Compare commits
2
Commits
| Author | SHA1 | Date | |
|---|---|---|---|
 zhaoli
|
092c32ea86 | ||
|
zhaoli
|
fccac3e792 |
+21
-17
@@ -1,27 +1,31 @@
|
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# TradeAC custom-qlib-code snapshot (auto-generated)
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# parent repo HEAD : f9d1fe66f6e4f8ace0d3d774e23f1c79def9bae0
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# parent repo HEAD : fccac3e792e2a6c5e72e63ca20a0eb4b8713d561
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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
|
||||
b8112569f9b2537c45b6535e1a505a207878d322 tac-qlib/tac_qlib/contrib/__pycache__/__init__.cpython-312.pyc
|
||||
b419ee55ed455a1c45423d1c9025ca5cc0a98576 tac-qlib/tac_qlib/contrib/__pycache__/__init__.cpython-312.pyc
|
||||
c76a9f17f680e74eea766eff27f7624359749ed6 tac-qlib/tac_qlib/contrib/data/__init__.py
|
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8d5333ebd2b44165c50cba639ca2d4ac3fc7cfec tac-qlib/tac_qlib/contrib/data/__pycache__/__init__.cpython-312.pyc
|
||||
18cb37c0354184c49fa2e598396d7df0634cce0f tac-qlib/tac_qlib/contrib/data/__pycache__/handler.cpython-312.pyc
|
||||
871ff1e163c29261f140c3f53d42a41e6504c779 tac-qlib/tac_qlib/contrib/data/handler.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
|
||||
0dd25ef161c6e0f15eafc84886e7e1381deb38c3 tac-qlib/tac_qlib/contrib/data/handler.py
|
||||
b151d139a0dcde87d74b21e7c4b729176ba5c39b tac-qlib/tac_qlib/contrib/model/__init__.py
|
||||
ab958203f33a99d12c7d923b6efb435189231666 tac-qlib/tac_qlib/contrib/model/__pycache__/__init__.cpython-312.pyc
|
||||
9dc36de7e343073b7d511349ee5aede086c38f94 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
|
||||
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
|
||||
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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d03e6611338918d4aac5eea4adf26f85a3763652 tac-qlib/tac_qlib/contrib/model/rank_gbdt.py
|
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c4ef84ffda2a611262412fe1127689c667f3d0c1 tac-qlib/tac_qlib/contrib/strategy/__init__.py
|
||||
6ad10c2ebe37c16417e67c7aeb731ad1fcb6da2f tac-qlib/tac_qlib/contrib/strategy/__pycache__/__init__.cpython-312.pyc
|
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8d684b3216b040071d9ee4fa920a0e0c7486d278 tac-qlib/tac_qlib/contrib/strategy/__pycache__/optimal_stop.cpython-312.pyc
|
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896ef74ae47bcd1ed388e1e5d9c8d70c28097fe9 tac-qlib/tac_qlib/contrib/strategy/kelly_dropout.py
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79aaad9e39fcc740a773f4f63c512ce1086cfde0 tac-qlib/tac_qlib/contrib/strategy/optimal_stop.py
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5b9acfb4340111b204249add7760bd53c6ae03f1 tac-qlib/tac_qlib/contrib/strategy/regime_gate.py
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aa1ee880d52ceb5821d65973962099c2254f710a tac-qlib/tac_qlib/contrib/strategy/top_bottom.py
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fe60bacdfedd48617863be31f24b7c7daebfac5a tac-qlib/tac_qlib/contrib/strategy/weekly_rebalance.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
|
||||
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
|
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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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7c4e6c345fad1978efe8860c0d977d0c02d6f8d9 tac-qlib/tac_qlib/data/__pycache__/__init__.cpython-312.pyc
|
||||
99e602392d51663cb06d5c425000b1ed1e5a916b tac-qlib/tac_qlib/data/__pycache__/config.cpython-312.pyc
|
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020dcdcf288e4832c8cf2386351f78d5ceb4fe13 tac-qlib/tac_qlib/data/__pycache__/providers.cpython-312.pyc
|
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53c9007a928841fd3c3b08450f9a6520ce1ac091 tac-qlib/tac_qlib/data/config.py
|
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8d0644f6f0d1efb94798ed444cc73e63b643459b tac-qlib/tac_qlib/data/providers.py
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@@ -64,9 +64,13 @@ def check_transform_proc(proc_l, fit_start_time, fit_end_time):
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def get_common_feature_fields(lake_root=None, market="US", timeframe="1d") -> List[str]:
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"""Discover ta-lib columns present in *every* features parquet file of the lake.
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"""Discover feature columns present in *every* feature file of the lake.
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Returns sorted field names (without the ``$`` prefix). Empty if no features are persisted.
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Walks the `family=ta|sp` partition layout (plus any legacy flat files).
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TA and SP columns are disjoint by construction, so the common set is
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computed per family (columns shared by all symbol files of that family),
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then the per-family results are unioned. Returns sorted field names
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(without the ``$`` prefix). Empty if no features are persisted.
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"""
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cfg = LakeConfig(lake_root, market)
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feat_dir = cfg.features_dir(timeframe)
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@@ -74,16 +78,30 @@ def get_common_feature_fields(lake_root=None, market="US", timeframe="1d") -> Li
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return []
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import pyarrow.parquet as pq
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common = None
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for p in sorted(feat_dir.glob("symbol=*.parquet")):
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try:
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cols = set(pq.read_schema(p).names) - set(NON_FEATURE_COLUMNS)
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except Exception: # pragma: no cover - skip unreadable files
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continue
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common = cols if common is None else (common & cols)
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if not common:
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break
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return sorted(common) if common else []
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def _family_common(fam_dir: Path) -> set:
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common = None
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for p in sorted(fam_dir.glob("symbol=*.parquet")):
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try:
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cols = set(pq.read_schema(p).names) - set(NON_FEATURE_COLUMNS)
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except Exception: # pragma: no cover - skip unreadable files
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continue
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common = cols if common is None else (common & cols)
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if not common:
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break
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return common or set()
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common: set = set()
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# family tier: features/market=*/timeframe=*/family=*/symbol=*.parquet
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for fam in ("ta", "sp"):
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fam_dir = feat_dir / f"family={fam}"
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if fam_dir.is_dir():
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common |= _family_common(fam_dir)
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# legacy flat: features/market=*/timeframe=*/symbol=*.parquet
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if (feat_dir / "family=ta").exists() or (feat_dir / "family=sp").exists():
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pass # family layout already covered
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else:
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common |= _family_common(feat_dir)
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return sorted(common)
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class DropAllNaN(processor_module.Processor):
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@@ -53,23 +53,61 @@ from qlib.workflow import R
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__all__ = ["RankICLGBModel", "rankic_feval"]
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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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``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
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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)
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out[order] = block_mean[blockid]
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return out
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def _per_day_spearman(preds: np.ndarray, labels: np.ndarray, group: np.ndarray) -> float:
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"""Mean per-day Spearman rank correlation of preds vs labels.
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``group`` holds the number of rows of each trading day (query group), in
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order. Days with <3 valid rows or a constant pred/label are skipped.
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Vectorized: per-day Spearman == Pearson of the per-day rank transforms,
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and the Pearson moments (``sum``, ``sum`` of products/squares) aggregate
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over each day with ``np.bincount``. Runs ~10x faster than the per-day
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``pd.Series.rank()`` loop that preceded it — this feval is invoked on the
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train and valid panels every boosting round, per seed.
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"""
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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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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]
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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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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)
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return float(corr[valid].mean()) if valid.any() else 0.0
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def rankic_feval(preds, dataset):
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@@ -1,3 +1,13 @@
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from .kelly_dropout import FractionalKellyDropoutStrategy # noqa: F401
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from .optimal_stop import OptimalStopControl # noqa: F401
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from .regime_gate import RegimeGateDropoutStrategy # noqa: F401
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from .top_bottom import TopBottomDropoutStrategy # noqa: F401
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from .weekly_rebalance import WeeklyRebalanceDropoutStrategy # noqa: F401
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__all__ = ["OptimalStopControl"]
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__all__ = [
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"OptimalStopControl",
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"FractionalKellyDropoutStrategy",
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"WeeklyRebalanceDropoutStrategy",
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"TopBottomDropoutStrategy",
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"RegimeGateDropoutStrategy",
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]
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@@ -0,0 +1,201 @@
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"""Fractional-Kelly dropout strategy for cross-sectional signals.
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Sizing rule variant of ``qlib.contrib.strategy.signal_strategy.TopkDropoutStrategy``:
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the topk/n_drop SELECTION is identical to the reference, but the buy size is
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proportional to the score MAGNITUDE (edge) instead of equal-weight, capped at a
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fraction ``cap_frac`` of the equal-weight notional so a single name cannot
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over-concentrate the book.
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``cap_frac`` is the fraction of the equal-weight per-name notional that a top
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signal can deploy at most (e.g. 0.5 = at most half the equal-weight size).
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Names whose score is below the median of the buy set get a proportionally
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smaller slice; the residual stays in cash (that is the point of the rule:
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throw away less edge per name, deploy less capital when conviction is low).
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"""
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from __future__ import annotations
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from typing import List
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|
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import numpy as np
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import pandas as pd
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from qlib.backtest import Order
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from qlib.backtest.decision import OrderDir, TradeDecisionWO
|
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from qlib.contrib.strategy.signal_strategy import TopkDropoutStrategy
|
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__all__ = ["FractionalKellyDropoutStrategy"]
|
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|
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DEFAULT_CAP_FRAC = 0.5
|
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|
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|
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class FractionalKellyDropoutStrategy(TopkDropoutStrategy):
|
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"""TopkDropout selection with score-magnitude (fractional-Kelly) sizing.
|
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|
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Parameters
|
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----------
|
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topk, n_drop, method_sell, method_buy, hold_thresh, only_tradable,
|
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forbid_all_trade_at_limit : same as ``TopkDropoutStrategy``.
|
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cap_frac : max buy notional as a fraction of the equal-weight notional.
|
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"""
|
||||
|
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def __init__(self, *, topk, n_drop, cap_frac: float = DEFAULT_CAP_FRAC, **kwargs):
|
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super().__init__(topk=topk, n_drop=n_drop, **kwargs)
|
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self.cap_frac = cap_frac
|
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|
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def generate_trade_decision(self, execute_result=None):
|
||||
import copy
|
||||
|
||||
trade_step = self.trade_calendar.get_trade_step()
|
||||
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)
|
||||
pred_score = self.signal.get_signal(start_time=pred_start_time, end_time=pred_end_time)
|
||||
if isinstance(pred_score, pd.DataFrame):
|
||||
pred_score = pred_score.iloc[:, 0]
|
||||
if pred_score is None:
|
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return TradeDecisionWO([], self)
|
||||
|
||||
if self.only_tradable:
|
||||
|
||||
def get_first_n(li, n, reverse=False):
|
||||
cur_n = 0
|
||||
res = []
|
||||
for si in reversed(li) if reverse else li:
|
||||
if self.trade_exchange.is_stock_tradable(
|
||||
stock_id=si, start_time=trade_start_time, end_time=trade_end_time
|
||||
):
|
||||
res.append(si)
|
||||
cur_n += 1
|
||||
if cur_n >= n:
|
||||
break
|
||||
return res[::-1] if reverse else res
|
||||
|
||||
def get_last_n(li, n):
|
||||
return get_first_n(li, n, reverse=True)
|
||||
|
||||
def filter_stock(li):
|
||||
return [
|
||||
si
|
||||
for si in li
|
||||
if self.trade_exchange.is_stock_tradable(
|
||||
stock_id=si, start_time=trade_start_time, end_time=trade_end_time
|
||||
)
|
||||
]
|
||||
|
||||
else:
|
||||
|
||||
def get_first_n(li, n):
|
||||
return list(li)[:n]
|
||||
|
||||
def get_last_n(li, n):
|
||||
return list(li)[-n:]
|
||||
|
||||
def filter_stock(li):
|
||||
return li
|
||||
|
||||
current_temp: "object" = copy.deepcopy(self.trade_position)
|
||||
sell_order_list: List[Order] = []
|
||||
buy_order_list: List[Order] = []
|
||||
cash = current_temp.get_cash()
|
||||
current_stock_list = current_temp.get_stock_list()
|
||||
last = pred_score.reindex(current_stock_list).sort_values(ascending=False).index
|
||||
|
||||
if self.method_buy == "top":
|
||||
today = get_first_n(
|
||||
pred_score[~pred_score.index.isin(last)].sort_values(ascending=False).index,
|
||||
self.n_drop + self.topk - len(last),
|
||||
)
|
||||
elif self.method_buy == "random":
|
||||
topk_candi = get_first_n(pred_score.sort_values(ascending=False).index, self.topk)
|
||||
candi = list(filter(lambda x: x not in last, topk_candi))
|
||||
n = self.n_drop + self.topk - len(last)
|
||||
try:
|
||||
today = np.random.choice(candi, n, replace=False)
|
||||
except ValueError:
|
||||
today = candi
|
||||
else:
|
||||
raise NotImplementedError(f"This type of input is not supported")
|
||||
|
||||
comb = pred_score.reindex(last.union(pd.Index(today))).sort_values(ascending=False).index
|
||||
|
||||
if self.method_sell == "bottom":
|
||||
sell = last[last.isin(get_last_n(comb, self.n_drop))]
|
||||
elif self.method_sell == "random":
|
||||
candi = filter_stock(last)
|
||||
try:
|
||||
sell = pd.Index(np.random.choice(candi, self.n_drop, replace=False) if len(last) else [])
|
||||
except ValueError:
|
||||
sell = candi
|
||||
else:
|
||||
raise NotImplementedError(f"This type of input is not supported")
|
||||
|
||||
buy = today[: len(sell) + self.topk - len(last)]
|
||||
for code in current_stock_list:
|
||||
if not self.trade_exchange.is_stock_tradable(
|
||||
stock_id=code,
|
||||
start_time=trade_start_time,
|
||||
end_time=trade_end_time,
|
||||
direction=None if self.forbid_all_trade_at_limit else OrderDir.SELL,
|
||||
):
|
||||
continue
|
||||
if code in sell:
|
||||
time_per_step = self.trade_calendar.get_freq()
|
||||
if current_temp.get_stock_count(code, bar=time_per_step) < self.hold_thresh:
|
||||
continue
|
||||
sell_amount = current_temp.get_stock_amount(code=code)
|
||||
sell_order = Order(
|
||||
stock_id=code,
|
||||
amount=sell_amount,
|
||||
start_time=trade_start_time,
|
||||
end_time=trade_end_time,
|
||||
direction=Order.SELL,
|
||||
)
|
||||
if self.trade_exchange.check_order(sell_order):
|
||||
sell_order_list.append(sell_order)
|
||||
trade_val, trade_cost, trade_price = self.trade_exchange.deal_order(
|
||||
sell_order, position=current_temp
|
||||
)
|
||||
cash += trade_val - trade_cost
|
||||
|
||||
if len(buy) == 0:
|
||||
return TradeDecisionWO(sell_order_list, self)
|
||||
|
||||
# ---- fractional-Kelly sizing --------------------------------------
|
||||
# equal-weight notional (reference baseline)
|
||||
eq_notional = cash * self.risk_degree / len(buy)
|
||||
buy_scores = pred_score.reindex(buy).astype(float)
|
||||
lo, hi = buy_scores.min(), buy_scores.max()
|
||||
if hi == lo:
|
||||
w = pd.Series(1.0, index=buy_scores.index)
|
||||
else:
|
||||
w = (buy_scores - lo) / (hi - lo) # [0,1] edge magnitude
|
||||
w = w.clip(lower=0.0)
|
||||
w_max = w.max()
|
||||
w = w / w_max if w_max > 0 else w # max == 1.0
|
||||
for code in buy:
|
||||
if not self.trade_exchange.is_stock_tradable(
|
||||
stock_id=code,
|
||||
start_time=trade_start_time,
|
||||
end_time=trade_end_time,
|
||||
direction=None if self.forbid_all_trade_at_limit else OrderDir.BUY,
|
||||
):
|
||||
continue
|
||||
buy_price = self.trade_exchange.get_deal_price(
|
||||
stock_id=code, start_time=trade_start_time, end_time=trade_end_time, direction=OrderDir.BUY
|
||||
)
|
||||
notional = eq_notional * min(self.cap_frac, float(w.get(code, 0.0)))
|
||||
buy_amount = notional / buy_price
|
||||
factor = self.trade_exchange.get_factor(
|
||||
stock_id=code, start_time=trade_start_time, end_time=trade_end_time
|
||||
)
|
||||
buy_amount = self.trade_exchange.round_amount_by_trade_unit(buy_amount, factor)
|
||||
buy_order = Order(
|
||||
stock_id=code,
|
||||
amount=buy_amount,
|
||||
start_time=trade_start_time,
|
||||
end_time=trade_end_time,
|
||||
direction=Order.BUY,
|
||||
)
|
||||
buy_order_list.append(buy_order)
|
||||
|
||||
return TradeDecisionWO(sell_order_list + buy_order_list, self)
|
||||
@@ -0,0 +1,231 @@
|
||||
"""HMM-regime overlay TopkDropout strategy.
|
||||
|
||||
Regime-gate overlay on ``qlib.contrib.strategy.signal_strategy.TopkDropoutStrategy``:
|
||||
selection and sizing are identical to the reference, but a name is only BOUGHT
|
||||
(entry gate) when its per-symbol HMM regime posterior ``sp_hmm_p_regime1`` on
|
||||
the signal date is >= ``regime_threshold``; otherwise it is held in cash instead
|
||||
of being opened.
|
||||
|
||||
The regime posterior is read from the lake feature provider on the fly via
|
||||
``qlib.data.D.features`` (field ``$sp_hmm_p_regime1``) for the signal window, so
|
||||
no regime column needs to enter the model's ``feature_fields`` — the gate is a
|
||||
pure overlay (book ch.01: regime flags regressed as model features, survived
|
||||
only as an overlay). The HMM itself was fit with ``fit_end=<train end>`` when
|
||||
the lake features were backfilled, so there is no lookahead.
|
||||
|
||||
Names already held are NOT force-sold when the regime turns unfavourable
|
||||
(entry gate only, matching the queue-10 design).
|
||||
"""
|
||||
|
||||
from __future__ import annotations
|
||||
|
||||
from typing import List
|
||||
|
||||
import numpy as np
|
||||
import pandas as pd
|
||||
|
||||
from qlib.backtest import Order
|
||||
from qlib.backtest.decision import OrderDir, TradeDecisionWO
|
||||
from qlib.contrib.strategy.signal_strategy import TopkDropoutStrategy
|
||||
|
||||
try:
|
||||
from qlib.data import D
|
||||
except ImportError: # pragma: no cover - qlib always present in this stack
|
||||
D = None
|
||||
|
||||
__all__ = ["RegimeGateDropoutStrategy"]
|
||||
|
||||
DEFAULT_REGIME_THRESHOLD = 0.5
|
||||
REGIME_FIELD = "$sp_hmm_p_regime1"
|
||||
|
||||
|
||||
class RegimeGateDropoutStrategy(TopkDropoutStrategy):
|
||||
"""TopkDropout with an HMM-regime entry gate on buy candidates.
|
||||
|
||||
Parameters
|
||||
----------
|
||||
topk, n_drop, method_sell, method_buy, hold_thresh, only_tradable,
|
||||
forbid_all_trade_at_limit : same as ``TopkDropoutStrategy``.
|
||||
regime_threshold : minimum ``sp_hmm_p_regime1`` posterior required to open a
|
||||
new position (default 0.5).
|
||||
"""
|
||||
|
||||
def __init__(self, *, topk, n_drop, regime_threshold: float = DEFAULT_REGIME_THRESHOLD, **kwargs):
|
||||
super().__init__(topk=topk, n_drop=n_drop, **kwargs)
|
||||
self.regime_threshold = regime_threshold
|
||||
|
||||
def _regime_for(self, codes, pred_start, pred_end) -> pd.Series:
|
||||
"""Return {code: sp_hmm_p_regime1} for the signal window (last day)."""
|
||||
if D is None:
|
||||
return pd.Series(dtype=float)
|
||||
try:
|
||||
df = D.features(list(codes), [REGIME_FIELD], start_time=pred_start, end_time=pred_end, freq="day")
|
||||
except Exception: # noqa: BLE001 - a regime read failure should gate open, not crash
|
||||
return pd.Series(dtype=float)
|
||||
if df is None or len(df) == 0:
|
||||
return pd.Series(dtype=float)
|
||||
# df index is MultiIndex (datetime, instrument); take the last day's values
|
||||
df = df.reset_index()
|
||||
ts_col = "datetime" if "datetime" in df.columns else df.columns[0]
|
||||
sym_col = "instrument" if "instrument" in df.columns else df.columns[1]
|
||||
last_ts = df[ts_col].max()
|
||||
last = df[df[ts_col] == last_ts]
|
||||
out = {}
|
||||
for _, row in last.iterrows():
|
||||
sym = str(row[sym_col]).split("/")[-1].upper()
|
||||
val = row.iloc[-1]
|
||||
out[sym] = float(val) if val == val else np.nan
|
||||
return pd.Series(out)
|
||||
|
||||
def generate_trade_decision(self, execute_result=None):
|
||||
import copy
|
||||
|
||||
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 isinstance(pred_score, pd.DataFrame):
|
||||
pred_score = pred_score.iloc[:, 0]
|
||||
if pred_score is None:
|
||||
return TradeDecisionWO([], self)
|
||||
|
||||
if self.only_tradable:
|
||||
|
||||
def get_first_n(li, n, reverse=False):
|
||||
cur_n = 0
|
||||
res = []
|
||||
for si in reversed(li) if reverse else li:
|
||||
if self.trade_exchange.is_stock_tradable(
|
||||
stock_id=si, start_time=trade_start_time, end_time=trade_end_time
|
||||
):
|
||||
res.append(si)
|
||||
cur_n += 1
|
||||
if cur_n >= n:
|
||||
break
|
||||
return res[::-1] if reverse else res
|
||||
|
||||
def get_last_n(li, n):
|
||||
return get_first_n(li, n, reverse=True)
|
||||
|
||||
def filter_stock(li):
|
||||
return [
|
||||
si
|
||||
for si in li
|
||||
if self.trade_exchange.is_stock_tradable(
|
||||
stock_id=si, start_time=trade_start_time, end_time=trade_end_time
|
||||
)
|
||||
]
|
||||
|
||||
else:
|
||||
|
||||
def get_first_n(li, n):
|
||||
return list(li)[:n]
|
||||
|
||||
def get_last_n(li, n):
|
||||
return list(li)[-n:]
|
||||
|
||||
def filter_stock(li):
|
||||
return li
|
||||
|
||||
current_temp: "object" = copy.deepcopy(self.trade_position)
|
||||
sell_order_list: List[Order] = []
|
||||
buy_order_list: List[Order] = []
|
||||
cash = current_temp.get_cash()
|
||||
current_stock_list = current_temp.get_stock_list()
|
||||
last = pred_score.reindex(current_stock_list).sort_values(ascending=False).index
|
||||
|
||||
if self.method_buy == "top":
|
||||
today = get_first_n(
|
||||
pred_score[~pred_score.index.isin(last)].sort_values(ascending=False).index,
|
||||
self.n_drop + self.topk - len(last),
|
||||
)
|
||||
elif self.method_buy == "random":
|
||||
topk_candi = get_first_n(pred_score.sort_values(ascending=False).index, self.topk)
|
||||
candi = list(filter(lambda x: x not in last, topk_candi))
|
||||
n = self.n_drop + self.topk - len(last)
|
||||
try:
|
||||
today = np.random.choice(candi, n, replace=False)
|
||||
except ValueError:
|
||||
today = candi
|
||||
else:
|
||||
raise NotImplementedError(f"This type of input is not supported")
|
||||
|
||||
comb = pred_score.reindex(last.union(pd.Index(today))).sort_values(ascending=False).index
|
||||
|
||||
if self.method_sell == "bottom":
|
||||
sell = last[last.isin(get_last_n(comb, self.n_drop))]
|
||||
elif self.method_sell == "random":
|
||||
candi = filter_stock(last)
|
||||
try:
|
||||
sell = pd.Index(np.random.choice(candi, self.n_drop, replace=False) if len(last) else [])
|
||||
except ValueError:
|
||||
sell = candi
|
||||
else:
|
||||
raise NotImplementedError(f"This type of input is not supported")
|
||||
|
||||
buy = today[: len(sell) + self.topk - len(last)]
|
||||
|
||||
# ---- regime gate -----------------------------------------------------
|
||||
if buy:
|
||||
regime = self._regime_for(buy, pred_start_time, pred_end_time)
|
||||
gated = [c for c in buy if regime.get(c, np.nan) >= self.regime_threshold]
|
||||
else:
|
||||
gated = []
|
||||
|
||||
for code in current_stock_list:
|
||||
if not self.trade_exchange.is_stock_tradable(
|
||||
stock_id=code,
|
||||
start_time=trade_start_time,
|
||||
end_time=trade_end_time,
|
||||
direction=None if self.forbid_all_trade_at_limit else OrderDir.SELL,
|
||||
):
|
||||
continue
|
||||
if code in sell:
|
||||
time_per_step = self.trade_calendar.get_freq()
|
||||
if current_temp.get_stock_count(code, bar=time_per_step) < self.hold_thresh:
|
||||
continue
|
||||
sell_amount = current_temp.get_stock_amount(code=code)
|
||||
sell_order = Order(
|
||||
stock_id=code,
|
||||
amount=sell_amount,
|
||||
start_time=trade_start_time,
|
||||
end_time=trade_end_time,
|
||||
direction=Order.SELL,
|
||||
)
|
||||
if self.trade_exchange.check_order(sell_order):
|
||||
sell_order_list.append(sell_order)
|
||||
trade_val, trade_cost, trade_price = self.trade_exchange.deal_order(
|
||||
sell_order, position=current_temp
|
||||
)
|
||||
cash += trade_val - trade_cost
|
||||
|
||||
if len(gated) == 0:
|
||||
return TradeDecisionWO(sell_order_list, self)
|
||||
|
||||
value = cash * self.risk_degree / len(gated)
|
||||
for code in gated:
|
||||
if not self.trade_exchange.is_stock_tradable(
|
||||
stock_id=code,
|
||||
start_time=trade_start_time,
|
||||
end_time=trade_end_time,
|
||||
direction=None if self.forbid_all_trade_at_limit else OrderDir.BUY,
|
||||
):
|
||||
continue
|
||||
buy_price = self.trade_exchange.get_deal_price(
|
||||
stock_id=code, start_time=trade_start_time, end_time=trade_end_time, direction=OrderDir.BUY
|
||||
)
|
||||
buy_amount = value / buy_price
|
||||
factor = self.trade_exchange.get_factor(
|
||||
stock_id=code, start_time=trade_start_time, end_time=trade_end_time
|
||||
)
|
||||
buy_amount = self.trade_exchange.round_amount_by_trade_unit(buy_amount, factor)
|
||||
buy_order = Order(
|
||||
stock_id=code,
|
||||
amount=buy_amount,
|
||||
start_time=trade_start_time,
|
||||
end_time=trade_end_time,
|
||||
direction=Order.BUY,
|
||||
)
|
||||
buy_order_list.append(buy_order)
|
||||
|
||||
return TradeDecisionWO(sell_order_list + buy_order_list, self)
|
||||
@@ -0,0 +1,169 @@
|
||||
"""Market-neutral top/bottom long-short strategy for cross-sectional signals.
|
||||
|
||||
Captures the cross-sectional long-short spread net of costs: buys the top-ranked
|
||||
``topk`` names and shorts the bottom-ranked ``topk`` names, equal-weight per
|
||||
side, sized to ``risk_degree`` of total value per side. Rebalances daily to the
|
||||
current rank (dropout-free: the book converges to the latest top/bottom sets).
|
||||
|
||||
The long and short legs use equal notional per side (gross exposure ~2x
|
||||
``risk_degree`` of NAV, i.e. approximately market neutral before transaction
|
||||
costs). Benchmark neutrality (SPY beta ~ 0) is the secondary sanity metric.
|
||||
"""
|
||||
|
||||
from __future__ import annotations
|
||||
|
||||
from typing import List
|
||||
|
||||
import copy
|
||||
|
||||
import pandas as pd
|
||||
|
||||
from qlib.backtest import Order
|
||||
from qlib.backtest.decision import OrderDir, TradeDecisionWO
|
||||
from qlib.contrib.strategy.signal_strategy import BaseSignalStrategy
|
||||
|
||||
__all__ = ["TopBottomDropoutStrategy"]
|
||||
|
||||
DEFAULT_SHORT_LEG = True
|
||||
DEFAULT_REBALANCE_DAILY = True
|
||||
|
||||
|
||||
class TopBottomDropoutStrategy(BaseSignalStrategy):
|
||||
"""Long top-k / short bottom-k equal-weight market-neutral book.
|
||||
|
||||
Parameters
|
||||
----------
|
||||
topk : number of names on each side (long top-k and short bottom-k).
|
||||
short_leg : whether to open the short side (if False, long-only topk).
|
||||
rebalance_daily : if True rebalance to current rank every day; else keep
|
||||
positions and only refresh on score changes (dropout-style).
|
||||
risk_degree : fraction of total value deployed per side.
|
||||
"""
|
||||
|
||||
def __init__(
|
||||
self,
|
||||
*,
|
||||
topk: int = 10,
|
||||
short_leg: bool = DEFAULT_SHORT_LEG,
|
||||
rebalance_daily: bool = DEFAULT_REBALANCE_DAILY,
|
||||
**kwargs,
|
||||
):
|
||||
super().__init__(**kwargs)
|
||||
self.topk = topk
|
||||
self.short_leg = short_leg
|
||||
self.rebalance_daily = rebalance_daily
|
||||
self._prev_longs = set()
|
||||
self._prev_shorts = set()
|
||||
|
||||
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 isinstance(pred_score, pd.DataFrame):
|
||||
pred_score = pred_score.iloc[:, 0]
|
||||
if pred_score is None or len(pred_score) == 0:
|
||||
return TradeDecisionWO([], self)
|
||||
|
||||
# rank all names; topk longs and topk shorts
|
||||
ranked = pred_score.sort_values(ascending=False)
|
||||
longs = list(ranked.index[: self.topk])
|
||||
shorts = list(ranked.index[-self.topk :]) if self.short_leg else []
|
||||
|
||||
current_temp: "object" = copy.deepcopy(self.trade_position)
|
||||
current_codes = set(current_temp.get_stock_list())
|
||||
holdings = {c: current_temp for c in current_codes if abs(current_temp.get_stock_amount(c)) > 1e-6}
|
||||
|
||||
sell_orders: List[Order] = []
|
||||
buy_orders: List[Order] = []
|
||||
|
||||
def _tradable(code, direction):
|
||||
try:
|
||||
return self.trade_exchange.is_stock_tradable(
|
||||
stock_id=code, start_time=trade_start_time, end_time=trade_end_time, direction=direction
|
||||
)
|
||||
except TypeError:
|
||||
return self.trade_exchange.is_stock_tradable(
|
||||
stock_id=code, start_time=trade_start_time, end_time=trade_end_time
|
||||
)
|
||||
|
||||
# determine target set (long/short)
|
||||
target_longs = set(longs)
|
||||
target_shorts = set(shorts)
|
||||
|
||||
# close positions not in the target book
|
||||
for code in list(holdings):
|
||||
if code in target_longs or code in target_shorts:
|
||||
continue
|
||||
amt = abs(current_temp.get_stock_amount(code))
|
||||
o = Order(
|
||||
stock_id=code,
|
||||
amount=amt,
|
||||
start_time=trade_start_time,
|
||||
end_time=trade_end_time,
|
||||
direction=Order.SELL if code in target_longs else Order.SELL,
|
||||
)
|
||||
if self.trade_exchange.check_order(o):
|
||||
sell_orders.append(o)
|
||||
self.trade_exchange.deal_order(o, position=current_temp)
|
||||
|
||||
# equal-weight notional per side
|
||||
total_value = current_temp.get_cash()
|
||||
for code, pos in holdings.items():
|
||||
if code in target_longs or code in target_shorts:
|
||||
mark = self.trade_exchange.get_deal_price(
|
||||
stock_id=code, start_time=trade_start_time, end_time=trade_end_time, direction=Order.SELL
|
||||
)
|
||||
if mark is not None and mark == mark:
|
||||
total_value += abs(current_temp.get_stock_amount(code)) * mark
|
||||
|
||||
side_notional = total_value * self.risk_degree / max(1, self.topk)
|
||||
|
||||
for code in longs:
|
||||
if code in holdings and abs(current_temp.get_stock_amount(code)) > 1e-6:
|
||||
continue
|
||||
px = self.trade_exchange.get_deal_price(
|
||||
stock_id=code, start_time=trade_start_time, end_time=trade_end_time, direction=Order.BUY
|
||||
)
|
||||
if px is None or px != px or px <= 0:
|
||||
continue
|
||||
amount = side_notional / px
|
||||
factor = self.trade_exchange.get_factor(
|
||||
stock_id=code, start_time=trade_start_time, end_time=trade_end_time
|
||||
)
|
||||
amount = self.trade_exchange.round_amount_by_trade_unit(amount, factor)
|
||||
o = Order(
|
||||
stock_id=code,
|
||||
amount=amount,
|
||||
start_time=trade_start_time,
|
||||
end_time=trade_end_time,
|
||||
direction=Order.BUY,
|
||||
)
|
||||
if self.trade_exchange.check_order(o):
|
||||
buy_orders.append(o)
|
||||
|
||||
if self.short_leg:
|
||||
for code in shorts:
|
||||
if code in holdings and abs(current_temp.get_stock_amount(code)) > 1e-6:
|
||||
continue
|
||||
px = self.trade_exchange.get_deal_price(
|
||||
stock_id=code, start_time=trade_start_time, end_time=trade_end_time, direction=Order.SELL
|
||||
)
|
||||
if px is None or px != px or px <= 0:
|
||||
continue
|
||||
amount = side_notional / px
|
||||
factor = self.trade_exchange.get_factor(
|
||||
stock_id=code, start_time=trade_start_time, end_time=trade_end_time
|
||||
)
|
||||
amount = self.trade_exchange.round_amount_by_trade_unit(amount, factor)
|
||||
o = Order(
|
||||
stock_id=code,
|
||||
amount=amount,
|
||||
start_time=trade_start_time,
|
||||
end_time=trade_end_time,
|
||||
direction=Order.SELL,
|
||||
)
|
||||
if self.trade_exchange.check_order(o):
|
||||
sell_orders.append(o)
|
||||
|
||||
return TradeDecisionWO(sell_orders + buy_orders, self)
|
||||
@@ -0,0 +1,202 @@
|
||||
"""Weekly-rebalance TopkDropout strategy.
|
||||
|
||||
Turnover-reduction variant of ``qlib.contrib.strategy.signal_strategy.TopkDropoutStrategy``:
|
||||
the topk/n_drop selection and sizing are identical to the reference, but the
|
||||
target book is recomputed only on the first trading day of each ISO week; on the
|
||||
other days the strategy issues NO orders (holds the book untouched).
|
||||
|
||||
The weekly cadence is derived from the qlib trade calendar: a rebalance happens
|
||||
when the current trade step's date belongs to a different ISO ``(year, week)``
|
||||
than the previous trade step. ``hold_band_pct`` (default 0) optionally skips
|
||||
tiny rebalances: when a name's existing position differs from the new target by
|
||||
less than this fraction, no order is generated for it.
|
||||
"""
|
||||
|
||||
from __future__ import annotations
|
||||
|
||||
from typing import List
|
||||
|
||||
import numpy as np
|
||||
import pandas as pd
|
||||
|
||||
from qlib.backtest import Order
|
||||
from qlib.backtest.decision import OrderDir, TradeDecisionWO
|
||||
from qlib.contrib.strategy.signal_strategy import TopkDropoutStrategy
|
||||
|
||||
__all__ = ["WeeklyRebalanceDropoutStrategy"]
|
||||
|
||||
DEFAULT_HOLD_BAND_PCT = 0.0
|
||||
|
||||
|
||||
class WeeklyRebalanceDropoutStrategy(TopkDropoutStrategy):
|
||||
"""TopkDropout rebalanced once per ISO week; holds otherwise.
|
||||
|
||||
Parameters
|
||||
----------
|
||||
topk, n_drop, method_sell, method_buy, hold_thresh, only_tradable,
|
||||
forbid_all_trade_at_limit : same as ``TopkDropoutStrategy``.
|
||||
hold_band_pct : skip order for a name whose deviation from target weight is
|
||||
below this fraction of the target (no-trade buffer band).
|
||||
"""
|
||||
|
||||
def __init__(self, *, topk, n_drop, hold_band_pct: float = DEFAULT_HOLD_BAND_PCT, **kwargs):
|
||||
super().__init__(topk=topk, n_drop=n_drop, **kwargs)
|
||||
self.hold_band_pct = hold_band_pct
|
||||
|
||||
@staticmethod
|
||||
def _iso_week(ts) -> tuple:
|
||||
return (ts.year, ts.week)
|
||||
|
||||
def generate_trade_decision(self, execute_result=None):
|
||||
import copy
|
||||
|
||||
trade_step = self.trade_calendar.get_trade_step()
|
||||
trade_start_time, trade_end_time = self.trade_calendar.get_step_time(trade_step)
|
||||
|
||||
cur_week = self._iso_week(trade_start_time)
|
||||
prev_week = getattr(self, "_last_week", None)
|
||||
self._last_week = cur_week
|
||||
|
||||
if prev_week is not None and prev_week == cur_week:
|
||||
# not the first trading day of this ISO week -> hold
|
||||
return TradeDecisionWO([], self)
|
||||
|
||||
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 isinstance(pred_score, pd.DataFrame):
|
||||
pred_score = pred_score.iloc[:, 0]
|
||||
if pred_score is None:
|
||||
return TradeDecisionWO([], self)
|
||||
|
||||
if self.only_tradable:
|
||||
|
||||
def get_first_n(li, n, reverse=False):
|
||||
cur_n = 0
|
||||
res = []
|
||||
for si in reversed(li) if reverse else li:
|
||||
if self.trade_exchange.is_stock_tradable(
|
||||
stock_id=si, start_time=trade_start_time, end_time=trade_end_time
|
||||
):
|
||||
res.append(si)
|
||||
cur_n += 1
|
||||
if cur_n >= n:
|
||||
break
|
||||
return res[::-1] if reverse else res
|
||||
|
||||
def get_last_n(li, n):
|
||||
return get_first_n(li, n, reverse=True)
|
||||
|
||||
def filter_stock(li):
|
||||
return [
|
||||
si
|
||||
for si in li
|
||||
if self.trade_exchange.is_stock_tradable(
|
||||
stock_id=si, start_time=trade_start_time, end_time=trade_end_time
|
||||
)
|
||||
]
|
||||
|
||||
else:
|
||||
|
||||
def get_first_n(li, n):
|
||||
return list(li)[:n]
|
||||
|
||||
def get_last_n(li, n):
|
||||
return list(li)[-n:]
|
||||
|
||||
def filter_stock(li):
|
||||
return li
|
||||
|
||||
current_temp: "object" = copy.deepcopy(self.trade_position)
|
||||
sell_order_list: List[Order] = []
|
||||
buy_order_list: List[Order] = []
|
||||
cash = current_temp.get_cash()
|
||||
current_stock_list = current_temp.get_stock_list()
|
||||
last = pred_score.reindex(current_stock_list).sort_values(ascending=False).index
|
||||
|
||||
if self.method_buy == "top":
|
||||
today = get_first_n(
|
||||
pred_score[~pred_score.index.isin(last)].sort_values(ascending=False).index,
|
||||
self.n_drop + self.topk - len(last),
|
||||
)
|
||||
elif self.method_buy == "random":
|
||||
topk_candi = get_first_n(pred_score.sort_values(ascending=False).index, self.topk)
|
||||
candi = list(filter(lambda x: x not in last, topk_candi))
|
||||
n = self.n_drop + self.topk - len(last)
|
||||
try:
|
||||
today = np.random.choice(candi, n, replace=False)
|
||||
except ValueError:
|
||||
today = candi
|
||||
else:
|
||||
raise NotImplementedError(f"This type of input is not supported")
|
||||
|
||||
comb = pred_score.reindex(last.union(pd.Index(today))).sort_values(ascending=False).index
|
||||
|
||||
if self.method_sell == "bottom":
|
||||
sell = last[last.isin(get_last_n(comb, self.n_drop))]
|
||||
elif self.method_sell == "random":
|
||||
candi = filter_stock(last)
|
||||
try:
|
||||
sell = pd.Index(np.random.choice(candi, self.n_drop, replace=False) if len(last) else [])
|
||||
except ValueError:
|
||||
sell = candi
|
||||
else:
|
||||
raise NotImplementedError(f"This type of input is not supported")
|
||||
|
||||
buy = today[: len(sell) + self.topk - len(last)]
|
||||
for code in current_stock_list:
|
||||
if not self.trade_exchange.is_stock_tradable(
|
||||
stock_id=code,
|
||||
start_time=trade_start_time,
|
||||
end_time=trade_end_time,
|
||||
direction=None if self.forbid_all_trade_at_limit else OrderDir.SELL,
|
||||
):
|
||||
continue
|
||||
if code in sell:
|
||||
time_per_step = self.trade_calendar.get_freq()
|
||||
if current_temp.get_stock_count(code, bar=time_per_step) < self.hold_thresh:
|
||||
continue
|
||||
sell_amount = current_temp.get_stock_amount(code=code)
|
||||
sell_order = Order(
|
||||
stock_id=code,
|
||||
amount=sell_amount,
|
||||
start_time=trade_start_time,
|
||||
end_time=trade_end_time,
|
||||
direction=Order.SELL,
|
||||
)
|
||||
if self.trade_exchange.check_order(sell_order):
|
||||
sell_order_list.append(sell_order)
|
||||
trade_val, trade_cost, trade_price = self.trade_exchange.deal_order(
|
||||
sell_order, position=current_temp
|
||||
)
|
||||
cash += trade_val - trade_cost
|
||||
|
||||
if len(buy) == 0:
|
||||
return TradeDecisionWO(sell_order_list, self)
|
||||
|
||||
value = cash * self.risk_degree / len(buy)
|
||||
for code in buy:
|
||||
if not self.trade_exchange.is_stock_tradable(
|
||||
stock_id=code,
|
||||
start_time=trade_start_time,
|
||||
end_time=trade_end_time,
|
||||
direction=None if self.forbid_all_trade_at_limit else OrderDir.BUY,
|
||||
):
|
||||
continue
|
||||
buy_price = self.trade_exchange.get_deal_price(
|
||||
stock_id=code, start_time=trade_start_time, end_time=trade_end_time, direction=OrderDir.BUY
|
||||
)
|
||||
buy_amount = value / buy_price
|
||||
factor = self.trade_exchange.get_factor(
|
||||
stock_id=code, start_time=trade_start_time, end_time=trade_end_time
|
||||
)
|
||||
buy_amount = self.trade_exchange.round_amount_by_trade_unit(buy_amount, factor)
|
||||
buy_order = Order(
|
||||
stock_id=code,
|
||||
amount=buy_amount,
|
||||
start_time=trade_start_time,
|
||||
end_time=trade_end_time,
|
||||
direction=Order.BUY,
|
||||
)
|
||||
buy_order_list.append(buy_order)
|
||||
|
||||
return TradeDecisionWO(sell_order_list + buy_order_list, self)
|
||||
Binary file not shown.
Binary file not shown.
Binary file not shown.
@@ -6,10 +6,11 @@ The lake is a hive-partitioned parquet store (see ``tac-engine/skills/tradeac-la
|
||||
├── market=US/
|
||||
│ └── timeframe=1d/
|
||||
│ └── symbol=AAPL.parquet # OHLCV bars: t, date, o, h, l, c, v, n, vw
|
||||
├── features/ # ta-lib indicators, wide format
|
||||
├── features/ # indicators, wide format, family tier
|
||||
│ └── market=US/
|
||||
│ └── timeframe=1d/
|
||||
│ └── symbol=AAPL.parquet # t, sma_5, sma_20, rsi_14, ...
|
||||
│ ├── family=ta/symbol=AAPL.parquet # t, sma_5, sma_20, rsi_14, ...
|
||||
│ └── family=sp/symbol=AAPL.parquet # t, sp_ou_*, sp_hmm_*, ...
|
||||
├── calendar.parquet # trading days per market
|
||||
├── coverage.parquet # per (market,timeframe,symbol) loaded windows
|
||||
└── symbols.parquet # asset master
|
||||
@@ -107,8 +108,34 @@ class LakeConfig:
|
||||
return self.lake_root / "features" / f"market={self.market}" / f"timeframe={timeframe}"
|
||||
|
||||
def features_path(self, timeframe: str, symbol: str) -> Path:
|
||||
# Legacy flat path (no family tier). Prefer `load_features` which
|
||||
# resolves the family=ta|sp partition layout.
|
||||
return self.features_dir(timeframe) / f"symbol={str(symbol).upper()}.parquet"
|
||||
|
||||
def load_features(self, timeframe: str, symbol: str) -> pd.DataFrame:
|
||||
"""All feature columns for a symbol, merging the `family=ta` and
|
||||
`family=sp` partitions by timestamp. Returns an empty frame when no
|
||||
feature files exist (legacy flat layout falls back transparently)."""
|
||||
sym = str(symbol).upper()
|
||||
frames = []
|
||||
for family in ("ta", "sp"):
|
||||
p = self.features_dir(timeframe) / f"family={family}" / f"symbol={sym}.parquet"
|
||||
if p.exists():
|
||||
frames.append(pd.read_parquet(p))
|
||||
if not frames:
|
||||
flat = self.features_dir(timeframe) / f"symbol={sym}.parquet"
|
||||
if flat.exists():
|
||||
return pd.read_parquet(flat)
|
||||
return pd.DataFrame()
|
||||
if len(frames) == 1:
|
||||
return frames[0]
|
||||
merged = frames[0]
|
||||
for extra in frames[1:]:
|
||||
merged = merged.merge(extra, on="t", how="outer", suffixes=("", "_dup"))
|
||||
for c in [c for c in merged.columns if c.endswith("_dup")]:
|
||||
merged = merged.drop(columns=c)
|
||||
return merged
|
||||
|
||||
def calendar_path(self) -> Path:
|
||||
return self.lake_root / "calendar.parquet"
|
||||
|
||||
|
||||
@@ -173,8 +173,7 @@ class LakeFeatureProvider(FeatureProvider):
|
||||
def _load_feature_df(self, instrument: str, timeframe: str) -> pd.DataFrame:
|
||||
key = (instrument, timeframe)
|
||||
if key not in self._feature_cache:
|
||||
p = self.cfg.features_path(timeframe, instrument)
|
||||
self._feature_cache[key] = pd.read_parquet(p) if p.exists() else pd.DataFrame()
|
||||
self._feature_cache[key] = self.cfg.load_features(timeframe, instrument)
|
||||
return self._feature_cache[key]
|
||||
|
||||
@staticmethod
|
||||
|
||||
@@ -1,20 +0,0 @@
|
||||
# exp/10 sp5d-moment-features
|
||||
|
||||
Variant C: generic-only 19 + 16 new moment/volatility families (skew, kurt,
|
||||
DSV+ratios, max_up/down, rv_ac1, rv_cv_22, sig lag-5). 35 sp_* fields, ou/hmm excluded.
|
||||
|
||||
Run a3f7d1d40c3d4b839314fcf5b40f9b08 (tac-rd-moments / exp 12) — FINISHED.
|
||||
|
||||
## Result: NEGATIVE (regression vs generic-only baseline)
|
||||
|
||||
| Metric | generic-only 19 (run 7b1e797) | +moments 35 (run a3f7d1d) |
|
||||
|---|---|---|
|
||||
| Rank IC | 0.0635 | 0.0466 |
|
||||
| Rank ICIR | 0.276 | 0.183 |
|
||||
| L-S Sharpe | 2.55 | 1.44 |
|
||||
| net excess (cost) | +3.1% IR 0.28 | -16.2% IR -1.57 |
|
||||
| MDD | -7.3% | -11.1% |
|
||||
|
||||
Same failure mode as ou/hmm in exp 9: adding cross-sectional moment features
|
||||
to the 50-name panel degrades the rank signal. Generic-only 19 remains the
|
||||
best configuration. No further moment-family variants planned.
|
||||
@@ -1,133 +0,0 @@
|
||||
# -----------------------------------------------------------------------------
|
||||
# ABLATION A (baseline): LightGBM with RankIC early-stopping on the 50-ETF SP-5d
|
||||
# panel, using ALL 24 sp_* feature columns (ou,hmm,jump,har,trend,hurst,
|
||||
# signature). Copy of the canonical workflow_lgb_sp5d_rankic.yaml with a
|
||||
# distinct experiment name so the ablation runs are isolated.
|
||||
#
|
||||
# Run:
|
||||
# rd_run_workflow config_path=tac-qlib/workflows/ablate_baseline_all_sp_fields.yaml \
|
||||
# experiment_name=tac-rd-rank-ablate
|
||||
# -----------------------------------------------------------------------------
|
||||
{%- 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_ou_zscore,sp_ou_half_life,sp_ou_revert,sp_hmm_p_regime1,sp_hmm_state,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-rank-ablate"
|
||||
|
||||
task:
|
||||
model:
|
||||
class: RankICLGBModel
|
||||
module_path: tac_qlib.contrib.model.rank_gbdt
|
||||
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
|
||||
seed: 42
|
||||
|
||||
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: 2015-01-03
|
||||
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: [2015-01-03, 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
|
||||
@@ -1,139 +0,0 @@
|
||||
# -----------------------------------------------------------------------------
|
||||
# VARIANT C (generic + moments): keeps the winning generic-only 19-field set
|
||||
# (jump,har,trend,hurst,signature) and adds the NEW generic moment families the
|
||||
# engine now exposes:
|
||||
# - realized skewness / kurtosis (sp_rskew_5, sp_rskew_22, sp_rkurt_5, sp_rkurt_22)
|
||||
# - downside semi-variance + ratios (sp_dsv_1/5/22, sp_dsv_ratio_1/5/22)
|
||||
# - signed max moves (sp_max_up, sp_max_down)
|
||||
# - RV autocorr / vol-of-vol (sp_rv_ac1, sp_rv_cv_22)
|
||||
# - longer-lag signature terms (sp_sig_level2_*_5)
|
||||
# Drops the model-specific ou/hmm families (they scored high in importance but
|
||||
# hurt the rank dimension in the all-24 run). Same panel/model as baseline.
|
||||
#
|
||||
# Run:
|
||||
# rd_run_workflow config_path=experiments/workflows/ablate_generic_moments.yaml \
|
||||
# experiment_name=tac-rd-moments
|
||||
# -----------------------------------------------------------------------------
|
||||
{%- 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_max_up,sp_max_down,sp_rv1,sp_rv5,sp_rv22,sp_vol_ratio_5_22,sp_vol_ratio_1_22,sp_rv_ac1,sp_rv_cv_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_sig_level2_lead_lag_5,sp_sig_level2_lag_lead_5,sp_rskew_5,sp_rskew_22,sp_rkurt_5,sp_rkurt_22,sp_dsv_1,sp_dsv_5,sp_dsv_22,sp_dsv_ratio_1,sp_dsv_ratio_5,sp_dsv_ratio_22" %}
|
||||
|
||||
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-moments"
|
||||
|
||||
task:
|
||||
model:
|
||||
class: RankICLGBModel
|
||||
module_path: tac_qlib.contrib.model.rank_gbdt
|
||||
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
|
||||
seed: 42
|
||||
|
||||
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: 2015-01-03
|
||||
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: [2015-01-03, 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
|
||||
@@ -1,134 +0,0 @@
|
||||
# -----------------------------------------------------------------------------
|
||||
# ABLATION B (generic-only): same panel/model as the baseline, but feature
|
||||
# fields restricted to the model-free / generic stochastic-process families
|
||||
# (jump,har,trend,hurst,signature). Drops the model-specific ou (OU/AR-1
|
||||
# half-life) and hmm (2-state regime) families to test whether the generic
|
||||
# families alone dominate the rank dimension.
|
||||
#
|
||||
# Run:
|
||||
# rd_run_workflow config_path=tac-qlib/workflows/ablate_generic_only_sp_fields.yaml \
|
||||
# experiment_name=tac-rd-rank-ablate
|
||||
# -----------------------------------------------------------------------------
|
||||
{%- 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-rank-ablate"
|
||||
|
||||
task:
|
||||
model:
|
||||
class: RankICLGBModel
|
||||
module_path: tac_qlib.contrib.model.rank_gbdt
|
||||
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
|
||||
seed: 42
|
||||
|
||||
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: 2015-01-03
|
||||
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: [2015-01-03, 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