"""导入常用模块"""https://www.jianshu.com/p/2199116cbfe5import numpy as npimport pandas as pdimport matplotlib.pyplot as pltimport datetimefrom jqdata import *from environment import * # 导入大树工作室开发的回测模块
MACD计算函数
import talib as tl# MACD 公用函数def MACD(close, fastperiod, slowperiod, signalperiod): macdDIFF, macdDEA, macd = tl.MACDEXT(close, fastperiod=fastperiod, fastmatype=1, slowperiod=slowperiod, slowmatype=1, signalperiod=signalperiod, signalmatype=1) macd = macd * 2 return macdDIFF, macdDEA, macd # 查寻一个时间段内某标的的macd信息def get_macd(stock, count, end_date, unit): data = get_bars(security=stock, count=count, unit=unit,include_now=False, end_dt=end_date, fq_ref_date=None) close = data['close'] open = data['open'] high = data['high'] low = data['low'] return MACD(close, 12, 26, 9)
MACD 的计算与使用，可参考
https://www.joinquant.com/view/community/detail/16472MACD常用方法一
DIF,DEA均大于0，DIF向上突破DEA，即在0轴上形成金叉的时候，买入；
DIF,DEA均小于0，DIF向下突破DEA，即在0轴下形成死叉的时候，卖出；
在形成金叉或死叉时，需要用前后两个时间点所对应的DIF与DEA的值进行比较，在日回测中，最短的是当天与昨天进行比较，但时间太短，容易发生虚假信号，因此，将这个前后比较的时间做为一个可变参数。设这个时间段为N，N的取值为1到9，接下回来对方法一进行回测，并生成比较结果。
trade_list = []N = range(1, 10)for n in N: """初始化以下内容""" context = Context() # 账户对象 order = Order(context) # 下单对象 trade = Trade(context, order) # 回测对旬 context.start_date = '2005-05-01' context.end_date = '2019-02-22' context.universe = ['000300.XSHG'] context.base = '000300.XSHG' """策略主体""" def handle(context, order): stock = context.universe[0] current_date = trade.context.current_dt macd_day = get_macd(stock, 100, current_date, '1d') dif1 = macd_day[0][-1] dea1 = macd_day[1][-1] macd1 = macd_day[2][-1] dif2 = macd_day[0][-n-1] dea2 = macd_day[1][-n-1] macd2 = macd_day[2][-n-1] close = get_price(security=stock, end_date=context.current_dt, frequency='daily', fields=None, skip_paused=False, fq='pre', count=5)['close'] if dif1 > 0 and dea1 > 0 and dif2 < dea2 and dif1 > dea2: if stock in context.position.keys(): return order.buy(stock, close[-1], context.cash // close[-1]) elif dif1 < 0 and dea1 < 0 and dif2 > dea2 and dif1 < dea2: if stock not in context.position.keys(): return order.sell(stock, close[-1], context.position[stock]['count']) """执行策略""" trade.trade(handle, show=False, log=False) trade_list.append(trade)# 展示Trade.show_ratio_compare('n', N, trade_list, 3, 3)
Trade.show_result('n', N, trade_list)
回测结果显示，在n等于9时的回报率最大，整体说来，回撤很大。但本回测是采用单一指标，未添加止损止盈，仓位管理等条件，所以不能太苛刻。
MACD常用方法二
当DEA与价格趋势发生背离时，即当股价不断走低，而DEA不断走高时，进行买入操作；当股价不断走高，而DEA值不断走低时，进行卖出操作。背离值需要一个时间段来进行比较，我们将这个时间段设N,取值1~9，然后对方法二进行回测。
trade_list = []N = range(1, 10)for n in N: """初始化以下内容""" context = Context() # 账户对象 order = Order(context) # 下单对象 trade = Trade(context, order) # 回测对旬 context.start_date = '2005-05-01' context.end_date = '2019-02-22' context.universe = ['000300.XSHG'] context.base = '000300.XSHG' """策略主体""" def handle(context, order): stock = context.universe[0] current_date = trade.context.current_dt macd_day = get_macd(stock, 100, current_date, '1d') close = get_price(security=stock, end_date=context.current_dt, frequency='daily', fields=None, skip_paused=False, fq='pre', count=n+1)['close'] dif1 = macd_day[0][-1] dea1 = macd_day[1][-1] macd1 = macd_day[2][-1] dif2 = macd_day[0][-n-1] dea2 = macd_day[1][-n-1] macd2 = macd_day[2][-n-1] close1 = close[-1] close2 = close[-n-1] if close1 < close2 and dea1 > dea2: if stock in context.position.keys(): return order.buy(stock, close[-1], context.cash // close[-1]) elif close1 > close2 and dea1 < dea2: if stock not in context.position.keys(): return order.sell(stock, close[-1], context.position[stock]['count']) """执行策略""" trade.trade(handle, show=False, log=False) trade_list.append(trade)# 展示Trade.show_ratio_compare('n', N, trade_list, 3, 3)
Trade.show_result('n', N, trade_list)
从回测看来，n取值等于4时，效果最好，但整体回测的效果并不理想。原因可以有以下，MACD本身具有延迟性，而如果想在底背离与顶背离时买卖，延迟的时间则更长，而大多数情况下，合适的买卖点不一定都会发生背离情况。因此，方法二回测的情况是符合逻辑的。
MACD常用方法三
当MACD柱由绿变红时，买入；当MACD由红变绿时，卖出。这里同样为本方法设定一个时间段，N的取值是1~9.来看下面的回测。
trade_list = []N = range(1, 10)for n in N: """初始化以下内容""" context = Context() # 账户对象 order = Order(context) # 下单对象 trade = Trade(context, order) # 回测对旬 context.start_date = '2005-05-01' context.end_date = '2019-02-22' context.universe = ['000300.XSHG'] context.base = '000300.XSHG' """策略主体""" def handle(context, order): stock = context.universe[0] current_date = trade.context.current_dt macd_day = get_macd(stock, 100, current_date, '1d') close = get_price(security=stock, end_date=context.current_dt, frequency='daily', fields=None, skip_paused=False, fq='pre', count=n+1)['close'] dif1 = macd_day[0][-1] dea1 = macd_day[1][-1] macd1 = macd_day[2][-1] dif2 = macd_day[0][-n-1] dea2 = macd_day[1][-n-1] macd2 = macd_day[2][-n-1] close1 = close[-1] close2 = close[-n-1] if macd2 < 0 and macd1 > 0: if stock in context.position.keys(): return order.buy(stock, close[-1], context.cash // close[-1]) elif macd2 > 0 and macd1 < 0: if stock not in context.position.keys(): return order.sell(stock, close[-1], context.position[stock]['count']) """执行策略""" trade.trade(handle, show=False, log=False) trade_list.append(trade)# 展示Trade.show_ratio_compare('n', N, trade_list, 3, 3)
Trade.show_result('n', N, trade_list)
通过回测来看，当n等于1时，效果最好。macd值由红转绿或由绿转红的时候，其实也正是死叉或金叉形成的时候。
通过比较，发现方法一的情况下，N=9时，回测效果最好。如下图：
"""初始化以下内容"""context = Context() # 账户对象order = Order(context) # 下单对象trade = Trade(context, order) # 回测对旬context.start_date = '2005-05-01'context.end_date = '2019-02-22'context.universe = ['000300.XSHG']context.base = '000300.XSHG'"""策略主体"""def handle(context, order): stock = context.universe[0] current_date = trade.context.current_dt macd_day = get_macd(stock, 100, current_date, '1d') dif1 = macd_day[0][-1] dea1 = macd_day[1][-1] macd1 = macd_day[2][-1] dif2 = macd_day[0][-9] dea2 = macd_day[1][-9] macd2 = macd_day[2][-9] close = get_price(security=stock, end_date=context.current_dt, frequency='daily', fields=None, skip_paused=False, fq='pre', count=5)['close'] if dif1 > 0 and dea1 > 0 and dif2 < dea2 and dif1 > dea2: if stock in context.position.keys(): return order.buy(stock, close[-1], context.cash // close[-1]) elif dif1 < 0 and dea1 < 0 and dif2 > dea2 and dif1 < dea2: if stock not in context.position.keys(): return order.sell(stock, close[-1], context.position[stock]['count'])"""执行策略"""trade.trade(handle, log=False)
以上几种情况，可以结合使用，有兴趣的朋友可以做更多的推广测试。
