做交易，一根均线上多下空，能做到稳定盈利？是“大道至简”，还是嘴盘忽悠？Python量化交易均线策略测试二

本文主要是介绍做交易，一根均线上多下空，能做到稳定盈利？是“大道至简”，还是嘴盘忽悠？Python量化交易均线策略测试二，希望对大家解决编程问题提供一定的参考价值，需要的开发者们随着小编来一起学习吧！

 
参考视频教程:  
 **首门程序员理财课 Python量化交易系统实战  **
交易，一根均线上多下空，能做到稳定盈利？是"大道至简"，还是嘴盘忽悠？Python量化交易均线策略测试一中，我们用python程序量化测试了10日均线，50日均线作为单根均线策略，测试发现收益十分不理想。

今天我们继续对单根均线策略做量化测试，今天突然想起昨天的测试还有60日均线没有做量化测试，为什么要测试60日均线，因为60日均线太有名了，外号有牛熊分界线，万能均线，生命线等。

仅仅以股价大于60均线作为买入条件，最近三年收益如下：

60均线最近三年收益

我们再把昨天python量化交易测试的10日均线，50日均线收益图贴在下面，很明显60日均线收益确实整体要稍微好一些。

10日均线，最近三年收益

50日均线，最近三年收益

我们对60日均线策略买点做一些优化

1.股价大于60日均线，且均线趋势向上

2.股价大于30日均线，且均线趋势向上

3.股价大于10日均线，且均线趋势向上

回测最近三年收益如下：

擦，貌似越优化收益越差，这也是意料之外的事情，这个优化策略，是我在百度找的一篇洋洋洒洒写了1万多字，图文并茂，貌似，看起来大神给的策略，看起来大神给的策略，量化交易回测不行啊，未完待续。。。。。。

如果你有策略愿意让我量化测试一下，请加公众号：Python量化交易探索，留言告诉我。

60日均线策略，python量化交易代码如下：

-*- coding: utf-8 -*-

import numpy as np

import pandas as pd

import matplotlib.pyplot as plt

import os

import shutil

import time

import matplotlib

def danjunxian(start,end):

mairuhou_mark = 0

zhengshouyi_num = 0

fushouyi_num = 0

cur_dir = os.getcwd() # get current path

folder_name = ‘result’

dir_new = os.path.join(cur_dir, folder_name)

end_date = [‘2019/12/31’,‘2019/12/30’,‘2019/12/29’,‘2019/12/28’]

删掉结果

if os.path.exists(dir_new + “\\” + start.replace(“/”,“_”) + “__” + end.replace(“/”,“_”) + “celie_xiangxi” + ‘.txt’):

os.remove(dir_new + “\\” + start.replace(“/”,“_”) + “__” + end.replace(“/”,“_”) + “celie_xiangxi” + ‘.txt’)

if os.path.exists(dir_new + “\\” + start.replace(“/”,“_”) + “__” + end.replace(“/”,“_”) + “dangtian_buy” +‘.txt’):

os.remove(dir_new + “\\” + start.replace(“/”,“_”) + “__” + end.replace(“/”,“_”) + “dangtian_buy” +‘.txt’)

if os.path.exists(dir_new + “\\” + start.replace(“/”,“_”) + “__” + end.replace(“/”,“_”) + ‘“dangtian_sell”’ +‘.txt’):

os.remove(dir_new + “\\” + start.replace(“/”,“_”) + “__” + end.replace(“/”,“_”) + ‘“dangtian_sell”’ +‘.txt’)

设置路径

dir_list = []

lujing = r’C:\gupiao\gupiaoci’

for i in os.listdir(r’C:\gupiao\gupiaoci’):

a = i.split(‘.’)[0]

if a[0] != ‘3’:

dir_list.append(lujing+‘\\’+i)

个股

total_shouyi=1

total_date_buy = []

total_date_sell = []

buy_date_zhengshouyi = []

buy_date_fushouyi = []

for j in dir_list:

获取数据

name = (j.split(‘\\’)[-1]).split(‘.’)[0]

df = pd.read_table(j,header=1,usecols=range(6), parse_dates=[0], index_col=0,encoding=‘gb2312’)

df.index.rename(‘date’, inplace=True)

df.rename(columns={’ 开盘’:‘open’, ’ 最高’:‘high’, ’ 最低’:‘low’, ’ 收盘’:‘close’,’ 成交量’:‘vol’}, inplace=True)

df = df.drop(‘数据来源:通达信’)

df.close = df.close.astype(np.float32)#设置为32位，4字节，默认64位，8字节，append到list之后就会多小数位

df.low = df.low.astype(np.float32)#设置为32位，4字节，默认64位，8字节，append到list之后就会多小数位

df.vol = df.vol.astype(np.int64)

df2 = df[start:end][:len(df.vol)-1]

df = df[start:end]

均线

ma5 = df2.close.rolling(window=5,center=False).mean()

ma5= ma5[start:end]

ma3 = df2.close.rolling(window=3,center=False).mean()

ma3= ma3[start:end]

ma8 = df2.close.rolling(window=8, center=False).mean()

ma8 = ma8[start:end]

ma15 = df2.close.rolling(window=15, center=False).mean()

ma15 = ma15[start:end]

ma10 = df2.close.rolling(window=10,center=False).mean()

ma10= ma10[start:end]

ma12 = df2.close.rolling(window=12, center=False).mean()

ma12 = ma12[start:end]

ma20 = df2.close.rolling(window=20,center=False).mean()

ma20= ma20[start:end]

ma30 = df2.close.rolling(window=30,center=False).mean()

ma30 = ma30[start:end]

ma60 = df2.close.rolling(window=60,center=False).mean()

ma60 = ma60[start:end]

ma120 = df2.close.rolling(window=120,center=False).mean()

ma120 = ma120[start:end]

ma200 = df2.close.rolling(window=200, center=False).mean()

ma200 = ma200[start:end]

ma250 = df2.close.rolling(window=250,center=False).mean()

ma250 = ma250[start:end]

均量线

vol5 = df2.vol.rolling(window=5,center=False).mean()

vol5 = vol5[start:end]

vol3 = df2.vol.rolling(window=3,center=False).mean()

vol3 = vol3[start:end]

vol8 = df2.vol.rolling(window=8,center=False).mean()

vol8 = vol8[start:end]

vol10 = df2.vol.rolling(window=10,center=False).mean()

vol10 = vol10[start:end]

vol20 = df2.vol.rolling(window=20,center=False).mean()

vol20 = vol20[start:end]

vol15 = df2.vol.rolling(window=15,center=False).mean()

vol15 = vol15[start:end]

vol30 = df2.vol.rolling(window=30,center=False).mean()

vol30 = vol30[start:end]

vol60 = df2.vol.rolling(window=60, center=False).mean()

vol60 = vol60[start:end]

设置初始化数据

买入条件，buy_status = True，其次是chicang_status = False

chicang_status = False#chicang_status==False的时候表示空仓状态，可以买入，买入之后需要将其设置为True，表示股票为持有状态

buy_status = False#初始化buy的状态为False，当遇到买点出现时，设置状态为True，表示之后可以买入

buy_price = []

buy_date = []

sell_price = []

sell_date = []

low_vol = []

low_vol_index = []

fengexian_mark = 0

low_vol

df_temp = df

temp_vol = df.vol

temp_index = df.index

ma3_temp = ma3

ma5_temp = ma5

ma8_temp = ma8

ma15_temp = ma15

ma10_temp = ma10

ma12_temp = ma12

ma20_temp = ma20

ma30_temp = ma30

ma60_temp = ma60

ma120_temp = ma120

ma200_temp = ma200

ma250_temp = ma250

vol5_temp = vol5

vol3_temp = vol3

vol8_temp = vol8

vol15_temp = vol15

vol10_temp = vol10

vol20_temp = vol20

vol30_temp = vol30

vol60_temp = vol60

temp_vol_max = 0

for j in range(60,len(temp_vol)):

if buy_status == False and chicang_status == False:

buy_status = True#买点出现时，设置状态为True，表示之后可以买入

vol_max_dangtian_index = temp_index[j]

close_min_new = df_temp.close[j]

elif buy_status == True and chicang_status == False:

if df_temp.at[temp_index[j-1],‘close’] >= 300 or (df_temp.high[j]-df_temp.close[j-1])/df_temp.close[j-1] < -0.09:

chicang_status = False

buy_status = False

break

elif df_temp.close[j] > ma60_temp[j]:#股价大于60均线​,买入

if len(gupiaochichang) >= 1:

for gupiaochichang_item in gupiaochichang:

if gupiaochichang_item[0] == temp_index[j] and gupiaochichang_item[1] >= 1:

print(gupiaochichang_item)

chicang_status = False

buy_status = False

if chicang_status == False and buy_status == False:

continue

chicang_status = True

buy_status = False#买入之后设置状态为False

buy_price_m = df_temp.at[temp_index[j],‘close’]

buy_date_temp = temp_index[j]

buy_price.append(df_temp.at[temp_index[j],‘close’])

buy_date.append(temp_index[j])

total_date_buy.append([name,temp_index[j]])

fengexian_mark = 1

close_max = df_temp.close[j]#加入买入当天就是当前最高价

股票持仓，买日期加1,买日期不在list，则加入

for gupiaochichuang_item in gupiaochichang:

if gupiaochichuang_item[0] == temp_index[j]:

gupiaochichuang_item[1] += 1

if len(gupiaochichang) == 0:

gupiaochichang.append([temp_index[j],1])

else:

for i22 in range(len(gupiaochichang)):

if len(gupiaochichang) != 0 and i22 == len(gupiaochichang) -1 and gupiaochichang[i22][0] != temp_index[j]:#找到最后一个还没有找到买日期，加将买日期加入

gupiaochichang.append([temp_index[j],1])

存结果

with open(dir_new + “\\” + start.replace(“/”,“_”) + “__” +end.replace(“/”,“_”) + “celie_xiangxi” + ‘.txt’,‘a’) as f:

f.write(“股票名称–” + name + ‘\n’ + “开始日期–” + start + ‘\n’ + “结束日期–” + end + ‘\n’ )

f.write(“最大vol/当天vol 大于20当天日期—–>” + str(vol_max_dangtian_index) + ‘\n’)

f.write(‘\n’ + “股票名称–” + name + “–买入价格–” + “–步长–” + “–” + “–j–” + str(j) + “–” + str(df_temp.at[temp_index[j],‘close’]) + “–买入日期–” + str(temp_index[j]))

elif chicang_status == True:

if df_temp.close[j] < ma10_temp[j]:#股价小于10日均线卖出

chicang_status = False

sell_price.append(df_temp.close[j])

sell_date.append(temp_index[j])

total_date_sell.append([name,temp_index[j]])

if (df_temp.close[j]-buy_price_m)/buy_price_m > 0:

zhengshouyi_num += 1#正收益次数加1

buy_date_zhengshouyi.append(buy_date_temp)

else:

fushouyi_num += 1#负收益次数加1

buy_date_fushouyi.append(buy_date_temp)

total_shouyi *= (1 + (df_temp.close[j]-buy_price_m)/buy_price_m)

mairuhou_mark = 0

存结果

with open(dir_new + “\\” + start.replace(“/”,“_”) + “__” +end.replace(“/”,“_”) + “celie_xiangxi” + ‘.txt’,‘a’) as f:

f.write(‘\n’ + “股票名称–” + name + “–最高点下跌10%,卖出价格–” + “–步长–” + “–” + “–j–” + str(j) + “–” + str(df_temp.close[j]) + “–卖出日期–” + str(temp_index[j]))

elif (df_temp.close[j] - buy_price_m)/buy_price_m < -0.05:

chicang_status = False

sell_price.append(df_temp.close[j])

sell_date.append(temp_index[j])

total_date_sell.append([name,temp_index[j]])

fushouyi_num += 1#负收益次数加1

total_shouyi *= (1 - 0.05)

mairuhou_mark = 0

buy_date_fushouyi.append([name,buy_date_temp])

buy_date_fushouyi.append(buy_date_temp)

存结果

with open(dir_new + “\\” + start.replace(“/”,“_”) + “__” +end.replace(“/”,“_”) + “celie_xiangxi” + ‘.txt’,‘a’) as f:

f.write(‘\n’ + “股票名称–” + name + “–止损-0.05,卖出价格–” + “–步长–” + “–” + “–j–” + str(j) + “–” + str(df_temp.close[j]) + “–卖出日期–” + str(temp_index[j]))

elif mairuhou_mark >= 10:

total_shouyi *= (1 + (df_temp.close[j] - buy_price_m)/buy_price_m)

chicang_status = False

sell_price.append(df_temp.close[j])

sell_date.append(temp_index[j])

total_date_sell.append([name,temp_index[j]])

mairuhou_mark = 0

if (df_temp.close[j] - buy_price_m)/(buy_price_m) > 0:

zhengshouyi_num += 1

buy_date_zhengshouyi.append(buy_date_temp)

print(“持有超过10天卖出：”,(df_temp.close[j] - buy_price_m)/(buy_price_m))

存结果

with open(dir_new + “\\” + start.replace(“/”,“_”) + “__” +end.replace(“/”,“_”) + “celie_xiangxi” + ‘.txt’,‘a’) as f:

f.write(‘\n’ + “股票名称–” + name + “–持有超过5天卖出，大于0.01,卖出价格–” + “–” + “–j–” + str(j) + “–” + str(df_temp.close[j]) + “–卖出日期–” + str(temp_index[j]))

else:

fushouyi_num += 1

buy_date_fushouyi.append(buy_date_temp)

print(“持有超过10天卖出：”,(df_temp.close[j] - buy_price_m) / (buy_price_m ))

存结果

with open(dir_new + “\\” + start.replace(“/”,“_”) + “__” +end.replace(“/”,“_”) + “celie_xiangxi” + ‘.txt’,‘a’) as f:

f.write(‘\n’ + “股票名称–” + name + “–持有超过15天卖出，小于0.01，大于0,卖出价格–” + “–j–” + str(j) + “–” + str(df_temp.close[j]) + “–卖出日期–” + str(temp_index[j]))

else:

mairuhou_mark += 1

gupiaochichang.sort(key=lambda x:x[0])

if zhengshouyi_num+fushouyi_num != 0:

print(start,“—-”,end,“total_shouyi=”,‘%.2f’%(total_shouyi),“概率”,‘%.2f’%(zhengshouyi_num/(zhengshouyi_num+fushouyi_num)),“正收益次数->”,zhengshouyi_num,“负收益次数->”,fushouyi_num)

存结果

cur_dir = os.getcwd() # get current path

folder_name = ‘result’

dir_new = os.path.join(cur_dir, folder_name)

存买入，卖出价格，日期

if len(buy_price) > len(sell_price):

if buy_date[-1] in end_date:

with open(dir_new + “\\” + start.replace(“/”,“_”) + “__” +end.replace(“/”,“_”) + “dangtian_buy” +‘.txt’,‘a’) as f:

f.writelines(‘\n’ + name + “–” + str(buy_date[-1]) + ‘\n’ + ’buy: ’ + str(buy_price[-1]) + ‘\n’)

if len(sell_price) != 0:

if sell_date[-1] in end_date:

with open(dir_new + “\\” + start.replace(“/”,“_”) + “__” +end.replace(“/”,“_”) + “dangtian_sell” +‘.txt’,‘a’) as f:

for rr in range(len(sell_price)):

if rr == 0:

f.writelines(‘\n’ + name + ‘\n’ + ’buy: ’ + str(buy_price[rr]) + str(buy_date[rr]) + ‘\n’ + ’sell: ’ + str(sell_price[rr]) + str(sell_date[rr]) + ‘\n’)

else:

f.writelines( ’buy: ’ +str(buy_price[rr]) + str(buy_date[rr]) + ‘\n’ + ’sell: ’ + str(sell_price[rr]) + str(sell_date[rr]) + ‘\n’)

if zhengshouyi_num+fushouyi_num != 0:

zshouyi = float(zhengshouyi_num/(zhengshouyi_num+fushouyi_num))

fshouyi = float(fushouyi_num/(zhengshouyi_num+fushouyi_num))

total_num = zhengshouyi_num+fushouyi_num

total_date_buy.sort(key=lambda x:x[1])

total_date_sell.sort(key=lambda x:x[1])

x = []

y = []

for i in buy_date_zhengshouyi:

if i not in x:

x.append(i)

for i in buy_date_fushouyi:

if i not in y:

y.append(i)

if fengexian_mark == 1:

with open(dir_new + “\\” + start.replace(“/”,“_”) + “__” + end.replace(“/”,“_”) + “celie_xiangxi” + ‘.txt’,‘a’) as f:

f.write(‘\n’ + ‘\n’ + “——————–分割线———————-” + ‘\n’ + ‘\n’)

buy_date_zhengshouyi_count = []

buy_date_fushouyi_count = []

i = 0

x.sort()

y.sort()

for i in x:

if buy_date_zhengshouyi.count(i) >= 0:

with open(dir_new + “\\” + start.replace(“/”,“_”) + “__” +end.replace(“/”,“_”) + “celie_xiangxi” + ‘.txt’,‘a’) as f:

f.write(‘\n’ + “正收益买日期–” + str(buy_date_zhengshouyi.count(i)) + “–” +str(i))

buy_date_zhengshouyi_count.append(buy_date_zhengshouyi.count(i))

with open(dir_new + “\\” + start.replace(“/”,“_”) + “__” +end.replace(“/”,“_”) + “celie_xiangxi” + ‘.txt’,‘a’) as f:

f.write(‘\n’ )

for i in y:

if buy_date_fushouyi.count(i) >= 0:

with open(dir_new + “\\” + start.replace(“/”,“_”) + “__” +end.replace(“/”,“_”) + “celie_xiangxi” + ‘.txt’,‘a’) as f:

f.write(‘\n’ + “负收益买日期–” + str(buy_date_fushouyi.count(i)) + “–” +str(i))

buy_date_fushouyi_count.append(buy_date_fushouyi.count(i))

if zhengshouyi_num+fushouyi_num != 0:

print(start,“—-”,end,“total_shouyi=”,‘%.2f’%(total_shouyi),“概率”,‘%.2f’%(zhengshouyi_num/(zhengshouyi_num+fushouyi_num)),“正收益次数->”,zhengshouyi_num,“负收益次数->”,fushouyi_num)

with open(dir_new + “\\” + start.replace(“/”,“_”) + “__” +end.replace(“/”,“_”) + “celie_xiangxi” + ‘.txt’,‘a’) as f:

f.write(‘\n’ +‘\n’ + “正收益买日期count–” + str(len(buy_date_zhengshouyi_count)) + “–” + str(buy_date_zhengshouyi_count))

f.write(‘\n’ + “负收益买日期count–” + str(len(buy_date_fushouyi_count)) + “–” + str(buy_date_fushouyi_count))

f.write(‘\n’ + “买卖总次数” + str(zhengshouyi_num+fushouyi_num) + “–正收益买概率–” + str(‘%.2f’%(zhengshouyi_num/(zhengshouyi_num + fushouyi_num))) + “–正收益次数–” + str(zhengshouyi_num))

for i in range(len(gupiaochichang)):

if i == 0:

f.write(‘\n’ + “买日期count:” + ‘\n’ + str(gupiaochichang[i]))

else:

f.write(‘\n’ + str(gupiaochichang[i]))

shutil.copy(dir_new + “\\” + start.replace(“/”,“_”) + “__” +end.replace(“/”,“_”) + “celie_xiangxi” + ‘.txt’,dir_new + “\\” + start.replace(“/”,“_”) + “__” +end.replace(“/”,“_”) + ‘_’ + “–正收益买概率–” + str(‘%.2f’%(zhengshouyi_num / (zhengshouyi_num + fushouyi_num))) + “–倍数–” + str(‘%.2f’%(total_shouyi))+ “–正收益次数–” + str(zhengshouyi_num) + “–负收益次数–” + str(fushouyi_num)+ ‘.txt’)

return [total_shouyi,zhengshouyi_num / (zhengshouyi_num + fushouyi_num)]

if name == ‘main’:

print(‘start’,time.strftime(“%Y-%m-%d %H:%M:%S”, time.localtime()))

cur_dir = os.getcwd() # get current path

folder_name = ‘result’

dir_new = os.path.join(cur_dir, folder_name)

start = [‘2016/09/01’,‘2017/09/01’,‘2018/09/01’]

end = [‘2017/12/31’,‘2018/12/31’,‘2019/12/31’]

gupiaochichang = []

total_shouyi_gailv = []

shouyi_huizong = []

gailv_huizong = []

for i in range(len(start)):

total_shouyi_gailv.append(danjunxian(start[i],end[i]))

for i in range(len(total_shouyi_gailv)):

shouyi_huizong.append(total_shouyi_gailv[i][0])

gailv_huizong.append(total_shouyi_gailv[i][1])

绘图

matplotlib.rcParams[‘font.family’] = ‘SimHei’ # SimHei黑体

matplotlib.rcParams[‘font.size’] = 10

dir_new = os.path.join(cur_dir, folder_name)

file_name = dir_new + r’/’ + ‘shouyi’

收益图

plt.subplots_adjust(hspace=0.5)

fig1 = plt.subplot(211)

fig1.set_title(“收益”)

设置坐标轴范围

fig1.set_xlim(-1, 3)

fig1.set_ylim(0, 50)

设置坐标轴名称

fig1.set_xlabel(‘日期’)

fig1.set_ylabel(‘收益’)

设置坐标轴刻度

fig1.set_xticks = np.arange(-1, 4, 1)

for a, b in zip(end, shouyi_huizong):

fig1.text(a, b + 0.1, ‘%.2f’ % b, ha=‘center’, va=‘bottom’, color=‘red’, fontsize=20)

fig1.plot(end, shouyi_huizong, color=‘blue’, marker=‘o’)

正收益概率图

fig2 = plt.subplot(212)

fig2.set_title(“收益概率”)

设置坐标轴范围

fig2.set_xlim(-1, 3)

fig2.set_ylim(0, 1.2)

设置坐标轴名称

fig2.set_xlabel(‘日期’)

fig2.set_ylabel(‘收益概率’)

设置坐标轴刻度

fig2.set_xticks = np.arange(-1, 4, 1)

for a, b in zip(end, gailv_huizong):

fig2.text(a, b + 0.1, ‘%.2f’ % b, ha=‘center’, va=‘bottom’, color=‘blue’, fontsize=20)

fig2.plot(end, gailv_huizong, color=‘blue’, marker=‘o’)

plt.savefig(file_name, dpi=300)

print(‘end’,time.strftime(“%Y-%m-%d %H:%M:%S”, time.localtime()))

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