From ec18aef4225607a9ff6472f0ff7164be6fcc6ccf Mon Sep 17 00:00:00 2001 From: chenzongwei <17695480342@163.com> Date: Tue, 14 May 2024 10:36:25 +0800 Subject: [PATCH] v4.2.2 --- pure_ocean_breeze/jason/data/read_data.py | 40 ++++++++++ pure_ocean_breeze/jason/labor/process.py | 95 ++++++++++++----------- 2 files changed, 88 insertions(+), 47 deletions(-) diff --git a/pure_ocean_breeze/jason/data/read_data.py b/pure_ocean_breeze/jason/data/read_data.py index 09a710e..2a0750d 100644 --- a/pure_ocean_breeze/jason/data/read_data.py +++ b/pure_ocean_breeze/jason/data/read_data.py @@ -9,6 +9,7 @@ from pure_ocean_breeze.jason.state.states import STATES from pure_ocean_breeze.jason.state.homeplace import HomePlace from pure_ocean_breeze.jason.state.decorators import * +from cachier import cachier try: homeplace = HomePlace() @@ -16,6 +17,7 @@ print("您暂未初始化，功能将受限") +@cachier() def read_daily( open: bool = 0, close: bool = 0, @@ -333,6 +335,7 @@ def database_read_primary_factors(name: str) -> pd.DataFrame: df = pd.read_parquet(homeplace.factor_data_file + name+'.parquet') return df +@cachier() def read_market( open: bool = 0, close: bool = 0, @@ -406,3 +409,40 @@ def read_market( tr = read_daily(tr=1, start=start) df = pd.DataFrame({k: list(df) for k in list(tr.columns)}, index=df.index) return df + + +@cachier() +def moon_read_dummy(freq): + 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(STATES["START"])) + ] + return industry_dummy + + # week_here + swindustry_dummy = ( + pd.read_parquet( + homeplace.daily_data_file + "sw_industry_level1_dummies.parquet" + ) + .fillna(0) + .set_index("date") + .groupby("code") + .resample(freq) + .last() + ) + return deal_dummy(swindustry_dummy) + +@cachier() +def moon_read_barra(): + styles = os.listdir(homeplace.barra_data_file) + styles = [i for i in styles if (i.endswith(".parquet")) and (i[0] != ".")] + barras = {} + for s in styles: + k = s.split(".")[0] + v = pd.read_parquet(homeplace.barra_data_file + s).resample("W").last() + barras[k] = v + return barras \ No newline at end of file diff --git a/pure_ocean_breeze/jason/labor/process.py b/pure_ocean_breeze/jason/labor/process.py index 86603ea..9df1640 100644 --- a/pure_ocean_breeze/jason/labor/process.py +++ b/pure_ocean_breeze/jason/labor/process.py @@ -42,6 +42,8 @@ read_daily, read_market, get_industry_dummies, + moon_read_dummy, + moon_read_barra, ) from pure_ocean_breeze.jason.state.homeplace import HomePlace @@ -817,29 +819,30 @@ def __init__( cls.freq = freq cls.freq_ctrl = frequency_controller(freq) - 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(STATES["START"])) - ] - 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(STATES["START"])) + # ] + # return industry_dummy if not no_read_indu: # week_here - cls.swindustry_dummy = ( - pd.read_parquet( - cls.homeplace.daily_data_file + "sw_industry_level1_dummies.parquet" - ) - .fillna(0) - .set_index("date") - .groupby("code") - .resample(freq) - .last() - ) - cls.swindustry_dummy = deal_dummy(cls.swindustry_dummy) + # cls.swindustry_dummy = ( + # pd.read_parquet( + # cls.homeplace.daily_data_file + "sw_industry_level1_dummies.parquet" + # ) + # .fillna(0) + # .set_index("date") + # .groupby("code") + # .resample(freq) + # .last() + # ) + # cls.swindustry_dummy = deal_dummy(cls.swindustry_dummy) + cls.swindustry_dummy=moon_read_dummy(freq) @property def factors_out(self): @@ -1181,12 +1184,7 @@ def get_long_short_comments(self, on_paper=False): self.inner_short_ret_yearly = self.inner_short_net_values[-1] * ( self.freq_ctrl.counts_one_year / len(self.inner_short_net_values) ) - self.group1_ret_yearly= self.group_net_values["group1"][-1] * ( - self.freq_ctrl.counts_one_year / len(self.group_net_values.group1) - ) - self.group10_ret_yearly = self.group_net_values["group10"][-1] * ( - self.freq_ctrl.counts_one_year / len(self.group_net_values.group10) - ) + # week_here self.long_short_vol_yearly = np.std(self.long_short_rets) * ( self.freq_ctrl.counts_one_year**0.5 @@ -1256,6 +1254,18 @@ def get_total_comments(self, groups_num): ].mean() else: self.factor_turnover_rate = self.factor_turnover_rates["group1"].mean() + self.group_mean_rets_monthly = self.group_rets.drop( + columns=["long_short"] + ).mean() + # self.group_mean_rets_monthly = ( + # self.group_mean_rets_monthly - self.group_mean_rets_monthly.mean() + # ) + mar=self.market_ret.loc[self.factors_out.index] + self.group_mean_rets_monthly = ( + self.group_mean_rets_monthly - mar.mean() + )*self.freq_ctrl.counts_one_year + self.group1_ret_yearly= self.group_mean_rets_monthly.loc['group1'] + self.group10_ret_yearly = self.group_mean_rets_monthly.loc['group10'] self.total_comments = pd.concat( [ self.ic_icir_and_rank, @@ -1306,16 +1316,7 @@ def get_total_comments(self, groups_num): ] ) # print(self.total_comments) - self.group_mean_rets_monthly = self.group_rets.drop( - columns=["long_short"] - ).mean() - # self.group_mean_rets_monthly = ( - # self.group_mean_rets_monthly - self.group_mean_rets_monthly.mean() - # ) - mar=self.market_ret.loc[self.factors_out.index] - self.group_mean_rets_monthly = ( - self.group_mean_rets_monthly - mar.mean() - )*self.freq_ctrl.counts_one_year + def plot_net_values(self, y2, filename, iplot=1, ilegend=1, without_breakpoint=0): """使用matplotlib来画图，y2为是否对多空组合采用双y轴""" @@ -2209,13 +2210,14 @@ def __init__( """ cls.homeplace = HomePlace() # barra因子数据 - styles = os.listdir(cls.homeplace.barra_data_file) - styles = [i for i in styles if (i.endswith(".parquet")) and (i[0] != ".")] - barras = {} - for s in styles: - k = s.split(".")[0] - v = pd.read_parquet(cls.homeplace.barra_data_file + s).resample("W").last() - barras[k] = v + # styles = os.listdir(cls.homeplace.barra_data_file) + # styles = [i for i in styles if (i.endswith(".parquet")) and (i[0] != ".")] + # barras = {} + # for s in styles: + # k = s.split(".")[0] + # v = pd.read_parquet(cls.homeplace.barra_data_file + s).resample("W").last() + # barras[k] = v + barras=moon_read_barra() rename_dict = { "size": "市值", "nonlinearsize": "非线性市值", @@ -2642,11 +2644,10 @@ def sing(dfs: list[pd.DataFrame], date: pd.Timestamp): @do_on_dfs -def sun(factor:pd.DataFrame,rolling_5:int=1): +def sun(factor:pd.DataFrame,rolling_days:int=10): '''先单因子测试，再测试其与常用风格之间的关系''' - if rolling_5: - factor=boom_one(factor) - ractor=boom_one(factor.rank(axis=1)) + ractor=boom_one(factor.rank(axis=1),rolling_days) + factor=boom_one(factor,rolling_days) shen=pure_moonnight(factor) pfi=pure_snowtrain(ractor) shen=pure_moonnight(pfi,neutralize=1)