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文库首页行业金融backtrader参考手册.pdf

backtrader参考手册.pdf

backtrader

量化投资

Python

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一个功能丰富的Python测试和交易框架。backtrader能够让策略研究员专注于编写可重用的交易策略、指标和分析器，而不是花时间构建基础设施。

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1 Installation

Requirements and versions

backtrader is self-contained with no external dependencies (except if you want to plot)

Basic requirements are:

Python 2.7

Python 3.2/3.3/3.4/3.5

pypy/pypy3



Additional requirements if plotting is wished:

Matplotlib >= 1.4.1



It may work with previous versions, but this the one used for development



Note

At the time of writing Matplotlib is not supported under pypy/pypy3

Python 2.x/3.x compatibility

Development takes place under Python 2.7 and sometimes under 3.4. Tests are run locally with both

versions.

Compatibility with 3.2/3.3/3.5 and pypy/pyp3 is checked with continuous integration under Travis

Install from pypi

For example using pip:

pip install backtrader

easy_install with the same syntax can also be applied

Install from pypi (including matplotlib )

Use this if plotting capabilities are wished:

pip install backtrader[plotting]

This pulls in matplotlib which will in turn pull in other dependencies.

Again you may prefer (or only have access to ...) easy_install

Install from source

First downloading a release or the latest tarball from the github site:

https://github.com/mementum/backtrader



And after unpacking run the command:

python setup.py install

Run from source in your project

Download a release or the latest tarball from the github site:

https://github.com/mementum/backtrader

And then copy the backtrader package directory to your own project. Under a Unix-like OS for example:

Again tar xzf backgrader.tgz

cd backtrader

cp -r backtrader project_directory

Remember that you would then need to manually install matplotlib for plotting.



2 Quickstart

Note

The data ﬁles used in the quickstart guide are updated from time to time, which means that the

adjusted close changes and with it the close (and the other components). That means that the actual

output may be diﬀerent to what was put in the documentation at the time of writing.

Using the platform

Let’s run through a series of examples (from almost an empty one to a fully ﬂedged strategy) but not

without before roughly explaining 2 basic concepts when working with backtrader

1. Lines



Data Feeds, Indicators and Strategies have lines.



A line is a succession of points that when joined together form this line. When talking about the

markets, a Data Feed has usually the following set of points per day:



Open, High, Low, Close, Volume, OpenInterest



The series of “Open”s along time is a Line. And therefore a Data Feed has usually 6 lines.



If we also consider “DateTime” (which is the actual reference for a single point), we could count 7

lines.



2. Index 0 Approach



When accessing the values in a line, the current value is accessed with index: 0



And the “last” output value is accessed with -1. This in line with Python conventions for iterables

(and a line can be iterated and is therefore an iterable) where index -1 is used to access the “last” item of

the iterable/array.



In our case is the last output value what’s getting accessed.



As such and being index 0 right after -1 , it is used to access the current moment in line.



With that in mind and if we imagine a Strategy featuring a Simple Moving average created during

initialization:

self.sma = SimpleMovingAverage(.....)

The easiest and simplest way to access the current value of this moving average:

av = self.sma[0]

There is no need to know how many bars/minutes/days/months have been processed, because “0”

uniquely identiﬁes the current instant.

Following pythonic tradition, the “last” output value is accessed using -1 :

previous_value = self.sma[-1]

Of course earlier output values can be accessed with -2, -3, ...

From 0 to 100: the samples

Basic Setup

Let’s get running.

from __future__ import (absolute_import, division, print_function,

unicode_literals)

import backtrader as bt

if __name__ == '__main__':

cerebro = bt.Cerebro()

print('Starting Portfolio Value: %.2f' % cerebro.broker.getvalue())

cerebro.run()

print('Final Portfolio Value: %.2f' % cerebro.broker.getvalue())

After the execution the output is:

Starting Portfolio Value: 10000.00

Final Portfolio Value: 10000.00

In this example:

backtrader was imported

The Cerebro engine was instantiated

The resulting cerebro instance was told to run (loop over data)

And the resulting outcome was printed out



Although it doesn’t seem much, let’s point out something explicitly shown:

The Cerebro engine has created a broker instance in the background

The instance already has some cash to start with



This behind the scenes broker instantiation is a constant trait in the platform to simplify the life of the

user. If no broker is set by the user, a default one is put in place.

And 10K monetary units is a usual value with some brokers to begin with.

Setting the Cash

In the world of ﬁnance, for sure only “losers” start with 10k. Let’s change the cash and run the example

again.

from __future__ import (absolute_import, division, print_function,

unicode_literals)

import backtrader as bt

if __name__ == '__main__':

cerebro = bt.Cerebro()

cerebro.broker.setcash(100000.0)

print('Starting Portfolio Value: %.2f' % cerebro.broker.getvalue())

cerebro.run()

print('Final Portfolio Value: %.2f' % cerebro.broker.getvalue())

After the execution the output is:

Starting Portfolio Value: 1000000.00

Final Portfolio Value: 1000000.00

Mission accomplished. Let’s move to tempestuous waters.

Adding a Data Feed

Having cash is fun, but the purpose behind all this is to let an automated strategy multiply the cash

without moving a ﬁnger by operating on an asset which we see as a Data Feed

Ergo ... No Data Feed - > No Fun. Let’s add one to the ever growing example.

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 the backtrader platform

import backtrader as bt

if __name__ == '__main__':

# Create a cerebro entity

cerebro = bt.Cerebro()

# Datas are in a subfolder of the samples. Need to find where the script is

# because it could have been called from anywhere

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niqinggood

2021-04-16

这是英文的，收费贵了
Terryinn

2020-11-24

只是官方英文的
扫地僧量化

2020-11-19

可以参考这本最新的系统化的backtrader技术教程，按中国人的思维写的：https://blog.csdn.net/qtbgo/article/details/109313249
gray428

2020-07-20

英文的，内容比较齐全。
MuSaCn

2019-11-02

就是英文版文档。