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Q1 #1

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kzil88 Apr 23, 2018
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42 changes: 42 additions & 0 deletions BP.py
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from keras.models import Sequential
from keras.layers import Dense, Dropout, Activation
from keras.optimizers import SGD
import DC
from keras.layers import Flatten
import numpy as np
import keras


if __name__ == '__main__':
dc = DC.DC('600519')
score_list = []
resu_list = []
train = dc.data_train
target = dc.data_target


model = Sequential()
model.add(Dense(64, activation='linear', input_dim=13))
model.add(Dropout(0.5))
model.add(Dense(64, activation='sigmoid'))
model.add(Dropout(0.5))
model.add(Dense(1, activation='relu'))
sgd = SGD(lr=0.01, decay=1e-6, momentum=0.9, nesterov=True)
model.compile(loss='logcosh', optimizer=sgd, metrics=['accuracy'])


for i in range(5):
model.fit(train, target, epochs=20000)
score = model.evaluate(train, target, batch_size=128)
print('SCORE:' + str(score[0]))
test_case = np.array([dc.test_case])
ans2 = model.predict(test_case)
resu_list.append(ans2[0][0])
score_list.append(score)
print('RESU '+str(i+1)+' : '+str(ans2[0][0]))
dc.refreshDATA(ans2[0][0])
train = dc.data_train
target = dc.data_target

print(score_list)
print(resu_list)
161 changes: 161 additions & 0 deletions ChooseStock.py
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# -*- coding:utf8 -*-
import tushare as ts
import re
import numpy as np
from pandas import DataFrame
import matplotlib.pyplot as plt
import pandas as pd
import datetime
import talib as ta
import random
import pymysql.cursors



class CS(object):
code = ''
avg = []
good_factor = 0.02
bad_factor = 0.05
cnt_bad_sell = 0
cnt_good_buy = 0
cnt_good_sell = 0
cnt_risk = 0
af = []
fq = []
process = []
dd_list = []
dd_list_show = []
macd_list = []
kdj_list = []
bool_up = 0.00
bool_mid = 0.00
bool_dn = 0.00
data_train = []
data_target = []
close = []
test_case = []

def __init__(self, in_code):
self.collectDATA(in_code)

def collectDATA(self,in_code):
self.code = in_code

self.data_src = temp_day = ts.get_k_data(code=in_code)
if temp_day.shape[0] < 150 :
raise Exception
list_open = np.array(temp_day.iloc[temp_day.shape[0]-20:temp_day.shape[0], [1]].astype('float32'), dtype=np.float).ravel()
list_close = np.array(temp_day.iloc[temp_day.shape[0]-20:temp_day.shape[0], [2]].astype('float32'), dtype=np.float).ravel()
list_high = np.array(temp_day.iloc[temp_day.shape[0]-20:temp_day.shape[0], [3]].astype('float32'), dtype=np.float).ravel()
list_low = np.array(temp_day.iloc[temp_day.shape[0]-20:temp_day.shape[0], [4]].astype('float32'), dtype=np.float).ravel()
self.list_vol = np.array(temp_day.iloc[temp_day.shape[0]-20:temp_day.shape[0], [5]].astype('float32'), dtype=np.float).ravel()
period = 20
self.close = list_close
self.avg = ta.MA(list_close,period)
self.avg = [x for x in self.avg if str(x) != 'nan']
self.good_buy = [x* (1.00 - self.good_factor) for x in self.avg]
self.good_sell = [x * (1.00 + self.good_factor) for x in self.avg]
self.bad_sell = [x * (1.00 - self.bad_factor) for x in self.avg]
self.cnt_risk = [0]*len(self.avg)
self.cnt_good_sell = [0]*len(self.avg)
self.cnt_good_buy = [0]*len(self.avg)
self.cnt_bad_sell = [0]*len(self.avg)
for a in range(len(self.avg)):
self.cnt_bad_sell[a] = len([x for x in list_low[:a+period-1] if x <= self.bad_sell[a]])
self.cnt_good_sell[a] = len([x for x in list_high[:a + period - 1] if x >= self.good_sell[a]])
self.cnt_bad_sell[a] = len([x for x in list_low[:a + period - 1] if self.bad_sell[a] < x <= self.good_buy[a]])
self.cnt_risk[a] = len([x for x in list_low[:a+period-1] if x <= list_close[a+period-1]])

#ARFQ
for b in range(len(self.avg)):
af,fq,process = get_arfq(list_high[b:b+period],list_low[b:b+period],self.good_sell[b],self.bad_sell[b],self.good_buy[b])
self.af.append(af/self.avg[-1])
self.fq.append(fq)
self.process.append(process)



def get_arfq(list_high,list_low,good_sell,bad_sell,good_buy):
# 振幅af = (high-low)/len 频率 freq = 从good_selld到good_buy（或反之）的所需步长之和除以len
af = ((list_high - list_low).sum()) / (len(list_high))
start_flag = 0
list_index = 0
#list_high = [float(x) for x in list_high]
#list_low = [float(x) for x in list_low]
for k in range(len(list_high)):
if list_high[len(list_high) - k - 1] >= good_sell:
start_flag = 1
list_index = len(list_high) - k - 1
elif bad_sell < list_low[len(list_high) - k - 1] <= good_buy:
start_flag = 2
list_index = len(list_high) - k - 1
freq_list = []
freq = 0
freq_step = []
process = 0
for l in range(list_index, len(list_high)):
if start_flag > 0:
if (divmod(start_flag, 2)[1] == 1) and (bad_sell < list_low[l] <= good_buy):
freq_list.append(l)
start_flag = start_flag + 1
elif (divmod(start_flag, 2)[1] == 0) and (list_high[l] >= good_sell):
freq_list.append(l)
start_flag = start_flag + 1
else:
freq = 0
if len(freq_list) == 1:
freq = 0 - (len(list_high) - freq_list[0] - 1)
elif len(freq_list) > 1:
for m in range(1, len(freq_list)):
freq_step.append(freq_list[len(freq_list) - m] - freq_list[len(freq_list) - 1 - m])
freq = np.array(freq_step).sum() / (len(freq_step))

if freq > 0:
process = (len(list_high) - freq_list[-1] - 1) / (freq)
else:
process = 0
return af,freq,process

if __name__ == '__main__':
# 取股票代码清单
src = ts.get_stock_basics()
code_list = src.index
code_set = set()
for i in range(len(code_list)):
resu = re.search(r'\d+', code_list[i]).string
code_set.add(resu)
print('Done:Code_Set')

# 建立数据库连接
db = pymysql.connect(host='127.0.0.1', user='root', passwd='admin', db='stock', charset='utf8')
cursor = db.cursor()
sql_done_set = "SELECT distinct stock_code FROM q1_cs"
try:
cursor.execute(sql_done_set)
done_set = cursor.fetchall()
for row in done_set:
code_set.remove(row[0])

cnt = 1
total = len(code_set)
for i in code_set:
print(i)
try:
cs = CS(i)
sql = "INSERT INTO q1_cs(stock_code,af,freq,process) VALUES ('%s', '%f', '%f','%f')" % (i, cs.af[-1], cs.fq[-1], cs.process[-1])
cursor.execute(sql)
db.commit()
cnt += 1
print('Seq: '+str(cnt)+' of ' + str(total) +'Code: ' + str(i) + ' AF: ' + str(cs.af[-1]) + ' Freq: ' + str(cs.fq[-1]) + ' Process: ' + str(cs.process[-1]))
except Exception as excp:
# db.rollback()
print(str('Errr') + str(i))
print('All Finished!')

except Exception as excp:
#db.rollback()
print(str('Errr')+ str(i))

db.close()

4 changes: 4 additions & 0 deletions README.md
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Original file line number Diff line number Diff line change
Expand Up @@ -19,3 +19,7 @@ Init_Profit.py ------ update the profit and divide shares of stock on Market.
Init_RZRQ.py ------ update the RZRQ information of stock on Market.
Init_RZRQ_Detail.py ------ update the RZRQ detail of stock on Market.
Init_Single.py ------ update specific stock daily price information on Market.

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BP.py ------ update Q1.BP.py to branch Q1.