1、策略更新
2、新增qmt
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main/factor/all_factors.py
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256
main/factor/all_factors.py
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"""
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统一因子计算模块
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提供统一接口来计算所有类型的因子
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"""
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import polars as pl
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from typing import List, Dict, Any
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from main.factor.operator_framework import FactorGraph
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from main.factor import (
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# 技术指标因子
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SMAFactor,
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EMAFactor,
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ATRFactor,
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OBVFactor,
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MACDFactor,
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RSI_Factor,
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CrossSectionalRankFactor,
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# 动量因子
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ReturnFactor,
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VolatilityFactor,
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MomentumFactor,
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MomentumAcceleration,
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TrendEfficiency,
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# 资金流因子
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LGFlowFactor,
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FlowIntensityFactor,
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FlowDivergenceFactor,
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FlowStructureFactor,
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FlowAccelerationFactor,
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# 筹码分布因子
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ChipConcentrationFactor,
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ChipSkewnessFactor,
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FloatingChipFactor,
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CostSupportFactor,
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WinnerPriceZoneFactor,
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CostSqueeze,
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HighCostSelling,
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LowCostAccumulation,
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InstNetAccum,
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ChipLockin,
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RetailOutInstIn,
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AccumAccel,
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# 市场情绪因子
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SentimentPanicGreedFactor,
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SentimentBreadthFactor,
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SentimentReversalFactor,
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PriceDeductionFactor,
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PriceDeductionRatioFactor,
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IndustryMomentumLeadership,
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LeadershipPersistenceScore,
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DynamicIndustryLeadership,
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# 行业/横截面因子
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IndustryMomentumFactor,
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MarketBreadthFactor,
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SectorRotationFactor,
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# 财务因子
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CashflowToEVFactor,
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BookToPriceFactor,
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DebtToEquityFactor,
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ProfitMarginFactor,
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BMFactor,
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# 特殊因子
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LimitFactor,
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VolumeRatioFactor,
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BBI_RATIO_FACTOR,
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VolatilitySlopeFactor,
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PriceVolumeTrendFactor,
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)
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def calculate_all_factors(
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df: pl.DataFrame,
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stock_factor_configs: List[Dict[str, Any]] = None,
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date_factor_configs: List[Dict[str, Any]] = None,
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) -> pl.DataFrame:
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"""
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统一计算所有因子的函数
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Parameters:
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df (pl.DataFrame): 输入的股票数据表
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stock_factor_configs (List[Dict]): 股票截面因子配置列表
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date_factor_configs (List[Dict]): 日期截面因子配置列表
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Returns:
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pl.DataFrame: 包含所有计算因子的DataFrame
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"""
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# 初始化因子图
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factor_graph = FactorGraph()
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# 如果没有提供配置，则使用默认配置
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if stock_factor_configs is None:
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stock_factor_configs = [
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{"class": SMAFactor, "params": {"window": 5}},
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{"class": SMAFactor, "params": {"window": 20}},
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{"class": EMAFactor, "params": {"window": 12}},
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{"class": EMAFactor, "params": {"window": 26}},
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{"class": ATRFactor, "params": {"window": 14}},
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{"class": OBVFactor, "params": {}},
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{
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"class": MACDFactor,
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"params": {"fast_period": 12, "slow_period": 26, "signal_period": 9},
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},
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{"class": RSI_Factor, "params": {"window": 14}},
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# 资金流因子
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{"class": LGFlowFactor, "params": {}},
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{"class": FlowIntensityFactor, "params": {}},
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{"class": FlowDivergenceFactor, "params": {}},
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{"class": FlowStructureFactor, "params": {}},
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{"class": FlowAccelerationFactor, "params": {}},
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{"class": InstNetAccum, "params": {}},
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{"class": ChipLockin, "params": {}},
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{"class": RetailOutInstIn, "params": {}},
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{"class": AccumAccel, "params": {}},
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# 筹码分布因子
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{"class": ChipConcentrationFactor, "params": {}},
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{"class": ChipSkewnessFactor, "params": {}},
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{"class": FloatingChipFactor, "params": {}},
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{"class": CostSupportFactor, "params": {}},
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{"class": WinnerPriceZoneFactor, "params": {}},
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{"class": LowCostAccumulation, "params": {}},
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{"class": HighCostSelling, "params": {}},
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{"class": CostSqueeze, "params": {}},
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# 市场情绪因子
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{
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"class": SentimentPanicGreedFactor,
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"params": {"window_atr": 14, "window_smooth": 5},
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},
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{
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"class": SentimentBreadthFactor,
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"params": {"window_vol": 20, "window_smooth": 3},
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},
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{
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"class": SentimentReversalFactor,
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"params": {"window_ret": 5, "window_vol": 5},
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},
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{"class": PriceDeductionFactor, "params": {"n": 10}},
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{"class": PriceDeductionRatioFactor, "params": {"n": 10}},
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{"class": IndustryMomentumLeadership, "params": {}},
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{"class": LeadershipPersistenceScore, "params": {}},
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# {"class": DynamicIndustryLeadership, "params": {}},
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# 财务因子
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# {"class": CashflowToEVFactor, "params": {}},
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# {"class": BookToPriceFactor, "params": {}},
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# {"class": ROEFactor, "params": {}},
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# {"class": DebtToEquityFactor, "params": {}},
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# {"class": ProfitMarginFactor, "params": {}},
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{"class": BMFactor, "params": {}},
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# 特殊因子
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{"class": LimitFactor, "params": {}},
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{"class": VolumeRatioFactor, "params": {}},
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{"class": BBI_RATIO_FACTOR, "params": {}},
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{
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"class": VolatilitySlopeFactor,
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"params": {"window_vol": 20, "window_slope": 5},
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},
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{"class": PriceVolumeTrendFactor, "params": {}},
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# 动量因子 - 添加20日收益率因子
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{"class": ReturnFactor, "params": {"period": 20}},
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{"class": ReturnFactor, "params": {"period": 5}},
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{"class": VolatilityFactor, "params": {"period": 10}},
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{
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"class": MomentumAcceleration,
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"params": {"short_period": 5, "long_period": 60},
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},
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{"class": TrendEfficiency, "params": {"period": 10}},
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{
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"class": CrossSectionalRankFactor,
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"params": {"column": "circ_mv", "name": "size_rank"},
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},
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]
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if date_factor_configs is None:
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date_factor_configs = [
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{"class": CrossSectionalRankFactor, "params": {"column": "return_5"}},
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{"class": CrossSectionalRankFactor, "params": {"column": "return_5"}},
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{
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"class": CrossSectionalRankFactor,
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"params": {"column": "return_20"},
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},
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{
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"class": CrossSectionalRankFactor,
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"params": {"column": "volatility_10"},
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},
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{
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"class": CrossSectionalRankFactor,
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"params": {"column": "circ_mv"},
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},
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# {
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# "class": CrossSectionalRankFactor,
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# "params": {"factor_name": "momentum_10"},
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# },
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]
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# 添加股票截面因子
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stock_factors = []
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for config in stock_factor_configs:
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factor_class = config["class"]
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params = config["params"]
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try:
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factor = factor_class(**params)
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factor_graph.add_factor(factor)
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stock_factors.append(factor)
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except Exception as e:
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print(f"创建股票因子 {factor_class.__name__} 时出错: {e}")
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# 添加日期截面因子
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date_factors = []
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for config in date_factor_configs:
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factor_class = config["class"]
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params = config["params"]
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try:
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factor = factor_class(**params)
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factor_graph.add_factor(factor)
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date_factors.append(factor)
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except Exception as e:
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print(f"创建日期因子 {factor_class.__name__} 时出错: {e}")
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# 先计算股票截面因子
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result_df = df.clone()
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# 获取所有需要的因子ID
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stock_factor_ids = [factor.get_factor_id() for factor in stock_factors]
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date_factor_ids = [factor.get_factor_id() for factor in date_factors]
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# 计算股票因子
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if stock_factor_ids:
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result_df = factor_graph.compute(result_df, stock_factor_ids)
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# 计算日期因子
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if date_factor_ids:
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result_df = factor_graph.compute(result_df, date_factor_ids)
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all_ids = []
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for ids in stock_factor_ids:
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all_ids.append(ids)
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for ids in date_factor_ids:
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all_ids.append(ids)
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return result_df, all_ids
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# 为了兼容旧的函数调用方式，提供一个简化的统一接口
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def compute_factors(df: pl.DataFrame):
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"""
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简化版因子计算接口
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Parameters:
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df (pl.DataFrame): 输入的股票数据表
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Returns:
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pl.DataFrame: 包含所有计算因子的DataFrame
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"""
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return calculate_all_factors(df)
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