多股回测（backtrader+quantstats+akshare）
导包
#引入技术指标数据
from __future__ import (absolute_import ,division,print_function,unicode_literals)
import datetime #用于datetime对象操作
import os.path #用于管理路径
import sys #用于在argvTo[0]中找到脚本名称
import backtrader as bt #引入backtrader框架
%matplotlib inline策略
#创建策略
class TestStrategy(bt.Strategy):
params = (
('maperiod1',5),
('maperiod2',13),
('maperiod3',21),
('maperiod4',34),
('maperiod5',55),
('printlog',True),
('poneplot' , False),#是否打印到同一张图
('pstake' , 100000) #单笔交易股票数据
)
def log(self,txt,dt=None,doprint = False):
dt = dt or self.datas[0].datetime.date(0)
#print('%s,%s' % (dt.isoformat(),txt))
"""策略的日志函数"""
if self.params.printlog or doprint:
dt = dt or self.datas[0].datetime.date(0)
print('%s,%s' % (dt.isoformat(),txt))
def __init__(self):
self.inds = dict()
for i, d in enumerate(self.datas):
self.inds[d] = dict()
self.inds[d]['ma1'] = bt.indicators.SimpleMovingAverage( d.close,period = self.params.maperiod1)
self.inds[d]['ma2'] = bt.indicators.SimpleMovingAverage( d.close,period = self.params.maperiod2)
self.inds[d]['ma3'] = bt.indicators.SimpleMovingAverage( d.close,period = self.params.maperiod3)
self.inds[d]['ma4'] = bt.indicators.SimpleMovingAverage( d.close,period = self.params.maperiod4)
self.inds[d]['ma5'] = bt.indicators.SimpleMovingAverage( d.close,period = self.params.maperiod5)
self.inds[d]['D1'] = bt.ind.CrossOver(self.inds[d]['ma5'],self.inds[d]['ma4']) #交叉信号
self.inds[d]['A1'] = bt.ind.CrossOver(self.inds[d]['ma1'],self.inds[d]['ma2']) #交叉信号
self.inds[d]['C1'] = bt.ind.CrossOver(self.inds[d]['ma2'],self.inds[d]['ma3'])
#跳过第一只股票data，第一只股票data作为主图数据
if i > 0:
if self.p.poneplot:
d.plotinfo.plotmaster = self.datas[0]
def notify_trade(self,trade):
if not trade.isclosed:
return
self.log('OPERATION PROFIT,GROSS %.2F,NET %.2F' %
(trade.pnl,trade.pnlcomm))
#多股回测时使用，数据读取。
def prenext(self):
self.next()
def next(self):
# 获取当天日期
date = self.datas[0].datetime.date(0)
# 获取当天value
value = self.broker.getvalue()
for i , d in enumerate(self.datas):
dt,dn = self.datetime.date(),d._name #获取时间及股票代码
pos = self.getposition(d).size
sig1 = ((self.inds[d]['D1'][-1]>0) and (self.inds[d]['A1'][0]>0)) and (self.inds[d]['ma2'][0] >self.inds[d]['ma4'][0])and (self.inds[d]['ma4'][0] >self.inds[d]['ma4'][-1])
sig2 = ((self.inds[d]['D1'][-1]>0) or (self.inds[d]['A1'][0]>0 ))and(self.inds[d]['ma2'][0] >self.inds[d]['ma2'][-1])and(d.close[0]/d.open[0]>1.05)and(d.volume[0] /d.volume[-1]>2)
sig3 = ((self.inds[d]['D1'][-1]>0) or (self.inds[d]['A1'][0]>0 ))and(self.inds[d]['ma2'][0] >self.inds[d]['ma3'][0] )and(self.inds[d]['ma3'][0] >self.inds[d]['ma4'][0] )and(self.inds[d]['ma4'][0] >self.inds[d]['ma4'][-1] )
sig4 = self.inds[d]['C1'][0]<0
#print('sig1',sig1)
if not pos: # 不在场内，则可以买入 vol成交量， ref日前
if sig1 or sig2 and sig3: #如果金叉
self.buy(data =d,size =self.p.pstake) #买
self.log('%s,BUY CREATE, %.2f ,%s' % (dt, d.close[0] ,d._name))
#self.order = self.buy()
elif sig4: #在场内。且死叉
self.close(data = d) #卖
self.log('%s,SELL CREATE,%.2f,%s' % (dt, d.close[0] ,d._name))
#self.order = self.sell()印花税
class stampDutyCommissionScheme(bt.CommInfoBase):
params = (
('stamp_duty',0.005),#印花税率
('percabs',True),
)
def _gotcommission(self,size,price,pseudoexec):
if size >0:#买入，不考虑印花税
return size*price * self.p.commission
elif size<0:#卖出，考虑印花税
return -size*price*(self.p.stamp_duty + self.p.commission)
else:
return 0开始回测
#创建cerebro实体
cerebro = bt.Cerebro()
#添加策略
cerebro.addstrategy(TestStrategy)添加数据
#创建价格数据
import akshare as ak
import baostock as bs
import pandas as pd
import datetime
#获取股票池数据
from os import listdir
filename = listdir('D:/stock_data')
stk_pools = filename
for i in stk_pools[:]:
try:
datapath = 'D:/stock_data/'+i
df = pd.read_csv('D:/stock_data/'+i)
#将数据长度不足的股票删去
if len(df)<55:
pass
else:
try:
data = bt.feeds.GenericCSVData(
dataname = datapath,
fromdate = datetime.datetime(2010,4,1),
todate = datetime.datetime(2021,7,8),
nullvalue = 0.0,
dtformat = ('%Y-%m-%d'),
datetime = 1,
open =2,
high = 3,
low = 4,
close = 5,
volume = 6,
openinterest = -1
)
cerebro.adddata(data,name = i)
except:
continue
except:
continue设置参数
#设置启动资金
cerebro.broker.setcash(len(stk_pools[:50])*10000)
#设置交易单位大小
cerebro.addsizer(bt.sizers.FixedSize,stake = 100)
#设置佣金为千分之一
comminfo = stampDutyCommissionScheme(stamp_duty=0.005,commission=0.001)
cerebro.broker.addcommissioninfo(comminfo)
#不显示曲线
for d in cerebro.datas:
d.plotinfo.plot = False
#打印开始信息
print('Starting Portfolio Value: %.2f' % cerebro.broker.getvalue())回测数据分析
#查看策略效果
cerebro.addanalyzer(bt.analyzers.PyFolio, _name='pyfolio')
back = cerebro.run(maxcpus=12,exactbars=True,stdstats=False)
import warnings
warnings.filterwarnings('ignore')
strat = back[0]
portfolio_stats = strat.analyzers.getbyname('pyfolio')
returns, positions, transactions, gross_lev = portfolio_stats.get_pf_items()
returns.index = returns.index.tz_convert(None)
import quantstats
quantstats.reports.html(returns, output='stats.html', title='Stock Sentiment')
import webbrowser
f = webbrowser.open('stats.html')
#打印最后结果
print('Final Profolio Value : %.2f' %cerebro.broker.getvalue())