parent
d671f820d4
commit
0977e8c9fd
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@ -0,0 +1,68 @@
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import numpy as np
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def check_buy_exclude(buy_exclude):
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buy_exclude_dict = dict()
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# 检查剔除条件
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for cond in ['amt_20', 'list_days', 'mkt', 'price']:
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if cond == 'amt_20':
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# 20日成交量均值
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if cond in buy_exclude:
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amt_exclude = buy_exclude[cond]
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else:
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buy_exclude[cond] = (0, np.inf)
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continue
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if isinstance(buy_exclude[cond], (set,list,tuple)):
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if len(amt_exclude) == 1:
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buy_exclude_dict[cond] = (amt_exclude[0], np.inf)
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else:
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buy_exclude_dict[cond] = (amt_exclude[0], amt_exclude[1])
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else:
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raise Exception('wrong input type for buy exclude: amt_20, `set`, `list` or `tuple` is required')
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if cond == 'list_days':
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# 上市时间
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if cond in buy_exclude:
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list_exclude = buy_exclude[cond]
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else:
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buy_exclude[cond] = (0, np.inf)
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continue
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if isinstance(buy_exclude[cond], (set,list,tuple)):
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if len(list_exclude) == 1:
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buy_exclude_dict[cond] = (list_exclude[0], np.inf)
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else:
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buy_exclude_dict[cond] = (list_exclude[0], list_exclude[1])
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else:
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raise Exception('wrong input type for buy exclude: list_days, `set`, `list` or `tuple` is required')
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if cond == 'mkt':
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# 市值
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if cond in buy_exclude:
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mkt_exclude = buy_exclude[cond]
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else:
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buy_exclude[cond] = (0, np.inf)
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continue
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if isinstance(buy_exclude[cond], (set,list,tuple)):
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if len(mkt_exclude) == 1:
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buy_exclude_dict[cond] = (mkt_exclude[0], np.inf)
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else:
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buy_exclude_dict[cond] = (mkt_exclude[0], mkt_exclude[1])
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else:
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raise Exception('wrong input type for buy exclude: mkt, `set`, `list` or `tuple` is required')
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if cond == 'price':
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# 价格
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if cond in buy_exclude:
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price_exclude = buy_exclude[cond]
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else:
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buy_exclude[cond] = (0, np.inf)
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continue
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if isinstance(buy_exclude[cond], (set,list,tuple)):
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if len(price_exclude) == 1:
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buy_exclude_dict[cond] = (price_exclude[0], np.inf)
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else:
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buy_exclude_dict[cond] = (price_exclude[0], price_exclude[1])
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else:
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raise Exception('wrong input type for buy exclude: price, `set`, `list` or `tuple` is required')
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return buy_exclude_dict
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@ -10,8 +10,8 @@ if __name__ == '__main__':
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data_dir = '/home/lenovo/quant/tools/detail_testing/basic_data'
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save_dir = '/home/lenovo/quant/data/backtest/basic_data'
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for i,f in enumerate(['open_post','close_post','down_limit','up_limit','size','amount_20','opening_info','ipo_days','margin_list',
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'abnormal', 'recession']):
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for i,f in enumerate(['open_post','close_post','open_pre','close_pre','down_limit','up_limit','size','amount_20',
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'opening_info','ipo_days','margin_list','abnormal', 'recession']):
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if f in ['margin_list']:
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tmp = gft.get_stock_factor(f, start='2012-01-01').fillna(0)
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else:
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@ -34,7 +34,7 @@ if __name__ == '__main__':
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# 更新下一日的数据用于筛选
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next_date = gft.days_after(df.index.max(), 1)
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next_list = []
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for i,f in enumerate(['amount_20','opening_info','ipo_days','margin_list','abnormal','recession']):
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for i,f in enumerate(['close_pre','size','amount_20','opening_info','ipo_days','margin_list','abnormal','recession']):
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if f in ['margin_list']:
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next_list.append(pd.Series(gft.get_stock_factor(f, start='2012-01-01').fillna(0).iloc[-1], name=f))
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else:
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98
trader.py
98
trader.py
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@ -1,5 +1,4 @@
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import pandas as pd
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import numpy as np
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import sys
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import os
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import copy
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@ -8,6 +7,7 @@ from typing import Union, Iterable, Dict
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from account import Account
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from dataloader import DataLoader
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from check_funcs import check_buy_exclude
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sys.path.append("/home/lenovo/quant/tools/get_factor_tools/")
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from db_tushare import get_factor_tools
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@ -35,7 +35,7 @@ class Trader(Account):
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slippage (tuple): 买入和卖出滑点
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commission (float): 佣金
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tax (dict): 印花税
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exclude_list (list): 额外的剔除列表，会优先满足该剔除列表中的条件，之后再进行正常的调仓
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force_exclude (list): 额外的剔除列表，会优先满足该剔除列表中的条件，之后再进行正常的调仓
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- abnormal: 异常公告剔除，包含中止上市、立案调查、警示函等异常情况的剔除
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- receesion: 财报同比或环比下降50%以上
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- qualified_opinion: 会计保留意见
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@ -50,18 +50,17 @@ class Trader(Account):
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data_root:dict={},
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tick: bool=False,
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weight: str='avg',
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amt_filter: set=(0,),
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ipo_days: int=20,
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slippage :tuple=(0.001,0.001),
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commission: float=0.0001,
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buy_exclude: Dict[str, Union[set,int,float]]={},
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force_exclude: list=[],
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account: dict={},
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tax: dict={
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'1990-01-01': (0.001,0.001),
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'2008-04-24': (0.001,0.001),
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'2008-09-19': (0, 0.001),
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'2023-08-28': (0, 0.0005)
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},
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exclude_list: list=[],
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account: dict={},
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**kwargs) -> None:
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# 初始化账户
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super().__init__(**account)
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@ -100,21 +99,6 @@ class Trader(Account):
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self.weight = weight
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else:
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raise ValueError('invalid type for `weight`')
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# amt_filter
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if isinstance(amt_filter, (set,list,tuple)):
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if len(amt_filter) == 1:
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self.amt_filter_min = amt_filter[0]
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self.amt_filter_max = np.inf
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else:
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self.amt_filter_min = amt_filter[0]
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self.amt_filter_max = amt_filter[1]
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else:
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raise Exception('wrong type for amt_filter, `set` `list` or `tuple` is required')
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# ipo_days
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if isinstance(ipo_days, int):
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self.ipo_days = ipo_days
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else:
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raise Exception('wrong type for ipo_days, `int` is required')
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# slippage
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if isinstance(slippage, tuple) and len(slippage) == 2:
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self.slippage = slippage
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@ -130,17 +114,19 @@ class Trader(Account):
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self.tax = tax
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else:
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raise ValueError('tax should be dict.')
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# exclude
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if isinstance(exclude_list, list):
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self.exclude_list = exclude_list
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# buy exclude
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self.buy_exclude = check_buy_exclude(buy_exclude)
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# force exclude
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if isinstance(force_exclude, list):
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self.force_exclude = force_exclude
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optional_list = ['abnormal', 'recession']
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for item in exclude_list:
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for item in force_exclude:
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if item in optional_list:
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pass
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else:
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raise ValueError(f"Unexpected keyword argument '{item}'")
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else:
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raise ValueError('exclude_list should be list.')
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raise ValueError('force_exclude should be list.')
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# data_root
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# 至少包含basic data路径，open信号默认使用basic_data
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if len(data_root) <= 0:
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@ -276,36 +262,58 @@ class Trader(Account):
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# 获取用于筛选的数据
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stock_status = self.today_data['basic'].get(factor.index.values, 'opening_info').sort_index()
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exclude_data = dict()
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for cond in self.buy_exclude:
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if cond == 'amt_20':
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stock_amt20 = self.today_data['basic'].get(factor.index.values, 'amount_20').sort_index()
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stock_amt_filter = stock_amt20.copy()
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stock_amt_filter.loc[:] = 0
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stock_amt_filter.loc[(stock_amt20 > self.amt_filter_min) & (stock_amt20 < self.amt_filter_max)] = 1
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stock_amt_filter = stock_amt_filter.sort_index()
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stock_amt_exclude = stock_amt20.copy()
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stock_amt_exclude.loc[:] = 0
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stock_amt_exclude.loc[(stock_amt20 > self.buy_exclude[cond][0]) & (stock_amt20 < self.buy_exclude[cond][1])] = 1
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stock_amt_exclude = stock_amt_exclude.sort_index()
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exclude_data[cond] = stock_amt_exclude
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if cond == 'list_days':
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stock_ipo_days = self.today_data['basic'].get(factor.index.values, 'ipo_days').sort_index()
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stock_ipo_filter = stock_ipo_days.copy()
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stock_ipo_filter.loc[:] = 0
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stock_ipo_filter.loc[stock_ipo_days > self.ipo_days] = 1
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stock_ipo_exclude = stock_ipo_days.copy()
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stock_ipo_exclude.loc[:] = 0
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stock_ipo_exclude.loc[(stock_ipo_days > self.buy_exclude[cond][0]) & (stock_ipo_days < self.buy_exclude[cond][1])] = 1
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stock_ipo_exclude = stock_ipo_exclude.sort_index()
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exclude_data[cond] = stock_ipo_exclude
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if cond == 'mkt':
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stock_size = self.today_data['basic'].get(factor.index.values, 'size').sort_index()
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stock_size_exclude = stock_size.copy()
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stock_size_exclude.loc[:] = 0
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stock_size_exclude.loc[(stock_size > self.buy_exclude[cond][0]) & (stock_size < self.buy_exclude[cond][1])] = 1
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stock_size_exclude = stock_size_exclude.sort_index()
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exclude_data[cond] = stock_size_exclude
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if cond == 'price':
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stock_price = self.today_data['basic'].get(factor.index.values, 'close_pre').sort_index()
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stock_price_exclude = stock_price.copy()
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stock_price_exclude.loc[:] = 0
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stock_price_exclude.loc[(stock_price > self.buy_exclude[cond][0]) & (stock_price < self.buy_exclude[cond][1])] = 1
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stock_price_exclude = stock_price_exclude.sort_index()
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exclude_data[cond] = stock_price_exclude
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# 剔除列表
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# 包含强制列表和普通列表：
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# 包含强制剔除列表和买入剔除列表：
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# 强制列表会将已经持仓的也强制剔除并且不算在换手率限制中
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# 普通列表如果已经持有不会过滤只对新买入的过滤
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# 买入剔除列表如果已经持有不会过滤只对新买入的过滤
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# 强制过滤列表
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exclude_stock = []
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for exclude in self.exclude_list:
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if exclude == 'abnormal':
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for cond in self.force_exclude:
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if cond == 'abnormal':
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stock_abnormal = self.today_data['basic'].get(factor.index.values, 'abnormal').sort_index()
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exclude_stock += stock_abnormal.loc[stock_abnormal > 0].index.to_list()
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if exclude == 'recession':
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if cond == 'recession':
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stock_recession = self.today_data['basic'].get(factor.index.values, 'recession').sort_index()
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exclude_stock += stock_recession.loc[stock_recession > 0].index.to_list()
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exclude_stock = list(set(exclude_stock))
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force_exclude = copy.deepcopy(exclude_stock)
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# 普通过滤列表
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normal_exclude = []
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normal_exclude += stock_ipo_filter.loc[stock_ipo_filter != 1].index.to_list()
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normal_exclude += stock_amt_filter.loc[stock_amt_filter != 1].index.to_list()
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normal_exclude = list(set(normal_exclude))
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# 买入过滤列表
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buy_exclude = []
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for cond in self.buy_exclude:
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buy_exclude += exclude_data[cond].loc[exclude_data[cond] != 1].index.to_list()
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buy_exclude = list(set(buy_exclude))
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# 交易列表
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# 仓位判断给与计算误差冗余
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@ -360,7 +368,7 @@ class Trader(Account):
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buy_list = []
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# 剔除过滤条件后可买入列表
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after_filter_list = list(set(factor.index) - set(normal_exclude) - set(force_exclude))
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after_filter_list = list(set(factor.index) - set(buy_exclude) - set(force_exclude))
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target_list = factor.loc[after_filter_list].dropna().sort_values(ascending=self.ascending).index.to_list()
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# 更新卖出后的持仓列表
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@ -469,7 +477,7 @@ class Trader(Account):
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buy_list = []
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# 剔除过滤条件后可买入列表
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after_filter_list = list(set(factor.index) - set(normal_exclude) - set(force_exclude))
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after_filter_list = list(set(factor.index) - set(buy_exclude) - set(force_exclude))
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target_list = factor.loc[after_filter_list].dropna().sort_values(ascending=self.ascending).index.to_list()
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# 更新卖出后的持仓列表
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