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哈尔兹法则策略量化二(优化买点,加入均线)

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400 3 克拉克亮 Lv.3 发表于 · 2020-1-13 23:07 举报 显示全部楼层 复制 正序浏览 |
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       在哈尔兹法则策略量化一,我们的买点完全遵照哈尔兹法则的买入条件:即股从低价上涨10%之后才介入,然而我们看到最近三年收益及正收益概率都不太理想,如下图:


图一,哈尔兹法则买入条件最近三年收益及正收益概率


今天我们尝试买点做一些优化,即在哈尔兹法则的买入条件,股从低价上涨10%的基础上加入5日均线大于10均线,最近三年收益如下图:


图二,哈尔兹法则买入条件加5日均线大于10日均线,最近三年收益及正收益概率



从图二对比图一原始的哈尔兹法则的买入条件收益,我们可以看到,收益,以及正收益率都是有所提高,尤其是2019年,收益达到5.58倍,但是,但是,我们看到最近年正收益率都很低,2018年尤其差0.26,2019年最好,也只有0.45,连50%都没有超过,这个胜率,实战操作很尴尬了。
另外贴上最近三年收益汇总,可以看到买入之后亏损的次数非常多
2017年 收益,0.64 概率 0.37 正收益次数- gt; 74 负收益次数- gt; 126
2018年 收益0.06 概率 0.26 正收益次数- gt; 41 负收益次数- gt; 117
2019年 total_shouyi= 5.58 概率 0.45 正收益次数- gt; 74 负收益次数- gt; 91

代码部分如下:

# -*- 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 Haerzi(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)

#创业板指数
df = pd.read_table(r‘C:\gupiao\gupiaoci‘ + \\399006.txt ,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]
#均线
ma5_chuangzhi = df2.close.rolling(window=5,center=False).mean()
ma5_chuangzhi = ma5_chuangzhi [start:end]
ma10_chuangzhi = df2.close.rolling(window=10,center=False).mean()
ma10_chuangzhi = ma10_chuangzhi [start:end]
ma30_chuangzhi = df2.close.rolling(window=30,center=False).mean()
ma30_chuangzhi = ma30_chuangzhi [start:end]

#深圳指数
df = pd.read_table(r‘C:\gupiao\gupiaoci‘ + \\399107.txt ,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]
#均线
ma5_shenzhen = df2.close.rolling(window=5,center=False).mean()
ma5_shenzhen = ma5_shenzhen[start:end]
ma10_shenzhen = df2.close.rolling(window=10,center=False).mean()
ma10_shenzhen = ma10_shenzhen[start:end]
ma30_shenzhen = df2.close.rolling(window=30,center=False).mean()
ma30_shenzhen = ma30_shenzhen[start:end]

#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)):
close_min = df_temp.close[j-60:j-1].min()#60天内最低价
if buy_status == False and chicang_status == False and df_temp.close[j] = 300 or (df_temp.high[j]-df_temp.close[j-1])/df_temp.close[j-1] 0.1 and ma5_temp[j] gt; ma10_temp[j]:#买点优化,即在哈尔兹法则的买入条件,股从低价上涨10%的基础上加入5日均线大于10均线if len(gupiaochichang) gt;= 1:
for gupiaochichang_item in gupiaochichang:
if gupiaochichang_item[0] == temp_index[j] and gupiaochichang_item[1] gt;= 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----- gt; + 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 close_max = 0.1:#最高点下跌10%,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 gt; 0:
zhengshouyi_num += 1#正收益次数加1
else:
fushouyi_num += 1#负收益次数加1
total_shouyi *= (1 + (df_temp.close[j]-buy_price_m)/buy_price_m)
mairuhou_mark = 0
buy_date_zhengshouyi.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 + --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 = 10 and df_temp.close[j] 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- gt; ,zhengshouyi_num, 负收益次数- gt; ,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) gt; 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 + ‘\n‘ + ‘buy: ‘ + str(buy_price[-1]) + str(buy_date[-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) gt;= 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) gt;= 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- gt; ,zhengshouyi_num, 负收益次数- gt; ,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))
else:
f.write(‘\n‘ + str(gupiaochichang))

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(Haerzi(start,end))
for i in range(len(total_shouyi_gailv)):
shouyi_huizong.append(total_shouyi_gailv[0])
gailv_huizong.append(total_shouyi_gailv[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, 20)
# 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()))
春天水乡
Lv.6
发表于 2020-1-14 16:22 复制 查看全部楼层
最近行情确实好,但也不能指望天天逼空;毕竟春节快到了,有些资金会撤离。对股民而言,如果2019年被当成“猪”宰了,那么“鼠”于自己的红包,就不能错过!
贵溪银矿部
Lv.6
发表于 2020-1-14 09:33 复制 查看全部楼层
有用信息!!!!!!!!!!!
海蓝蓝111
Lv.2
发表于 2020-1-13 23:14 复制 查看全部楼层
逢低介入滞涨股,不追高
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