File size: 8,686 Bytes
590a501 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 | """Load pre-mined factors from GP parquet, QuantaAlpha JSON, qlib expressions, or pred pickles."""
from __future__ import annotations
import json
from pathlib import Path
from typing import Any
import pandas as pd
from config.settings import PROJECT_ROOT, load_settings
from data_pipeline.init_qlib import init_qlib
def _normalize_symbol(code: str) -> str:
code = str(code).upper()
if code.startswith(("SH", "SZ", "BJ")):
return code
if code and code[0] == "6":
return f"SH{code}"
return f"SZ{code}"
def _to_signal_series(df: pd.DataFrame, score_col: str = "score") -> pd.Series:
"""Convert panel to qlib MultiIndex (instrument, datetime) signal."""
if isinstance(df, pd.Series):
s = df.copy()
if s.index.names != ["instrument", "datetime"]:
s = s.swaplevel().sort_index()
s.index.names = ["instrument", "datetime"]
return s
panel = df.copy()
if "date" in panel.columns:
panel = panel.rename(columns={"date": "datetime"})
if "symbol" in panel.columns:
panel = panel.rename(columns={"symbol": "instrument"})
panel["instrument"] = panel["instrument"].map(_normalize_symbol)
panel["datetime"] = pd.to_datetime(panel["datetime"])
out = panel.set_index(["instrument", "datetime"])[score_col].sort_index()
out.index.names = ["instrument", "datetime"]
return out
def load_factor_panel(path: str | Path) -> pd.DataFrame:
path = Path(path)
if not path.is_absolute():
path = PROJECT_ROOT / path
if not path.exists():
raise FileNotFoundError(f"Factor panel not found: {path}")
if path.suffix == ".parquet":
df = pd.read_parquet(path)
elif path.suffix == ".csv":
df = pd.read_csv(path)
elif path.suffix == ".json":
return load_quantaalpha_library(path)
else:
raise ValueError(f"Unsupported factor format: {path.suffix}")
if "date" in df.columns:
df["date"] = pd.to_datetime(df["date"])
return df
def load_quantaalpha_library(path: str | Path) -> pd.DataFrame:
"""Parse QuantaAlpha factor library JSON into a metadata table."""
path = Path(path)
with open(path, encoding="utf-8") as f:
data = json.load(f)
rows = []
for fid, info in data.get("factors", {}).items():
backtest = info.get("backtest_results", {}) or {}
rows.append(
{
"factor_id": fid,
"factor_name": info.get("factor_name", fid),
"factor_expression": info.get("factor_expression", ""),
"factor_description": info.get("factor_description", ""),
"ic": backtest.get("IC", backtest.get("ic")),
"icir": backtest.get("ICIR", backtest.get("icir")),
"rank_ic": backtest.get("Rank IC", backtest.get("rank_ic")),
"rank_icir": backtest.get("Rank ICIR", backtest.get("rank_icir")),
"source": "quantaalpha",
}
)
return pd.DataFrame(rows)
def load_qlib_expression_factor(
expression: str,
instruments: str | list | None = None,
start_time: str | None = None,
end_time: str | None = None,
name: str = "factor",
) -> pd.Series:
settings = load_settings()
init_qlib()
from qlib.data import D
market = instruments or settings.market
start_time = start_time or settings.raw["data"]["start_time"]
end_time = end_time or settings.raw["data"]["end_time"]
df = D.features(
D.instruments(market),
[expression],
start_time=start_time,
end_time=end_time,
freq=settings.freq,
)
df.columns = [name]
s = df[name]
s.index.names = ["instrument", "datetime"]
return s
def load_pred_pickle(path: str | Path) -> pd.Series:
import pickle
path = Path(path)
if not path.is_absolute():
path = PROJECT_ROOT / path
with open(path, "rb") as f:
obj = pickle.load(f)
if isinstance(obj, pd.Series):
return obj
if isinstance(obj, pd.DataFrame):
col = "score" if "score" in obj.columns else obj.columns[0]
return _to_signal_series(obj, col)
raise TypeError(f"Unsupported pred pickle type: {type(obj)}")
def combine_factor_columns(
panel: pd.DataFrame,
factor_cols: list[str] | None = None,
method: str = "equal",
ic_weights: dict[str, float] | None = None,
) -> pd.Series:
"""Combine multiple factor columns into one cross-sectional score."""
factor_cols = factor_cols or [c for c in panel.columns if c.startswith("factor_") or c.startswith("alpha_")]
if not factor_cols:
raise ValueError("No factor columns found to combine")
work = panel[["date" if "date" in panel.columns else "datetime", "symbol" if "symbol" in panel.columns else "instrument", *factor_cols]].copy()
date_col = "date" if "date" in work.columns else "datetime"
sym_col = "symbol" if "symbol" in work.columns else "instrument"
def _zscore(x: pd.DataFrame) -> pd.DataFrame:
return x.apply(lambda s: (s - s.mean()) / (s.std() + 1e-8))
grouped = work.groupby(date_col)
norm = grouped[factor_cols].transform(lambda x: (x - x.mean()) / (x.std() + 1e-8))
if method == "equal":
score = norm.mean(axis=1)
elif method == "rank_mean":
score = grouped[factor_cols].rank(pct=True).mean(axis=1)
elif method == "ic_weighted":
if not ic_weights:
raise ValueError("ic_weighted requires ic_weights dict")
weights = pd.Series({c: ic_weights.get(c, 0.0) for c in factor_cols})
weights = weights / weights.abs().sum()
score = norm.mul(weights, axis=1).sum(axis=1)
else:
raise ValueError(f"Unknown combine method: {method}")
out = work[[date_col, sym_col]].copy()
out["score"] = score.values
return _to_signal_series(out, "score")
def build_signal_from_source(source_cfg: dict[str, Any]) -> pd.Series:
"""Build qlib signal Series from a signal source config block."""
src_type = source_cfg.get("type", "factor_panel")
if src_type == "pred_pickle":
return load_pred_pickle(source_cfg["path"])
if src_type == "qlib_expression":
return load_qlib_expression_factor(
expression=source_cfg["expression"],
instruments=source_cfg.get("instruments"),
start_time=source_cfg.get("start_time"),
end_time=source_cfg.get("end_time"),
name=source_cfg.get("name", "score"),
)
if src_type == "quantaalpha_library":
from integrations.quantaalpha.factor_library import build_signal_from_library
return build_signal_from_library(
library_path=source_cfg["path"],
factor_ids=source_cfg.get("factor_ids"),
top_k=source_cfg.get("top_k"),
combine=source_cfg.get("combine", "ic_weighted"),
quality_filter=source_cfg.get("quality_filter"),
)
if src_type == "factor_registry":
from factor_engine.formula_registry import compute_factor
return compute_factor(
source_cfg["name"],
start_time=source_cfg.get("start_time"),
end_time=source_cfg.get("end_time"),
cache=source_cfg.get("cache", True),
registry_path=source_cfg.get("registry_path"),
)
if src_type == "factor_registry_panel":
from factor_engine.formula_registry import build_combined_panel
panel = build_combined_panel(
factor_names=source_cfg.get("names"),
enabled_only=source_cfg.get("enabled_only", True),
use_cache=source_cfg.get("use_cache", True),
registry_path=source_cfg.get("registry_path"),
)
return combine_factor_columns(
panel,
factor_cols=source_cfg.get("factor_cols"),
method=source_cfg.get("combine", "equal"),
)
panel = load_factor_panel(source_cfg["path"])
if source_cfg.get("score_col"):
return _to_signal_series(panel, source_cfg["score_col"])
factor_cols = source_cfg.get("factor_cols")
if factor_cols or any(c.startswith("factor_") for c in panel.columns):
return combine_factor_columns(
panel,
factor_cols=factor_cols,
method=source_cfg.get("combine", "equal"),
ic_weights=source_cfg.get("ic_weights"),
)
if "score" in panel.columns:
return _to_signal_series(panel, "score")
if "pred_score" in panel.columns:
return _to_signal_series(panel, "pred_score")
raise ValueError(f"Cannot infer signal from source config: {source_cfg}")
|