Backtrader量化平台教程（一）:backtrader的整体框架

imelee 2018-03-07

展开全文

backtrader是一个量化策略的回测分析平台，功能还是很强大的。

1.安装backtrader

安装很简单，和别的lib安装一模一样，pip install backtrader。

2.从代码开始

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from __future__ import (absolute_import, division, print_function,
unicode_literals)
import datetime # For datetime objects
import os.path # To manage paths
import sys # To find out the script name (in argv[0])
import pandas as pd
from WindPy import w
# Import the backtrader platform
import backtrader as bt
# Create a Stratey
class TestStrategy(bt.Strategy):
def log(self, txt, dt=None):
''''' Logging function fot this strategy'''
dt = dt or self.datas[0].datetime.date(0)
print('%s, %s' % (dt.isoformat(), txt))
def __init__(self):
# Keep a reference to the "close" line in the data[0] dataseries
self.dataclose = self.datas[0].close
# To keep track of pending orders
self.order = None
def notify(self, order):
if order.status in [order.Submitted, order.Accepted]:
# Buy/Sell order submitted/accepted to/by broker - Nothing to do
return
# Check if an order has been completed
# Attention: broker could reject order if not enougth cash
if order.status in [order.Completed, order.Canceled, order.Margin]:
if order.isbuy():
self.log('BUY EXECUTED, %.2f' % order.executed.price)
elif order.issell():
self.log('SELL EXECUTED, %.2f' % order.executed.price)
self.bar_executed = len(self)
# Write down: no pending order
self.order = None
def next(self):
# Simply log the closing price of the series from the reference
self.log('Close, %.2f' % self.dataclose[0])
# Check if an order is pending ... if yes, we cannot send a 2nd one
if self.order:
return
# Check if we are in the market
if not self.position:
# Not yet ... we MIGHT BUY if ...
if self.dataclose[0] < self.dataclose[-1]:
# current close less than previous close
if self.dataclose[-1] < self.dataclose[-2]:
# previous close less than the previous close
# BUY, BUY, BUY!!! (with default parameters)
self.log('BUY CREATE, %.2f' % self.dataclose[0])
# Keep track of the created order to avoid a 2nd order
self.order = self.buy()
else:
# Already in the market ... we might sell
if len(self) >= (self.bar_executed + 5):
# SELL, SELL, SELL!!! (with all possible default parameters)
self.log('SELL CREATE, %.2f' % self.dataclose[0])
# Keep track of the created order to avoid a 2nd order
self.order = self.sell()
if __name__ == '__main__':
# Create a cerebro entity
cerebro = bt.Cerebro()
# Add a strategy
cerebro.addstrategy(TestStrategy)
# Create a Data Feed
# 本地数据，笔者用Wind获取的东风汽车数据以csv形式存储在本地。
# parase_dates = True是为了读取csv为dataframe的时候能够自动识别datetime格式的字符串，big作为index
# 注意，这里最后的pandas要符合backtrader的要求的格式
dataframe = pd.read_csv('dfqc.csv', index_col=0, parse_dates=True)
dataframe['openinterest'] = 0
data = bt.feeds.PandasData(dataname=dataframe,
fromdate = datetime.datetime(2015, 1, 1),
todate = datetime.datetime(2016, 12, 31)
)
# Add the Data Feed to Cerebro
cerebro.adddata(data)
# Set our desired cash start
cerebro.broker.setcash(100000.0)
# Print out the starting conditions
print('Starting Portfolio Value: %.2f' % cerebro.broker.getvalue())
# Run over everything
cerebro.run()
# Print out the final result
print('Final Portfolio Value: %.2f' % cerebro.broker.getvalue())
# Plot the result
cerebro.plot()

上面这段代码，大家安装完backtrader之后可以复制过去运行一样，一般来说，运行通过就说明环境没有问题。接下来我们就分析一下这个不赚钱的策略。

我们想想，量化回测，首先得有数据。

在backtrader里面，DataFeed就是这样一个数据的概念。

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dataframe = pd.read_csv('dfqc.csv', index_col=0, parse_dates=True)
dataframe['openinterest'] = 0
data = bt.feeds.PandasData(dataname=dataframe,
fromdate = datetime.datetime(2015, 1, 1),
todate = datetime.datetime(2016, 12, 31)
)

这一段代码从本地csv文件获得某一个股票的交易数据，包括open，close，high，low，volume。大家注意到，这里我们的存储形式的pandas。backtrader是支持pandas这样的一种数据格式的，无形中方便了很多

关于该数据文件，大家可以去笔者的百度云下载。

链接：https://pan.baidu.com/s/1slnULbv 密码：wi3k

后面的很多教程案例，都会用到这一数据。

backtrader对每种数据的来源都有一定的标准，要不然咱们相信，不订立标准，框架怎么会知道每一列数据都是干嘛的呢。backtrader对于pandas的标准就是这些列的名字得是open，close，high，low，volume，openinterest。我们这里没有用到openinterest，所以把它设置为零就可以了，这里提示一下，backtrader要求pandas下的DataFeed，pandas的DataFrame的index是时间。

数据的问题解决了，那么数据要交给回测平台。这里整个回测功能的实现都是依赖于cercbro这个类实现的。这个单词的意思，在西班牙语里就是大脑。

我们创造一个大脑，

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