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shy3130
e0cd625ef4
- 因子平台: /factors 一级页(检验/因子库/编辑器/组合/挖掘), DSL 公式因子(25 算子点选、双语字段、我的因子模板、脏公式守卫), 版本与生命周期, 自动挖掘 L1 统计筛选 - 因子↔策略四条桥: 触发器 Zap 快建因子条件信号、因子一键生成排名策略、自定义信号 AI 提示词接入因子分组、策略回测因子归因(胜/败单入场信号日因子均值, 独立 tab, 双语因子名) - 回测: 统计卡新增盈亏比(≥1 红/<1 绿), 蒙卡回撤合并为中位/95% 双值卡(自适应字号), 高级设置基础过滤与策略编辑器参数对齐(5 组区间) - 信号库独立页 /signals(原设置 tab 迁出), 持仓提醒入导航; 挖掘并入因子页第 5 tab, /mining 旧链接重定向 - 研究线配套: 因子目录 61→77(评分/矩阵双内核), stats_v2(Newey-West/BH-FDR/DSR), enriched 管道与异动/报价服务配套调整 - 文档: README 导航与特性表、features.md 因子平台章节、操作说明书 9.2、factor-platform-plan 执行状态与 §5、二开文档桥接说明; 交流与支持节改版 - 版本 0.2.2 → 0.2.3; 后端全量 1625 passed(1 例环境性跳过), 前端 build 通过
323 lines
14 KiB
Python
323 lines
14 KiB
Python
"""策略评分字段解析。"""
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from __future__ import annotations
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from collections.abc import Collection, Mapping
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from typing import Any
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import polars as pl
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from app.factors.registry import (
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factor_dependencies as _registry_factor_dependencies,
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)
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from app.factors.registry import get_factor as _registry_get_factor
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from app.factors.registry import scoring_warmups as _registry_scoring_warmups
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from app.factors.registry import virtual_dependencies as _registry_virtual_dependencies
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SCORING_DIRECTION_HIGH = "high"
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SCORING_DIRECTION_LOW = "low"
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SCORING_DIRECTIONS = frozenset({SCORING_DIRECTION_HIGH, SCORING_DIRECTION_LOW})
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# P1 起依赖声明与预热窗口的单一权威来源为 app/factors/registry.py;
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# 本常量为兼容别名, 键集合与历史版本逐项一致 (见 tests/test_factor_registry.py 快照测试)。
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VIRTUAL_SCORING_DEPENDENCIES: dict[str, frozenset[str]] = dict(_registry_virtual_dependencies())
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_ROLLING_SCORING_WARMUP: dict[str, int] = dict(_registry_scoring_warmups())
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def effective_scoring(
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defaults: Mapping[str, Any] | None,
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overrides: Mapping[str, Any] | None,
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) -> dict[str, Any]:
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"""解析有效评分；新配置可完整替换，历史配置保持局部覆盖。"""
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override_values = (overrides or {}).get("scoring")
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if (overrides or {}).get("scoring_replace") is True:
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return dict(override_values) if isinstance(override_values, Mapping) else {}
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scoring = dict(defaults or {})
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if isinstance(override_values, Mapping):
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scoring.update(override_values)
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return scoring
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def effective_scoring_directions(overrides: Mapping[str, Any] | None) -> dict[str, str]:
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values = (overrides or {}).get("scoring_directions")
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if not isinstance(values, Mapping):
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return {}
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return {
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str(name): str(direction)
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for name, direction in values.items()
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if direction in SCORING_DIRECTIONS
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}
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def scoring_warmup_bars(scoring: Mapping[str, Any]) -> int:
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warmups: list[int] = [
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_ROLLING_SCORING_WARMUP.get(str(name), 1)
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for name, weight in scoring.items()
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if weight
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]
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# composite/custom 因子的预热来自注册表 (P3)
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for name, weight in scoring.items():
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if not weight:
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continue
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spec = _registry_get_factor(str(name))
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if spec is not None and spec.kind in ("custom", "composite"):
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warmups.append(spec.warmup_bars)
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return max(warmups, default=1)
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def scoring_dependencies(scoring: Mapping[str, Any]) -> set[str]:
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"""把受控虚拟评分字段展开为实际数据依赖 (含 composite/custom 递归展开)。"""
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dependencies: set[str] = set()
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for name, weight in scoring.items():
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if not weight:
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continue
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dependencies.update(_registry_factor_dependencies([str(name)]))
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return dependencies
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def _composite_value_expr(available: set[str], name: str) -> pl.Expr | None:
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"""复合因子值 = Σ w_i * 截面 zscore(成员值); 成员可为已物化列或虚拟因子。"""
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spec = _registry_get_factor(name)
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if spec is None or not spec.components:
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return None
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total: pl.Expr | None = None
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for member_id, weight in spec.components:
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member_expr = (
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pl.col(member_id)
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if member_id in available
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else scoring_value_expr(available, member_id)
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)
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if member_expr is None:
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return None
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mean = member_expr.mean().over("date")
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std = member_expr.std().over("date")
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piece = pl.when(std > 0).then((member_expr - mean) / std).otherwise(None) * weight
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total = piece if total is None else total + piece
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return total
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def scoring_value_expr(columns: Collection[str], name: str) -> pl.Expr | None:
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"""返回评分值表达式；依赖不完整时返回 None。"""
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available = set(columns)
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if name in available:
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return pl.col(name)
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# composite 在 VIRTUAL 字典门控之前分派 (依赖经注册表递归展开) —— P3
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spec = _registry_get_factor(name)
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if spec is not None and spec.kind == "composite":
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return _composite_value_expr(available, name)
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dependencies = VIRTUAL_SCORING_DEPENDENCIES.get(name)
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if dependencies is None or not dependencies.issubset(available):
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return None
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if name.startswith("ma") and name.endswith("_bias"):
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period = name.removeprefix("ma").removesuffix("_bias")
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if period.isdigit():
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return _relative(pl.col("close"), pl.col(f"ma{period}"))
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if name.startswith("ema") and name.endswith("_bias"):
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period = name.removeprefix("ema").removesuffix("_bias")
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if period.isdigit():
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return _relative(pl.col("close"), pl.col(f"ema{period}"))
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if name in {"macd_dif_pct", "macd_dea_pct", "macd_hist_pct"}:
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source = name.removesuffix("_pct")
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return _ratio(pl.col(source), pl.col("close"))
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if name == "atr_pct":
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return _ratio(pl.col("atr_14"), pl.col("close"))
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if name == "boll_position":
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return _ratio(
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pl.col("close") - pl.col("boll_lower"),
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pl.col("boll_upper") - pl.col("boll_lower"),
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)
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if name == "boll_width":
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return _ratio(pl.col("boll_upper") - pl.col("boll_lower"), pl.col("ma20"))
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if name == "vol_ratio_10d":
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return _ratio(
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pl.col("volume"),
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pl.col("volume").shift(1).rolling_mean(10).over("symbol"),
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)
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if name == "vol_trend_5_10":
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return _relative(pl.col("vol_ma5"), pl.col("vol_ma10"))
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if name == "turnover_ratio_5d":
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return _relative(
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pl.col("turnover_rate"),
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pl.col("turnover_rate").shift(1).rolling_mean(5).over("symbol"),
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)
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if name == "log_amount":
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return pl.when(pl.col("amount") >= 0).then((pl.col("amount") + 1).log()).otherwise(None)
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if name == "amount_ratio_5d":
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return _relative(
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pl.col("amount"),
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pl.col("amount").shift(1).rolling_mean(5).over("symbol"),
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)
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if name == "gap_return":
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return _relative(pl.col("open"), pl.col("prev_close"))
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if name == "intraday_return":
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return _relative(pl.col("close"), pl.col("open"))
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if name == "close_position":
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return _ratio(pl.col("close") - pl.col("low"), pl.col("high") - pl.col("low"))
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if name == "distance_to_high_60d":
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return _relative(pl.col("close"), pl.col("high_60d"))
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if name == "distance_from_low_60d":
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return _relative(pl.col("close"), pl.col("low_60d"))
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if name in {
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"max_ret_20d", "ret_skew_20d", "up_days_20d",
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"amihud_20d", "vol_price_corr_20d",
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}:
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change = _daily_change_expr()
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if name == "max_ret_20d":
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return change.rolling_max(20, min_samples=20).over("symbol")
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if name == "ret_skew_20d":
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return change.rolling_skew(20, bias=True).over("symbol")
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if name == "up_days_20d":
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return (
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(change > 0).cast(pl.Float64)
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.rolling_sum(20, min_samples=20).over("symbol")
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)
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if name == "amihud_20d":
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illiquidity = _ratio(change.abs(), pl.col("amount") / 1e8)
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return illiquidity.rolling_mean(20, min_samples=20).over("symbol")
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volume = pl.col("volume")
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product = change * volume
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return _rolling_corr_expr(change, volume, product, 20).over("symbol")
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if name == "turnover_z_60d":
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baseline = pl.col("turnover_rate").shift(1)
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mean = baseline.rolling_mean(60, min_samples=60)
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std = baseline.rolling_std(60, min_samples=60)
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return (
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pl.when(std > 0).then((pl.col("turnover_rate") - mean) / std)
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.otherwise(None)
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.over("symbol")
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)
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if name == "vwap_bias":
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vwap = _ratio(pl.col("amount"), pl.col("volume") * 100.0)
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return _relative(pl.col("close"), vwap)
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if name == "vol_trend_5_60":
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fast = pl.col("volume").rolling_mean(5)
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slow = pl.col("volume").rolling_mean(60)
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return _relative(fast, slow).over("symbol")
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if name in {"limit_up_count_20d", "limit_up_count_60d"}:
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window = 20 if name == "limit_up_count_20d" else 60
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hit = (pl.col("consecutive_limit_ups").fill_null(0) > 0).cast(pl.Float64)
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return hit.rolling_sum(window, min_samples=window).over("symbol")
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# ── 扩充批次 (2026-09-05): 全部滚动窗口默认 min_samples=窗口长 (fail-closed) ──
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if name == "log_float_mv":
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# 换手率 = 成交量/流通股本 → 股本 = volume/turnover_rate, 市值 = close x 股本
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return (
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pl.when((pl.col("turnover_rate") > 0) & (pl.col("volume") > 0))
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.then((pl.col("close") * pl.col("volume") / pl.col("turnover_rate")).log())
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.otherwise(None)
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)
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if name == "momentum_120d":
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return _relative(
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pl.col("close"),
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pl.col("close").shift(120),
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).over("symbol")
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if name == "mom_accel_20_60":
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return pl.col("momentum_20d") - pl.col("momentum_60d")
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if name == "rsi_14_delta_5d":
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return pl.col("rsi_14") - pl.col("rsi_14").shift(5).over("symbol")
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if name == "overnight_ret_20d":
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overnight = _relative(pl.col("open"), pl.col("prev_close"))
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return overnight.rolling_sum(20, min_samples=20).over("symbol")
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if name == "intraday_ret_20d":
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intraday = _relative(pl.col("close"), pl.col("open"))
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return intraday.rolling_sum(20, min_samples=20).over("symbol")
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if name == "downside_vol_20d":
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downside = (_daily_change_expr().clip(upper_bound=0.0) ** 2)
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return downside.rolling_mean(20, min_samples=20).sqrt().over("symbol")
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if name == "vol_regime_5_60":
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change = _daily_change_expr()
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fast = change.rolling_std(5, min_samples=5)
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slow = change.rolling_std(60, min_samples=60)
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return _ratio(fast, slow).over("symbol")
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if name == "amplitude_trend_20_60":
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fast = pl.col("amplitude").rolling_mean(20, min_samples=20)
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slow = pl.col("amplitude").rolling_mean(60, min_samples=60)
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return _relative(fast, slow).over("symbol")
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if name == "obv_trend_20d":
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change = _daily_change_expr()
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signed = change.sign() * pl.col("volume")
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total = signed.rolling_sum(20, min_samples=20)
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scale = pl.col("volume").rolling_mean(20, min_samples=20) * 20.0
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return _ratio(total, scale).over("symbol")
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if name == "amount_mean_20d":
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return (pl.col("amount") / 1e8).rolling_mean(20, min_samples=20).over("symbol")
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if name == "turnover_mean_20d":
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return pl.col("turnover_rate").rolling_mean(20, min_samples=20).over("symbol")
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if name == "turnover_std_20d":
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mean = pl.col("turnover_rate").rolling_mean(20, min_samples=20)
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std = pl.col("turnover_rate").rolling_std(20, min_samples=20)
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return _ratio(std, mean).over("symbol")
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if name == "position_240d":
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high = pl.col("close").rolling_max(240, min_samples=240)
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low = pl.col("close").rolling_min(240, min_samples=240)
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return _ratio(pl.col("close") - low, high - low).over("symbol")
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if name == "distance_to_high_240d":
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return _relative(
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pl.col("close"),
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pl.col("close").rolling_max(240, min_samples=240),
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).over("symbol")
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if name == "kdj_kd_diff":
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return pl.col("kdj_k") - pl.col("kdj_d")
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return None
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def _daily_change_expr() -> pl.Expr:
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previous = pl.col("close").shift(1)
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return _ratio(pl.col("close"), previous) - 1.0
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def _rolling_corr_expr(
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left: pl.Expr, right: pl.Expr, product: pl.Expr, window: int
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) -> pl.Expr:
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"""Pearson correlation over a rolling window, matching the matrix kernel formula."""
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mean_left = left.rolling_mean(window, min_samples=window)
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mean_right = right.rolling_mean(window, min_samples=window)
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mean_product = product.rolling_mean(window, min_samples=window)
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mean_left_sq = (left * left).rolling_mean(window, min_samples=window)
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mean_right_sq = (right * right).rolling_mean(window, min_samples=window)
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covariance = mean_product - mean_left * mean_right
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variance_left = mean_left_sq - mean_left * mean_left
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variance_right = mean_right_sq - mean_right * mean_right
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return pl.when(
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(variance_left > 0) & (variance_right > 0)
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).then(
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covariance / (variance_left * variance_right).sqrt()
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).otherwise(None)
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def materialize_scoring_columns(
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frame: pl.DataFrame,
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names: Collection[str],
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) -> pl.DataFrame:
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# custom (DSL) 因子先物化: frame_transform 可能需要多阶段临时列 (嵌套窗口规避),
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# 与单表达式路径不同, 必须整体走帧变换 —— 与检验/试算共用同一条计算路径 (P3)。
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from app.factors.dsl import FACTOR_COLUMN, compile_formula_cached
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for name in names:
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spec = _registry_get_factor(str(name))
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if spec is None or spec.kind != "custom" or name in frame.columns:
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continue
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compiled = compile_formula_cached(spec.formula_text)
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if compiled.frame_transform is None:
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continue
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transformed = compiled.frame_transform(frame)
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if transformed is None:
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continue
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frame = transformed.with_columns(pl.col(FACTOR_COLUMN).alias(str(name))).drop(FACTOR_COLUMN)
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expressions = [
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expression.alias(name)
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for name in names
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if name not in frame.columns
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and (expression := scoring_value_expr(frame.columns, str(name))) is not None
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]
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return frame.with_columns(expressions) if expressions else frame
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def _ratio(numerator: pl.Expr, denominator: pl.Expr) -> pl.Expr:
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return pl.when(denominator.is_not_null() & (denominator != 0)).then(
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numerator / denominator
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).otherwise(None)
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def _relative(numerator: pl.Expr, denominator: pl.Expr) -> pl.Expr:
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return _ratio(numerator, denominator) - 1.0
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