python量化交易学习笔记（三）——第一个策略回测程序Demo6

C:Python38python.exe F:/test/test/demo6.py
Starting Portfolio Value: 100000.00
2019-10-08, Close, 5.22
2019-10-09, Close, 5.27
2019-10-10, Close, 5.26
2019-10-11, Close, 5.24
2019-10-14, Close, 5.23
2019-10-15, Close, 5.17
2019-10-16, Close, 5.20
2019-10-17, Close, 5.25
2019-10-18, Close, 5.12
2019-10-21, Close, 5.10
2019-10-22, Close, 5.25
2019-10-23, Close, 5.23
2019-10-24, Close, 5.29
2019-10-25, Close, 5.29
2019-10-28, Close, 5.22
2019-10-29, Close, 5.23
2019-10-30, Close, 5.17
2019-10-31, Close, 5.12
2019-11-01, Close, 5.23
2019-11-04, Close, 5.24
2019-11-05, Close, 5.22
2019-11-06, Close, 5.12
2019-11-07, Close, 5.15
2019-11-08, Close, 5.12
2019-11-11, Close, 5.02
2019-11-12, Close, 5.02
2019-11-13, Close, 5.00
2019-11-14, Close, 5.07
2019-11-15, Close, 5.00
2019-11-18, Close, 4.94
2019-11-19, Close, 5.05
2019-11-20, Close, 5.07
2019-11-21, Close, 5.00
2019-11-22, Close, 4.95
2019-11-25, Close, 4.98
2019-11-26, Close, 4.95
2019-11-27, Close, 4.92
2019-11-28, Close, 4.89
2019-11-29, Close, 4.91
2019-12-02, Close, 4.91
2019-12-03, Close, 4.95
2019-12-04, Close, 4.94
2019-12-05, Close, 5.05
2019-12-06, Close, 5.10
2019-12-09, Close, 5.10
2019-12-10, Close, 5.03
2019-12-11, Close, 5.06
2019-12-12, Close, 5.02
2019-12-13, Close, 5.03
2019-12-16, Close, 5.01
2019-12-17, Close, 5.09
2019-12-18, Close, 5.10
2019-12-19, Close, 5.06
2019-12-20, Close, 5.00
2019-12-23, Close, 4.95
2019-12-24, Close, 4.98
2019-12-25, Close, 5.20
2019-12-26, Close, 5.26
2019-12-27, Close, 5.16
2019-12-30, Close, 5.18
2019-12-31, Close, 5.21
2020-01-02, Close, 5.21
2020-01-03, Close, 5.27
2020-01-06, Close, 5.23
2020-01-07, Close, 5.22
2020-01-08, Close, 5.08
2020-01-09, Close, 5.24
2020-01-10, Close, 5.21
2020-01-13, Close, 5.21
2020-01-14, Close, 5.17
2020-01-15, Close, 5.11
2020-01-16, Close, 5.06
2020-01-17, Close, 5.01
2020-01-20, Close, 4.99
2020-01-21, Close, 4.99
2020-01-22, Close, 4.99
2020-01-23, Close, 4.88
2020-02-03, Close, 4.39
2020-02-04, Close, 4.43
2020-02-05, Close, 4.43
2020-02-06, Close, 4.66
2020-02-07, Close, 4.73
2020-02-10, Close, 4.72
2020-02-11, Close, 4.70
2020-02-12, Close, 4.77
2020-02-13, Close, 4.68
2020-02-14, Close, 4.66
2020-02-17, Close, 4.75
2020-02-18, Close, 4.67
2020-02-19, Close, 4.64
2020-02-20, Close, 4.66
2020-02-21, Close, 4.77
2020-02-24, Close, 4.70
2020-02-25, Close, 4.73
2020-02-26, Close, 4.85
2020-02-27, Close, 4.86
2020-02-28, Close, 4.84
Final Portfolio Value: 100000.00

Process finished with exit code 0

　　

from __future__ import (absolute_import, division, print_function,
                        unicode_literals)
import datetime  # 用于datetime对象操作
import os.path  # 用于管理路径
import sys  # 用于在argvTo[0]中找到脚本名称
import backtrader as bt # 引入backtrader框架

# 创建策略
class TestStrategy(bt.Strategy):
    def log(self, txt, dt=None):
        ''' 策略的日志函数'''
        dt = dt or self.datas[0].datetime.date(0)
        print('%s, %s' % (dt.isoformat(), txt))
    def __init__(self):
        # 引用data[0]数据的收盘价数据
        self.dataclose = self.datas[0].close
    def next(self):
        # 日志输出收盘价数据
        self.log('Close, %.2f' % self.dataclose[0])

# 创建cerebro实体
cerebro = bt.Cerebro()
# 添加策略
cerebro.addstrategy(TestStrategy)
# 先找到脚本的位置，然后根据脚本与数据的相对路径关系找到数据位置
# 这样脚本从任意地方被调用，都可以正确地访问到数据
modpath = os.path.dirname(os.path.abspath(sys.argv[0]))
datapath = os.path.join(modpath, 'F:/GZH/自动化交易/历史数据/sh.600173history_k_data2021-12-31-2021-12-31.csv')
# 创建价格数据
data = bt.feeds.GenericCSVData(
    dataname = datapath,
    fromdate = datetime.datetime(2019, 10, 1),
    todate = datetime.datetime(2020, 2, 29),
    nullvalue = 0.0,
    dtformat = ('%Y/%m/%d'),
    datetime = 0,
    open = 1,
    high = 2,
    low = 3,
    close = 4,
    volume = 5,
    openinterest = -1
)
# 在Cerebro中添加价格数据
cerebro.adddata(data)
# 设置启动资金
cerebro.broker.setcash(100000.0)
# 打印开始信息
print('Starting Portfolio Value: %.2f' % cerebro.broker.getvalue())
# 遍历所有数据
cerebro.run()
# 打印最后结果
print('Final Portfolio Value: %.2f' % cerebro.broker.getvalue())

　　程序中创建了一个backtrader.Strategy的子类，并将其添加至cerebro中。这里实际就是新建了一个自定义的空策略，在这个策略里可以添加买入卖出条件，供框架进行回测。当前的策略只是做了按天输出收盘价格。

几点简单的解释：

当__init__方法被调用时，策略就有了一个数据列表，这个列表是标准的Python语言列表，存储的是按顺序加载的数据
self.dataclose = self.datas[0].close引用列表中的收盘价数据，用于后续交易
next方法在每个K线数据上都会被调用
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参考：https://blog.csdn.net/m0_46603114/article/details/104971989