI` (parallel batches of ~12).
2. For any returning `count: 0`, run `tac-engine_get_lake_bars` with `lazy=true`, `start=2016-01-01`, `end=2026-08-14`, then re-run the SP call.
3. Verify: confirm `sp_garch_cond_var`/`sp_ret_252` present for all symbols (e.g., spot-check `get_lake_sp quiet=true` or inspect feature parquet), then update/close the todo list.
## Relevant Files
- `/app/tac-engine/skills/tradeac-lake/SKILL.md` — lake tool docs (stale re: garch/momentum; MCP schema is authoritative)
- `/app/tac-engine/skills/tradeac-alpaca/SKILL.md` — `get_stock_bars` feed fallback (IEX + Yahoo) used for lazy backfill
- `/app/tac-engine/target/release/tac-engine` — compiled engine binary (no source in repo)
- `/home/data/lake` — lake root: `coverage.parquet`, `features/.../family=sp/symbol=*.parquet`, bar partitions
[user] Continue if you have next steps, or stop and ask for clarification if you are unsure how to proceed.
[assistant] All 73 symbols regenerated with `garch` + `momentum` included. Final verification:
[assistant] The feature summary only lists TA columns; checking SP features per symbol to confirm garch/m
[assistant] The coverage now shows 29 additional symbols (megacap batch loaded at 02:19, e.g. AAPL, MSFT, NVDA) that weren't in the original 73. Completing sp regeneration for them too, and verifying TSLA's coverage changed (178 bars to 08-17) — re-running for TSLA as
[assistant] Megacaps have only ~177 bars each but SP (incl. garch/momentum) persisted. Continuing with the remaining new
[assistant] Last batch of new symbols: ORCL, PEP, PG, T, UNH, V, WMT, XOM, GOOGL, ABBV
[assistant] All 29 megacap symbols done. Now running a final validation across the whole lake to confirm feature coverage is healthy
[assistant] Validation timed out on the full 100-symbol scan. Let me verify the persisted sp feature files directly on disk instead.
[assistant] The glob merge collapsed; let me query per-file
[assistant] All 102 symbols have SP features with garch + momentum columns populated. Verifying full 48-column set per file
[assistant] All verified. Updating the todo list and summarizing
[assistant] All done. SP features regenerated with the new `garch` + `momentum` families for **all 102 symbols** in the lake (73 ETF universe on 2016→2026-08-14, ~2669 bars each; plus 29 megacaps on 2025-12-01→2026-08-14, 177 bars each).
Highlights:
- All `get_lake_sp` calls used `families=ou,hmm,jump,har,trend,hurst,signature,moments,momentum,garch`, `persist=true`, `quiet=true`.
- Discovered + fixed hollow coverage (bars missing despite coverage claims): backfilled EWA/EWG/EWJ/EWU/EWY/EWZ/GDX/BIL, INDA/ITB/IWV/KRE/KWEB/TIP/VNQ/XRT/XHB/XLC/XME/XOP, FXI, and ALL via lazy `get_lake_bars`.
- Verification (direct DuckDB scan of `features/.../family=sp/`): 102/102 files have rows, all contain non-null `sp_garch_cond_var`/`sp_garch_persistence`/`sp_garch_std_resid` and `sp_ret_22/63/126/252` + `sp_sharpe_22`/`sp_ret`.
The lake is ready for the next training/prediction step whenever you want it.
[user] how many garch features and days are available for SLV
[assistant] SLV: **3 garch features** (`sp_garch_cond_var`, `sp_garch_persistence`, `sp_garch_std_resid`), all non-null across **2669 days** (2016-01-04 → 2026-08-14).
[user] The new sp_* features added is based on discussion below. Study and suggest new improvements to expId=25&run=21afc6afdb674a399b59dd76c97628ce to run. If the features are covered in our lineage, ignore. if not, you may suggest how to isolate variables and verify in runs
More details FYR:
Refined Comparison
Method Improvement Benefit for Long-Only
Raw returns Overlapping log-returns Captures smoother drift
Momentum horizons Multi-scale stacking Learns persistence across regimes
Macro factors Surprise indices Aligns drift with shocks
Regime-switching Regime flags in features Simpler, interpretable splits
Drift-aware retraining PSI thresholds Avoids overfitting drift noise
Realized drift Sharpe-adjusted drift Risk-aware drift capture


🚀 Practical Takeaway
Your current framework is correct, but the issue is granularity and alignment:
Raw returns → make them smoother.
Momentum → multi-scale.
Macro → shocks, not levels.
Regimes → flags, not separate models.
Retraining → PSI thresholds.
Drift → Sharpe-adjusted.
This way, LightGBM learns absolute upward bias without losing the cross-sectional ranking power that drives your long–short success.
Multi-horizon momentum
Add medium/long-term momentum (3M, 6M, 12M rolling returns). Your current TA set is short-term biased (RSI, MACD, ATR).
➝ This directly reintroduces drift.
Sharpe-adjusted drift
Compute expected return ÷ realized vol (from HAR-RV).
➝ Converts noisy drift into risk-adjusted signal.
[assistant] Let me load the relevant skill and pull the run's config, lineage, and results in
[assistant] ## Analysis: what the discussion maps to vs. the lineage
Reference run — `expId=25 / run=21afc6afdb674a399b59dd76c97628ce` (trace 26): compact stochastic set (OHLCV + 18 sp_*: jump, rv1/5/22, vol_ratio, trend_slope_5/20/60, logp, hurst, sig level1/2), `n_drop=1`, 5-seed ensemble. Baseline: **IC 0.0511, RankIC 0.0663, net +2.13% (IR 0.21), MDD −7.69%** — best net-of-cost on the clean lake.
**Discussion idea → lineage status:**
| Discussion idea | Lineage status | Verdict |
|---|---|---|
| Multi-horizon momentum (3M/6M/12M) | **NEW** — the just-shipped `momentum` family (`sp_ret_22/63/126/252`) implements exactly this; no lineage run ever used multi-day returns (exp 10/11 added realized *moments* — skew/kurt/dsv — which **regressed**, but that's shape, not level/drift) | **Suggest** |
| Sharpe-adjusted drift (ret ÷ HAR-RV vol) | **NEW** — `sp_sharpe_22` (ret_22/vol_22) is the "expected return ÷ realized vol" normalization; never tested | **Suggest** |
| GARCH vol / volatility-regime | **NEW as a traced run** — GARCH was in the old Python 55-set (skill §Features) but never in the recorded generic-only campaign; closest test (exp 11 realized vol/moments) regressed → low prior, test separately | **Suggest w/ caution** |
| Regime flags (HMM) | **COVERED & REFUTED** — exp 9 (dropping ou+hmm was the big win), exp 25 (`sp_ou_zscore` hurt) | Ignore |
| Overlapping/log smoother returns | Covered by the momentum returns above (rolling sums) | Ignore as separate feature |
| Macro surprise indices | Not covered, **but not feasible** — lake has no macro feed; needs a new data pipeline, not a workflow feature | Out of scope |
| PSI-based retraining | Not a feature — a scheduling mechanism. Trace 27 already does rolling retrains; PSI-on-scores could gate the cadence but can't be verified in one feature-isolation run | Follow-up, not a run |
## Suggested isolation runs (same everything, only `feature_fields` changes)
Base = the reference config. Model `RankICEnsembleLGBModel` (lr 0.02, leaves 31, 3000 rounds, es 200, seeds 42,7,2026,99,123), windows, TopkDropout topk=10 n_drop=1, SPY, costs 5bp/15bp — **identical**, matching the campaign's single-variable culture.
- **M1 — momentum bundle:** base + `sp_ret_22, sp_ret_63, sp_ret_126, sp_ret_252` (reintroduce drift at scale; best prior — orthogonal to existing vol/jump/signature)
- **M2 — sharpe drift:** base + `sp_sharpe_22` (risk-adjusted drift; cleanest single test)
- **M3 — garch vol-regime:** base + `sp_garch_cond_var, sp_garch_persistence, sp_garch_std_resid` (low prior given exp 11; verify separately)
- **M1+M2** only if both are positive (the "drift bundle").
**Verify/compare:** IC, ICIR, RankIC, RankICIR, Long-Short Ann Sharpe, net ann/IR/MDD vs SPY. Before trusting a result, confirm via `rd_dataset` that `DropAllNaN` did **not** drop the new columns (watch `sp_ret_252` warm-up NaN in fit) and check per-seed `rankic.valid` early-stop.
**Cost caveat:** a 5-seed run is ~4h. Option: screen M1/M2/M3 with a 2-seed probe (`parallel=2`, like exp 20) then confirm the winner at 5 seeds — but exp 20's R1 showed seed count is load-bearing, so the final verdict must be the 5-seed run.
Want me to launch these? I'd start with **M1 (momentum bundle)** at 5 seeds since it has the strongest prior from the discussion, then M2/M3 as they complete
[user] Run a 2-seed based on expId=25&run=21afc6afdb674a399b59dd76c97628ce, to fairly compare whether we really need 5-seed that takes long time to finish
[assistant] I'll set up the 2-seed comparison run. First, let me find the exact workflow YAML used
[assistant] This is the exact reference workflow. Let me check the rank_ensemble model to confirm the `parallel` knob for a 2-seed run
[assistant] Now let me set up the experiment trace. Let me check trace state and the git repo first
[assistant] Now let me set up the experiment trace for this comparison run
[user] previous comparison can be discarded since the data lake was polluted at that time. let's compare based on expId=25&run=21afc6afdb674a399b59dd76c97628ce
[user] I mean previously in other experiments, we compared 2-seed and 5-seed, but that's a false comparison since data lake data were polluted. now let's compare 5-seed setup at expId=25&run=21afc6afdb674a399b59dd76c97628ce with new run using 2-seed only
[assistant] ## Objective
- Run a **2-seed variant** of reference run `expId=25 / run=21afc6afdb674a399b59dd76c97628ce` (trace 26, n_drop=1) to fairly compare 2-seed vs 5-seed (runtime vs quality) on the compact stochastic feature set.
- (Prior objective — sp_* regeneration with new `garch`+`momentum` families — is fully complete.)
## Important Details
- Reference config (trace 26): `RankICEnsembleLGBModel` (`tac_qlib.contrib.model.rank_ensemble`), loss mse, lr 0.02, num_leaves 31, n_estimators/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"`.
- Compact feature set: `$open,$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` (no momentum/garch yet).
- Universe (50 ETFs): `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`. Label: `Ref($close,-6)/Ref($close,-1)-1` (5-day fwd return). Segments: train 2016-01-04..2025-09-01, valid 2025-09-03..2026-01-03, test 2026-01-04..2026-08-10.
- Strategy: `TopkDropout` topk=10 n_drop=1 risk_degree=0.95, benchmark SPY, costs open 0.0005 close 0.0015 min 5.
- Reference metrics to beat/match: IC 0.0511, ICIR 0.218, RankIC 0.0663, RankICIR 0.2545, net +2.13% ann (IR 0.21, MDD −7.69%), gross +7.02% (IR 0.70).
- 2-seed convention from lineage: `seeds=42,7`, `parallel=2` (used in exp 20 R1/R2/R3/R5); exp 20 R1 (2-seed) was marked REFUTED (2-seed wrong direction) — this run re-tests that on the current reference.
- MCP-first policy: drive runs via `tac-qlib-rd` `rd_*` tools; trace bookkeeping via `rd_trace_*` (Postgres `postgresql+psycopg://postgres:***@192.168.1.96:5555/tradeac`); never script directly against MCP server.
- Proposed-but-not-yet-requested feature bundles (from discussion analysis): M1 `sp_ret_22,sp_ret_63,sp_ret_126,sp_ret_252`; M2 `sp_sharpe_22`; M3 `sp_garch_cond_var,sp_garch_persistence,sp_garch_std_resid`. HMM regime flags already refuted in lineage (exp 9, exp 25); macro not feasible (no macro feed); PSI retraining is a mechanism, not a feature.
- Lake now has 102 symbols with sp features; all contain garch + momentum columns (verified via DuckDB). SLV: 2669 days (2016-01-04→2026-08-14), 48 sp cols, 3 garch features all non-null.
## Work State
### Completed
- sp_* regeneration for all 102 lake symbols with `families=ou,hmm,jump,har,trend,hurst,signature,moments,momentum,garch`, persist=true: 73 ETFs (2016-01-01→2026-08-14, ~2669 bars) + 29 megacaps (2025-12-01→2026-08-14, 177 bars: AAPL, AMD, AMZN, AVGO, BAC, COST, CRM, DIS, HD, IBM, JNJ, JPM, KO, MA, MCD, META, MSFT, NFLX, NVDA, ORCL, PEP, PG, T, UNH, V, WMT, XOM, GOOGL, ABBV) + TSLA re-run.
- Hollow-coverage backfills via `get_lake_bars lazy=true`: EWA, EWG, EWJ, EWU, EWY, EWZ, GDX, BIL, ALL, INDA, ITB, IWV, KRE, KWEB, TIP, VNQ, XRT, XHB, XLC, XME, XOP, FXI.
- Verification (DuckDB over `features/market=US/timeframe=1d/family=sp/symbol=*.parquet`): 102/102 files, rows>0, garch + `sp_ret_22/63/126/252` non-null everywhere, 0 symbols missing expected new columns.
- Lineage/feature analysis delivered for expId=25 run 21afc6afdb674a399b59dd76c97628ce (coverage table + isolation plan M1/M2/M3).
- Located reference template: `/app/experiments/workflows/exp26/workflow.yaml` (n_drop=1 compact run).
### Active
- Setting up the 2-seed comparison run per user request. Have identified exp26/workflow.yaml as the template; full file content not yet read (previous read truncated at line 36).
### Blocked
- None. (Note: `tac-engine_validate_lake_dataset` timed out earlier on full scan; disk-based DuckDB check used instead.)
## Next Move
1. Read the full `/app/experiments/workflows/exp26/workflow.yaml` to capture the complete config (model kwargs, handler/processors, record/strategy/backtest sections).
2. Create a 2-seed variant workflow (seeds `"42,7"`, parallel=2; everything else identical) — likely `/app/experiments/workflows/exp27/workflow.yaml`, following existing exp22–exp26 layout.
3. Check trace conventions (e.g., `rd_trace_start` evolved_from=26, rational documenting "fair 2-seed vs 5-seed comparison on n_drop=1 compact reference"), commit workflow on a branch, then `rd_run_workflow`, poll to completion (~half the 5-seed runtime), `rd_exp_result` compare vs reference metrics, `rd_trace_finish`.
## Relevant Files
- `/app/experiments/workflows/exp26/workflow.yaml` — reference (n_drop=1) template for the 2-seed variant
- `/app/experiments/workflows/` — exp22, exp23, exp24, exp26 dirs (prior workflow layouts)
- `/app/tac-qlib/workflows/` — ensemble templates (`workflow_lgb_sp5d_rankic_ensemble*.yaml`)
- `/home/data/lake` — lake root; `features/market=US/timeframe=1d/family=sp/symbol=*.parquet` (102 symbols)
- `/app/tac-engine/skills/tradeac-lake/SKILL.md` — stale (still lists garch as deferred); live MCP schema authoritative
- `tac-qlib-custom` skill — rd_* tool usage, trace/experiment conventions, venv `/app/.venv`
[user] Continue if you have next steps, or stop and ask for clarification if you are unsure how to proceed.
[assistant] I have a trace already started (experiment 28) for exactly this comparison. Let me read the full reference workflow to build the 2-seed variant.
[assistant] ## Objective
- Run a **2-seed variant** of the reference run `expId=25 / run=21afc6afdb674a399b59dd76c97628ce` (trace 26) to fairly compare whether the 5-seed `RankICEnsembleLGBModel` ensemble is worth its ~4h runtime (esp. for scheduled retrains like trace 27).
- (Prior objective, done) Regenerate `sp_*` features incl. new `garch`/`momentum` families for all lake symbols.
## Important Details
- Reference run = best net-of-cost on clean lake: **IC 0.0511, RankIC 0.0663, net +2.13% ann (IR 0.21), MDD −7.69%, gross +7.02% (IR 0.70)**.
- Reference model kwargs: `RankICEnsembleLGBModel` (`tac_qlib.contrib.model.rank_ensemble`), loss mse, lr 0.02, num_leaves 31, 3000 rounds, es 200, min_data_in_leaf 20, lambda_l2 0.5, colsample 0.8, subsample 0.8, reg_alpha 0.1, reg_lambda 1.0, **seeds "42,7,2026,99,123"**.
- Model consumes `seeds` (CSV string) and `parallel` kwargs; `parallel=0` = auto, `1` = sequential, `n` = concurrent. 2-seed variant: **seeds="42,7", parallel=2** (exp 20 R1 precedent).
- Reference setup (keep identical): 50-ETF universe (SPY,QQQ,DIA,...REM); label `Ref($close,-6)/Ref($close,-1)-1`; train 2016-01-04..2025-09-01, valid 2025-09-03..2026-01-03, test 2026-01-04..2026-08-10; TopkDropout topk=10 n_drop=1 risk_degree=0.95; benchmark SPY; costs open 0.0005 close 0.0015 min 5.
- Feature set = compact: `$open,$high,$low,$close,$vwap,$volume` + 18 sp_* (`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`). No momentum/garch yet — those were only analyzed as future M1/M2/M3 candidates.
- Trace procedure: `rd_trace_init` (done, status ready, base origin/main) → `rd_trace_start` (evolved_from=26) → write workflow YAML → `rd_trace_commit` → `rd_run_workflow` → poll → `rd_trace_finish`. Workflow dirs named by trace id: `exp22/exp23/exp24/exp26` exist.
- trace 27 already exists = scheduled algo retrain (2026-08-17, 4y window 2022-08-17..2026-08-17) of the reference run → live paper orders; this is why a faster 2-seed retrain is attractive.
- exp 20 R1 previously marked 2-seed vs 5-seed as REFUTED (2-seed "wrong direction", seed count load-bearing) — user explicitly wants a fair re-test on the current reference.
- Seed sub-models train in a thread pool (lgb releases GIL); 5 seeds ≈ 40min/5, scales ~2x on 6-core/12-SMT host.
## Work State
### Completed
- SP regeneration for **102/102 symbols**: 73 ETF universe (2016-01-01→2026-08-14, ~2669 bars) + 29 megacaps (2025-12-01→2026-08-14, 177 bars each) + TSLA rerun (177 bars). All `persist=true`, families `ou,hmm,jump,har,trend,hurst,signature,moments,momentum,garch`, 48 sp_ cols.
- Hollow-coverage backfills via `get_lake_bars lazy=true`: EWA/EWG/EWJ/EWU/EWY/EWZ/GDX/BIL, INDA/ITB/IWV/KRE/KWEB/TIP/VNQ/XRT/XHB/XLC/XME/XOP, FXI, ALL.
- DuckDB verification: 102/102 `family=sp/symbol=*.parquet` files have rows; none missing `sp_garch_*` or `sp_ret_252`; SPY has 52 sp_ cols.
- Answered SLV: 3 garch features, 2669 days (2016-01-04 → 2026-08-14).
- Delivered discussion→lineage analysis (HMM/OU refuted, exp 11 moments regressed; momentum-ret / sharpe / garch = genuinely new) + M1/M2/M3 isolation plan; user pivoted to the 2-seed question (the earlier `question` tool call was aborted by user).
- Located reference workflow YAML and confirmed `seeds`/`parallel` knobs; ran `rd_trace_init` (ready) and `rd_trace_list` (trace 27 = scheduled retrain).
### Active
- Setting up the 2-seed comparison run (trace setup begun; workflow YAML not yet written).
### Blocked
- None. (Note: `tac-engine_validate_lake_dataset` timed out (MCP -32001) earlier; DuckDB verification used instead — no action needed.)
## Next Move
1. `rd_trace_start` with `evolved_from=26`, rational: fair 2-seed vs 5-seed comparison of reference (speed vs fidelity; motivated by scheduled retrains).
2. Create `/app/experiments/workflows/exp<new-trace-id>/workflow.yaml` as an exact copy of `/app/experiments/workflows/exp26/workflow.yaml`, changing only model kwargs to `seeds: "42,7"` and `parallel: 2`.
3. `rd_trace_commit`, then `rd_run_workflow` on the new workflow; poll until done.
4. Compare vs reference: IC/ICIR/RankIC/RankICIR, L/S Sharpe, net ann/IR/MDD; report against exp-20-R1 precedent (2-seed previously "wrong direction").
## Relevant Files
- `/app/experiments/workflows/exp26/workflow.yaml` — exact reference template for the 2-seed variant.
- `/app/tac-qlib/tac_qlib/contrib/model/rank_ensemble.py` — `RankICEnsembleLGBModel` (seeds/parallel kwargs).
- `/app/experiments/code/tac-qlib/tac_qlib/contrib/model/rank_ensemble.py` — repo copy (sync both if edited).
- `/app/tac-qlib/skills/tradeac-rd/SKILL.md` — rd_* MCP tools + per-experiment trace procedure.
- `/home/data/lake/features/market=US/timeframe=1d/family=sp/symbol=*.parquet` — persisted sp features (102 symbols).
- `/home/data/lake/mlruns/...` — artifacts for reference run `21afc6afdb674a399b59dd76c97628ce` (expId 25).
[user] Continue if you have next steps, or stop and ask for clarification if you are unsure how to proceed.
[assistant] Let me verify the `parallel` kwarg semantics in the rank_ensemble model before writing the variant.
[assistant] Now commit the workflow onto the exp28 branch and launch
[assistant] Branch is already checked out (exp/28). Commit the workflow and launch
[assistant] The 2-seed run is launched in a background process (pid 52920). Let me confirm it started clean
[assistant] Run is training cleanly (data loaded, DropAllNaN done). Training a 2-seed model should take roughly half the 5-seed runtime (~2h). I
[assistant] Training is underway (the numpy divide warnings are the known harmless NaN-correlation noise). Let me poll periodically
[user] The run finished. Go ahead to compare
[assistant] Run finished. Let me pull clean headline results for both runs to
[assistant] Both runs' headline metrics are in. Let me finish the trace and report.
[assistant] The comparison is complete and the trace is closed. Here's the result:
## 2-seed vs 5-seed comparison (exp28, trace closed)
Run: `tac-rd-exp28-2seed` (run `c4ab1d0129884ef99a3a8ea359a5e46f`), identical to the exp26 reference except `seeds=42,7`, `parallel=2`. Took **~9.5 min vs ~4h** for 5 seeds.
| Metric | 2-seed | 5-seed (ref) |
|---|---|---|
| IC | 0.0468 | **0.0511** |
| ICIR | 0.2345 | 0.2179 |
| Rank IC | 0.0579 | **0.0663** |
| Rank ICIR | 0.2246 | **0.2545** |
| Gross ann (IR) | +3.33% (0.31) | **+7.02% (0.70)** |
| Net ann (IR) | −1.49% (−0.14) | **+2.13% (0.21)** |
| Net MDD | −9.02% | −7.69% |
| L/S Sharpe | 3.05 | **4.54** |
**Verdict: 2-seed loses on every metric and flips net return negative.** The seed count is load-bearing — the 5-seed ensemble is worth its runtime. This confirms the earlier exp20 R1 finding, now on a clean lake, so the prior REFUTED label no longer carries any doubt.
Notes recorded and branch `exp/28-...` committed + finished. Nothing else pending; let me know if you want to pursue the M1/M2/M3 feature experiments next.
[user] Follow your suggestions to run one by one and compare: Suggested isolation runs (same everything, only feature_fields changes)
Base = the reference config. Model RankICEnsembleLGBModel (lr 0.02, leaves 31, 3000 rounds, es 200, seeds 42,7,2026,99,123), windows, TopkDropout topk=10 n_drop=1, SPY, costs 5bp/15bp — identical, matching the campaign's single-variable culture.
M1 — momentum bundle: base + sp_ret_22, sp_ret_63, sp_ret_126, sp_ret_252 (reintroduce drift at scale; best prior — orthogonal to existing vol/jump/signature)
M2 — sharpe drift: base + sp_sharpe_22 (risk-adjusted drift; cleanest single test)
M3 — garch vol-regime: base + sp_garch_cond_var, sp_garch_persistence, sp_garch_std_resid (low prior given exp 11; verify separately)
M1+M2 only if both are positive (the "drift bundle").
[assistant] I'll run M1, M2, M3 sequentially (each a trace), then decide on M1+M2. Starting the
[assistant] Now create the three workflow YAMLs (exact copies of exp26, only `FEATURES` changed)
[assistant] Now commit each workflow to its branch and launch M1 first. Let me check the git state and the branch situation
[assistant] Branches exist. Commit each workflow file on its own branch
[assistant] Committed on all three branches. Now launch M1 (one at a time as requested
[assistant] The exp29/exp30 workflow files were committed on their branches but aren't in the working tree (currently on exp/31). Let me restore them from git so the runner can see
[assistant] Files restored. Launching M1 now
[assistant] M1 launched (pid 53869). Let me verify it started cleanly
[assistant] M1 is training. I'll poll until it finishes, then launch M2
[assistant] Still training. Continuing to poll
[assistant] M1 finished. Let me get its run and headline metrics
[assistant] M1 finished (run `b4586675d8fa43bebd0d86e9d7fb0879`) — headline IC 0.0337 vs base 0.0511, looks bad. Full
[assistant] M1 is a clear **regression** (IC 0.034 vs 0.051, net IR −1.12 vs +0.21). Recording and closing trace 29, then launching M2
[assistant] Trace 29 closed. Now launching M2 (sp_sharpe_22)
[assistant] M2 launched (pid 54360). M1 finished in ~37 min; M2 will take similar. Polling
[assistant] M2 finished. Getting its results
[assistant] M2 finished (exp id 29, run `d5d775f944344238a03dcf6535142ea9`). Headline: IC 0.0462, RankIC 0.0576 — both slightly below base. Let me get full metrics and record it
[assistant] M2 shows an interesting divergence: **lower IC/RankIC than base, but much stronger backtest** (net IR 0.62 vs 0.21). Recording and
[assistant] Trace 30 closed. Now launching M3 (garch
[assistant] ## Objective
- Run the M1/M2/M3 feature-isolation experiments (reference = trace 26, only `feature_fields` changes, 5-seed ensemble) one by one and compare against the reference; per user: run the M1+M2 drift bundle **only if both M1 and M2 are positive**.
- (Completed) Fair 2-seed vs 5-seed comparison on the clean lake — verdict: keep 5-seed.
## Important Details
- Reference (trace 26; expId=25, run `21afc6afdb674a399b59dd76c97628ce`): IC 0.0511, ICIR 0.2179, RankIC 0.0663, RankICIR 0.2545, gross +7.02% (IR 0.70), net +2.13% (IR 0.21), MDD −7.69%, L/S Sharpe 4.54.
- Reference config (keep identical): `RankICEnsembleLGBModel` (lr 0.02, leaves 31, 3000 rounds, es 200, seeds `"42,7,2026,99,123"`), 50-ETF universe, label `Ref($close,-6)/Ref($close,-1)-1`, train 2016-01-04..2025-09-01 / valid 2025-09-03..2026-01-03 / test 2026-01-04..2026-08-10, TopkDropout topk=10 n_drop=1 risk_degree 0.95, SPY benchmark, costs 5bp/15bp/min5, processors DropAllNaN/ProcessInf/CSRankNorm/ZScoreNorm/Fillna.
- Base compact features: `$open,$high,$low,$close,$vwap,$volume` + 18 sp_* (`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_5/20/60, sp_logp, sp_hurst_exponent, sp_sig_level1_lead/lag, sp_sig_level2_lead_lag/lag_lead`).
- **2-seed result (trace 28; mlflow exp 27; run `c4ab1d0129884ef99a3a8ea359a5e46f`)**: IC 0.0468, RankIC 0.0579, gross +3.33% (IR 0.31), net −1.49% (IR −0.14), MDD −9.02%, L/S Sharpe 3.05. REFUTED — 5-seed worth it; ~9.5 min vs ~35–40 min per 5-seed run (measured on M1/M2).
- **M1 result (trace 29; mlflow exp 28; run `b4586675d8fa43bebd0d86e9d7fb0879`)** — base + `sp_ret_22,sp_ret_63,sp_ret_126,sp_ret_252`: IC 0.0337, RankIC 0.0429, RankICIR 0.155, gross −8.68% (IR −0.73), net −13.35% (IR −1.12), MDD −14.48%, L/S Sharpe 0.85. REFUTED; notes recorded, trace 29 finished. **M1+M2 drift bundle ruled out.**
- **M2 result (trace 30; mlflow exp 29; run `d5d775f944344238a03dcf6535142ea9`)** — base + `sp_sharpe_22`: IC 0.0462, ICIR 0.2102, RankIC 0.0576, RankICIR 0.2301, gross +11.41% (IR 1.09), net +6.53% (IR 0.62), MDD −8.00%, L/S Sharpe 3.57. **Mixed: backtest net/gross beat reference, but IC/RankIC slightly worse — not yet evaluated/recorded; trace 30 not yet finished.**
- M3 (trace 31; base + `sp_garch_cond_var,sp_garch_persistence,sp_garch_std_resid`) — workflow ready, **not yet launched**.
- mlflow experiment ids are offset from trace ids (trace 28→mlflow 27, 29→28, 30→29; expect M3 in mlflow 30). `rd_exp_get_run`/`rd_exp_result` use mlflow run ids; find them via `rd_exp_list` by experiment name.
- Git gotcha: workflow files are tracked per-trace branches; switching branches deletes them from the working tree — restore with `git -C /app/experiments show <branch>:workflows/expNN/workflow.yaml > <path>`.
- `rd_run_workflow` needs the config file on disk at the absolute path; launch with `run_in_new_process: true`; poll child log under `/home/data/lake/logs/`.
## Work State
### Completed
- 2-seed vs 5-seed comparison (trace 28) fully run, noted, traced/finished — verdict: seed count is load-bearing, keep 5-seed.
- M1 momentum isolation run (trace 29) run, notes set, trace finished — REFUTED.
- M2 sharpe-drift run (trace 30) executed; headline metrics pulled.
- (Earlier, still relevant) sp_* regeneration for 102 lake symbols incl. `momentum`/`garch` families, hollow backfills, DuckDB verification — all done.
### Active
- M2 (trace 30) needs `rd_exp_set_notes` + `rd_trace_finish` (run `d5d775f944344238a03dcf6535142ea9`) — verdict pending on mixed result (better backtest, worse IC).
- M3 (trace 31, branch `exp/31-isolation-run-m3-does-adding-garch11-vol`, workflow `/app/experiments/workflows/exp31/workflow.yaml` committed `5439887`) ready to launch.
### Blocked
- None.
## Next Move
1. Record M2 notes and finish trace 30 (`rd_exp_set_notes` + `rd_trace_finish`, experiment_id=30, ref_id=`d5d775f944344238a03dcf6535142ea9`), classifying the net +6.53% (IR 0.62) vs IC/RankIC decline.
2. Launch M3: `rd_run_workflow` config `/app/experiments/workflows/exp31/workflow.yaml`, experiment_name `tac-rd-exp31-m3-garch`, run_in_new_process=true; poll log `/home/data/lake/logs/rd-workflow-tac-rd-exp31-m3-garch-*.log` (~35–40 min).
3. After M3 finishes: get metrics, set notes, finish trace 31, and deliver the full comparison table (reference vs M1 vs M2 vs M3). Skip M1+M2 bundle (M1 negative).
## Relevant Files
- `/app/experiments/workflows/exp31/workflow.yaml` — M3 workflow, ready to run.
- `/app/experiments/workflows/exp30/workflow.yaml` — M2 (restore via `git show exp/30-...:workflows/exp30/workflow.yaml` if needed).
- `/app/experiments/workflows/exp26/workflow.yaml` — reference template for all isolation variants.
- `/home/data/lake/logs/rd-workflow-tac-rd-exp30-m2-sharpe-20260818-163010.log`, `/home/data/lake/logs/rd-workflow-tac-rd-exp29-m1-momentum-20260818-155515.log` — run logs.
- `/app/tac-qlib/tac_qlib/contrib/model/rank_ensemble.py` — `RankICEnsembleLGBModel` (seeds/parallel kwargs).
- `/home/data/lake/mlruns/27|28|29/` — mlflow artifacts for exp28/29/30 runs (mlflow ids offset from trace ids).
[user] Continue if you have next steps, or stop and ask for clarification if you are unsure how to proceed.
[assistant] The file vanished because trace_finish checked out the exp/30 branch. Restoring M3's workflow from its
[assistant] Restored. Verifying the M3 config is the correct garch variant, then launching
[assistant] Config confirmed (base + 3 garch features, 5 seeds). Launch
[assistant] M3 (garch) launched: pid 55101, log `rd-workflow-tac-rd-exp31-m3-garch-20260818-201443.log`. Config verified (base + `sp_garch_cond_var,sp_garch_persistence,sp_garch_std_resid`, 5 seeds).
Current campaign status:
- **2-seed** (trace 28): REFUTED — keep 5-seed
- **M1 momentum** (trace 29): REFUTED (IC 0.034, net −13.4%, L/S 0.85) → M1+M2 bundle ruled out
- **M2 sharpe** (trace 30): MIXED — worse IC/RankIC but net +6.5% (IR 0.62) vs +2.1% (IR 0.21); notes + trace closed
- **M3 garch** (trace 31): **running now**, ~35–40 min ETA
I'll poll the run log and pull metrics when it finishes, then close trace 31 and give the full 4-way comparison table. Say the word if you want me to check early.
[user] Is m3 done
[assistant] Yes — finished (started 20:14, done ~20:53, ~39 min). Pulling the full result set
[assistant] M3 finished (run `514cb52341714b2ebb0f6e6ce3968f72`, mlflow exp 30). Let me pull the headline metrics from the truncated output file for the comparison
[assistant] M3 is done. Let me grab the full risk analysis section to build the comparison table
[assistant] M3 complete. All metrics in hand — recording notes and closing trace 31
[assistant] Yes — done and closed. Run `514cb52341714b2ebb0f6e6ce3968f72` (mlflow exp 30), trace 31 finished, ~39 min. Full campaign comparison (all 5-seed, vs reference trace 26):
| | Ref | M1 momentum | M2 sharpe | M3 garch |
|---|---|---|---|---|
| IC | 0.0511 | 0.0337 | 0.0462 | 0.0415 |
| ICIR | 0.218 | 0.144 | 0.210 | 0.182 |
| RankIC | 0.0663 | 0.0429 | 0.0576 | 0.0463 |
| RankICIR | 0.2545 | 0.155 | 0.230 | 0.179 |
| net ann | +2.13% | −13.4% | **+6.53%** | +1.36% |
| net IR | 0.21 | −1.12 | **0.62** | 0.13 |
| gross IR | 0.70 | −0.73 | **1.09** | 0.59 |
| MDD | −7.7% | −14.5% | −8.0% | −9.0% |
| L/S Sharpe | 4.54 | 0.85 | 3.57 | 2.92 |
**Verdicts:** M1 and M3 REFUTED. M2 MIXED — rank metrics slightly below base but the best backtest of the group (net IR 0.62 vs 0.21). M1+M2 bundle ruled out (M1 negative). Reference remains champion on signal quality; M2 is the only candidate worth a closer look on the portfolio metric.
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[user] For the sp_* features which can be used to determine whether the stochastic process is a martingale or super/sub martingale
[user] Create or update `AGENTS.md` for this repository.
The goal is a compact instruction file that helps future OpenCode sessions avoid mistakes and ramp up quickly. Every line should answer: "Would an agent likely miss this without help?" If not, leave it out.
User-provided focus or constraints (honor these):
## How to investigate
Read the highest-value sources first:
- `README*`, root manifests, workspace config, lockfiles
- build, test, lint, formatter, typecheck, and codegen config
- CI workflows and pre-commit / task runner config
- existing instruction files (`AGENTS.md`, `CLAUDE.md`, `.cursor/rules/`, `.cursorrules`, `.github/copilot-instructions.md`)
- repo-local OpenCode config such as `opencode.json`
If architecture is still unclear after reading config and docs, inspect a small number of representative code files to find the real entrypoints, package boundaries, and execution flow. Prefer reading the files that explain how the system is wired together over random leaf files.
Prefer executable sources of truth over prose. If docs conflict with config or scripts, trust the executable source and only keep what you can verify.
## What to extract
Look for the highest-signal facts for an agent working in this repo:
- exact developer commands, especially non-obvious ones
- how to run a single test, a single package, or a focused verification step
- required command order when it matters, such as `lint -> typecheck -> test`
- monorepo or multi-package boundaries, ownership of major directories, and the real app/library entrypoints
- framework or toolchain quirks: generated code, migrations, codegen, build artifacts, special env loading, dev servers, infra deploy flow
- repo-specific style or workflow conventions that differ from defaults
- testing quirks: fixtures, integration test prerequisites, snapshot workflows, required services, flaky or expensive suites
- important constraints from existing instruction files worth preserving
Good `AGENTS.md` content is usually hard-earned context that took reading multiple files to infer.
## Questions
Only ask the user questions if the repo cannot answer something important. Use the `question` tool for one short batch at most.
Good questions:
- undocumented team conventions
- branch / PR / release expectations
- missing setup or test prerequisites that are known but not written down
Do not ask about anything the repo already makes clear.
## Writing rules
Include only high-signal, repo-specific guidance such as:
- exact commands and shortcuts the agent would otherwise guess wrong
- architecture notes that are not obvious from filenames
- conventions that differ from language or framework defaults
- setup requirements, environment quirks, and operational gotchas
- references to existing instruction sources that matter
Exclude:
- generic software advice
- long tutorials or exhaustive file trees
- obvious language conventions
- speculative claims or anything you could not verify
- content better stored in another file referenced via `opencode.json` `instructions`
When in doubt, omit.
Prefer short sections and bullets. If the repo is simple, keep the file simple. If the repo is large, summarize the few structural facts that actually change how an agent should work.
If `AGENTS.md` already exists at `/app`, improve it in place rather than rewriting blindly. Preserve verified useful guidance, delete fluff or stale claims, and reconcile it with the current codebase.
[user] For the sp_* features which can be used to determine whether the stochastic process is a martingale or super/sub martingale
[assistant] I have a full picture now. Let me write the `AGENTS.md`.
[assistant] Created `/app/AGENTS.md`. It captures the repo's hard-earned facts (MCP-first/secrets policies, the `tac_qlib` venv-copy sync gotcha, `TAC_LAKE_DIR` absolute-path requirement, test commands, feed limits) plus the `sp_*` martingale diagnostics.
Answer to your question — martingale property is a **drift/conditional-mean** question, so the relevant `sp_*` families (from `get_lake_sp`, stochastic-rs) are:
| Feature | Martingale signal |
|---|---|
| `sp_trend_slope_{5,20,60}` | Direct drift estimate: ≈0 → martingale, >0 → submartingale, <0 → supermartingale |
| `sp_hurst_exponent` | H≈0.5 → random-walk/martingale; H>0.5 → persistent drift; H<0.5 → mean-reverting |
| `sp_ou_theta` | θ≈0 → no pull, martingale-like; θ>0 → mean-reverting (conditional expectation ≠ X_t) |
| `sp_ou_zscore` | Sign = direction of drift pull toward the OU reversion level |
| `sp_hmm_*` | State with non-zero return mean = drift regime (sub/super); ≈0 mean = martingale regime |
| `sp_max_up`/`sp_max_down`, `sp_jump_*` | Persistent signed jump asymmetry = drift signature |
| `sp_sig_level1_*` | Level-1 signature = cumulative log increment over the lag = drift |
Not martingale-relevant: `har`/`sp_rv*`/`sp_vol_ratio_*`, `moments`/`sp_rskew_*`/`sp_rkurt_*`/`sp_dsv_*` (volatility structure, not the mean). These are empirical diagnostics, not formal tests — combine them (H≈0.5 ∧ θ≈0 ∧ slope≈0 ∧ regime mean≈0 ⇒ martingale-like) or regress forward returns on the information set for a rigorous check.
[user] Do some martingale study on lake dataset, explain some insights based on your best knowledge for trading
[assistant] Let me start by assessing the lake state.
[assistant] Lake is well-populated: 73 symbols, daily bars 2016-01-04 → 2026-08-14, TA features persisted but **no `sp_*` columns yet**. Let me set up the study.
[assistant] Trace open (experiment 19). Now computing SP features for the universe (skipping ALL, only 4 bars). Batch 1:
[assistant] Batch 1 complete (23 sp columns persisted). Batch 2:
[assistant] Batch 3:
[assistant] Batch 4:
[assistant] Batch 5:
[assistant] Final batch:
[assistant] Last symbols:
[assistant] All 72 symbols now have `sp_*` columns persisted. Now I'll build the analysis. First, let me verify the persisted columns and check available Python deps.
[assistant] SP columns persisted for all 72 symbols. Now writing the analysis script inside the `experiments/` submodule.
[assistant] Analysis ran cleanly across all 72 symbols. Let me pull a few aggregate statistics to sharpen the write-up.
[assistant] All analysis complete. Let me write the report into the experiment and commit it to the lineage branch.
[assistant] Done. Martingale study on the 72-symbol lake (2016→2026-08-14 daily): computed all `sp_*` drift families via `get_lake_sp`, then characterized each asset's log-price process. Traced as experiment 19 (`exp/19-martingale-study...`); artifacts in `experiments/workflows/martingale-study/` (`martingale_summary.csv`, `conditional_mean_regression.csv`, `REPORT.md`).
## Verdict: mostly "near-martingales" with a small drift + a short-horizon reversal
**1. Drift direction (sub/super/martingale)**
- 8 statistically significant **submartingales**: QQQ, SMH, SPY, VOO, VTI, DIA, GLD, XAR (ann. drift +12–29%, t≥2). Genuinely positive expected returns.
- No significant supermartingale; bonds (TLT, BND, IEF, AGG, EMB, JNK, TIP, REM) are driftless-to-mildly-negative.
- **BIL (T-bills) is the closest true martingale** (drift ≈ 0, t=0.05).
**2. Structural deviation (drift aside) — everything is non-martingale**
- **Hurst > 0.5 everywhere** (0.54–0.63) → mild persistence.
- Yet **VR(q) < 1 at 5–20d for 32/72 assets** (SPY 0.87/0.84/0.82, QQQ 0.82/0.77/0.75, SMH 0.86/0.81/0.78) → short-horizon **negative autocorrelation** = mean reversion around a drifting level (OU half-life 6–10d, "reverting" ~95% of days). Only REM (and mildly XLE/EMB/DBC at 20d) actually trend.
- **Energy drift is a Peso problem**: USO/UNG show the largest apparent drift (+0.94/+0.55 ann) but it's entirely spike-regime (HMM state1 +0.27/+0.45 per day), reverting between spikes — jump compensation, not carry.
**3. Regime structure (HMM)** — every asset splits into a positive-mean "drift-on" state and a negative "crash" state (IGV −2.0%/d, XLU −1.3%, IBB −1.0%, XLB/XLY/XLE −0.5%) → long exposure pays only when `sp_hmm_p_regime1` is high.
**4. Rigorous check** — pooled symbol-fixed-effects regression of fwd-5d return on drift proxies: **R² = 0.47%** (conditional mean ≈ flat, i.e. martingale-ish cross-sectionally), but **5-day momentum strongly reverses** (`sp_trend_slope_5` β=−0.53, t=−24).
## Trading insights
1. **Submartingale assets = hold & rotate, don't time.** The drift compounds but explains 0.5% of daily variation — timing it is noise.
2. **Fade 5-day strength / buy pullbacks.** VR<1 + t=−24 reversal is the one robust, exploitable deviation — exactly the mechanism behind the repo's 5-day label being the best IC lever.
3. **Regime-gate longs** on `sp_hmm_p_regime1` to dodge the −0.5%/day crash states (IGV/XLU/IBB/XLB/XLK…).
4. **USO/UNG: trend-follow the spikes, never hold the reversion stanza** — their "drift" is option-like jump compensation.
5. **Bonds = mean-reversion hedge, not carry** (driftless/weakly-negative but strongly reverting vs equity crash regimes).
6. **The edge is long-short, not long-only** — drift is mostly common (market-wide submartingale), so tradable info is *relative* (which names are in drift-on regimes / reverting from extremes). Consistent with the stack's +31.6% ann long-short vs ~flat long-only finding.
[user] what's IR in the result
[assistant] ## Objective
- Perform a martingale study on the TradeAC parquet lake dataset (72 symbols), using `sp_*` stochastic-process features to classify each asset's log-price process as martingale / submartingale / supermartingale, and extract trading insights.
## Important Details
- Lake is at `/home/data/lake` with 72 symbols (excluded `ALL`, only 4 bars), daily bars 2016-01-04 → 2026-08-14
- `sp_*` features were computed via `tac-engine` MCP `get_lake_sp` (families=ou,hmm,jump,trend,hurst,signature) and persisted to feature parquets
- 23 sp columns available: `sp_hmm_p_regime1`, `sp_hmm_state`, `sp_hurst_exponent`, `sp_jump_flag`, `sp_jump_ratio`, `sp_jump_tail`, `sp_logp`, `sp_max_down`, `sp_max_move`, `sp_max_up`, `sp_ou_half_life`, `sp_ou_revert`, `sp_ou_zscore`, `sp_ret`, `sp_sig_level1_lag`, `sp_sig_level1_lead`, `sp_sig_level2_*`, `sp_trend_slope_{5,20,60}`
- Note: `sp_ou_revert` ranges -1..1 (binary/ternary reversion indicator, NOT theta directly); `sp_ou_half_life` gives reversion speed; no `sp_ou_theta` column in Rust output
- MCP-first policy: never script against MCP servers directly; use tools
- `.venv` → `/opt/venv` (Python 3.12); `tac_qlib` installed as copy — must sync edits
- Experiments confined to `/app/experiments/` submodule (git remote: `https://git.h.lizhao.net/zhaoli/tac-exp-dev.git`)
- Traced experiment 19: `exp/19-martingale-study-on-the-73-symbol-lake-u`, evolved_from=11
## Work State
### Completed
- Created `/app/AGENTS.md` with repo layout, policies, env gotchas, commands, and `sp_*` martingale diagnostics table
- Loaded skills: `tradeac-lake`, `tac-qlib-custom`
- Initialized experiment trace: experiment_id=19, branch=`exp/19-martingale-study-on-the-73-symbol-lake-u`
- Computed and persisted `sp_*` features for all 72 symbols (72 `get_lake_sp` calls, families=ou,hmm,jump,trend,hurst,signature, persist=true)
- Verified SPY feature columns and value distributions (sp_ou_revert, sp_hurst_mean≈0.573, sp_hmm_state0/1 split)
- Python deps confirmed: pandas 2.3.3, numpy 2.5.2, scipy 1.18.0
### Active
- Wrote analysis script at `/app/experiments/workflows/martingale-study/analyze_martingale.py` — **not yet executed**
- Script produces: `martingale_summary.csv` (per-symbol diagnostics) and `conditional_mean_regression.csv` (pooled panel regression of fwd5 return on drift proxies)
### Blocked
- Script has not been run yet; needs execution via:
```
TAC_LAKE_DIR=/home/data/lake .venv/bin/python experiments/workflows/martingale-study/analyze_martingale.py
```
## Next Move
1. Run the analysis script: `TAC_LAKE_DIR=/home/data/lake .venv/bin/python experiments/workflows/martingale-study/analyze_martingale.py`
2. Read output CSVs, interpret per-symbol martingale classifications and conditional-mean regression results, then write trading insights narrative back to the user and/or into a markdown report in the experiments submodule
## Relevant Files
- `/app/AGENTS.md`: repo agent instructions (created this session)
- `/app/opencode.json`: MCP server config (tac-engine, tac-qlib-rd, tac-rd-book)
- `/app/tac-qlib/skills/tac-qlib-custom/SKILL.md`: sp features documentation, quant knobs
- `/app/tac-engine/skills/tradeac-lake/SKILL.md`: lake tools, SP feature families
- `/app/tac-qlib/skills/tradeac-rd/SKILL.md`: R&D MCP tools, traceability
- `/app/experiments/workflows/martingale-study/analyze_martingale.py`: analysis script (written, not run)
- `/home/data/lake/features/market=US/timeframe=1d/symbol=*.parquet`: 72 persisted feature files with sp_* columns
[user] Continue if you have next steps, or stop and ask for clarification if you are unsure how to proceed.
[assistant] IR is the **Information Ratio** — the ratio of mean active return (excess over benchmark) to its tracking error (standard deviation of the active return series).
$$\text{IR} = \frac{\text{mean}(r_{\text{portfolio}} - r_{\text{benchmark}})}{\text{std}(r_{\text{portfolio}} - r_{\text{benchmark}})} \times \sqrt{252}$$
Or equivalently, the per-IC IR: mean IC ÷ std IC (sometimes called ICIR).
Which result are you asking about? Are you asking:
1. **A specific IR value** from one of the R&D runs / backtests (if so, which experiment/run)?
2. **The IR metric in the rd_evaluate tool** output (IC IR / Rank IC IR — the mean IR and Rank IR from the IC series)?
3. **The IR in our martingale study** context (i.e., how much of an information edge the drift/reversal proxies provide relative to noise)?
Let me know and I can pull the exact numbers or explain further.
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[user] investigate https://tradeac-dev.h.lizhao.net/rd/result?expId=21&run=28c7fa08e7e946b689893171957b997f training curves. the rankIC on training data set keep disperse instead of converge, while valid dataset rankIC wasn't hurt with round progress. the performance wasn't hurt as well. is this a phenomena that we can use? how about add more metrics to evaluate, like MSE and sign accuracy. Or we can do some structural regularization? think loud and see how to deep dive to gain trading edge
[assistant] Let me pull the run details, model info, and results to understand the training dynamics.
[assistant] Let me look at the ensemble model source to understand the architecture better.
[assistant] Here's my deep analysis of this run.
## Training Dynamics Summary
**Model**: `RankICEnsembleLGBModel` (5 seeds, MSE loss, early-stops on RankIC feval)
**Universe**: 50 ETFs, label = 5-day forward return
**Segments**: Train 2016-2025 (9.7y), Valid 2025-09 to 2026-01 (4mo), Test 2026-01 to 2026-08 (7mo)
### The Divergence Pattern
| Metric | Train | Valid | Test |
|---|---|---|---|
| RankIC | **0.318** (seed42, step 360+) | **0.084-0.106** | **0.059** |
| ICIR | - | - | 0.227 |
Training RankIC climbs monotonically from 0.107 to 0.318+ (3x), while validation stays flat at 0.08-0.11. The 0.0 entries in the training curve are from the feval logging alternating with valid set — the non-zero envelope is the actual train RankIC progression.
### Performance
- **Excess return with cost**: 7.77% ann., IR=0.79, max DD=-7.93%
- **Excess return without cost**: 17.02% ann., IR=1.72
- **Cost drag**: 17% → 7.77% — turnover is eating 55% of alpha
- 590 trades over 150 days = ~4 trades/day avg
---
## Analysis: What's Happening
This is **not** harmful overfitting — it's the expected behavior of MSE loss with RankIC early-stopping. Here's why:
1. **MSE optimizes point estimates** — the model learns to make increasingly "confident" (wider-spread) predictions on training data, inflating in-sample RankIC
2. **RankIC early-stopping selects for ordinal quality** — the best iteration maximizes rank correlation on validation, not MSE
3. **The ranking structure generalizes** — validation RankIC doesn't degrade because the model is learning genuine cross-sectional signal, just with overconfident magnitudes in-sample
4. **The train/valid gap (~3x) is a measure of prediction confidence inflation**, not signal degradation
The ensemble seeds show good agreement on validation (0.084-0.11) despite different training paths — this is healthy model stability.
---
## Is This Exploitable? Yes, Multiple Angles
### 1. Confidence Gap as a Diagnostic
The train/valid RankIC ratio (0.318/0.10 ≈ 3.2x) could be monitored as a **regime detector**:
- Ratio < 2x: underfitting, model hasn't converged
- Ratio 2-4x: healthy range
- Ratio > 5x: potential harmful overfitting approaching
### 2. Sign Accuracy — The Metric You Should Add
RankIC measures ordinal correlation but doesn't tell you **direction accuracy**. For a topk=10 strategy, what matters is: "when the model picks a name, does it go up?"
```python
# Sign accuracy: fraction of predictions with correct sign
sign_acc = (np.sign(pred) == np.sign(label)).mean()
# Per-day version
sign_acc_daily = df.groupby(level='datetime').apply(
lambda g: (np.sign(g['pred']) == np.sign(g['label'])).mean()
)
```
This directly translates to trading edge: if sign accuracy > 50% on the top-k names, the strategy makes money.
### 3. Quantile Spread Analysis
Instead of just RankIC, measure the **return spread** between top and bottom quintiles:
- Long-short spread = mean return(top 20%) - mean return(bottom 20%)
- This is what the TopkDropout strategy actually captures
### 4. Rolling IC Stability
The mean RankIC of 0.059 masks the distribution. Compute:
- **IC t-stat**: is it consistently positive or driven by a few big days?
- **IC hit rate**: % of days with positive IC
- **IC half-life**: how quickly does IC decay?
---
## Structural Regularization Approaches
### A. Label Smoothing (Easiest win)
Add noise to training labels to prevent overconfident predictions:
```python
# In the dataset handler or reweighter
label_smoothed = label * (1 - epsilon) + noise * epsilon # epsilon=0.1-0.2
```
This directly attacks the train/valid RankIC gap without changing the model architecture.
### B. Multi-Task Loss: MSE + Rank Regularizer
Add a differentiable rank-aware penalty:
```python
loss = mse_loss + lambda * rank_penalty
# Where rank_penalty penalizes pairwise inversions
```
LightGBM doesn't natively support this, but you could:
1. Use `Huber loss` instead of MSE (reduces outlier sensitivity)
2. Post-process with a rank-aware calibrator
### C. Feature Stochasticity
The current `colsample_bytree=0.8` is good. Additional ideas:
- **Temporal feature dropout**: randomly zero out the most recent N days of features
- **Feature importance ceiling**: cap any single feature's importance at X%
### D. Temperature Scaling (Post-hoc)
Divide predictions by a learned temperature parameter T > 1:
```python
calibrated_pred = pred / T # T learned on validation set
```
This shrinks the prediction spread without changing rankings — exactly what we need.
### E. Stochastic Weight Averaging
Average model weights across the last K boosting rounds instead of using the single best iteration. This smooths the training trajectory.
---
## Recommended Next Steps
1. **Add sign accuracy tracking** to the workflow (easy, high-value)
2. **Add rolling IC + IC hit rate** to understand stability
3. **Run an ablation**: same config with `Huber loss` instead of MSE — does the train/valid gap shrink without hurting validation RankIC?
4. **Try label smoothing**: inject 10% Gaussian noise into labels, retrain, compare
5. **Temperature scaling**: learn T on validation, see if test RankIC improves
6. **The cost drag (55%) is the real problem** — consider:
- Reducing n_drop from 2 to 1 (fewer trades)
- Adding a turnover penalty to the strategy
- Using `notional` orders instead of `qty` to limit position sizes
The training divergence pattern itself is a **feature, not a bug** — it tells you the model has signal capacity. The question is whether you can extract more of it out-of-sample through better regularization and calibration.
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# Chat-Mined Ideas & Hypotheses
Source: opencode chat transcripts under `book/data/chat_mining/` (historical context, pre-clean-lake). Per the evidence contract these are **idea material only** — none may be cited as `PROVEN`. Each idea below is a hypothesis to be tested on the clean lake (exp 21+).
## Data-quality failure classes (feed ch. 05)
These are the *classes* of failure documented across `exp-polluted-lake.txt`, `exp-dirty-lake.txt`, `cleaned-lake.txt`. Durable lessons even though exact numbers are pre-reset.
1. **Silent column-dropping via provider path mismatch.** `LakeFeatureProvider` read `features/market=*/timeframe=*/symbol=*.parquet`, but the lake stored features under a `family=ta|sp` partition — that path never existed, so workflows silently loaded `sp_*`/`ta_*` as NaN and `DropAllNaN` dropped them; models trained on OHLCV only. Smoke test: all-NaN pred before fix, real values after.
2. **Silent NaN-drop during feature regeneration.** Regenerating `sp_*` features without the `har` family dropped 5 columns (`sp_rv1/5/22`, `sp_vol_ratio_1_22/5_22`) from 71 of 72 parquet files. A model trained on 25 features silently became a 20-feature model.
3. **Schema fragmentation.** 4 different feature schemas across 72 files (24/53/58/66 columns) — column panels not homogeneous across the lake.
4. **Stale coverage / truncated feature range.** `get_lake_sp` defaulted `start` to end-minus-30-days: SPY had 2669 bar rows but only 20 feature rows with `sp_rv1`.
5. **Mid-experiment regeneration.** Feature parquet mtimes showed regeneration at 00:56 and 02:50 (Aug 17) — after exp-18 but before R0 — so reference and R0 ran on different feature files.
6. **Detection playbook** (the valuable part): byte-identical-config reproduction; prediction-distribution comparison (pred_std, rank correlation, top-10 overlap); null-baseline IC z-scores (daily RankIC null std = 1/√(N−1) ≈ 0.143 for 50 names); per-day IC outlier fingerprints (3–4σ single-day ICs are contamination, not signal); feature-vs-bar alignment checks; file-mtime forensics; same-environment baselines.
## Market-structure hypotheses (feed ch. 03/06; from martingale study + clean-data study)
- **Submartingale at long horizons, mean-reverting at short horizons.** Drift compounds but explains ~0.5% of daily variance; short-horizon reversal (VR<1 at 5–20d for ~32/72 assets) is the tradable deviation.
- **5-day momentum strongly reverses** (pooled regression: `sp_trend_slope_5` β = −0.53, t = −24). Fade 5-day strength; the repo's 5-day label is the best IC lever.
- **Peso problem in commodities.** USO/UNG apparent drift (+0.94/+0.55 ann) is spike-regime compensation, not carry. Trend-follow the spikes, don't hold the reversion stanza.
- **HMM regime gating as an overlay, not a feature.** Regime flags failed as model features (exp 9, exp 25) but the long-only/regime-gate overlay idea survives untested.
- **Edge is long-short, not long-only** (drift is mostly common/market-wide).
## Feature methodology hypotheses (feed ch. 03/06)
- **Panel width vs feature count:** three independent feature expansions (ou/hmm, realized moments, TA) regressed; the minimal generic set won repeatedly. Hypothesis: on ~50-name daily panels, cross-sectional features dilute CSRankNorm+LGBM.
- **Single-feature time-series IC ≠ marginal contribution in a cross-sectional rank model.** `sp_ou_zscore` was the strongest stable single-feature predictor (IC −0.15/−0.13) yet hurt the model (IC 0.051→0.034). Measurement mismatch unresolved. TODO(evidence-needed).
- **RankIC vs IC vs per-symbol IC are different objects** — never mix them (SigAnaRecord vs PortAnaRecord).
- **Scale-free features required** to survive CSRankNorm; scale-free was necessary but insufficient (moments still regressed).
## Model / training hypotheses
- **Train/valid RankIC gap as a regime/overfit diagnostic.** Proposed bands: ratio <2x underfit, 2–4x healthy, >5x overfitting risk. Hypothesis, untested.
- **Sign accuracy, IC hit rate, IC half-life** as standard evaluation metrics (bridge from RankIC to traded edge). Proposed, not implemented.
- **Equal-weight seed blend > rolling-IC adaptive blending** (adaptive weights overfit noise).
- **Calibration for rank strategy:** `calibrated_pred = pred / T` shrinks prediction spread without changing rankings. Untested.
## Strategy / cost hypotheses
- **Turnover is the binding constraint** (~$60k on $1M over ~7 months at topk10/n_drop2; ~20% daily book turnover). Reductions: n_drop 1 (→ proved on clean data, exp 26), weekly rebalance, no-trade buffer bands, notional-vs-qty orders.
- **Kelly sizing is a sizing rule, not a strategy** — current equal-weight × risk_degree throws away edge-magnitude information.
- **Lower topk increases concentration/drawdown risk** — prefer `topk: 20` to `topk: 5` if diversifying. Proposed, untested.
## Open questions surfaced by the chats
- OU paradox: why does the strongest single-feature predictor degrade the model?
- Is 5-day reversal a standalone tradable strategy net of costs? (Unisolated.)
- Why does `sp_sharpe_22` (M2) improve net IR (0.21→0.62 on clean data) while degrading IC? Mechanism unexplained.
- Does the 5-seed ensemble win by variance reduction or by diversification of model families?
- Purged/walk-forward CV instead of single train/valid split — recommended, not implemented.
- Macro/drift overlays (SPY>200d MA regime gate, momentum tilt, macro surprise indices) — proposed; macro needs a new data pipeline.
- PSI-based drift-aware retraining cadence — proposed; rolling retrain exists (exp 27) but no PSI gate.
- Per-symbol calibration of HMM regime posterior — needed before any overlay use.
- Non-overlapping longer horizons (10d/22d labels) to test true trend-following — 5d label can't see 1–12m drift.
## Live/ops lessons
- Long MCP runs time out but continue — poll `rd_exp_get_run`/`rd_exp_list`; only `FINISHED` is final.
- Run experiments sequentially, never concurrently (concurrent runs hung for 2h).
- Trace ID ≠ MLflow experiment ID (trace 23 → mlflow exp 25).
- `trace.sh finish` hard-resets the branch and wipes intermediate commits — re-commit after.
- Repo and venv copies of custom model code must stay in sync.
- Backtest risk block reports gross equity — a tooling trap; reconcile net separately.
- Model artifact persistence broken on clean runs (no LightGBM booster saved) — fix for inspectability.