v4.0.3

README.md

@@ -5,7 +5,7 @@
 
 ### 全新大版本📢
 * v4.0.0 — 2023.06.28
-> 因子框架4.0版本来啦！逐笔数据&任意秒级数据来啦！
+> 因子框架4.0版本来啦！逐笔数据&任意秒级数据&全新的因子成果数据库来啦！
 
 * v3.0.0 — 2022.08.16
 

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__ = "2023-07-03 01:29:04"
-__version__ = "4.0.2"
+__updated__ = "2023-07-03 20:59:40"
+__version__ = "4.0.3"
 __author__ = "chenzongwei"
 __author_email__ = "winterwinter999@163.com"
 __url__ = "https://github.com/chen-001/pure_ocean_breeze"

pure_ocean_breeze/data/read_data.py

@@ -1,12 +1,14 @@
-__updated__ = "2023-06-21 14:48:09"
+__updated__ = "2023-07-04 09:13:50"
 
 import os
 import numpy as np
 import pandas as pd
 import datetime
-from typing import Union, Dict, Tuple
+import deprecation
+from typing import Any, Union, Dict, Tuple
 from loguru import logger
 
+from pure_ocean_breeze import __version__
 from pure_ocean_breeze.state.states import STATES
 from pure_ocean_breeze.state.homeplace import HomePlace
 from pure_ocean_breeze.state.decorators import *
@@ -172,33 +174,33 @@ def read_daily(
  elif open:
  opens = pd.read_parquet(
  homeplace.daily_data_file + "opens.parquet"
- ) * read_daily(state=1,start=start)
+ ) * read_daily(state=1, start=start)
  df = opens
  elif close:
  closes = pd.read_parquet(
  homeplace.daily_data_file + "closes.parquet"
- ) * read_daily(state=1,start=start)
+ ) * read_daily(state=1, start=start)
  df = closes
  elif high:
  highs = pd.read_parquet(
  homeplace.daily_data_file + "highs.parquet"
- ) * read_daily(state=1,start=start)
+ ) * read_daily(state=1, start=start)
  df = highs
  elif low:
  lows = pd.read_parquet(
  homeplace.daily_data_file + "lows.parquet"
- ) * read_daily(state=1,start=start)
+ ) * read_daily(state=1, start=start)
  df = lows
  elif vwap:
  df = (
  pd.read_parquet(homeplace.daily_data_file + "vwaps.parquet")
  * read_daily(adjfactor=1, start=start)
- * read_daily(state=1,start=start)
+ * read_daily(state=1, start=start)
  )
  elif tr:
  trs = pd.read_parquet(homeplace.daily_data_file + "trs.parquet").replace(
  0, np.nan
- ) * read_daily(state=1,start=start)
+ ) * read_daily(state=1, start=start)
  df = trs
  elif sharenum:
  sharenums = pd.read_parquet(homeplace.daily_data_file + "sharenums.parquet")
@@ -208,7 +210,7 @@ def read_daily(
  elif amount:
  volumes = pd.read_parquet(
  homeplace.daily_data_file + "amounts.parquet"
- ) * read_daily(state=1,start=start)
+ ) * read_daily(state=1, start=start)
  df = volumes
  elif money:
  df = pd.read_parquet(
@@ -220,14 +222,14 @@ def read_daily(
  elif flow_cap:
  closes = pd.read_parquet(
  homeplace.daily_data_file + "closes_unadj.parquet"
- ) * read_daily(state=1,start=start)
+ ) * read_daily(state=1, start=start)
  sharenums = pd.read_parquet(homeplace.daily_data_file + "sharenums.parquet")
  flow_cap = closes * sharenums
  df = flow_cap
  elif total_cap:
  closes = pd.read_parquet(
  homeplace.daily_data_file + "closes_unadj.parquet"
- ) * read_daily(state=1,start=start)
+ ) * read_daily(state=1, start=start)
  sharenums = pd.read_parquet(
  homeplace.daily_data_file + "total_sharenums.parquet"
  )
@@ -237,9 +239,9 @@ def read_daily(
  # df=pd.read_parquet(homeplace.daily_data_file+'adjfactors.parquet')
  df = (
  read_daily(close=1, start=start)
- * read_daily(state=1,start=start)
+ * read_daily(state=1, start=start)
  / read_daily(close=1, start=start, unadjust=1)
- * read_daily(state=1,start=start)
+ * read_daily(state=1, start=start)
  )
  elif st:
  st = pd.read_parquet(homeplace.daily_data_file + "sts.parquet")
@@ -278,46 +280,46 @@ def read_daily(
  ) / read_daily(close=1, start=start)
  elif pb:
  df = pd.read_parquet(homeplace.daily_data_file + "pb.parquet") * read_daily(
- state=1,start=start
+ state=1, start=start
  )
  elif pe:
  df = pd.read_parquet(homeplace.daily_data_file + "pe.parquet") * read_daily(
- state=1,start=start
+ state=1, start=start
  )
  elif pettm:
- df = pd.read_parquet(homeplace.daily_data_file + "pettm.parquet") * read_daily(
- state=1,start=start
- )
+ df = pd.read_parquet(
+ homeplace.daily_data_file + "pettm.parquet"
+ ) * read_daily(state=1, start=start)
  elif iret:
  df = pd.read_parquet(
  homeplace.daily_data_file + "idiosyncratic_ret.parquet"
- ) * read_daily(state=1,start=start)
+ ) * read_daily(state=1, start=start)
  elif ivol:
  df = read_daily(iret=1, start=start)
  df = df.rolling(20, min_periods=10).std()
  elif illiquidity:
  df = pd.read_parquet(
  homeplace.daily_data_file + "illiquidity.parquet"
- ) * read_daily(state=1,start=start)
+ ) * read_daily(state=1, start=start)
  elif swindustry_ret:
  df = pd.read_parquet(
  homeplace.daily_data_file + "股票对应申万一级行业每日收益率.parquet"
- ) * read_daily(state=1,start=start)
+ ) * read_daily(state=1, start=start)
  elif zxindustry_ret:
  df = pd.read_parquet(
  homeplace.daily_data_file + "股票对应中信一级行业每日收益率.parquet"
- ) * read_daily(state=1,start=start)
+ ) * read_daily(state=1, start=start)
  elif stop_up:
  df = (
  pd.read_parquet(homeplace.daily_data_file + "stop_ups.parquet")
  * read_daily(adjfactor=1, start=start)
- * read_daily(state=1,start=start)
+ * read_daily(state=1, start=start)
  )
  elif stop_down:
  df = (
  pd.read_parquet(homeplace.daily_data_file + "stop_downs.parquet")
  * read_daily(adjfactor=1, start=start)
- * read_daily(state=1,start=start)
+ * read_daily(state=1, start=start)
  )
  elif zxindustry_dummy_code:
  df = pd.read_parquet(homeplace.daily_data_file + "中信一级行业哑变量代码版.parquet")
@@ -349,35 +351,35 @@ def read_daily(
  if open:
  opens = pd.read_parquet(
  homeplace.daily_data_file + "opens_unadj.parquet"
- ) * read_daily(state=1,start=start)
+ ) * read_daily(state=1, start=start)
  df = opens
  elif close:
  closes = pd.read_parquet(
  homeplace.daily_data_file + "closes_unadj.parquet"
- ) * read_daily(state=1,start=start)
+ ) * read_daily(state=1, start=start)
  df = closes
  elif high:
  highs = pd.read_parquet(
  homeplace.daily_data_file + "highs_unadj.parquet"
- ) * read_daily(state=1,start=start)
+ ) * read_daily(state=1, start=start)
  df = highs
  elif low:
  lows = pd.read_parquet(
  homeplace.daily_data_file + "lows_unadj.parquet"
- ) * read_daily(state=1,start=start)
+ ) * read_daily(state=1, start=start)
  df = lows
  elif vwap:
  df = pd.read_parquet(
  homeplace.daily_data_file + "vwaps.parquet"
- ) * read_daily(state=1,start=start)
+ ) * read_daily(state=1, start=start)
  elif stop_up:
  df = pd.read_parquet(
  homeplace.daily_data_file + "stop_ups.parquet"
- ) * read_daily(state=1,start=start)
+ ) * read_daily(state=1, start=start)
  elif stop_down:
  df = pd.read_parquet(
  homeplace.daily_data_file + "stop_downs.parquet"
- ) * read_daily(state=1,start=start)
+ ) * read_daily(state=1, start=start)
  else:
  raise IOError("阁下总得读点什么吧？🤒")
  if "date" not in df.columns:
@@ -755,6 +757,12 @@ def get_industry_dummies(
  return ress
 
 
+@deprecation.deprecated(
+ deprecated_in="4.0",
+ removed_in="5.0",
+ current_version=__version__,
+ details="由于因子成果数据库升级，3.x版本的因子成果读取函数将下线",
+)
 def database_read_final_factors(
  name: str = None,
  order: int = None,
@@ -877,6 +885,12 @@ def database_read_final_factors(
  return df, ""
 
 
+@deprecation.deprecated(
+ deprecated_in="4.0",
+ removed_in="5.0",
+ current_version=__version__,
+ details="由于因子成果数据库升级，3.x版本的因子成果读取函数将下线",
+)
 def database_read_primary_factors(name: str, name2: str = None) -> pd.DataFrame:
  """根据因子名字，读取初级因子的因子值
 
@@ -900,3 +914,77 @@ def database_read_primary_factors(name: str, name2: str = None) -> pd.DataFrame:
  df = pd.read_parquet(homeplace.factor_data_file + name)
  df = df[sorted(list(df.columns))]
  return df
+
+
+class FactorDone(object):
+ def __init__(
+ self,
+ order: str,
+ name: str = None,
+ place: str = None,
+ son_name: str = None,
+ freq: str = "月",
+ ) -> None:
+ self.homeplace = HomePlace()
+ self.order = order
+ self.freq = freq
+ self.qdb = Questdb()
+ try:
+ self.factor_infos = self.qdb.get_data(
+ f"select * from factor_infos where order='{self.order}' and freq='{self.freq}'"
+ )
+ except Exception:
+ self.factor_infos = pd.DataFrame()
+ self.name = name
+ self.place = place
+ self.son_name = son_name
+ if (self.place is None) or (self.name is None):
+ final_line = self.qdb.get_data(
+ f"select * from factor_infos where order='{self.order}' and freq='{self.freq}'"
+ )
+ self.name = final_line.name.iloc[0]
+ self.place = final_line.place.iloc[0]
+ if son_name is None:
+ self.file = f"因子{self.order}_{self.name}_{self.freq}_{self.place}.parquet"
+ self.son_factors = {}
+ if self.factor_infos.shape[0] > 0:
+ for row in self.factor_infos.dropna().itertuples():
+ self.son_factors[row.son_name] = FactorDone(
+ order=row.order,
+ name=row.name,
+ place=row.place,
+ son_name=row.son_name,
+ freq=row.freq,
+ )
+ else:
+ self.file = f"因子{self.order}_{self.name}_{self.son_name}_{self.freq}_{self.place}.parquet"
+
+
+ def __call__(self, son_name: str = None) -> Union[pd.DataFrame, dict]:
+ if son_name is None:
+ return pd.read_parquet(self.homeplace.final_factor_file + self.file)
+ else:
+ return self.son_factors[son_name]()
+
+ def save_factor(self, factor: pd.DataFrame):
+ try:
+ son_info = self.qdb.get_data(
+ f"select * from factor_infos where order='{self.order}' and son_name='{self.son_name}' and freq='{self.freq}'"
+ )
+ except Exception:
+ logger.warning(f"本次为第一次写入{self.name}_{self.son_name}因子")
+ son_info = pd.DataFrame()
+ if son_info.shape[0] == 0:
+ self.qdb.write_via_df(
+ pd.DataFrame(
+ {
+ "order": [self.order],
+ "name": [self.name],
+ "place": [self.place],
+ "son_name": [self.son_name],
+ "freq": [self.freq],
+ }
+ ),
+ "factor_infos",
+ )
+ factor.to_parquet(self.homeplace.final_factor_file + self.file)