Compare commits
3
Commits
| Author | SHA1 | Date | |
|---|---|---|---|
 zhaoli
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3d845306fe | ||
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zhaoli
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1075525d6e | ||
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zhaoli
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63c1ea763e |
+7
-7
@@ -1,19 +1,19 @@
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# TradeAC custom-qlib-code snapshot (auto-generated)
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# parent repo HEAD : 71211552910ee2ebaed853696e9ad8d89641c3de
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# parent repo HEAD : 1075525d6e954dca0bb31daf6675904f8b569f1a
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# tac-qlib/tac_qlib/contrib
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# tac-qlib/tac_qlib/data
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# per-file hashes (git hash-object):
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1b6298c4a5652f2e863cbdc385a1014a570fcd59 tac-qlib/tac_qlib/contrib/__init__.py
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c76a9f17f680e74eea766eff27f7624359749ed6 tac-qlib/tac_qlib/contrib/data/__init__.py
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871ff1e163c29261f140c3f53d42a41e6504c779 tac-qlib/tac_qlib/contrib/data/handler.py
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0dd25ef161c6e0f15eafc84886e7e1381deb38c3 tac-qlib/tac_qlib/contrib/data/handler.py
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b151d139a0dcde87d74b21e7c4b729176ba5c39b tac-qlib/tac_qlib/contrib/model/__init__.py
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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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79aaad9e39fcc740a773f4f63c512ce1086cfde0 tac-qlib/tac_qlib/contrib/strategy/optimal_stop.py
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92e6e90eb0cd0a25142034560f27adb6b705b1a8 tac-qlib/tac_qlib/data/__init__.py
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0eefe65b3210b764ebf52ecd27e405c4b5ecaa8f tac-qlib/tac_qlib/data/__pycache__/__init__.cpython-312.pyc
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4d5f85b06931f153942870c922f13713dd632ace tac-qlib/tac_qlib/data/__pycache__/config.cpython-312.pyc
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158481e2d49653b6d8c71d88dc4ba3ac1148c57a 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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b6dc9ced54f4044f5954b60ddd199acae9eef456 tac-qlib/tac_qlib/data/__pycache__/__init__.cpython-312.pyc
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5cede5184d17910b0232d343f8ecca460098ea11 tac-qlib/tac_qlib/data/__pycache__/config.cpython-312.pyc
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3a5accd6d239f342354cf1fa9410a6d2fb921de0 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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@@ -74,8 +78,9 @@ 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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def _family_common(fam_dir: Path) -> set:
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common = None
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for p in sorted(feat_dir.glob("symbol=*.parquet")):
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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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@@ -83,7 +88,20 @@ def get_common_feature_fields(lake_root=None, market="US", timeframe="1d") -> Li
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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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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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Binary file not shown.
Binary file not shown.
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@@ -6,10 +6,11 @@ The lake is a hive-partitioned parquet store (see ``tac-engine/skills/tradeac-la
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├── market=US/
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│ └── timeframe=1d/
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│ └── symbol=AAPL.parquet # OHLCV bars: t, date, o, h, l, c, v, n, vw
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├── features/ # ta-lib indicators, wide format
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├── features/ # indicators, wide format, family tier
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│ └── market=US/
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│ └── timeframe=1d/
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│ └── symbol=AAPL.parquet # t, sma_5, sma_20, rsi_14, ...
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│ ├── family=ta/symbol=AAPL.parquet # t, sma_5, sma_20, rsi_14, ...
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│ └── family=sp/symbol=AAPL.parquet # t, sp_ou_*, sp_hmm_*, ...
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├── calendar.parquet # trading days per market
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├── coverage.parquet # per (market,timeframe,symbol) loaded windows
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└── symbols.parquet # asset master
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@@ -107,8 +108,34 @@ class LakeConfig:
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return self.lake_root / "features" / f"market={self.market}" / f"timeframe={timeframe}"
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def features_path(self, timeframe: str, symbol: str) -> Path:
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# Legacy flat path (no family tier). Prefer `load_features` which
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# resolves the family=ta|sp partition layout.
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return self.features_dir(timeframe) / f"symbol={str(symbol).upper()}.parquet"
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def load_features(self, timeframe: str, symbol: str) -> pd.DataFrame:
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"""All feature columns for a symbol, merging the `family=ta` and
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`family=sp` partitions by timestamp. Returns an empty frame when no
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feature files exist (legacy flat layout falls back transparently)."""
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sym = str(symbol).upper()
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frames = []
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for family in ("ta", "sp"):
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p = self.features_dir(timeframe) / f"family={family}" / f"symbol={sym}.parquet"
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if p.exists():
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frames.append(pd.read_parquet(p))
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if not frames:
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flat = self.features_dir(timeframe) / f"symbol={sym}.parquet"
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if flat.exists():
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return pd.read_parquet(flat)
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return pd.DataFrame()
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if len(frames) == 1:
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return frames[0]
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merged = frames[0]
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for extra in frames[1:]:
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merged = merged.merge(extra, on="t", how="outer", suffixes=("", "_dup"))
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for c in [c for c in merged.columns if c.endswith("_dup")]:
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merged = merged.drop(columns=c)
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return merged
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def calendar_path(self) -> Path:
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return self.lake_root / "calendar.parquet"
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@@ -173,8 +173,7 @@ class LakeFeatureProvider(FeatureProvider):
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def _load_feature_df(self, instrument: str, timeframe: str) -> pd.DataFrame:
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key = (instrument, timeframe)
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if key not in self._feature_cache:
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p = self.cfg.features_path(timeframe, instrument)
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self._feature_cache[key] = pd.read_parquet(p) if p.exists() else pd.DataFrame()
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self._feature_cache[key] = self.cfg.load_features(timeframe, instrument)
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return self._feature_cache[key]
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@staticmethod
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@@ -1,96 +0,0 @@
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# -----------------------------------------------------------------------------
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# exp17 — short 3-month lake workflow (trace id 17)
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# train 2026-05-01..06-30 / valid 07-01..07-15 / test 07-16..08-14
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# 20-ETF universe, 5d forward label, LGBModel mse, TopkDropout topk=3/n_drop=1
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# -----------------------------------------------------------------------------
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{%- set LAKE = TAC_LAKE_DIR %}
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qlib_init:
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provider_uri: "{{ LAKE }}"
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region: us
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expression_cache: null
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dataset_cache: null
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calendar_provider:
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class: tac_qlib.data.providers.LakeCalendarProvider
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kwargs: { lake_root: "{{ LAKE }}", market: US }
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instrument_provider:
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class: tac_qlib.data.providers.LakeInstrumentProvider
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kwargs: { lake_root: "{{ LAKE }}", market: US, markets: {} }
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feature_provider:
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class: tac_qlib.data.providers.LakeFeatureProvider
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kwargs: { lake_root: "{{ LAKE }}", market: US }
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exp_manager:
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class: MLflowExpManager
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module_path: qlib.workflow.expm
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kwargs: { uri: "sqlite:///mlruns.db", default_exp_name: "tac-rd-short-lake" }
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task:
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model:
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class: LGBModel
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module_path: qlib.contrib.model.gbdt
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kwargs:
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loss: mse
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learning_rate: 0.05
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num_leaves: 15
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n_estimators: 200
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colsample_bytree: 0.8
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subsample: 0.8
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subsample_freq: 1
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reg_alpha: 0.01
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reg_lambda: 0.01
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dataset:
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class: DatasetH
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module_path: qlib.data.dataset
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kwargs:
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handler:
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class: TACHandler
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module_path: tac_qlib.contrib.data.handler
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kwargs:
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instruments: SPY,QQQ,IWM,EEM,TLT,GLD,USO,SMH,XLF,XLE,XLY,XLK,XLV,XBI,IBB,KWEB,FXI,EWJ,VNQ,GDX
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start_time: 2026-05-01
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end_time: 2026-08-14
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fit_start_time: 2026-05-01
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fit_end_time: 2026-06-30
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freq: day
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lake_root: "{{ LAKE }}"
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market: US
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label: "Ref($close,-6)/Ref($close,-1)-1"
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segments:
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train: [2026-05-01, 2026-06-30]
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valid: [2026-07-01, 2026-07-15]
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test: [2026-07-16, 2026-08-14]
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record:
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- { class: SignalRecord, module_path: qlib.workflow.record_temp, kwargs: {} }
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- class: SigAnaRecord
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module_path: qlib.workflow.record_temp
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kwargs: { ana_long_short: true, ann_scaler: 252 }
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- class: PortAnaRecord
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module_path: qlib.workflow.record_temp
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kwargs:
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config:
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strategy:
|
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class: TopkDropoutStrategy
|
||||
module_path: qlib.contrib.strategy
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kwargs:
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signal: "<PRED>"
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topk: 3
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n_drop: 1
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only_tradable: true
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risk_degree: 0.95
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backtest:
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start_time: 2026-07-16
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end_time: 2026-08-14
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account: 1000000
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benchmark: SPY
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exchange_kwargs:
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codes: SPY,QQQ,IWM,EEM,TLT,GLD,USO,SMH,XLF,XLE,XLY,XLK,XLV,XBI,IBB,KWEB,FXI,EWJ,VNQ,GDX
|
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deal_price: $close
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freq: day
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open_cost: 0.0005
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close_cost: 0.0015
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min_cost: 5.0
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risk_analysis_freq: 1d
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@@ -0,0 +1,97 @@
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# Re-run of experiment 16 with validated family=ta and family=sp lake features.
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{%- set LAKE = TAC_LAKE_DIR %}
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{%- set UNIVERSE = "SPY,QQQ,DIA,IWM,MDY,VTI,VOO,VEA,VWO,VT,EFA,EEM,TLT,IEF,SHY,AGG,BND,LQD,HYG,JNK,EMB,GLD,SLV,USO,UNG,DBA,DBC,XLK,XLF,XLE,XLV,XLI,XLY,XLP,XLU,XLB,XLRE,ARKK,SMH,SOXX,IBB,XBI,ITA,XAR,ICLN,TAN,FDN,IGV,ESPO,REM" %}
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{%- set FEATURES = "$open,$high,$low,$close,$vwap,$volume,sma_5,sma_20,ema_12,ema_26,rsi_14,macd,macd_signal,macd_hist,bb_upper,bb_middle,bb_lower,atr_14,adx_14,sp_ret,sp_ou_half_life,sp_ou_revert,sp_ou_zscore,sp_hmm_p_regime1,sp_hmm_state,sp_jump_ratio,sp_jump_flag,sp_jump_tail,sp_max_down,sp_max_move,sp_max_up,sp_rv1,sp_rv5,sp_rv22,sp_rv_ac1,sp_rv_cv_22,sp_vol_ratio_1_22,sp_vol_ratio_5_22,sp_trend_slope_5,sp_trend_slope_20,sp_trend_slope_60,sp_logp,sp_hurst_exponent,sp_rskew_5,sp_rskew_22,sp_rkurt_5,sp_rkurt_22,sp_dsv_1,sp_dsv_5,sp_dsv_22,sp_dsv_ratio_1,sp_dsv_ratio_5,sp_dsv_ratio_22,sp_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" %}
|
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|
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qlib_init:
|
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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-exp16-db-ta-sp" }
|
||||
|
||||
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: 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