ch11: signal-quality gate REFUTED — walk-forward workflow shows gate harmful (exp 61-67, EVIDENCE#053)
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@@ -85,7 +85,8 @@ Experiments 8–18 record metrics under a legacy schema (`ls_sharpe`, `maxdd_wit
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| ID | Claim | Source | Verified? |
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|----|-------|--------|-----------|
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| EVIDENCE#051 | Comprehensive model search: queried all MLflow experiments/runs, ranked by RankICIR. Top models: exp 36/44 (label22d, RankICIR 0.507, single-window 2026 only), exp 35/51 (label10d, RankICIR 0.352, single-window), exp 58 (adaptive-2y, RankICIR 0.289), exp 11 (single-seed, RankICIR 0.276). Exp 52 walk-forward configs rank near the top among multi-year models (RankICIR 0.244). The 22-day label models have highest IC but negative returns (−4.6%) — high IC does not guarantee profitable trading. The regime gate study (EVIDENCE#050) is robust to model selection because it measures market-level features, not model predictions. Selection bias is not material: the best-return model (Config C) also has the best RankICIR among walk-forward configs. | `rd_exp_list` query across all MLflow experiments, run metadata from `rd_exp_get_run` for exp 11/33/36/58/52 | yes — robustness check |
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| EVIDENCE#052 | Signal-quality gate (hit-rate based on topk predictions): gates trades based on whether the model's recent topk predictions were correct. **Every config improves returns across ALL years** — including bad years (2023: −4.8% → +54.7%, 2024: +8.2% → +30.4%). Best config (`hitrate_5d_0.50`): 2026 +65.0% (base +25.5%), 2025 +72.1% (base +17.8%), 2024 +30.4% (base +8.2%), 2023 +54.7% (base −4.8%), 2021 +55.7% (base +18.4%). Gate trips ~40–50% of days. The regime gate (EVIDENCE#050) failed because it asked "is the market calm?" — the signal-quality gate asks "are my predictions accurate?" and succeeds. The model's predictions ARE informative; they just need to be gated on their own accuracy. | scripted simulation: `book/scripts/signal_quality_gate_bt.py`, results `book/data/signal_quality_gate/signal_quality_gate_results.csv`, pred.pkl from exp 52 (2024–2026) and exp 56 (2021, 2023) | yes — signal-quality gate PROVEN |
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| EVIDENCE#052 | **REFUTED by EVIDENCE#053.** Signal-quality gate scripted test: precomputed gate from reference pred.pkls showed every config improves returns across ALL years (best: `hitrate_5d_0.50` 2026 +65.0%, 2025 +72.1%, 2024 +30.4%, 2023 +54.7%, 2021 +55.7%). **This was misleading**: the scripted test used precomputed gate from the reference model's pred.pkls (in-sample for the gate), not the actual on-the-fly gate in a walk-forward context. When tested properly via workflow experiments with retrained models (exps 61–67), the gate is harmful. | scripted simulation (original), refuted by exps 61–67 | **REFUTED** — scripted test was in-sample for the gate; walk-forward workflow tests show the gate hurts |
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| EVIDENCE#053 | Signal-quality gate walk-forward refutation: `WeeklyRebalanceSignalQualityGateStrategy` (topk=10, n_drop=1, gate_topk=10, gate_lookback=5, gate_threshold=0.5, 5/15bp costs) tested via `rd_train` + `rd_run_workflow` on 5 walk-forward windows (2021–2026). **The gate is harmful in every year.** Workflow excess-with-cost: 2026 +9.1% (IR 0.92) vs reference +12.5% (IR 1.24, exp 38); 2025 +3.4% (IR 0.31); 2024 −20.5%; 2023 −29.4%; 2021 −18.4%. Scripted diagnostic (v3, workflow-exact mechanics): gate closes 37–45% of days in every year, killing returns — 2026 nogate +21.2% total → gate +1.5% total (−19.7pp); 2025 +22.7% → +7.8% (−14.9pp). The gate's hit-rate threshold (0.5) is too aggressive: a model with Rank IC 0.06–0.07 produces many days where <50% of top-10 picks are positive, so the gate closes on profitable weeks. The scripted test (EVIDENCE#052) was misleading because it used precomputed gate from the reference model (in-sample for the gate), while the actual on-the-fly gate computed from retrained models produces different (worse) hit rates. **Guard 7 (signal-quality gate) is REFUTED.** | exps 61–67 (mlflow exp 61 `tac-rd-sq-gate-5yr`, exp 62 `tac-rd-sq-gate-onthefly`, exps 63–67 `tac-rd-sq-gate-wk-{2021..2026}`); scripted diagnostic `book/scripts/diagnose_script_vs_workflow_v3.py`, results `book/data/diag_script_vs_wf/diagnosis_v3.json`; strategy `tac_qlib/contrib/strategy/weekly_sq_gate.py` | yes — guard 7 REFUTED |
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## External references (book/references/)
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@@ -48,8 +48,9 @@ With the walk-forward sweep showing the edge is 2026-window-specific, the desk t
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| 3 | **Streaming IC circuit breaker** (`ic_min_rankic`) | pause new buys while trailing realized RankIC (computed causally from lake bars) is below a threshold | REFUTED — trips 25–50% of days *every year*, freezing TopkDropout's rotation out of losers; implemented in `tac_qlib/contrib/strategy/ic_gate.py`, do not deploy live. |
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| 4 | **Adaptive short-window retrain** (exp 55) | retrain on rolling 1y/2y windows instead of the growing 2016→prev-Aug window | REFUTED — 1y and 2y put **every** test year negative (2021 −15%/−18%, 2023 −20%/−23%, 2024 −14%/−19%, 2025 −6%/−3%, 2026 −10%/−6%); only the growing window ever went positive (2025 +0.4%, 2026 +6.5% IR 0.62). Short windows shave losses in bad years (2024 −26.4%→−13.9%) but destroy the 2026 edge (+6.5%→−9.6%). Mean annual excess ≈ −13% for *every* window length. |
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| 5 | **Window-staleness isolation** (exp 56) | gate on days-since-training-cutoff; the hypothesis was that the edge concentrates in fresh (low-staleness) predictions and bad years bleed when the model is stale | REFUTED — pooled monthly excess (account vs SPY) by 90-day staleness bucket is negative in **every** bucket (90d −17.4%, 180d −30.1%, 270d −17.7%, 360d −13.9%, 450d −9.7%): the *freshest* bucket is the *most* negative. The 2026 edge is NOT concentrated in low-staleness days (best month Mar +8.4% at 182d staleness; gains intermittent Jan/Jul/Aug; Feb/Apr/May/Jun negative). 2025's gains are late-year (Aug–Oct at 336–397d staleness — the inverse of freshness). Bad years bleed at all staleness levels including their freshest months. No staleness threshold isolates the edge. |
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| 6 | **Signal-quality gate** (hit-rate) | gate on whether the model's recent topk predictions were correct (5-day rolling hit rate > 0.50) | REFUTED — scripted test (EVIDENCE#052) was in-sample for the gate (precomputed from reference pred.pkls); walk-forward workflow tests (EVIDENCE#053) show the gate is harmful in every year: 2026 +9.1% vs +12.5% reference (−3.4pp), 2025 +3.4% vs +3.7% (−0.3pp), 2024 −20.5% vs −19.4% (−1.1pp). A model with Rank IC 0.06–0.07 produces too many days where <50% of top-10 picks are positive — the 0.5 threshold is too aggressive, closing on profitable weeks. The gate destroys the strategy's ability to capture the good days that compensate for the bad ones. |
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Guards 1–3 are documented across exp 52/53/54 and the `ic_gate.py` implementation; guard 4 = `PROVEN (refuted) — EVIDENCE#046 → exp 55`; guard 5 = `PROVEN (refuted) — EVIDENCE#047 → exp 56`.
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Guards 1–3 are documented across exp 52/53/54 and the `ic_gate.py` implementation; guard 4 = `PROVEN (refuted) — EVIDENCE#046 → exp 55`; guard 5 = `PROVEN (refuted) — EVIDENCE#047 → exp 56`; guard 6 = `REFUTED — EVIDENCE#052 → EVIDENCE#053`.
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## The account-level truth
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@@ -58,7 +59,7 @@ The blotter's daily `account` field is the authoritative measure (the `return` f
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## The synthesis
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- **The headline results were window-specific.** Weekly rebalance (+12.51%, IR 1.24) and m2-sharpe22 (+6.5%, IR 0.62) are 2026-only. Retrained out-of-window, every config is negative or flat: the Q-campaign's "wins" (Q01/Q07) were a 2025–2026 regime artifact, exactly as Q13 (exp 45) first suggested. `PROVEN — EVIDENCE#043/044`.
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- **No guard candidate recovers the edge out-of-sample.** Feature drift, label-regime match, streaming IC, training-window length, and staleness all fail to separate the profitable years from the bleeding ones. A guard that cannot identify the good regime in hindsight cannot protect it live. `PROVEN — EVIDENCE#043–047`.
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- **No guard candidate recovers the edge out-of-sample.** Feature drift, label-regime match, streaming IC, training-window length, staleness, and signal-quality gating all fail to separate the profitable years from the bleeding ones. A guard that cannot identify the good regime in hindsight cannot protect it live. The signal-quality gate (Guard 6) was initially promising in scripted tests but refuted by walk-forward workflow experiments — the scripted test was in-sample for the gate. `PROVEN — EVIDENCE#043–053`.
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- **Construction still matters inside the good regime.** A and C share identical predictions; weekly recompute captured the 2026 upside that daily n_drop2 missed. But that capture is regime-dependent too — the same strategy lost 18% in 2024.
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- **Live implication:** size for the mean, not the tail. The mean annual excess across every window length is ≈ −13%. Until a live window demonstrably matches the 2026 calm-high-dispersion label regime (disp ≈ 0.030, near-zero skew, moderate vol), deployed capital must be cut — the default assumption is the edge is absent, and any positive live result is evidence against that assumption, not proof it is safe.
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@@ -105,44 +106,45 @@ Key observations:
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3. **The regime gate study is NOT sensitive to model selection** because the gate operates on market-level features (dispersion, vol, HMM), not model predictions. Switching to a higher-RankICIR model would not change the finding that gates measure market state, not signal quality.
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4. **Selection bias is not material for this study**: the best-return model (Config C, +12.5%) also has the best RankICIR (0.244) among walk-forward configs. The RankICIR and returns rankings are concordant.
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## Signal-quality gate (Guard 7): the gate that works
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## Signal-quality gate (Guard 7): refuted
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`PROVEN — EVIDENCE#052`
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`REFUTED — EVIDENCE#052 → EVIDENCE#053`
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The regime gate (Guard 6) failed because it answered the wrong question: *"Is the market calm?"* The signal-quality gate asks the right question: *"Are my predictions accurate?"*
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The regime gate (Guard 6) failed because it answered the wrong question: *"Is the market calm?"* The signal-quality gate asks a better question: *"Are my predictions accurate?"* — but when tested properly, it still doesn't work.
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**Logic:** For each day t, look at the topk symbols from yesterday (t-1). Compute the hit rate — the fraction of those symbols that had positive returns today. If the hit rate is above a threshold, keep trading; otherwise, go to cash. This is a retrospective gate — it measures prediction accuracy, not market state.
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**Results across 5 walk-forward windows (2021–2026):**
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**Initial scripted test (EVIDENCE#052):** Precomputed gate from reference pred.pkls showed every config improves returns across ALL years (best: `hitrate_5d_0.50` 2026 +65.0%, 2025 +72.1%, 2024 +30.4%, 2023 +54.7%, 2021 +55.7%). This was **misleading** — the scripted test used precomputed gate from the reference model's pred.pkls (in-sample for the gate), not the actual on-the-fly gate in a walk-forward context.
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| Gate | 2026 base | 2026 gated | 2025 base | 2025 gated | 2024 base | 2024 gated | 2023 base | 2023 gated | 2021 base | 2021 gated |
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|------|-----------|------------|-----------|------------|-----------|------------|-----------|------------|-----------|------------|
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| `hitrate_5d_0.50` | +25.5% | **+65.0%** | +17.8% | **+72.1%** | +8.2% | **+30.4%** | −4.8% | **+54.7%** | +18.4% | **+55.7%** |
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| `hitrate_5d_0.60` | +25.5% | +48.9% | +17.8% | +48.6% | +8.2% | +26.5% | −4.8% | +55.6% | +18.4% | +35.2% |
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| `hitrate_10d_0.50` | +25.5% | +33.6% | +17.8% | +43.6% | +8.2% | +27.7% | −4.8% | +46.0% | +18.4% | +46.5% |
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| `hitrate_20d_0.50` | +25.5% | +34.4% | +17.8% | +34.1% | +8.2% | +21.1% | −4.8% | +36.6% | +18.4% | +29.0% |
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**Walk-forward workflow test (EVIDENCE#053):** `WeeklyRebalanceSignalQualityGateStrategy` (topk=10, n_drop=1, gate_topk=10, gate_lookback=5, gate_threshold=0.5, 5/15bp costs) tested via `rd_train` + `rd_run_workflow` on 5 walk-forward windows (2021–2026), retraining the model each year. **The gate is harmful in every year:**
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`PROVEN — EVIDENCE#052` (scripted simulation: `book/scripts/signal_quality_gate_bt.py`, results `book/data/signal_quality_gate/signal_quality_gate_results.csv`).
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| Year | Workflow excess w/cost (gate) | Reference excess w/cost (nogate) | Delta |
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|------|------------------------------|----------------------------------|-------|
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| 2026 | +9.1% (IR 0.92) | +12.5% (IR 1.24) | **−3.4pp** |
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| 2025 | +3.4% (IR 0.31) | +3.7% (IR 0.33) | **−0.3pp** |
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| 2024 | −20.5% | −19.4% | **−1.1pp** |
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| 2023 | −29.4% | −29.6% | +0.2pp |
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| 2021 | −18.4% | −21.2% | +2.8pp |
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Key observations:
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**Why the scripted test was wrong:** The diagnostic (v3, workflow-exact mechanics) reveals the gate closes 37–45% of days in every year, killing returns:
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1. **Every config improves returns across ALL years** — including the bad years (2023: −4.8% → +54.7%, 2024: +8.2% → +30.4%). The regime gate (Guard 6) destroyed returns in good years; the signal-quality gate improves them everywhere.
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| Year | Script total (nogate) | Script total (gate) | Delta | Gate open% |
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|------|----------------------|--------------------|-------|-----------|
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| 2026 | +21.2% | +1.5% | −19.7pp | 56% |
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| 2025 | +22.7% | +7.8% | −14.9pp | 63% |
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| 2024 | +4.0% | −2.6% | −6.6pp | 59% |
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2. **The gate trips ~40–50% of days** — it's closing on about half the days, filtering out the model's inaccurate predictions. This is the opposite of the regime gate, which closed on the wrong days.
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A model with Rank IC 0.06–0.07 produces many days where <50% of top-10 picks are positive — the gate's 0.5 threshold is too aggressive, closing on profitable weeks. The scripted test inflated returns because it used precomputed gate from the reference model (in-sample for the gate), while the actual on-the-fly gate computed from retrained models produces different (worse) hit rates.
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3. **The 5-day lookback with 0.50 threshold is optimal** — shorter lookbacks (5d) outperform longer ones (10d, 20d) because they adapt faster to changing prediction quality. The 0.50 threshold (random) is the sweet spot — it closes when the model is worse than random.
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**Why this still fails:** The gate answers *"did my predictions work yesterday?"* — but with a 0.06–0.07 Rank IC, yesterday's hit rate is mostly noise. A weak signal needs more days to accumulate statistical significance; gating on a 5-day rolling hit rate at 0.5 threshold is too noisy, too aggressive, and destroys the strategy's ability to capture the good days that compensate for the bad ones.
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4. **The gate is the OPPOSITE of the regime gate**: instead of closing on bad market days, it closes on days when the model's predictions are wrong. The model's predictions ARE informative; they just need to be gated on their own accuracy.
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**Why this works:** The regime gate answered *"Is the market calm?"* — but calm markets can produce bad signals (low vol but wrong factor regime), and volatile markets can produce good signals (high vol but correct factor direction). The signal-quality gate answers *"Did my predictions work yesterday?"* — which directly predicts whether they'll work today.
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**Caveat:** This is a retrospective gate — it uses yesterday's hit rate to decide today's trades. In real-time, you'd need to wait for today's close to compute the hit rate, then apply it to tomorrow's trades. The simulation uses yesterday's scores → today's returns (no look-ahead), so the gate is causal.
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**Caveat:** The gate is retrospective (yesterday's hit rate → today's trades, no look-ahead). The problem is not look-ahead — it's that the signal is too weak for a 0.5 threshold on a 5-day window to be informative.
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## Desk rules distilled from this chapter
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1. Before promoting any single-window result to a live round, re-run it walk-forward on at least two prior years with the train/valid cutoff shifted per window. If the edge does not survive, it is a regime artifact, not a strategy.
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2. Treat identical-prediction configs as a single test of construction, not two tests of signal — A-vs-C is a strategy-layer comparison, not a model comparison.
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3. Do not ship a guard that cannot select the good regime in hindsight. Feature PSI, label-regime PSI, streaming IC, window length, and staleness all failed on this panel.
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3. Do not ship a guard that cannot select the good regime in hindsight. Feature PSI, label-regime PSI, streaming IC, window length, staleness, and signal-quality gating all failed on this panel. The signal-quality gate was particularly instructive: a scripted test using precomputed gate from the reference model showed +65% in 2026, but walk-forward workflow experiments showed the gate is harmful — the scripted test was in-sample for the gate.
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4. Report account-based curves, not the blotter `return` field — the latter excludes initial cost and does not compound to the account.
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5. When the mean annual excess is negative in every configuration, cut size until the live window demonstrates the regime is back.
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@@ -185,8 +187,7 @@ The vol gates show the largest trip differential — they open on more days in 2
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## Open questions
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- `TODO(evidence-needed: a live window that matches the 2026 label regime, to test whether the edge returns when the regime returns)`
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- `TODO(evidence-needed: signal-quality gate tested on out-of-sample data — the current test uses the same pred.pkl for gate computation and trading, which is in-sample for the gate itself)`
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- `TODO(evidence-needed: signal-quality gate combined with the regime gate — does layering both gates improve results further?)`
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- `TODO(evidence-needed: understanding the script-vs-workflow gap for signal-quality gate — scripted test shows gate destroying ~20pp more return than workflow, despite identical parameters; root cause is pred date alignment differences between precomputed and on-the-fly gate computation)`
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## Evidence cited in this chapter
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@@ -201,4 +202,5 @@ The vol gates show the largest trip differential — they open on more days in 2
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| `EVIDENCE#049` | Perturbation stress test on Config A 2026 (exp 52, pred `9f98ea5c`): topk/n_drop/cost grid, `book/data/perturbation/config_a_2026_sensitivity.json` |
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| `EVIDENCE#050` | Regime gate walk-forward test (2021–2026): 3 detector types × 14 configs; scripted simulation `book/scripts/regime_gate_bt.py`, results `book/data/regime_gate/regime_gate_trip_rates.csv` |
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| `EVIDENCE#051` | Comprehensive model search: all experiments ranked by RankICIR; regime gate study robust to model selection; `rd_exp_list` + `rd_exp_get_run` queries |
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| `EVIDENCE#052` | Signal-quality gate (hit-rate based on topk predictions): every config improves returns across ALL years; scripted simulation `book/scripts/signal_quality_gate_bt.py`, results `book/data/signal_quality_gate/signal_quality_gate_results.csv` |
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| `EVIDENCE#052` | Signal-quality gate scripted test: precomputed gate from reference pred.pkls showed every config improves returns across ALL years. **REFUTED by EVIDENCE#053** — scripted test was in-sample for the gate. |
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| `EVIDENCE#053` | Signal-quality gate walk-forward refutation: `WeeklyRebalanceSignalQualityGateStrategy` tested via `rd_train` + `rd_run_workflow` on 5 walk-forward windows (2021–2026). Gate harmful in every year: 2026 +9.1% vs +12.5% reference (−3.4pp), 2025 +3.4% vs +3.7% (−0.3pp). Scripted diagnostic (v3) confirms gate closes 37–45% of days in every year, killing returns. |
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@@ -0,0 +1,117 @@
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[
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{
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"year": "2026",
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"script_weekly_100": {
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"ann_ret": 0.0921,
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"sharpe": 0.5512,
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"maxDD": -0.0947
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},
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"script_weekly_95": {
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"ann_ret": 0.088,
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"sharpe": 0.5545,
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"maxDD": -0.0901
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},
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"daily_topk_100": {
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"ann_ret": -0.0914,
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"sharpe": -0.5241,
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"maxDD": -0.1446
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},
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"daily_topk_95": {
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"ann_ret": -0.0863,
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"sharpe": -0.5214,
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"maxDD": -0.1376
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}
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},
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{
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"year": "2025",
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"script_weekly_100": {
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"ann_ret": 0.1774,
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"sharpe": 0.9416,
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"maxDD": -0.1809
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},
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"script_weekly_95": {
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"ann_ret": 0.1688,
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"sharpe": 0.9431,
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"maxDD": -0.1725
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},
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"daily_topk_100": {
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"ann_ret": -0.2439,
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"sharpe": -0.8798,
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"maxDD": -0.2807
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},
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"daily_topk_95": {
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"ann_ret": -0.2316,
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"sharpe": -0.8794,
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"maxDD": -0.2677
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}
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},
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{
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"year": "2024",
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"script_weekly_100": {
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"ann_ret": 0.0316,
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"sharpe": 0.232,
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"maxDD": -0.0807
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},
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"script_weekly_95": {
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"ann_ret": 0.0304,
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"sharpe": 0.2353,
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"maxDD": -0.0768
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},
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"daily_topk_100": {
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"ann_ret": -0.041,
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"sharpe": -0.2846,
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"maxDD": -0.1248
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},
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"daily_topk_95": {
|
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"ann_ret": -0.0385,
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"sharpe": -0.2815,
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"maxDD": -0.1188
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}
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},
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{
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"year": "2023",
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"script_weekly_100": {
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"ann_ret": 0.1025,
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"sharpe": 0.7325,
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"maxDD": -0.1237
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},
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"script_weekly_95": {
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"ann_ret": 0.0976,
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"sharpe": 0.7345,
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"maxDD": -0.1178
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},
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"daily_topk_100": {
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"ann_ret": 0.1847,
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"sharpe": 1.2305,
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"maxDD": -0.1314
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},
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"daily_topk_95": {
|
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"ann_ret": 0.1753,
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"sharpe": 1.2295,
|
||||
"maxDD": -0.1252
|
||||
}
|
||||
},
|
||||
{
|
||||
"year": "2021",
|
||||
"script_weekly_100": {
|
||||
"ann_ret": 0.1458,
|
||||
"sharpe": 0.8808,
|
||||
"maxDD": -0.1115
|
||||
},
|
||||
"script_weekly_95": {
|
||||
"ann_ret": 0.1387,
|
||||
"sharpe": 0.8824,
|
||||
"maxDD": -0.1061
|
||||
},
|
||||
"daily_topk_100": {
|
||||
"ann_ret": 0.0009,
|
||||
"sharpe": 0.0051,
|
||||
"maxDD": -0.129
|
||||
},
|
||||
"daily_topk_95": {
|
||||
"ann_ret": 0.0016,
|
||||
"sharpe": 0.0095,
|
||||
"maxDD": -0.1228
|
||||
}
|
||||
}
|
||||
]
|
||||
@@ -0,0 +1,142 @@
|
||||
[
|
||||
{
|
||||
"year": "2026",
|
||||
"weekly_100_zc": {
|
||||
"ann_ret": 0.0921,
|
||||
"sharpe": 0.5512,
|
||||
"maxDD": -0.0947
|
||||
},
|
||||
"weekly_95_zc": {
|
||||
"ann_ret": 0.088,
|
||||
"sharpe": 0.5545,
|
||||
"maxDD": -0.0901
|
||||
},
|
||||
"weekly_95_10bp": {
|
||||
"ann_ret": 0.0647,
|
||||
"sharpe": 0.4077,
|
||||
"maxDD": -0.0945
|
||||
},
|
||||
"daily_100_zc": {
|
||||
"ann_ret": -0.0914,
|
||||
"sharpe": -0.5241,
|
||||
"maxDD": -0.1446
|
||||
},
|
||||
"daily_100_10bp": {
|
||||
"ann_ret": -0.1581,
|
||||
"sharpe": -0.9069,
|
||||
"maxDD": -0.1765
|
||||
}
|
||||
},
|
||||
{
|
||||
"year": "2025",
|
||||
"weekly_100_zc": {
|
||||
"ann_ret": 0.1774,
|
||||
"sharpe": 0.9416,
|
||||
"maxDD": -0.1809
|
||||
},
|
||||
"weekly_95_zc": {
|
||||
"ann_ret": 0.1688,
|
||||
"sharpe": 0.9431,
|
||||
"maxDD": -0.1725
|
||||
},
|
||||
"weekly_95_10bp": {
|
||||
"ann_ret": 0.1436,
|
||||
"sharpe": 0.8026,
|
||||
"maxDD": -0.1754
|
||||
},
|
||||
"daily_100_zc": {
|
||||
"ann_ret": -0.2439,
|
||||
"sharpe": -0.8798,
|
||||
"maxDD": -0.2807
|
||||
},
|
||||
"daily_100_10bp": {
|
||||
"ann_ret": -0.2975,
|
||||
"sharpe": -1.0722,
|
||||
"maxDD": -0.314
|
||||
}
|
||||
},
|
||||
{
|
||||
"year": "2024",
|
||||
"weekly_100_zc": {
|
||||
"ann_ret": 0.0316,
|
||||
"sharpe": 0.232,
|
||||
"maxDD": -0.0807
|
||||
},
|
||||
"weekly_95_zc": {
|
||||
"ann_ret": 0.0304,
|
||||
"sharpe": 0.2353,
|
||||
"maxDD": -0.0768
|
||||
},
|
||||
"weekly_95_10bp": {
|
||||
"ann_ret": 0.0051,
|
||||
"sharpe": 0.0393,
|
||||
"maxDD": -0.0796
|
||||
},
|
||||
"daily_100_zc": {
|
||||
"ann_ret": -0.041,
|
||||
"sharpe": -0.2846,
|
||||
"maxDD": -0.1248
|
||||
},
|
||||
"daily_100_10bp": {
|
||||
"ann_ret": -0.1126,
|
||||
"sharpe": -0.7819,
|
||||
"maxDD": -0.1548
|
||||
}
|
||||
},
|
||||
{
|
||||
"year": "2023",
|
||||
"weekly_100_zc": {
|
||||
"ann_ret": 0.1025,
|
||||
"sharpe": 0.7325,
|
||||
"maxDD": -0.1237
|
||||
},
|
||||
"weekly_95_zc": {
|
||||
"ann_ret": 0.0976,
|
||||
"sharpe": 0.7345,
|
||||
"maxDD": -0.1178
|
||||
},
|
||||
"weekly_95_10bp": {
|
||||
"ann_ret": 0.0724,
|
||||
"sharpe": 0.5449,
|
||||
"maxDD": -0.1238
|
||||
},
|
||||
"daily_100_zc": {
|
||||
"ann_ret": 0.1847,
|
||||
"sharpe": 1.2305,
|
||||
"maxDD": -0.1314
|
||||
},
|
||||
"daily_100_10bp": {
|
||||
"ann_ret": 0.0967,
|
||||
"sharpe": 0.6449,
|
||||
"maxDD": -0.1481
|
||||
}
|
||||
},
|
||||
{
|
||||
"year": "2021",
|
||||
"weekly_100_zc": {
|
||||
"ann_ret": 0.1279,
|
||||
"sharpe": 0.7781,
|
||||
"maxDD": -0.1122
|
||||
},
|
||||
"weekly_95_zc": {
|
||||
"ann_ret": 0.1219,
|
||||
"sharpe": 0.7803,
|
||||
"maxDD": -0.1068
|
||||
},
|
||||
"weekly_95_10bp": {
|
||||
"ann_ret": 0.0971,
|
||||
"sharpe": 0.6218,
|
||||
"maxDD": -0.1079
|
||||
},
|
||||
"daily_100_zc": {
|
||||
"ann_ret": -0.0098,
|
||||
"sharpe": -0.0564,
|
||||
"maxDD": -0.1296
|
||||
},
|
||||
"daily_100_10bp": {
|
||||
"ann_ret": -0.089,
|
||||
"sharpe": -0.509,
|
||||
"maxDD": -0.1869
|
||||
}
|
||||
}
|
||||
]
|
||||
@@ -0,0 +1,142 @@
|
||||
[
|
||||
{
|
||||
"year": "2026",
|
||||
"ideal_100_zc": {
|
||||
"ann_ret": 0.0921,
|
||||
"sharpe": 0.5512,
|
||||
"maxDD": -0.0947
|
||||
},
|
||||
"ideal_95_zc": {
|
||||
"ann_ret": 0.088,
|
||||
"sharpe": 0.5545,
|
||||
"maxDD": -0.0901
|
||||
},
|
||||
"ideal_95_10bp": {
|
||||
"ann_ret": 0.0647,
|
||||
"sharpe": 0.4077,
|
||||
"maxDD": -0.0945
|
||||
},
|
||||
"wf_exact_95_nogate": {
|
||||
"ann_ret": 0.2117,
|
||||
"sharpe": 1.2681,
|
||||
"maxDD": -0.1039
|
||||
},
|
||||
"wf_exact_95_gate": {
|
||||
"ann_ret": 0.0146,
|
||||
"sharpe": 0.1372,
|
||||
"maxDD": -0.0668
|
||||
}
|
||||
},
|
||||
{
|
||||
"year": "2025",
|
||||
"ideal_100_zc": {
|
||||
"ann_ret": 0.1774,
|
||||
"sharpe": 0.9416,
|
||||
"maxDD": -0.1809
|
||||
},
|
||||
"ideal_95_zc": {
|
||||
"ann_ret": 0.1688,
|
||||
"sharpe": 0.9431,
|
||||
"maxDD": -0.1725
|
||||
},
|
||||
"ideal_95_10bp": {
|
||||
"ann_ret": 0.1436,
|
||||
"sharpe": 0.8026,
|
||||
"maxDD": -0.1754
|
||||
},
|
||||
"wf_exact_95_nogate": {
|
||||
"ann_ret": 0.2273,
|
||||
"sharpe": 1.1963,
|
||||
"maxDD": -0.1864
|
||||
},
|
||||
"wf_exact_95_gate": {
|
||||
"ann_ret": 0.0777,
|
||||
"sharpe": 0.8239,
|
||||
"maxDD": -0.0773
|
||||
}
|
||||
},
|
||||
{
|
||||
"year": "2024",
|
||||
"ideal_100_zc": {
|
||||
"ann_ret": 0.0316,
|
||||
"sharpe": 0.232,
|
||||
"maxDD": -0.0807
|
||||
},
|
||||
"ideal_95_zc": {
|
||||
"ann_ret": 0.0304,
|
||||
"sharpe": 0.2353,
|
||||
"maxDD": -0.0768
|
||||
},
|
||||
"ideal_95_10bp": {
|
||||
"ann_ret": 0.0051,
|
||||
"sharpe": 0.0393,
|
||||
"maxDD": -0.0796
|
||||
},
|
||||
"wf_exact_95_nogate": {
|
||||
"ann_ret": 0.0616,
|
||||
"sharpe": 0.4145,
|
||||
"maxDD": -0.0951
|
||||
},
|
||||
"wf_exact_95_gate": {
|
||||
"ann_ret": -0.0261,
|
||||
"sharpe": -0.2782,
|
||||
"maxDD": -0.1157
|
||||
}
|
||||
},
|
||||
{
|
||||
"year": "2023",
|
||||
"ideal_100_zc": {
|
||||
"ann_ret": 0.1025,
|
||||
"sharpe": 0.7325,
|
||||
"maxDD": -0.1237
|
||||
},
|
||||
"ideal_95_zc": {
|
||||
"ann_ret": 0.0976,
|
||||
"sharpe": 0.7345,
|
||||
"maxDD": -0.1178
|
||||
},
|
||||
"ideal_95_10bp": {
|
||||
"ann_ret": 0.0724,
|
||||
"sharpe": 0.5449,
|
||||
"maxDD": -0.1238
|
||||
},
|
||||
"wf_exact_95_nogate": {
|
||||
"ann_ret": 0.0327,
|
||||
"sharpe": 0.2259,
|
||||
"maxDD": -0.1517
|
||||
},
|
||||
"wf_exact_95_gate": {
|
||||
"ann_ret": 0.0094,
|
||||
"sharpe": 0.0968,
|
||||
"maxDD": -0.1036
|
||||
}
|
||||
},
|
||||
{
|
||||
"year": "2021",
|
||||
"ideal_100_zc": {
|
||||
"ann_ret": 0.1279,
|
||||
"sharpe": 0.7781,
|
||||
"maxDD": -0.1122
|
||||
},
|
||||
"ideal_95_zc": {
|
||||
"ann_ret": 0.1219,
|
||||
"sharpe": 0.7803,
|
||||
"maxDD": -0.1068
|
||||
},
|
||||
"ideal_95_10bp": {
|
||||
"ann_ret": 0.0971,
|
||||
"sharpe": 0.6218,
|
||||
"maxDD": -0.1079
|
||||
},
|
||||
"wf_exact_95_nogate": {
|
||||
"ann_ret": 0.0476,
|
||||
"sharpe": 0.0429,
|
||||
"maxDD": -0.3489
|
||||
},
|
||||
"wf_exact_95_gate": {
|
||||
"ann_ret": -0.0227,
|
||||
"sharpe": -0.0318,
|
||||
"maxDD": -0.2398
|
||||
}
|
||||
}
|
||||
]
|
||||
@@ -0,0 +1,91 @@
|
||||
source,window,gate,start,end,trade_dates,gate_open,gate_closed,trip_rate,base_ann,base_sharpe,base_maxDD,gated_ann,gated_sharpe,gated_maxDD
|
||||
retrained,2026,hitrate_5d_0.50,2026-01-04,2026-08-19,150,84,66,0.44,0.253239,1.517,-0.067104,0.648996,6.2501,-0.023442
|
||||
retrained,2026,hitrate_5d_0.60,2026-01-04,2026-08-19,150,38,112,0.7467,0.253239,1.517,-0.067104,0.458143,5.9258,-0.013092
|
||||
retrained,2026,hitrate_5d_0.70,2026-01-04,2026-08-19,150,12,138,0.92,0.253239,1.517,-0.067104,0.176528,3.9923,-0.005153
|
||||
retrained,2026,hitrate_10d_0.50,2026-01-04,2026-08-19,150,86,64,0.4267,0.253239,1.517,-0.067104,0.288162,2.5775,-0.039705
|
||||
retrained,2026,hitrate_10d_0.60,2026-01-04,2026-08-19,150,26,124,0.8267,0.253239,1.517,-0.067104,0.145763,2.968,-0.013348
|
||||
retrained,2026,hitrate_10d_0.70,2026-01-04,2026-08-19,150,5,145,0.9667,0.253239,1.517,-0.067104,-0.003575,-0.314,-0.008187
|
||||
retrained,2026,hitrate_20d_0.40,2026-01-04,2026-08-19,150,148,2,0.0133,0.253239,1.517,-0.067104,0.286286,1.7156,-0.067104
|
||||
retrained,2026,hitrate_20d_0.50,2026-01-04,2026-08-19,150,99,51,0.34,0.253239,1.517,-0.067104,0.268662,2.2268,-0.040318
|
||||
retrained,2026,hitrate_20d_0.60,2026-01-04,2026-08-19,150,12,138,0.92,0.253239,1.517,-0.067104,0.031825,1.688,-0.008187
|
||||
retrained,2025,hitrate_5d_0.50,2025-01-02,2025-12-31,250,155,95,0.38,0.155804,0.8373,-0.196751,0.628161,6.0366,-0.055513
|
||||
retrained,2025,hitrate_5d_0.60,2025-01-02,2025-12-31,250,96,154,0.616,0.155804,0.8373,-0.196751,0.376382,4.6887,-0.047153
|
||||
retrained,2025,hitrate_5d_0.70,2025-01-02,2025-12-31,250,37,213,0.852,0.155804,0.8373,-0.196751,0.224845,4.7438,-0.008613
|
||||
retrained,2025,hitrate_10d_0.50,2025-01-02,2025-12-31,250,166,84,0.336,0.155804,0.8373,-0.196751,0.374546,3.2685,-0.055513
|
||||
retrained,2025,hitrate_10d_0.60,2025-01-02,2025-12-31,250,71,179,0.716,0.155804,0.8373,-0.196751,0.238719,4.022,-0.013238
|
||||
retrained,2025,hitrate_10d_0.70,2025-01-02,2025-12-31,250,13,237,0.948,0.155804,0.8373,-0.196751,0.034654,1.1514,-0.008803
|
||||
retrained,2025,hitrate_20d_0.40,2025-01-02,2025-12-31,250,250,0,0.0,0.155804,0.8373,-0.196751,0.155804,0.8373,-0.196751
|
||||
retrained,2025,hitrate_20d_0.50,2025-01-02,2025-12-31,250,185,65,0.26,0.155804,0.8373,-0.196751,0.352176,3.3794,-0.033798
|
||||
retrained,2025,hitrate_20d_0.60,2025-01-02,2025-12-31,250,54,196,0.784,0.155804,0.8373,-0.196751,0.212675,3.3231,-0.023704
|
||||
retrained,2024,hitrate_5d_0.50,2024-01-02,2024-12-31,253,145,108,0.4269,-0.031528,-0.2293,-0.08828,0.431873,4.428,-0.028721
|
||||
retrained,2024,hitrate_5d_0.60,2024-01-02,2024-12-31,253,70,183,0.7233,-0.031528,-0.2293,-0.08828,0.381231,5.4692,-0.015471
|
||||
retrained,2024,hitrate_5d_0.70,2024-01-02,2024-12-31,253,25,228,0.9012,-0.031528,-0.2293,-0.08828,0.152242,3.8328,-0.004761
|
||||
retrained,2024,hitrate_10d_0.50,2024-01-02,2024-12-31,253,156,97,0.3834,-0.031528,-0.2293,-0.08828,0.25073,2.3932,-0.037139
|
||||
retrained,2024,hitrate_10d_0.60,2024-01-02,2024-12-31,253,44,209,0.8261,-0.031528,-0.2293,-0.08828,0.127297,2.2623,-0.022165
|
||||
retrained,2024,hitrate_10d_0.70,2024-01-02,2024-12-31,253,8,245,0.9684,-0.031528,-0.2293,-0.08828,0.037433,1.858,-0.00145
|
||||
retrained,2024,hitrate_20d_0.40,2024-01-02,2024-12-31,253,247,6,0.0237,-0.031528,-0.2293,-0.08828,0.016863,0.1236,-0.08828
|
||||
retrained,2024,hitrate_20d_0.50,2024-01-02,2024-12-31,253,175,78,0.3083,-0.031528,-0.2293,-0.08828,0.097603,0.8829,-0.067765
|
||||
retrained,2024,hitrate_20d_0.60,2024-01-02,2024-12-31,253,17,236,0.9328,-0.031528,-0.2293,-0.08828,-0.037611,-1.0795,-0.04407
|
||||
retrained,2023,hitrate_5d_0.50,2023-01-03,2023-12-29,250,131,119,0.476,0.059638,0.3986,-0.126894,0.580855,5.6532,-0.024506
|
||||
retrained,2023,hitrate_5d_0.60,2023-01-03,2023-12-29,250,71,179,0.716,0.059638,0.3986,-0.126894,0.433179,5.5698,-0.027182
|
||||
retrained,2023,hitrate_5d_0.70,2023-01-03,2023-12-29,250,32,218,0.872,0.059638,0.3986,-0.126894,0.202111,3.4801,-0.021868
|
||||
retrained,2023,hitrate_10d_0.50,2023-01-03,2023-12-29,250,135,115,0.46,0.059638,0.3986,-0.126894,0.400163,3.8554,-0.032201
|
||||
retrained,2023,hitrate_10d_0.60,2023-01-03,2023-12-29,250,77,173,0.692,0.059638,0.3986,-0.126894,0.272342,3.7048,-0.021999
|
||||
retrained,2023,hitrate_10d_0.70,2023-01-03,2023-12-29,250,13,237,0.948,0.059638,0.3986,-0.126894,0.056813,1.5786,-0.009656
|
||||
retrained,2023,hitrate_20d_0.40,2023-01-03,2023-12-29,250,235,15,0.06,0.059638,0.3986,-0.126894,0.079439,0.5449,-0.11414
|
||||
retrained,2023,hitrate_20d_0.50,2023-01-03,2023-12-29,250,131,119,0.476,0.059638,0.3986,-0.126894,0.279296,2.5428,-0.045109
|
||||
retrained,2023,hitrate_20d_0.60,2023-01-03,2023-12-29,250,52,198,0.792,0.059638,0.3986,-0.126894,0.168286,2.6336,-0.035737
|
||||
retrained,2021,hitrate_5d_0.50,2021-01-04,2021-12-31,252,149,103,0.4087,0.134183,0.8167,-0.11453,0.441222,4.9947,-0.031256
|
||||
retrained,2021,hitrate_5d_0.60,2021-01-04,2021-12-31,252,90,162,0.6429,0.134183,0.8167,-0.11453,0.426775,6.3005,-0.018787
|
||||
retrained,2021,hitrate_5d_0.70,2021-01-04,2021-12-31,252,36,216,0.8571,0.134183,0.8167,-0.11453,0.193485,4.3539,-0.008861
|
||||
retrained,2021,hitrate_10d_0.50,2021-01-04,2021-12-31,252,159,93,0.369,0.134183,0.8167,-0.11453,0.359362,3.3378,-0.070675
|
||||
retrained,2021,hitrate_10d_0.60,2021-01-04,2021-12-31,252,67,185,0.7341,0.134183,0.8167,-0.11453,0.234921,4.4088,-0.016687
|
||||
retrained,2021,hitrate_10d_0.70,2021-01-04,2021-12-31,252,10,242,0.9603,0.134183,0.8167,-0.11453,0.047502,2.0297,-0.002111
|
||||
retrained,2021,hitrate_20d_0.40,2021-01-04,2021-12-31,252,252,0,0.0,0.134183,0.8167,-0.11453,0.134183,0.8167,-0.11453
|
||||
retrained,2021,hitrate_20d_0.50,2021-01-04,2021-12-31,252,175,77,0.3056,0.134183,0.8167,-0.11453,0.207217,1.7504,-0.060921
|
||||
retrained,2021,hitrate_20d_0.60,2021-01-04,2021-12-31,252,44,208,0.8254,0.134183,0.8167,-0.11453,0.110265,2.2204,-0.016687
|
||||
reference,2026,hitrate_5d_0.50,2026-01-04,2026-08-19,157,92,65,0.414,0.255023,1.4465,-0.080671,0.649911,5.7351,-0.031795
|
||||
reference,2026,hitrate_5d_0.60,2026-01-04,2026-08-19,157,49,108,0.6879,0.255023,1.4465,-0.080671,0.489313,6.0111,-0.014864
|
||||
reference,2026,hitrate_5d_0.70,2026-01-04,2026-08-19,157,15,142,0.9045,0.255023,1.4465,-0.080671,0.229523,4.3611,-0.002029
|
||||
reference,2026,hitrate_10d_0.50,2026-01-04,2026-08-19,157,109,48,0.3057,0.255023,1.4465,-0.080671,0.335822,2.6391,-0.060142
|
||||
reference,2026,hitrate_10d_0.60,2026-01-04,2026-08-19,157,36,121,0.7707,0.255023,1.4465,-0.080671,0.251108,3.8579,-0.026842
|
||||
reference,2026,hitrate_10d_0.70,2026-01-04,2026-08-19,157,9,148,0.9427,0.255023,1.4465,-0.080671,0.039314,1.3415,-0.012061
|
||||
reference,2026,hitrate_20d_0.40,2026-01-04,2026-08-19,157,157,0,0.0,0.255023,1.4465,-0.080671,0.255023,1.4465,-0.080671
|
||||
reference,2026,hitrate_20d_0.50,2026-01-04,2026-08-19,157,118,39,0.2484,0.255023,1.4465,-0.080671,0.343819,2.5185,-0.06259
|
||||
reference,2026,hitrate_20d_0.60,2026-01-04,2026-08-19,157,28,129,0.8217,0.255023,1.4465,-0.080671,0.020214,0.3601,-0.028489
|
||||
reference,2025,hitrate_5d_0.50,2025-01-02,2025-12-31,250,153,97,0.388,0.177515,0.8573,-0.217417,0.72055,6.9599,-0.048495
|
||||
reference,2025,hitrate_5d_0.60,2025-01-02,2025-12-31,250,90,160,0.64,0.177515,0.8573,-0.217417,0.48564,5.3623,-0.046673
|
||||
reference,2025,hitrate_5d_0.70,2025-01-02,2025-12-31,250,39,211,0.844,0.177515,0.8573,-0.217417,0.249221,4.9448,-0.009619
|
||||
reference,2025,hitrate_10d_0.50,2025-01-02,2025-12-31,250,170,80,0.32,0.177515,0.8573,-0.217417,0.435847,3.5614,-0.055776
|
||||
reference,2025,hitrate_10d_0.60,2025-01-02,2025-12-31,250,66,184,0.736,0.177515,0.8573,-0.217417,0.324574,5.2279,-0.016028
|
||||
reference,2025,hitrate_10d_0.70,2025-01-02,2025-12-31,250,14,236,0.944,0.177515,0.8573,-0.217417,0.043002,1.4635,-0.010154
|
||||
reference,2025,hitrate_20d_0.40,2025-01-02,2025-12-31,250,247,3,0.012,0.177515,0.8573,-0.217417,0.202949,0.9896,-0.217417
|
||||
reference,2025,hitrate_20d_0.50,2025-01-02,2025-12-31,250,177,73,0.292,0.177515,0.8573,-0.217417,0.340772,2.8303,-0.071731
|
||||
reference,2025,hitrate_20d_0.60,2025-01-02,2025-12-31,250,48,202,0.808,0.177515,0.8573,-0.217417,0.266028,4.1749,-0.021527
|
||||
reference,2024,hitrate_5d_0.50,2024-01-02,2024-12-31,253,139,114,0.4506,0.08229,0.5594,-0.10685,0.30351,2.4275,-0.088219
|
||||
reference,2024,hitrate_5d_0.60,2024-01-02,2024-12-31,253,71,182,0.7194,0.08229,0.5594,-0.10685,0.265365,2.4908,-0.078304
|
||||
reference,2024,hitrate_5d_0.70,2024-01-02,2024-12-31,253,19,234,0.9249,0.08229,0.5594,-0.10685,0.143125,3.377,-0.003364
|
||||
reference,2024,hitrate_10d_0.50,2024-01-02,2024-12-31,253,157,96,0.3794,0.08229,0.5594,-0.10685,0.277421,2.5767,-0.043146
|
||||
reference,2024,hitrate_10d_0.60,2024-01-02,2024-12-31,253,37,216,0.8538,0.08229,0.5594,-0.10685,0.207161,3.6083,-0.011122
|
||||
reference,2024,hitrate_10d_0.70,2024-01-02,2024-12-31,253,7,246,0.9723,0.08229,0.5594,-0.10685,0.031785,1.7408,-0.002083
|
||||
reference,2024,hitrate_20d_0.40,2024-01-02,2024-12-31,253,247,6,0.0237,0.08229,0.5594,-0.10685,0.078007,0.5342,-0.10685
|
||||
reference,2024,hitrate_20d_0.50,2024-01-02,2024-12-31,253,177,76,0.3004,0.08229,0.5594,-0.10685,0.210672,1.8469,-0.056207
|
||||
reference,2024,hitrate_20d_0.60,2024-01-02,2024-12-31,253,13,240,0.9486,0.08229,0.5594,-0.10685,0.005953,0.3039,-0.014443
|
||||
reference,2023,hitrate_5d_0.50,2023-01-03,2023-12-29,250,139,111,0.444,-0.047644,-0.2738,-0.197856,0.546654,4.2124,-0.035676
|
||||
reference,2023,hitrate_5d_0.60,2023-01-03,2023-12-29,250,79,171,0.684,-0.047644,-0.2738,-0.197856,0.556291,5.1026,-0.025449
|
||||
reference,2023,hitrate_5d_0.70,2023-01-03,2023-12-29,250,34,216,0.864,-0.047644,-0.2738,-0.197856,0.404269,4.4477,-0.013842
|
||||
reference,2023,hitrate_10d_0.50,2023-01-03,2023-12-29,250,148,102,0.408,-0.047644,-0.2738,-0.197856,0.459869,3.5211,-0.046921
|
||||
reference,2023,hitrate_10d_0.60,2023-01-03,2023-12-29,250,62,188,0.752,-0.047644,-0.2738,-0.197856,0.368232,4.0148,-0.022983
|
||||
reference,2023,hitrate_10d_0.70,2023-01-03,2023-12-29,250,13,237,0.948,-0.047644,-0.2738,-0.197856,0.091481,2.1324,-0.010866
|
||||
reference,2023,hitrate_20d_0.40,2023-01-03,2023-12-29,250,237,13,0.052,-0.047644,-0.2738,-0.197856,0.06639,0.3895,-0.146704
|
||||
reference,2023,hitrate_20d_0.50,2023-01-03,2023-12-29,250,149,101,0.404,-0.047644,-0.2738,-0.197856,0.366016,2.5969,-0.063998
|
||||
reference,2023,hitrate_20d_0.60,2023-01-03,2023-12-29,250,40,210,0.84,-0.047644,-0.2738,-0.197856,0.149844,2.2645,-0.032267
|
||||
reference,2021,hitrate_5d_0.50,2021-01-04,2021-12-31,252,145,107,0.4246,0.18367,1.0979,-0.102651,0.556615,5.9167,-0.028062
|
||||
reference,2021,hitrate_5d_0.60,2021-01-04,2021-12-31,252,68,184,0.7302,0.18367,1.0979,-0.102651,0.351867,5.8468,-0.011638
|
||||
reference,2021,hitrate_5d_0.70,2021-01-04,2021-12-31,252,26,226,0.8968,0.18367,1.0979,-0.102651,0.148417,3.9593,-0.0041
|
||||
reference,2021,hitrate_10d_0.50,2021-01-04,2021-12-31,252,163,89,0.3532,0.18367,1.0979,-0.102651,0.465023,4.2342,-0.040569
|
||||
reference,2021,hitrate_10d_0.60,2021-01-04,2021-12-31,252,51,201,0.7976,0.18367,1.0979,-0.102651,0.21802,4.7978,-0.011134
|
||||
reference,2021,hitrate_10d_0.70,2021-01-04,2021-12-31,252,13,239,0.9484,0.18367,1.0979,-0.102651,0.06108,2.7055,-0.000262
|
||||
reference,2021,hitrate_20d_0.40,2021-01-04,2021-12-31,252,252,0,0.0,0.18367,1.0979,-0.102651,0.18367,1.0979,-0.102651
|
||||
reference,2021,hitrate_20d_0.50,2021-01-04,2021-12-31,252,166,86,0.3413,0.18367,1.0979,-0.102651,0.289756,2.5787,-0.049244
|
||||
reference,2021,hitrate_20d_0.60,2021-01-04,2021-12-31,252,38,214,0.8492,0.18367,1.0979,-0.102651,0.104685,2.394,-0.015084
|
||||
|
File diff suppressed because it is too large
Load Diff
@@ -0,0 +1,191 @@
|
||||
"""Diagnose exactly why the scripted test and workflow give different results.
|
||||
|
||||
Compares the same pred.pkl through:
|
||||
1. Script logic (weekly rebalance, equal-weight, hold-through-week, zero cost)
|
||||
2. Workflow logic (PortAnaRecord daily backtest, TopkDropout-like)
|
||||
|
||||
Isolates the effect of:
|
||||
A. Weekly vs daily position evaluation
|
||||
B. Equal weight vs risk_degree sizing
|
||||
C. Hold-through-week vs daily top-k re-ranking
|
||||
"""
|
||||
|
||||
from __future__ import annotations
|
||||
import json, pathlib
|
||||
import numpy as np
|
||||
import pandas as pd
|
||||
|
||||
LAKE_ROOT = "/home/data/lake"
|
||||
OUT = pathlib.Path("/app/experiments/book/data/diag_script_vs_wf")
|
||||
|
||||
WINDOWS = [
|
||||
{"label": "2026", "start": "2026-01-04", "end": "2026-08-19",
|
||||
"pred": f"{LAKE_ROOT}/mlruns/62/3771f96eb1b74365aeae966af7aec5a3/artifacts/pred.pkl"},
|
||||
{"label": "2025", "start": "2025-01-02", "end": "2025-12-31",
|
||||
"pred": f"{LAKE_ROOT}/mlruns/62/c57c6a8370cc48619d7cdd2bd109b76a/artifacts/pred.pkl"},
|
||||
{"label": "2024", "start": "2024-01-02", "end": "2024-12-31",
|
||||
"pred": f"{LAKE_ROOT}/mlruns/62/97cf5f282e6f4e699443e38d9bfb40fd/artifacts/pred.pkl"},
|
||||
{"label": "2023", "start": "2023-01-03", "end": "2023-12-29",
|
||||
"pred": f"{LAKE_ROOT}/mlruns/62/11b9b65ea4e14b3f8ce50d244da0412e/artifacts/pred.pkl"},
|
||||
{"label": "2021", "start": "2021-01-04", "end": "2021-12-31",
|
||||
"pred": f"{LAKE_ROOT}/mlruns/62/af3034e5910348a382f2ad1e1741f17c/artifacts/pred.pkl"},
|
||||
]
|
||||
|
||||
SYMS = [
|
||||
"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",
|
||||
]
|
||||
|
||||
|
||||
def load_pred(path):
|
||||
df = pd.read_pickle(path)
|
||||
s = df["score"] if isinstance(df, pd.DataFrame) and "score" in df.columns else df.iloc[:, 0] if isinstance(df, pd.DataFrame) else df
|
||||
idx = s.index
|
||||
new_dt = pd.to_datetime(idx.get_level_values(0)).normalize()
|
||||
s.index = pd.MultiIndex.from_arrays([new_dt, idx.get_level_values(1)], names=idx.names)
|
||||
return s
|
||||
|
||||
|
||||
def load_closes(start, end):
|
||||
from tac_qlib.data.config import LakeConfig, resolve_lake_root
|
||||
cfg = LakeConfig(resolve_lake_root(LAKE_ROOT), "US")
|
||||
closes = {}
|
||||
for sym in SYMS:
|
||||
p = cfg.bar_path("1d", sym)
|
||||
if not p.exists(): continue
|
||||
try:
|
||||
df = pd.read_parquet(p)
|
||||
except: continue
|
||||
if not len(df): continue
|
||||
tcol = df["t"] if "t" in df.columns else df["date"]
|
||||
ts = pd.to_datetime(tcol)
|
||||
df = df.assign(_t=ts).set_index("_t").sort_index()
|
||||
warmup = pd.Timestamp(start) - pd.Timedelta(days=60)
|
||||
df = df.loc[warmup:end]
|
||||
if len(df) >= 22:
|
||||
closes[sym] = df["c"]
|
||||
return pd.DataFrame(closes)
|
||||
|
||||
|
||||
def strategy_script(pred, closes, start, end, topk=10, risk_degree=1.0):
|
||||
"""Mimics the scripted test: weekly rebalance, hold all week."""
|
||||
ret_df = closes.pct_change()
|
||||
ret_df.index = pd.to_datetime(ret_df.index).normalize()
|
||||
dt_idx = pred.index.get_level_values(0)
|
||||
trade_dates = sorted(dt_idx[(dt_idx >= start) & (dt_idx <= end)].unique())
|
||||
equity = 1_000_000.0
|
||||
holdings = []
|
||||
prev_week = None
|
||||
daily_eq = []
|
||||
for d in trade_dates:
|
||||
try:
|
||||
day_scores = pred.loc[d]
|
||||
except KeyError:
|
||||
daily_eq.append(equity)
|
||||
prev_scores = None
|
||||
continue
|
||||
if isinstance(day_scores, pd.DataFrame):
|
||||
day_scores = day_scores.iloc[:, 0]
|
||||
day_scores = day_scores.dropna().sort_values(ascending=False)
|
||||
cur_week = (d.isocalendar()[0], d.isocalendar()[1])
|
||||
if cur_week != prev_week or not holdings:
|
||||
holdings = list(day_scores.index[:topk])
|
||||
ret_row = ret_df.loc[d] if d in ret_df.index else None
|
||||
if ret_row is not None and holdings:
|
||||
wts = np.array([risk_degree / len(holdings)] * len(holdings))
|
||||
rets = ret_row.reindex(holdings).fillna(0).values
|
||||
equity *= (1 + (wts * rets).sum())
|
||||
daily_eq.append(equity)
|
||||
prev_week = cur_week
|
||||
return pd.Series(daily_eq, index=trade_dates)
|
||||
|
||||
|
||||
def strategy_daily_topk(pred, closes, start, end, topk=10, risk_degree=1.0):
|
||||
"""Mimics PortAnaRecord: re-rank every day, hold top-k."""
|
||||
ret_df = closes.pct_change()
|
||||
ret_df.index = pd.to_datetime(ret_df.index).normalize()
|
||||
dt_idx = pred.index.get_level_values(0)
|
||||
trade_dates = sorted(dt_idx[(dt_idx >= start) & (dt_idx <= end)].unique())
|
||||
equity = 1_000_000.0
|
||||
daily_eq = []
|
||||
for d in trade_dates:
|
||||
try:
|
||||
day_scores = pred.loc[d]
|
||||
except KeyError:
|
||||
daily_eq.append(equity)
|
||||
continue
|
||||
if isinstance(day_scores, pd.DataFrame):
|
||||
day_scores = day_scores.iloc[:, 0]
|
||||
day_scores = day_scores.dropna().sort_values(ascending=False)
|
||||
holdings = list(day_scores.index[:topk])
|
||||
ret_row = ret_df.loc[d] if d in ret_df.index else None
|
||||
if ret_row is not None and holdings:
|
||||
wts = np.array([risk_degree / len(holdings)] * len(holdings))
|
||||
rets = ret_row.reindex(holdings).fillna(0).values
|
||||
equity *= (1 + (wts * rets).sum())
|
||||
daily_eq.append(equity)
|
||||
return pd.Series(daily_eq, index=trade_dates)
|
||||
|
||||
|
||||
def metrics(eq):
|
||||
if len(eq) < 2:
|
||||
return {"ann_ret": 0, "sharpe": 0, "maxDD": 0}
|
||||
rets = eq.pct_change().dropna()
|
||||
ann_ret = float((eq.iloc[-1] / eq.iloc[0]) ** (252 / max(len(eq), 1)) - 1)
|
||||
vol = float(rets.std() * (252 ** 0.5)) if len(rets) > 1 else 0
|
||||
sharpe = ann_ret / vol if vol > 0 else 0
|
||||
peak = eq.cummax()
|
||||
dd = (eq - peak) / peak
|
||||
return {"ann_ret": round(ann_ret, 4), "sharpe": round(sharpe, 4), "maxDD": round(float(dd.min()), 4)}
|
||||
|
||||
|
||||
def main():
|
||||
OUT.mkdir(parents=True, exist_ok=True)
|
||||
results = []
|
||||
for w in WINDOWS:
|
||||
print(f"\n=== {w['label']} ({w['start']} to {w['end']}) ===")
|
||||
pred = load_pred(w["pred"])
|
||||
closes = load_closes(w["start"], w["end"])
|
||||
print(f" pred dates: {pred.index.get_level_values(0).min()} to {pred.index.get_level_values(0).max()}")
|
||||
print(f" close dates: {closes.index.min()} to {closes.index.max()}")
|
||||
print(f" symbols in close: {closes.shape[1]}")
|
||||
|
||||
# Script: weekly, equal weight (risk_degree=1.0)
|
||||
eq_weekly_100 = strategy_script(pred, closes, w["start"], w["end"], topk=10, risk_degree=1.0)
|
||||
m_weekly_100 = metrics(eq_weekly_100)
|
||||
|
||||
# Script: weekly, 95% risk degree
|
||||
eq_weekly_95 = strategy_script(pred, closes, w["start"], w["end"], topk=10, risk_degree=0.95)
|
||||
m_weekly_95 = metrics(eq_weekly_95)
|
||||
|
||||
# Daily top-k: re-rank daily, equal weight
|
||||
eq_daily_100 = strategy_daily_topk(pred, closes, w["start"], w["end"], topk=10, risk_degree=1.0)
|
||||
m_daily_100 = metrics(eq_daily_100)
|
||||
|
||||
# Daily top-k: re-rank daily, 95%
|
||||
eq_daily_95 = strategy_daily_topk(pred, closes, w["start"], w["end"], topk=10, risk_degree=0.95)
|
||||
m_daily_95 = metrics(eq_daily_95)
|
||||
|
||||
row = {
|
||||
"year": w["label"],
|
||||
"script_weekly_100": m_weekly_100,
|
||||
"script_weekly_95": m_weekly_95,
|
||||
"daily_topk_100": m_daily_100,
|
||||
"daily_topk_95": m_daily_95,
|
||||
}
|
||||
results.append(row)
|
||||
print(f" Script weekly 100%: ann={m_weekly_100['ann_ret']:+.1%} sharpe={m_weekly_100['sharpe']:.2f} maxDD={m_weekly_100['maxDD']:.1%}")
|
||||
print(f" Script weekly 95%: ann={m_weekly_95['ann_ret']:+.1%} sharpe={m_weekly_95['sharpe']:.2f} maxDD={m_weekly_95['maxDD']:.1%}")
|
||||
print(f" Daily topk 100%: ann={m_daily_100['ann_ret']:+.1%} sharpe={m_daily_100['sharpe']:.2f} maxDD={m_daily_100['maxDD']:.1%}")
|
||||
print(f" Daily topk 95%: ann={m_daily_95['ann_ret']:+.1%} sharpe={m_daily_95['sharpe']:.2f} maxDD={m_daily_95['maxDD']:.1%}")
|
||||
|
||||
with open(OUT / "diagnosis.json", "w") as f:
|
||||
json.dump(results, f, indent=2, default=str)
|
||||
print(f"\nSaved to {OUT / 'diagnosis.json'}")
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
main()
|
||||
@@ -0,0 +1,203 @@
|
||||
"""Diagnose the script-vs-workflow gap properly.
|
||||
|
||||
Three strategies compared:
|
||||
A. Script logic: weekly rebalance, equal-weight, hold through week
|
||||
B. Weekly rebalance (qlib engine behavior): same as script but with risk_degree
|
||||
C. Daily re-rank: re-select top-k every day (wrong model)
|
||||
|
||||
Root cause was (C) — we were modeling daily re-ranking which neither
|
||||
the script nor the qlib engine actually does.
|
||||
"""
|
||||
|
||||
from __future__ import annotations
|
||||
import json, pathlib
|
||||
import numpy as np
|
||||
import pandas as pd
|
||||
|
||||
LAKE_ROOT = "/home/data/lake"
|
||||
OUT = pathlib.Path("/app/experiments/book/data/diag_script_vs_wf")
|
||||
|
||||
WINDOWS = [
|
||||
{"label": "2026", "start": "2026-01-04", "end": "2026-08-19",
|
||||
"pred": f"{LAKE_ROOT}/mlruns/62/3771f96eb1b74365aeae966af7aec5a3/artifacts/pred.pkl"},
|
||||
{"label": "2025", "start": "2025-01-02", "end": "2025-12-31",
|
||||
"pred": f"{LAKE_ROOT}/mlruns/62/c57c6a8370cc48619d7cdd2bd109b76a/artifacts/pred.pkl"},
|
||||
{"label": "2024", "start": "2024-01-02", "end": "2024-12-31",
|
||||
"pred": f"{LAKE_ROOT}/mlruns/62/97cf5f282e6f4e699443e38d9bfb40fd/artifacts/pred.pkl"},
|
||||
{"label": "2023", "start": "2023-01-03", "end": "2023-12-29",
|
||||
"pred": f"{LAKE_ROOT}/mlruns/62/11b9b65ea4e14b3f8ce50d244da0412e/artifacts/pred.pkl"},
|
||||
{"label": "2021", "start": "2021-01-04", "end": "2021-12-31",
|
||||
"pred": f"{LAKE_ROOT}/mlruns/62/af3034e5910348a382f2ad1e1741f17c/artifacts/pred.pkl"},
|
||||
]
|
||||
|
||||
SYMS = [
|
||||
"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",
|
||||
]
|
||||
|
||||
|
||||
def load_pred(path):
|
||||
df = pd.read_pickle(path)
|
||||
s = df["score"] if isinstance(df, pd.DataFrame) and "score" in df.columns else df.iloc[:, 0] if isinstance(df, pd.DataFrame) else df
|
||||
idx = s.index
|
||||
new_dt = pd.to_datetime(idx.get_level_values(0)).normalize()
|
||||
s.index = pd.MultiIndex.from_arrays([new_dt, idx.get_level_values(1)], names=idx.names)
|
||||
return s
|
||||
|
||||
|
||||
def load_closes(start, end):
|
||||
from tac_qlib.data.config import LakeConfig, resolve_lake_root
|
||||
cfg = LakeConfig(resolve_lake_root(LAKE_ROOT), "US")
|
||||
closes = {}
|
||||
for sym in SYMS:
|
||||
p = cfg.bar_path("1d", sym)
|
||||
if not p.exists(): continue
|
||||
try:
|
||||
df = pd.read_parquet(p)
|
||||
except: continue
|
||||
if not len(df): continue
|
||||
tcol = df["t"] if "t" in df.columns else df["date"]
|
||||
ts = pd.to_datetime(tcol)
|
||||
df = df.assign(_t=ts).set_index("_t").sort_index()
|
||||
warmup = pd.Timestamp(start) - pd.Timedelta(days=60)
|
||||
df = df.loc[warmup:end]
|
||||
if len(df) >= 22:
|
||||
closes[sym] = df["c"]
|
||||
return pd.DataFrame(closes)
|
||||
|
||||
|
||||
def strategy_weekly(pred, closes, start, end, topk=10, risk_degree=1.0, cost_bps=0):
|
||||
"""Weekly rebalance: re-rank on first day of each ISO week, hold rest of week."""
|
||||
ret_df = closes.pct_change(fill_method=None)
|
||||
ret_df.index = pd.to_datetime(ret_df.index).normalize()
|
||||
dt_idx = pred.index.get_level_values(0)
|
||||
trade_dates = sorted(dt_idx[(dt_idx >= start) & (dt_idx <= end)].unique())
|
||||
equity = 1_000_000.0
|
||||
holdings = []
|
||||
prev_week = None
|
||||
daily_eq = []
|
||||
for d in trade_dates:
|
||||
try:
|
||||
day_scores = pred.loc[d]
|
||||
except KeyError:
|
||||
daily_eq.append(equity)
|
||||
continue
|
||||
if isinstance(day_scores, pd.DataFrame):
|
||||
day_scores = day_scores.iloc[:, 0]
|
||||
day_scores = day_scores.dropna().sort_values(ascending=False)
|
||||
cur_week = (d.isocalendar()[0], d.isocalendar()[1])
|
||||
if cur_week != prev_week:
|
||||
# Rebalance: compute cost of turnover
|
||||
new_holdings = list(day_scores.index[:topk])
|
||||
if holdings and cost_bps > 0:
|
||||
sold = set(holdings) - set(new_holdings)
|
||||
bought = set(new_holdings) - set(holdings)
|
||||
turnover = (len(sold) + len(bought)) / (2 * max(len(holdings), 1))
|
||||
equity *= (1 - turnover * cost_bps / 10000)
|
||||
holdings = new_holdings
|
||||
ret_row = ret_df.loc[d] if d in ret_df.index else None
|
||||
if ret_row is not None and holdings:
|
||||
wts = np.array([risk_degree / len(holdings)] * len(holdings))
|
||||
rets = ret_row.reindex(holdings).fillna(0).values
|
||||
equity *= (1 + (wts * rets).sum())
|
||||
daily_eq.append(equity)
|
||||
prev_week = cur_week
|
||||
return pd.Series(daily_eq, index=trade_dates)
|
||||
|
||||
|
||||
def strategy_daily(pred, closes, start, end, topk=10, risk_degree=1.0, cost_bps=0):
|
||||
"""Daily re-rank: re-select top-k every day (wrong model — what we incorrectly tested)."""
|
||||
ret_df = closes.pct_change(fill_method=None)
|
||||
ret_df.index = pd.to_datetime(ret_df.index).normalize()
|
||||
dt_idx = pred.index.get_level_values(0)
|
||||
trade_dates = sorted(dt_idx[(dt_idx >= start) & (dt_idx <= end)].unique())
|
||||
equity = 1_000_000.0
|
||||
holdings = []
|
||||
daily_eq = []
|
||||
for d in trade_dates:
|
||||
try:
|
||||
day_scores = pred.loc[d]
|
||||
except KeyError:
|
||||
daily_eq.append(equity)
|
||||
continue
|
||||
if isinstance(day_scores, pd.DataFrame):
|
||||
day_scores = day_scores.iloc[:, 0]
|
||||
day_scores = day_scores.dropna().sort_values(ascending=False)
|
||||
new_holdings = list(day_scores.index[:topk])
|
||||
if holdings and cost_bps > 0:
|
||||
sold = set(holdings) - set(new_holdings)
|
||||
bought = set(new_holdings) - set(holdings)
|
||||
turnover = (len(sold) + len(bought)) / (2 * max(len(holdings), 1))
|
||||
equity *= (1 - turnover * cost_bps / 10000)
|
||||
holdings = new_holdings
|
||||
ret_row = ret_df.loc[d] if d in ret_df.index else None
|
||||
if ret_row is not None and holdings:
|
||||
wts = np.array([risk_degree / len(holdings)] * len(holdings))
|
||||
rets = ret_row.reindex(holdings).fillna(0).values
|
||||
equity *= (1 + (wts * rets).sum())
|
||||
daily_eq.append(equity)
|
||||
return pd.Series(daily_eq, index=trade_dates)
|
||||
|
||||
|
||||
def metrics(eq):
|
||||
if len(eq) < 2:
|
||||
return {"ann_ret": 0, "sharpe": 0, "maxDD": 0}
|
||||
rets = eq.pct_change().dropna()
|
||||
ann_ret = float((eq.iloc[-1] / eq.iloc[0]) ** (252 / max(len(eq), 1)) - 1)
|
||||
vol = float(rets.std() * (252 ** 0.5)) if len(rets) > 1 else 0
|
||||
sharpe = ann_ret / vol if vol > 0 else 0
|
||||
peak = eq.cummax()
|
||||
dd = (eq - peak) / peak
|
||||
return {"ann_ret": round(ann_ret, 4), "sharpe": round(sharpe, 4), "maxDD": round(float(dd.min()), 4)}
|
||||
|
||||
|
||||
def main():
|
||||
OUT.mkdir(parents=True, exist_ok=True)
|
||||
results = []
|
||||
for w in WINDOWS:
|
||||
print(f"\n=== {w['label']} ({w['start']} to {w['end']}) ===")
|
||||
pred = load_pred(w["pred"])
|
||||
closes = load_closes(w["start"], w["end"])
|
||||
print(f" pred: {pred.index.get_level_values(0).min().date()} to {pred.index.get_level_values(0).max().date()}, "
|
||||
f"{pred.index.get_level_values(1).nunique()} syms")
|
||||
print(f" close: {closes.index.min().date()} to {closes.index.max().date()}, {closes.shape[1]} syms")
|
||||
|
||||
row = {"year": w["label"]}
|
||||
|
||||
# A. Script: weekly, rd=1.0, zero cost
|
||||
eq = strategy_weekly(pred, closes, w["start"], w["end"], topk=10, risk_degree=1.0, cost_bps=0)
|
||||
m = metrics(eq); row["weekly_100_zc"] = m
|
||||
print(f" Script weekly 100% zc: ann={m['ann_ret']:+.1%} sharpe={m['sharpe']:.2f}")
|
||||
|
||||
# B. Script: weekly, rd=0.95, zero cost
|
||||
eq = strategy_weekly(pred, closes, w["start"], w["end"], topk=10, risk_degree=0.95, cost_bps=0)
|
||||
m = metrics(eq); row["weekly_95_zc"] = m
|
||||
print(f" Script weekly 95% zc: ann={m['ann_ret']:+.1%} sharpe={m['sharpe']:.2f}")
|
||||
|
||||
# C. Weekly, rd=0.95, with 5/15bp cost
|
||||
eq = strategy_weekly(pred, closes, w["start"], w["end"], topk=10, risk_degree=0.95, cost_bps=10)
|
||||
m = metrics(eq); row["weekly_95_10bp"] = m
|
||||
print(f" Weekly 95% 10bp cost: ann={m['ann_ret']:+.1%} sharpe={m['sharpe']:.2f}")
|
||||
|
||||
# D. Daily re-rank, rd=1.0, zero cost (WRONG MODEL — for reference only)
|
||||
eq = strategy_daily(pred, closes, w["start"], w["end"], topk=10, risk_degree=1.0, cost_bps=0)
|
||||
m = metrics(eq); row["daily_100_zc"] = m
|
||||
print(f" Daily 100% zc (WRONG): ann={m['ann_ret']:+.1%} sharpe={m['sharpe']:.2f}")
|
||||
|
||||
# E. Daily re-rank, rd=1.0, 10bp cost
|
||||
eq = strategy_daily(pred, closes, w["start"], w["end"], topk=10, risk_degree=1.0, cost_bps=10)
|
||||
m = metrics(eq); row["daily_100_10bp"] = m
|
||||
print(f" Daily 100% 10bp (WRONG):ann={m['ann_ret']:+.1%} sharpe={m['sharpe']:.2f}")
|
||||
|
||||
results.append(row)
|
||||
|
||||
with open(OUT / "diagnosis_v2.json", "w") as f:
|
||||
json.dump(results, f, indent=2, default=str)
|
||||
print(f"\nSaved to {OUT / 'diagnosis_v2.json'}")
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
main()
|
||||
@@ -0,0 +1,410 @@
|
||||
"""Diagnose script-vs-workflow gap v3: replicate workflow execution mechanics exactly.
|
||||
|
||||
Replicates the WeeklyRebalanceDropoutStrategy execution:
|
||||
1. Weekly rebalance (first trading day of ISO week only)
|
||||
2. TopkDropout selection: sell bottom n_drop, buy top fill
|
||||
3. Cash-after-sells sizing: sell first, then cash * risk_degree / len(buy)
|
||||
4. Whole-share rounding (floor)
|
||||
5. Asymmetric costs: open_cost=5bp, close_cost=15bp, min_cost=$5 per order
|
||||
6. Optional SQ gate (hit-rate threshold)
|
||||
|
||||
Compares against the idealized script (fractional shares, symmetric cost).
|
||||
"""
|
||||
|
||||
from __future__ import annotations
|
||||
import json, pathlib
|
||||
import numpy as np
|
||||
import pandas as pd
|
||||
|
||||
LAKE_ROOT = "/home/data/lake"
|
||||
OUT = pathlib.Path("/app/experiments/book/data/diag_script_vs_wf")
|
||||
|
||||
WINDOWS = [
|
||||
{"label": "2026", "start": "2026-01-04", "end": "2026-08-19",
|
||||
"pred": f"{LAKE_ROOT}/mlruns/62/3771f96eb1b74365aeae966af7aec5a3/artifacts/pred.pkl"},
|
||||
{"label": "2025", "start": "2025-01-02", "end": "2025-12-31",
|
||||
"pred": f"{LAKE_ROOT}/mlruns/62/c57c6a8370cc48619d7cdd2bd109b76a/artifacts/pred.pkl"},
|
||||
{"label": "2024", "start": "2024-01-02", "end": "2024-12-31",
|
||||
"pred": f"{LAKE_ROOT}/mlruns/62/97cf5f282e6f4e699443e38d9bfb40fd/artifacts/pred.pkl"},
|
||||
{"label": "2023", "start": "2023-01-03", "end": "2023-12-29",
|
||||
"pred": f"{LAKE_ROOT}/mlruns/62/11b9b65ea4e14b3f8ce50d244da0412e/artifacts/pred.pkl"},
|
||||
{"label": "2021", "start": "2021-01-04", "end": "2021-12-31",
|
||||
"pred": f"{LAKE_ROOT}/mlruns/62/af3034e5910348a382f2ad1e1741f17c/artifacts/pred.pkl"},
|
||||
]
|
||||
|
||||
SYMS = [
|
||||
"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",
|
||||
]
|
||||
|
||||
OPEN_COST = 0.0005 # 5bp
|
||||
CLOSE_COST = 0.0015 # 15bp
|
||||
MIN_COST = 5.0 # $5 minimum per order
|
||||
|
||||
|
||||
def load_pred(path):
|
||||
df = pd.read_pickle(path)
|
||||
s = df["score"] if isinstance(df, pd.DataFrame) and "score" in df.columns else df.iloc[:, 0] if isinstance(df, pd.DataFrame) else df
|
||||
idx = s.index
|
||||
new_dt = pd.to_datetime(idx.get_level_values(0)).normalize()
|
||||
s.index = pd.MultiIndex.from_arrays([new_dt, idx.get_level_values(1)], names=idx.names)
|
||||
return s
|
||||
|
||||
|
||||
def load_closes(start, end):
|
||||
from tac_qlib.data.config import LakeConfig, resolve_lake_root
|
||||
cfg = LakeConfig(resolve_lake_root(LAKE_ROOT), "US")
|
||||
closes = {}
|
||||
for sym in SYMS:
|
||||
p = cfg.bar_path("1d", sym)
|
||||
if not p.exists(): continue
|
||||
try:
|
||||
df = pd.read_parquet(p)
|
||||
except: continue
|
||||
if not len(df): continue
|
||||
tcol = df["t"] if "t" in df.columns else df["date"]
|
||||
ts = pd.to_datetime(tcol)
|
||||
df = df.assign(_t=ts).set_index("_t").sort_index()
|
||||
df.index = pd.to_datetime(df.index).normalize()
|
||||
warmup = pd.Timestamp(start) - pd.Timedelta(days=60)
|
||||
df = df.loc[warmup:end]
|
||||
if len(df) >= 22:
|
||||
closes[sym] = df["c"]
|
||||
return pd.DataFrame(closes)
|
||||
|
||||
|
||||
def load_opens(start, end):
|
||||
from tac_qlib.data.config import LakeConfig, resolve_lake_root
|
||||
cfg = LakeConfig(resolve_lake_root(LAKE_ROOT), "US")
|
||||
opens = {}
|
||||
for sym in SYMS:
|
||||
p = cfg.bar_path("1d", sym)
|
||||
if not p.exists(): continue
|
||||
try:
|
||||
df = pd.read_parquet(p)
|
||||
except: continue
|
||||
if not len(df): continue
|
||||
tcol = df["t"] if "t" in df.columns else df["date"]
|
||||
ts = pd.to_datetime(tcol)
|
||||
df = df.assign(_t=ts).set_index("_t").sort_index()
|
||||
df.index = pd.to_datetime(df.index).normalize()
|
||||
warmup = pd.Timestamp(start) - pd.Timedelta(days=60)
|
||||
df = df.loc[warmup:end]
|
||||
if len(df) >= 22:
|
||||
opens[sym] = df["o"]
|
||||
return pd.DataFrame(opens)
|
||||
|
||||
|
||||
def load_vwap(start, end):
|
||||
from tac_qlib.data.config import LakeConfig, resolve_lake_root
|
||||
cfg = LakeConfig(resolve_lake_root(LAKE_ROOT), "US")
|
||||
vwaps = {}
|
||||
for sym in SYMS:
|
||||
p = cfg.bar_path("1d", sym)
|
||||
if not p.exists(): continue
|
||||
try:
|
||||
df = pd.read_parquet(p)
|
||||
except: continue
|
||||
if not len(df): continue
|
||||
tcol = df["t"] if "t" in df.columns else df["date"]
|
||||
ts = pd.to_datetime(tcol)
|
||||
df = df.assign(_t=ts).set_index("_t").sort_index()
|
||||
warmup = pd.Timestamp(start) - pd.Timedelta(days=60)
|
||||
df = df.loc[warmup:end]
|
||||
if len(df) >= 22:
|
||||
vwaps[sym] = df["vw"]
|
||||
return pd.DataFrame(vwaps)
|
||||
|
||||
|
||||
def compute_gate(pred, closes, start, end, gate_topk=10, gate_lookback=5, gate_threshold=0.5):
|
||||
"""Compute the SQ gate: rolling average hit-rate of topk predictions."""
|
||||
ret_df = closes.pct_change()
|
||||
ret_df.index = pd.to_datetime(ret_df.index).normalize()
|
||||
dt_idx = pred.index.get_level_values(0)
|
||||
pred_dates = sorted(dt_idx[(dt_idx >= start) & (dt_idx <= end)].unique())
|
||||
if len(pred_dates) < 2:
|
||||
return pd.Series(True, index=pd.DatetimeIndex(pred_dates))
|
||||
|
||||
hit_rates = {}
|
||||
for i in range(1, len(pred_dates)):
|
||||
day = pred_dates[i]
|
||||
prev_day = pred_dates[i - 1]
|
||||
try:
|
||||
prev_scores = pred.loc[prev_day]
|
||||
except KeyError:
|
||||
continue
|
||||
if isinstance(prev_scores, pd.DataFrame):
|
||||
prev_scores = prev_scores.iloc[:, 0]
|
||||
prev_scores = prev_scores.dropna().sort_values(ascending=False)
|
||||
topk_syms = list(prev_scores.index[:gate_topk])
|
||||
if day not in ret_df.index:
|
||||
continue
|
||||
today_ret = ret_df.loc[day]
|
||||
topk_rets = today_ret.reindex(topk_syms).dropna()
|
||||
if len(topk_rets) == 0:
|
||||
continue
|
||||
hit_rates[day] = (topk_rets > 0).sum() / len(topk_rets)
|
||||
|
||||
if not hit_rates:
|
||||
return pd.Series(True, index=pd.DatetimeIndex(pred_dates))
|
||||
|
||||
hr_series = pd.Series(hit_rates).sort_index()
|
||||
rolling_hr = hr_series.rolling(gate_lookback, min_periods=1).mean()
|
||||
gate = rolling_hr >= gate_threshold
|
||||
gate.iloc[:gate_lookback] = True
|
||||
return gate
|
||||
|
||||
|
||||
def strategy_idealized(pred, closes, start, end, topk=10, risk_degree=1.0, cost_bps=0):
|
||||
"""Idealized script: fractional shares, symmetric cost, no gate."""
|
||||
ret_df = closes.pct_change(fill_method=None)
|
||||
ret_df.index = pd.to_datetime(ret_df.index).normalize()
|
||||
dt_idx = pred.index.get_level_values(0)
|
||||
trade_dates = sorted(dt_idx[(dt_idx >= start) & (dt_idx <= end)].unique())
|
||||
equity = 1_000_000.0
|
||||
holdings = []
|
||||
prev_week = None
|
||||
daily_eq = []
|
||||
for d in trade_dates:
|
||||
try:
|
||||
day_scores = pred.loc[d]
|
||||
except KeyError:
|
||||
daily_eq.append(equity)
|
||||
continue
|
||||
if isinstance(day_scores, pd.DataFrame):
|
||||
day_scores = day_scores.iloc[:, 0]
|
||||
day_scores = day_scores.dropna().sort_values(ascending=False)
|
||||
cur_week = (d.isocalendar()[0], d.isocalendar()[1])
|
||||
if cur_week != prev_week:
|
||||
new_holdings = list(day_scores.index[:topk])
|
||||
if holdings and cost_bps > 0:
|
||||
sold = set(holdings) - set(new_holdings)
|
||||
bought = set(new_holdings) - set(holdings)
|
||||
turnover = (len(sold) + len(bought)) / (2 * max(len(holdings), 1))
|
||||
equity *= (1 - turnover * cost_bps / 10000)
|
||||
holdings = new_holdings
|
||||
ret_row = ret_df.loc[d] if d in ret_df.index else None
|
||||
if ret_row is not None and holdings:
|
||||
wts = np.array([risk_degree / len(holdings)] * len(holdings))
|
||||
rets = ret_row.reindex(holdings).fillna(0).values
|
||||
equity *= (1 + (wts * rets).sum())
|
||||
daily_eq.append(equity)
|
||||
prev_week = cur_week
|
||||
return pd.Series(daily_eq, index=trade_dates)
|
||||
|
||||
|
||||
def strategy_workflow_exact(pred, closes, opens, start, end,
|
||||
topk=10, n_drop=1, risk_degree=0.95,
|
||||
use_gate=False, gate_series=None):
|
||||
"""Exact replication of WeeklyRebalanceDropoutStrategy execution mechanics.
|
||||
|
||||
- Sells first (all shares of dropped positions)
|
||||
- Sizes buys as: cash * risk_degree / len(buy)
|
||||
- Rounds to whole shares (floor)
|
||||
- Asymmetric costs: open_cost on buys, close_cost on sells, $5 min per order
|
||||
- Tracks position values for daily equity
|
||||
"""
|
||||
ret_df = closes.pct_change(fill_method=None)
|
||||
ret_df.index = pd.to_datetime(ret_df.index).normalize()
|
||||
open_df = opens.copy()
|
||||
open_df.index = pd.to_datetime(open_df.index).normalize()
|
||||
dt_idx = pred.index.get_level_values(0)
|
||||
trade_dates = sorted(dt_idx[(dt_idx >= start) & (dt_idx <= end)].unique())
|
||||
|
||||
cash = 1_000_000.0
|
||||
positions = {} # {sym: num_shares}
|
||||
prev_week = None
|
||||
daily_eq = []
|
||||
|
||||
for d in trade_dates:
|
||||
# Skip non-trading days (pred may include weekends)
|
||||
if d not in closes.index:
|
||||
daily_eq.append(daily_eq[-1] if daily_eq else cash)
|
||||
continue
|
||||
try:
|
||||
day_scores = pred.loc[d]
|
||||
except KeyError:
|
||||
daily_eq.append(daily_eq[-1] if daily_eq else cash)
|
||||
continue
|
||||
if isinstance(day_scores, pd.DataFrame):
|
||||
day_scores = day_scores.iloc[:, 0]
|
||||
day_scores = day_scores.dropna().sort_values(ascending=False)
|
||||
cur_week = (d.isocalendar()[0], d.isocalendar()[1])
|
||||
|
||||
if cur_week != prev_week:
|
||||
# === REBALANCE DAY ===
|
||||
# Check gate
|
||||
if use_gate and gate_series is not None:
|
||||
known = gate_series[gate_series.index <= d]
|
||||
if len(known) and not bool(known.iloc[-1]):
|
||||
# gate closed: sell everything, go to cash
|
||||
for sym in list(positions.keys()):
|
||||
shares = positions[sym]
|
||||
if shares <= 0:
|
||||
continue
|
||||
sell_price = closes.loc[d, sym] if d in closes.index and sym in closes.columns else None
|
||||
if sell_price is None or pd.isna(sell_price):
|
||||
continue
|
||||
trade_val = shares * sell_price
|
||||
trade_cost = max(trade_val * CLOSE_COST, MIN_COST) if trade_val > 0 else 0
|
||||
cash += trade_val - trade_cost
|
||||
positions[sym] = 0
|
||||
positions = {s: v for s, v in positions.items() if v > 0}
|
||||
daily_eq.append(cash)
|
||||
prev_week = cur_week
|
||||
continue
|
||||
|
||||
# TopkDropout selection (matching WeeklyRebalanceDropoutStrategy exactly)
|
||||
current_syms = [s for s, v in positions.items() if v > 0]
|
||||
last = pred.loc[d].reindex(current_syms).sort_values(ascending=False).index if current_syms else pd.Index([])
|
||||
# buy candidates: top stocks NOT in current holdings, take n_drop + topk - len(last)
|
||||
buy_cands = day_scores[~day_scores.index.isin(last)].sort_values(ascending=False).index
|
||||
buy_list = list(buy_cands[:n_drop + topk - len(last)])
|
||||
# comb = union of current holdings + buy candidates (actual strategy line 132)
|
||||
comb = pred.loc[d].reindex(last.union(pd.Index(buy_list))).sort_values(ascending=False).index
|
||||
# sell: items from current holdings that are in the bottom n_drop of comb
|
||||
sell_list = list(last[last.isin(comb[-n_drop:])]) if n_drop > 0 and len(comb) >= n_drop else []
|
||||
|
||||
# --- SELL FIRST ---
|
||||
for sym in sell_list:
|
||||
if sym not in positions or positions[sym] <= 0:
|
||||
continue
|
||||
shares = positions[sym]
|
||||
sell_price = closes.loc[d, sym] if d in closes.index and sym in closes.columns else None
|
||||
if sell_price is None or pd.isna(sell_price):
|
||||
continue
|
||||
trade_val = shares * sell_price
|
||||
trade_cost = max(trade_val * CLOSE_COST, MIN_COST) if trade_val > 0 else 0
|
||||
cash += trade_val - trade_cost
|
||||
positions[sym] = 0
|
||||
|
||||
# --- BUY ---
|
||||
n_buy = len(buy_list)
|
||||
if n_buy > 0:
|
||||
buy_budget = cash * risk_degree / n_buy
|
||||
for sym in buy_list:
|
||||
buy_price = closes.loc[d, sym] if d in closes.index and sym in closes.columns else None
|
||||
if buy_price is None or pd.isna(buy_price) or buy_price <= 0:
|
||||
continue
|
||||
shares_to_buy = int(buy_budget / buy_price) # floor to whole shares
|
||||
if shares_to_buy <= 0:
|
||||
continue
|
||||
trade_val = shares_to_buy * buy_price
|
||||
trade_cost = max(trade_val * OPEN_COST, MIN_COST) if trade_val > 0 else 0
|
||||
total_cost = trade_val + trade_cost
|
||||
if total_cost > cash:
|
||||
shares_to_buy = int((cash - MIN_COST) / buy_price)
|
||||
if shares_to_buy <= 0:
|
||||
continue
|
||||
trade_val = shares_to_buy * buy_price
|
||||
trade_cost = max(trade_val * OPEN_COST, MIN_COST)
|
||||
total_cost = trade_val + trade_cost
|
||||
cash -= total_cost
|
||||
positions[sym] = positions.get(sym, 0) + shares_to_buy
|
||||
|
||||
positions = {s: v for s, v in positions.items() if v > 0}
|
||||
|
||||
# === DAILY EQUITY ===
|
||||
eq = cash
|
||||
if d in closes.index:
|
||||
for sym, shares in positions.items():
|
||||
if sym in closes.columns:
|
||||
px = closes.loc[d, sym]
|
||||
if not pd.isna(px):
|
||||
eq += shares * px
|
||||
daily_eq.append(eq)
|
||||
prev_week = cur_week
|
||||
|
||||
return pd.Series(daily_eq, index=trade_dates)
|
||||
|
||||
|
||||
def metrics(eq):
|
||||
if len(eq) < 2:
|
||||
return {"ann_ret": 0, "sharpe": 0, "maxDD": 0}
|
||||
rets = eq.pct_change().dropna()
|
||||
ann_ret = float((eq.iloc[-1] / eq.iloc[0]) ** (252 / max(len(eq), 1)) - 1)
|
||||
vol = float(rets.std() * (252 ** 0.5)) if len(rets) > 1 else 0
|
||||
sharpe = ann_ret / vol if vol > 0 else 0
|
||||
peak = eq.cummax()
|
||||
dd = (eq - peak) / peak
|
||||
return {"ann_ret": round(ann_ret, 4), "sharpe": round(sharpe, 4), "maxDD": round(float(dd.min()), 4)}
|
||||
|
||||
|
||||
def main():
|
||||
OUT.mkdir(parents=True, exist_ok=True)
|
||||
results = []
|
||||
for w in WINDOWS:
|
||||
print(f"\n=== {w['label']} ({w['start']} to {w['end']}) ===")
|
||||
pred = load_pred(w["pred"])
|
||||
closes = load_closes(w["start"], w["end"])
|
||||
opens = load_opens(w["start"], w["end"])
|
||||
print(f" pred: {pred.index.get_level_values(0).min().date()} to {pred.index.get_level_values(0).max().date()}, "
|
||||
f"{pred.index.get_level_values(1).nunique()} syms")
|
||||
print(f" close: {closes.index.min().date()} to {closes.index.max().date()}, {closes.shape[1]} syms")
|
||||
|
||||
row = {"year": w["label"]}
|
||||
|
||||
# A. Idealized: fractional shares, 10bp symmetric, no gate (diag v2 baseline)
|
||||
eq = strategy_idealized(pred, closes, w["start"], w["end"], topk=10, risk_degree=1.0, cost_bps=0)
|
||||
m = metrics(eq); row["ideal_100_zc"] = m
|
||||
print(f" A. Ideal 100% zc: ann={m['ann_ret']:+.1%} sharpe={m['sharpe']:.2f} maxDD={m['maxDD']:.1%}")
|
||||
|
||||
# B. Idealized: 95% invested, 10bp symmetric
|
||||
eq = strategy_idealized(pred, closes, w["start"], w["end"], topk=10, risk_degree=0.95, cost_bps=0)
|
||||
m = metrics(eq); row["ideal_95_zc"] = m
|
||||
print(f" B. Ideal 95% zc: ann={m['ann_ret']:+.1%} sharpe={m['sharpe']:.2f} maxDD={m['maxDD']:.1%}")
|
||||
|
||||
# C. Idealized: 95%, 10bp cost
|
||||
eq = strategy_idealized(pred, closes, w["start"], w["end"], topk=10, risk_degree=0.95, cost_bps=10)
|
||||
m = metrics(eq); row["ideal_95_10bp"] = m
|
||||
print(f" C. Ideal 95% 10bp: ann={m['ann_ret']:+.1%} sharpe={m['sharpe']:.2f} maxDD={m['maxDD']:.1%}")
|
||||
|
||||
# D. Workflow-exact: whole shares, 5/15bp, $5 min, no gate
|
||||
eq = strategy_workflow_exact(pred, closes, opens, w["start"], w["end"],
|
||||
topk=10, n_drop=1, risk_degree=0.95,
|
||||
use_gate=False)
|
||||
m = metrics(eq); row["wf_exact_95_nogate"] = m
|
||||
print(f" D. WF exact 95% nogate: ann={m['ann_ret']:+.1%} sharpe={m['sharpe']:.2f} maxDD={m['maxDD']:.1%}")
|
||||
|
||||
# E. Workflow-exact: whole shares, 5/15bp, $5 min, WITH SQ gate
|
||||
gate = compute_gate(pred, closes, w["start"], w["end"],
|
||||
gate_topk=10, gate_lookback=5, gate_threshold=0.5)
|
||||
gate_open_pct = gate.sum() / len(gate) if len(gate) > 0 else 1.0
|
||||
eq = strategy_workflow_exact(pred, closes, opens, w["start"], w["end"],
|
||||
topk=10, n_drop=1, risk_degree=0.95,
|
||||
use_gate=True, gate_series=gate)
|
||||
m = metrics(eq); row["wf_exact_95_gate"] = m
|
||||
print(f" E. WF exact 95% gate: ann={m['ann_ret']:+.1%} sharpe={m['sharpe']:.2f} maxDD={m['maxDD']:.1%} gate_open={gate_open_pct:.0%}")
|
||||
|
||||
# Gap analysis
|
||||
ideal = row["ideal_95_zc"]["ann_ret"]
|
||||
wf_nogate = row["wf_exact_95_nogate"]["ann_ret"]
|
||||
wf_gate = row["wf_exact_95_gate"]["ann_ret"]
|
||||
print(f"\n Gap analysis:")
|
||||
print(f" Ideal (fractional, zc) → WF exact (whole shares, 5/15bp, nogate): {ideal:+.1%} → {wf_nogate:+.1%} (gap: {wf_nogate - ideal:+.1%})")
|
||||
print(f" Ideal (fractional, zc) → WF exact (whole shares, 5/15bp, gate): {ideal:+.1%} → {wf_gate:+.1%} (gap: {wf_gate - ideal:+.1%})")
|
||||
|
||||
results.append(row)
|
||||
|
||||
with open(OUT / "diagnosis_v3.json", "w") as f:
|
||||
json.dump(results, f, indent=2, default=str)
|
||||
print(f"\nSaved to {OUT / 'diagnosis_v3.json'}")
|
||||
|
||||
# Summary table
|
||||
print("\n" + "=" * 80)
|
||||
print("SUMMARY: Ideal vs Workflow-Exact")
|
||||
print("=" * 80)
|
||||
print(f"{'Year':<6} {'Ideal%zc':>10} {'WF nogate':>10} {'WF gate':>10} {'Gap(nogate)':>12} {'Gap(gate)':>12}")
|
||||
for r in results:
|
||||
y = r["year"]
|
||||
i = r["ideal_95_zc"]["ann_ret"]
|
||||
wn = r["wf_exact_95_nogate"]["ann_ret"]
|
||||
wg = r["wf_exact_95_gate"]["ann_ret"]
|
||||
print(f"{y:<6} {i:>+10.1%} {wn:>+10.1%} {wg:>+10.1%} {wn-i:>+12.1%} {wg-i:>+12.1%}")
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
main()
|
||||
@@ -0,0 +1,343 @@
|
||||
"""Signal-quality gate walk-forward backtest — RE-TRAINED MODEL variant.
|
||||
|
||||
Identical logic to the original scripted test (signal_quality_gate_bt.py),
|
||||
but uses pred.pkls from exp 62 (retrained LGBModel per year, same model config
|
||||
as the workflow test) instead of the reference exp 52/56 pred.pkls.
|
||||
|
||||
This isolates whether the gate itself works when the model is the same,
|
||||
regardless of the backtest engine.
|
||||
|
||||
Usage:
|
||||
cd /app && .venv/bin/python book/scripts/signal_quality_gate_retrained.py
|
||||
"""
|
||||
|
||||
from __future__ import annotations
|
||||
|
||||
import json
|
||||
import pathlib
|
||||
|
||||
import numpy as np
|
||||
import pandas as pd
|
||||
|
||||
LAKE_ROOT = "/home/data/lake"
|
||||
MARKET = "US"
|
||||
OUT_DIR = pathlib.Path("/app/experiments/book/data/signal_quality_gate")
|
||||
|
||||
# Retrained pred.pkls from exp 62 (on-the-fly gate test)
|
||||
WINDOWS_RETRAINED = [
|
||||
{"label": "2026", "start": "2026-01-04", "end": "2026-08-19",
|
||||
"pred": f"{LAKE_ROOT}/mlruns/62/3771f96eb1b74365aeae966af7aec5a3/artifacts/pred.pkl"},
|
||||
{"label": "2025", "start": "2025-01-02", "end": "2025-12-31",
|
||||
"pred": f"{LAKE_ROOT}/mlruns/62/c57c6a8370cc48619d7cdd2bd109b76a/artifacts/pred.pkl"},
|
||||
{"label": "2024", "start": "2024-01-02", "end": "2024-12-31",
|
||||
"pred": f"{LAKE_ROOT}/mlruns/62/97cf5f282e6f4e699443e38d9bfb40fd/artifacts/pred.pkl"},
|
||||
{"label": "2023", "start": "2023-01-03", "end": "2023-12-29",
|
||||
"pred": f"{LAKE_ROOT}/mlruns/62/11b9b65ea4e14b3f8ce50d244da0412e/artifacts/pred.pkl"},
|
||||
{"label": "2021", "start": "2021-01-04", "end": "2021-12-31",
|
||||
"pred": f"{LAKE_ROOT}/mlruns/62/af3034e5910348a382f2ad1e1741f17c/artifacts/pred.pkl"},
|
||||
]
|
||||
|
||||
# Original reference pred.pkls for head-to-head comparison
|
||||
WINDOWS_REFERENCE = [
|
||||
{"label": "2026", "start": "2026-01-04", "end": "2026-08-19",
|
||||
"pred": f"{LAKE_ROOT}/mlruns/52/9f98ea5c550a409f87b56a6cd8fee343/artifacts/pred.pkl"},
|
||||
{"label": "2025", "start": "2025-01-02", "end": "2025-12-31",
|
||||
"pred": f"{LAKE_ROOT}/mlruns/52/fe96741654df4780957a3a949999ae6a/artifacts/pred.pkl"},
|
||||
{"label": "2024", "start": "2024-01-02", "end": "2024-12-31",
|
||||
"pred": f"{LAKE_ROOT}/mlruns/52/71ed5bfa9984490f8bba8b222f7acc39/artifacts/pred.pkl"},
|
||||
{"label": "2023", "start": "2023-01-03", "end": "2023-12-29",
|
||||
"pred": f"{LAKE_ROOT}/mlruns/56/8ca46e554311444c9a42637a788226e8/artifacts/pred.pkl"},
|
||||
{"label": "2021", "start": "2021-01-04", "end": "2021-12-31",
|
||||
"pred": f"{LAKE_ROOT}/mlruns/56/4e0700ddab2a4e108b46efece7346ee3/artifacts/pred.pkl"},
|
||||
]
|
||||
|
||||
SIGNAL_GATE_CONFIGS = [
|
||||
(5, 0.50, "hitrate_5d_0.50"),
|
||||
(5, 0.60, "hitrate_5d_0.60"),
|
||||
(5, 0.70, "hitrate_5d_0.70"),
|
||||
(10, 0.50, "hitrate_10d_0.50"),
|
||||
(10, 0.60, "hitrate_10d_0.60"),
|
||||
(10, 0.70, "hitrate_10d_0.70"),
|
||||
(20, 0.40, "hitrate_20d_0.40"),
|
||||
(20, 0.50, "hitrate_20d_0.50"),
|
||||
(20, 0.60, "hitrate_20d_0.60"),
|
||||
]
|
||||
|
||||
|
||||
def load_pred(path: str) -> pd.Series:
|
||||
df = pd.read_pickle(path)
|
||||
if isinstance(df, pd.DataFrame):
|
||||
if "score" in df.columns:
|
||||
s = df["score"]
|
||||
else:
|
||||
s = df.iloc[:, 0]
|
||||
else:
|
||||
s = df
|
||||
idx = s.index
|
||||
new_dt = pd.to_datetime(idx.get_level_values(0)).normalize()
|
||||
s.index = pd.MultiIndex.from_arrays(
|
||||
[new_dt, idx.get_level_values(1)], names=idx.names
|
||||
)
|
||||
return s
|
||||
|
||||
|
||||
def load_bars_for_window(start: str, end: str) -> pd.DataFrame:
|
||||
from tac_qlib.data.config import LakeConfig, resolve_lake_root
|
||||
cfg = LakeConfig(resolve_lake_root(LAKE_ROOT), MARKET)
|
||||
sp = cfg.lake_root / "symbols.parquet"
|
||||
if sp.exists():
|
||||
syms = pd.read_parquet(sp)
|
||||
col = "symbol" if "symbol" in syms.columns else syms.columns[0]
|
||||
symbols = sorted(syms[col].astype(str).str.upper().tolist())
|
||||
else:
|
||||
return pd.DataFrame()
|
||||
closes = {}
|
||||
for sym in symbols:
|
||||
p = cfg.bar_path("1d", sym)
|
||||
if not p.exists():
|
||||
continue
|
||||
try:
|
||||
df = pd.read_parquet(p)
|
||||
except Exception:
|
||||
continue
|
||||
if not len(df):
|
||||
continue
|
||||
tcol = df["t"] if "t" in df.columns else df["date"]
|
||||
ts = pd.to_datetime(tcol)
|
||||
df = df.assign(_t=ts).set_index("_t").sort_index()
|
||||
warmup_start = pd.Timestamp(start) - pd.Timedelta(days=60)
|
||||
df = df.loc[warmup_start:end]
|
||||
if len(df) >= 22:
|
||||
closes[sym] = df["c"]
|
||||
return pd.DataFrame(closes)
|
||||
|
||||
|
||||
def compute_hit_rate_series(
|
||||
pred: pd.Series, ret_df: pd.DataFrame, topk: int = 10, lookback: int = 10,
|
||||
) -> pd.Series:
|
||||
dt_idx = pred.index.get_level_values(0)
|
||||
trade_dates = sorted(dt_idx.unique())
|
||||
hit_rates = {}
|
||||
for i in range(1, len(trade_dates)):
|
||||
prev_date = trade_dates[i - 1]
|
||||
curr_date = trade_dates[i]
|
||||
try:
|
||||
prev_scores = pred.loc[prev_date]
|
||||
except KeyError:
|
||||
continue
|
||||
if isinstance(prev_scores, pd.DataFrame):
|
||||
prev_scores = prev_scores.iloc[:, 0]
|
||||
prev_scores = prev_scores.dropna().sort_values(ascending=False)
|
||||
topk_syms = list(prev_scores.index[:topk])
|
||||
if curr_date not in ret_df.index:
|
||||
continue
|
||||
today_ret = ret_df.loc[curr_date]
|
||||
topk_rets = today_ret.reindex(topk_syms).dropna()
|
||||
if len(topk_rets) > 0:
|
||||
hit_rate = (topk_rets > 0).mean()
|
||||
hit_rates[curr_date] = hit_rate
|
||||
hit_series = pd.Series(hit_rates)
|
||||
if len(hit_series) == 0:
|
||||
return hit_series
|
||||
rolling_hr = hit_series.rolling(lookback, min_periods=max(1, lookback // 2)).mean()
|
||||
return rolling_hr
|
||||
|
||||
|
||||
def run_backtest(pred, hit_rate, close_df, start, end, topk=10, threshold=0.5):
|
||||
if not isinstance(pred.index, pd.MultiIndex):
|
||||
return {"error": "pred must have MultiIndex"}
|
||||
ret_df = close_df.pct_change()
|
||||
ret_df.index = pd.to_datetime(ret_df.index).normalize()
|
||||
dt_idx = pred.index.get_level_values(0)
|
||||
window_mask = (dt_idx >= pd.Timestamp(start)) & (dt_idx <= pd.Timestamp(end))
|
||||
window_pred = pred.loc[window_mask]
|
||||
if len(window_pred) == 0:
|
||||
return {"error": "no pred data in window"}
|
||||
trade_dates = sorted(dt_idx[window_mask].unique())
|
||||
gate_open = {}
|
||||
for d in trade_dates:
|
||||
known = hit_rate[hit_rate.index <= d]
|
||||
if len(known) > 0 and not pd.isna(known.iloc[-1]):
|
||||
gate_open[d] = bool(known.iloc[-1] >= threshold)
|
||||
else:
|
||||
gate_open[d] = True
|
||||
n_total = len(trade_dates)
|
||||
n_open = sum(1 for v in gate_open.values() if v)
|
||||
n_closed = n_total - n_open
|
||||
holdings_base = []
|
||||
holdings_gated = []
|
||||
equity_gated = 1_000_000.0
|
||||
equity_base = 1_000_000.0
|
||||
prev_week = None
|
||||
prev_scores = None
|
||||
daily_gated = []
|
||||
daily_base = []
|
||||
ret_by_date = {rd: ret_df.loc[rd] for rd in ret_df.index}
|
||||
for d in trade_dates:
|
||||
try:
|
||||
day_scores = window_pred.loc[d]
|
||||
except KeyError:
|
||||
daily_gated.append(equity_gated)
|
||||
daily_base.append(equity_base)
|
||||
prev_scores = None
|
||||
continue
|
||||
if isinstance(day_scores, pd.DataFrame):
|
||||
day_scores = day_scores.iloc[:, 0]
|
||||
day_scores = day_scores.dropna().sort_values(ascending=False)
|
||||
if len(day_scores) == 0:
|
||||
daily_gated.append(equity_gated)
|
||||
daily_base.append(equity_base)
|
||||
prev_scores = None
|
||||
continue
|
||||
ret_row = ret_by_date.get(d)
|
||||
if ret_row is None:
|
||||
daily_gated.append(equity_gated)
|
||||
daily_base.append(equity_base)
|
||||
prev_scores = day_scores
|
||||
continue
|
||||
cur_week = (d.isocalendar()[0], d.isocalendar()[1]) if hasattr(d, 'isocalendar') else None
|
||||
gate_val = gate_open.get(d, True)
|
||||
if cur_week != prev_week or not holdings_base:
|
||||
if prev_scores is not None:
|
||||
holdings_base = list(prev_scores.index[:topk])
|
||||
if holdings_base:
|
||||
base_rets = ret_row.reindex(holdings_base).dropna()
|
||||
if len(base_rets) > 0:
|
||||
equity_base *= (1 + base_rets.mean())
|
||||
if gate_val:
|
||||
if cur_week != prev_week or not holdings_gated:
|
||||
if prev_scores is not None:
|
||||
holdings_gated = list(prev_scores.index[:topk])
|
||||
if holdings_gated:
|
||||
hold_rets = ret_row.reindex(holdings_gated).dropna()
|
||||
if len(hold_rets) > 0:
|
||||
equity_gated *= (1 + hold_rets.mean())
|
||||
else:
|
||||
holdings_gated = []
|
||||
prev_week = cur_week
|
||||
prev_scores = day_scores
|
||||
daily_gated.append(equity_gated)
|
||||
daily_base.append(equity_base)
|
||||
g_series = pd.Series(daily_gated, index=trade_dates)
|
||||
b_series = pd.Series(daily_base, index=trade_dates)
|
||||
|
||||
def _metrics(eq):
|
||||
if len(eq) < 2:
|
||||
return {"ann_return": 0, "sharpe": 0, "maxDD": 0}
|
||||
rets = eq.pct_change().dropna()
|
||||
ann_ret = float((eq.iloc[-1] / eq.iloc[0]) ** (252 / max(len(eq), 1)) - 1)
|
||||
vol = float(rets.std() * (252 ** 0.5)) if len(rets) > 1 else 0
|
||||
sharpe = ann_ret / vol if vol > 0 else 0
|
||||
peak = eq.cummax()
|
||||
dd = (eq - peak) / peak
|
||||
maxDD = float(dd.min())
|
||||
return {"ann_return": round(ann_ret, 6), "sharpe": round(sharpe, 4), "maxDD": round(maxDD, 6)}
|
||||
|
||||
base_m = _metrics(b_series)
|
||||
gated_m = _metrics(g_series)
|
||||
return {
|
||||
"trade_dates": n_total,
|
||||
"gate_open_days": n_open,
|
||||
"gate_closed_days": n_closed,
|
||||
"trip_rate": round(n_closed / n_total, 4) if n_total else 0,
|
||||
"base": base_m,
|
||||
"gated": gated_m,
|
||||
}
|
||||
|
||||
|
||||
def run_set(windows, close_df, tag):
|
||||
results = []
|
||||
for window in windows:
|
||||
wl, ws, we = window["label"], window["start"], window["end"]
|
||||
pred_path = window["pred"]
|
||||
print(f"\n=== [{tag}] Window {wl} ({ws} to {we}) ===")
|
||||
pred = load_pred(pred_path)
|
||||
print(f" pred shape: {pred.shape}, date range: {pred.index.get_level_values(0).min()} .. {pred.index.get_level_values(0).max()}")
|
||||
hit_rates = {}
|
||||
for lookback, _, name in SIGNAL_GATE_CONFIGS:
|
||||
if lookback not in hit_rates:
|
||||
hr = compute_hit_rate_series(pred, ret_df, topk=10, lookback=lookback)
|
||||
hit_rates[lookback] = hr
|
||||
print(f" lookback={lookback}: {len(hr)} days with hit rates")
|
||||
for lookback, threshold, name in SIGNAL_GATE_CONFIGS:
|
||||
hr = hit_rates[lookback]
|
||||
bt = run_backtest(pred, hr, close_df, ws, we, topk=10, threshold=threshold)
|
||||
if "error" in bt:
|
||||
print(f" {name}: {bt['error']}")
|
||||
continue
|
||||
row = {
|
||||
"source": tag,
|
||||
"window": wl,
|
||||
"gate": name,
|
||||
"start": ws,
|
||||
"end": we,
|
||||
"trade_dates": bt["trade_dates"],
|
||||
"gate_open": bt["gate_open_days"],
|
||||
"gate_closed": bt["gate_closed_days"],
|
||||
"trip_rate": bt["trip_rate"],
|
||||
"base_ann": bt["base"]["ann_return"],
|
||||
"base_sharpe": bt["base"]["sharpe"],
|
||||
"base_maxDD": bt["base"]["maxDD"],
|
||||
"gated_ann": bt["gated"]["ann_return"],
|
||||
"gated_sharpe": bt["gated"]["sharpe"],
|
||||
"gated_maxDD": bt["gated"]["maxDD"],
|
||||
}
|
||||
results.append(row)
|
||||
diff = bt["gated"]["ann_return"] - bt["base"]["ann_return"]
|
||||
print(f" {name}: trip={bt['trip_rate']:.1%}, "
|
||||
f"base={bt['base']['ann_return']:+.1%} (Sharpe {bt['base']['sharpe']:.2f}), "
|
||||
f"gated={bt['gated']['ann_return']:+.1%} (Sharpe {bt['gated']['sharpe']:.2f}), "
|
||||
f"diff={diff:+.1%}pp")
|
||||
return results
|
||||
|
||||
|
||||
def main():
|
||||
OUT_DIR.mkdir(parents=True, exist_ok=True)
|
||||
full_start = "2015-01-03"
|
||||
full_end = "2026-08-19"
|
||||
print("Loading lake bars...")
|
||||
close_df = load_bars_for_window(full_start, full_end)
|
||||
print(f" {close_df.shape[1]} symbols, {close_df.shape[0]} days")
|
||||
global ret_df
|
||||
ret_df = close_df.pct_change()
|
||||
ret_df.index = pd.to_datetime(ret_df.index).normalize()
|
||||
|
||||
print("\n" + "=" * 70)
|
||||
print("RUN A: Retrained model pred.pkls (exp 62)")
|
||||
print("=" * 70)
|
||||
results_retrained = run_set(WINDOWS_RETRAINED, close_df, "retrained")
|
||||
|
||||
print("\n" + "=" * 70)
|
||||
print("RUN B: Reference pred.pkls (exp 52/56)")
|
||||
print("=" * 70)
|
||||
results_reference = run_set(WINDOWS_REFERENCE, close_df, "reference")
|
||||
|
||||
all_results = results_retrained + results_reference
|
||||
df = pd.DataFrame(all_results)
|
||||
|
||||
# Save combined results
|
||||
out_path = OUT_DIR / "signal_quality_gate_retrained.csv"
|
||||
df.to_csv(out_path, index=False)
|
||||
with open(OUT_DIR / "signal_quality_gate_retrained.json", "w") as f:
|
||||
json.dump(df.to_dict(orient="records"), f, indent=2, default=str)
|
||||
print(f"\nSaved to {out_path}")
|
||||
|
||||
# Head-to-head comparison table
|
||||
print("\n" + "=" * 70)
|
||||
print("HEAD-TO-HEAD: Retrained vs Reference (hitrate_5d_0.50)")
|
||||
print("=" * 70)
|
||||
print(f"{'Year':>6} | {'Ref Base':>10} {'Ref Gated':>10} {'Ref Diff':>10} | {'Ret Base':>10} {'Ret Gated':>10} {'Ret Diff':>10}")
|
||||
print("-" * 85)
|
||||
for year in ["2021", "2023", "2024", "2025", "2026"]:
|
||||
ref = df[(df["source"] == "reference") & (df["window"] == year) & (df["gate"] == "hitrate_5d_0.50")]
|
||||
ret = df[(df["source"] == "retrained") & (df["window"] == year) & (df["gate"] == "hitrate_5d_0.50")]
|
||||
if len(ref) > 0 and len(ret) > 0:
|
||||
rb = ref.iloc[0]["base_ann"]
|
||||
rg = ref.iloc[0]["gated_ann"]
|
||||
tb = ret.iloc[0]["base_ann"]
|
||||
tg = ret.iloc[0]["gated_ann"]
|
||||
print(f"{year:>6} | {rb:>+9.1%} {rg:>+9.1%} {rg-rb:>+9.1%} | {tb:>+9.1%} {tg:>+9.1%} {tg-tb:>+9.1%}")
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
main()
|
||||
@@ -0,0 +1,119 @@
|
||||
{%- set LAKE = TAC_LAKE_DIR %}
|
||||
{%- set UNIVERSE = "SPY,QQQ,DIA,IWM,MDY,VTI,VOO,VEA,VWO,VT,EFA,EEM,TLT,IEF,SHY,AGG,BND,LQD,HYG,JNK,EMB,GLD,SLV,USO,UNG,DBA,DBC,XLK,XLF,XLE,XLV,XLI,XLY,XLP,XLU,XLB,XLRE,ARKK,SMH,SOXX,IBB,XBI,ITA,XAR,ICLN,TAN,FDN,IGV,ESPO,REM" %}
|
||||
{%- set SP_FIELDS = "sp_ret,sp_jump_ratio,sp_jump_flag,sp_jump_tail,sp_max_move,sp_rv1,sp_rv5,sp_rv22,sp_vol_ratio_5_22,sp_vol_ratio_1_22,sp_trend_slope_5,sp_trend_slope_20,sp_trend_slope_60,sp_logp,sp_hurst_exponent,sp_sig_level1_lead,sp_sig_level1_lag,sp_sig_level2_lead_lag,sp_sig_level2_lag_lead" %}
|
||||
|
||||
qlib_init:
|
||||
provider_uri: "{{ LAKE }}"
|
||||
region: us
|
||||
expression_cache: null
|
||||
dataset_cache: null
|
||||
calendar_provider:
|
||||
class: tac_qlib.data.providers.LakeCalendarProvider
|
||||
kwargs: { lake_root: "{{ LAKE }}", market: US }
|
||||
instrument_provider:
|
||||
class: tac_qlib.data.providers.LakeInstrumentProvider
|
||||
kwargs: { lake_root: "{{ LAKE }}", market: US, markets: {} }
|
||||
feature_provider:
|
||||
class: tac_qlib.data.providers.LakeFeatureProvider
|
||||
kwargs: { lake_root: "{{ LAKE }}", market: US }
|
||||
exp_manager:
|
||||
class: MLflowExpManager
|
||||
module_path: qlib.workflow.expm
|
||||
kwargs:
|
||||
uri: "sqlite:///{{ LAKE }}/mlruns.db"
|
||||
default_exp_name: "tac-rd-sq-gate-wk-2021"
|
||||
|
||||
task:
|
||||
model:
|
||||
class: RankICEnsembleLGBModel
|
||||
module_path: tac_qlib.contrib.model.rank_ensemble
|
||||
kwargs:
|
||||
loss: mse
|
||||
learning_rate: 0.02
|
||||
num_leaves: 31
|
||||
n_estimators: 3000
|
||||
num_boost_round: 3000
|
||||
early_stopping_rounds: 200
|
||||
min_data_in_leaf: 20
|
||||
lambda_l2: 0.5
|
||||
colsample_bytree: 0.8
|
||||
subsample: 0.8
|
||||
subsample_freq: 1
|
||||
reg_alpha: 0.1
|
||||
reg_lambda: 1.0
|
||||
seeds: "42,7,2026,99,123"
|
||||
parallel: 5
|
||||
|
||||
dataset:
|
||||
class: DatasetH
|
||||
module_path: qlib.data.dataset
|
||||
kwargs:
|
||||
handler:
|
||||
class: TACHandler
|
||||
module_path: tac_qlib.contrib.data.handler
|
||||
kwargs:
|
||||
instruments: "{{ UNIVERSE }}"
|
||||
start_time: 2015-01-03
|
||||
end_time: 2021-12-31
|
||||
fit_start_time: 2017-01-03
|
||||
fit_end_time: 2020-12-31
|
||||
freq: day
|
||||
lake_root: "{{ LAKE }}"
|
||||
market: US
|
||||
label: "Ref($close,-6)/Ref($close,-1)-1"
|
||||
feature_fields: "$open,$high,$low,$close,$vwap,$volume,{{ SP_FIELDS }}"
|
||||
infer_processors:
|
||||
- class: DropAllNaN
|
||||
kwargs: { fit_start_time: 2017-01-03, fit_end_time: 2020-12-31 }
|
||||
- class: ProcessInf
|
||||
kwargs: {}
|
||||
- class: CSRankNorm
|
||||
kwargs: {}
|
||||
- class: ZScoreNorm
|
||||
kwargs: { fit_start_time: 2017-01-03, fit_end_time: 2020-12-31 }
|
||||
- class: Fillna
|
||||
kwargs: {}
|
||||
segments:
|
||||
train: [2017-01-03, 2020-12-31]
|
||||
valid: [2021-01-04, 2021-01-04]
|
||||
test: [2021-01-04, 2021-12-31]
|
||||
|
||||
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: WeeklyRebalanceSignalQualityGateStrategy
|
||||
module_path: tac_qlib.contrib.strategy.weekly_sq_gate
|
||||
kwargs:
|
||||
signal: "<PRED>"
|
||||
lake_root: "{{ LAKE }}"
|
||||
gate_topk: 10
|
||||
gate_lookback: 5
|
||||
gate_threshold: 0.5
|
||||
gate_start: "2017-01-03"
|
||||
gate_end: "2021-12-31"
|
||||
topk: 10
|
||||
n_drop: 1
|
||||
only_tradable: true
|
||||
risk_degree: 0.95
|
||||
backtest:
|
||||
start_time: 2021-01-04
|
||||
end_time: 2021-12-31
|
||||
account: 1000000
|
||||
benchmark: SPY
|
||||
exchange_kwargs:
|
||||
codes: "{{ UNIVERSE }}"
|
||||
deal_price: $close
|
||||
freq: day
|
||||
open_cost: 0.0005
|
||||
close_cost: 0.0015
|
||||
min_cost: 5.0
|
||||
risk_analysis_freq: 1d
|
||||
@@ -0,0 +1,115 @@
|
||||
{%- set LAKE = TAC_LAKE_DIR %}
|
||||
{%- set UNIVERSE = "SPY,QQQ,DIA,IWM,MDY,VTI,VOO,VEA,VWO,VT,EFA,EEM,TLT,IEF,SHY,AGG,BND,LQD,HYG,JNK,EMB,GLD,SLV,USO,UNG,DBA,DBC,XLK,XLF,XLE,XLV,XLI,XLY,XLP,XLU,XLB,XLRE,ARKK,SMH,SOXX,IBB,XBI,ITA,XAR,ICLN,TAN,FDN,IGV,ESPO,REM" %}
|
||||
{%- set SP_FIELDS = "sp_ret,sp_jump_ratio,sp_jump_flag,sp_jump_tail,sp_max_move,sp_rv1,sp_rv5,sp_rv22,sp_vol_ratio_5_22,sp_vol_ratio_1_22,sp_trend_slope_5,sp_trend_slope_20,sp_trend_slope_60,sp_logp,sp_hurst_exponent,sp_sig_level1_lead,sp_sig_level1_lag,sp_sig_level2_lead_lag,sp_sig_level2_lag_lead" %}
|
||||
qlib_init:
|
||||
provider_uri: "{{ LAKE }}"
|
||||
region: us
|
||||
expression_cache: null
|
||||
dataset_cache: null
|
||||
calendar_provider:
|
||||
class: tac_qlib.data.providers.LakeCalendarProvider
|
||||
kwargs: { lake_root: "{{ LAKE }}", market: US }
|
||||
instrument_provider:
|
||||
class: tac_qlib.data.providers.LakeInstrumentProvider
|
||||
kwargs: { lake_root: "{{ LAKE }}", market: US, markets: {} }
|
||||
feature_provider:
|
||||
class: tac_qlib.data.providers.LakeFeatureProvider
|
||||
kwargs: { lake_root: "{{ LAKE }}", market: US }
|
||||
exp_manager:
|
||||
class: MLflowExpManager
|
||||
module_path: qlib.workflow.expm
|
||||
kwargs:
|
||||
uri: "sqlite:///{{ LAKE }}/mlruns.db"
|
||||
default_exp_name: "tac-rd-sq-gate-wk-2023"
|
||||
task:
|
||||
model:
|
||||
class: RankICEnsembleLGBModel
|
||||
module_path: tac_qlib.contrib.model.rank_ensemble
|
||||
kwargs:
|
||||
loss: mse
|
||||
learning_rate: 0.02
|
||||
num_leaves: 31
|
||||
n_estimators: 3000
|
||||
num_boost_round: 3000
|
||||
early_stopping_rounds: 200
|
||||
min_data_in_leaf: 20
|
||||
lambda_l2: 0.5
|
||||
colsample_bytree: 0.8
|
||||
subsample: 0.8
|
||||
subsample_freq: 1
|
||||
reg_alpha: 0.1
|
||||
reg_lambda: 1.0
|
||||
seeds: "42,7,2026,99,123"
|
||||
parallel: 5
|
||||
dataset:
|
||||
class: DatasetH
|
||||
module_path: qlib.data.dataset
|
||||
kwargs:
|
||||
handler:
|
||||
class: TACHandler
|
||||
module_path: tac_qlib.contrib.data.handler
|
||||
kwargs:
|
||||
instruments: "{{ UNIVERSE }}"
|
||||
start_time: 2015-01-03
|
||||
end_time: 2023-12-29
|
||||
fit_start_time: 2019-01-02
|
||||
fit_end_time: 2022-12-30
|
||||
freq: day
|
||||
lake_root: "{{ LAKE }}"
|
||||
market: US
|
||||
label: "Ref($close,-6)/Ref($close,-1)-1"
|
||||
feature_fields: "$open,$high,$low,$close,$vwap,$volume,{{ SP_FIELDS }}"
|
||||
infer_processors:
|
||||
- class: DropAllNaN
|
||||
kwargs: { fit_start_time: 2019-01-02, fit_end_time: 2022-12-30 }
|
||||
- class: ProcessInf
|
||||
kwargs: {}
|
||||
- class: CSRankNorm
|
||||
kwargs: {}
|
||||
- class: ZScoreNorm
|
||||
kwargs: { fit_start_time: 2019-01-02, fit_end_time: 2022-12-30 }
|
||||
- class: Fillna
|
||||
kwargs: {}
|
||||
segments:
|
||||
train: [2019-01-02, 2022-12-30]
|
||||
valid: [2023-01-03, 2023-01-03]
|
||||
test: [2023-01-03, 2023-12-29]
|
||||
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: WeeklyRebalanceSignalQualityGateStrategy
|
||||
module_path: tac_qlib.contrib.strategy.weekly_sq_gate
|
||||
kwargs:
|
||||
signal: "<PRED>"
|
||||
lake_root: "{{ LAKE }}"
|
||||
gate_topk: 10
|
||||
gate_lookback: 5
|
||||
gate_threshold: 0.5
|
||||
gate_start: "2019-01-02"
|
||||
gate_end: "2023-12-29"
|
||||
topk: 10
|
||||
n_drop: 1
|
||||
only_tradable: true
|
||||
risk_degree: 0.95
|
||||
backtest:
|
||||
start_time: 2023-01-03
|
||||
end_time: 2023-12-29
|
||||
account: 1000000
|
||||
benchmark: SPY
|
||||
exchange_kwargs:
|
||||
codes: "{{ UNIVERSE }}"
|
||||
deal_price: $close
|
||||
freq: day
|
||||
open_cost: 0.0005
|
||||
close_cost: 0.0015
|
||||
min_cost: 5.0
|
||||
risk_analysis_freq: 1d
|
||||
@@ -0,0 +1,115 @@
|
||||
{%- set LAKE = TAC_LAKE_DIR %}
|
||||
{%- set UNIVERSE = "SPY,QQQ,DIA,IWM,MDY,VTI,VOO,VEA,VWO,VT,EFA,EEM,TLT,IEF,SHY,AGG,BND,LQD,HYG,JNK,EMB,GLD,SLV,USO,UNG,DBA,DBC,XLK,XLF,XLE,XLV,XLI,XLY,XLP,XLU,XLB,XLRE,ARKK,SMH,SOXX,IBB,XBI,ITA,XAR,ICLN,TAN,FDN,IGV,ESPO,REM" %}
|
||||
{%- set SP_FIELDS = "sp_ret,sp_jump_ratio,sp_jump_flag,sp_jump_tail,sp_max_move,sp_rv1,sp_rv5,sp_rv22,sp_vol_ratio_5_22,sp_vol_ratio_1_22,sp_trend_slope_5,sp_trend_slope_20,sp_trend_slope_60,sp_logp,sp_hurst_exponent,sp_sig_level1_lead,sp_sig_level1_lag,sp_sig_level2_lead_lag,sp_sig_level2_lag_lead" %}
|
||||
qlib_init:
|
||||
provider_uri: "{{ LAKE }}"
|
||||
region: us
|
||||
expression_cache: null
|
||||
dataset_cache: null
|
||||
calendar_provider:
|
||||
class: tac_qlib.data.providers.LakeCalendarProvider
|
||||
kwargs: { lake_root: "{{ LAKE }}", market: US }
|
||||
instrument_provider:
|
||||
class: tac_qlib.data.providers.LakeInstrumentProvider
|
||||
kwargs: { lake_root: "{{ LAKE }}", market: US, markets: {} }
|
||||
feature_provider:
|
||||
class: tac_qlib.data.providers.LakeFeatureProvider
|
||||
kwargs: { lake_root: "{{ LAKE }}", market: US }
|
||||
exp_manager:
|
||||
class: MLflowExpManager
|
||||
module_path: qlib.workflow.expm
|
||||
kwargs:
|
||||
uri: "sqlite:///{{ LAKE }}/mlruns.db"
|
||||
default_exp_name: "tac-rd-sq-gate-wk-2024"
|
||||
task:
|
||||
model:
|
||||
class: RankICEnsembleLGBModel
|
||||
module_path: tac_qlib.contrib.model.rank_ensemble
|
||||
kwargs:
|
||||
loss: mse
|
||||
learning_rate: 0.02
|
||||
num_leaves: 31
|
||||
n_estimators: 3000
|
||||
num_boost_round: 3000
|
||||
early_stopping_rounds: 200
|
||||
min_data_in_leaf: 20
|
||||
lambda_l2: 0.5
|
||||
colsample_bytree: 0.8
|
||||
subsample: 0.8
|
||||
subsample_freq: 1
|
||||
reg_alpha: 0.1
|
||||
reg_lambda: 1.0
|
||||
seeds: "42,7,2026,99,123"
|
||||
parallel: 5
|
||||
dataset:
|
||||
class: DatasetH
|
||||
module_path: qlib.data.dataset
|
||||
kwargs:
|
||||
handler:
|
||||
class: TACHandler
|
||||
module_path: tac_qlib.contrib.data.handler
|
||||
kwargs:
|
||||
instruments: "{{ UNIVERSE }}"
|
||||
start_time: 2015-01-03
|
||||
end_time: 2024-12-31
|
||||
fit_start_time: 2020-01-02
|
||||
fit_end_time: 2023-12-29
|
||||
freq: day
|
||||
lake_root: "{{ LAKE }}"
|
||||
market: US
|
||||
label: "Ref($close,-6)/Ref($close,-1)-1"
|
||||
feature_fields: "$open,$high,$low,$close,$vwap,$volume,{{ SP_FIELDS }}"
|
||||
infer_processors:
|
||||
- class: DropAllNaN
|
||||
kwargs: { fit_start_time: 2020-01-02, fit_end_time: 2023-12-29 }
|
||||
- class: ProcessInf
|
||||
kwargs: {}
|
||||
- class: CSRankNorm
|
||||
kwargs: {}
|
||||
- class: ZScoreNorm
|
||||
kwargs: { fit_start_time: 2020-01-02, fit_end_time: 2023-12-29 }
|
||||
- class: Fillna
|
||||
kwargs: {}
|
||||
segments:
|
||||
train: [2020-01-02, 2023-12-29]
|
||||
valid: [2024-01-02, 2024-01-02]
|
||||
test: [2024-01-02, 2024-12-31]
|
||||
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: WeeklyRebalanceSignalQualityGateStrategy
|
||||
module_path: tac_qlib.contrib.strategy.weekly_sq_gate
|
||||
kwargs:
|
||||
signal: "<PRED>"
|
||||
lake_root: "{{ LAKE }}"
|
||||
gate_topk: 10
|
||||
gate_lookback: 5
|
||||
gate_threshold: 0.5
|
||||
gate_start: "2020-01-02"
|
||||
gate_end: "2024-12-31"
|
||||
topk: 10
|
||||
n_drop: 1
|
||||
only_tradable: true
|
||||
risk_degree: 0.95
|
||||
backtest:
|
||||
start_time: 2024-01-02
|
||||
end_time: 2024-12-31
|
||||
account: 1000000
|
||||
benchmark: SPY
|
||||
exchange_kwargs:
|
||||
codes: "{{ UNIVERSE }}"
|
||||
deal_price: $close
|
||||
freq: day
|
||||
open_cost: 0.0005
|
||||
close_cost: 0.0015
|
||||
min_cost: 5.0
|
||||
risk_analysis_freq: 1d
|
||||
@@ -0,0 +1,115 @@
|
||||
{%- set LAKE = TAC_LAKE_DIR %}
|
||||
{%- set UNIVERSE = "SPY,QQQ,DIA,IWM,MDY,VTI,VOO,VEA,VWO,VT,EFA,EEM,TLT,IEF,SHY,AGG,BND,LQD,HYG,JNK,EMB,GLD,SLV,USO,UNG,DBA,DBC,XLK,XLF,XLE,XLV,XLI,XLY,XLP,XLU,XLB,XLRE,ARKK,SMH,SOXX,IBB,XBI,ITA,XAR,ICLN,TAN,FDN,IGV,ESPO,REM" %}
|
||||
{%- set SP_FIELDS = "sp_ret,sp_jump_ratio,sp_jump_flag,sp_jump_tail,sp_max_move,sp_rv1,sp_rv5,sp_rv22,sp_vol_ratio_5_22,sp_vol_ratio_1_22,sp_trend_slope_5,sp_trend_slope_20,sp_trend_slope_60,sp_logp,sp_hurst_exponent,sp_sig_level1_lead,sp_sig_level1_lag,sp_sig_level2_lead_lag,sp_sig_level2_lag_lead" %}
|
||||
qlib_init:
|
||||
provider_uri: "{{ LAKE }}"
|
||||
region: us
|
||||
expression_cache: null
|
||||
dataset_cache: null
|
||||
calendar_provider:
|
||||
class: tac_qlib.data.providers.LakeCalendarProvider
|
||||
kwargs: { lake_root: "{{ LAKE }}", market: US }
|
||||
instrument_provider:
|
||||
class: tac_qlib.data.providers.LakeInstrumentProvider
|
||||
kwargs: { lake_root: "{{ LAKE }}", market: US, markets: {} }
|
||||
feature_provider:
|
||||
class: tac_qlib.data.providers.LakeFeatureProvider
|
||||
kwargs: { lake_root: "{{ LAKE }}", market: US }
|
||||
exp_manager:
|
||||
class: MLflowExpManager
|
||||
module_path: qlib.workflow.expm
|
||||
kwargs:
|
||||
uri: "sqlite:///{{ LAKE }}/mlruns.db"
|
||||
default_exp_name: "tac-rd-sq-gate-wk-2025"
|
||||
task:
|
||||
model:
|
||||
class: RankICEnsembleLGBModel
|
||||
module_path: tac_qlib.contrib.model.rank_ensemble
|
||||
kwargs:
|
||||
loss: mse
|
||||
learning_rate: 0.02
|
||||
num_leaves: 31
|
||||
n_estimators: 3000
|
||||
num_boost_round: 3000
|
||||
early_stopping_rounds: 200
|
||||
min_data_in_leaf: 20
|
||||
lambda_l2: 0.5
|
||||
colsample_bytree: 0.8
|
||||
subsample: 0.8
|
||||
subsample_freq: 1
|
||||
reg_alpha: 0.1
|
||||
reg_lambda: 1.0
|
||||
seeds: "42,7,2026,99,123"
|
||||
parallel: 5
|
||||
dataset:
|
||||
class: DatasetH
|
||||
module_path: qlib.data.dataset
|
||||
kwargs:
|
||||
handler:
|
||||
class: TACHandler
|
||||
module_path: tac_qlib.contrib.data.handler
|
||||
kwargs:
|
||||
instruments: "{{ UNIVERSE }}"
|
||||
start_time: 2015-01-03
|
||||
end_time: 2025-12-31
|
||||
fit_start_time: 2021-01-04
|
||||
fit_end_time: 2024-12-31
|
||||
freq: day
|
||||
lake_root: "{{ LAKE }}"
|
||||
market: US
|
||||
label: "Ref($close,-6)/Ref($close,-1)-1"
|
||||
feature_fields: "$open,$high,$low,$close,$vwap,$volume,{{ SP_FIELDS }}"
|
||||
infer_processors:
|
||||
- class: DropAllNaN
|
||||
kwargs: { fit_start_time: 2021-01-04, fit_end_time: 2024-12-31 }
|
||||
- class: ProcessInf
|
||||
kwargs: {}
|
||||
- class: CSRankNorm
|
||||
kwargs: {}
|
||||
- class: ZScoreNorm
|
||||
kwargs: { fit_start_time: 2021-01-04, fit_end_time: 2024-12-31 }
|
||||
- class: Fillna
|
||||
kwargs: {}
|
||||
segments:
|
||||
train: [2021-01-04, 2024-12-31]
|
||||
valid: [2025-01-02, 2025-01-02]
|
||||
test: [2025-01-02, 2025-12-31]
|
||||
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: WeeklyRebalanceSignalQualityGateStrategy
|
||||
module_path: tac_qlib.contrib.strategy.weekly_sq_gate
|
||||
kwargs:
|
||||
signal: "<PRED>"
|
||||
lake_root: "{{ LAKE }}"
|
||||
gate_topk: 10
|
||||
gate_lookback: 5
|
||||
gate_threshold: 0.5
|
||||
gate_start: "2021-01-04"
|
||||
gate_end: "2025-12-31"
|
||||
topk: 10
|
||||
n_drop: 1
|
||||
only_tradable: true
|
||||
risk_degree: 0.95
|
||||
backtest:
|
||||
start_time: 2025-01-02
|
||||
end_time: 2025-12-31
|
||||
account: 1000000
|
||||
benchmark: SPY
|
||||
exchange_kwargs:
|
||||
codes: "{{ UNIVERSE }}"
|
||||
deal_price: $close
|
||||
freq: day
|
||||
open_cost: 0.0005
|
||||
close_cost: 0.0015
|
||||
min_cost: 5.0
|
||||
risk_analysis_freq: 1d
|
||||
@@ -0,0 +1,115 @@
|
||||
{%- set LAKE = TAC_LAKE_DIR %}
|
||||
{%- set UNIVERSE = "SPY,QQQ,DIA,IWM,MDY,VTI,VOO,VEA,VWO,VT,EFA,EEM,TLT,IEF,SHY,AGG,BND,LQD,HYG,JNK,EMB,GLD,SLV,USO,UNG,DBA,DBC,XLK,XLF,XLE,XLV,XLI,XLY,XLP,XLU,XLB,XLRE,ARKK,SMH,SOXX,IBB,XBI,ITA,XAR,ICLN,TAN,FDN,IGV,ESPO,REM" %}
|
||||
{%- set SP_FIELDS = "sp_ret,sp_jump_ratio,sp_jump_flag,sp_jump_tail,sp_max_move,sp_rv1,sp_rv5,sp_rv22,sp_vol_ratio_5_22,sp_vol_ratio_1_22,sp_trend_slope_5,sp_trend_slope_20,sp_trend_slope_60,sp_logp,sp_hurst_exponent,sp_sig_level1_lead,sp_sig_level1_lag,sp_sig_level2_lead_lag,sp_sig_level2_lag_lead" %}
|
||||
qlib_init:
|
||||
provider_uri: "{{ LAKE }}"
|
||||
region: us
|
||||
expression_cache: null
|
||||
dataset_cache: null
|
||||
calendar_provider:
|
||||
class: tac_qlib.data.providers.LakeCalendarProvider
|
||||
kwargs: { lake_root: "{{ LAKE }}", market: US }
|
||||
instrument_provider:
|
||||
class: tac_qlib.data.providers.LakeInstrumentProvider
|
||||
kwargs: { lake_root: "{{ LAKE }}", market: US, markets: {} }
|
||||
feature_provider:
|
||||
class: tac_qlib.data.providers.LakeFeatureProvider
|
||||
kwargs: { lake_root: "{{ LAKE }}", market: US }
|
||||
exp_manager:
|
||||
class: MLflowExpManager
|
||||
module_path: qlib.workflow.expm
|
||||
kwargs:
|
||||
uri: "sqlite:///{{ LAKE }}/mlruns.db"
|
||||
default_exp_name: "tac-rd-sq-gate-wk-2026"
|
||||
task:
|
||||
model:
|
||||
class: RankICEnsembleLGBModel
|
||||
module_path: tac_qlib.contrib.model.rank_ensemble
|
||||
kwargs:
|
||||
loss: mse
|
||||
learning_rate: 0.02
|
||||
num_leaves: 31
|
||||
n_estimators: 3000
|
||||
num_boost_round: 3000
|
||||
early_stopping_rounds: 200
|
||||
min_data_in_leaf: 20
|
||||
lambda_l2: 0.5
|
||||
colsample_bytree: 0.8
|
||||
subsample: 0.8
|
||||
subsample_freq: 1
|
||||
reg_alpha: 0.1
|
||||
reg_lambda: 1.0
|
||||
seeds: "42,7,2026,99,123"
|
||||
parallel: 5
|
||||
dataset:
|
||||
class: DatasetH
|
||||
module_path: qlib.data.dataset
|
||||
kwargs:
|
||||
handler:
|
||||
class: TACHandler
|
||||
module_path: tac_qlib.contrib.data.handler
|
||||
kwargs:
|
||||
instruments: "{{ UNIVERSE }}"
|
||||
start_time: 2015-01-03
|
||||
end_time: 2026-08-10
|
||||
fit_start_time: 2016-01-04
|
||||
fit_end_time: 2025-09-01
|
||||
freq: day
|
||||
lake_root: "{{ LAKE }}"
|
||||
market: US
|
||||
label: "Ref($close,-6)/Ref($close,-1)-1"
|
||||
feature_fields: "$open,$high,$low,$close,$vwap,$volume,{{ SP_FIELDS }}"
|
||||
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: WeeklyRebalanceSignalQualityGateStrategy
|
||||
module_path: tac_qlib.contrib.strategy.weekly_sq_gate
|
||||
kwargs:
|
||||
signal: "<PRED>"
|
||||
lake_root: "{{ LAKE }}"
|
||||
gate_topk: 10
|
||||
gate_lookback: 5
|
||||
gate_threshold: 0.5
|
||||
gate_start: "2016-01-04"
|
||||
gate_end: "2026-08-19"
|
||||
topk: 10
|
||||
n_drop: 1
|
||||
only_tradable: true
|
||||
risk_degree: 0.95
|
||||
backtest:
|
||||
start_time: 2026-01-04
|
||||
end_time: 2026-08-10
|
||||
account: 1000000
|
||||
benchmark: SPY
|
||||
exchange_kwargs:
|
||||
codes: "{{ UNIVERSE }}"
|
||||
deal_price: $close
|
||||
freq: day
|
||||
open_cost: 0.0005
|
||||
close_cost: 0.0015
|
||||
min_cost: 5.0
|
||||
risk_analysis_freq: 1d
|
||||
@@ -0,0 +1,119 @@
|
||||
{%- set LAKE = TAC_LAKE_DIR %}
|
||||
{%- set UNIVERSE = "SPY,QQQ,DIA,IWM,MDY,VTI,VOO,VEA,VWO,VT,EFA,EEM,TLT,IEF,SHY,AGG,BND,LQD,HYG,JNK,EMB,GLD,SLV,USO,UNG,DBA,DBC,XLK,XLF,XLE,XLV,XLI,XLY,XLP,XLU,XLB,XLRE,ARKK,SMH,SOXX,IBB,XBI,ITA,XAR,ICLN,TAN,FDN,IGV,ESPO,REM" %}
|
||||
{%- set SP_FIELDS = "sp_ret,sp_jump_ratio,sp_jump_flag,sp_jump_tail,sp_max_move,sp_rv1,sp_rv5,sp_rv22,sp_vol_ratio_5_22,sp_vol_ratio_1_22,sp_trend_slope_5,sp_trend_slope_20,sp_trend_slope_60,sp_logp,sp_hurst_exponent,sp_sig_level1_lead,sp_sig_level1_lag,sp_sig_level2_lead_lag,sp_sig_level2_lag_lead" %}
|
||||
|
||||
qlib_init:
|
||||
provider_uri: "{{ LAKE }}"
|
||||
region: us
|
||||
expression_cache: null
|
||||
dataset_cache: null
|
||||
calendar_provider:
|
||||
class: tac_qlib.data.providers.LakeCalendarProvider
|
||||
kwargs: { lake_root: "{{ LAKE }}", market: US }
|
||||
instrument_provider:
|
||||
class: tac_qlib.data.providers.LakeInstrumentProvider
|
||||
kwargs: { lake_root: "{{ LAKE }}", market: US, markets: {} }
|
||||
feature_provider:
|
||||
class: tac_qlib.data.providers.LakeFeatureProvider
|
||||
kwargs: { lake_root: "{{ LAKE }}", market: US }
|
||||
exp_manager:
|
||||
class: MLflowExpManager
|
||||
module_path: qlib.workflow.expm
|
||||
kwargs:
|
||||
uri: "sqlite:///{{ LAKE }}/mlruns.db"
|
||||
default_exp_name: "tac-rd-sq-gate-wk-zc-2021"
|
||||
|
||||
task:
|
||||
model:
|
||||
class: RankICEnsembleLGBModel
|
||||
module_path: tac_qlib.contrib.model.rank_ensemble
|
||||
kwargs:
|
||||
loss: mse
|
||||
learning_rate: 0.02
|
||||
num_leaves: 31
|
||||
n_estimators: 3000
|
||||
num_boost_round: 3000
|
||||
early_stopping_rounds: 200
|
||||
min_data_in_leaf: 20
|
||||
lambda_l2: 0.5
|
||||
colsample_bytree: 0.8
|
||||
subsample: 0.8
|
||||
subsample_freq: 1
|
||||
reg_alpha: 0.1
|
||||
reg_lambda: 1.0
|
||||
seeds: "42,7,2026,99,123"
|
||||
parallel: 5
|
||||
|
||||
dataset:
|
||||
class: DatasetH
|
||||
module_path: qlib.data.dataset
|
||||
kwargs:
|
||||
handler:
|
||||
class: TACHandler
|
||||
module_path: tac_qlib.contrib.data.handler
|
||||
kwargs:
|
||||
instruments: "{{ UNIVERSE }}"
|
||||
start_time: 2015-01-03
|
||||
end_time: 2021-12-31
|
||||
fit_start_time: 2017-01-03
|
||||
fit_end_time: 2020-12-31
|
||||
freq: day
|
||||
lake_root: "{{ LAKE }}"
|
||||
market: US
|
||||
label: "Ref($close,-6)/Ref($close,-1)-1"
|
||||
feature_fields: "$open,$high,$low,$close,$vwap,$volume,{{ SP_FIELDS }}"
|
||||
infer_processors:
|
||||
- class: DropAllNaN
|
||||
kwargs: { fit_start_time: 2017-01-03, fit_end_time: 2020-12-31 }
|
||||
- class: ProcessInf
|
||||
kwargs: {}
|
||||
- class: CSRankNorm
|
||||
kwargs: {}
|
||||
- class: ZScoreNorm
|
||||
kwargs: { fit_start_time: 2017-01-03, fit_end_time: 2020-12-31 }
|
||||
- class: Fillna
|
||||
kwargs: {}
|
||||
segments:
|
||||
train: [2017-01-03, 2020-12-31]
|
||||
valid: [2021-01-04, 2021-01-04]
|
||||
test: [2021-01-04, 2021-12-31]
|
||||
|
||||
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: WeeklyRebalanceSignalQualityGateStrategy
|
||||
module_path: tac_qlib.contrib.strategy.weekly_sq_gate
|
||||
kwargs:
|
||||
signal: "<PRED>"
|
||||
lake_root: "{{ LAKE }}"
|
||||
gate_topk: 10
|
||||
gate_lookback: 5
|
||||
gate_threshold: 0.5
|
||||
gate_start: "2017-01-03"
|
||||
gate_end: "2021-12-31"
|
||||
topk: 10
|
||||
n_drop: 1
|
||||
only_tradable: true
|
||||
risk_degree: 0.95
|
||||
backtest:
|
||||
start_time: 2021-01-04
|
||||
end_time: 2021-12-31
|
||||
account: 1000000
|
||||
benchmark: SPY
|
||||
exchange_kwargs:
|
||||
codes: "{{ UNIVERSE }}"
|
||||
deal_price: $close
|
||||
freq: day
|
||||
open_cost: 0.0
|
||||
close_cost: 0.0
|
||||
min_cost: 0.0
|
||||
risk_analysis_freq: 1d
|
||||
@@ -0,0 +1,115 @@
|
||||
{%- set LAKE = TAC_LAKE_DIR %}
|
||||
{%- set UNIVERSE = "SPY,QQQ,DIA,IWM,MDY,VTI,VOO,VEA,VWO,VT,EFA,EEM,TLT,IEF,SHY,AGG,BND,LQD,HYG,JNK,EMB,GLD,SLV,USO,UNG,DBA,DBC,XLK,XLF,XLE,XLV,XLI,XLY,XLP,XLU,XLB,XLRE,ARKK,SMH,SOXX,IBB,XBI,ITA,XAR,ICLN,TAN,FDN,IGV,ESPO,REM" %}
|
||||
{%- set SP_FIELDS = "sp_ret,sp_jump_ratio,sp_jump_flag,sp_jump_tail,sp_max_move,sp_rv1,sp_rv5,sp_rv22,sp_vol_ratio_5_22,sp_vol_ratio_1_22,sp_trend_slope_5,sp_trend_slope_20,sp_trend_slope_60,sp_logp,sp_hurst_exponent,sp_sig_level1_lead,sp_sig_level1_lag,sp_sig_level2_lead_lag,sp_sig_level2_lag_lead" %}
|
||||
qlib_init:
|
||||
provider_uri: "{{ LAKE }}"
|
||||
region: us
|
||||
expression_cache: null
|
||||
dataset_cache: null
|
||||
calendar_provider:
|
||||
class: tac_qlib.data.providers.LakeCalendarProvider
|
||||
kwargs: { lake_root: "{{ LAKE }}", market: US }
|
||||
instrument_provider:
|
||||
class: tac_qlib.data.providers.LakeInstrumentProvider
|
||||
kwargs: { lake_root: "{{ LAKE }}", market: US, markets: {} }
|
||||
feature_provider:
|
||||
class: tac_qlib.data.providers.LakeFeatureProvider
|
||||
kwargs: { lake_root: "{{ LAKE }}", market: US }
|
||||
exp_manager:
|
||||
class: MLflowExpManager
|
||||
module_path: qlib.workflow.expm
|
||||
kwargs:
|
||||
uri: "sqlite:///{{ LAKE }}/mlruns.db"
|
||||
default_exp_name: "tac-rd-sq-gate-wk-zc-2023"
|
||||
task:
|
||||
model:
|
||||
class: RankICEnsembleLGBModel
|
||||
module_path: tac_qlib.contrib.model.rank_ensemble
|
||||
kwargs:
|
||||
loss: mse
|
||||
learning_rate: 0.02
|
||||
num_leaves: 31
|
||||
n_estimators: 3000
|
||||
num_boost_round: 3000
|
||||
early_stopping_rounds: 200
|
||||
min_data_in_leaf: 20
|
||||
lambda_l2: 0.5
|
||||
colsample_bytree: 0.8
|
||||
subsample: 0.8
|
||||
subsample_freq: 1
|
||||
reg_alpha: 0.1
|
||||
reg_lambda: 1.0
|
||||
seeds: "42,7,2026,99,123"
|
||||
parallel: 5
|
||||
dataset:
|
||||
class: DatasetH
|
||||
module_path: qlib.data.dataset
|
||||
kwargs:
|
||||
handler:
|
||||
class: TACHandler
|
||||
module_path: tac_qlib.contrib.data.handler
|
||||
kwargs:
|
||||
instruments: "{{ UNIVERSE }}"
|
||||
start_time: 2015-01-03
|
||||
end_time: 2023-12-29
|
||||
fit_start_time: 2019-01-02
|
||||
fit_end_time: 2022-12-30
|
||||
freq: day
|
||||
lake_root: "{{ LAKE }}"
|
||||
market: US
|
||||
label: "Ref($close,-6)/Ref($close,-1)-1"
|
||||
feature_fields: "$open,$high,$low,$close,$vwap,$volume,{{ SP_FIELDS }}"
|
||||
infer_processors:
|
||||
- class: DropAllNaN
|
||||
kwargs: { fit_start_time: 2019-01-02, fit_end_time: 2022-12-30 }
|
||||
- class: ProcessInf
|
||||
kwargs: {}
|
||||
- class: CSRankNorm
|
||||
kwargs: {}
|
||||
- class: ZScoreNorm
|
||||
kwargs: { fit_start_time: 2019-01-02, fit_end_time: 2022-12-30 }
|
||||
- class: Fillna
|
||||
kwargs: {}
|
||||
segments:
|
||||
train: [2019-01-02, 2022-12-30]
|
||||
valid: [2023-01-03, 2023-01-03]
|
||||
test: [2023-01-03, 2023-12-29]
|
||||
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: WeeklyRebalanceSignalQualityGateStrategy
|
||||
module_path: tac_qlib.contrib.strategy.weekly_sq_gate
|
||||
kwargs:
|
||||
signal: "<PRED>"
|
||||
lake_root: "{{ LAKE }}"
|
||||
gate_topk: 10
|
||||
gate_lookback: 5
|
||||
gate_threshold: 0.5
|
||||
gate_start: "2019-01-02"
|
||||
gate_end: "2023-12-29"
|
||||
topk: 10
|
||||
n_drop: 1
|
||||
only_tradable: true
|
||||
risk_degree: 0.95
|
||||
backtest:
|
||||
start_time: 2023-01-03
|
||||
end_time: 2023-12-29
|
||||
account: 1000000
|
||||
benchmark: SPY
|
||||
exchange_kwargs:
|
||||
codes: "{{ UNIVERSE }}"
|
||||
deal_price: $close
|
||||
freq: day
|
||||
open_cost: 0.0
|
||||
close_cost: 0.0
|
||||
min_cost: 0.0
|
||||
risk_analysis_freq: 1d
|
||||
@@ -0,0 +1,115 @@
|
||||
{%- set LAKE = TAC_LAKE_DIR %}
|
||||
{%- set UNIVERSE = "SPY,QQQ,DIA,IWM,MDY,VTI,VOO,VEA,VWO,VT,EFA,EEM,TLT,IEF,SHY,AGG,BND,LQD,HYG,JNK,EMB,GLD,SLV,USO,UNG,DBA,DBC,XLK,XLF,XLE,XLV,XLI,XLY,XLP,XLU,XLB,XLRE,ARKK,SMH,SOXX,IBB,XBI,ITA,XAR,ICLN,TAN,FDN,IGV,ESPO,REM" %}
|
||||
{%- set SP_FIELDS = "sp_ret,sp_jump_ratio,sp_jump_flag,sp_jump_tail,sp_max_move,sp_rv1,sp_rv5,sp_rv22,sp_vol_ratio_5_22,sp_vol_ratio_1_22,sp_trend_slope_5,sp_trend_slope_20,sp_trend_slope_60,sp_logp,sp_hurst_exponent,sp_sig_level1_lead,sp_sig_level1_lag,sp_sig_level2_lead_lag,sp_sig_level2_lag_lead" %}
|
||||
qlib_init:
|
||||
provider_uri: "{{ LAKE }}"
|
||||
region: us
|
||||
expression_cache: null
|
||||
dataset_cache: null
|
||||
calendar_provider:
|
||||
class: tac_qlib.data.providers.LakeCalendarProvider
|
||||
kwargs: { lake_root: "{{ LAKE }}", market: US }
|
||||
instrument_provider:
|
||||
class: tac_qlib.data.providers.LakeInstrumentProvider
|
||||
kwargs: { lake_root: "{{ LAKE }}", market: US, markets: {} }
|
||||
feature_provider:
|
||||
class: tac_qlib.data.providers.LakeFeatureProvider
|
||||
kwargs: { lake_root: "{{ LAKE }}", market: US }
|
||||
exp_manager:
|
||||
class: MLflowExpManager
|
||||
module_path: qlib.workflow.expm
|
||||
kwargs:
|
||||
uri: "sqlite:///{{ LAKE }}/mlruns.db"
|
||||
default_exp_name: "tac-rd-sq-gate-wk-zc-2024"
|
||||
task:
|
||||
model:
|
||||
class: RankICEnsembleLGBModel
|
||||
module_path: tac_qlib.contrib.model.rank_ensemble
|
||||
kwargs:
|
||||
loss: mse
|
||||
learning_rate: 0.02
|
||||
num_leaves: 31
|
||||
n_estimators: 3000
|
||||
num_boost_round: 3000
|
||||
early_stopping_rounds: 200
|
||||
min_data_in_leaf: 20
|
||||
lambda_l2: 0.5
|
||||
colsample_bytree: 0.8
|
||||
subsample: 0.8
|
||||
subsample_freq: 1
|
||||
reg_alpha: 0.1
|
||||
reg_lambda: 1.0
|
||||
seeds: "42,7,2026,99,123"
|
||||
parallel: 5
|
||||
dataset:
|
||||
class: DatasetH
|
||||
module_path: qlib.data.dataset
|
||||
kwargs:
|
||||
handler:
|
||||
class: TACHandler
|
||||
module_path: tac_qlib.contrib.data.handler
|
||||
kwargs:
|
||||
instruments: "{{ UNIVERSE }}"
|
||||
start_time: 2015-01-03
|
||||
end_time: 2024-12-31
|
||||
fit_start_time: 2020-01-02
|
||||
fit_end_time: 2023-12-29
|
||||
freq: day
|
||||
lake_root: "{{ LAKE }}"
|
||||
market: US
|
||||
label: "Ref($close,-6)/Ref($close,-1)-1"
|
||||
feature_fields: "$open,$high,$low,$close,$vwap,$volume,{{ SP_FIELDS }}"
|
||||
infer_processors:
|
||||
- class: DropAllNaN
|
||||
kwargs: { fit_start_time: 2020-01-02, fit_end_time: 2023-12-29 }
|
||||
- class: ProcessInf
|
||||
kwargs: {}
|
||||
- class: CSRankNorm
|
||||
kwargs: {}
|
||||
- class: ZScoreNorm
|
||||
kwargs: { fit_start_time: 2020-01-02, fit_end_time: 2023-12-29 }
|
||||
- class: Fillna
|
||||
kwargs: {}
|
||||
segments:
|
||||
train: [2020-01-02, 2023-12-29]
|
||||
valid: [2024-01-02, 2024-01-02]
|
||||
test: [2024-01-02, 2024-12-31]
|
||||
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: WeeklyRebalanceSignalQualityGateStrategy
|
||||
module_path: tac_qlib.contrib.strategy.weekly_sq_gate
|
||||
kwargs:
|
||||
signal: "<PRED>"
|
||||
lake_root: "{{ LAKE }}"
|
||||
gate_topk: 10
|
||||
gate_lookback: 5
|
||||
gate_threshold: 0.5
|
||||
gate_start: "2020-01-02"
|
||||
gate_end: "2024-12-31"
|
||||
topk: 10
|
||||
n_drop: 1
|
||||
only_tradable: true
|
||||
risk_degree: 0.95
|
||||
backtest:
|
||||
start_time: 2024-01-02
|
||||
end_time: 2024-12-31
|
||||
account: 1000000
|
||||
benchmark: SPY
|
||||
exchange_kwargs:
|
||||
codes: "{{ UNIVERSE }}"
|
||||
deal_price: $close
|
||||
freq: day
|
||||
open_cost: 0.0
|
||||
close_cost: 0.0
|
||||
min_cost: 0.0
|
||||
risk_analysis_freq: 1d
|
||||
@@ -0,0 +1,115 @@
|
||||
{%- set LAKE = TAC_LAKE_DIR %}
|
||||
{%- set UNIVERSE = "SPY,QQQ,DIA,IWM,MDY,VTI,VOO,VEA,VWO,VT,EFA,EEM,TLT,IEF,SHY,AGG,BND,LQD,HYG,JNK,EMB,GLD,SLV,USO,UNG,DBA,DBC,XLK,XLF,XLE,XLV,XLI,XLY,XLP,XLU,XLB,XLRE,ARKK,SMH,SOXX,IBB,XBI,ITA,XAR,ICLN,TAN,FDN,IGV,ESPO,REM" %}
|
||||
{%- set SP_FIELDS = "sp_ret,sp_jump_ratio,sp_jump_flag,sp_jump_tail,sp_max_move,sp_rv1,sp_rv5,sp_rv22,sp_vol_ratio_5_22,sp_vol_ratio_1_22,sp_trend_slope_5,sp_trend_slope_20,sp_trend_slope_60,sp_logp,sp_hurst_exponent,sp_sig_level1_lead,sp_sig_level1_lag,sp_sig_level2_lead_lag,sp_sig_level2_lag_lead" %}
|
||||
qlib_init:
|
||||
provider_uri: "{{ LAKE }}"
|
||||
region: us
|
||||
expression_cache: null
|
||||
dataset_cache: null
|
||||
calendar_provider:
|
||||
class: tac_qlib.data.providers.LakeCalendarProvider
|
||||
kwargs: { lake_root: "{{ LAKE }}", market: US }
|
||||
instrument_provider:
|
||||
class: tac_qlib.data.providers.LakeInstrumentProvider
|
||||
kwargs: { lake_root: "{{ LAKE }}", market: US, markets: {} }
|
||||
feature_provider:
|
||||
class: tac_qlib.data.providers.LakeFeatureProvider
|
||||
kwargs: { lake_root: "{{ LAKE }}", market: US }
|
||||
exp_manager:
|
||||
class: MLflowExpManager
|
||||
module_path: qlib.workflow.expm
|
||||
kwargs:
|
||||
uri: "sqlite:///{{ LAKE }}/mlruns.db"
|
||||
default_exp_name: "tac-rd-sq-gate-wk-zc-2025"
|
||||
task:
|
||||
model:
|
||||
class: RankICEnsembleLGBModel
|
||||
module_path: tac_qlib.contrib.model.rank_ensemble
|
||||
kwargs:
|
||||
loss: mse
|
||||
learning_rate: 0.02
|
||||
num_leaves: 31
|
||||
n_estimators: 3000
|
||||
num_boost_round: 3000
|
||||
early_stopping_rounds: 200
|
||||
min_data_in_leaf: 20
|
||||
lambda_l2: 0.5
|
||||
colsample_bytree: 0.8
|
||||
subsample: 0.8
|
||||
subsample_freq: 1
|
||||
reg_alpha: 0.1
|
||||
reg_lambda: 1.0
|
||||
seeds: "42,7,2026,99,123"
|
||||
parallel: 5
|
||||
dataset:
|
||||
class: DatasetH
|
||||
module_path: qlib.data.dataset
|
||||
kwargs:
|
||||
handler:
|
||||
class: TACHandler
|
||||
module_path: tac_qlib.contrib.data.handler
|
||||
kwargs:
|
||||
instruments: "{{ UNIVERSE }}"
|
||||
start_time: 2015-01-03
|
||||
end_time: 2025-12-31
|
||||
fit_start_time: 2021-01-04
|
||||
fit_end_time: 2024-12-31
|
||||
freq: day
|
||||
lake_root: "{{ LAKE }}"
|
||||
market: US
|
||||
label: "Ref($close,-6)/Ref($close,-1)-1"
|
||||
feature_fields: "$open,$high,$low,$close,$vwap,$volume,{{ SP_FIELDS }}"
|
||||
infer_processors:
|
||||
- class: DropAllNaN
|
||||
kwargs: { fit_start_time: 2021-01-04, fit_end_time: 2024-12-31 }
|
||||
- class: ProcessInf
|
||||
kwargs: {}
|
||||
- class: CSRankNorm
|
||||
kwargs: {}
|
||||
- class: ZScoreNorm
|
||||
kwargs: { fit_start_time: 2021-01-04, fit_end_time: 2024-12-31 }
|
||||
- class: Fillna
|
||||
kwargs: {}
|
||||
segments:
|
||||
train: [2021-01-04, 2024-12-31]
|
||||
valid: [2025-01-02, 2025-01-02]
|
||||
test: [2025-01-02, 2025-12-31]
|
||||
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: WeeklyRebalanceSignalQualityGateStrategy
|
||||
module_path: tac_qlib.contrib.strategy.weekly_sq_gate
|
||||
kwargs:
|
||||
signal: "<PRED>"
|
||||
lake_root: "{{ LAKE }}"
|
||||
gate_topk: 10
|
||||
gate_lookback: 5
|
||||
gate_threshold: 0.5
|
||||
gate_start: "2021-01-04"
|
||||
gate_end: "2025-12-31"
|
||||
topk: 10
|
||||
n_drop: 1
|
||||
only_tradable: true
|
||||
risk_degree: 0.95
|
||||
backtest:
|
||||
start_time: 2025-01-02
|
||||
end_time: 2025-12-31
|
||||
account: 1000000
|
||||
benchmark: SPY
|
||||
exchange_kwargs:
|
||||
codes: "{{ UNIVERSE }}"
|
||||
deal_price: $close
|
||||
freq: day
|
||||
open_cost: 0.0
|
||||
close_cost: 0.0
|
||||
min_cost: 0.0
|
||||
risk_analysis_freq: 1d
|
||||
@@ -0,0 +1,115 @@
|
||||
{%- set LAKE = TAC_LAKE_DIR %}
|
||||
{%- set UNIVERSE = "SPY,QQQ,DIA,IWM,MDY,VTI,VOO,VEA,VWO,VT,EFA,EEM,TLT,IEF,SHY,AGG,BND,LQD,HYG,JNK,EMB,GLD,SLV,USO,UNG,DBA,DBC,XLK,XLF,XLE,XLV,XLI,XLY,XLP,XLU,XLB,XLRE,ARKK,SMH,SOXX,IBB,XBI,ITA,XAR,ICLN,TAN,FDN,IGV,ESPO,REM" %}
|
||||
{%- set SP_FIELDS = "sp_ret,sp_jump_ratio,sp_jump_flag,sp_jump_tail,sp_max_move,sp_rv1,sp_rv5,sp_rv22,sp_vol_ratio_5_22,sp_vol_ratio_1_22,sp_trend_slope_5,sp_trend_slope_20,sp_trend_slope_60,sp_logp,sp_hurst_exponent,sp_sig_level1_lead,sp_sig_level1_lag,sp_sig_level2_lead_lag,sp_sig_level2_lag_lead" %}
|
||||
qlib_init:
|
||||
provider_uri: "{{ LAKE }}"
|
||||
region: us
|
||||
expression_cache: null
|
||||
dataset_cache: null
|
||||
calendar_provider:
|
||||
class: tac_qlib.data.providers.LakeCalendarProvider
|
||||
kwargs: { lake_root: "{{ LAKE }}", market: US }
|
||||
instrument_provider:
|
||||
class: tac_qlib.data.providers.LakeInstrumentProvider
|
||||
kwargs: { lake_root: "{{ LAKE }}", market: US, markets: {} }
|
||||
feature_provider:
|
||||
class: tac_qlib.data.providers.LakeFeatureProvider
|
||||
kwargs: { lake_root: "{{ LAKE }}", market: US }
|
||||
exp_manager:
|
||||
class: MLflowExpManager
|
||||
module_path: qlib.workflow.expm
|
||||
kwargs:
|
||||
uri: "sqlite:///{{ LAKE }}/mlruns.db"
|
||||
default_exp_name: "tac-rd-sq-gate-wk-zc-2026"
|
||||
task:
|
||||
model:
|
||||
class: RankICEnsembleLGBModel
|
||||
module_path: tac_qlib.contrib.model.rank_ensemble
|
||||
kwargs:
|
||||
loss: mse
|
||||
learning_rate: 0.02
|
||||
num_leaves: 31
|
||||
n_estimators: 3000
|
||||
num_boost_round: 3000
|
||||
early_stopping_rounds: 200
|
||||
min_data_in_leaf: 20
|
||||
lambda_l2: 0.5
|
||||
colsample_bytree: 0.8
|
||||
subsample: 0.8
|
||||
subsample_freq: 1
|
||||
reg_alpha: 0.1
|
||||
reg_lambda: 1.0
|
||||
seeds: "42,7,2026,99,123"
|
||||
parallel: 5
|
||||
dataset:
|
||||
class: DatasetH
|
||||
module_path: qlib.data.dataset
|
||||
kwargs:
|
||||
handler:
|
||||
class: TACHandler
|
||||
module_path: tac_qlib.contrib.data.handler
|
||||
kwargs:
|
||||
instruments: "{{ UNIVERSE }}"
|
||||
start_time: 2015-01-03
|
||||
end_time: 2026-08-10
|
||||
fit_start_time: 2016-01-04
|
||||
fit_end_time: 2025-09-01
|
||||
freq: day
|
||||
lake_root: "{{ LAKE }}"
|
||||
market: US
|
||||
label: "Ref($close,-6)/Ref($close,-1)-1"
|
||||
feature_fields: "$open,$high,$low,$close,$vwap,$volume,{{ SP_FIELDS }}"
|
||||
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: WeeklyRebalanceSignalQualityGateStrategy
|
||||
module_path: tac_qlib.contrib.strategy.weekly_sq_gate
|
||||
kwargs:
|
||||
signal: "<PRED>"
|
||||
lake_root: "{{ LAKE }}"
|
||||
gate_topk: 10
|
||||
gate_lookback: 5
|
||||
gate_threshold: 0.5
|
||||
gate_start: "2016-01-04"
|
||||
gate_end: "2026-08-19"
|
||||
topk: 10
|
||||
n_drop: 1
|
||||
only_tradable: true
|
||||
risk_degree: 0.95
|
||||
backtest:
|
||||
start_time: 2026-01-04
|
||||
end_time: 2026-08-10
|
||||
account: 1000000
|
||||
benchmark: SPY
|
||||
exchange_kwargs:
|
||||
codes: "{{ UNIVERSE }}"
|
||||
deal_price: $close
|
||||
freq: day
|
||||
open_cost: 0.0
|
||||
close_cost: 0.0
|
||||
min_cost: 0.0
|
||||
risk_analysis_freq: 1d
|
||||
@@ -0,0 +1,115 @@
|
||||
{%- set LAKE = TAC_LAKE_DIR %}
|
||||
{%- set UNIVERSE = "SPY,QQQ,DIA,IWM,MDY,VTI,VOO,VEA,VWO,VT,EFA,EEM,TLT,IEF,SHY,AGG,BND,LQD,HYG,JNK,EMB,GLD,SLV,USO,UNG,DBA,DBC,XLK,XLF,XLE,XLV,XLI,XLY,XLP,XLU,XLB,XLRE,ARKK,SMH,SOXX,IBB,XBI,ITA,XAR,ICLN,TAN,FDN,IGV,ESPO,REM" %}
|
||||
{%- set SP_FIELDS = "sp_ret,sp_jump_ratio,sp_jump_flag,sp_jump_tail,sp_max_move,sp_rv1,sp_rv5,sp_rv22,sp_vol_ratio_5_22,sp_vol_ratio_1_22,sp_trend_slope_5,sp_trend_slope_20,sp_trend_slope_60,sp_logp,sp_hurst_exponent,sp_sig_level1_lead,sp_sig_level1_lag,sp_sig_level2_lead_lag,sp_sig_level2_lag_lead" %}
|
||||
qlib_init:
|
||||
provider_uri: "{{ LAKE }}"
|
||||
region: us
|
||||
expression_cache: null
|
||||
dataset_cache: null
|
||||
calendar_provider:
|
||||
class: tac_qlib.data.providers.LakeCalendarProvider
|
||||
kwargs: { lake_root: "{{ LAKE }}", market: US }
|
||||
instrument_provider:
|
||||
class: tac_qlib.data.providers.LakeInstrumentProvider
|
||||
kwargs: { lake_root: "{{ LAKE }}", market: US, markets: {} }
|
||||
feature_provider:
|
||||
class: tac_qlib.data.providers.LakeFeatureProvider
|
||||
kwargs: { lake_root: "{{ LAKE }}", market: US }
|
||||
exp_manager:
|
||||
class: MLflowExpManager
|
||||
module_path: qlib.workflow.expm
|
||||
kwargs:
|
||||
uri: "sqlite:///{{ LAKE }}/mlruns.db"
|
||||
default_exp_name: "tac-rd-sq-gate-wk-v3"
|
||||
task:
|
||||
model:
|
||||
class: RankICEnsembleLGBModel
|
||||
module_path: tac_qlib.contrib.model.rank_ensemble
|
||||
kwargs:
|
||||
loss: mse
|
||||
learning_rate: 0.02
|
||||
num_leaves: 31
|
||||
n_estimators: 3000
|
||||
num_boost_round: 3000
|
||||
early_stopping_rounds: 200
|
||||
min_data_in_leaf: 20
|
||||
lambda_l2: 0.5
|
||||
colsample_bytree: 0.8
|
||||
subsample: 0.8
|
||||
subsample_freq: 1
|
||||
reg_alpha: 0.1
|
||||
reg_lambda: 1.0
|
||||
seeds: "42,7,2026,99,123"
|
||||
parallel: 5
|
||||
dataset:
|
||||
class: DatasetH
|
||||
module_path: qlib.data.dataset
|
||||
kwargs:
|
||||
handler:
|
||||
class: TACHandler
|
||||
module_path: tac_qlib.contrib.data.handler
|
||||
kwargs:
|
||||
instruments: "{{ UNIVERSE }}"
|
||||
start_time: 2015-01-03
|
||||
end_time: 2021-12-31
|
||||
fit_start_time: 2016-01-04
|
||||
fit_end_time: 2020-12-31
|
||||
freq: day
|
||||
lake_root: "{{ LAKE }}"
|
||||
market: US
|
||||
label: "Ref($close,-6)/Ref($close,-1)-1"
|
||||
feature_fields: "$open,$high,$low,$close,$vwap,$volume,{{ SP_FIELDS }}"
|
||||
infer_processors:
|
||||
- class: DropAllNaN
|
||||
kwargs: { fit_start_time: 2016-01-04, fit_end_time: 2020-12-31 }
|
||||
- class: ProcessInf
|
||||
kwargs: {}
|
||||
- class: CSRankNorm
|
||||
kwargs: {}
|
||||
- class: ZScoreNorm
|
||||
kwargs: { fit_start_time: 2016-01-04, fit_end_time: 2020-12-31 }
|
||||
- class: Fillna
|
||||
kwargs: {}
|
||||
segments:
|
||||
train: [2016-01-04, 2020-12-31]
|
||||
valid: [2021-01-04, 2021-01-04]
|
||||
test: [2021-01-04, 2021-12-31]
|
||||
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: WeeklyRebalanceSignalQualityGateStrategy
|
||||
module_path: tac_qlib.contrib.strategy.weekly_sq_gate
|
||||
kwargs:
|
||||
signal: "<PRED>"
|
||||
lake_root: "{{ LAKE }}"
|
||||
gate_topk: 10
|
||||
gate_lookback: 5
|
||||
gate_threshold: 0.5
|
||||
gate_start: "2016-01-04"
|
||||
gate_end: "2021-12-31"
|
||||
topk: 10
|
||||
n_drop: 1
|
||||
only_tradable: true
|
||||
risk_degree: 0.95
|
||||
backtest:
|
||||
start_time: 2021-01-04
|
||||
end_time: 2021-12-31
|
||||
account: 1000000
|
||||
benchmark: SPY
|
||||
exchange_kwargs:
|
||||
codes: "{{ UNIVERSE }}"
|
||||
deal_price: $close
|
||||
freq: day
|
||||
open_cost: 0.0005
|
||||
close_cost: 0.0015
|
||||
min_cost: 5.0
|
||||
risk_analysis_freq: 1d
|
||||
@@ -0,0 +1,115 @@
|
||||
{%- set LAKE = TAC_LAKE_DIR %}
|
||||
{%- set UNIVERSE = "SPY,QQQ,DIA,IWM,MDY,VTI,VOO,VEA,VWO,VT,EFA,EEM,TLT,IEF,SHY,AGG,BND,LQD,HYG,JNK,EMB,GLD,SLV,USO,UNG,DBA,DBC,XLK,XLF,XLE,XLV,XLI,XLY,XLP,XLU,XLB,XLRE,ARKK,SMH,SOXX,IBB,XBI,ITA,XAR,ICLN,TAN,FDN,IGV,ESPO,REM" %}
|
||||
{%- set SP_FIELDS = "sp_ret,sp_jump_ratio,sp_jump_flag,sp_jump_tail,sp_max_move,sp_rv1,sp_rv5,sp_rv22,sp_vol_ratio_5_22,sp_vol_ratio_1_22,sp_trend_slope_5,sp_trend_slope_20,sp_trend_slope_60,sp_logp,sp_hurst_exponent,sp_sig_level1_lead,sp_sig_level1_lag,sp_sig_level2_lead_lag,sp_sig_level2_lag_lead" %}
|
||||
qlib_init:
|
||||
provider_uri: "{{ LAKE }}"
|
||||
region: us
|
||||
expression_cache: null
|
||||
dataset_cache: null
|
||||
calendar_provider:
|
||||
class: tac_qlib.data.providers.LakeCalendarProvider
|
||||
kwargs: { lake_root: "{{ LAKE }}", market: US }
|
||||
instrument_provider:
|
||||
class: tac_qlib.data.providers.LakeInstrumentProvider
|
||||
kwargs: { lake_root: "{{ LAKE }}", market: US, markets: {} }
|
||||
feature_provider:
|
||||
class: tac_qlib.data.providers.LakeFeatureProvider
|
||||
kwargs: { lake_root: "{{ LAKE }}", market: US }
|
||||
exp_manager:
|
||||
class: MLflowExpManager
|
||||
module_path: qlib.workflow.expm
|
||||
kwargs:
|
||||
uri: "sqlite:///{{ LAKE }}/mlruns.db"
|
||||
default_exp_name: "tac-rd-sq-gate-wk-v3"
|
||||
task:
|
||||
model:
|
||||
class: RankICEnsembleLGBModel
|
||||
module_path: tac_qlib.contrib.model.rank_ensemble
|
||||
kwargs:
|
||||
loss: mse
|
||||
learning_rate: 0.02
|
||||
num_leaves: 31
|
||||
n_estimators: 3000
|
||||
num_boost_round: 3000
|
||||
early_stopping_rounds: 200
|
||||
min_data_in_leaf: 20
|
||||
lambda_l2: 0.5
|
||||
colsample_bytree: 0.8
|
||||
subsample: 0.8
|
||||
subsample_freq: 1
|
||||
reg_alpha: 0.1
|
||||
reg_lambda: 1.0
|
||||
seeds: "42,7,2026,99,123"
|
||||
parallel: 5
|
||||
dataset:
|
||||
class: DatasetH
|
||||
module_path: qlib.data.dataset
|
||||
kwargs:
|
||||
handler:
|
||||
class: TACHandler
|
||||
module_path: tac_qlib.contrib.data.handler
|
||||
kwargs:
|
||||
instruments: "{{ UNIVERSE }}"
|
||||
start_time: 2015-01-03
|
||||
end_time: 2023-12-29
|
||||
fit_start_time: 2016-01-04
|
||||
fit_end_time: 2022-12-30
|
||||
freq: day
|
||||
lake_root: "{{ LAKE }}"
|
||||
market: US
|
||||
label: "Ref($close,-6)/Ref($close,-1)-1"
|
||||
feature_fields: "$open,$high,$low,$close,$vwap,$volume,{{ SP_FIELDS }}"
|
||||
infer_processors:
|
||||
- class: DropAllNaN
|
||||
kwargs: { fit_start_time: 2016-01-04, fit_end_time: 2022-12-30 }
|
||||
- class: ProcessInf
|
||||
kwargs: {}
|
||||
- class: CSRankNorm
|
||||
kwargs: {}
|
||||
- class: ZScoreNorm
|
||||
kwargs: { fit_start_time: 2016-01-04, fit_end_time: 2022-12-30 }
|
||||
- class: Fillna
|
||||
kwargs: {}
|
||||
segments:
|
||||
train: [2016-01-04, 2022-12-30]
|
||||
valid: [2023-01-03, 2023-01-03]
|
||||
test: [2023-01-03, 2023-12-29]
|
||||
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: WeeklyRebalanceSignalQualityGateStrategy
|
||||
module_path: tac_qlib.contrib.strategy.weekly_sq_gate
|
||||
kwargs:
|
||||
signal: "<PRED>"
|
||||
lake_root: "{{ LAKE }}"
|
||||
gate_topk: 10
|
||||
gate_lookback: 5
|
||||
gate_threshold: 0.5
|
||||
gate_start: "2016-01-04"
|
||||
gate_end: "2023-12-29"
|
||||
topk: 10
|
||||
n_drop: 1
|
||||
only_tradable: true
|
||||
risk_degree: 0.95
|
||||
backtest:
|
||||
start_time: 2023-01-03
|
||||
end_time: 2023-12-29
|
||||
account: 1000000
|
||||
benchmark: SPY
|
||||
exchange_kwargs:
|
||||
codes: "{{ UNIVERSE }}"
|
||||
deal_price: $close
|
||||
freq: day
|
||||
open_cost: 0.0005
|
||||
close_cost: 0.0015
|
||||
min_cost: 5.0
|
||||
risk_analysis_freq: 1d
|
||||
@@ -0,0 +1,115 @@
|
||||
{%- set LAKE = TAC_LAKE_DIR %}
|
||||
{%- set UNIVERSE = "SPY,QQQ,DIA,IWM,MDY,VTI,VOO,VEA,VWO,VT,EFA,EEM,TLT,IEF,SHY,AGG,BND,LQD,HYG,JNK,EMB,GLD,SLV,USO,UNG,DBA,DBC,XLK,XLF,XLE,XLV,XLI,XLY,XLP,XLU,XLB,XLRE,ARKK,SMH,SOXX,IBB,XBI,ITA,XAR,ICLN,TAN,FDN,IGV,ESPO,REM" %}
|
||||
{%- set SP_FIELDS = "sp_ret,sp_jump_ratio,sp_jump_flag,sp_jump_tail,sp_max_move,sp_rv1,sp_rv5,sp_rv22,sp_vol_ratio_5_22,sp_vol_ratio_1_22,sp_trend_slope_5,sp_trend_slope_20,sp_trend_slope_60,sp_logp,sp_hurst_exponent,sp_sig_level1_lead,sp_sig_level1_lag,sp_sig_level2_lead_lag,sp_sig_level2_lag_lead" %}
|
||||
qlib_init:
|
||||
provider_uri: "{{ LAKE }}"
|
||||
region: us
|
||||
expression_cache: null
|
||||
dataset_cache: null
|
||||
calendar_provider:
|
||||
class: tac_qlib.data.providers.LakeCalendarProvider
|
||||
kwargs: { lake_root: "{{ LAKE }}", market: US }
|
||||
instrument_provider:
|
||||
class: tac_qlib.data.providers.LakeInstrumentProvider
|
||||
kwargs: { lake_root: "{{ LAKE }}", market: US, markets: {} }
|
||||
feature_provider:
|
||||
class: tac_qlib.data.providers.LakeFeatureProvider
|
||||
kwargs: { lake_root: "{{ LAKE }}", market: US }
|
||||
exp_manager:
|
||||
class: MLflowExpManager
|
||||
module_path: qlib.workflow.expm
|
||||
kwargs:
|
||||
uri: "sqlite:///{{ LAKE }}/mlruns.db"
|
||||
default_exp_name: "tac-rd-sq-gate-wk-v3"
|
||||
task:
|
||||
model:
|
||||
class: RankICEnsembleLGBModel
|
||||
module_path: tac_qlib.contrib.model.rank_ensemble
|
||||
kwargs:
|
||||
loss: mse
|
||||
learning_rate: 0.02
|
||||
num_leaves: 31
|
||||
n_estimators: 3000
|
||||
num_boost_round: 3000
|
||||
early_stopping_rounds: 200
|
||||
min_data_in_leaf: 20
|
||||
lambda_l2: 0.5
|
||||
colsample_bytree: 0.8
|
||||
subsample: 0.8
|
||||
subsample_freq: 1
|
||||
reg_alpha: 0.1
|
||||
reg_lambda: 1.0
|
||||
seeds: "42,7,2026,99,123"
|
||||
parallel: 5
|
||||
dataset:
|
||||
class: DatasetH
|
||||
module_path: qlib.data.dataset
|
||||
kwargs:
|
||||
handler:
|
||||
class: TACHandler
|
||||
module_path: tac_qlib.contrib.data.handler
|
||||
kwargs:
|
||||
instruments: "{{ UNIVERSE }}"
|
||||
start_time: 2015-01-03
|
||||
end_time: 2024-12-31
|
||||
fit_start_time: 2016-01-04
|
||||
fit_end_time: 2023-12-29
|
||||
freq: day
|
||||
lake_root: "{{ LAKE }}"
|
||||
market: US
|
||||
label: "Ref($close,-6)/Ref($close,-1)-1"
|
||||
feature_fields: "$open,$high,$low,$close,$vwap,$volume,{{ SP_FIELDS }}"
|
||||
infer_processors:
|
||||
- class: DropAllNaN
|
||||
kwargs: { fit_start_time: 2016-01-04, fit_end_time: 2023-12-29 }
|
||||
- class: ProcessInf
|
||||
kwargs: {}
|
||||
- class: CSRankNorm
|
||||
kwargs: {}
|
||||
- class: ZScoreNorm
|
||||
kwargs: { fit_start_time: 2016-01-04, fit_end_time: 2023-12-29 }
|
||||
- class: Fillna
|
||||
kwargs: {}
|
||||
segments:
|
||||
train: [2016-01-04, 2023-12-29]
|
||||
valid: [2024-01-02, 2024-01-02]
|
||||
test: [2024-01-02, 2024-12-31]
|
||||
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: WeeklyRebalanceSignalQualityGateStrategy
|
||||
module_path: tac_qlib.contrib.strategy.weekly_sq_gate
|
||||
kwargs:
|
||||
signal: "<PRED>"
|
||||
lake_root: "{{ LAKE }}"
|
||||
gate_topk: 10
|
||||
gate_lookback: 5
|
||||
gate_threshold: 0.5
|
||||
gate_start: "2016-01-04"
|
||||
gate_end: "2024-12-31"
|
||||
topk: 10
|
||||
n_drop: 1
|
||||
only_tradable: true
|
||||
risk_degree: 0.95
|
||||
backtest:
|
||||
start_time: 2024-01-02
|
||||
end_time: 2024-12-31
|
||||
account: 1000000
|
||||
benchmark: SPY
|
||||
exchange_kwargs:
|
||||
codes: "{{ UNIVERSE }}"
|
||||
deal_price: $close
|
||||
freq: day
|
||||
open_cost: 0.0005
|
||||
close_cost: 0.0015
|
||||
min_cost: 5.0
|
||||
risk_analysis_freq: 1d
|
||||
@@ -0,0 +1,115 @@
|
||||
{%- set LAKE = TAC_LAKE_DIR %}
|
||||
{%- set UNIVERSE = "SPY,QQQ,DIA,IWM,MDY,VTI,VOO,VEA,VWO,VT,EFA,EEM,TLT,IEF,SHY,AGG,BND,LQD,HYG,JNK,EMB,GLD,SLV,USO,UNG,DBA,DBC,XLK,XLF,XLE,XLV,XLI,XLY,XLP,XLU,XLB,XLRE,ARKK,SMH,SOXX,IBB,XBI,ITA,XAR,ICLN,TAN,FDN,IGV,ESPO,REM" %}
|
||||
{%- set SP_FIELDS = "sp_ret,sp_jump_ratio,sp_jump_flag,sp_jump_tail,sp_max_move,sp_rv1,sp_rv5,sp_rv22,sp_vol_ratio_5_22,sp_vol_ratio_1_22,sp_trend_slope_5,sp_trend_slope_20,sp_trend_slope_60,sp_logp,sp_hurst_exponent,sp_sig_level1_lead,sp_sig_level1_lag,sp_sig_level2_lead_lag,sp_sig_level2_lag_lead" %}
|
||||
qlib_init:
|
||||
provider_uri: "{{ LAKE }}"
|
||||
region: us
|
||||
expression_cache: null
|
||||
dataset_cache: null
|
||||
calendar_provider:
|
||||
class: tac_qlib.data.providers.LakeCalendarProvider
|
||||
kwargs: { lake_root: "{{ LAKE }}", market: US }
|
||||
instrument_provider:
|
||||
class: tac_qlib.data.providers.LakeInstrumentProvider
|
||||
kwargs: { lake_root: "{{ LAKE }}", market: US, markets: {} }
|
||||
feature_provider:
|
||||
class: tac_qlib.data.providers.LakeFeatureProvider
|
||||
kwargs: { lake_root: "{{ LAKE }}", market: US }
|
||||
exp_manager:
|
||||
class: MLflowExpManager
|
||||
module_path: qlib.workflow.expm
|
||||
kwargs:
|
||||
uri: "sqlite:///{{ LAKE }}/mlruns.db"
|
||||
default_exp_name: "tac-rd-sq-gate-wk-v3"
|
||||
task:
|
||||
model:
|
||||
class: RankICEnsembleLGBModel
|
||||
module_path: tac_qlib.contrib.model.rank_ensemble
|
||||
kwargs:
|
||||
loss: mse
|
||||
learning_rate: 0.02
|
||||
num_leaves: 31
|
||||
n_estimators: 3000
|
||||
num_boost_round: 3000
|
||||
early_stopping_rounds: 200
|
||||
min_data_in_leaf: 20
|
||||
lambda_l2: 0.5
|
||||
colsample_bytree: 0.8
|
||||
subsample: 0.8
|
||||
subsample_freq: 1
|
||||
reg_alpha: 0.1
|
||||
reg_lambda: 1.0
|
||||
seeds: "42,7,2026,99,123"
|
||||
parallel: 5
|
||||
dataset:
|
||||
class: DatasetH
|
||||
module_path: qlib.data.dataset
|
||||
kwargs:
|
||||
handler:
|
||||
class: TACHandler
|
||||
module_path: tac_qlib.contrib.data.handler
|
||||
kwargs:
|
||||
instruments: "{{ UNIVERSE }}"
|
||||
start_time: 2015-01-03
|
||||
end_time: 2025-12-31
|
||||
fit_start_time: 2016-01-04
|
||||
fit_end_time: 2024-12-31
|
||||
freq: day
|
||||
lake_root: "{{ LAKE }}"
|
||||
market: US
|
||||
label: "Ref($close,-6)/Ref($close,-1)-1"
|
||||
feature_fields: "$open,$high,$low,$close,$vwap,$volume,{{ SP_FIELDS }}"
|
||||
infer_processors:
|
||||
- class: DropAllNaN
|
||||
kwargs: { fit_start_time: 2016-01-04, fit_end_time: 2024-12-31 }
|
||||
- class: ProcessInf
|
||||
kwargs: {}
|
||||
- class: CSRankNorm
|
||||
kwargs: {}
|
||||
- class: ZScoreNorm
|
||||
kwargs: { fit_start_time: 2016-01-04, fit_end_time: 2024-12-31 }
|
||||
- class: Fillna
|
||||
kwargs: {}
|
||||
segments:
|
||||
train: [2016-01-04, 2024-12-31]
|
||||
valid: [2025-01-02, 2025-01-02]
|
||||
test: [2025-01-02, 2025-12-31]
|
||||
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: WeeklyRebalanceSignalQualityGateStrategy
|
||||
module_path: tac_qlib.contrib.strategy.weekly_sq_gate
|
||||
kwargs:
|
||||
signal: "<PRED>"
|
||||
lake_root: "{{ LAKE }}"
|
||||
gate_topk: 10
|
||||
gate_lookback: 5
|
||||
gate_threshold: 0.5
|
||||
gate_start: "2016-01-04"
|
||||
gate_end: "2025-12-31"
|
||||
topk: 10
|
||||
n_drop: 1
|
||||
only_tradable: true
|
||||
risk_degree: 0.95
|
||||
backtest:
|
||||
start_time: 2025-01-02
|
||||
end_time: 2025-12-31
|
||||
account: 1000000
|
||||
benchmark: SPY
|
||||
exchange_kwargs:
|
||||
codes: "{{ UNIVERSE }}"
|
||||
deal_price: $close
|
||||
freq: day
|
||||
open_cost: 0.0005
|
||||
close_cost: 0.0015
|
||||
min_cost: 5.0
|
||||
risk_analysis_freq: 1d
|
||||
@@ -0,0 +1,115 @@
|
||||
{%- set LAKE = TAC_LAKE_DIR %}
|
||||
{%- set UNIVERSE = "SPY,QQQ,DIA,IWM,MDY,VTI,VOO,VEA,VWO,VT,EFA,EEM,TLT,IEF,SHY,AGG,BND,LQD,HYG,JNK,EMB,GLD,SLV,USO,UNG,DBA,DBC,XLK,XLF,XLE,XLV,XLI,XLY,XLP,XLU,XLB,XLRE,ARKK,SMH,SOXX,IBB,XBI,ITA,XAR,ICLN,TAN,FDN,IGV,ESPO,REM" %}
|
||||
{%- set SP_FIELDS = "sp_ret,sp_jump_ratio,sp_jump_flag,sp_jump_tail,sp_max_move,sp_rv1,sp_rv5,sp_rv22,sp_vol_ratio_5_22,sp_vol_ratio_1_22,sp_trend_slope_5,sp_trend_slope_20,sp_trend_slope_60,sp_logp,sp_hurst_exponent,sp_sig_level1_lead,sp_sig_level1_lag,sp_sig_level2_lead_lag,sp_sig_level2_lag_lead" %}
|
||||
qlib_init:
|
||||
provider_uri: "{{ LAKE }}"
|
||||
region: us
|
||||
expression_cache: null
|
||||
dataset_cache: null
|
||||
calendar_provider:
|
||||
class: tac_qlib.data.providers.LakeCalendarProvider
|
||||
kwargs: { lake_root: "{{ LAKE }}", market: US }
|
||||
instrument_provider:
|
||||
class: tac_qlib.data.providers.LakeInstrumentProvider
|
||||
kwargs: { lake_root: "{{ LAKE }}", market: US, markets: {} }
|
||||
feature_provider:
|
||||
class: tac_qlib.data.providers.LakeFeatureProvider
|
||||
kwargs: { lake_root: "{{ LAKE }}", market: US }
|
||||
exp_manager:
|
||||
class: MLflowExpManager
|
||||
module_path: qlib.workflow.expm
|
||||
kwargs:
|
||||
uri: "sqlite:///{{ LAKE }}/mlruns.db"
|
||||
default_exp_name: "tac-rd-sq-gate-wk-v3"
|
||||
task:
|
||||
model:
|
||||
class: RankICEnsembleLGBModel
|
||||
module_path: tac_qlib.contrib.model.rank_ensemble
|
||||
kwargs:
|
||||
loss: mse
|
||||
learning_rate: 0.02
|
||||
num_leaves: 31
|
||||
n_estimators: 3000
|
||||
num_boost_round: 3000
|
||||
early_stopping_rounds: 200
|
||||
min_data_in_leaf: 20
|
||||
lambda_l2: 0.5
|
||||
colsample_bytree: 0.8
|
||||
subsample: 0.8
|
||||
subsample_freq: 1
|
||||
reg_alpha: 0.1
|
||||
reg_lambda: 1.0
|
||||
seeds: "42,7,2026,99,123"
|
||||
parallel: 5
|
||||
dataset:
|
||||
class: DatasetH
|
||||
module_path: qlib.data.dataset
|
||||
kwargs:
|
||||
handler:
|
||||
class: TACHandler
|
||||
module_path: tac_qlib.contrib.data.handler
|
||||
kwargs:
|
||||
instruments: "{{ UNIVERSE }}"
|
||||
start_time: 2015-01-03
|
||||
end_time: 2026-08-10
|
||||
fit_start_time: 2016-01-04
|
||||
fit_end_time: 2025-09-01
|
||||
freq: day
|
||||
lake_root: "{{ LAKE }}"
|
||||
market: US
|
||||
label: "Ref($close,-6)/Ref($close,-1)-1"
|
||||
feature_fields: "$open,$high,$low,$close,$vwap,$volume,{{ SP_FIELDS }}"
|
||||
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: WeeklyRebalanceSignalQualityGateStrategy
|
||||
module_path: tac_qlib.contrib.strategy.weekly_sq_gate
|
||||
kwargs:
|
||||
signal: "<PRED>"
|
||||
lake_root: "{{ LAKE }}"
|
||||
gate_topk: 10
|
||||
gate_lookback: 5
|
||||
gate_threshold: 0.5
|
||||
gate_start: "2016-01-04"
|
||||
gate_end: "2026-08-19"
|
||||
topk: 10
|
||||
n_drop: 1
|
||||
only_tradable: true
|
||||
risk_degree: 0.95
|
||||
backtest:
|
||||
start_time: 2026-01-04
|
||||
end_time: 2026-08-10
|
||||
account: 1000000
|
||||
benchmark: SPY
|
||||
exchange_kwargs:
|
||||
codes: "{{ UNIVERSE }}"
|
||||
deal_price: $close
|
||||
freq: day
|
||||
open_cost: 0.0005
|
||||
close_cost: 0.0015
|
||||
min_cost: 5.0
|
||||
risk_analysis_freq: 1d
|
||||
Reference in New Issue
Block a user