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