tac-exp-dev/book/data/chat_mining/greeting-setup.txt

[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.