641 lines
31 KiB
Python
641 lines
31 KiB
Python
import pandas as pd
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import numpy as np
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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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class Trader(Account):
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"""
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交易类: 用于控制每日交易情况
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Args:
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signal (dict[str, pd.DataFrame]): 目标因子，按顺序执行
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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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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, pd.DataFrame]): 权重分配
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- avg (str): 平均分配，每天早盘重新分配，日中交易不重新分配
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- (pd.DataFrame): 自定义股票权重，包含每天个股指定的权重，会自动归一化
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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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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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- 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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- report: 财报同比下降50%以上剔除
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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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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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if isinstance(signal, dict):
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self.signal = signal
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if 'close' in signal:
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raise ValueError('signal key cannot be close')
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for s in self.signal:
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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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if s in interval:
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s_interval = interval[s]
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if isinstance(s_interval, int):
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df_interval = pd.Series(index=signal[s].index, data=[0]*len(signal[s].index))
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df_interval[::s_interval] = 1
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elif isinstance(s_interval, tuple):
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df_interval = pd.Series(index=signal[s].index, data=[0]*len(signal[s].index))
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df_interval[::s_interval[0]] = s_interval[1]
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elif isinstance(s_interval, pd.Series):
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df_interval = s_interval
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else:
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raise ValueError('invalid interval type')
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self.interval.append(df_interval)
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else:
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raise ValueError(f'not found interval for signal {s}')
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self.interval = pd.concat(self.interval)
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# num
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if isinstance(num, int):
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self.num = int(num)
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else:
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raise ValueError('num should be int')
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# ascending
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if isinstance(ascending, bool):
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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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# with_st
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if isinstance(with_st, bool):
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self.with_st = with_st
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else:
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raise ValueError('invalid type for `with_st`')
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# weight
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if isinstance(weight, (str, pd.DataFrame)):
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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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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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raise ValueError('num of data_root should be equal or greater than 1')
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if 'basic' in data_root:
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# 可执行日期
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self.avaliable_date = pd.Series(index=[f.split('.')[0] for f in os.listdir(data_root['basic'])]).sort_index()
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else:
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raise ValueError('data_root should contain basic data root')
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for s in self.signal:
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if s == 'open':
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continue
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else:
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if s not in data_root:
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raise ValueError(f'data for signal {s} is not provided')
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self.data_root = data_root
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def load_data(self,
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date: str,
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update_type: str='rtn'):
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"""
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加载每日基础数据
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Args:
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update_type (str): 更新模式
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- rtn: 更新所有信号数据
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- position: 只更新basic数据，用于持仓判断
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"""
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self.today_data = dict()
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self.today_data['basic'] = DataLoader(os.path.join(self.data_root['basic'],f'{date}.csv'))
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if update_type == 'position':
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return True
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for s in self.signal:
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if s == 'open':
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if s in self.data_root:
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continue
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else:
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self.today_data[s] = DataLoader(self.today_data['basic'].data[['open_post']].rename(columns={'open_post':'price'}))
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else:
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self.today_data[s] = DataLoader(os.path.join(self.data_root[s],f'{date}.csv'))
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if 'close' in self.signal:
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pass
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else:
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self.today_data['close'] = DataLoader(self.today_data['basic'].data[['close_post']].rename(columns={'close_post':'price'}))
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def get_weight(self, date, total_weight, next_position):
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"""
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计算个股仓位
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"""
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if isinstance(self.weight, str):
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if self.weight == 'avg':
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return total_weight / len(next_position)
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if isinstance(self.weight, pd.DataFrame):
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date_weight = self.weight.loc[date].dropna().sort_index()
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try:
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weight_list = date_weight.loc[next_position['stock_code'].to_list()].values
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weight_list = total_weight * weight_list / sum(weight_list)
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return weight_list
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except:
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raise ValueError(f'not found stock weight in {date}')
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def get_next_position(self, date, factor):
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"""
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计算下一时刻持仓
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"""
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# 计算持仓和最大可交易数量
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if len(self.position) > 0:
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last_position = self.position['stock_code'].values
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last_position = factor.loc[last_position].sort_values(ascending=self.ascending)
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if len(self.position) <= self.num:
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# 如果昨日持仓本身就不足持仓数量则不用足额换仓
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max_sell_num = min(int(self.interval.loc[date]*self.num), int(self.interval.loc[date]*self.num)+len(self.position)-self.num)
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else:
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# 如果昨日持仓本身就超额持仓数量则超额换仓
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max_sell_num = max(int(self.interval.loc[date]*self.num), int(self.interval.loc[date]*self.num)+len(self.position)-self.num)
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else:
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last_position = pd.Series()
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max_sell_num = self.num
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target_list = []
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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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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_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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# 剔除列表
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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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# 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 [0,2,5,7]:
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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 [3,6]:
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target_list.append(stock)
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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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# ----- 卖出 -----
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# 按照反向排名逐个卖出
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if self.ascending:
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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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if stock in target_list:
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continue
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else:
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if stock_status.loc[stock] in [0,2,5,7]:
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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_sell_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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cant_buy_list = [] # 涨停股记录
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max_buy_num = max(0, self.num-len(last_position)+len(sell_list))
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for stock in target_list:
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if stock in after_sell_list:
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continue
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else:
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if stock_status.loc[stock] in [4,6]:
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cant_buy_list.append(stock)
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buy_list.append(stock)
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if len(buy_list) == max_buy_num:
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break
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next_position = pd.DataFrame({'stock_code': list((set(last_position.index) - set(sell_list)) | set(buy_list))})
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next_position['date'] = date
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next_position['weight'] = self.get_weight(date, self.leverage, next_position)
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# 剔除无法买入的涨停股，这部分仓位空出
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next_position = next_position[~next_position['stock_code'].isin(cant_buy_list)]
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next_position['margin_trade'] = 0
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else:
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# 如果有杠杆
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def assign_stock(normal_list, margin_list, margin_needed, stock, status):
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if status == 1:
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if len(margin_list) < margin_needed:
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margin_list.append(stock)
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else:
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if len(normal_list) < self.num - margin_needed:
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normal_list.append(stock)
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return normal_list, margin_list
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# 计算需要融资融券标的数量
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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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# 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 [0,2,5,7]:
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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 [3,6]:
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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 [0,2,7]:
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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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break
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target_list = normal_list + margin_list
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# ----- 卖出 -----
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buy_list = []
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sell_list = []
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# 融资融券池的和非融资融券池的分开更新
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# 更新融资融券池
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if len(last_position) > 0:
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last_margin_list = self.position.loc[self.position['margin_trade'] == 1, 'stock_code'].to_list()
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else:
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last_margin_list = []
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for stock in factor.loc[last_margin_list].sort_values(ascending=self.ascending).index.values[::-1]:
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if stock in normal_list:
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continue
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else:
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if stock_status.loc[stock] in [0,2,5,7]:
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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) >= int(max_trade_num * margin_ratio) + 1:
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break
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next_margin_list = list(set(last_margin_list) - set(sell_list))
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# 更新非融资融券池
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if len(last_position) > 0:
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last_normal_list = self.position.loc[self.position['margin_trade'] == 0, 'stock_code'].to_list()
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else:
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last_normal_list = []
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for stock in factor.loc[last_normal_list].sort_values(ascending=self.ascending).index.values[::-1]:
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if stock in normal_list:
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continue
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else:
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if stock_status.loc[stock] in [0,2,5,7]:
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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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break
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next_normal_list = list(set(last_normal_list) - set(sell_list))
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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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max_trade_num = min(max_trade_num, self.num-len(last_position)+len(sell_list))
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# 融资融券池的和非融资融券池的分开更新
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# 更新融资融券池
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for stock in margin_list:
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if stock in after_sell_list:
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continue
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else:
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next_margin_list.append(stock)
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if len(next_margin_list) == margin_needed:
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break
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# 更新非融资融券池
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for stock in normal_list:
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if stock in after_sell_list:
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continue
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else:
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next_normal_list.append(stock)
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if len(next_normal_list) >= self.num - margin_needed:
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break
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next_position = pd.DataFrame({'stock_code': next_margin_list + next_normal_list})
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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.get_weight(date, 1 + (margin_num / self.num), next_position)
|
||
next_position['margin_trade'] = 0
|
||
next_position = next_position.set_index(['stock_code'])
|
||
next_position.loc[next_margin_list, 'margin_trade'] = 1
|
||
next_position = next_position.reset_index()
|
||
# 检测当前持仓是否可以交易
|
||
frozen_list = []
|
||
if len(self.position) > 0:
|
||
for stock in next_position['stock_code']:
|
||
if stock_status.loc[stock] in [0,2]:
|
||
frozen_list.append(stock)
|
||
return sell_list, buy_list, frozen_list, next_position
|
||
|
||
def get_price(self,
|
||
trade_time: str='open',
|
||
target: Iterable[str]=[],
|
||
buy_list: Iterable[str] = [],
|
||
sell_list: Iterable[str] = []):
|
||
"""
|
||
获取价格
|
||
Args:
|
||
trade_time (str): 交易时间
|
||
target (Iterable[float]): 目标
|
||
buy_list (Iterable[str]): 买入目标
|
||
sell_list (Iterable[str]): 卖出目标
|
||
"""
|
||
stock_price = self.today_data[trade_time]
|
||
target_price = pd.Series(index=target)
|
||
sell_list = list(set(target) & set(sell_list))
|
||
buy_list = list(set(target) & set(buy_list))
|
||
target_price.loc[target] = stock_price.get(target, 'price').fillna(0)
|
||
target_price.loc[sell_list] = stock_price.get(sell_list, 'price') * (1 - self.current_fee[1])
|
||
target_price.loc[buy_list] = stock_price.get(buy_list, 'price') * (1 + self.current_fee[0])
|
||
return target_price
|
||
|
||
def check_update_status(self,
|
||
date: str,
|
||
trade_time: str):
|
||
# 判断当前更新状态
|
||
# 如果日期和交易时间已经存在则返回True
|
||
if len(self.account_history) == 0:
|
||
return False
|
||
elif date < self.account_history['date'].max():
|
||
return True
|
||
else:
|
||
exist_list = self.account_history['date'].str.cat(self.account_history['trade_time'], sep='-').values
|
||
if f'{date}-{trade_time}' in exist_list:
|
||
return True
|
||
else:
|
||
return False
|
||
|
||
def reblance_weight(self,
|
||
trade_time: str,
|
||
cur_pos: pd.DataFrame,
|
||
next_position: pd.DataFrame):
|
||
"""
|
||
动态平衡权重
|
||
"""
|
||
# 判断冻结列表
|
||
stock_status = self.today_data['basic'].get(cur_pos['stock_code'].values, 'opening_info')
|
||
buy_frozen_list = []
|
||
for stock in cur_pos['stock_code']:
|
||
if stock_status.loc[stock] in [0,2,4,6]:
|
||
buy_frozen_list.append(stock)
|
||
sell_frozen_list = []
|
||
for stock in cur_pos['stock_code']:
|
||
if stock_status.loc[stock] in [0,2,5,7]:
|
||
sell_frozen_list.append(stock)
|
||
# 设定目标仓位
|
||
next_position['target_weight'] = next_position['weight']
|
||
next_position['current_weight'] = 0
|
||
cur_pos = cur_pos.set_index(['stock_code'])
|
||
next_position = next_position.set_index(['stock_code'])
|
||
current_list = list(set(cur_pos.index) & set(next_position.index))
|
||
next_position.loc[current_list, 'current_weight'] = cur_pos.loc[current_list, 'weight']
|
||
next_position['open'] = 0
|
||
next_position.loc[current_list, 'open'] = cur_pos.loc[current_list, 'close']
|
||
# 计算理想仓位变动
|
||
next_position['weight_chg'] = next_position['weight'] - next_position['current_weight']
|
||
# 根据冻结判断是否能够变动
|
||
next_position['final_weight'] = 0
|
||
buy_frozen_list = set(buy_frozen_list) & set(next_position.index) & set(next_position.loc[next_position['weight_chg'] > 0].index)
|
||
sell_frozen_list = set(sell_frozen_list) & set(next_position.index) & set(next_position.loc[next_position['weight_chg'] < 0].index)
|
||
next_position.loc[next_position.index, 'final_weight'] = next_position['weight']
|
||
next_position.loc[buy_frozen_list, 'final_weight'] = next_position.loc[buy_frozen_list, 'current_weight']
|
||
next_position.loc[sell_frozen_list, 'final_weight'] = next_position.loc[sell_frozen_list, 'current_weight']
|
||
# 动态平衡仓位
|
||
next_position['final_weight'] /= next_position['final_weight'].sum()
|
||
next_position['final_weight'] *= next_position['weight'].sum()
|
||
# 计算理想仓位变动
|
||
next_position['weight_chg'] = next_position['final_weight'] - next_position['current_weight']
|
||
next_position.loc[list(buy_frozen_list | sell_frozen_list), 'weight_chg'] = 0
|
||
# 动态平衡价格
|
||
next_position['adjust_price'] = 0
|
||
buy_adjust_list = next_position[next_position['weight_chg'] > 0].index.values
|
||
sell_adjust_list = next_position[next_position['weight_chg'] < 0].index.values
|
||
next_position.loc[buy_adjust_list, 'adjust_price'] = self.get_price(trade_time, buy_adjust_list, buy_adjust_list, []).values
|
||
next_position.loc[sell_adjust_list, 'adjust_price'] = self.get_price(trade_time, sell_adjust_list, [], sell_adjust_list).values
|
||
# 价格调整
|
||
next_position['adjust_open'] = (next_position['current_weight']*next_position['open'] + next_position['weight_chg']*next_position['adjust_price'])
|
||
next_position['adjust_open'] = next_position['adjust_open'] / next_position['final_weight']
|
||
next_position.loc[list(buy_frozen_list | sell_frozen_list), 'adjust_open'] = next_position.loc[list(buy_frozen_list | sell_frozen_list), 'open']
|
||
# 当日买入不调整
|
||
next_position['open'] = next_position['adjust_open']
|
||
next_position['weight'] = next_position['final_weight']
|
||
next_position = next_position.reset_index()
|
||
next_position = next_position[['stock_code','date','open','weight','margin_trade']]
|
||
return next_position
|
||
|
||
def update_account(self,
|
||
date: str,
|
||
trade_time: str,
|
||
cur_pos: pd.DataFrame,
|
||
next_position: pd.DataFrame):
|
||
"""
|
||
更新账户
|
||
Args:
|
||
date (str): 日期
|
||
trade_time (str): 交易时间
|
||
cur_pos (DataFrame): 当前持仓
|
||
next_position (Iterable[str]): 下一刻持仓
|
||
"""
|
||
turnover = pd.concat([
|
||
cur_pos.set_index(['stock_code'])['weight'].fillna(0).rename('cur'),
|
||
next_position.set_index(['stock_code'])['weight'].rename('next'),
|
||
], axis=1)
|
||
turnover = (turnover['next'] - turnover['cur'].fillna(0)).abs().sum()
|
||
leverage = next_position['weight'].sum()
|
||
if cur_pos['weight'].sum() == 0:
|
||
pnl = 0
|
||
else:
|
||
pnl = (cur_pos['end_weight'].sum() - cur_pos['weight'].sum())
|
||
self.account *= 1+pnl
|
||
self.account_history = self.account_history.append({
|
||
'date': date,
|
||
'trade_time': trade_time,
|
||
'turnover': turnover,
|
||
'leverage': leverage,
|
||
'pnl': pnl
|
||
}, ignore_index=True)
|
||
return True
|
||
|
||
def update_signal(self,
|
||
date:str,
|
||
update_type='rtn'):
|
||
"""
|
||
更新信号收益
|
||
|
||
Args:
|
||
update_type (str): 更新类型
|
||
- position: 只更新持仓不更新收益
|
||
- rtn: 更新收益和持仓
|
||
"""
|
||
# 如果更新日期的close已经记录，则跳过，否则删除现有日期相关记录继续更新
|
||
if f'{date}-close' in self.account_history['date'].str.cat(self.account_history['trade_time'], sep='-').values:
|
||
return True
|
||
else:
|
||
self.account_history = self.account_history.query(f'date != "{date}" ', engine='python')
|
||
if date in self.position_history:
|
||
self.position_history.pop(date)
|
||
# 更新持仓信号
|
||
self.load_data(date, update_type)
|
||
# 更新费用
|
||
fee = (self.commission + self.slippage[0], self.commission + self.slippage[1])
|
||
current_tax = (0.001, 0.001)
|
||
for time,tax_rate in self.tax.items():
|
||
if date > time:
|
||
current_tax = tax_rate
|
||
fee = (fee[0] + current_tax[0], fee[1] + current_tax[1])
|
||
self.current_fee = fee
|
||
# 如果当前持仓不空，添加隔夜收益，否则直接买入
|
||
if len(self.position) == 0:
|
||
cur_pos = pd.DataFrame(columns=['stock_code','date','weight','open','close','margin_trade'])
|
||
else:
|
||
cur_pos = self.position.copy()
|
||
# 冻结列表
|
||
frozen_list = []
|
||
# 遍历各个交易时间的信号
|
||
for idx,trade_time in enumerate(self.signal):
|
||
if self.check_update_status(date, trade_time):
|
||
continue
|
||
if date in self.signal[trade_time].index:
|
||
factor = self.signal[trade_time].loc[date]
|
||
else:
|
||
continue
|
||
factor = self.signal[trade_time].loc[date]
|
||
# 获取当前、持仓
|
||
sell_list, buy_list, frozen_list, next_position = self.get_next_position(date, factor)
|
||
# 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
|
||
if update_type == 'position':
|
||
self.position_history[date] = next_position.copy()
|
||
return True
|
||
if len(cur_pos) > 0:
|
||
cur_pos['close'] = self.get_price(trade_time, cur_pos['stock_code'].values, [], sell_list).values
|
||
# 停牌股价格不变
|
||
cur_pos.loc[cur_pos['stock_code'].isin(frozen_list), 'close'] = cur_pos.loc[cur_pos['stock_code'].isin(frozen_list), 'open']
|
||
# 计算收益
|
||
cur_pos['rtn'] = (cur_pos['close'] / cur_pos['open'])
|
||
cur_pos['end_weight'] = cur_pos['weight'] * cur_pos['rtn']
|
||
self.update_account(date, trade_time, cur_pos, next_position)
|
||
# 更新仓位
|
||
cur_pos['weight'] = (cur_pos['end_weight'] / cur_pos['end_weight'].sum()) * cur_pos['weight'].sum()
|
||
# 调整权重：买入、卖出、仓位再平衡
|
||
next_position = self.reblance_weight(trade_time, cur_pos, next_position)
|
||
else:
|
||
next_position['open'] = self.get_price(trade_time, next_position['stock_code'].values, buy_list, []).values
|
||
self.position = next_position.copy()
|
||
# 收盘统计当日收益
|
||
trade_time = 'close'
|
||
if self.check_update_status(date, trade_time):
|
||
return True
|
||
cur_pos = self.position.copy()
|
||
cur_pos['close'] = self.get_price(trade_time, cur_pos['stock_code'].values, [], []).values
|
||
# 停牌价格不变
|
||
cur_pos.loc[cur_pos['stock_code'].isin(frozen_list), 'close'] = cur_pos.loc[cur_pos['stock_code'].isin(frozen_list), 'open']
|
||
cur_pos.loc[cur_pos['open'] == 0, 'close'] = cur_pos.loc[cur_pos['open'] == 0, 'open']
|
||
cur_pos['rtn'] = (cur_pos['close'] / cur_pos['open']) - 1
|
||
cur_pos['end_weight'] = cur_pos['weight'] * (cur_pos['rtn'] + 1)
|
||
position_record = cur_pos.copy()
|
||
position_record['end_weight'] = (position_record['end_weight'] / position_record['end_weight'].sum()) * position_record['weight'].sum()
|
||
cur_pos['weight'] = (cur_pos['end_weight'] / cur_pos['end_weight'].sum()) * cur_pos['weight'].sum()
|
||
next_position = cur_pos.copy()[['stock_code','date','weight','margin_trade']]
|
||
next_position['open'] = cur_pos['close']
|
||
self.update_account(date, trade_time, cur_pos, cur_pos)
|
||
self.position = next_position.copy()
|
||
self.position_history[date] = position_record.copy()
|
||
return True |