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Solo_Stock_strategy.py
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import tushare as ts
import pandas as pd
import matplotlib.pyplot as plt
from datetime import datetime
import time
class AstockTrading(): # 策略类
def __init__(self, stratege_name, Start_date, End_date, origin_total_value):
token = '' # 改成自己的token
self._pro = ts.pro_api(token)
day = self._pro.trade_cal(exchange='', start_date=Start_date,
end_date=End_date)
self._current_buy = [] # 当天买入
self._current_sell = [] # 当天卖出
self._hands_list = [] # 当天买入手数
self.start_date = Start_date
self.end_date = End_date
self._datum_target = '399300.SZ' # 基准标的，沪深300
self._stratege_name = stratege_name
self._origin_total_value = origin_total_value # 持仓总市值
self._total_value = self._origin_total_value # 持仓初始市值
########## 主要属性 ↓
self._daily_tick = pd.DataFrame([]) # 初始化每日行情，在get_tick函数里面赋值
self._history_order = [] # 历史指令
self._calendar = day[day['is_open'] == 1].cal_date.apply(str)
# ↑ 交易日历,一次性赋值,列表,字符串
self._buy_list = [] # 每日买入股票列表
self._trade_number = 0 # 交易日期序号
self._history_value = [] # 历史市值
self._hold_stock = {} # 目前持仓股票
self._connot_sell_stock = [] # 跌停股，不能卖
self._base_rate = [] # 基准收益率
self._my_rate = [] # 个人收益率
self._new_calendar = pd.DataFrame([]) # 交易日历（作图用）
self._result = pd.DataFrame([]) # 最后用于统计
def get_tick(self): # 获取行情
self._daily_tick = ts.pro_bar(ts_code=self._code, adj='qfq',
start_date=self.start_date, end_date=self.end_date)
# 前复权个股数据
def order_target_value(self, stock, target_value): # 交易函数,设置手续费也是在这里
# stock 是代码
# target_value 是目标份额
# 这里结算总市值self._total_value
target_value = int(target_value)
df = self._daily_tick # 为了便于书写，self._daily_tick赋值为当日行情数据df
if target_value == 0:
# 卖出后，新市值加等于新价格减与成本的差乘以手数再乘以100
self._total_value += ((df[df.ts_code==stock]['close'] - \
self._hold_stock[stock][1])*\
self._hold_stock[stock][0]) * 100
self._total_value = float(self._total_value)
self._current_sell.append(stock)
else: # 买入
hands = target_value / df[df.ts_code==stock]['close'].iloc[0] \
// 100 - 1
self._hands_list.append(hands)
# ↑可买手数
cost = hands * 100 * df[df.ts_code==stock]['close'].iloc[0] * 0.0015
if cost < 5: # 手续费不低于5元
cost = 5
self._total_value -= cost
# 买入就是总权益减去手续费
self._current_buy.append(stock)
self._total_value = float(self._total_value)
def before_market_open(self): # 盘前函数
self._connot_sell_stock = []
df2 = self._pro.suspend_d(suspend_type='S', \
trade_date=self._calendar.iloc[self._trade_number]).ts_code
self.get_tick() # 模拟交易前获取每日行情，相当于预先知道行情了，都是以收盘价交易
limit_down_list = self._pro.limit_list(
trade_date=self._calendar.iloc[self._trade_number],
limit_type='D').ts_code # 当天跌停股票
for stock in self._hold_stock: # 统计持仓里面哪些是跌停的
if stock in list(limit_down_list) or stock in list(df2):
self._connot_sell_stock.append(stock) # 持仓里面的跌停或停牌股列表
def after_market_close(self): # 盘后函数
self._trade_number += 1 # 今日行情获取完毕之后，序号加1，下次获取下一日行情数据
def strategy(self): # 策略重点修改这里
# 选股策略
df = self._daily_tick
# ↑为了便于书写，self._daily_tick赋值为当日行情数据df
self._buy_list = [] # 重新初始化买入列表
'''
在这里写交易策略，如果确定要买，将股票代码传入 self._buy_list 列表，即便只有一个股票
'''
def trade(self):# 选股结束，开始交易
df = self._daily_tick
MonPerStock = self._total_value / len(self._buy_list)
for stock in self._hold_stock: # stock 应当是代码字符串
if stock not in self._buy_list and stock not in self._connot_sell_stock:
# 如果已经持有的股票不在买入列表也没跌停或停牌，卖出
self.order_target_value(stock, 0) # 目标卖空
self._history_order.append(['{}, {}, {}手, 现价{}元, 卖空'.format(
df[df.ts_code==stock]['trade_date'].iloc[0],
stock,
'all',
float(df[df.ts_code==stock]['close'].iloc[0]))]) # 增加记录
for stock in self._buy_list: # stock 应当是代码字符串
self.order_target_value(stock, MonPerStock) # 目标买入
self._history_order.append(['{}, {}, {}手 现价{}元, 买入'.format(
df[df.ts_code==stock]['trade_date'].iloc[0],
stock,
int(MonPerStock//float(df[df.ts_code==stock]['close'])//100),
float(df[df.ts_code==stock]['close'].iloc[0]))]) # 增加记录
self.update_hold() # 更新持仓和市值
self.after_market_close() # 盘后，日期加一
def update_hold(self): # 更新持仓和市值
df = self._daily_tick
for num in range(len(self._current_sell)):
del self._hold_stock[self._current_sell[num]]
for num in range(len(self._current_buy)):
self._hold_stock[self._current_buy[num]] = \
[int(self._hands_list[num]),
float(df[df.ts_code==self._current_buy[num]]['close'])]
self._current_buy = [] # 当天买入
self._current_sell = [] # 当天卖出
self._hands_list = [] # 当天买入手数
self._history_value.append(self._total_value) # 记录历史市值
def picture_all(self): # 交易结束,作图和结算
print('>> 最终市值{:.3f}元，收益率{:.3f}%\n'.format(self._total_value,
(self._total_value - \
self._origin_total_value)/\
self._origin_total_value*100))
self._result['历史市值'] = self._my_rate
self._result['沪深300'] = self._base_rate
self._result['超额收益'] = self._result['历史市值'] - self._result['沪深300']
plt.plot(self._new_calendar, self._result)
plt.legend(['历史市值', '沪深300', '超额收益'])
plt.xlabel('日期',fontsize='15')
plt.ylabel('收益率',fontsize='15')
plt.title('{}策略'.format(self._stratege_name),fontsize=15)
plt.grid()
plt.show()
def statistics(self): # 统计结果
# 把历史市值改成pd.Series形式
plt.rcParams['font.sans-serif'] = ['Microsoft YaHei']
self._new_calendar['trade_date'] = [datetime.strptime(day, '%Y%m%d').date()\
for day in self._calendar]
hs300 = self._pro.index_daily(ts_code=self._datum_target,
start_date=self.start_date,
end_date=self.end_date)
hs300_close = list(hs300['close'][::-1]) # 沪深300收盘价
for i in range(len(hs300_close)):
self._base_rate.append((hs300_close[i] - hs300_close[0])/hs300_close[0])
# ↑ 沪深300累计收益率
for i in range(len(self._history_value)):
self._my_rate.append((self._history_value[i] - \
self._origin_total_value)/self._origin_total_value)
def count_day(self): # 计算交易日期数
day = self._pro.trade_cal(exchange='', start_date=self.start_date,
end_date=self.end_date)
calendar = day[day['is_open'] == 1].cal_date.apply(str)
return len(calendar)
def run(self): # 运行全部交易流程
days = self.count_day()
for i in range(days): # 循环若干交易日
time.sleep(0.05)
self.before_market_open()
self.strategy()
self.trade()
if (i+1)%5 == 0:
print('>> 已完成{}天交易，还剩下{}天'.format(i+1,days-i-1)) # 通报进度
print('## 交易完毕\n')
self.statistics()
self.picture_all() # 统计作图
mys = AstockTrading('amount_strategy', '20190101', '20190531', 1000000)
# 设定策略名称，交易起止日期，本金
mys.run() # 运行
print('利用以下命令查看相关信息：\n>> mys.picture_all() 显示图像')
print('>> mys._history_order 显示历史指令')