【backtrader】BackTrader官方文档02-快速上手
目录
- 1.快速上手
- 2.使用这个框架
- 3.从0-100：例子
-
- 3.1 设置现金
- 3.2 添加数据源
- 4.我们的第一个策略
- 5. 经纪人说：给我钱！
- 6. 自定义策略：参数
- 7.添加一个技术指标
1.快速上手
笔记：
本文档中数据会时不时更新，这意味着实际的输出结果可能与编写文档时放在文档中的内容不同。
2.使用这个框架
让我们来运行这一系列例子（从一个几乎空的策略到一个成熟的策略），但在此之前，我要大致解释一下backtrader的两个基本概念。
-
线
- 在backtrader中，数据源（data feeds），指标（indicators），策略（strategies）都有线（line）这个概念。
- 在数学中，线是由一系列连续的点构成的。当我们把这个概念带到金融市场上，一个数据源通常有下面这几个点组成：
- Open, High, Low, Close, Volume, OpenInterest
一个数据提要通常有6行，如果我们还考虑“DateTime”(它是单个点的实际引用)，我们可以数到7行。
- Open, High, Low, Close, Volume, OpenInterest
-
index 0 访问当前值
- 当访问一行中的值时，使用index: 0访问当前值
- “最后一个”输出值用-1访问。 这符合Python对可迭代对象的约定(一行可以被迭代，因此是一个可迭代对象)，其中index -1用于访问可迭代对象/数组的“最后”项。
- 在我们的例子中我们得到的是最后一个输出值。
- 由于-1后面的索引为0，因此用于访问当前时刻。
考虑到这一点，如果我们想象一个策略，在初始化期间创建一个简单的移动均线:
self.sma = SimpleMovingAverage(.....)
获取该移动平均线当前值最简单的方法是:
av = self.sma[0]
不需要知道已经处理了多少条/分钟/天/月，因为“0”唯一地标识了当前时刻。
遵循python的传统，使用-1访问“last”输出值:
previous_value = self.sma[-1]
当然，之前的值可以用-2,-3访问。
3.从0-100：例子
基础设置
让我们来运行一下：
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())
然后输出结果是：
Starting Portfolio Value: 10000.00
Final Portfolio Value: 10000.00
在这个例子中：
- 导入backtrader包
- 大脑引擎（cerebro engine）被初始化
- 生成的cerebro实例被告知运行(对数据进行循环)
- 并且输出结果被打印
尽管代码不是很多，但还是让我们明确地指出一些东西：
- cerebro engine创建了一个cerebro实例
- 这个实例已经拥有了一些启动资金
为了简化用户的操作，broker实例化是框架默认执行的。 如果用户没有设置代理，则会设置一个默认代理。
对一些经纪人来说通常启动资金为10k。
3.1 设置现金
在金融世界里，只有失败者以10k资金开始，让我们来修改一下启动资金。
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)
print('Starting Portfolio Value: %.2f' % cerebro.broker.getvalue())
cerebro.run()
print('Final Portfolio Value: %.2f' % cerebro.broker.getvalue())
执行结果为：
Starting Portfolio Value: 1000000.00
Final Portfolio Value: 1000000.00
任务完成，让我们进一步探索！
3.2 添加数据源
很开心拥有资金，但这一切背后的目的是，让自动化策略无需动一根手指，就能通过操作我们视为数据来源的资产，让现金成倍增长。
No Data Feed -> No Fun. 让我们在再来举一个例子吧。
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())
数据可以在这里获取
结果如下：
Starting Portfolio Value: 1000000.00
Final Portfolio Value: 1000000.00
内存中数据有所增加：
- 通过实例脚本的位置来定位数据文件
- 通过datetime对象去筛选出我们要操作的数据源
除此之外，数据源也被创建并且添加到了cerebro实例中。
输出结果没有改变，因为我们没有对数据进行操作。如果改变了，那就是奇迹了。
笔记
Yahoo Online数据是以日期降序发送CSV数据，这不是标准约定。 reverse =True参数考虑到文件中的CSV数据已经被反转，并且按照标准预期日期升序排列。
4.我们的第一个策略
broker有了资金也有了数据，看来危险的交易就要来了。
让我们在框架中加入一个策略，并且输出每个bar的“close”价格。
DataSeries (Data Feeds中的子类)对象有别名来访问著名的OHLC (Open High Low Close)每日值。 这应该会简化print操作。
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())
执行后输出为：
2000-11-15, SELL CREATE, 25.68
2000-11-16, SELL EXECUTED, 25.57
2000-11-16, Close, 24.35
2000-11-17, Close, 25.63
2000-11-20, Close, 22.01
2000-11-21, Close, 21.24
2000-11-21, BUY CREATE, 21.24
2000-11-22, BUY EXECUTED, 21.01
2000-11-22, Close, 19.85
2000-11-24, Close, 21.46
2000-11-27, Close, 20.57
2000-11-28, Close, 20.15
2000-11-29, Close, 20.35
2000-11-30, Close, 23.57
2000-11-30, SELL CREATE, 23.57
2000-12-01, SELL EXECUTED, 23.46
2000-12-01, Close, 23.52
2000-12-04, Close, 25.07
2000-12-05, Close, 28.02
2000-12-06, Close, 26.85
2000-12-07, Close, 25.18
2000-12-07, BUY CREATE, 25.18
2000-12-08, BUY EXECUTED, 26.74
2000-12-08, Close, 26.74
2000-12-11, Close, 28.41
2000-12-12, Close, 27.35
2000-12-13, Close, 25.24
2000-12-14, Close, 24.46
2000-12-15, Close, 25.41
2000-12-15, SELL CREATE, 25.41
2000-12-18, SELL EXECUTED, 26.68
2000-12-18, Close, 28.46
2000-12-19, Close, 27.24
2000-12-20, Close, 25.35
2000-12-20, BUY CREATE, 25.35
2000-12-21, BUY EXECUTED, 24.74
2000-12-21, Close, 26.24
2000-12-22, Close, 28.35
2000-12-26, Close, 27.52
2000-12-27, Close, 27.30
2000-12-28, Close, 27.63
2000-12-29, Close, 25.85
2000-12-29, SELL CREATE, 25.85
Final Portfolio Value: 100017.52
系统竟然赚钱了…一定有问题
5. 经纪人说：给我钱！
而这笔钱被称作为“佣金”
让我们为每次操作添加一个合理的0.1%佣金率（无论是买卖……是的，经纪人很狂热……）
一行就足够了：
cerebro.broker.setcommission(commission=0.001)
为了更好的了解框架，我们希望在买/卖周期后看到盈利或亏损，无论有没有佣金
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):
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())
执行结果是这样的：
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
幸运女神眷顾！！！这个系统仍然赚钱。
在继续之前，让我们通过过滤“net”来注意一些事情。
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
加和“net”得到总利润，最后结果是：
15.83
但是系统告诉我们是：
2000-12-29T00:00:00, SELL CREATE, 27.41
Final Portfolio Value: 100016.98
显然15.83不等于16.98，这是哪里出现问题了呢？获利的15.83已经在钱包里了。
不幸的是，平台在数据反馈的最后一天有一个空缺。即使已经发送了卖出操作，但是它还没有被执行。
但是经纪人计算最终投资组合价值的时候考虑了2000 年 12 月 29 日的“收盘价”。实际执行价格将在下一个交易日（恰好是 2001 年 1 月 2 日）设定。扩展数据源以考虑这一天的输出是：
2001-01-02T00:00:00, SELL EXECUTED, Price: 27.87, Cost: 27.87, Commission 0.03
2001-01-02T00:00:00, OPERATION PROFIT, GROSS 1.64, NET 1.59
2001-01-02T00:00:00, Close, 24.87
2001-01-02T00:00:00, BUY CREATE, 24.87
Final Portfolio Value: 100017.41
现在将之前的净利润添加到已完成操作的净利润中：
15.83 + 1.59 = 17.42
6. 自定义策略：参数
硬编码策略中的某些值并且没有机会轻松更改它们有点不切实际。参数可以派上用场。
参数的定义很简单，看起来像：
params = (('myparam', 27), ('exitbars', 5),)
这是一个标准的 Python 元组，里面有一些元组，下面的内容可能看起来更美观：
params = (
( 'myparam' , 27 ),
( 'exitbars' , 5 ),
)
在将策略添加到 Cerebro 引擎时，允许对策略进行格式化参数化：
# Add a strategy
cerebro.addstrategy(TestStrategy, myparam=20, exitbars=7)
笔记
以下setsizing方法已弃用。此内容保留在此处，供查看源旧样本的任何人使用。源已更新以使用：
cerebro.addsizer(bt.sizers.FixedSize, stake=10)``
请阅读关于sizers的相关章节
在策略中使用参数也比较容易，因为它们存储在“params”属性中。例如，如果我们想设置stake，我们可以像这样下面这个例子一样将stack参数传递给position sizer：
# Set the sizer stake from the params
self.sizer.setsizing(self.params.stake)
我们也可以使用stake参数和 self.params.stake作为值来调用买卖。
退出的逻辑被修改：
# Already in the market ... we might sell
if len(self) >= (self.bar_executed + self.params.exitbars):
综上所述，示例演变为如下所示：
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())
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, 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
为了看出差异，打印输出也会展示每笔买入多少股。
将买入的股票数量乘以 10，显而易见的事情发生了：盈亏乘以 10。盈余现在是169.80 ，而不是16.98
7.添加一个技术指标
当你知道一个指标后，肯定会想方设法去尝试将它添加到策略中。当然，它们一定比简单的“三连低收盘价” 策略要好得多。
PyAlgoTrade 中的一个示例讲解了如何使用简单移动平均线的策略。
-
如果收盘价高于平均水平，则购买“AtMarket”
-
如果收盘价小于平均线，则卖出
-
市场上只允许单向操作
大多数现有代码可以保留在原处。让我们在初始化期间添加平均值 并保留对它的引用：
self.sma = bt.indicators.MovingAverageSimple(self.datas[0], period=self.params.maperiod)
进出场的逻辑将依赖于均线值。让我们看看代码中的逻辑。
笔记
开始的现金是1000，与 PyAlgoTrade 示例一致，不收取佣金
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())
现在，让我们看看输出的第一个交易日是几号：
- 可以发现，不再是2000-01-03，而是2000-01-24，这是为什么呢？
其实，1月24号之前的数据没有丢失，而是平台做了自适应。 - 简单移动平均线已经呗添加到策略中去
- 这个指标需要15根k线才能生成结果
- 2000-01-24是第15根k线出现的日期
原文翻译：
交易平台假设策略有一个合适的指标，在决策过程中使用它。 如果指标还没有准备好并产生价值，那么试图做出决策是没有意义的。
意思就是说：如果这个策略需要15根k线才能输出结果，而数据没有那么多，平台就不会输出值。
- next函数将会调用第一个已经被处理好的指标数据来输出结果
- 在这个例子中，只有一个简单的指标，但是策略可以有很多个指标
然后输出结果如下：
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
一个可以盈利的策略变成了不能赚钱的了，并且没有算手续费。可能简单的添加一个策略并不能让系统变好。