Simon
73d191b43a
- 新增 finance/tests/ 6 个测试套件（agents/backtest/dao_upsert/factors/features/fundamental_lookahead） - 数据层: data_manager / dao 优化，新增 upsert 逻辑 - 因子层: 基本面因子抽象定位 _mapping、ROE/PE/PB 重构 - 回测层: vectorbt/engine 大改动（251 行），report 增强 - ML 层: features/backtest_integration 特征工程与回测优化 - CLI: agent_cli 重构 - config/settings 扩充配置项
89 lines
2.5 KiB
Python
89 lines
2.5 KiB
Python
"""
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PE / PB 估值因子。
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基于日线收盘价 + 财务数据（EPS/每股净资产）计算。
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"""
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import pandas as pd
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from factors.base import BaseFactor
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from factors.fundamental._mapping import map_fundamental_to_daily
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class PEFactor(BaseFactor):
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"""
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市盈率因子 = close / eps。
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eps 来自财务数据中的 'eps' 列或 TTM EPS。
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因子值越大表示估值越贵。
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"""
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category = "fundamental"
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def __init__(self, financial_df: pd.DataFrame | None = None):
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"""
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参数:
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financial_df: 含 'end_date' 和 'eps' 的 DataFrame。
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"""
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self._financial_df = financial_df
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self.name = "pe"
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def calculate(self, df: pd.DataFrame) -> pd.Series:
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if self._financial_df is None or self._financial_df.empty:
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return pd.Series(float("nan"), index=df.index)
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eps_series = map_fundamental_to_daily(df, self._financial_df, "eps")
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close = df["close"]
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return close / eps_series.replace(0, float("nan"))
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def get_required_columns(self) -> list[str]:
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return ["close"]
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class PBFactor(BaseFactor):
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"""
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市净率因子 = close / bvps（每股净资产）。
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因子值越大表示估值越贵。
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"""
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category = "fundamental"
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def __init__(self, financial_df: pd.DataFrame | None = None):
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self._financial_df = financial_df
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self.name = "pb"
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def calculate(self, df: pd.DataFrame) -> pd.Series:
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if self._financial_df is None or self._financial_df.empty:
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return pd.Series(float("nan"), index=df.index)
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bvps_series = map_fundamental_to_daily(df, self._financial_df, "bvps")
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return df["close"] / bvps_series.replace(0, float("nan"))
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def get_required_columns(self) -> list[str]:
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return ["close"]
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class EPFactor(BaseFactor):
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"""
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盈利收益率因子 = eps / close = 1 / PE。
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值越大表示估值越便宜，适合与动量等因子同向排序。
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"""
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category = "fundamental"
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def __init__(self, financial_df: pd.DataFrame | None = None):
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self._financial_df = financial_df
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self.name = "ep"
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def calculate(self, df: pd.DataFrame) -> pd.Series:
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if self._financial_df is None or self._financial_df.empty:
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return pd.Series(float("nan"), index=df.index)
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eps_series = map_fundamental_to_daily(df, self._financial_df, "eps")
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return eps_series / df["close"].replace(0, float("nan")) * 100
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def get_required_columns(self) -> list[str]:
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return ["close"]
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