python-双均线策略的预测能力研究

做量化，不仅要懂编程，有交易经验，而且要数学和统计学要好。恶补统计理论中，好后悔上统计课的时候做白日梦了。。。。。。。。。。。

下面试着尝试了分析双均线能否产生预测力，我们知道很多回测产生的盈利，有时候并不是策略本身的问题，有可能是数据的趋势。本文尝试通过消除平均趋势或者采用随机基准来对比策略的收益，似乎，双均线蛮好。

# -*- coding: utf-8 -*-"""Created on Tue Jun  6 14:50:04 2017@author: yunjinqi  E-mail:[e-mail]  Differentiate yourself in the world from anyone else."""# -*- coding: utf-8 -*-  """ Created on Thu May 25 08:55:12 2017  @author: yunjinqi    E-mail:[e-mail]    Differentiate yourself in the world from anyone else. """  import pandas as pd  import numpy as np  import datetime  import time  #获取数据  df=pd.read_csv('C:/Users/HXWD/Desktop/rb000.csv',encoding='gbk')  df.head()df.columns=['date','open','high','low','close','volume','amt']  df.head()  value=[]  for i in range(5,6):      for j in range(20,21):          df['ma5']=df['close'].rolling(i).mean()          df['ma20']=df['close'].rolling(j).mean()          df.ix[df['ma5']>df['ma20'],'cross']=1          df.ix[df['ma5']<=df['ma20'],'cross']=-1          #df[['close','ma5','ma20']][-200:].plot()          df['ret']=(df['close']-df['close'].shift(1))          df['profit']=df['ret']*df['cross']          #df['profit'].plot()          target=df['profit'].sum()          s=[i,j,target]          ts=time.strftime('%Y-%m-%d %X', time.localtime() )          value.append(s)          print('当前时间:{}短期参数:{},长期参数:{}优化完毕,净利润{}'.format(ts,i,j,s)) data=pd.DataFrame(value)  #data.to_csv('参数优化.csv')  #消除趋势后的df_up=df.cross[df.cross>0]df_down=df.cross[df.cross<0]p=len(df_up)/(len(df_down)+len(df_up))ll=len(df)mu=p*len(df)muimport randomsuiji_value=[]for i in range(10000):    total=[]    for i in range(len(df)):        v=random.randint(0,ll)        if v<mu:            total.append(1)        if v>=mu:            total.append(-1)    total=pd.Series(total)    suiji_profit=(df['ret']*total).sum()    print(suiji_profit)                   suiji_value.append(suiji_profit)suiji_value=pd.Series(suiji_value).sum()/10000suiji_value#随机信号只能够产生平均83.9的收益，而双均线能够产生15593的利润，#有理由相信这是一个好策略#我们尝试消除平均趋势之后再计算相应的盈利df['new_ret']=(df['close']-df['close'].shift(1))/df['close'].shift(1)aa=df['new_ret'].mean()df['new_ret1']=df['new_ret']-pd.Series([aa]*len(df))df['ret2']=df['new_ret1']*df['close'].shift(1)(df['ret2']*df['cross']).sum()#计算出来消除平均趋势之后的收益15571，只差了20多。#似乎均线策略，马马虎虎证明了，是可以产生预测能力的。