Compare commits
3
Commits
| Author | SHA1 | Date | |
|---|---|---|---|
 zhaoli
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a9e4464668 | ||
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zhaoli
|
ea75065fb5 | ||
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zhaoli
|
06e3b16544 |
+13
-13
@@ -1,27 +1,27 @@
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# TradeAC custom-qlib-code snapshot (auto-generated)
|
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# parent repo HEAD : a4bfb930986d8c86edc547e0b5d50fed680bbd56
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# parent repo HEAD : ea75065fb517388e417ad0fea76acf2731fef84e
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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
|
||||
b419ee55ed455a1c45423d1c9025ca5cc0a98576 tac-qlib/tac_qlib/contrib/__pycache__/__init__.cpython-312.pyc
|
||||
b5e28b7c0a10ab553eaa6303013a880731742378 tac-qlib/tac_qlib/contrib/__pycache__/__init__.cpython-312.pyc
|
||||
c76a9f17f680e74eea766eff27f7624359749ed6 tac-qlib/tac_qlib/contrib/data/__init__.py
|
||||
2f6c67620aa2f9e6aaaef3369361d9b3eac3d6ca tac-qlib/tac_qlib/contrib/data/__pycache__/__init__.cpython-312.pyc
|
||||
fdd5923a70a399e8680913593ff111641947898e tac-qlib/tac_qlib/contrib/data/__pycache__/handler.cpython-312.pyc
|
||||
715ab312125a446af751b928fe41cf41b99ffcd0 tac-qlib/tac_qlib/contrib/data/__pycache__/__init__.cpython-312.pyc
|
||||
a82a6a236db93097d48748e541c2cf9caa4e48b5 tac-qlib/tac_qlib/contrib/data/__pycache__/handler.cpython-312.pyc
|
||||
0dd25ef161c6e0f15eafc84886e7e1381deb38c3 tac-qlib/tac_qlib/contrib/data/handler.py
|
||||
b151d139a0dcde87d74b21e7c4b729176ba5c39b tac-qlib/tac_qlib/contrib/model/__init__.py
|
||||
08dec87ccdf6bb5d2cf611ca3032a4280aaab8cf tac-qlib/tac_qlib/contrib/model/__pycache__/__init__.cpython-312.pyc
|
||||
6fb61946ea9a83dfb560de3717f5fbf482c4c00e tac-qlib/tac_qlib/contrib/model/__pycache__/rank_ensemble.cpython-312.pyc
|
||||
3e80f2e08b661ddd2f58ffe5a6196063fa41ae51 tac-qlib/tac_qlib/contrib/model/__pycache__/rank_gbdt.cpython-312.pyc
|
||||
483221cf1d5bad779d3cf9505269b7fb0b3f165d tac-qlib/tac_qlib/contrib/model/__pycache__/__init__.cpython-312.pyc
|
||||
1286dec07a861f25691b24f71ecc22d8eb3537df tac-qlib/tac_qlib/contrib/model/__pycache__/rank_ensemble.cpython-312.pyc
|
||||
5185764bd55dbc63d16582bfcb6adc11b7e6d342 tac-qlib/tac_qlib/contrib/model/__pycache__/rank_gbdt.cpython-312.pyc
|
||||
d3f051f3a8650c42fedc7b367b966f7c74fb5789 tac-qlib/tac_qlib/contrib/model/rank_ensemble.py
|
||||
d03e6611338918d4aac5eea4adf26f85a3763652 tac-qlib/tac_qlib/contrib/model/rank_gbdt.py
|
||||
ccfe7d554989aa7f3e5a2128ae663e51b2207149 tac-qlib/tac_qlib/contrib/model/rank_gbdt.py
|
||||
4afcf9058231111c412925f4c4b84e81d656db87 tac-qlib/tac_qlib/contrib/strategy/__init__.py
|
||||
6ad10c2ebe37c16417e67c7aeb731ad1fcb6da2f tac-qlib/tac_qlib/contrib/strategy/__pycache__/__init__.cpython-312.pyc
|
||||
8d684b3216b040071d9ee4fa920a0e0c7486d278 tac-qlib/tac_qlib/contrib/strategy/__pycache__/optimal_stop.cpython-312.pyc
|
||||
13a8e4a41ce9afde97a55756d666d3ff5cbbec0e tac-qlib/tac_qlib/contrib/strategy/__pycache__/__init__.cpython-312.pyc
|
||||
0bb5804a79611c66bc25988226ca0f1cdbe1a7eb tac-qlib/tac_qlib/contrib/strategy/__pycache__/optimal_stop.cpython-312.pyc
|
||||
79aaad9e39fcc740a773f4f63c512ce1086cfde0 tac-qlib/tac_qlib/contrib/strategy/optimal_stop.py
|
||||
92e6e90eb0cd0a25142034560f27adb6b705b1a8 tac-qlib/tac_qlib/data/__init__.py
|
||||
7c4e6c345fad1978efe8860c0d977d0c02d6f8d9 tac-qlib/tac_qlib/data/__pycache__/__init__.cpython-312.pyc
|
||||
99e602392d51663cb06d5c425000b1ed1e5a916b tac-qlib/tac_qlib/data/__pycache__/config.cpython-312.pyc
|
||||
020dcdcf288e4832c8cf2386351f78d5ceb4fe13 tac-qlib/tac_qlib/data/__pycache__/providers.cpython-312.pyc
|
||||
3cbc0686e6f863d306d93c589a607a5bdb7201f2 tac-qlib/tac_qlib/data/__pycache__/__init__.cpython-312.pyc
|
||||
46bcdbcc173ff3a6281fed33d10c1bcac96876b0 tac-qlib/tac_qlib/data/__pycache__/config.cpython-312.pyc
|
||||
6df4f88af22ab4c026c14ef1835f2e630cdf850c tac-qlib/tac_qlib/data/__pycache__/providers.cpython-312.pyc
|
||||
53c9007a928841fd3c3b08450f9a6520ce1ac091 tac-qlib/tac_qlib/data/config.py
|
||||
8d0644f6f0d1efb94798ed444cc73e63b643459b tac-qlib/tac_qlib/data/providers.py
|
||||
|
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@@ -53,61 +53,23 @@ from qlib.workflow import R
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__all__ = ["RankICLGBModel", "rankic_feval"]
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|
||||
|
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def _group_averaged_rank(values: np.ndarray, gid: np.ndarray, offs: np.ndarray) -> np.ndarray:
|
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"""Averaged (tie-corrected) rank of ``values`` within each group, vectorized.
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|
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``gid`` maps each row to its group id; ``offs`` holds the cumulative row
|
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offsets so that group ``i`` occupies rows ``[offs[i], offs[i+1])``. Returns
|
||||
the same result as ``pandas.Series.rank(method='average')`` applied per
|
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group, but in one pass (``np.lexsort`` is the only non-linear step).
|
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"""
|
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n = len(values)
|
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order = np.lexsort((values, gid))
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ord_rank = np.empty(n, dtype=np.float64)
|
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ord_rank[order] = np.arange(n, dtype=np.float64) - offs[gid[order]] + 1.0
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sg = gid[order]
|
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sv = values[order]
|
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newblock = np.empty(n, dtype=bool)
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newblock[0] = True
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newblock[1:] = (sg[1:] != sg[:-1]) | (sv[1:] != sv[:-1])
|
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blockid = np.cumsum(newblock) - 1
|
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block_mean = np.bincount(blockid, weights=ord_rank[order]) / np.bincount(blockid)
|
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out = np.empty(n)
|
||||
out[order] = block_mean[blockid]
|
||||
return out
|
||||
|
||||
|
||||
def _per_day_spearman(preds: np.ndarray, labels: np.ndarray, group: np.ndarray) -> float:
|
||||
"""Mean per-day Spearman rank correlation of preds vs labels.
|
||||
|
||||
``group`` holds the number of rows of each trading day (query group), in
|
||||
order. Days with <3 valid rows or a constant pred/label are skipped.
|
||||
|
||||
Vectorized: per-day Spearman == Pearson of the per-day rank transforms,
|
||||
and the Pearson moments (``sum``, ``sum`` of products/squares) aggregate
|
||||
over each day with ``np.bincount``. Runs ~10x faster than the per-day
|
||||
``pd.Series.rank()`` loop that preceded it — this feval is invoked on the
|
||||
train and valid panels every boosting round, per seed.
|
||||
"""
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if group is None or len(group) == 0:
|
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return 0.0
|
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offs = np.concatenate([[0], np.cumsum(group.astype(int))])
|
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gid = np.repeat(np.arange(len(group)), group.astype(int))
|
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rp = _group_averaged_rank(preds, gid, offs)
|
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rl = _group_averaged_rank(labels, gid, offs)
|
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n_g = group.astype(float)
|
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s_p = np.bincount(gid, weights=rp)
|
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s_l = np.bincount(gid, weights=rl)
|
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s_pl = np.bincount(gid, weights=rp * rl)
|
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s_pp = np.bincount(gid, weights=rp * rp)
|
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s_ll = np.bincount(gid, weights=rl * rl)
|
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cov = n_g * s_pl - s_p * s_l
|
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var_p = n_g * s_pp - s_p ** 2
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var_l = n_g * s_ll - s_l ** 2
|
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denom = np.sqrt(var_p * var_l)
|
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valid = (n_g >= 3) & (denom > 0)
|
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corr = np.where(valid, cov / np.where(denom == 0, 1, denom), 0.0)
|
||||
return float(corr[valid].mean()) if valid.any() else 0.0
|
||||
vals = []
|
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for i in range(len(group)):
|
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s = slice(offs[i], offs[i + 1])
|
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p, l = preds[s], labels[s]
|
||||
if len(p) < 3 or np.std(p) == 0 or np.std(l) == 0:
|
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continue
|
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vals.append(np.corrcoef(pd.Series(p).rank(), pd.Series(l).rank())[0, 1])
|
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return float(np.mean(vals)) if vals else 0.0
|
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|
||||
|
||||
def rankic_feval(preds, dataset):
|
||||
|
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@@ -1,140 +0,0 @@
|
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# -----------------------------------------------------------------------------
|
||||
# QUEUE-01 — M2 reproduction: risk-adjusted 22d Sharpe drift (sp_sharpe_22).
|
||||
#
|
||||
# Hypothesis (book ch.01/ch.07, EVIDENCE#018 -> exp 30): adding the
|
||||
# risk-adjusted 22d Sharpe drift feature (sp_sharpe_22) to the compact
|
||||
# stochastic reference IMPROVES net portfolio performance (exp 30: net +6.53%
|
||||
# IR 0.62 vs reference +2.13% IR 0.21) while rank metrics dip (RankIC 0.0576 vs
|
||||
# 0.0663). exp 30 is a SINGLE clean-lake run, unreproduced -> HYPOTHESIS.
|
||||
#
|
||||
# Change vs exp-26 reference (EVIDENCE#015, run 21afc6af...): ONE feature added,
|
||||
# feature_fields = compact set + sp_sharpe_22. Everything else byte-identical.
|
||||
#
|
||||
# Acceptance: net_ann_return > +2.13% AND net_IR > 0.21 (else HYPOTHESIS -> REFUTED).
|
||||
# Run: rd_run_workflow config_path=<repo>/experiments/queue/workflows/q01_m2_sharpe22_repro.yaml \
|
||||
# experiment_name=tac-rd-q01-m2-sharpe22-repro
|
||||
# -----------------------------------------------------------------------------
|
||||
{%- 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 FEATURES = "$open,$high,$low,$close,$vwap,$volume,sp_ret,sp_jump_ratio,sp_jump_flag,sp_jump_tail,sp_max_move,sp_rv1,sp_rv5,sp_rv22,sp_vol_ratio_5_22,sp_vol_ratio_1_22,sp_trend_slope_5,sp_trend_slope_20,sp_trend_slope_60,sp_logp,sp_hurst_exponent,sp_sig_level1_lead,sp_sig_level1_lag,sp_sig_level2_lead_lag,sp_sig_level2_lag_lead,sp_sharpe_22" %}
|
||||
|
||||
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-q01-m2-sharpe22-repro"
|
||||
|
||||
task:
|
||||
model:
|
||||
class: RankICEnsembleLGBModel
|
||||
module_path: tac_qlib.contrib.model.rank_ensemble
|
||||
kwargs:
|
||||
loss: mse
|
||||
learning_rate: 0.02
|
||||
num_leaves: 31
|
||||
n_estimators: 3000
|
||||
num_boost_round: 3000
|
||||
early_stopping_rounds: 200
|
||||
min_data_in_leaf: 20
|
||||
lambda_l2: 0.5
|
||||
colsample_bytree: 0.8
|
||||
subsample: 0.8
|
||||
subsample_freq: 1
|
||||
reg_alpha: 0.1
|
||||
reg_lambda: 1.0
|
||||
seeds: "42,7,2026,99,123"
|
||||
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: "{{ FEATURES }}"
|
||||
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: 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: $close
|
||||
freq: day
|
||||
open_cost: 0.0005
|
||||
close_cost: 0.0015
|
||||
min_cost: 5.0
|
||||
risk_analysis_freq: 1d
|
||||
@@ -1,141 +0,0 @@
|
||||
# -----------------------------------------------------------------------------
|
||||
# QUEUE-04 — Non-overlapping 10-day label horizon.
|
||||
#
|
||||
# Hypothesis (book ch.01/chat-ideas): the 5d label is the campaign's best IC
|
||||
# lever but sees short-horizon reversal only; a non-overlapping 10d label
|
||||
# (`Ref($close,-11)/Ref($close,-1)-1`) tests whether a longer, cleaner horizon
|
||||
# captures trend/reversal better and survives cost (lower effective turnover).
|
||||
#
|
||||
# Change vs exp-26 reference: ONE variable — label 5d -> 10d. Everything else
|
||||
# identical (features, model, strategy).
|
||||
#
|
||||
# Acceptance: net_IR > 0.21 AND net_ann_return > +2.13%; secondary: ICIR and
|
||||
# L/S Sharpe >= reference. A flat-but-not-worse result still settles the
|
||||
# horizon-decomposition question (TODO: 5d can't see 1-12m drift).
|
||||
# Run: rd_run_workflow config_path=<repo>/experiments/queue/workflows/q04_label10d.yaml \
|
||||
# experiment_name=tac-rd-q04-label10d
|
||||
# -----------------------------------------------------------------------------
|
||||
{%- set LAKE = TAC_LAKE_DIR %}
|
||||
{%- set UNIVERSE = "SPY,QQQ,DIA,IWM,MDY,VTI,VOO,VEA,VWO,VT,EFA,EEM,TLT,IEF,SHY,AGG,BND,LQD,HYG,JNK,EMB,GLD,SLV,USO,UNG,DBA,DBC,XLK,XLF,XLE,XLV,XLI,XLY,XLP,XLU,XLB,XLRE,ARKK,SMH,SOXX,IBB,XBI,ITA,XAR,ICLN,TAN,FDN,IGV,ESPO,REM" %}
|
||||
{%- set FEATURES = "$open,$high,$low,$close,$vwap,$volume,sp_ret,sp_jump_ratio,sp_jump_flag,sp_jump_tail,sp_max_move,sp_rv1,sp_rv5,sp_rv22,sp_vol_ratio_5_22,sp_vol_ratio_1_22,sp_trend_slope_5,sp_trend_slope_20,sp_trend_slope_60,sp_logp,sp_hurst_exponent,sp_sig_level1_lead,sp_sig_level1_lag,sp_sig_level2_lead_lag,sp_sig_level2_lag_lead" %}
|
||||
|
||||
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-q04-label10d"
|
||||
|
||||
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,-11)/Ref($close,-1)-1"
|
||||
feature_fields: "{{ FEATURES }}"
|
||||
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: 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: $close
|
||||
freq: day
|
||||
open_cost: 0.0005
|
||||
close_cost: 0.0015
|
||||
min_cost: 5.0
|
||||
risk_analysis_freq: 1d
|
||||
@@ -1,9 +1,9 @@
|
||||
# M2 isolation run: base compact set + risk-adjusted drift sp_sharpe_22.
|
||||
# 2-seed RankICEnsemble comparison on the compact stochastic set, n_drop=1.
|
||||
# Exact copy of exp26 (reference: expId=25 run=21afc6afdb674a399b59dd76c97628ce)
|
||||
# except feature_fields. 5-seed ensemble.
|
||||
# except seeds=42,7 and parallel=2. Test whether 5 seeds are needed vs 2.
|
||||
{%- set LAKE = TAC_LAKE_DIR %}
|
||||
{%- set UNIVERSE = "SPY,QQQ,DIA,IWM,MDY,VTI,VOO,VEA,VWO,VT,EFA,EEM,TLT,IEF,SHY,AGG,BND,LQD,HYG,JNK,EMB,GLD,SLV,USO,UNG,DBA,DBC,XLK,XLF,XLE,XLV,XLI,XLY,XLP,XLU,XLB,XLRE,ARKK,SMH,SOXX,IBB,XBI,ITA,XAR,ICLN,TAN,FDN,IGV,ESPO,REM" %}
|
||||
{%- set FEATURES = "$open,$high,$low,$close,$vwap,$volume,sp_ret,sp_jump_ratio,sp_jump_flag,sp_jump_tail,sp_max_move,sp_rv1,sp_rv5,sp_rv22,sp_vol_ratio_5_22,sp_vol_ratio_1_22,sp_trend_slope_5,sp_trend_slope_20,sp_trend_slope_60,sp_logp,sp_hurst_exponent,sp_sig_level1_lead,sp_sig_level1_lag,sp_sig_level2_lead_lag,sp_sig_level2_lag_lead,sp_sharpe_22" %}
|
||||
{%- set FEATURES = "$open,$high,$low,$close,$vwap,$volume,sp_ret,sp_jump_ratio,sp_jump_flag,sp_jump_tail,sp_max_move,sp_rv1,sp_rv5,sp_rv22,sp_vol_ratio_5_22,sp_vol_ratio_1_22,sp_trend_slope_5,sp_trend_slope_20,sp_trend_slope_60,sp_logp,sp_hurst_exponent,sp_sig_level1_lead,sp_sig_level1_lag,sp_sig_level2_lead_lag,sp_sig_level2_lag_lead" %}
|
||||
|
||||
qlib_init:
|
||||
provider_uri: "{{ LAKE }}"
|
||||
@@ -22,7 +22,7 @@ qlib_init:
|
||||
exp_manager:
|
||||
class: MLflowExpManager
|
||||
module_path: qlib.workflow.expm
|
||||
kwargs: { uri: "sqlite:///mlruns.db", default_exp_name: "tac-rd-exp30-m2-sharpe" }
|
||||
kwargs: { uri: "sqlite:///mlruns.db", default_exp_name: "tac-rd-exp28-2seed" }
|
||||
|
||||
task:
|
||||
model:
|
||||
@@ -42,7 +42,8 @@ task:
|
||||
subsample_freq: 1
|
||||
reg_alpha: 0.1
|
||||
reg_lambda: 1.0
|
||||
seeds: "42,7,2026,99,123"
|
||||
seeds: "42,7"
|
||||
parallel: 2
|
||||
|
||||
dataset:
|
||||
class: DatasetH
|
||||
@@ -1,99 +0,0 @@
|
||||
# M3 isolation run: base compact set + GARCH(1,1) vol-regime trio.
|
||||
# Exact copy of exp26 (reference: expId=25 run=21afc6afdb674a399b59dd76c97628ce)
|
||||
# except feature_fields. 5-seed ensemble.
|
||||
{%- set LAKE = TAC_LAKE_DIR %}
|
||||
{%- set UNIVERSE = "SPY,QQQ,DIA,IWM,MDY,VTI,VOO,VEA,VWO,VT,EFA,EEM,TLT,IEF,SHY,AGG,BND,LQD,HYG,JNK,EMB,GLD,SLV,USO,UNG,DBA,DBC,XLK,XLF,XLE,XLV,XLI,XLY,XLP,XLU,XLB,XLRE,ARKK,SMH,SOXX,IBB,XBI,ITA,XAR,ICLN,TAN,FDN,IGV,ESPO,REM" %}
|
||||
{%- set FEATURES = "$open,$high,$low,$close,$vwap,$volume,sp_ret,sp_jump_ratio,sp_jump_flag,sp_jump_tail,sp_max_move,sp_rv1,sp_rv5,sp_rv22,sp_vol_ratio_5_22,sp_vol_ratio_1_22,sp_trend_slope_5,sp_trend_slope_20,sp_trend_slope_60,sp_logp,sp_hurst_exponent,sp_sig_level1_lead,sp_sig_level1_lag,sp_sig_level2_lead_lag,sp_sig_level2_lag_lead,sp_garch_cond_var,sp_garch_persistence,sp_garch_std_resid" %}
|
||||
|
||||
qlib_init:
|
||||
provider_uri: "{{ LAKE }}"
|
||||
region: us
|
||||
expression_cache: null
|
||||
dataset_cache: null
|
||||
calendar_provider:
|
||||
class: tac_qlib.data.providers.LakeCalendarProvider
|
||||
kwargs: { lake_root: "{{ LAKE }}", market: US }
|
||||
instrument_provider:
|
||||
class: tac_qlib.data.providers.LakeInstrumentProvider
|
||||
kwargs: { lake_root: "{{ LAKE }}", market: US, markets: {} }
|
||||
feature_provider:
|
||||
class: tac_qlib.data.providers.LakeFeatureProvider
|
||||
kwargs: { lake_root: "{{ LAKE }}", market: US }
|
||||
exp_manager:
|
||||
class: MLflowExpManager
|
||||
module_path: qlib.workflow.expm
|
||||
kwargs: { uri: "sqlite:///mlruns.db", default_exp_name: "tac-rd-exp31-m3-garch" }
|
||||
|
||||
task:
|
||||
model:
|
||||
class: RankICEnsembleLGBModel
|
||||
module_path: tac_qlib.contrib.model.rank_ensemble
|
||||
kwargs:
|
||||
loss: mse
|
||||
learning_rate: 0.02
|
||||
num_leaves: 31
|
||||
n_estimators: 3000
|
||||
num_boost_round: 3000
|
||||
early_stopping_rounds: 200
|
||||
min_data_in_leaf: 20
|
||||
lambda_l2: 0.5
|
||||
colsample_bytree: 0.8
|
||||
subsample: 0.8
|
||||
subsample_freq: 1
|
||||
reg_alpha: 0.1
|
||||
reg_lambda: 1.0
|
||||
seeds: "42,7,2026,99,123"
|
||||
|
||||
dataset:
|
||||
class: DatasetH
|
||||
module_path: qlib.data.dataset
|
||||
kwargs:
|
||||
handler:
|
||||
class: TACHandler
|
||||
module_path: tac_qlib.contrib.data.handler
|
||||
kwargs:
|
||||
instruments: "{{ 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: "{{ FEATURES }}"
|
||||
infer_processors:
|
||||
- { class: DropAllNaN, kwargs: { fit_start_time: "2016-01-04", fit_end_time: "2025-09-01" } }
|
||||
- { class: ProcessInf, kwargs: {} }
|
||||
- { class: CSRankNorm, kwargs: {} }
|
||||
- { class: ZScoreNorm, kwargs: { fit_start_time: "2016-01-04", fit_end_time: "2025-09-01" } }
|
||||
- { class: Fillna, kwargs: {} }
|
||||
segments:
|
||||
train: [2016-01-04, 2025-09-01]
|
||||
valid: [2025-09-03, 2026-01-03]
|
||||
test: [2026-01-04, 2026-08-10]
|
||||
|
||||
record:
|
||||
- { class: SignalRecord, module_path: qlib.workflow.record_temp, kwargs: {} }
|
||||
- { class: SigAnaRecord, module_path: qlib.workflow.record_temp, kwargs: { ana_long_short: true, ann_scaler: 252 } }
|
||||
- class: PortAnaRecord
|
||||
module_path: qlib.workflow.record_temp
|
||||
kwargs:
|
||||
config:
|
||||
strategy:
|
||||
class: TopkDropoutStrategy
|
||||
module_path: qlib.contrib.strategy
|
||||
kwargs: { signal: "<PRED>", topk: 10, n_drop: 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