量化投资分析-回测框架BackTrader-基础教程
第一章：BackTrader入门
1.1 安装
1.1.1 通过pip安装backtrader：
pip install backtrader
此外通过easy_install也可以使用相同语法进行安装。
1.1.2 通过源码安装backtrader
- 首先从github网站下载一个版本或最新的backtrader tar包: https://github.com/mementum/backtrader
- 运行命令
python setup.py install
backtrader需要matplotlib进行绘图
1.2第一个backtrader项目
运行backtrader平台的只需要:
- 创建
Strategy对象
-
确定可调参数
-
实例化策略中需要的指标
Indicators -
写下进入/退出市场的逻辑
或者准备一些指标，作为做空、做多的信号
Signal
- 创建
Cerebro引擎
- 将创建的
Strategy实例（或者基于信号的策略）添加到Cerebro引擎 - 载入数据Data Feed (
cerebro.adddata()) - 运行
cerebro.run() - 可视化结果:
cerebro.plot()
1.2.1 下面使用一个案例进行讲解。
from __future__ import (absolute_import, division, print_function,
unicode_literals)
import backtrader as bt
if __name__ == '__main__':
# 创建Cerebro实例
cerebro = bt.Cerebro()
# 输出Cerebro引擎默认本金
print('Starting Portfolio Value: %.2f' % cerebro.broker.getvalue())
# 运行引擎
cerebro.run()
# 输出Cerebro引擎运行之后本金情况
print('Final Portfolio Value: %.2f' % cerebro.broker.getvalue())
上述代码做了以下几件事情：
- 导入
backtrader包 - 实例化
Cerebro引擎对象 - 循环遍历数据，运行生成的cerebro实例(还没有添加数据进去)
- 输出运行结果
代码看起来不是很多，但是从代码可以看出:
Cerebro引擎在后台通过初始化创建了broker代理实例（对象）- 实例化的
broker对象拥有初始化本金
1.2.2 如何设置本金
在实例化Cerebro对象后，通过Cerebro.broker.setcash()函数初始化本金
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) # 设置本金
print('Starting Portfolio Value: %.2f' % cerebro.broker.getvalue())
cerebro.run()
print('Final Portfolio Value: %.2f' % cerebro.broker.getvalue())
1.2.3 如何添加数据
通过backtrader.feeds.YahooFinanceCSVData()添加从雅虎网站下载的.CSV金融数据（从其他地方获取，按照Yahoo CSV格式存取也可，直接从Yahoo网站下载，不保存直接放进Data Feeds易可）
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
modpath = os.path.dirname(os.path.abspath(sys.argv[0]))
datapath = os.path.join(modpath, '../../datas/orcl-1995-2014.txt')
# Create a Data Feed
data = bt.feeds.YahooFinanceCSVData(
dataname=datapath,
# Do not pass values before this date
fromdate=datetime.datetime(2000, 1, 1),
# Do not pass values after this date
todate=datetime.datetime(2000, 12, 31),
reverse=False)
# 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())
Yahoo Online按日期降序发送CSV数据，这不是标准约定。参数 reverse =True 考虑到文件中的CSV数据已经被反转，并且具有标准的预期日期升序。
下面再给出一段从网上获取数据作为Data Feeds的案例
下面再给出一段读取Excel文件的案例
# -*- coding: utf-8 -*-
"""
Created on Thu Mar 19 08:35:36 2020
@author: CHERN
"""
# 使用Backtrader进行回测
from __future__ import (absolute_import, division, print_function,
unicode_literals)
# Import the backtrader platform
import backtrader as bt
import datetime # For datetime objects
import os.path # To manage paths
import sys # To find out the script name (in argv[0])
# from WindPy import w
import pandas as pd
import tushare as ts
# 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] and self.dataclose[-1] < self.dataclose[-2]:
# current close less than previous close
# 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 + 1):
# 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__':
cerebro = bt.Cerebro()
# Add a strategy
cerebro.addstrategy(TestStrategy)
# Add the Data Feed to Cerebro
stock_daily_data = pd.read_excel('./data/stock_data_000001.SZ.xlsx' ,index_col='trade_date', parse_dates=True)
# 读取出来之后，针对tushare和backtrader列名不同，进行必要的列重命名
stock_daily_data.rename(columns={
'vol':'volume'}, inplace = True)
# 读取出来之后针对tushare把最近时间放在第一行，最前时间放在最后一行的数据，进行升序排列
stock_daily_data = stock_daily_data.sort_index(ascending=True)
data = bt.feeds.PandasData(dataname=stock_daily_data['2012'])
cerebro.adddata(data)
# Set our desired cash start
cerebro.broker.setcash(100000.0)
print('Starting Portfolio Value: %.2f' % cerebro.broker.getvalue())
cerebro.run()
# Print out the final result
print('Final Portfolio Value: %.2f' % cerebro.broker.getvalue())
# Plot the result
cerebro.plot()
其中Excel文件表是这样的：
[外链图片转存失败,源站可能有防盗链机制,建议将图片保存下来直接上传(img-1g1Jtr5U-1585366345261)(assets\1585135333170.png)]
**注意：**这里我在学习使用的时候，因为对pandas包不熟悉，浪费了很多时间。这里说明一下pandas读取Excel文件赋值到
pandas.dataframe变量，作为Data Feed的时候，需要注意的地方：
pandas.read_excel()函数读取日期需要进行日期的解析- 读取到的
DataFrame类型的数据是没有设置index的，需要手动设置。并且不能为整数类型的时间戳，否则会报错 AttributeError: ‘numpy.int64’ object has no attribute ‘to_pydatetime’. 也不能为字符串类型的数据，否则会报错 AttributeError: ‘str’ object has no attribute ‘to_pydatetime’.- 读取到的
DataFrame中index有可能是降序排列的（离当前日期最近的一天在dataframe中第一行，最小的日期在最后一行），这在backtrader中不符合逻辑，需要进行重新排列
1.2.4 指定一个买卖股票的策略（实例化Strategy对象）
下面创建一个策略Strategy对象，在Strategy对象的回调方法中，打印出每天（每一个bar）的“收盘价”. 注意这里其实说是每一天的数据并不准确，在BackTrader中，认为时间的最小单位是bar（在迭代遍历数据是，每次取一行，依次进行，一行可以认为是一个bar），而没有假设你给的时间是分钟，天，周或者月数据。这里只是恰好给的是日K而已，所以说打印每天的数据。注意二者区别。
时间序列DataSeries是Data Feeds中重要的类，别名为OHLC(Open, High, Low, Close)，在每一个迭代的bar中，我们不必使用self.data.open[],self.data.close[]等访问数据，而只需要使用使用self.dataclose[]等来进行访问开盘价，收盘价，最低价和最高价
如果对Pandas不熟悉的读者可以查看下面快速上手教程
- 10分钟学会 Pandas教程 10 Mins to Pandas
- Pandas金融分析常用方法 Pandas 金融数据处理
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
# Create a Stratey
class TestStrategy(bt.Strategy):
def log(self, txt, dt=None):
''' Logging function for 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
def next(self):
# Simply log the closing price of the series from the reference
self.log('Close, %.2f' % self.dataclose[0])
if __name__ == '__main__':
# Create a cerebro entity
cerebro = bt.Cerebro()
# Add a strategy
cerebro.addstrategy(TestStrategy)
# Datas are in a subfolder of the samples. Need to find where the script is
# because it could have been called from anywhere
modpath = os.path.dirname(os.path.abspath(sys.argv[0]))
datapath = os.path.join(modpath, '../../datas/orcl-1995-2014.txt')
# Create a Data Feed
data = bt.feeds.YahooFinanceCSVData(
dataname=datapath,
# Do not pass values before this date
fromdate=datetime.datetime(2000, 1, 1),
# Do not pass values before this date
todate=datetime.datetime(2000, 12, 31),
# Do not pass values after this date
reverse=False)
# 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())
解释一下上面的代码：
- 初始化函数
init()在引擎添加完数据之后，被自动调用。数据加入引擎后被转化为Python标准list列表， 可以按它们被插入的顺序访问(这句是真么意思？？？)。data中的第一个数据self.datas[0]为默认为系统时钟，用来进行交易操作和保持策略的同步self.dataclose = self.datas[0].close是收盘线(***close line***什么鬼？？)的索引. 索引操作使得只需要一个间接层来访问收盘价格(close values)next()方法在系统时钟的每个bar(就是每一行数据)上都被自动运行。（有一种例外，比如当使用indicators还没有生成必要数据，如30日均线，在数据的前29个bar中，并没有这些数据，自然无法调用next()方法进行股票操作）
1.2.5 在策略中加入买卖操作
下面的程序演示了如果价格连续3个交易日下跌，就买入股票的策略
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
# 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
def next(self):
# Simply log the closing price of the series from the reference
self.log('Close, %.2f' % self.dataclose[0])
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 all possible default parameters)
self.log('BUY CREATE, %.2f' % self.dataclose[0])
self.buy()
if __name__ == '__main__':
# Create a cerebro entity
cerebro = bt.Cerebro()
# Add a strategy
cerebro.addstrategy(TestStrategy)
# Datas are in a subfolder of the samples. Need to find where the script is
# because it could have been called from anywhere
modpath = os.path.dirname(os.path.abspath(sys.argv[0]))
datapath = os.path.join(modpath, '../../datas/orcl-1995-2014.txt')
# Create a Data Feed
data = bt.feeds.YahooFinanceCSVData(
dataname=datapath,
# Do not pass values before this date
fromdate=datetime.datetime(2000, 1, 1),
# Do not pass values before this date
todate=datetime.datetime(2000, 12, 31),
# Do not pass values after this date
reverse=False)
# 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())
从输出结果来看，在发出几个“购买”命令之后，本金在减少。但是
- 订单已经创建，但不知道是否执行，何时执行，以什么价格执行。下一个示例将通过侦听订单状态的通知来构建此功能。
好奇的读者可能会问，有多少股票被买进了，有哪些资产被买进了，订单是如何执行的。在可能的情况下(在这种情况下)，平台会填补空白:
self.datas[0](主数据又称系统时钟)是目标资产，如果没有指定其他资产- 买了多少股份(stake)是有一个
sizer指定的，默认值是1，可以手动指定 - 订单是在“市场上”执行的，代理(Cerebro实例)使用下一个bar的开盘价执行买操作，因为它是当前检查bar之后的第一个tick（满足下单条件之后的下一个bar中的开盘价-强调一下，这里未必是下一个开盘日，因为backtrader没有假设你的数据采样间隔是D）
- 到目前为止，该命令是在没有任何佣金的情况下执行的(稍后会详细介绍)
1.2.6 如何设置卖单
明白如何进入市场（做多），我们还需要知道当前策略是否在市场中，和如何退出市场（做空）
Strategy对象提供了访问position属性- 方法
buy()和sell()返回创建的订单（还未被执行） - 订单状态的改变，将会通过
notify()方法通知给Strategy对象
我们假设一个简单的做空策略：
五个bars之后就卖出（不管上行或者下行）（再次强调，这里并没有假设时间或者时间戳的概念，只是单纯的五个bars，bars之间的时间间隔可能是1min,1 hour,1day,1week，也可以是其他任意时间周期，虽然我们知道数据源是日K，但是该策略并没有对此进行假设。）
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
# 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_order(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 enough cash
if order.status in [order.Completed]:
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)
elif order.status in [order.Canceled, order.Margin, order.Rejected]:
self.log('Order Canceled/Margin/Rejected')
# 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)
# Datas are in a subfolder of the samples. Need to find where the script is
# because it could have been called from anywhere
modpath = os.path.dirname(os.path.abspath(sys.argv[0]))
datapath = os.path.join(modpath, '../../datas/orcl-1995-2014.txt')
# Create a Data Feed
data = bt.feeds.YahooFinanceCSVData(
dataname=datapath,
# Do not pass values before this date
fromdate=datetime.datetime(2000, 1, 1),
# Do not pass values before this date
todate=datetime.datetime(2000, 12, 31),
# Do not pass values after this date
reverse=False)
# 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())
需要说明两点：
-
如果还没有在市场中，那么只允许进行卖操作（这里是为了简化模型，实际中如果我们没有买这只股票，也可以通过融券的方式获得先卖，后买操作）
def notify_order(self, order):
-
next()方法没有类似于"bar index"的数值传递进去，因此很难理解什么时候5个bar可能已经过去了，但是可以通过python内置方法实现:对一个对象调用len()函数会返回它的行长度。我们可以在对象中定义self.bar_executed属性，用来记录订单完成时对象的行长度（放在sefl.notify()回调函数中赋值），并在next()方法中检查当前长度len(self)是否为5+self.bar_executed。
这里len(self)返回的是什么？是BackTrader在Strategy类中重写的__len__()吗？还是仅仅是一个python的一个我不知道的语法知识？
1.2.7 添加操作佣金
如何设置操作佣金？假设我们要在每次买卖操作的时候给0.1%的佣金率，可以通过如下方法设置
# 0.1% ... divide by 100 to remove the %
cerebro.broker.setcommission(commission=0.001)
完整的代码如下，并且输出有佣金和无佣金情况下
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
# 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 and buy price/commission
self.order = None
self.buyprice = None
self.buycomm = None
def notify_order(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 enough cash
if order.status in [order.Completed]:
if order.isbuy():
self.log(
'BUY EXECUTED, Price: %.2f, Cost: %.2f, Comm %.2f' %
(order.executed.price,
order.executed.value,
order.executed.comm))
self.buyprice = order.executed.price
self.buycomm = order.executed.comm
else: # Sell
self.log('SELL EXECUTED, Price: %.2f, Cost: %.2f, Comm %.2f' %
(order.executed.price,
order.executed.value,
order.executed.comm))
self.bar_executed = len(self)
elif order.status in [order.Canceled, order.Margin, order.Rejected]:
self.log('Order Canceled/Margin/Rejected')
self.order = None
def notify_trade(self, trade):
if not trade.isclosed:
return
self.log('OPERATION PROFIT, GROSS %.2f, NET %.2f' %
(trade.pnl, trade.pnlcomm))
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)
# Datas are in a subfolder of the samples. Need to find where the script is
# because it could have been called from anywhere
modpath = os.path.dirname(os.path.abspath(sys.argv[0]))
datapath = os.path.join(modpath, '../../datas/orcl-1995-2014.txt')
# Create a Data Feed
data = bt.feeds.YahooFinanceCSVData(
dataname=datapath,
# Do not pass values before this date
fromdate=datetime.datetime(2000, 1, 1),
# Do not pass values before this date
todate=datetime.datetime(2000, 12, 31),
# Do not pass values after this date
reverse=False)
# Add the Data Feed to Cerebro
cerebro.adddata(data)
# Set our desired cash start
cerebro.broker.setcash(100000.0)
# Set the commission - 0.1% ... divide by 100 to remove the %
cerebro.broker.setcommission(commission=0.001)
# 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())
输出结果如下
Starting Portfolio Value: 100000.00
2000-01-03T00:00:00, Close, 27.85
2000-01-04T00:00:00, Close, 25.39
2000-01-05T00:00:00, Close, 24.05
2000-01-05T00:00:00, BUY CREATE, 24.05
2000-01-06T00:00:00, BUY EXECUTED, Price: 23.61, Cost: 23.61, Commission 0.02
2000-01-06T00:00:00, Close, 22.63
2000-01-07T00:00:00, Close, 24.37
2000-01-10T00:00:00, Close, 27.29
2000-01-11T00:00:00, Close, 26.49
2000-01-12T00:00:00, Close, 24.90
2000-01-13T00:00:00, Close, 24.77
2000-01-13T00:00:00, SELL CREATE, 24.77
2000-01-14T00:00:00, SELL EXECUTED, Price: 25.70, Cost: 25.70, Commission 0.03
2000-01-14T00:00:00, OPERATION PROFIT, GROSS 2.09, NET 2.04
2000-01-14T00:00:00, Close, 25.18
...
...
...
2000-12-15T00:00:00, SELL CREATE, 26.93
2000-12-18T00:00:00, SELL EXECUTED, Price: 28.29, Cost: 28.29, Commission 0.03
2000-12-18T00:00:00, OPERATION PROFIT, GROSS -0.06, NET -0.12
2000-12-18T00:00:00, Close, 30.18
2000-12-19T00:00:00, Close, 28.88
2000-12-20T00:00:00, Close, 26.88
2000-12-20T00:00:00, BUY CREATE, 26.88
2000-12-21T00:00:00, BUY EXECUTED, Price: 26.23, Cost: 26.23, Commission 0.03
2000-12-21T00:00:00, Close, 27.82
2000-12-22T00:00:00, Close, 30.06
2000-12-26T00:00:00, Close, 29.17
2000-12-27T00:00:00, Close, 28.94
2000-12-28T00:00:00, Close, 29.29
2000-12-29T00:00:00, Close, 27.41
2000-12-29T00:00:00, SELL CREATE, 27.41
Final Portfolio Value: 100016.98
过滤一下只包含操作利润行，输出如下结果
2000-01-14T00:00:00, OPERATION PROFIT, GROSS 2.09, NET 2.04
2000-02-07T00:00:00, OPERATION PROFIT, GROSS 3.68, NET 3.63
2000-02-28T00:00:00, OPERATION PROFIT, GROSS 4.48, NET 4.42
2000-03-13T00:00:00, OPERATION PROFIT, GROSS 3.48, NET 3.41
2000-03-22T00:00:00, OPERATION PROFIT, GROSS -0.41, NET -0.49
2000-04-07T00:00:00, OPERATION PROFIT, GROSS 2.45, NET 2.37
2000-04-20T00:00:00, OPERATION PROFIT, GROSS -1.95, NET -2.02
2000-05-02T00:00:00, OPERATION PROFIT, GROSS 5.46, NET 5.39
2000-05-11T00:00:00, OPERATION PROFIT, GROSS -3.74, NET -3.81
2000-05-30T00:00:00, OPERATION PROFIT, GROSS -1.46, NET -1.53
2000-07-05T00:00:00, OPERATION PROFIT, GROSS -1.62, NET -1.69
2000-07-14T00:00:00, OPERATION PROFIT, GROSS 2.08, NET 2.01
2000-07-28T00:00:00, OPERATION PROFIT, GROSS 0.14, NET 0.07
2000-08-08T00:00:00, OPERATION PROFIT, GROSS 4.36, NET 4.29
2000-08-21T00:00:00, OPERATION PROFIT, GROSS 1.03, NET 0.95
2000-09-15T00:00:00, OPERATION PROFIT, GROSS -4.26, NET -4.34
2000-09-27T00:00:00, OPERATION PROFIT, GROSS 1.29, NET 1.22
2000-10-13T00:00:00, OPERATION PROFIT, GROSS -2.98, NET -3.04
2000-10-26T00:00:00, OPERATION PROFIT, GROSS 3.01, NET 2.95
2000-11-06T00:00:00, OPERATION PROFIT, GROSS -3.59, NET -3.65
2000-11-16T00:00:00, OPERATION PROFIT, GROSS 1.28, NET 1.23
2000-12-01T00:00:00, OPERATION PROFIT, GROSS 2.59, NET 2.54
2000-12-18T00:00:00, OPERATION PROFIT, GROSS -0.06, NET -0.12
把纯利润“net profits”加起来得到15.83，但是系统在最后说最终本金是100016.98，这是为什么呢？是因为15.83是已经落袋为安的那份钱，你的买卖操作都已经得到执行，赚的差价已经到手。最有一次本金值里面包含了一部分股票卖出订单已经提交，但是还未执行。经纪人计算的“最终投资组合价值”考虑了2000年12月至29日的“收盘价”。
1.2.8定制策略:参数
如果你的策略中有一些参数，比如使用MA5,MA10这些均线等，但是又想优化这个策略，你总不能每次在Strategy对象中修改，然后运行，把结果记录下来再修改，再运行吧。这个一点都不自动化。那怎么办呢？我们就不在Strategy对象中，直接把参数写死，而是抽象出参数出来，然后把Strategy对象和参数一同放进Cerebro实例中，进行优化还是怎么样干啥都行
使用如下方法定义一个参数
params = (('myparam', 27), ('exitbars', 5),)
在将策略添加到Cerebro引擎时，可以使用任意一种格式化参数化策略:
# Add a strategy
cerebro.addstrategy(TestStrategy, myparam=20, exitbars=7)
cerebro.addsizer(bt.sizers.FixedSize, stake=10)
在新版本中
setsizing()方法已经被弃用了，尽量不要使用这个方法来设置sizer
在策略中使用参数很容易，因为它们存储在“params”属性中。例如，如果我们想要设置sizer stake(操作时的手数)，我们可以在init()方法中，把参数传递给sizer:
# Set the sizer stake from the params
self.sizer.setsizing(self.params.stake)
我们也可以在调用buy(),sell()方法时，使用stake参数
# Already in the market ... we might sell
if len(self) >= (self.bar_executed + self.params.exitbars):
通过以上分析，代码变成下面这样
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
# Create a Stratey
class TestStrategy(bt.Strategy):
params = (
('exitbars', 5),
)
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 and buy price/commission
self.order = None
self.buyprice = None
self.buycomm = None
def notify_order(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 enough cash
if order.status in [order.Completed]:
if order.isbuy():
self.log(
'BUY EXECUTED, Price: %.2f, Cost: %.2f, Comm %.2f' %
(order.executed.price,
order.executed.value,
order.executed.comm))
self.buyprice = order.executed.price
self.buycomm = order.executed.comm
else: # Sell
self.log('SELL EXECUTED, Price: %.2f, Cost: %.2f, Comm %.2f' %
(order.executed.price,
order.executed.value,
order.executed.comm))
self.bar_executed = len(self)
elif order.status in [order.Canceled, order.Margin, order.Rejected]:
self.log('Order Canceled/Margin/Rejected')
self.order = None
def notify_trade(self, trade):
if not trade.isclosed:
return
self.log('OPERATION PROFIT, GROSS %.2f, NET %.2f' %
(trade.pnl, trade.pnlcomm))
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 + self.params.exitbars):
# 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)
# Datas are in a subfolder of the samples. Need to find where the script is
# because it could have been called from anywhere
modpath = os.path.dirname(os.path.abspath(sys.argv[0]))
datapath = os.path.join(modpath, '../../datas/orcl-1995-2014.txt')
# Create a Data Feed
data = bt.feeds.YahooFinanceCSVData(
dataname=datapath,
# Do not pass values before this date
fromdate=datetime.datetime(2000, 1, 1),
# Do not pass values before this date
todate=datetime.datetime(2000, 12, 31),
# Do not pass values after this date
reverse=False)
# Add the Data Feed to Cerebro
cerebro.adddata(data)
# Set our desired cash start
cerebro.broker.setcash(100000.0)
# Add a FixedSize sizer according to the stake
cerebro.addsizer(bt.sizers.FixedSize, stake=10)
# Set the commission - 0.1% ... divide by 100 to remove the %
cerebro.broker.setcommission(commission=0.001)
# 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())
程序输出
Starting Portfolio Value: 100000.00
2000-01-03T00:00:00, Close, 27.85
2000-01-04T00:00:00, Close, 25.39
2000-01-05T00:00:00, Close, 24.05
2000-01-05T00:00:00, BUY CREATE, 24.05
2000-01-06T00:00:00, BUY EXECUTED, Size 10, Price: 23.61, Cost: 236.10, Commission 0.24
2000-01-06T00:00:00, Close, 22.63
...
...
...
2000-12-20T00:00:00, BUY CREATE, 26.88
2000-12-21T00:00:00, BUY EXECUTED, Size 10, Price: 26.23, Cost: 262.30, Commission 0.26
2000-12-21T00:00:00, Close, 27.82
2000-12-22T00:00:00, Close, 30.06
2000-12-26T00:00:00, Close, 29.17
2000-12-27T00:00:00, Close, 28.94
2000-12-28T00:00:00, Close, 29.29
2000-12-29T00:00:00, Close, 27.41
2000-12-29T00:00:00, SELL CREATE, 27.41
Final Portfolio Value: 100169.80
可以看出，将股份stake乘以10之后，显而易见的事情发生了:损益乘以10。现在的盈余不是16.98，而是169.80
1.2.9 添加indicator指示器（股票历史价格的一个指标）
了解指示器之后，我们自然想到在策略中添加一个，这灵感来自PyAlgoTrade 使用的移动均值：
- 如果收盘价高于平均价格，做多，买入
- 在市场上后，若价格低于移动均值，则卖出
- 市场上只允许进行一次主动操作
为了实现，这样的策略，我们在适当位置添加indicator
self.sma = bt.indicators.MovingAverageSimple(self.datas[0], period=self.params.maperiod)
初始现金为1000个货币单位，以符合PyAlgoTrade示例，且不应用佣金
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
# Create a Stratey
class TestStrategy(bt.Strategy):
params = (
('maperiod', 15),
)
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 and buy price/commission
self.order = None
self.buyprice = None
self.buycomm = None
# Add a MovingAverageSimple indicator
self.sma = bt.indicators.SimpleMovingAverage(
self.datas[0], period=self.params.maperiod)
def notify_order(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 enough cash
if order.status in [order.Completed]:
if order.isbuy():
self.log(
'BUY EXECUTED, Price: %.2f, Cost: %.2f, Comm %.2f' %
(order.executed.price,
order.executed.value,
order.executed.comm))
self.buyprice = order.executed.price
self.buycomm = order.executed.comm
else: # Sell
self.log('SELL EXECUTED, Price: %.2f, Cost: %.2f, Comm %.2f' %
(order.executed.price,
order.executed.value,
order.executed.comm))
self.bar_executed = len(self)
elif order.status in [order.Canceled, order.Margin, order.Rejected]:
self.log('Order Canceled/Margin/Rejected')
self.order = None
def notify_trade(self, trade):
if not trade.isclosed:
return
self.log('OPERATION PROFIT, GROSS %.2f, NET %.2f' %
(trade.pnl, trade.pnlcomm))
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.sma[0]:
# BUY, BUY, BUY!!! (with all possible 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:
if self.dataclose[0] < self.sma[0]:
# 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)
# Datas are in a subfolder of the samples. Need to find where the script is
# because it could have been called from anywhere
modpath = os.path.dirname(os.path.abspath(sys.argv[0]))
datapath = os.path.join(modpath, '../../datas/orcl-1995-2014.txt')
# Create a Data Feed
data = bt.feeds.YahooFinanceCSVData(
dataname=datapath,
# Do not pass values before this date
fromdate=datetime.datetime(2000, 1, 1),
# Do not pass values before this date
todate=datetime.datetime(2000, 12, 31),
# Do not pass values after this date
reverse=False)
# Add the Data Feed to Cerebro
cerebro.adddata(data)
# Set our desired cash start
cerebro.broker.setcash(1000.0)
# Add a FixedSize sizer according to the stake
cerebro.addsizer(bt.sizers.FixedSize, stake=10)
# Set the commission
cerebro.broker.setcommission(commission=0.0)
# 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())
程序输出为
Starting Portfolio Value: 1000.00
2000-01-24T00:00:00, Close, 25.55
2000-01-25T00:00:00, Close, 26.61
2000-01-25T00:00:00, BUY CREATE, 26.61
2000-01-26T00:00:00, BUY EXECUTED, Size 10, Price: 26.76, Cost: 267.60, Commission 0.00
2000-01-26T00:00:00, Close, 25.96
2000-01-27T00:00:00, Close, 24.43
2000-01-27T00:00:00, SELL CREATE, 24.43
2000-01-28T00:00:00, SELL EXECUTED, Size 10, Price: 24.28, Cost: 242.80, Commission 0.00
2000-01-28T00:00:00, OPERATION PROFIT, GROSS -24.80, NET -24.80
2000-01-28T00:00:00, Close, 22.34
2000-01-31T00:00:00, Close, 23.55
2000-02-01T00:00:00, Close, 25.46
2000-02-02T00:00:00, Close, 25.61
2000-02-02T00:00:00, BUY CREATE, 25.61
2000-02-03T00:00:00, BUY EXECUTED, Size 10, Price: 26.11, Cost: 261.10, Commission 0.00
...
...
...
2000-12-20T00:00:00, SELL CREATE, 26.88
2000-12-21T00:00:00, SELL EXECUTED, Size 10, Price: 26.23, Cost: 262.30, Commission 0.00
2000-12-21T00:00:00, OPERATION PROFIT, GROSS -20.60, NET -20.60
2000-12-21T00:00:00, Close, 27.82
2000-12-21T00:00:00, BUY CREATE, 27.82
2000-12-22T00:00:00, BUY EXECUTED, Size 10, Price: 28.65, Cost: 286.50, Commission 0.00
2000-12-22T00:00:00, Close, 30.06
2000-12-26T00:00:00, Close, 29.17
2000-12-27T00:00:00, Close, 28.94
2000-12-28T00:00:00, Close, 29.29
2000-12-29T00:00:00, Close, 27.41
2000-12-29T00:00:00, SELL CREATE, 27.41
Final Portfolio Value: 973.90
可以看到，打印日期是从2002-01-24开始的(next()方法的输出)，而不是从2002-01-03为什么呢？因为在刚开始的14天，程序还没有准备好数据，因此，在准备好必要的数据之前，策略并没有启动，next()方法也没有被调用
backtrader平台假设该策略有一个合适的指示器，可以在决策过程中使用它。如果指标还没有准备好并产生价值，试图做出决策是没有意义的。记住：
- next()方法在所有Indicators全部准备好数据之后，才被首次调用
- 在本例中，只有一个指示器，但是策略可以有任意数量的指示器。
1.2.10 可视化
一个打印输出或日志的实际位置的系统在每条-即时是好的，但人类往往是可视化的，因此它似乎是正确的，提供了相同的位置作为图表。
backtrader绘图依赖matplotlib工具包
在backtrader中绘图很简单，在运行Cerebro.run()方法之后的适当位置加入即可：
cerebro.plot()
我们如何做一些定制呢？
假设我们需要话数据的一些indicator的曲线。。我们在初始化函数中添加一下Indicators
# Indicators for the plotting show
bt.indicators.ExponentialMovingAverage(self.datas[0], period=25)
bt.indicators.WeightedMovingAverage(self.datas[0], period=25).subplot = True
bt.indicators.StochasticSlow(self.datas[0])
bt.indicators.MACDHisto(self.datas[0])
rsi = bt.indicators.RSI(self.datas[0])
bt.indicators.SmoothedMovingAverage(rsi, period=10)
bt.indicators.ATR(self.datas[0]).plot = False
即使没有显式地将指标添加到策略的成员变量(如self)中。，他们将自动注册的战略，并将影响下一个最短周期，并将成为绘图的一部分。
在本例中，只有RSI被添加到一个临时变量RSI，其唯一目的是创建一个移动平均值。
程序为：
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
# Create a Stratey
class TestStrategy(bt.Strategy):
params = (
('maperiod', 15),
)
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 and buy price/commission
self.order = None
self.buyprice = None
self.buycomm = None
# Add a MovingAverageSimple indicator
self.sma = bt.indicators.SimpleMovingAverage(
self.datas[0], period=self.params.maperiod)
# Indicators for the plotting show
bt.indicators.ExponentialMovingAverage(self.datas[0], period=25)
bt.indicators.WeightedMovingAverage(self.datas[0], period=25,
subplot=True)
bt.indicators.StochasticSlow(self.datas[0])
bt.indicators.MACDHisto(self.datas[0])
rsi = bt.indicators.RSI(self.datas[0])
bt.indicators.SmoothedMovingAverage(rsi, period=10)
bt.indicators.ATR(self.datas[0], plot=False)
def notify_order(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 enough cash
if order.status in [order.Completed]:
if order.isbuy():
self.log(
'BUY EXECUTED, Price: %.2f, Cost: %.2f, Comm %.2f' %
(order.executed.price,
order.executed.value,
order.executed.comm))
self.buyprice = order.executed.price
self.buycomm = order.executed.comm
else: # Sell
self.log('SELL EXECUTED, Price: %.2f, Cost: %.2f, Comm %.2f' %
(order.executed.price,
order.executed.value,
order.executed.comm))
self.bar_executed = len(self)
elif order.status in [order.Canceled, order.Margin, order.Rejected]:
self.log('Order Canceled/Margin/Rejected')
# Write down: no pending order
self.order = None
def notify_trade(self, trade):
if not trade.isclosed:
return
self.log('OPERATION PROFIT, GROSS %.2f, NET %.2f' %
(trade.pnl, trade.pnlcomm))
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.sma[0]:
# BUY, BUY, BUY!!! (with all possible 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:
if self.dataclose[0] < self.sma[0]:
# 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)
# Datas are in a subfolder of the samples. Need to find where the script is
# because it could have been called from anywhere
modpath = os.path.dirname(os.path.abspath(sys.argv[0]))
datapath = os.path.join(modpath, '../../datas/orcl-1995-2014.txt')
# Create a Data Feed
data = bt.feeds.YahooFinanceCSVData(
dataname=datapath,
# Do not pass values before this date
fromdate=datetime.datetime(2000, 1, 1),
# Do not pass values before this date
todate=datetime.datetime(2000, 12, 31),
# Do not pass values after this date
reverse=False)
# Add the Data Feed to Cerebro
cerebro.adddata(data)
# Set our desired cash start
cerebro.broker.setcash(1000.0)
# Add a FixedSize sizer according to the stake
cerebro.addsizer(bt.sizers.FixedSize, stake=10)
# Set the commission
cerebro.broker.setcommission(commission=0.0)
# 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()
输出结果为：
Starting Portfolio Value: 1000.00
2000-02-18T00:00:00, Close, 27.61
2000-02-22T00:00:00, Close, 27.97
2000-02-22T00:00:00, BUY CREATE, 27.97
2000-02-23T00:00:00, BUY EXECUTED, Size 10, Price: 28.38, Cost: 283.80, Commission 0.00
2000-02-23T00:00:00, Close, 29.73
...
...
...
2000-12-21T00:00:00, BUY CREATE, 27.82
2000-12-22T00:00:00, BUY EXECUTED, Size 10, Price: 28.65, Cost: 286.50, Commission 0.00
2000-12-22T00:00:00, Close, 30.06
2000-12-26T00:00:00, Close, 29.17
2000-12-27T00:00:00, Close, 28.94
2000-12-28T00:00:00, Close, 29.29
2000-12-29T00:00:00, Close, 27.41
2000-12-29T00:00:00, SELL CREATE, 27.41
Final Portfolio Value: 981.00
即使逻辑没有改变，最终的结果也已经改变了。这是真的，但是逻辑并没有应用到相同数量的条上。
如前所述，当所有指示器都准备好生成一个值时，平台将首先调用next。在这个绘图示例中(在图表中非常清楚)，MACD是最后一个完全准备好的指示器(所有3行都产生一个输出)。第一个购买订单不再安排在2000年1月，而是接近2000年2月底。
[外链图片转存失败,源站可能有防盗链机制,建议将图片保存下来直接上传(img-Cf0yeVZl-1585366345264)(assets/2020-03-27-17-28-36.png)]
1.2.11 如何优化策略中包含的参数
许多交易书籍说，每个市场和每个交易的股票(或商品或…)都有不同的价格。没有放之四海而皆准的东西。在绘图样例之前，当策略开始使用指示器时，期间默认值为15条。它是一个策略参数，可以用于优化来改变参数的值，看看哪个更适合市场。
关于优化和相关利弊的文献有很多，但是建议总是指向同一个方向:不要过度优化。如果交易的想法是不健全的，优化可能最终产生一个积极的结果，这是唯一有效的回测数据集。
对样本进行修正以优化简单移动平均线的周期。为清楚起见，有关买卖订单的任何输出都已删除.示例:
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
# Create a Stratey
class TestStrategy(bt.Strategy):
params = (
('maperiod', 15),
('printlog', False),
)
def log(self, txt, dt=None, doprint=False):
''' Logging function fot this strategy'''
if self.params.printlog or doprint:
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 and buy price/commission
self.order = None
self.buyprice = None
self.buycomm = None
# Add a MovingAverageSimple indicator
self.sma = bt.indicators.SimpleMovingAverage(
self.datas[0], period=self.params.maperiod)
def notify_order(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 enough cash
if order.status in [order.Completed]:
if order.isbuy():
self.log(
'BUY EXECUTED, Price: %.2f, Cost: %.2f, Comm %.2f' %
(order.executed.price,
order.executed.value,
order.executed.comm))
self.buyprice = order.executed.price
self.buycomm = order.executed.comm
else: # Sell
self.log('SELL EXECUTED, Price: %.2f, Cost: %.2f, Comm %.2f' %
(order.executed.price,
order.executed.value,
order.executed.comm))
self.bar_executed = len(self)
elif order.status in [order.Canceled, order.Margin, order.Rejected]:
self.log('Order Canceled/Margin/Rejected')
# Write down: no pending order
self.order = None
def notify_trade(self, trade):
if not trade.isclosed:
return
self.log('OPERATION PROFIT, GROSS %.2f, NET %.2f' %
(trade.pnl, trade.pnlcomm))
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.sma[0]:
# BUY, BUY, BUY!!! (with all possible 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:
if self.dataclose[0] < self.sma[0]:
# 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()
def stop(self):
self.log('(MA Period %2d) Ending Value %.2f' %
(self.params.maperiod, self.broker.getvalue()), doprint=True)
if __name__ == '__main__':
# Create a cerebro entity
cerebro = bt.Cerebro()
# Add a strategy
strats = cerebro.optstrategy(
TestStrategy,
maperiod=range(10, 31))
# Datas are in a subfolder of the samples. Need to find where the script is
# because it could have been called from anywhere
modpath = os.path.dirname(os.path.abspath(sys.argv[0]))
datapath = os.path.join(modpath, '../../datas/orcl-1995-2014.txt')
# Create a Data Feed
data = bt.feeds.YahooFinanceCSVData(
dataname=datapath,
# Do not pass values before this date
fromdate=datetime.datetime(2000, 1, 1),
# Do not pass values before this date
todate=datetime.datetime(2000, 12, 31),
# Do not pass values after this date
reverse=False)
# Add the Data Feed to Cerebro
cerebro.adddata(data)
# Set our desired cash start
cerebro.broker.setcash(1000.0)
# Add a FixedSize sizer according to the stake
cerebro.addsizer(bt.sizers.FixedSize, stake=10)
# Set the commission
cerebro.broker.setcommission(commission=0.0)
# Run over everything
cerebro.run(maxcpus=1)
输出为：
2000-12-29, (MA Period 10) Ending Value 880.30
2000-12-29, (MA Period 11) Ending Value 880.00
2000-12-29, (MA Period 12) Ending Value 830.30
2000-12-29, (MA Period 13) Ending Value 893.90
2000-12-29, (MA Period 14) Ending Value 896.90
2000-12-29, (MA Period 15) Ending Value 973.90
2000-12-29, (MA Period 16) Ending Value 959.40
2000-12-29, (MA Period 17) Ending Value 949.80
2000-12-29, (MA Period 18) Ending Value 1011.90
2000-12-29, (MA Period 19) Ending Value 1041.90
2000-12-29, (MA Period 20) Ending Value 1078.00
2000-12-29, (MA Period 21) Ending Value 1058.80
2000-12-29, (MA Period 22) Ending Value 1061.50
2000-12-29, (MA Period 23) Ending Value 1023.00
2000-12-29, (MA Period 24) Ending Value 1020.10
2000-12-29, (MA Period 25) Ending Value 1013.30
2000-12-29, (MA Period 26) Ending Value 998.30
2000-12-29, (MA Period 27) Ending Value 982.20
2000-12-29, (MA Period 28) Ending Value 975.70
2000-12-29, (MA Period 29) Ending Value 983.30
2000-12-29, (MA Period 30) Ending Value 979.80
不是调用addstrategy()向Cerebro添加一个stratey类，而是调用optstrategy()。而不是传递一个值，而是传递一个值范围。
添加了其中一个策略回调函数，即stop()方法，该方法将在数据耗尽，针对一个策略回调测试结束后自动执行。它用于在broker中打印投资组合的最终净值(以前在Cerebro中是这样做的)，系统将对范围的每个值执行策略。
从结果可以看出：
- 在低于18周期时，策略亏损（无佣金）
- 18-26之间，策略赚钱
- 超过26也亏损s
在这个数据集上，20bars的时间周期，最赚钱
1.3 总结
逐步增加的例子展示了如何从一个简单的脚本到一个完全运行的交易系统，甚至可以绘制结果并进行优化。要想提高获胜的几率，我们还有很多工作要做:
-
自定义Indicators（创建一个指示器很容易(甚至绘制它们也很容易)）
-
Sizers的制定
-
订单类型(limit,stop，stoplimit)
-
其他
e commission
cerebro.broker.setcommission(commission=0.0)
# Run over everything
cerebro.run(maxcpus=1)
输出为：
```shell
2000-12-29, (MA Period 10) Ending Value 880.30
2000-12-29, (MA Period 11) Ending Value 880.00
2000-12-29, (MA Period 12) Ending Value 830.30
2000-12-29, (MA Period 13) Ending Value 893.90
2000-12-29, (MA Period 14) Ending Value 896.90
2000-12-29, (MA Period 15) Ending Value 973.90
2000-12-29, (MA Period 16) Ending Value 959.40
2000-12-29, (MA Period 17) Ending Value 949.80
2000-12-29, (MA Period 18) Ending Value 1011.90
2000-12-29, (MA Period 19) Ending Value 1041.90
2000-12-29, (MA Period 20) Ending Value 1078.00
2000-12-29, (MA Period 21) Ending Value 1058.80
2000-12-29, (MA Period 22) Ending Value 1061.50
2000-12-29, (MA Period 23) Ending Value 1023.00
2000-12-29, (MA Period 24) Ending Value 1020.10
2000-12-29, (MA Period 25) Ending Value 1013.30
2000-12-29, (MA Period 26) Ending Value 998.30
2000-12-29, (MA Period 27) Ending Value 982.20
2000-12-29, (MA Period 28) Ending Value 975.70
2000-12-29, (MA Period 29) Ending Value 983.30
2000-12-29, (MA Period 30) Ending Value 979.80
不是调用addstrategy()向Cerebro添加一个stratey类，而是调用optstrategy()。而不是传递一个值，而是传递一个值范围。
添加了其中一个策略回调函数，即stop()方法，该方法将在数据耗尽，针对一个策略回调测试结束后自动执行。它用于在broker中打印投资组合的最终净值(以前在Cerebro中是这样做的)，系统将对范围的每个值执行策略。
从结果可以看出：
- 在低于18周期时，策略亏损（无佣金）
- 18-26之间，策略赚钱
- 超过26也亏损s
在这个数据集上，20bars的时间周期，最赚钱
1.3 总结
逐步增加的例子展示了如何从一个简单的脚本到一个完全运行的交易系统，甚至可以绘制结果并进行优化。要想提高获胜的几率，我们还有很多工作要做:
-
自定义Indicators（创建一个指示器很容易(甚至绘制它们也很容易)）
-
Sizers的制定
-
订单类型(limit,stop，stoplimit)
-
其他
为了确保能够充分利用上述所有项目，文档提供了对它们(和其他主题)的深入了解。在目录中查找并继续阅读…和发展。