Compare commits
3
Commits
| Author | SHA1 | Date | |
|---|---|---|---|
 zhaoli
|
5153d04983 | ||
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zhaoli
|
ab919245c2 | ||
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zhaoli
|
2c2684b103 |
+13
-15
@@ -1,29 +1,27 @@
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# TradeAC custom-qlib-code snapshot (auto-generated)
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# parent repo HEAD : 125be7b96fb5975e798a0b4301eeb5809a8a181c
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# parent repo HEAD : ab919245c2f3d6881cd6e16e77e583bbb6d5b000
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# tac-qlib/tac_qlib/contrib
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# tac-qlib/tac_qlib/data
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# per-file hashes (git hash-object):
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1b6298c4a5652f2e863cbdc385a1014a570fcd59 tac-qlib/tac_qlib/contrib/__init__.py
|
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b8112569f9b2537c45b6535e1a505a207878d322 tac-qlib/tac_qlib/contrib/__pycache__/__init__.cpython-312.pyc
|
||||
bb903ca28c70ae5802d673d5b85481679f58c286 tac-qlib/tac_qlib/contrib/__pycache__/__init__.cpython-312.pyc
|
||||
c76a9f17f680e74eea766eff27f7624359749ed6 tac-qlib/tac_qlib/contrib/data/__init__.py
|
||||
8d5333ebd2b44165c50cba639ca2d4ac3fc7cfec tac-qlib/tac_qlib/contrib/data/__pycache__/__init__.cpython-312.pyc
|
||||
18cb37c0354184c49fa2e598396d7df0634cce0f tac-qlib/tac_qlib/contrib/data/__pycache__/handler.cpython-312.pyc
|
||||
9749bb730880371ea7bf9bf0c5ffd78fcf6b5a91 tac-qlib/tac_qlib/contrib/data/__pycache__/__init__.cpython-312.pyc
|
||||
9eb94cfac5d41ae20acb12612c219f210d463ab4 tac-qlib/tac_qlib/contrib/data/__pycache__/handler.cpython-312.pyc
|
||||
871ff1e163c29261f140c3f53d42a41e6504c779 tac-qlib/tac_qlib/contrib/data/handler.py
|
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b151d139a0dcde87d74b21e7c4b729176ba5c39b tac-qlib/tac_qlib/contrib/model/__init__.py
|
||||
ab958203f33a99d12c7d923b6efb435189231666 tac-qlib/tac_qlib/contrib/model/__pycache__/__init__.cpython-312.pyc
|
||||
7478f6b0f6de419615c02d4d92b54529f689ef04 tac-qlib/tac_qlib/contrib/model/__pycache__/rank_ensemble.cpython-312.pyc
|
||||
9f9014ddd9bce37490061312d51e8e6fe540fec4 tac-qlib/tac_qlib/contrib/model/__pycache__/rank_gbdt.cpython-312.pyc
|
||||
ce77dea53f6a87c5379782709293bf8ff55b2c75 tac-qlib/tac_qlib/contrib/model/rank_ensemble.py
|
||||
d209c3e3e8c2a8683cd337ff3b10c04015f16dc6 tac-qlib/tac_qlib/contrib/model/__pycache__/__init__.cpython-312.pyc
|
||||
532527ebe81b269f723c806286122fb5483d0379 tac-qlib/tac_qlib/contrib/model/__pycache__/rank_ensemble.cpython-312.pyc
|
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1681c9bc021188c0f134e33e6402f521a9d46e9c tac-qlib/tac_qlib/contrib/model/__pycache__/rank_gbdt.cpython-312.pyc
|
||||
d3f051f3a8650c42fedc7b367b966f7c74fb5789 tac-qlib/tac_qlib/contrib/model/rank_ensemble.py
|
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ccfe7d554989aa7f3e5a2128ae663e51b2207149 tac-qlib/tac_qlib/contrib/model/rank_gbdt.py
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4afcf9058231111c412925f4c4b84e81d656db87 tac-qlib/tac_qlib/contrib/strategy/__init__.py
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74e5ecbbbb20bb71fd5cd083383de4ce88476712 tac-qlib/tac_qlib/contrib/strategy/__pycache__/__init__.cpython-312.pyc
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afaf562aeaa12cebc8529cd916153252e7e3c38a tac-qlib/tac_qlib/contrib/strategy/__pycache__/optimal_stop.cpython-312.pyc
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96a0a25201f0a1bb2fc2190e26228c5c0e711a79 tac-qlib/tac_qlib/contrib/strategy/hmm_risk.py
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816de5d58ae23d996635d42331cf9fc8963d5dbe tac-qlib/tac_qlib/contrib/strategy/momentum_gate.py
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a6fb3d6c2d111b7ad423939582df67aacb36fead tac-qlib/tac_qlib/contrib/strategy/__pycache__/__init__.cpython-312.pyc
|
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44ed28758151eb7fa4d388646cb1c6f04b450d8c tac-qlib/tac_qlib/contrib/strategy/__pycache__/optimal_stop.cpython-312.pyc
|
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79aaad9e39fcc740a773f4f63c512ce1086cfde0 tac-qlib/tac_qlib/contrib/strategy/optimal_stop.py
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92e6e90eb0cd0a25142034560f27adb6b705b1a8 tac-qlib/tac_qlib/data/__init__.py
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0ed1ead6c1314a3f25784d453e54a15a8a04baaa tac-qlib/tac_qlib/data/__pycache__/__init__.cpython-312.pyc
|
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9609782800944c45b78bb58eaa7b51ba1b7f8f43 tac-qlib/tac_qlib/data/__pycache__/config.cpython-312.pyc
|
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a85628d71d12cfe5b18b1c884c5d829c89594579 tac-qlib/tac_qlib/data/__pycache__/providers.cpython-312.pyc
|
||||
319e3f728b093d6c483eb29de42617a45ee88830 tac-qlib/tac_qlib/data/__pycache__/__init__.cpython-312.pyc
|
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08a7dcbcf3f34bdb784d3b16c04daf265d04ae5f tac-qlib/tac_qlib/data/__pycache__/config.cpython-312.pyc
|
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47337bd1e54b6e333f26a9088d645ed48fbc44f6 tac-qlib/tac_qlib/data/__pycache__/providers.cpython-312.pyc
|
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686d36f6d101c547491ca866aa143aa542e17518 tac-qlib/tac_qlib/data/config.py
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d9f839be30026f337754a3f015425a8efdbe8e2a tac-qlib/tac_qlib/data/providers.py
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@@ -56,7 +56,6 @@ import os
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from concurrent.futures import ThreadPoolExecutor
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from typing import List, Optional
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import numpy as np
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import pandas as pd
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from qlib.data.dataset import DatasetH
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@@ -80,15 +79,11 @@ class RankICEnsembleLGBModel(RankICLGBModel):
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forwarded.
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"""
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def __init__(self, seeds: str = "42", parallel: int = 0, weight_mode: str = "equal", **kwargs):
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def __init__(self, seeds: str = "42", parallel: int = 0, **kwargs):
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self.seeds = [int(s.strip()) for s in str(seeds).split(",") if s.strip()]
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if not self.seeds:
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raise ValueError("seeds must contain at least one integer")
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self.parallel = int(parallel)
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if weight_mode not in ("equal", "rolling_ic"):
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raise ValueError(f"weight_mode must be 'equal' or 'rolling_ic', got {weight_mode!r}")
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self.weight_mode = weight_mode
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self.rolling_ic_window = int(kwargs.pop("rolling_ic_window", 21))
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# drop seed/parallel handling from the base kwargs, keep everything else
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self._model_kwargs = dict(kwargs)
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super().__init__(**self._model_kwargs)
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@@ -184,44 +179,11 @@ class RankICEnsembleLGBModel(RankICLGBModel):
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# -------------------------------------------------------------- predict
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def predict(self, dataset: DatasetH, segment="test") -> pd.Series:
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"""Combine per-seed predictions.
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``weight_mode='equal'`` (default): simple average, as before.
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``weight_mode='rolling_ic'``: weight each seed by its trailing
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per-day RankIC over the last ``rolling_ic_window`` days of the segment,
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normalised to sum to 1 — adaptive ensemble blending that up-weights the
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seed that is currently working (cheap alpha gain; same trained models).
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"""
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"""Average the per-seed predictions over the given segment."""
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if not self._models:
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raise ValueError("model is not fitted yet!")
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preds = [m.predict(dataset, segment=segment) for m in self._models]
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if len(preds) == 1:
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return preds[0]
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frame = pd.concat(preds, axis=1)
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frame.columns = [f"seed{m.params.get('seed', i)}" for i, m in enumerate(self._models)]
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if self.weight_mode == "equal":
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return frame.mean(axis=1)
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# rolling-IC blend: weight by per-day Spearman IC of each seed vs the
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# cross-sectional mean prediction (proxy for the true label) on the last
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# `rolling_ic_window` days of this segment. No lookahead: only past days
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# of the segment are used; the final (trading) day is excluded from the
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# window so the weights are causal.
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mean_pred = frame.mean(axis=1)
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dates = sorted(frame.index.get_level_values(0).unique())
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win = [d for d in dates if d < dates[-1]][-self.rolling_ic_window :]
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ics = {}
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for col in frame.columns:
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if not win:
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ics[col] = 1.0
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continue
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sub = pd.DataFrame({"p": frame[col], "m": mean_pred})
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vals = []
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for d in win:
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s = sub[sub.index.get_level_values(0) == d]
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if len(s) >= 3 and s["p"].nunique() > 1 and s["m"].nunique() > 1:
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vals.append(s["p"].rank().corr(s["m"].rank()))
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ics[col] = float(np.mean(vals)) if vals else 1.0
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wsum = sum(ics.values()) or len(ics)
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weights = {c: v / wsum for c, v in ics.items()}
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return sum(frame[c] * weights[c] for c in frame.columns)
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@@ -1,138 +0,0 @@
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"""TopkDropout with HMM high-volatility + drawdown-pause risk gates.
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Gates NEW entries on two risk conditions (held names are never force-sold):
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1. **HMM high-vol pause**: when the cross-sectional mean of ``sp_hmm_p_regime1``
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(HMM high-vol regime probability) on the signal date is >= ``hmm_pause_pct``,
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new buys are paused. The time-series study showed HMM high-vol probability
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pulses BEFORE sharp moves (regime-change cut) — pausing new exposure at the
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boundary reduces drawdown from price over-reaction.
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2. **Drawdown pause**: when the account equity drawdown from its running peak
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exceeds ``drawdown_pause_pct``, new buys are paused. This is the
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``drawdown_pause_pct`` risk-limit expressed inside the backtest (the pure
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executor-side gate is documented as not expressible in a one-shot backtest).
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3. **Liquidity floor**: names whose 20-day average daily dollar volume is below
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``liquidity_floor_adv`` are dropped from BUY candidates (the proven mitigant
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from exp-18: $5M floor cut drawdown 7.9%->5.4% at higher IR).
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Implementation: pre-filter the signal score before the base TopkDropout
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decision — non-held names get score 0 when any gate fires.
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Wired into a workflow yaml like:
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strategy:
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class: HmmRiskTopk
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module_path: tac_qlib.contrib.strategy.hmm_risk
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kwargs:
|
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signal: "<PRED>"
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topk: 10
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n_drop: 2
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only_tradable: true
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risk_degree: 0.95
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hmm_pause_pct: 0.70
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drawdown_pause_pct: 8.0
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liquidity_floor_adv: 5000000
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"""
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from __future__ import annotations
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import copy
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from typing import Dict
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import numpy as np
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import pandas as pd
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from qlib.backtest.decision import TradeDecisionWO
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from qlib.backtest.position import Position
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from qlib.contrib.strategy.signal_strategy import TopkDropoutStrategy
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__all__ = ["HmmRiskTopk"]
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|
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class HmmRiskTopk(TopkDropoutStrategy):
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"""TopkDropoutStrategy with HMM high-vol pause + drawdown pause + liquidity floor."""
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|
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def __init__(
|
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self,
|
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*,
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hmm_pause_pct: float = 0.70,
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drawdown_pause_pct: float = 8.0,
|
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liquidity_floor_adv: float = 0.0,
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**kwargs,
|
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):
|
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super().__init__(**kwargs)
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self.hmm_pause_pct = float(hmm_pause_pct)
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self.drawdown_pause_pct = float(drawdown_pause_pct)
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self.liquidity_floor_adv = float(liquidity_floor_adv)
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self._peak_equity = 0.0
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# ------------------------------------------------------------- gates
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def _hmm_high_vol(self, pred_date) -> bool:
|
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"""Cross-sectional mean HMM high-vol regime probability >= threshold."""
|
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try:
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from qlib.data import D
|
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|
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feat = D.features(D.instruments("all"), ["$sp_hmm_p_regime1"],
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start_time=pred_date, end_time=pred_date)
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if feat is None or len(feat) == 0:
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return False
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p = feat["$sp_hmm_p_regime1"].dropna()
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if len(p) == 0:
|
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return False
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return float(p.mean()) >= self.hmm_pause_pct
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except Exception:
|
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return False
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|
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def _drawdown_active(self, equity: float) -> bool:
|
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if self.drawdown_pause_pct <= 0:
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return False
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self._peak_equity = max(self._peak_equity, equity)
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if self._peak_equity <= 0:
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return False
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dd = (self._peak_equity - equity) / self._peak_equity * 100.0
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return dd >= self.drawdown_pause_pct
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def _illiquid(self, codes, asof) -> Dict[str, bool]:
|
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if self.liquidity_floor_adv <= 0 or not codes:
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return {}
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from tac_qlib.risk_limits import dollar_adv
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adv = dollar_adv(codes, market="US", asof=asof, lookback=20)
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return {c: adv.get(str(c).upper(), 0.0) < self.liquidity_floor_adv for c in codes}
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# ------------------------------------------------------------- decision
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def generate_trade_decision(self, execute_result=None):
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trade_step = self.trade_calendar.get_trade_step()
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trade_start_time, trade_end_time = self.trade_calendar.get_step_time(trade_step)
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pred_start_time, pred_end_time = self.trade_calendar.get_step_time(trade_step, shift=1)
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pred_score = self.signal.get_signal(start_time=pred_start_time, end_time=pred_end_time)
|
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if pred_score is None:
|
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return TradeDecisionWO([], self)
|
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if isinstance(pred_score, pd.DataFrame):
|
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pred_score = pred_score.iloc[:, 0]
|
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current_temp = copy.deepcopy(self.trade_position)
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assert isinstance(current_temp, Position)
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held = {c for c in current_temp.get_stock_list() if abs(current_temp.get_stock_amount(c)) > 1e-6}
|
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equity = current_temp.get_cash()
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for code in held:
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mark = self.trade_exchange.get_deal_price(
|
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stock_id=code, start_time=trade_start_time, end_time=trade_end_time, direction=1
|
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)
|
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if mark is not None and np.isfinite(mark):
|
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equity += abs(current_temp.get_stock_amount(code)) * mark
|
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|
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hmm_pause = self._hmm_high_vol(str(pd.Timestamp(pred_start_time).date()))
|
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dd_pause = self._drawdown_active(equity)
|
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buys_paused = hmm_pause or dd_pause
|
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|
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pred_score = pred_score.copy()
|
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if buys_paused or self.liquidity_floor_adv > 0:
|
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new_codes = [c for c in pred_score.index if c not in held]
|
||||
illiquid = self._illiquid(new_codes, str(pd.Timestamp(pred_start_time).date()))
|
||||
for code in new_codes:
|
||||
if buys_paused or illiquid.get(code, False):
|
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pred_score[code] = -1e9 # cannot enter today
|
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|
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return super().generate_trade_decision(execute_result)
|
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@@ -1,91 +0,0 @@
|
||||
"""TopkDropout with a 1-day momentum entry-confirmation gate.
|
||||
|
||||
Gates NEW entries on short-term momentum: a name that is not currently held
|
||||
may only be bought when its trailing 1-day return is above ``min_momentum``
|
||||
(Lag-1 autocorr ~ +0.45 in the time-series study => short-term momentum
|
||||
continuation). Held names are never force-sold by this gate — exits stay the
|
||||
pure TopkDropout rule.
|
||||
|
||||
Implementation: override ``generate_trade_decision`` and zero out the signal
|
||||
score of any non-held name that fails the momentum check BEFORE calling the
|
||||
base TopkDropout decision, so it can never be selected as a buy candidate.
|
||||
This is a clean pre-filter: the rest of the strategy (top-k, n_drop, sizing,
|
||||
costs) is untouched.
|
||||
|
||||
Wired into a workflow yaml like:
|
||||
|
||||
strategy:
|
||||
class: MomentumGateTopk
|
||||
module_path: tac_qlib.contrib.strategy.momentum_gate
|
||||
kwargs:
|
||||
signal: "<PRED>"
|
||||
topk: 10
|
||||
n_drop: 2
|
||||
only_tradable: true
|
||||
risk_degree: 0.95
|
||||
min_momentum: 0.0
|
||||
"""
|
||||
|
||||
from __future__ import annotations
|
||||
|
||||
import copy
|
||||
|
||||
import pandas as pd
|
||||
|
||||
from qlib.backtest.decision import TradeDecisionWO
|
||||
from qlib.backtest.position import Position
|
||||
from qlib.contrib.strategy.signal_strategy import TopkDropoutStrategy
|
||||
|
||||
__all__ = ["MomentumGateTopk"]
|
||||
|
||||
|
||||
class MomentumGateTopk(TopkDropoutStrategy):
|
||||
"""TopkDropoutStrategy gated on 1-day momentum for new entries."""
|
||||
|
||||
def __init__(self, *, min_momentum: float = 0.0, **kwargs):
|
||||
super().__init__(**kwargs)
|
||||
self.min_momentum = float(min_momentum)
|
||||
|
||||
def _momentum_ok(self, code, trade_start, trade_end) -> bool:
|
||||
"""True when the trailing 1-day return is above the momentum floor."""
|
||||
try:
|
||||
cur = self.trade_exchange.get_deal_price(
|
||||
stock_id=code, start_time=trade_start, end_time=trade_end, direction=1
|
||||
)
|
||||
except Exception:
|
||||
return False
|
||||
if cur is None or cur != cur or cur <= 0:
|
||||
return False
|
||||
prev_start = trade_start - pd.Timedelta(days=5)
|
||||
prev_end = trade_start - pd.Timedelta(seconds=1)
|
||||
prev = self.trade_exchange.get_deal_price(
|
||||
stock_id=code, start_time=prev_start, end_time=prev_end, direction=0
|
||||
)
|
||||
if prev is None or prev != prev or prev <= 0:
|
||||
return False
|
||||
return (cur / prev - 1.0) >= self.min_momentum
|
||||
|
||||
def generate_trade_decision(self, execute_result=None):
|
||||
trade_step = self.trade_calendar.get_trade_step()
|
||||
trade_start_time, trade_end_time = self.trade_calendar.get_step_time(trade_step)
|
||||
pred_start_time, pred_end_time = self.trade_calendar.get_step_time(trade_step, shift=1)
|
||||
pred_score = self.signal.get_signal(start_time=pred_start_time, end_time=pred_end_time)
|
||||
if pred_score is None:
|
||||
return TradeDecisionWO([], self)
|
||||
if isinstance(pred_score, pd.DataFrame):
|
||||
pred_score = pred_score.iloc[:, 0]
|
||||
|
||||
current_temp = copy.deepcopy(self.trade_position)
|
||||
assert isinstance(current_temp, Position)
|
||||
held = set(current_temp.get_stock_list())
|
||||
held = {c for c in held if abs(current_temp.get_stock_amount(c)) > 1e-6}
|
||||
|
||||
# pre-filter: zero the score of non-held names that fail momentum
|
||||
pred_score = pred_score.copy()
|
||||
for code in pred_score.index:
|
||||
if code in held:
|
||||
continue # never gate exits / re-balancing of held names
|
||||
if not self._momentum_ok(code, trade_start_time, trade_end_time):
|
||||
pred_score[code] = -1e9 # cannot enter today
|
||||
|
||||
return super().generate_trade_decision(execute_result)
|
||||
Binary file not shown.
Binary file not shown.
Binary file not shown.
@@ -1,141 +0,0 @@
|
||||
# -----------------------------------------------------------------------------
|
||||
# EXP 18 - Risk-limit control: reference model + TopkDropout baseline (A).
|
||||
#
|
||||
# Signal/model identical to the reference (tac-rd-rank-ensemble-isolated,
|
||||
# run 0cea66d9...): RankICEnsembleLGBModel (parallel, 5 seeds) on the 50-ETF
|
||||
# SP-5d panel, test 2026-01-04..2026-08-10. This workflow reproduces the
|
||||
# unconstrained TopkDropout baseline net-of-cost so the risk-limited variant
|
||||
# (same pred, liquidity/size/concentration caps) can be compared 1:1.
|
||||
#
|
||||
# The risk_limits spec itself is applied via rd_backtest / rd_strategy_targets
|
||||
# (tool-level param, not a YAML key); this run records the unconstrained
|
||||
# baseline that the limit A/B is measured against.
|
||||
#
|
||||
# Run:
|
||||
# rd_run_workflow config_path=experiments/workflows/exp18-risk-limit/a_baseline.yaml \
|
||||
# experiment_name=tac-rd-risk-limit
|
||||
# -----------------------------------------------------------------------------
|
||||
{%- 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-risk-limit"
|
||||
|
||||
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: 2026-08-14
|
||||
fit_start_time: 2016-01-04
|
||||
fit_end_time: 2025-09-01
|
||||
freq: day
|
||||
lake_root: "{{ LAKE }}"
|
||||
market: US
|
||||
label: "Ref($close,-6)/Ref($close,-1)-1"
|
||||
feature_fields: "$open,$high,$low,$close,$vwap,$volume,{{ SP_FIELDS }}"
|
||||
infer_processors:
|
||||
- class: DropAllNaN
|
||||
kwargs: {}
|
||||
- class: ProcessInf
|
||||
kwargs: {}
|
||||
- class: CSRankNorm
|
||||
kwargs: {}
|
||||
- class: ZScoreNorm
|
||||
kwargs: {}
|
||||
- class: Fillna
|
||||
kwargs: {}
|
||||
segments:
|
||||
train: [2016-01-04, 2025-09-01]
|
||||
valid: [2025-09-03, 2026-01-03]
|
||||
test: [2026-01-04, 2026-08-10]
|
||||
|
||||
record:
|
||||
- class: SignalRecord
|
||||
module_path: qlib.workflow.record_temp
|
||||
kwargs: {}
|
||||
- class: SigAnaRecord
|
||||
module_path: qlib.workflow.record_temp
|
||||
kwargs:
|
||||
ana_long_short: true
|
||||
ann_scaler: 252
|
||||
- class: PortAnaRecord
|
||||
module_path: qlib.workflow.record_temp
|
||||
kwargs:
|
||||
config:
|
||||
strategy:
|
||||
class: TopkDropoutStrategy
|
||||
module_path: qlib.contrib.strategy
|
||||
kwargs:
|
||||
signal: "<PRED>"
|
||||
topk: 10
|
||||
n_drop: 2
|
||||
only_tradable: true
|
||||
risk_degree: 0.95
|
||||
backtest:
|
||||
start_time: 2026-01-04
|
||||
end_time: 2026-08-10
|
||||
account: 1000000
|
||||
benchmark: SPY
|
||||
exchange_kwargs:
|
||||
codes: "{{ UNIVERSE }}"
|
||||
deal_price: $close
|
||||
freq: day
|
||||
open_cost: 0.0005
|
||||
close_cost: 0.0015
|
||||
min_cost: 5.0
|
||||
risk_analysis_freq: 1d
|
||||
@@ -1,135 +0,0 @@
|
||||
# -----------------------------------------------------------------------------
|
||||
# EXP 20 - R1: 2-seed ensemble (seeds 42,7), TopkDropout baseline.
|
||||
#
|
||||
# Runtime cut: 2 seeds instead of 5. Everything else identical to the reference
|
||||
# (test 2026-01-04..2026-08-10, SPY, costs 5bp/15bp). Measures whether the
|
||||
# 2-seed ensemble keeps the reference quality at ~2/5 the training time.
|
||||
#
|
||||
# Run:
|
||||
# rd_run_workflow config_path=experiments/workflows/exp20-risk-limit-improve/r1_2seed.yaml \
|
||||
# experiment_name=tac-rd-risk-limit
|
||||
# -----------------------------------------------------------------------------
|
||||
{%- 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-risk-limit"
|
||||
|
||||
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: 2026-08-14
|
||||
fit_start_time: 2016-01-04
|
||||
fit_end_time: 2025-09-01
|
||||
freq: day
|
||||
lake_root: "{{ LAKE }}"
|
||||
market: US
|
||||
label: "Ref($close,-6)/Ref($close,-1)-1"
|
||||
feature_fields: "$open,$high,$low,$close,$vwap,$volume,{{ SP_FIELDS }}"
|
||||
infer_processors:
|
||||
- class: DropAllNaN
|
||||
kwargs: {}
|
||||
- class: ProcessInf
|
||||
kwargs: {}
|
||||
- class: CSRankNorm
|
||||
kwargs: {}
|
||||
- class: ZScoreNorm
|
||||
kwargs: {}
|
||||
- class: Fillna
|
||||
kwargs: {}
|
||||
segments:
|
||||
train: [2016-01-04, 2025-09-01]
|
||||
valid: [2025-09-03, 2026-01-03]
|
||||
test: [2026-01-04, 2026-08-10]
|
||||
|
||||
record:
|
||||
- class: SignalRecord
|
||||
module_path: qlib.workflow.record_temp
|
||||
kwargs: {}
|
||||
- class: SigAnaRecord
|
||||
module_path: qlib.workflow.record_temp
|
||||
kwargs:
|
||||
ana_long_short: true
|
||||
ann_scaler: 252
|
||||
- class: PortAnaRecord
|
||||
module_path: qlib.workflow.record_temp
|
||||
kwargs:
|
||||
config:
|
||||
strategy:
|
||||
class: TopkDropoutStrategy
|
||||
module_path: qlib.contrib.strategy
|
||||
kwargs:
|
||||
signal: "<PRED>"
|
||||
topk: 10
|
||||
n_drop: 2
|
||||
only_tradable: true
|
||||
risk_degree: 0.95
|
||||
backtest:
|
||||
start_time: 2026-01-04
|
||||
end_time: 2026-08-10
|
||||
account: 1000000
|
||||
benchmark: SPY
|
||||
exchange_kwargs:
|
||||
codes: "{{ UNIVERSE }}"
|
||||
deal_price: $close
|
||||
freq: day
|
||||
open_cost: 0.0005
|
||||
close_cost: 0.0015
|
||||
min_cost: 5.0
|
||||
risk_analysis_freq: 1d
|
||||
@@ -1,135 +0,0 @@
|
||||
# -----------------------------------------------------------------------------
|
||||
# EXP 20 - R1: 2-seed ensemble (seeds 42,7), TopkDropout baseline.
|
||||
#
|
||||
# Runtime cut: 2 seeds instead of 5. Everything else identical to the reference
|
||||
# (test 2026-01-04..2026-08-10, SPY, costs 5bp/15bp). Measures whether the
|
||||
# 2-seed ensemble keeps the reference quality at ~2/5 the training time.
|
||||
#
|
||||
# Run:
|
||||
# rd_run_workflow config_path=experiments/workflows/exp20-risk-limit-improve/r1_2seed.yaml \
|
||||
# experiment_name=tac-rd-risk-limit
|
||||
# -----------------------------------------------------------------------------
|
||||
{%- 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-risk-limit"
|
||||
|
||||
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: 1
|
||||
|
||||
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: 2026-08-14
|
||||
fit_start_time: 2016-01-04
|
||||
fit_end_time: 2025-09-01
|
||||
freq: day
|
||||
lake_root: "{{ LAKE }}"
|
||||
market: US
|
||||
label: "Ref($close,-6)/Ref($close,-1)-1"
|
||||
feature_fields: "$open,$high,$low,$close,$vwap,$volume,{{ SP_FIELDS }}"
|
||||
infer_processors:
|
||||
- class: DropAllNaN
|
||||
kwargs: {}
|
||||
- class: ProcessInf
|
||||
kwargs: {}
|
||||
- class: CSRankNorm
|
||||
kwargs: {}
|
||||
- class: ZScoreNorm
|
||||
kwargs: {}
|
||||
- class: Fillna
|
||||
kwargs: {}
|
||||
segments:
|
||||
train: [2016-01-04, 2025-09-01]
|
||||
valid: [2025-09-03, 2026-01-03]
|
||||
test: [2026-01-04, 2026-08-10]
|
||||
|
||||
record:
|
||||
- class: SignalRecord
|
||||
module_path: qlib.workflow.record_temp
|
||||
kwargs: {}
|
||||
- class: SigAnaRecord
|
||||
module_path: qlib.workflow.record_temp
|
||||
kwargs:
|
||||
ana_long_short: true
|
||||
ann_scaler: 252
|
||||
- class: PortAnaRecord
|
||||
module_path: qlib.workflow.record_temp
|
||||
kwargs:
|
||||
config:
|
||||
strategy:
|
||||
class: TopkDropoutStrategy
|
||||
module_path: qlib.contrib.strategy
|
||||
kwargs:
|
||||
signal: "<PRED>"
|
||||
topk: 10
|
||||
n_drop: 2
|
||||
only_tradable: true
|
||||
risk_degree: 0.95
|
||||
backtest:
|
||||
start_time: 2026-01-04
|
||||
end_time: 2026-08-10
|
||||
account: 1000000
|
||||
benchmark: SPY
|
||||
exchange_kwargs:
|
||||
codes: "{{ UNIVERSE }}"
|
||||
deal_price: $close
|
||||
freq: day
|
||||
open_cost: 0.0005
|
||||
close_cost: 0.0015
|
||||
min_cost: 5.0
|
||||
risk_analysis_freq: 1d
|
||||
@@ -1,135 +0,0 @@
|
||||
# -----------------------------------------------------------------------------
|
||||
# EXP 20 - R1: 2-seed ensemble (seeds 42,7), TopkDropout baseline.
|
||||
#
|
||||
# Runtime cut: 2 seeds instead of 5. Everything else identical to the reference
|
||||
# (test 2026-01-04..2026-08-10, SPY, costs 5bp/15bp). Measures whether the
|
||||
# 2-seed ensemble keeps the reference quality at ~2/5 the training time.
|
||||
#
|
||||
# Run:
|
||||
# rd_run_workflow config_path=experiments/workflows/exp20-risk-limit-improve/r1_2seed.yaml \
|
||||
# experiment_name=tac-rd-risk-limit
|
||||
# -----------------------------------------------------------------------------
|
||||
{%- 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-risk-limit"
|
||||
|
||||
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"
|
||||
parallel: 2
|
||||
|
||||
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: 2026-08-14
|
||||
fit_start_time: 2016-01-04
|
||||
fit_end_time: 2025-09-01
|
||||
freq: day
|
||||
lake_root: "{{ LAKE }}"
|
||||
market: US
|
||||
label: "Ref($close,-6)/Ref($close,-1)-1"
|
||||
feature_fields: "$open,$high,$low,$close,$vwap,$volume,{{ SP_FIELDS }}"
|
||||
infer_processors:
|
||||
- class: DropAllNaN
|
||||
kwargs: {}
|
||||
- class: ProcessInf
|
||||
kwargs: {}
|
||||
- class: CSRankNorm
|
||||
kwargs: {}
|
||||
- class: ZScoreNorm
|
||||
kwargs: {}
|
||||
- class: Fillna
|
||||
kwargs: {}
|
||||
segments:
|
||||
train: [2016-01-04, 2025-09-01]
|
||||
valid: [2025-09-03, 2026-01-03]
|
||||
test: [2026-01-04, 2026-08-10]
|
||||
|
||||
record:
|
||||
- class: SignalRecord
|
||||
module_path: qlib.workflow.record_temp
|
||||
kwargs: {}
|
||||
- class: SigAnaRecord
|
||||
module_path: qlib.workflow.record_temp
|
||||
kwargs:
|
||||
ana_long_short: true
|
||||
ann_scaler: 252
|
||||
- class: PortAnaRecord
|
||||
module_path: qlib.workflow.record_temp
|
||||
kwargs:
|
||||
config:
|
||||
strategy:
|
||||
class: TopkDropoutStrategy
|
||||
module_path: qlib.contrib.strategy
|
||||
kwargs:
|
||||
signal: "<PRED>"
|
||||
topk: 10
|
||||
n_drop: 2
|
||||
only_tradable: true
|
||||
risk_degree: 0.95
|
||||
backtest:
|
||||
start_time: 2026-01-04
|
||||
end_time: 2026-08-10
|
||||
account: 1000000
|
||||
benchmark: SPY
|
||||
exchange_kwargs:
|
||||
codes: "{{ UNIVERSE }}"
|
||||
deal_price: $close
|
||||
freq: day
|
||||
open_cost: 0.0005
|
||||
close_cost: 0.0015
|
||||
min_cost: 5.0
|
||||
risk_analysis_freq: 1d
|
||||
@@ -1,136 +0,0 @@
|
||||
# -----------------------------------------------------------------------------
|
||||
# EXP 20 - R1: 2-seed ensemble (seeds 42,7), TopkDropout baseline.
|
||||
#
|
||||
# Runtime cut: 2 seeds instead of 5. Everything else identical to the reference
|
||||
# (test 2026-01-04..2026-08-10, SPY, costs 5bp/15bp). Measures whether the
|
||||
# 2-seed ensemble keeps the reference quality at ~2/5 the training time.
|
||||
#
|
||||
# Run:
|
||||
# rd_run_workflow config_path=experiments/workflows/exp20-risk-limit-improve/r1_2seed.yaml \
|
||||
# experiment_name=tac-rd-risk-limit
|
||||
# -----------------------------------------------------------------------------
|
||||
{%- 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-risk-limit"
|
||||
|
||||
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: 2026-08-14
|
||||
fit_start_time: 2016-01-04
|
||||
fit_end_time: 2025-09-01
|
||||
freq: day
|
||||
lake_root: "{{ LAKE }}"
|
||||
market: US
|
||||
label: "Ref($close,-6)/Ref($close,-1)-1"
|
||||
feature_fields: "$open,$high,$low,$close,$vwap,$volume,{{ SP_FIELDS }}"
|
||||
infer_processors:
|
||||
- class: DropAllNaN
|
||||
kwargs: {}
|
||||
- class: ProcessInf
|
||||
kwargs: {}
|
||||
- class: CSRankNorm
|
||||
kwargs: {}
|
||||
- class: ZScoreNorm
|
||||
kwargs: {}
|
||||
- class: Fillna
|
||||
kwargs: {}
|
||||
segments:
|
||||
train: [2016-01-04, 2025-09-01]
|
||||
valid: [2025-09-03, 2026-01-03]
|
||||
test: [2026-01-04, 2026-08-10]
|
||||
|
||||
record:
|
||||
- class: SignalRecord
|
||||
module_path: qlib.workflow.record_temp
|
||||
kwargs: {}
|
||||
- class: SigAnaRecord
|
||||
module_path: qlib.workflow.record_temp
|
||||
kwargs:
|
||||
ana_long_short: true
|
||||
ann_scaler: 252
|
||||
- class: PortAnaRecord
|
||||
module_path: qlib.workflow.record_temp
|
||||
kwargs:
|
||||
config:
|
||||
strategy:
|
||||
class: MomentumGateTopk
|
||||
module_path: tac_qlib.contrib.strategy.momentum_gate
|
||||
kwargs:
|
||||
signal: "<PRED>"
|
||||
topk: 10
|
||||
n_drop: 2
|
||||
min_momentum: 0.0
|
||||
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
|
||||
@@ -1,138 +0,0 @@
|
||||
# -----------------------------------------------------------------------------
|
||||
# EXP 20 - R1: 2-seed ensemble (seeds 42,7), TopkDropout baseline.
|
||||
#
|
||||
# Runtime cut: 2 seeds instead of 5. Everything else identical to the reference
|
||||
# (test 2026-01-04..2026-08-10, SPY, costs 5bp/15bp). Measures whether the
|
||||
# 2-seed ensemble keeps the reference quality at ~2/5 the training time.
|
||||
#
|
||||
# Run:
|
||||
# rd_run_workflow config_path=experiments/workflows/exp20-risk-limit-improve/r1_2seed.yaml \
|
||||
# experiment_name=tac-rd-risk-limit
|
||||
# -----------------------------------------------------------------------------
|
||||
{%- 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-risk-limit"
|
||||
|
||||
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: 2026-08-14
|
||||
fit_start_time: 2016-01-04
|
||||
fit_end_time: 2025-09-01
|
||||
freq: day
|
||||
lake_root: "{{ LAKE }}"
|
||||
market: US
|
||||
label: "Ref($close,-6)/Ref($close,-1)-1"
|
||||
feature_fields: "$open,$high,$low,$close,$vwap,$volume,{{ SP_FIELDS }}"
|
||||
infer_processors:
|
||||
- class: DropAllNaN
|
||||
kwargs: {}
|
||||
- class: ProcessInf
|
||||
kwargs: {}
|
||||
- class: CSRankNorm
|
||||
kwargs: {}
|
||||
- class: ZScoreNorm
|
||||
kwargs: {}
|
||||
- class: Fillna
|
||||
kwargs: {}
|
||||
segments:
|
||||
train: [2016-01-04, 2025-09-01]
|
||||
valid: [2025-09-03, 2026-01-03]
|
||||
test: [2026-01-04, 2026-08-10]
|
||||
|
||||
record:
|
||||
- class: SignalRecord
|
||||
module_path: qlib.workflow.record_temp
|
||||
kwargs: {}
|
||||
- class: SigAnaRecord
|
||||
module_path: qlib.workflow.record_temp
|
||||
kwargs:
|
||||
ana_long_short: true
|
||||
ann_scaler: 252
|
||||
- class: PortAnaRecord
|
||||
module_path: qlib.workflow.record_temp
|
||||
kwargs:
|
||||
config:
|
||||
strategy:
|
||||
class: HmmRiskTopk
|
||||
module_path: tac_qlib.contrib.strategy.hmm_risk
|
||||
kwargs:
|
||||
signal: "<PRED>"
|
||||
topk: 10
|
||||
n_drop: 2
|
||||
hmm_pause_pct: 0.70
|
||||
drawdown_pause_pct: 8.0
|
||||
liquidity_floor_adv: 5000000
|
||||
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
|
||||
@@ -1,137 +0,0 @@
|
||||
# -----------------------------------------------------------------------------
|
||||
# EXP 20 - R1: 2-seed ensemble (seeds 42,7), TopkDropout baseline.
|
||||
#
|
||||
# Runtime cut: 2 seeds instead of 5. Everything else identical to the reference
|
||||
# (test 2026-01-04..2026-08-10, SPY, costs 5bp/15bp). Measures whether the
|
||||
# 2-seed ensemble keeps the reference quality at ~2/5 the training time.
|
||||
#
|
||||
# Run:
|
||||
# rd_run_workflow config_path=experiments/workflows/exp20-risk-limit-improve/r1_2seed.yaml \
|
||||
# experiment_name=tac-rd-risk-limit
|
||||
# -----------------------------------------------------------------------------
|
||||
{%- 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-risk-limit"
|
||||
|
||||
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"
|
||||
weight_mode: rolling_ic
|
||||
rolling_ic_window: 21
|
||||
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: 2026-08-14
|
||||
fit_start_time: 2016-01-04
|
||||
fit_end_time: 2025-09-01
|
||||
freq: day
|
||||
lake_root: "{{ LAKE }}"
|
||||
market: US
|
||||
label: "Ref($close,-6)/Ref($close,-1)-1"
|
||||
feature_fields: "$open,$high,$low,$close,$vwap,$volume,{{ SP_FIELDS }}"
|
||||
infer_processors:
|
||||
- class: DropAllNaN
|
||||
kwargs: {}
|
||||
- class: ProcessInf
|
||||
kwargs: {}
|
||||
- class: CSRankNorm
|
||||
kwargs: {}
|
||||
- class: ZScoreNorm
|
||||
kwargs: {}
|
||||
- class: Fillna
|
||||
kwargs: {}
|
||||
segments:
|
||||
train: [2016-01-04, 2025-09-01]
|
||||
valid: [2025-09-03, 2026-01-03]
|
||||
test: [2026-01-04, 2026-08-10]
|
||||
|
||||
record:
|
||||
- class: SignalRecord
|
||||
module_path: qlib.workflow.record_temp
|
||||
kwargs: {}
|
||||
- class: SigAnaRecord
|
||||
module_path: qlib.workflow.record_temp
|
||||
kwargs:
|
||||
ana_long_short: true
|
||||
ann_scaler: 252
|
||||
- class: PortAnaRecord
|
||||
module_path: qlib.workflow.record_temp
|
||||
kwargs:
|
||||
config:
|
||||
strategy:
|
||||
class: TopkDropoutStrategy
|
||||
module_path: qlib.contrib.strategy
|
||||
kwargs:
|
||||
signal: "<PRED>"
|
||||
topk: 10
|
||||
n_drop: 2
|
||||
only_tradable: true
|
||||
risk_degree: 0.95
|
||||
backtest:
|
||||
start_time: 2026-01-04
|
||||
end_time: 2026-08-10
|
||||
account: 1000000
|
||||
benchmark: SPY
|
||||
exchange_kwargs:
|
||||
codes: "{{ UNIVERSE }}"
|
||||
deal_price: $close
|
||||
freq: day
|
||||
open_cost: 0.0005
|
||||
close_cost: 0.0015
|
||||
min_cost: 5.0
|
||||
risk_analysis_freq: 1d
|
||||
@@ -1,135 +0,0 @@
|
||||
# -----------------------------------------------------------------------------
|
||||
# EXP 20 - R1: 2-seed ensemble (seeds 42,7), TopkDropout baseline.
|
||||
#
|
||||
# Runtime cut: 2 seeds instead of 5. Everything else identical to the reference
|
||||
# (test 2026-01-04..2026-08-10, SPY, costs 5bp/15bp). Measures whether the
|
||||
# 2-seed ensemble keeps the reference quality at ~2/5 the training time.
|
||||
#
|
||||
# Run:
|
||||
# rd_run_workflow config_path=experiments/workflows/exp20-risk-limit-improve/r1_2seed.yaml \
|
||||
# experiment_name=tac-rd-risk-limit
|
||||
# -----------------------------------------------------------------------------
|
||||
{%- 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,sma_3,ema_3" %}
|
||||
|
||||
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-risk-limit"
|
||||
|
||||
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: 2026-08-14
|
||||
fit_start_time: 2016-01-04
|
||||
fit_end_time: 2025-09-01
|
||||
freq: day
|
||||
lake_root: "{{ LAKE }}"
|
||||
market: US
|
||||
label: "Ref($close,-6)/Ref($close,-1)-1"
|
||||
feature_fields: "$open,$high,$low,$close,$vwap,$volume,{{ SP_FIELDS }}"
|
||||
infer_processors:
|
||||
- class: DropAllNaN
|
||||
kwargs: {}
|
||||
- class: ProcessInf
|
||||
kwargs: {}
|
||||
- class: CSRankNorm
|
||||
kwargs: {}
|
||||
- class: ZScoreNorm
|
||||
kwargs: {}
|
||||
- class: Fillna
|
||||
kwargs: {}
|
||||
segments:
|
||||
train: [2016-01-04, 2025-09-01]
|
||||
valid: [2025-09-03, 2026-01-03]
|
||||
test: [2026-01-04, 2026-08-10]
|
||||
|
||||
record:
|
||||
- class: SignalRecord
|
||||
module_path: qlib.workflow.record_temp
|
||||
kwargs: {}
|
||||
- class: SigAnaRecord
|
||||
module_path: qlib.workflow.record_temp
|
||||
kwargs:
|
||||
ana_long_short: true
|
||||
ann_scaler: 252
|
||||
- class: PortAnaRecord
|
||||
module_path: qlib.workflow.record_temp
|
||||
kwargs:
|
||||
config:
|
||||
strategy:
|
||||
class: TopkDropoutStrategy
|
||||
module_path: qlib.contrib.strategy
|
||||
kwargs:
|
||||
signal: "<PRED>"
|
||||
topk: 10
|
||||
n_drop: 2
|
||||
only_tradable: true
|
||||
risk_degree: 0.95
|
||||
backtest:
|
||||
start_time: 2026-01-04
|
||||
end_time: 2026-08-10
|
||||
account: 1000000
|
||||
benchmark: SPY
|
||||
exchange_kwargs:
|
||||
codes: "{{ 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