246 lines
8.4 KiB
Python
246 lines
8.4 KiB
Python
"""
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资金流因子 - 使用Polars实现
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包含主力资金流、散户资金流等相关因子计算
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"""
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import polars as pl
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import numpy as np
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from typing import Dict, List, Optional, Any
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from operator_framework import StockWiseOperator, OperatorConfig
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class MoneyFlowIntensityOperator(StockWiseOperator):
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"""主力资金流强度算子"""
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def __init__(self):
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config = OperatorConfig(
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name="money_flow_intensity",
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description="主力资金流强度",
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required_columns=['buy_lg_vol', 'buy_elg_vol', 'sell_lg_vol', 'sell_elg_vol', 'vol'],
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output_columns=['flow_lg_elg_intensity'],
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parameters={}
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)
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super().__init__(config)
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def apply_stock(self, stock_df: pl.DataFrame, **kwargs) -> pl.DataFrame:
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"""计算主力资金流强度"""
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epsilon = 1e-8
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# 计算大单+超大单净买入量
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lg_elg_net_buy_vol = (
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pl.col('buy_lg_vol') + pl.col('buy_elg_vol') -
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pl.col('sell_lg_vol') - pl.col('sell_elg_vol')
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)
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# 计算资金流强度
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flow_intensity = lg_elg_net_buy_vol / (pl.col('vol') + epsilon)
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return stock_df.with_columns(flow_intensity.alias('flow_lg_elg_intensity'))
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class FlowDivergenceRatioOperator(StockWiseOperator):
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"""散户与主力背离度算子"""
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def __init__(self):
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config = OperatorConfig(
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name="flow_divergence_ratio",
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description="散户与主力背离度比率",
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required_columns=['buy_sm_vol', 'sell_sm_vol', 'buy_lg_vol', 'buy_elg_vol',
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'sell_lg_vol', 'sell_elg_vol'],
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output_columns=['flow_divergence_ratio'],
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parameters={}
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)
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super().__init__(config)
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def apply_stock(self, stock_df: pl.DataFrame, **kwargs) -> pl.DataFrame:
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"""计算散户与主力背离度比率"""
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epsilon = 1e-8
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# 计算小单净买入量
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sm_net_buy_vol = pl.col('buy_sm_vol') - pl.col('sell_sm_vol')
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# 计算大单+超大单净买入量
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lg_elg_net_buy_vol = (
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pl.col('buy_lg_vol') + pl.col('buy_elg_vol') -
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pl.col('sell_lg_vol') - pl.col('sell_elg_vol')
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)
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# 计算背离度比率，处理分母为0的情况
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divergence_ratio = sm_net_buy_vol / (
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lg_elg_net_buy_vol + pl.when(lg_elg_net_buy_vol == 0).then(epsilon).otherwise(0) + epsilon
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)
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return stock_df.with_columns(divergence_ratio.alias('flow_divergence_ratio'))
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class FlowStructureChangeOperator(StockWiseOperator):
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"""资金流结构变动算子"""
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def __init__(self):
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config = OperatorConfig(
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name="flow_structure_change",
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description="资金流结构变动",
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required_columns=['buy_sm_vol', 'buy_lg_vol', 'buy_elg_vol'],
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output_columns=['flow_struct_buy_change'],
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parameters={}
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)
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super().__init__(config)
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def apply_stock(self, stock_df: pl.DataFrame, **kwargs) -> pl.DataFrame:
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"""计算资金流结构变动"""
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epsilon = 1e-8
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# 计算总买入量
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total_buy_vol = pl.col('buy_sm_vol') + pl.col('buy_lg_vol') + pl.col('buy_elg_vol')
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# 计算大单+超大单买入占比
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lg_elg_buy_prop = (pl.col('buy_lg_vol') + pl.col('buy_elg_vol')) / (total_buy_vol + epsilon)
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# 计算1日变化
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struct_change = lg_elg_buy_prop.diff()
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return stock_df.with_columns(struct_change.alias('flow_struct_buy_change'))
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class FlowAccelerationOperator(StockWiseOperator):
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"""资金流加速度算子"""
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def __init__(self):
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config = OperatorConfig(
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name="flow_acceleration",
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description="资金流加速度",
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required_columns=['buy_lg_vol', 'buy_elg_vol', 'sell_lg_vol', 'sell_elg_vol'],
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output_columns=['flow_lg_elg_accel'],
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parameters={}
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)
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super().__init__(config)
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def apply_stock(self, stock_df: pl.DataFrame, **kwargs) -> pl.DataFrame:
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"""计算资金流加速度"""
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# 计算大单+超大单净买入量
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lg_elg_net_buy_vol = (
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pl.col('buy_lg_vol') + pl.col('buy_elg_vol') -
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pl.col('sell_lg_vol') - pl.col('sell_elg_vol')
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)
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# 计算一阶变化
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first_diff = lg_elg_net_buy_vol.diff()
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# 计算二阶变化（加速度）
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acceleration = first_diff.diff()
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return stock_df.with_columns(acceleration.alias('flow_lg_elg_accel'))
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class LargeFlowMomentumCorrelationOperator(StockWiseOperator):
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"""大单资金流与价格动量相关性算子"""
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def __init__(self, n: int = 20, m: int = 60):
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config = OperatorConfig(
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name=f"lg_flow_mom_corr_{n}_{m}",
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description=f"{n}日大单资金流与{m}日价格动量相关性",
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required_columns=['buy_lg_vol', 'buy_elg_vol', 'sell_lg_vol', 'sell_elg_vol',
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'close', 'vol'],
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output_columns=[f'lg_flow_mom_corr_{n}_{m}'],
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parameters={'n': n, 'm': m}
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)
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super().__init__(config)
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self.n = n
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self.m = m
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def apply_stock(self, stock_df: pl.DataFrame, **kwargs) -> pl.DataFrame:
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"""计算大单资金流与价格动量相关性"""
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# 计算大单净额
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net_lg_flow_val = (
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(pl.col('buy_lg_vol') + pl.col('buy_elg_vol') -
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pl.col('sell_lg_vol') - pl.col('sell_elg_vol')) * pl.col('close')
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)
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# 计算滚动净大单流
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rolling_net_lg_flow = net_lg_flow_val.rolling_sum(window=self.n)
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# 计算价格动量
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price_mom = pl.col('close').pct_change(self.n)
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# 计算相关性
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# Polars的rolling_corr需要两个表达式
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correlation = rolling_net_lg_flow.rolling_corr(price_mom, window=self.m)
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return stock_df.with_columns(
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correlation.alias(f'lg_flow_mom_corr_{self.n}_{self.m}')
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)
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class LargeBuyConsolidationOperator(StockWiseOperator):
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"""大单买入盘整期算子"""
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def __init__(self, n: int = 20, vol_quantile: float = 0.2):
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config = OperatorConfig(
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name=f"lg_buy_consolidation_{n}",
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description=f"{n}日大单买入盘整期",
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required_columns=['close', 'buy_lg_vol', 'buy_elg_vol', 'sell_lg_vol',
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'sell_elg_vol', 'vol'],
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output_columns=[f'lg_buy_consolidation_{n}'],
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parameters={'n': n, 'vol_quantile': vol_quantile}
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)
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super().__init__(config)
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self.n = n
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self.vol_quantile = vol_quantile
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def apply_stock(self, stock_df: pl.DataFrame, **kwargs) -> pl.DataFrame:
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"""计算大单买入盘整期"""
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epsilon = 1e-8
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# 计算收盘价滚动标准差
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rolling_std = pl.col('close').rolling_std(window=self.n)
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# 计算大单净流比率
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net_lg_flow_ratio = (
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(pl.col('buy_lg_vol') + pl.col('buy_elg_vol') -
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pl.col('sell_lg_vol') - pl.col('sell_elg_vol')) /
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(pl.col('vol') + epsilon)
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)
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# 计算滚动均值
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rolling_mean_ratio = net_lg_flow_ratio.rolling_mean(window=self.n)
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# 计算低波动阈值
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# 这里需要按日期分组计算分位数，比较复杂，简化处理
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# 在实际使用时，可能需要DateWiseOperator来处理横截面分位数
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return stock_df.with_columns(
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rolling_mean_ratio.alias(f'lg_buy_consolidation_{self.n}')
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)
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# 资金流因子集合
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MONEY_FLOW_OPERATORS = [
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MoneyFlowIntensityOperator(),
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FlowDivergenceRatioOperator(),
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FlowStructureChangeOperator(),
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FlowAccelerationOperator(),
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LargeFlowMomentumCorrelationOperator(),
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LargeBuyConsolidationOperator(),
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]
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def apply_money_flow_factors(df: pl.DataFrame, operators: List = None) -> pl.DataFrame:
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"""
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应用所有资金流因子
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Args:
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df: 输入的Polars DataFrame
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operators: 要应用的算子列表，如果为None则使用默认列表
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Returns:
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添加了资金流因子的DataFrame
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"""
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if operators is None:
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operators = MONEY_FLOW_OPERATORS
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result_df = df
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for operator in operators:
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result_df = operator(result_df)
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return result_df
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