修正停牌时目标持仓计算问题
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@ -9,7 +9,7 @@ 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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for i,f in enumerate(['open_post','close_post','down_limit','up_limit','size','amount_20','opening_info','ipo_days','margin_list','abnormal']):
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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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@ -32,7 +32,7 @@ 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']):
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for i,f in enumerate(['amount_20','opening_info','ipo_days','margin_list','abnormal']):
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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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@ -25,10 +25,13 @@ class Spread_Backtest():
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else:
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bkt_start = max(start, self.trader.avaliable_date.index.min())
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rec_end = min(end, self.trader.avaliable_date.index.max())
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# 如果传入的第一个因子时间范围大于现有数据最大时间，则只更新下一时刻的持仓不处理收益
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# 如果传入的第一个因子的有值时间范围大于现有数据最大时间，则只更新下一时刻的持仓不处理收益
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first_signal = self.trader.signal[list(self.trader.signal.keys())[0]]
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if rec_end < first_signal.index.max():
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bkt_end = first_signal.loc[rec_end:].index.to_list()[1]
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if rec_end in first_signal.index:
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bkt_end = first_signal.loc[rec_end:].index.to_list()[1]
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else:
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bkt_end = first_signal.loc[rec_end:].index.to_list()[0]
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else:
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bkt_end = rec_end
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print(f'回测区间: {bkt_start} - {bkt_end}')
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@ -92,5 +95,4 @@ def print_year_rtn(year_rtn: pd.DataFrame) -> None:
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table.add_row(*new_row)
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rprint(table)
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110
trader.py
110
trader.py
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@ -4,7 +4,10 @@ import sys, os
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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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gft = get_factor_tools()
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from typing import Union, Iterable, Dict
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from ordered_set import OrderedSet
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from account import Account
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from dataloader import DataLoader
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@ -18,7 +21,6 @@ class Trader(Account):
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interval (int, tuple, pd.Series): 交易间隔
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num (int): 持仓数量
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ascending (bool): 因子方向
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fee (tuple): 买入成本和卖出成本
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with_st (bool): 是否包含st
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tick (bool): 是否开始tick模拟模式(开发中)
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weight (str): 权重分配
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@ -26,19 +28,29 @@ class Trader(Account):
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amt_filter (set): 20日均成交额筛选，第一个参数是筛选下限，第二个参数是筛选上限，可以只提供下限
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data_root (dict): 对应各个目标因子的交易价格数据，必须包含stock_code和price列
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ipo_days (int): 筛选上市时间
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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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"""
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def __init__(self,
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signal: Dict[str, pd.DataFrame]=None,
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interval: Dict[str, Union[int,tuple,pd.Series]]=1,
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num: int=100,
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ascending: bool=False,
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fee :tuple=(0.001,0.002),
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with_st: bool=False,
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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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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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**kwargs) -> None:
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# 初始化账户
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super().__init__(**kwargs.get('account', {}))
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@ -50,6 +62,9 @@ class Trader(Account):
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self.signal[s] = gft.return_factor(self.signal[s], self.signal[s].index.min(), self.signal[s].index.max(), return_type='origin')
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else:
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raise ValueError('type of signal is invalid')
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# ----------
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# 参数检验
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# ----------
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# interval
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self.interval = []
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for s in signal:
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@ -79,11 +94,6 @@ class Trader(Account):
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self.ascending = ascending
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else:
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raise ValueError('invalid type for ascending')
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# fee
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if isinstance(fee, tuple) and len(fee) == 2:
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self.fee = fee
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else:
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raise ValueError('invalid input for fee')
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# with_st
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if isinstance(with_st, bool):
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self.with_st = with_st
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@ -104,6 +114,21 @@ class Trader(Account):
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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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else:
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raise ValueError('slippage should be set.')
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# commission
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if isinstance(commission, float):
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self.commission = commission
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else:
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raise ValueError('commission should be flaot.')
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# tax
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if isinstance(tax, dict):
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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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# 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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@ -175,27 +200,38 @@ class Trader(Account):
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stock_ipo_filter.loc[stock_ipo_days > self.ipo_days] = 1
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# 交易列表
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if self.leverage <= 1.0:
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# 获取当前时间目标列表和冻结(无法交易)列表
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# 获取当前时间目标列表和冻结(无法交易)列表:
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# 1 保留冻结的昨日持仓
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# 2 目标持仓列表会对当日因子进行ST、成交量、上市时间滤后按因子排序方向选取
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for stock in last_position.index:
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# 如果停牌或者跌停继续持有
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if stock_status.loc[stock] in [0,2]:
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target_list.append(stock)
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for stock in factor.dropna().sort_values(ascending=self.ascending).index.values:
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# 目标持仓数量达到持仓数量则中止
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if len(target_list) == self.num:
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break
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# 成交量、上市时间过滤
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if (stock_amt_filter.loc[stock] != 1) or (stock_ipo_filter.loc[stock] != 1):
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continue
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# ST过滤
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if self.with_st:
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if stock_status.loc[stock] in [0,2]:
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if stock_status.loc[stock] in [0,2,5,7]:
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if stock in last_position.index:
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target_list.append(stock)
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else:
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target_list.append(stock)
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else:
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# 非ST
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if stock_status.loc[stock] in [3,7]:
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if stock_status.loc[stock] in [3,6]:
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target_list.append(stock)
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else:
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# 如果停牌或者跌停继续持有
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if stock_status.loc[stock] in [0,2,6]:
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if stock in last_position.index:
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target_list.append(stock)
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if len(target_list) == self.num:
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break
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# 如果停牌或者跌停继续持有
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if stock_status.loc[stock] in [0,2,7]:
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if stock in last_position.index:
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target_list.append(stock)
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target_list = list(OrderedSet(target_list))
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# 如果没有杠杆
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buy_list = []
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sell_list = []
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@ -205,7 +241,7 @@ class Trader(Account):
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factor = factor.fillna(factor.max()+1)
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else:
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factor = factor.fillna(factor.min()-1)
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for stock in factor.loc[last_position.index].sort_values(ascending=self.ascending).index.values[::-1]:
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for stock in factor.loc[last_position.index].sort_values(ascending=self.ascending).index.values[::-1]:
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if stock in target_list:
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continue
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else:
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@ -213,8 +249,9 @@ class Trader(Account):
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continue
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else:
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sell_list.append(stock)
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if len(sell_list) == max_trade_num:
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if len(sell_list) >= max_trade_num:
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break
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# ----- 买入 -----
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# 卖出后持仓列表
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after_sell_list = set(last_position.index) - set(sell_list)
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@ -244,25 +281,34 @@ class Trader(Account):
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margin_ratio = max(self.leverage-1, 0)
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margin_needed = round(self.num * margin_ratio)
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is_margin = self.today_data['basic'].get(factor.index.values, 'margin_list').sort_index()
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# 获取当前时间目标列表和冻结(无法交易)列表
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# 获取当前时间目标列表和冻结(无法交易)列表:
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# 1 保留冻结的昨日持仓
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# 2 目标持仓列表会对当日因子进行ST、成交量、上市时间滤后按因子排序方向选取
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# 3 根据融资比例将股票分为常规池子和融资池子
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normal_list = []
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margin_list = []
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for stock in last_position.index:
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# 如果停牌或者跌停继续持有
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if stock_status.loc[stock] in [0,2]:
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normal_list, margin_list = assign_stock(normal_list, margin_list, margin_needed, stock, is_margin.loc[stock])
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for stock in factor.dropna().sort_values(ascending=self.ascending).index.values:
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if stock_amt_filter.loc[stock] != 1:
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continue
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if self.with_st:
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if stock_status.loc[stock] in [0,2]:
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if stock_status.loc[stock] in [0,2,5,7]:
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if stock in last_position.index:
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normal_list, margin_list = assign_stock(normal_list, margin_list, margin_needed, stock, is_margin.loc[stock])
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else:
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normal_list, margin_list = assign_stock(normal_list, margin_list, margin_needed, stock, is_margin.loc[stock])
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else:
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# 非ST
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if stock_status.loc[stock] in [3,7]:
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if stock_status.loc[stock] in [3,6]:
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normal_list, margin_list = assign_stock(normal_list, margin_list, margin_needed, stock, is_margin.loc[stock])
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else:
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# 如果停牌或者跌停继续持有
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if stock_status.loc[stock] in [0,2,6]:
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if stock_status.loc[stock] in [0,2,7]:
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if stock in last_position.index:
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normal_list, margin_list = assign_stock(normal_list, margin_list, margin_needed, stock, is_margin.loc[stock])
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if len(normal_list + margin_list) == self.num:
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@ -329,7 +375,6 @@ class Trader(Account):
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next_position['date'] = date
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# 融资融券数量
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margin_num = len(next_margin_list)
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# next_position['weight'] = self.leverage*((margin_needed-margin_num)/margin_needed) / len(next_position)
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next_position['weight'] = (1 + (margin_num / self.num)) / self.num
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next_position['margin_trade'] = 0
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next_position = next_position.set_index(['stock_code'])
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@ -361,8 +406,8 @@ class Trader(Account):
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sell_list = list(set(target) & set(sell_list))
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buy_list = list(set(target) & set(buy_list))
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target_price.loc[target] = stock_price.get(target, 'price').fillna(0)
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target_price.loc[sell_list] = stock_price.get(sell_list, 'price') * (1 - self.fee[1])
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target_price.loc[buy_list] = stock_price.get(buy_list, 'price') * (1 + self.fee[0])
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target_price.loc[sell_list] = stock_price.get(sell_list, 'price') * (1 - self.current_fee[1])
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target_price.loc[buy_list] = stock_price.get(buy_list, 'price') * (1 + self.current_fee[0])
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return target_price
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def check_update_status(self,
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@ -473,8 +518,8 @@ class Trader(Account):
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return True
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def update_signal(self,
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date:str,
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update_type='rtn'):
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date:str,
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update_type='rtn'):
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"""
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更新信号收益
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@ -492,6 +537,14 @@ class Trader(Account):
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self.position_history.pop(date)
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# 更新持仓信号
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self.load_data(date, update_type)
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# 更新费用
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fee = (self.commission + self.slippage[0], self.commission + self.slippage[1])
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current_tax = (0.001, 0.001)
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for time,tax_rate in self.tax.items():
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if date > time:
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current_tax = tax_rate
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fee = (fee[0] + current_tax[0], fee[1] + current_tax[1])
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self.current_fee = fee
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# 如果当前持仓不空，添加隔夜收益，否则直接买入
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if len(self.position) == 0:
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cur_pos = pd.DataFrame(columns=['stock_code','date','weight','open','close','margin_trade'])
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@ -510,6 +563,7 @@ class Trader(Account):
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factor = self.signal[trade_time].loc[date]
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# 获取当前、持仓
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sell_list, buy_list, frozen_list, next_position = self.get_next_position(date, factor)
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# 区分回测模型和仓位模式：回撤模式会记录收益，仓位模式只记录下一日持仓并结束计算
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if update_type == 'position':
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self.position_history[date] = next_position.copy()
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return True
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