（于北京疫情期间）
量化之所以做为投资的手段，早应该有共识了。下围棋象棋的人知道， 人脑不可能战胜有智能的程序，它的计算能力太强太快，加上严格的程序化执行。我们人在做交易的时候，受情绪身体状况影响，感知的偏差，消息的不对称，往往会经常性的犯错。
我们的量化历程也是发展的很不顺利，从最初C++Fix协议注重高频交易，C#算法编程。到如今终于归入正道，发现Python才是一统江山的金融量化的工具，而我们现在用的Backtrader更可以称为国之重器！为什么呢？ 首先它有强大丰富的支持库，简单易用且成熟。 具体对我们来说就是金融数据分析应用方面引用库numpy，pandas和matplotlib。可以非常快速部署，性能稳定高效。
又为什么说它一统江山呢？外汇，加密货币和金融股指期货可以一并用它作为量化工具了：
- FX-外汇有 IB， OANDA可以支持用（策略难度最高）
- 加密货币的大部分交易所；我们现在用OKex，Binance （难度适中）
- 国内的股指商品期货极星量化交易可以用 （相对简单）
通用的解决方案是Python封装API接口。这大大简化了各个平台策略开发。相信国内外宽客的同行团队越来越多的会像我们一样，转向Python-BT。也许很快就发展成为新的行业标准。
下面是BT应用的一个简单实例，只需要几分钟就安装好了，在你的Python的 IDE环境运行一下，看你是否认同它的强大简单和高效！？
首先安装BACKTRADER
pip install backtrader pip install backtrader[plotting]
下面我们运行一个简单的策略，将symbol orcl一年的数据导入策略运行，结果显示导入数据和价格曲线以图形方式绘制出来。设定起始资金，用这次导入价格数据运行后显示交易记录和资金状况。
下面是数据文件的链接，浏览打开另存到本地文件夹，程序中默认路径为当前程序目录。请根据需要修改。
https://nabidex.com/usercontent/orcl-1995-2014.txt
数据文件存好后， 复制下面的程序就可以运行了，结果看后面的截图。
#this source code is provided by Backtrader Team！
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, '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()
最后是运行后的截图：
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