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Account.py
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Account.py
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import numpy as np
import pandas as pd
class Account:
def __init__(self, money_init, start_date='', end_date='', stop_loss_rate=-0.03, stop_profit_rate=0.05,
max_hold_period=5):
self.cash = money_init # 现金
self.stock_value = 0 # 股票价值
self.market_value = money_init # 总市值
self.stock_name = [] # 记录持仓股票名字
self.stock_id = [] # 记录持仓股票id
self.buy_date = [] # 记录持仓股票买入日期
self.stock_num = [] # 记录持股股票剩余持股数量
self.stock_price = [] # 记录股票的买入价格
self.start_date = start_date
self.end_date = end_date
self.stock_asset = [] # 持仓数量
self.buy_rate = 0.0003 # 买入费率
self.buy_min = 5 # 最小买入费率
self.sell_rate = 0.0003 # 卖出费率
self.sell_min = 5 # 最大买入费率
self.stamp_duty = 0.001 # 印花税
# self.info = [] # 记录所有买入卖出记录
self.max_hold_period = max_hold_period # 最大持股周期
self.hold_day = [] # 股票持股时间
self.cost = [] # 记录真实花费
# self.profit = [] # 记录每次卖出股票收益
self.stop_loss_rate = stop_loss_rate # 止损比例
self.stop_profit_rate = stop_profit_rate # 止盈比例
self.victory = 0 # 记录交易胜利次数
self.defeat = 0 # 记录失败次数
self.cash_all = [money_init] # 记录每天收盘后所持现金
self.stock_value_all = [0.0] # 记录每天收盘后所持股票的市值
self.market_value_all = [money_init] # 记录每天收盘后的总市值
self.max_market_value = money_init # 记录最大的市值情况，用来计算回撤
self.min_after_max_makret_value = money_init # 记录最大市值后的最小市值
self.max_retracement = 0 #记录最大回撤率
self.info = pd.DataFrame(columns=['ts_code', 'name', 'buy_price', 'buy_date', 'buy_num', 'sell_price', 'sell_date',
'profit'])
# 股票买入
def buy_stock(self, buy_date, stock_name, stock_id, stock_price, buy_num):
"""
:param buy_date: 买入日期
:param stock_name: 买入股票的名字
:param stock_id: 买入股票的id
:param stcok_price: 买入股票的价格
:param buy_num: 买入股票的数量
:return:
"""
tmp_len = len(self.info)
if stock_id not in self.stock_id:
self.stock_id.append(stock_id)
self.stock_name.append(stock_name)
self.buy_date.append(buy_date)
self.stock_price.append(stock_price)
self.hold_day.append(1)
self.info.loc[tmp_len, 'ts_code'] = stock_id
self.info.loc[tmp_len, 'name'] = stock_name
self.info.loc[tmp_len, 'buy_price'] = stock_price
self.info.loc[tmp_len, 'buy_date'] = buy_date
# 更新市值、现金及股票价值
tmp_money = stock_price * buy_num
service_change = tmp_money * self.buy_rate
if service_change < self.buy_min:
service_change = self.buy_min
self.cash = self.cash - tmp_money - service_change
if self.cash < 0:
buy_num = buy_num - 100
tmp_money = stock_price * buy_num
service_change = tmp_money * self.buy_rate
if service_change < self.buy_min:
service_change = self.buy_min
self.cash = self.cash - tmp_money - service_change
self.info.loc[tmp_len, 'buy_num'] = buy_num
self.stock_num.append(buy_num)
# self.stock_value = self.stock_value + tmp_money
# self.market_value = self.cash + self.stock_value
self.cost.append(tmp_money + service_change)
info = str(buy_date) + ' 买入 ' + stock_name + ' (' + stock_id + ') ' \
+ str(int(buy_num)) + '股，股价：'+str(stock_price)+',花费：' + str(round(tmp_money, 2)) + ',手续费：' \
+ str(round(service_change, 2)) + '，剩余现金：' + str(round(self.cash, 2))
print(info)
# self.info.append(info)
def sell_stock(self, sell_date, stock_name, stock_id, sell_price, sell_num, flag):
"""
:param sell_date: 卖出日期
:param stock_name: 卖出股票的名字
:param stock_id: 卖出股票的id
:param sell_price: 卖出股票的价格
:param sell_num: 卖出股票的数量
:return:
"""
if stock_id not in self.stock_id:
raise TypeError('该股票未买入')
idx = self.stock_id.index(stock_id)
tmp_money = sell_num * sell_price
service_change = tmp_money * self.sell_rate
if service_change < self.sell_min:
service_change = self.sell_min
stamp_duty = self.stamp_duty * tmp_money
self.cash = self.cash + tmp_money - service_change - stamp_duty
# self.stock_value = self.stock_value - tmp_money
# self.market_value = self.cash + self.stock_value
service_change = stamp_duty + service_change
# self.profit.append(tmp_money-service_change)
profit = tmp_money-service_change - self.cost[idx]
if self.stock_num[idx] == sell_num:
# 全部卖出
del self.stock_num[idx]
del self.stock_id[idx]
del self.stock_name[idx]
del self.buy_date[idx]
del self.stock_price[idx]
del self.hold_day[idx]
del self.cost[idx]
else:
self.stock_num[idx] = self.stock_num[idx] - sell_num
# 还需要补充profit的计算先放着
pass
if flag == 0:
info = str(sell_date) + ' 到期卖出' + stock_name + ' (' + stock_id + ') ' \
+ str(int(sell_num)) + '股，股价：'+str(sell_price) + ',收入：' + str(round(tmp_money,2)) + ',手续费：' \
+ str(round(service_change, 2)) + '，剩余现金：' + str(round(self.cash, 2))
if profit > 0:
info = info + '，最终盈利：' + str(round(profit, 2))
self.victory += 1
else:
info = info + '，最终亏损：' + str(round(profit, 2))
self.defeat += 1
elif flag == 1:
info = str(sell_date) + ' 止盈卖出' + stock_name + ' (' + stock_id + ') ' \
+ str(int(sell_num)) + '股，股价：' + str(sell_price) + ',收入：' + str(round(tmp_money, 2)) + ',手续费：' \
+ str(round(service_change, 2)) + '，剩余现金：' + str(round(self.cash, 2)) \
+ '，最终盈利：' + str(round(profit, 2))
self.victory += 1
elif flag == 2:
info = str(sell_date) + ' 止损卖出' + stock_name + ' (' + stock_id + ') ' \
+ str(int(sell_num)) + '股，股价：' + str(sell_price) + ',收入：' + str(round(tmp_money, 2)) + ',手续费：' \
+ str(round(service_change, 2)) + '，剩余现金：' + str(round(self.cash, 2)) \
+ '，最终亏损：' + str(round(profit, 2))
self.defeat += 1
print(info)
idx = (self.info['ts_code'] == stock_id) & self.info['sell_date'].isna()
self.info.loc[idx, 'sell_date'] = sell_date
self.info.loc[idx, 'sell_price'] = sell_price
self.info.loc[idx, 'profit'] = profit
# 买入触发时间，后期可以补
def buy_trigger(self):
pass
# 判断是否达到卖出条件
def sell_trigger(self, stock_id, day, all_df, index_df):
"""
:param stock_id: 股票id
:param day: 回测时间
:param all_df: 所有数据的DataFrame
:param index_df: 指数的DataFram
:return: 第一个返回是否卖出，第二个返回卖出类型，第三个返回
卖出价格；若不卖出，后面两个值无意义
"""
# print(day, stock_id)
# 可能会有一些停牌企业，后期再改
idx = (all_df['trade_date'] == day) & (all_df['ts_code'] == stock_id)
# print(all_df[idx]['low'])
low = all_df[idx]['low'].values[0]
high = all_df[idx]['high'].values[0]
open = all_df[idx]['open'].values[0]
close = all_df[idx]['close'].values[0]
idx = self.stock_id.index(stock_id)
tmp_rate = (open - self.stock_price[idx]) / self.stock_price[idx]
if tmp_rate <= self.stop_loss_rate: # 止损卖出，开盘价卖出
return True, 2, open
elif tmp_rate >= self.stop_profit_rate: # 止盈卖出，开盘价卖出
return True, 1, open
# 这里有点bug，先判断最低吧，优先出现最差的可能
tmp_rate = (low - self.stock_price[idx]) / self.stock_price[idx]
if tmp_rate <= self.stop_loss_rate: # 止损卖出，止损价卖出
# 假设都止损价不能马上卖出，多损失 0.01%
sell_price = self.stock_price[idx] * (1 + self.stop_loss_rate - 0.01)
return True, 2, sell_price
tmp_rate = (high - self.stock_price[idx]) / self.stock_price[idx]
if tmp_rate >= self.stop_profit_rate: # 止盈卖出，止盈价卖出
sell_price = self.stock_price[idx] * (1 + self.stop_profit_rate)
return True, 1, sell_price
# 判断持股周期是否达到上限
hold_day = self.hold_day[idx]
if hold_day >= self.max_hold_period: # 收盘价卖出
return True, 0, close
return False, 3, 0
# 更新信息
def update(self, day, all_df):
stock_price = []
for j in range(len(self.stock_id)):
self.hold_day[j] = self.hold_day[j] + 1 # 更新持股时间
idx = (all_df['trade_date'] == day) & (all_df['ts_code'] == self.stock_id[j])
close = all_df.loc[idx]['close'].values[0]
stock_price.append(close)
# 更新市值等信息
# print(stock_price)
stock_price = np.array(stock_price)
stock_num = np.array(self.stock_num)
self.stock_value = np.sum(stock_num * stock_price)
self.market_value = self.cash + self.stock_value
self.market_value_all.append(self.market_value)
self.stock_value_all.append(self.stock_value)
self.cash_all.append(self.cash)
if self.max_market_value < self.market_value:
self.max_market_value = self.market_value
self.min_after_max_makret_value = 99999999999
else:
if self.min_after_max_makret_value > self.market_value:
self.min_after_max_makret_value = self.market_value
# 计算回撤率
retracement = np.abs((self.max_market_value - self.min_after_max_makret_value) / self.max_market_value)
if retracement > self.max_retracement:
self.max_retracement = retracement
def BackTest(self, buy_df, all_df, index_df, buy_price='close'):
"""
:param buy_df: 可以买入的股票，输入为DataFrame
:param all_df: 所有股票的DataFrame
:param index_df: 指数对应时间的df
:return:
"""
day_info = np.sort(index_df['trade_date'])
for i in range(len(day_info)):
day = day_info[i]
tmp_idx = buy_df['trade_date'] == day
# tmp_df = buy_df.loc[tmp_idx].reset_index()
tmp_df = buy_df.loc[tmp_idx].sort_values('label_prob', ascending=False).reset_index()
# 先买后卖吧
# ----买股
if len(tmp_df) != 0:
for j in range(len(tmp_df)):
money = self.market_value * 0.2
if money > self.cash:
money = self.cash
if money < 5000: # 假设小于5000RMB，就不买股票
break
# print(1)
# print(tmp_df)
# print(tmp_df['close'])
buy_num = (money / tmp_df[buy_price][j]) // 100
if buy_num == 0:
continue
buy_num = buy_num * 100
self.buy_stock(day, tmp_df['name'][j],
tmp_df['ts_code'][j], tmp_df[buy_price][j], buy_num)
# ----卖股
# import datetime
# start = datetime.datetime.now()
for j in range(len(self.stock_id) - 1, -1, -1):
if self.buy_date[j] == day:
continue
stock_id = self.stock_id[j]
stock_name = self.stock_name[j]
sell_num = self.stock_num[j] # 假设全卖出去
is_sell, sell_kind, sell_price = self.sell_trigger(stock_id, day, all_df, index_df)
if is_sell:
self.sell_stock(day, stock_name, stock_id, sell_price, sell_num, sell_kind)
# 更新持股周期及信息
self.update(day, all_df)
# end = datetime.datetime.now()
# print('running time:%s'%(end-start))
# self.info['buy_date'] = self.info['buy_date'].apply(lambda x: int(x))
# self.info['sell_date'] = self.info['sell_date'].apply(lambda x: int(x))
# self.info['buy_num'] = self.info['buy_num'].apply(lambda x: int(x))
try:
self.info[['buy_date', 'sell_date', 'buy_num']] = self.info[['buy_date', 'sell_date', 'buy_num']].astype(int)
except:
pass