diff --git a/pure_ocean_breeze/__init__.py b/pure_ocean_breeze/__init__.py index 66aa2d4..9840c16 100644 --- a/pure_ocean_breeze/__init__.py +++ b/pure_ocean_breeze/__init__.py @@ -2,8 +2,8 @@ 一个量化多因子研究的框架，包含数据、回测、因子加工等方面的功能 """ -__updated__ = "2022-09-30 19:41:26" -__version__ = "3.3.1" +__updated__ = "2022-10-01 02:34:56" +__version__ = "3.3.2" __author__ = "chenzongwei" __author_email__ = "winterwinter999@163.com" __url__ = "https://github.com/chen-001/pure_ocean_breeze" diff --git a/pure_ocean_breeze/data/read_data.py b/pure_ocean_breeze/data/read_data.py index 8043823..2b394b9 100644 --- a/pure_ocean_breeze/data/read_data.py +++ b/pure_ocean_breeze/data/read_data.py @@ -1,4 +1,4 @@ -__updated__ = "2022-09-30 23:50:56" +__updated__ = "2022-10-01 10:14:38" import os import numpy as np @@ -168,7 +168,7 @@ def read_daily( elif ret_night: df = ( read_daily(open=1, start=start) - / read_daily(close, start=start).shift(1) + / read_daily(close=1, start=start).shift(1) - 1 ) elif vol: diff --git a/pure_ocean_breeze/data/write_data.py b/pure_ocean_breeze/data/write_data.py index 4d02228..f8a452e 100644 --- a/pure_ocean_breeze/data/write_data.py +++ b/pure_ocean_breeze/data/write_data.py @@ -1,4 +1,4 @@ -__updated__ = "2022-09-30 23:50:57" +__updated__ = "2022-10-01 09:55:11" try: import rqdatac @@ -1264,8 +1264,8 @@ def save(df, old, file): def database_update_idiosyncratic_ret(): - pb = read_daily(pb=1, start=20040101) - cap = read_daily(flow_cap=1, start=20040101) + pb = read_daily(pb=1, start=20100101) + cap = read_daily(flow_cap=1, start=20100101).dropna(how='all') fama = pure_fama([cap, pb]) - fama().reset_index().to_feather("idiosyncratic_ret.feather") - logger.success("特质收益率已经更新完成") + fama().reset_index().to_feather(homeplace.daily_data_file+"idiosyncratic_ret.feather") + logger.success("特质收益率已经更新完成") \ No newline at end of file diff --git a/pure_ocean_breeze/labor/process.py b/pure_ocean_breeze/labor/process.py index 9d9f05b..2ddbef9 100644 --- a/pure_ocean_breeze/labor/process.py +++ b/pure_ocean_breeze/labor/process.py @@ -1,4 +1,4 @@ -__updated__ = "2022-10-01 02:01:16" +__updated__ = "2022-10-01 10:14:27" import warnings @@ -1367,7 +1367,7 @@ class pure_moon(object): "factor_turnover_rates", "factor_turnover_rate", "group_rets_std", - "group_rets_stds" + "group_rets_stds", ] @classmethod @@ -1375,6 +1375,7 @@ class pure_moon(object): def __init__( cls, startdate: int, + no_read_indu: bool = 0, ): cls.homeplace = HomePlace() # 已经算好的月度st状态文件 @@ -1382,37 +1383,38 @@ def __init__( # 已经算好的月度交易状态文件 cls.states_monthly_file = homeplace.daily_data_file + "states_monthly.feather" - cls.swindustry_dummy = ( - pd.read_feather(cls.homeplace.daily_data_file + "申万行业2021版哑变量.feather") - .fillna(0) - .set_index("date") - .groupby("code") - .resample("M") - .last() - ) + if not no_read_indu: + cls.swindustry_dummy = ( + pd.read_feather(cls.homeplace.daily_data_file + "申万行业2021版哑变量.feather") + .fillna(0) + .set_index("date") + .groupby("code") + .resample("M") + .last() + ) - cls.zxindustry_dummy = ( - pd.read_feather(cls.homeplace.daily_data_file + "中信一级行业哑变量代码版.feather") - .fillna(0) - .set_index("date") - .groupby("code") - .resample("M") - .last() - .fillna(0) - ) + cls.zxindustry_dummy = ( + pd.read_feather(cls.homeplace.daily_data_file + "中信一级行业哑变量代码版.feather") + .fillna(0) + .set_index("date") + .groupby("code") + .resample("M") + .last() + .fillna(0) + ) - def deal_dummy(industry_dummy): - industry_dummy = industry_dummy.drop(columns=["code"]).reset_index() - industry_ws = [f"w{i}" for i in range(1, industry_dummy.shape[1] - 1)] - col = ["code", "date"] + industry_ws - industry_dummy.columns = col - industry_dummy = industry_dummy[ - industry_dummy.date >= pd.Timestamp(str(startdate)) - ] - return industry_dummy + def deal_dummy(industry_dummy): + industry_dummy = industry_dummy.drop(columns=["code"]).reset_index() + industry_ws = [f"w{i}" for i in range(1, industry_dummy.shape[1] - 1)] + col = ["code", "date"] + industry_ws + industry_dummy.columns = col + industry_dummy = industry_dummy[ + industry_dummy.date >= pd.Timestamp(str(startdate)) + ] + return industry_dummy - cls.swindustry_dummy = deal_dummy(cls.swindustry_dummy) - cls.zxindustry_dummy = deal_dummy(cls.zxindustry_dummy) + cls.swindustry_dummy = deal_dummy(cls.swindustry_dummy) + cls.zxindustry_dummy = deal_dummy(cls.zxindustry_dummy) @property def factors_out(self): @@ -1540,7 +1542,10 @@ def neutralize_factors(cls, df): industry_codes = list(df.columns) industry_codes = [i for i in industry_codes if i.startswith("w")] industry_codes_str = "+".join(industry_codes) - ols_result = smf.ols("fac~cap_size+" + industry_codes_str, data=df).fit() + if len(industry_codes_str) > 0: + ols_result = smf.ols("fac~cap_size+" + industry_codes_str, data=df).fit() + else: + ols_result = smf.ols("fac~cap_size", data=df).fit() ols_w = ols_result.params["cap_size"] ols_b = ols_result.params["Intercept"] ols_bs = {} @@ -1569,11 +1574,9 @@ def single(x): cls.cap["cap_size"] = np.log(cls.cap["cap_size"]) def get_neutral_factors( - self, - zxindustry_dummies=0, - swindustry_dummies=0, + self, zxindustry_dummies=0, swindustry_dummies=0, only_cap=0 ): - """对因子进行市值中性化""" + """对因子进行行业市值中性化""" self.factors = self.factors.set_index("date") self.factors.index = self.factors.index + pd.DateOffset(months=1) self.factors = self.factors.resample("M").last() @@ -1589,14 +1592,15 @@ def get_neutral_factors( self.factors = pd.merge( self.factors, self.cap, how="inner", on=["date", "code"] ) - if swindustry_dummies: - self.factors = pd.merge( - self.factors, self.swindustry_dummy, on=["date", "code"] - ) - else: - self.factors = pd.merge( - self.factors, self.zxindustry_dummy, on=["date", "code"] - ) + if not only_cap: + if swindustry_dummies: + self.factors = pd.merge( + self.factors, self.swindustry_dummy, on=["date", "code"] + ) + else: + self.factors = pd.merge( + self.factors, self.zxindustry_dummy, on=["date", "code"] + ) self.factors = self.factors.set_index(["date", "code"]) self.factors = self.factors.groupby(["date"]).apply(self.neutralize_factors) self.factors = self.factors.reset_index() @@ -1803,7 +1807,7 @@ def in_g(df): self.group_rets_stds = self.data.groupby(["date", "group"]).apply( lambda x: x.ret.std() ) - self.group_rets_std = self.group_rets_stds.groupby('group').mean() + self.group_rets_std = self.group_rets_stds.groupby("group").mean() # dropna是因为如果股票行情数据比因子数据的截止日期晚，而最后一个月发生月初跌停时，会造成最后某组多出一个月的数据 self.group_rets = self.group_rets.unstack() self.group_rets = self.group_rets[ @@ -1944,6 +1948,7 @@ def run( sheetname=None, zxindustry_dummies=0, swindustry_dummies=0, + only_cap=0, ): """运行回测部分""" if comments_writer and not (comments_sheetname or sheetname): @@ -1964,6 +1969,7 @@ def run( self.get_neutral_factors( swindustry_dummies=swindustry_dummies, zxindustry_dummies=zxindustry_dummies, + only_cap=only_cap, ) self.deal_with_factors_after_neutralize() else: @@ -2113,6 +2119,8 @@ def __init__( opens: pd.DataFrame = None, closes: pd.DataFrame = None, capitals: pd.DataFrame = None, + no_read_indu: bool = 0, + only_cap: bool = 0, ) -> None: """一键回测框架，测试单因子的月频调仓的分组表现 每月月底计算因子值，月初第一天开盘时买入，月末收盘最后一天收盘时卖出 @@ -2168,16 +2176,20 @@ def __init__( 行业中性化时，选用申万一级行业, by default 0 ages : pd.DataFrame, optional 输入股票上市天数的数据，index是时间，columns是股票代码，values是天数, by default None - sts : pd.DataFrame, + sts : pd.DataFrame, optional 输入股票每天是否st的数据，是st股即为1，否则为0，index是时间，columns是股票代码，values是0或1, by default None - states : pd.DataFrame, + states : pd.DataFrame, optional 输入股票每天交易状态的数据，正常交易为1，否则为0，index是时间，columns是股票代码，values是0或1, by default None - opens : pd.DataFrame, + opens : pd.DataFrame, optional 输入股票的复权开盘价数据，index是时间，columns是股票代码，values是价格, by default None - closes : pd.DataFrame, + closes : pd.DataFrame, optional 输入股票的复权收盘价数据，index是时间，columns是股票代码，values是价格, by default None - capitals : pd.DataFrame, + capitals : pd.DataFrame, optional 输入股票的每月月末流通市值数据，index是时间，columns是股票代码，values是流通市值, by default None + no_read_indu : bool, optional + 不读入行业数据, by default 0 + only_cap : bool, optional + 仅做市值中性化, by default 0 """ if isinstance(factors, pure_fallmount): @@ -2203,8 +2215,13 @@ def __init__( from pure_ocean_breeze.state.states import NET_VALUES_WRITER net_values_writer = NET_VALUES_WRITER + if boxcox + neutralize == 0: + no_read_indu = 1 + if only_cap + no_read_indu > 0: + only_cap = no_read_indu = 1 self.shen = pure_moon( startdate=start, + no_read_indu=no_read_indu, ) self.shen.set_basic_data( age=ages, @@ -2238,6 +2255,7 @@ def __init__( sheetname=sheetname, swindustry_dummies=swindustry_dummies, zxindustry_dummies=zxindustry_dummies, + only_cap=only_cap, ) def __call__(self) -> pd.DataFrame: diff --git "a/\346\233\264\346\226\260\346\227\245\345\277\227/version3.md" "b/\346\233\264\346\226\260\346\227\245\345\277\227/version3.md" index a10d5a0..23d8c6f 100644 --- "a/\346\233\264\346\226\260\346\227\245\345\277\227/version3.md" +++ "b/\346\233\264\346\226\260\346\227\245\345\277\227/version3.md" @@ -1,5 +1,11 @@ ## 更新日志🗓 — v3 +* v3.3.2 — 2022.10.01 +> 1. 修复了读取隔夜收益率的bug +> 1. 将更新特质收益率数据的起始时间点改为2010年1月1日，并修复了其中的bug +> 1. 给pure_moon和pure_moonnight增加了no_read_indu参数，使回测时不必读入行业哑变量数据，便于调试 +> 1. 给pure_moon和pure_moonnight增加了only_cap参数，使回测时只做市值中性化，而不做行业中性化 +> 1. 优化了pure_moon和pure_moonnight的参数逻辑，当neutralize和boxcox均为0时，自动开启no_read_indu参数；当no_read_indu和only_cap任一为0时，自动开启另一个参数 * v3.3.1 — 2022.10.01 > 1. 给一键导入库的requires中，增加了import pyfinance.ols as go > 1. 增加了用于fama三因子与特质收益率相关的类pure_fama，可以计算各期因子收益率、个股各期特质收益率、个股各期因子暴露、超额收益率等内容