量化交易4-backtrader的执行策略编写

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我是靠谱客的博主靓丽猎豹，最近开发中收集的这篇文章主要介绍量化交易4-backtrader的执行策略编写，觉得挺不错的，现在分享给大家，希望可以做个参考。

概述

backtrader执行的策略的概念是，我们使用cerebro.adddata(data)把数据加载完成后，针对加载的数据，通过判断买卖股票的时间点，所买卖算法，以下面的代码为例子：class MyStrategy(bt.Strategy):中所写的代码，其实就是策略，下面回针对构建策略做简单的讲解

import tushare as ts
import pandas as pd

import datetime # For datetime objects
import os.path # To manage paths
import sys # To find out the script name (in argv[0])
# Import the backtrader platform
import backtrader as bt

class MyStrategy(bt.Strategy):
# 策略参数
params = dict(
period=20, # 均线周期
look_back_days=30,
printlog=False
)
def __init__(self):
self.mas = dict()
#遍历所有股票,计算20日均线
for data in self.datas:
self.mas[data._name] = bt.ind.SMA(data.close, period=self.p.period)
def next(self):
#计算截面收益率
rate_list=[]
for data in self.datas:
if len(data)>self.p.look_back_days:
p0=data.close[0]
pn=data.close[-self.p.look_back_days]
rate=(p0-pn)/pn
rate_list.append([data._name,rate])
#股票池
long_list=[]
sorted_rate=sorted(rate_list,key=lambda x:x[1],reverse=True)
long_list=[i[0] for i in sorted_rate[:10]]
# 得到当前的账户价值
total_value = self.broker.getvalue()
p_value = total_value*0.9/10
for data in self.datas:
#获取仓位
pos = self.getposition(data).size
if not pos and data._name in long_list and
self.mas[data._name][0]>data.close[0]:
size=int(p_value/100/data.close[0])*100
self.buy(data = data, size = size)
if pos!=0 and data._name not in long_list or
self.mas[data._name][0]<data.close[0]:
self.close(data = data)
def log(self, txt, dt=None,doprint=False):
if self.params.printlog or doprint:
dt = dt or self.datas[0].datetime.date(0)
print(f'{dt.isoformat()},{txt}')
#记录交易执行情况（可省略，默认不输出结果）
def notify_order(self, order):
# 如果order为submitted/accepted,返回空
if order.status in [order.Submitted, order.Accepted]:
return
# 如果order为buy/sell executed,报告价格结果
if order.status in [order.Completed]:
if order.isbuy():
self.log(f'买入:n价格:{order.executed.price:.2f},
成本:{order.executed.value:.2f},
手续费:{order.executed.comm:.2f}')
self.buyprice = order.executed.price
self.buycomm = order.executed.comm
else:
self.log(f'卖出:n价格：{order.executed.price:.2f},
成本: {order.executed.value:.2f},
手续费{order.executed.comm:.2f}')
self.bar_executed = len(self)
# 如果指令取消/交易失败, 报告结果
elif order.status in [order.Canceled, order.Margin, order.Rejected]:
self.log('交易失败')
self.order = None
#记录交易收益情况（可省略，默认不输出结果）
def notify_trade(self,trade):
if not trade.isclosed:
return
self.log(f'策略收益：n毛收益 {trade.pnl:.2f}, 净收益 {trade.pnlcomm:.2f}')

pro=ts.pro_api('cbb257058b7cb228769b4949437c27c27e5132e882747dc80f01a5a5')

def ts_get_daily_stock(code,start_dt,end_dt):
start_dt = start_dt.replace("'", "", 3);
end_dt = end_dt.replace("'", "", 3);
#start_dt = '20190101'
#end_dt=''
print(code,start_dt,end_dt)
data = pro.daily(ts_code = code,start_date = start_dt,end_date=end_dt)
print(data)
data['trade_date'] = pd.to_datetime(data['trade_date'])
data=data.sort_values(by = 'trade_date')
data.index = data['trade_date']
data['openinterest']=0
data['volume']=data['vol']
data = data[
['open','close','high','low','volume']
]
return data



#读取选股的结果
df=pd.read_csv('stock_alpha.csv')
df.columns=['ts_code','name','alpha','start_dt','end_dt']
min_a=df.sort_values(by='alpha')
min_a=min_a.iloc[:10,:]

code=[]
code=min_a['ts_code']#记录alpha最小的10支股票代码

start_dts=[]
start_dts=min_a['start_dt']#记录alpha最小的10支股票代码

end_dts=[]
end_dts=min_a['end_dt']#记录alpha最小的10支股票代码

for i in range(len(code)):
data=ts_get_daily_stock(code.iloc[i],start_dts.iloc[i],end_dts.iloc[i])#字段分别为股票代码、开始日期、结束日期
data.to_csv(code.iloc[i]+'.csv')



cerebro = bt.Cerebro()
for i in range(len(code)):#循环获取10支股票历史数据
dataframe = pd.read_csv(code.iloc[i]+'.csv', index_col=0, parse_dates=True)
dataframe['openinterest'] = 0
data = bt.feeds.PandasData(dataname=dataframe,
fromdate = datetime.datetime(2019, 1, 1),
todate = datetime.datetime(2022, 3, 22)
)
cerebro.adddata(data)

#回测设置
startcash=100000.0
cerebro.broker.setcash(startcash)
# 设置佣金为千分之一
cerebro.broker.setcommission(commission=0.001)
# 添加策略
cerebro.addstrategy(MyStrategy,printlog=True)
cerebro.run()
#获取回测结束后的总资金
portvalue = cerebro.broker.getvalue()
pnl = portvalue - startcash
#打印结果
print(f'总资金: {round(portvalue,2)}')
print(f'净收益: {round(pnl,2)}')