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()))
这篇关于做交易，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的文章就介绍到这儿，希望我们推荐的文章对编程师们有所帮助！
