forked from RoveAllOverTheWorld512/hyb_ta
-
Notifications
You must be signed in to change notification settings - Fork 1
/
supershort_study.py
173 lines (154 loc) · 6.38 KB
/
supershort_study.py
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
# -*- coding: utf-8 -*-
"""
Created on 2019-10-11 09:12:38
author: huangyunbin
email: huangyunbin@sina.com
QQ: 592440193
"""
import pandas as pd
from stock_pandas.tdx.tdxdayread import Tdxday
from stock_pandas.tdx.class_func import *
from stock_pandas.misc.supershort import *
import sys
import os
import datetime
import time
def selefirstsignal(df):
'''
删除与前一个信号小于3周的重复信号
'''
# df = df.sort_index()
if df.index.name == 'date' and ('date' not in df.columns):
df = df.reset_index()
df = df.sort_values(by=['gpdm', 'date'])
df['date'] = pd.to_datetime(df['date'])
df = df.assign(tmp=df['date']-df['date'].shift(1))
df.loc[df['gpdm'] != df['gpdm'].shift(1), 'tmp'] = None
df = df.loc[((df['tmp'] > pd.Timedelta('21 days')) | pd.isnull(df['tmp']))]
df = df.drop(columns=['tmp'])
return df
if __name__ == '__main__':
###############################################################################
# year = 2018
# csvfn = f'F:\pandas-ta_project\stock_pandas\misc\{year}st2.csv'
# df = pd.read_csv(csvfn, encoding='GBK')
# data = []
# j = 120
# k = 120
# n = 60
# m = 120
# for i, gpxx in df.iterrows():
# print(gpxx)
# filename = gpxx.gpdm[:6]
# gpmc = gpxx.gpmc
# date = str(gpxx.date)
# tdxday = Tdxday(filename)
# ohlc = tdxday.get_qfqdata(start='20170101')
# data.append([gpxx.gpdm, gpmc, date] + badnews(ohlc, date, j, k, n, m))
#
# rs = pd.DataFrame(data,
# columns=['gpdm', 'gpmc', 'date',
# 'date_max', 'days_max', 'close_max', 'zf_max',
# 'date_1', 'close_1',
# 'date_min', 'days_min', 'close_min', 'zf_min', 'zf_min_max',
# 'date1_max', 'days1_max', 'close1_max', 'zf1_max',
# 'date2_min', 'days2_min', 'close2_min', 'zf2_min',
# 'date3_max', 'days3_max', 'close3_max', 'zf3_max'])
# rs = rs.round(4)
# rs.to_csv(f'st{year}_{j}_{k}_{n}.csv', encoding='GBK')
#
###############################################################################
# csvfn = r'F:\pandas-ta_project\sgdf_144_34_21_0.5_-0.3.csv'
# start = '2019-01-01'
# end = '2019-12-31'
# df = pd.read_csv(csvfn, encoding='GBK')
# df1 = df.loc[(df['date'] >= start) & (df['date'] <= end)]
# df1 = df1.sort_values(by=['gpdm', 'date'])
# df2 = df1.drop_duplicates(subset=['gpdm'])
# df2 = df2.reset_index(drop=True)
## sys.exit()
# data = []
# j = 60
# k = 30
# n = 30
# m = 60
# ln = len(df2)
# for i, gpxx in df2.iterrows():
# print(i, ln, gpxx.gpdm, gpxx.gpmc, gpxx.date)
# dm = gpxx.gpdm[:6]
# gpmc = gpxx.gpmc
# date = gpxx.date
# tdxday = Tdxday(dm)
# ohlc = tdxday.get_qfqdata(start='20170101')
# data.append([gpxx.gpdm, gpmc, date] + badnews(ohlc, date, j, k, n, m))
#
# rs = pd.DataFrame(data,
# columns=['gpdm', 'gpmc', 'date',
# 'date_max', 'days_max', 'close_max', 'zf_max',
# 'date_1', 'close_1',
# 'date_min', 'days_min', 'close_min', 'zf_min', 'zf_min_max',
# 'date1_max', 'days1_max', 'close1_max', 'zf1_max',
# 'date2_min', 'days2_min', 'close2_min', 'zf2_min',
# 'date3_max', 'days3_max', 'close3_max', 'zf3_max'])
# rs = rs.round(4)
## rs.to_csv(f'doublebottom_{j}_{k}_{n}.csv', encoding='GBK')
# rs.to_csv(f'dou_bott_{start}_{end}_{j}_{k}_{n}.csv', encoding='GBK')
###############################################################################
df = None
start = '2018-01-01'
end = (datetime.datetime.now() - datetime.timedelta(5)).strftime('%Y-%m-%d')
gplblst = {'SHZBA': '沪市主板A股',
'SHKCBA': '沪市科创板A股',
'SZZBA': '深市主板A股',
'SZZXBA': '深市中小板A股',
'SZCYBA': '深市创业板A股'}
for lb in gplblst:
# csvfn = f'f:\data\{lb}_20180101_20191122_144_89_55_0.5_-0.3.csv'
csvfn = f'f:\data\{lb}_20180101_20191123_144_89_13_0.5_-0.3.csv'
if os.path.exists(csvfn):
df1 = pd.read_csv(csvfn, encoding='GBK', parse_dates=True, infer_datetime_format=True)
df1 = df1.loc[(df1['double_bott'] == 1)]
df1 = selefirstsignal(df1)
df1 = df1.loc[(df1['date'] >= start) & (df1['date'] <= end)]
df = pd.concat([df, df1])
df = df.reset_index(drop=True)
# df = df.loc[(df['decreasing_34'] < -0.25)]
# df = df.loc[(df['increasing_55'] > 0.50)]
# df = selefirstsignal(df)
# sys.exit()
data = []
j = 30
k = 20
n = 20
m = 60
ln = len(df)
start_time = time.time()
for i, gpxx in df.iterrows():
print(i, ln, gpxx.gpdm, gpxx.gpmc, gpxx.date)
dm = gpxx.gpdm[:6]
gpmc = gpxx.gpmc
date = gpxx.date.strftime('%Y-%m-%d')
tdxday = Tdxday(dm)
ohlc = tdxday.get_qfqdata(start='20160101')
data.append([gpxx.gpdm, gpmc, date] + supershort(ohlc, date, j, k, n, m))
if ((i + 1) % 50 == 0) or (i >= ln - 1):
now_time = time.time()
t1 = now_time - start_time
# 每只股票秒数
p = t1 / (i - k + 1)
# 估计剩余时间
t1 = t1 / 60
t2 = (ln - i) * p / 60
print('------已用时%d分钟，估计还需要%d分钟' % (t1, t2))
rs = pd.DataFrame(data,
columns=['gpdm', 'gpmc', 'date',
'date_max', 'days_max', 'close_max', 'zf_max',
'date_1', 'close_1',
'date_min', 'days_min', 'close_min', 'zf_min', 'zf_min_max',
'date1_max', 'days1_max', 'close1_max', 'zf1_max',
'date2_min', 'days2_min', 'close2_min', 'zf2_min',
'date3_max', 'days3_max', 'close3_max', 'zf3_max'])
rs = rs.round(4)
# rs.to_csv(f'doublebottom_{j}_{k}_{n}.csv', encoding='GBK')
# rs.to_csv(f'f:\data\supershort__89_55_0.5_-0.3_{start}_{end}_{j}_{k}_{n}_{m}.csv', encoding='GBK')
rs.to_csv(f'f:\data\supershort13__89_55_0.5_-0.3_{start}_{end}_{j}_{k}_{n}_{m}.csv', encoding='GBK')