v4.1.4

pure_ocean_breeze/__init__.py

@@ -3,7 +3,7 @@
 """
 
 __updated__ = "2023-07-18 14:49:23"
-__version__ = "4.1.3"
+__version__ = "4.1.4"
 __author__ = "chenzongwei"
 __author_email__ = "winterwinter999@163.com"
 __url__ = "https://github.com/chen-001/pure_ocean_breeze"

pure_ocean_breeze/jason/labor/process.py

@@ -36,6 +36,7 @@
 from typing import Callable, Union, Dict, List, Tuple
 from pure_ocean_breeze.jason.data.read_data import (
  read_daily,
+ read_market,
  get_industry_dummies,
 )
 from pure_ocean_breeze.jason.state.homeplace import HomePlace
@@ -762,6 +763,7 @@ class pure_moon(object):
  "limit_downs",
  "data",
  "ic_icir_and_rank",
+ "big_small_rankic",
  "rets_monthly_limit_downs",
  "group_rets",
  "long_short_rets",
@@ -814,6 +816,11 @@ class pure_moon(object):
  "group_mean_rets_monthly",
  "not_ups",
  "not_downs",
+ "group1_ret_yearly",
+ "group10_ret_yearly",
+ "market_ret",
+ "long_minus_market_rets",
+ "long_minus_market_nets",
  ]
 
  @classmethod
@@ -876,6 +883,8 @@ def set_basic_data(
  capitals = (
  read_daily(flow_cap=1, start=STATES["START"]).resample(cls.freq).last()
  )
+ market=read_market(zz500=1,every_stock=0,close=1).resample(cls.freq).last()
+ cls.market_ret=market/market.shift(1)-1
  # 交易状态文件
  cls.states = states
  # Monday vwap
@@ -919,6 +928,7 @@ def set_factor_df_date_as_index(self, df: pd.DataFrame):
  self.factors, self.factors.shift(2), plt_plot=0
  )
  self.factor_cross_stds = self.factors.std(axis=1)
+
 
  @classmethod
  @lru_cache(maxsize=None)
@@ -983,21 +993,27 @@ def get_ic_rankic(cls, df):
  df1 = df[["ret", "fac"]]
  ic = df1.corr(method="pearson").iloc[0, 1]
  rankic = df1.rank().corr().iloc[0, 1]
- df2 = pd.DataFrame({"ic": [ic], "rankic": [rankic]})
+ small_ic=df1[df1.fac<=df1.fac.median()].rank().corr().iloc[0, 1]
+ big_ic=df1[df1.fac>=df1.fac.median()].rank().corr().iloc[0, 1]
+ df2 = pd.DataFrame({"ic": [ic], "rankic": [rankic],"small_rankic":[small_ic],"big_rankic":[big_ic]})
  return df2
 
  def get_icir_rankicir(cls, df):
  """计算ICIR和RankICIR"""
  ic = df.ic.mean()
  rankic = df.rankic.mean()
+ small_rankic=df.small_rankic.mean()
+ big_rankic=df.big_rankic.mean()
  # week_here
  icir = ic / np.std(df.ic) * (cls.freq_ctrl.counts_one_year ** (0.5))
  # week_here
  rankicir = rankic / np.std(df.rankic) * (cls.freq_ctrl.counts_one_year ** (0.5))
+ small_rankicir = small_rankic / np.std(df.small_rankic) * (cls.freq_ctrl.counts_one_year ** (0.5))
+ big_rankicir = big_rankic / np.std(df.big_rankic) * (cls.freq_ctrl.counts_one_year ** (0.5))
  return pd.DataFrame(
  {"IC": [ic], "ICIR": [icir], "RankIC": [rankic], "RankICIR": [rankicir]},
  index=["评价指标"],
- )
+ ),pd.DataFrame({"1-5rankic":[small_rankic],"1-5ICIR":[small_rankicir],"6-10RankIC":[big_rankic],"6-10ICIR":[big_rankicir]},index=["评价指标"]).T
 
  def get_ic_icir_and_rank(cls, df):
  """计算IC、ICIR、RankIC、RankICIR"""
@@ -1006,13 +1022,14 @@ def get_ic_icir_and_rank(cls, df):
  cls.rankics = df1.rankic
  cls.ics = cls.ics.reset_index(drop=True, level=1).to_frame()
  cls.rankics = cls.rankics.reset_index(drop=True, level=1).to_frame()
- df2 = cls.get_icir_rankicir(df1)
+ df2,df5 = cls.get_icir_rankicir(df1)
  df2 = df2.T
  dura = (df.date.max() - df.date.min()).days / 365
  t_value = df2.iloc[3, 0] * (dura ** (1 / 2))
  df3 = pd.DataFrame({"评价指标": [t_value]}, index=["RankIC.t"])
  df4 = pd.concat([df2, df3])
- return df4
+ df
+ return df4,df5
 
  @classmethod
  def get_groups(cls, df, groups_num):
@@ -1040,7 +1057,7 @@ def get_data(self, groups_num):
  self.data = pd.merge(
  self.rets_monthly, self.factors, how="inner", on=["date", "code"]
  )
- self.ic_icir_and_rank = self.get_ic_icir_and_rank(self.data)
+ self.ic_icir_and_rank,self.big_small_rankic = self.get_ic_icir_and_rank(self.data)
  self.data = self.data.groupby("date").apply(
  lambda x: self.get_groups(x, groups_num)
  )
@@ -1134,6 +1151,8 @@ def in_g(df):
  self.long_short_net_values = self.make_start_to_one(
  self.long_short_rets.cumsum()
  )
+ self.long_minus_market_rets=self.group_rets.group1-self.market_ret
+
  if self.long_short_net_values[-1] <= self.long_short_net_values[0]:
  self.long_short_rets = (
  self.group_rets["group" + str(groups_num)] - self.group_rets["group1"]
@@ -1145,6 +1164,8 @@ def in_g(df):
  self.group_rets["group" + str(groups_num)] - self.rets_all
  )
  self.inner_rets_short = self.rets_all - self.group_rets.group1
+ self.long_minus_market_rets=self.group_rets['group'+str(groups_num)]-self.market_ret
+ self.long_minus_market_nets=self.make_start_to_one(self.long_minus_market_rets.dropna().cumsum())
  self.inner_long_net_values = self.make_start_to_one(
  self.inner_rets_long.cumsum()
  )
@@ -1181,6 +1202,12 @@ 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
@@ -1284,8 +1311,22 @@ def get_total_comments(self, groups_num):
  "一阶自相关性",
  ],
  ),
+ self.big_small_rankic,
+ pd.DataFrame(
+ {
+ "评价指标": [
+ self.group1_ret_yearly,
+ self.group10_ret_yearly,
+ ]
+ },
+ index=[
+ "1组年化收益",
+ "10组年化收益",
+ ]
+ )
  ]
  )
+ # print(self.total_comments)
  self.group_mean_rets_monthly = self.group_rets.drop(
  columns=["long_short"]
  ).mean()
@@ -1309,13 +1350,13 @@ def plot_net_values(self, y2, filename, iplot=1, ilegend=1, without_breakpoint=0
  plt.savefig(filename_path)
  else:
 
- tris = self.group_net_values
+ tris = self.group_net_values.drop(columns=['long_short'])
  if without_breakpoint:
  tris = tris.dropna()
  figs = cf.figures(
  tris,
  [
- dict(kind="line", y=list(self.group_net_values.columns)),
+ dict(kind="line", y=list(tris.columns)),
  # dict(kind="bar", y="各组月均超均收益"),
  # dict(kind="bar", y="rankic"),
  ],
@@ -1330,11 +1371,12 @@ def plot_net_values(self, y2, filename, iplot=1, ilegend=1, without_breakpoint=0
  [
  comments.iloc[:6, :].reset_index(drop=True),
  comments.iloc[6:12, :].reset_index(drop=True),
- comments.iloc[12:, :].reset_index(drop=True),
+ comments.iloc[12:18, :].reset_index(drop=True),
+ comments.iloc[18:, :].reset_index(drop=True),
  ],
  axis=1,
  )
- here.columns = ["信息系数", "结果", "绩效指标", "结果", "其他指标", "结果"]
+ here.columns = ["信息系数", "结果", "绩效指标", "结果", "其他指标", "结果","单侧","结果"]
  # here=here.to_numpy().tolist()+[['信息系数','结果','绩效指标','结果']]
  table = FF.create_table(here.iloc[::-1])
  table.update_yaxes(matches=None)
@@ -1348,16 +1390,16 @@ def plot_net_values(self, y2, filename, iplot=1, ilegend=1, without_breakpoint=0
  )
  )
  # table=go.Figure([go.Table(header=dict(values=list(here.columns)),cells=dict(values=here.to_numpy().tolist()))])
- pic3_data = go.Bar(y=list(self.rankics.rankic), x=list(self.rankics.index))
+ pic3_data = go.Bar(y=list(self.rankics.rankic), x=list(self.rankics.index),marker_color="red")
  pic3 = go.Figure(data=[pic3_data])
- pic4_data = go.Line(
+ pic5_data = go.Line(
  y=list(self.rankics.rankic.cumsum()),
  x=list(self.rankics.index),
  name="y2",
  yaxis="y2",
  )
- pic4_layout = go.Layout(yaxis2=dict(title="y2", side="right"))
- pic4 = go.Figure(data=[pic4_data], layout=pic4_layout)
+ pic5_layout = go.Layout(yaxis2=dict(title="y2", side="right"))
+ pic5 = go.Figure(data=[pic5_data], layout=pic5_layout)
  figs.append(table)
  figs = [figs[-1]] + figs[:-1]
  figs.append(pic2)
@@ -1366,38 +1408,50 @@ def plot_net_values(self, y2, filename, iplot=1, ilegend=1, without_breakpoint=0
  legend=dict(yanchor="top", y=0.99, xanchor="left", x=0.01)
  )
  figs[3].update_layout(yaxis2=dict(title="y2", side="right"))
+ # twins=pd.concat([self.long_short_net_values,self.long_minus_market_nets],axis=1)
+ pic4=go.Figure()
+ figs.append(pic4)
+ figs[4].add_trace(go.Line(x=self.long_short_net_values.index,y=self.long_short_net_values,name='long_short'))
+ figs[4].add_trace(go.Line(x=self.long_minus_market_nets.index,y=self.long_minus_market_nets,name='long_minus_market'))
+ figs.append(pic5)
  base_layout = cf.tools.get_base_layout(figs)
 
  sp = cf.subplots(
  figs,
- shape=(2, 12),
+ shape=(2, 14),
  base_layout=base_layout,
  vertical_spacing=0.15,
  horizontal_spacing=0.03,
  shared_yaxes=False,
  specs=[
  [
+ # None,
+ {"rowspan": 2, "colspan": 5},
  None,
- {"rowspan": 2, "colspan": 4},
  None,
  None,
  None,
- {"rowspan": 2, "colspan": 4},
+ {"rowspan": 2, "colspan": 3},
  None,
  None,
+ {"colspan": 3},
+ None,
  None,
  {"colspan": 3},
  None,
  None,
  ],
  [
+ # None,
+ None,
  None,
  None,
  None,
  None,
  None,
  None,
  None,
+ {"colspan": 3},
  None,
  None,
  {"colspan": 3},
@@ -1412,7 +1466,8 @@ def plot_net_values(self, y2, filename, iplot=1, ilegend=1, without_breakpoint=0
  "绩效指标",
  ],
  )
- sp["layout"].update(showlegend=ilegend)
+ sp["layout"].update(showlegend=ilegend,width=1800,height=230)
+ # sp['colors']=['#FF5733', '#33FF57', '#3357FF']
  # los=sp['layout']['annotations']
  # los[0]['font']['color']='#000000'
  # los[1]['font']['color']='#000000'
@@ -1711,7 +1766,7 @@ def __init__(
  no_read_indu: bool = 0,
  only_cap: bool = 0,
  iplot: bool = 1,
- ilegend: bool = 1,
+ ilegend: bool = 0,
  without_breakpoint: bool = 0,
  total_cap: bool = 0,
  ) -> None: