Backtrader：继续学习，把通达信的mace交易指标公式改为Backtrader交易策略

多改几个通达信交易指标有助于熟悉Backtrader开发。

这里记录一下通达信内置的mace交易指标公式的修改和测试结果。

通达信附图指标：

 

DIFF:=EMA(CLOSE,SHORT)-EMA(CLOSE,LONG);
DEA:=EMA(DIFF,MID);
MACD:=2*(DIFF-DEA);
平空开多:=CROSS(MACD,0);
平多开空:=CROSS(0,MACD);
STICKLINE(平空开多,0,30,2,0),COLOR0000FF;
STICKLINE(平多开空,0,30,2,0),COLORWHITE;

python代码：

# -*- coding: utf-8 -*-
"""
Created on Feb  21 2022

@author: freepy
"""

from __future__ import (absolute_import, division, print_function,
                        unicode_literals)
from datetime import datetime  # For datetime objects
# Import the backtrader platform
import backtrader as bt
import pandas as pd

# 创建策略继承bt.Strategy
class TestStrategy(bt.Strategy):
    params = (
        # 均线参数设置15天，15日均线
        ('para_mid', 12),
        ('para_short', 9),
        ('para_long', 26),
    )

    def log(self, txt, dt=None):
        # 记录策略的执行日志
        dt = dt or self.datas[0].datetime.date(0)
        print('%s, %s' % (dt.isoformat(), txt))

    def __init__(self):
        # 保存收盘价的引用
        self.dataclose = self.datas[0].close
        # 跟踪挂单
        self.order = None
        # 买入价格和手续费
        self.buyprice = None
        self.buycomm = None
        # # 加入均线指标
        # self.sma = bt.indicators.SimpleMovingAverage(self.datas[0], period=self.params.para_mid)
        
        # # 收盘价的 para_short 日简单移动平均
        # self.ma1 = bt.indicators.SimpleMovingAverage(self.data.close, period=self.params.para_short)
        # # 收盘价的 para_long 日简单移动平均
        # self.ma2 = bt.indicators.SimpleMovingAverage(self.data.close, period=self.params.para_long)
        
        
        # 交易条件
        # DIFF:=EMA(CLOSE,SHORT)-EMA(CLOSE,LONG);
        # DEA:=EMA(DIFF,MID);
        # MACD:=2*(DIFF-DEA);
        
        self.DIFF = bt.indicators.EMA(self.data.close, period=self.params.para_short) - \
                bt.indicators.EMA(self.data.close, period=self.params.para_long)
        self.DEA = bt.indicators.EMA(self.DIFF, period=self.params.para_mid)
        self.MACD = 2 * (self.DIFF - self.DEA) 

    # 交易函数
    def next(self):
        # 记录收盘价
        self.log('Close, %.2f' % self.dataclose[0])

        # 如果有订单正在挂起，不操作
        if self.order:
            return

        
        # 如果没有持仓则买入
        if not self.position:
            # MACD 上穿 0
            if self.MACD > 0: 
                # 买入
                self.log('买入单, %.2f' % self.dataclose[0])
                    # 跟踪订单避免重复
                self.order = self.buy()
        else:
            # 如果已经持仓， 0 上穿 MACD
            if 0 > self.MACD:
                # 全部卖出
                self.log('卖出单, %.2f' % self.dataclose[0])
                # 跟踪订单避免重复
                self.order = self.sell()
        

    # 订单状态通知，买入卖出都是下单
    def notify_order(self, order):
        if order.status in [order.Submitted, order.Accepted]:
            # broker 提交/接受了，买/卖订单则什么都不做
            return

        # 检查一个订单是否完成
        # 注意: 当资金不足时，broker会拒绝订单
        if order.status in [order.Completed]:
            if order.isbuy():
                self.log(
                    '已买入, 价格: %.2f, 费用: %.2f, 佣金 %.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('已卖出, 价格: %.2f, 费用: %.2f, 佣金 %.2f' %
                         (order.executed.price,
                          order.executed.value,
                          order.executed.comm))
            # 记录当前交易数量
            self.bar_executed = len(self)

        elif order.status in [order.Canceled, order.Margin, order.Rejected]:
            self.log('订单取消/保证金不足/拒绝')

        # 其他状态记录为：无挂起订单
        self.order = None

    # 交易状态通知，一买一卖算交易
    def notify_trade(self, trade):
        if not trade.isclosed:
            return
        self.log('交易利润, 毛利润 %.2f, 净利润 %.2f' %
                 (trade.pnl, trade.pnlcomm))

start = '2020-01-01'
end = '2022-01-31'

def get_data(code, start_date, end_date):
    df_tdx = pd.read_feather(r'./dataout/tdx/'+code+r'.day.feather')
    df_tdx.index=pd.to_datetime(df_tdx.date, format = '%Y%m%d')
    df_tdx_b=df_tdx.truncate(before=start_date, after = end_date)
    df_tdx_b['openinterest']=0
    df_tdx_b.rename(columns={'vol':'volume'}, inplace = True)
    df_tdx_b=df_tdx_b[['open','high','low','close','volume','openinterest']]
    return df_tdx_b

dataframe=get_data('sh600851', datetime.strptime(start,'%Y-%m-%d'), datetime.strptime(end,'%Y-%m-%d'))

if __name__ == '__main__':
	
    # 初始化cerebro回测系统设置
    cerebro = bt.Cerebro()

    # 取得股票历史数据
    data = bt.feeds.PandasData(dataname=dataframe, fromdate = datetime.strptime(start,'%Y-%m-%d'), todate = datetime.strptime(end,'%Y-%m-%d'))
	
    # 为Cerebro引擎添加策略
    cerebro.addstrategy(TestStrategy)
    # 加载交易数据
    cerebro.adddata(data)
	
	# 设置投资金额
    cerebro.broker.setcash(100000.0)
    # 设置佣金为0.001,除以100去掉%号
    cerebro.broker.setcommission(commission=0.001)
	
    #获取回测开始时的总资金
    print('期初资金: %.2f' % cerebro.broker.getvalue())
    #运行回测系统
    cerebro.run()
    #获取回测结束后的总资金
    print('期末资金: %.2f' % cerebro.broker.getvalue())

通过对比，可以看到python回测买卖日期与通达信是对应的。

但是这个策略的盈利能力很差：

。。。。。。

2022-01-20, 已卖出, 价格: 8.75, 费用: 8.53, 佣金 0.01
2022-01-20, 交易利润, 毛利润 0.22, 净利润 0.20
2022-01-20, Close, 8.65
2022-01-21, Close, 8.24
2022-01-24, Close, 8.10
2022-01-25, Close, 7.70
2022-01-26, Close, 7.64
2022-01-27, Close, 7.57
期末资金: 99999.84