简介
一般交易，交易所都要收手续费，这次在策略里面卖买的同时增加了手续费。具体可以参看Backtrader官方文档quickstart
原理
broker是经纪人/交易所的角色，通过broker.setcommission方法设置手续费
实践
实现目标：
- 买卖还是和以前的一样，手续费设置为：千一 0.001
- 显示出每次交易的有手续费和没有手续费的成交价格
- 显示每次教程成功后，毛利（gross price）和净利（net price）
# -*- coding: utf-8 -*-
"""
Created on Sun Mar 29 12:18:17 2020
@author: horace pei
"""
#############################################################
#import
#############################################################
from __future__ import (absolute_import, division, print_function,
unicode_literals)
import os,sys
import pandas as pd
import backtrader as bt
#############################################################
#global const values
#############################################################
#############################################################
#static function
#############################################################
#############################################################
#class
#############################################################
# 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
# To keep track of pending orders
self.order = None
# buy price
self.buyprice = None
# buy commission
self.buycomm = None
#订单状态改变回调方法 be notified through notify_order(order) of any status change in an order
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
elif order.issell():
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
#交易状态改变回调方法 be notified through notify_trade(trade) of any opening/updating/closing trade
def notify_trade(self, trade):
if not trade.isclosed:
return
# 每笔交易收益 毛利和净利
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(当前账户持股情况，size，price等等)
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()
#############################################################
#global values
#############################################################
#############################################################
#global function
#############################################################
def get_dataframe():
# Get a pandas dataframe
datapath = './data/stockinfo.csv'
tmpdatapath = './data/stockinfo_tmp.csv'
print('-----------------------read csv---------------------------')
dataframe = pd.read_csv(datapath,
skiprows=0,
header=0,
parse_dates=True,
index_col=0)
dataframe.trade_date = pd.to_datetime(dataframe.trade_date, format="%Y%m%d")
dataframe['openinterest'] = '0'
feedsdf = dataframe[['trade_date', 'open', 'high', 'low', 'close', 'vol', 'openinterest']]
feedsdf.columns =['datetime', 'open', 'high', 'low', 'close', 'volume', 'openinterest']
feedsdf.set_index(keys='datetime', inplace =True)
feedsdf.iloc[::-1].to_csv(tmpdatapath)
feedsdf = pd.read_csv(tmpdatapath, skiprows=0, header=0, parse_dates=True, index_col=0)
if os.path.isfile(tmpdatapath):
os.remove(tmpdatapath)
print(tmpdatapath+" removed!")
return feedsdf
########################################################################
#main
########################################################################
if __name__ == '__main__':
# Create a cerebro entity(创建cerebro)
cerebro = bt.Cerebro()
# Add a strategy(加入自定义策略)
cerebro.addstrategy(TestStrategy)
# Get a pandas dataframe(获取dataframe格式股票数据)
feedsdf = get_dataframe()
# Pass it to the backtrader datafeed and add it to the cerebro(加入数据)
data = bt.feeds.PandasData(dataname=feedsdf)
cerebro.adddata(data)
# Set our desired cash start(给经纪人，可以理解为交易所股票账户充钱)
cerebro.broker.setcash(100000.0)
# Set the commission - 0.1%(设置交易手续费，双向收取)
cerebro.broker.setcommission(commission=0.001)
# Print out the starting conditions(输出账户金额)
print('Starting Portfolio Value: %.2f' % cerebro.broker.getvalue())
# Run over everything(执行回测)
cerebro.run()
# Print out the final result(输出账户金额)
print('Final Portfolio Value: %.2f' % cerebro.broker.getvalue())
分析：
买的输出：
2020-04-01, BUY CREATE, 5.21
2020-04-02, BUY EXECUTED, Price: 5.20, Cost: 5.20, Comm 0.01
4月1日，触发了买的动作，4月2日开盘价 5.20购买1股，成本价5.20，手续费0.01。
卖的输出：
2020-02-11, SELL CREATE, 5.38
2020-02-12, SELL EXECUTED, Price: 5.39, Cost: 5.00, Comm 0.01
2020-02-12, OPERATION PROFIT, GROSS 0.39, NET 0.38
2月11日，触发了卖的动作，2月12日以开盘价 5.39出售1股，成本价5.20，手续费0.01，
这次交易毛利是0.39，扣除手续费后，净利是0.38。
整体上还还是比较清晰的。
全代码
全代码请到github上clone了。github地址：[qtbt](https://github.com/horacepei/qtbt.git）
