313 lines
11 KiB
Python
313 lines
11 KiB
Python
"""
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筹码分布因子 - 使用Polars实现
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包含筹码集中度、分布偏度、浮筹比例等相关因子计算
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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 ChipConcentrationOperator(StockWiseOperator):
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"""筹码集中度算子"""
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def __init__(self):
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config = OperatorConfig(
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name="chip_concentration",
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description="筹码集中度",
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required_columns=['cost_95pct', 'cost_5pct', 'close'],
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output_columns=['chip_concentration_range'],
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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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"""计算筹码集中度"""
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epsilon = 1e-8
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# 计算筹码集中度范围，相对于当前价格标准化
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concentration_range = (pl.col('cost_95pct') - pl.col('cost_5pct')) / (pl.col('close') + epsilon)
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return stock_df.with_columns(concentration_range.alias('chip_concentration_range'))
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class ChipSkewnessOperator(StockWiseOperator):
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"""筹码分布偏度算子"""
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def __init__(self):
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config = OperatorConfig(
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name="chip_skewness",
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description="筹码分布偏度",
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required_columns=['weight_avg', 'cost_50pct'],
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output_columns=['chip_skewness'],
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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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"""计算筹码分布偏度"""
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epsilon = 1e-8
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# 计算偏度：(加权平均成本 - 中位数成本) / 中位数成本
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skewness = (pl.col('weight_avg') - pl.col('cost_50pct')) / (pl.col('cost_50pct') + epsilon)
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return stock_df.with_columns(skewness.alias('chip_skewness'))
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class FloatingChipProxyOperator(StockWiseOperator):
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"""浮筹比例代理算子"""
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def __init__(self):
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config = OperatorConfig(
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name="floating_chip_proxy",
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description="浮筹比例代理",
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required_columns=['close', 'cost_15pct', 'winner_rate'],
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output_columns=['floating_chip_proxy'],
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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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"""计算浮筹比例代理"""
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# 计算价格与15%成本线的距离
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price_dist_cost15 = (pl.col('close') - pl.col('cost_15pct')) / pl.col('close')
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# 计算浮筹代理：获利盘比例 * max(0, 价格距离)
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floating_proxy = pl.col('winner_rate') * pl.max_horizontal(0, price_dist_cost15)
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return stock_df.with_columns(floating_proxy.alias('floating_chip_proxy'))
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class CostSupportChangeOperator(StockWiseOperator):
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"""成本支撑强度变化算子"""
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def __init__(self, n: int = 1):
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config = OperatorConfig(
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name=f"cost_support_change_{n}",
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description=f"{n}日成本支撑强度变化",
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required_columns=['cost_15pct'],
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output_columns=[f'cost_support_15pct_change_{n}'],
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parameters={'n': n}
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)
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super().__init__(config)
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self.n = n
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def apply_stock(self, stock_df: pl.DataFrame, **kwargs) -> pl.DataFrame:
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"""计算成本支撑强度变化"""
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# 计算百分比变化
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pct_change = pl.col('cost_15pct').pct_change(self.n) * 100
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return stock_df.with_columns(pct_change.alias(f'cost_support_15pct_change_{self.n}'))
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class WinnerPriceZoneOperator(StockWiseOperator):
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"""获利盘压力/支撑区分类算子"""
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def __init__(self):
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config = OperatorConfig(
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name="winner_price_zone",
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description="获利盘压力/支撑区分类",
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required_columns=['close', 'cost_85pct', 'cost_15pct', 'cost_50pct', 'winner_rate'],
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output_columns=['cat_winner_price_zone'],
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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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"""计算获利盘压力/支撑区分类"""
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# 定义条件
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conditions = [
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# 1: 高风险区 (高位 & 高获利盘)
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(pl.col('close') > pl.col('cost_85pct')) & (pl.col('winner_rate') > 0.8),
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# 2: 低潜力区 (低位 & 低获利盘)
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(pl.col('close') < pl.col('cost_15pct')) & (pl.col('winner_rate') < 0.2),
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# 3: 中上获利区 (中高位 & 多数获利)
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(pl.col('close') > pl.col('cost_50pct')) & (pl.col('winner_rate') > 0.5),
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# 4: 中下亏损区 (中低位 & 多数亏损)
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(pl.col('close') < pl.col('cost_50pct')) & (pl.col('winner_rate') < 0.5),
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]
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choices = [1, 2, 3, 4]
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# 使用select函数进行分类
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zone_classification = pl.select(
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conditions=conditions,
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choices=choices,
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default=0 # 0: 其他情况
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)
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return stock_df.with_columns(zone_classification.alias('cat_winner_price_zone'))
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class FlowChipConsistencyOperator(StockWiseOperator):
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"""主力行为与筹码结构一致性算子"""
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def __init__(self):
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config = OperatorConfig(
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name="flow_chip_consistency",
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description="主力行为与筹码结构一致性",
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required_columns=['buy_lg_vol', 'buy_elg_vol', 'sell_lg_vol', 'sell_elg_vol',
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'close', 'cost_15pct', 'cost_50pct'],
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output_columns=['flow_chip_consistency'],
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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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"""计算主力行为与筹码结构一致性"""
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# 计算大单净买入量
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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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# 判断价格是否接近下方筹码密集区
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price_near_low_support = (
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(pl.col('close') > pl.col('cost_15pct')) &
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(pl.col('close') < pl.col('cost_50pct'))
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)
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# 计算一致性：主力净买入 * 价格位置指示器
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consistency = lg_elg_net_buy_vol * price_near_low_support.cast(int)
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return stock_df.with_columns(consistency.alias('flow_chip_consistency'))
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class ProfitTakingVsAbsorptionOperator(StockWiseOperator):
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"""获利了结压力/承接盘强度算子"""
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def __init__(self):
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config = OperatorConfig(
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name="profit_taking_vs_absorb",
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description="获利了结压力vs承接盘强度",
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required_columns=['buy_lg_vol', 'buy_elg_vol', 'sell_lg_vol', 'sell_elg_vol',
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'winner_rate'],
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output_columns=['profit_taking_vs_absorb'],
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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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"""计算获利了结压力vs承接盘强度"""
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# 计算大单净买入量
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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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# 判断高获利盘
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high_winner_rate_flag = (pl.col('winner_rate') > 0.7).cast(int)
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# 计算因子：主力净买入 * 高获利盘指示器
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# 正值表示高获利盘下主力仍在买入（承接），负值表示主力在卖出（了结）
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factor = lg_elg_net_buy_vol * high_winner_rate_flag
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return stock_df.with_columns(factor.alias('profit_taking_vs_absorb'))
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class ChipConcentrationChangeOperator(StockWiseOperator):
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"""筹码集中度变化算子"""
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def __init__(self, n: int = 20):
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config = OperatorConfig(
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name=f"chip_conc_std_{n}",
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description=f"{n}日筹码集中度变化",
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required_columns=['cost_85pct', 'cost_15pct', 'weight_avg'],
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output_columns=[f'chip_conc_std_{n}'],
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parameters={'n': n}
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)
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super().__init__(config)
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self.n = n
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def apply_stock(self, stock_df: pl.DataFrame, **kwargs) -> pl.DataFrame:
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"""计算筹码集中度变化"""
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epsilon = 1e-8
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# 计算成本区间标准化值
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cost_range_norm = (pl.col('cost_85pct') - pl.col('cost_15pct')) / (pl.col('weight_avg') + epsilon)
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# 计算滚动标准差
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conc_std = cost_range_norm.rolling_std(window=self.n)
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return stock_df.with_columns(conc_std.alias(f'chip_conc_std_{self.n}'))
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class CostBreakoutConfirmationOperator(StockWiseOperator):
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"""成本突破确认算子"""
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def __init__(self, m: int = 5):
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config = OperatorConfig(
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name=f"cost_break_confirm_cnt_{m}",
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description=f"{m}日成本突破确认",
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required_columns=['close', 'cost_85pct', 'cost_15pct',
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'buy_lg_vol', 'buy_elg_vol', 'sell_lg_vol', 'sell_elg_vol'],
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output_columns=[f'cost_break_confirm_cnt_{m}'],
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parameters={'m': m}
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)
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super().__init__(config)
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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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"""计算成本突破确认"""
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# 获取前一日的成本位
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prev_cost_85 = pl.col('cost_85pct').shift(1)
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prev_cost_15 = pl.col('cost_15pct').shift(1)
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# 判断突破
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break_up = pl.col('close') > prev_cost_85
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break_down = pl.col('close') < prev_cost_15
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# 计算大单净流
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net_lg_flow_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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# 判断确认信号
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confirm_up = break_up & (net_lg_flow_vol > 0)
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confirm_down = break_down & (net_lg_flow_vol < 0)
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# 计算净确认信号
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net_confirm = confirm_up.cast(int) - confirm_down.cast(int)
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# 计算m日累计
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confirm_cnt = net_confirm.rolling_sum(window=self.m)
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return stock_df.with_columns(confirm_cnt.alias(f'cost_break_confirm_cnt_{self.m}'))
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# 筹码分布因子集合
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CHIP_DISTRIBUTION_OPERATORS = [
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ChipConcentrationOperator(),
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ChipSkewnessOperator(),
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FloatingChipProxyOperator(),
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CostSupportChangeOperator(),
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WinnerPriceZoneOperator(),
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FlowChipConsistencyOperator(),
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ProfitTakingVsAbsorptionOperator(),
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ChipConcentrationChangeOperator(),
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CostBreakoutConfirmationOperator(),
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]
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def apply_chip_distribution_factors(df: pl.DataFrame, operators: List = None) -> pl.DataFrame:
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"""
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应用所有筹码分布因子
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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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添加了筹码分布因子的DataFrame
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"""
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if operators is None:
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operators = CHIP_DISTRIBUTION_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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