def __init__(self): input_file_path = "input/PriceVolume/bench_mark.xlsx" bench_mark_list,self.bench_mark_name_list = self.load_bench_mark_data(input_file_path) #bench_mark_list = ["000016.SH","000001.SH","000300.SH","399006.SZ","399905.SZ"] #self.bench_mark_name_list = [u"上证50",u"上证综指",u"沪深300",u"创业板指",u"中证500"] #bench_mark_list = ["0806.HK","QIHU.N"] #self.bench_mark_name_list = [u"惠理",u"奇虎"] stock_data_list = LoadData.get_daily_stock_data(bench_mark_list,"20110101") self.price_index = 4 self.volume_index = 5 self.trade_cost = 1-0.001 self.range_day = 1 self.regression_day = [2,5] # short flag = 1 表示可以做空 short_flag = 1 self.price_list = [] self.volume_list = [] for ii in range(len(bench_mark_list)): self.price_list.append(list(zip(*stock_data_list[ii])[self.price_index])) self.volume_list.append(list(zip(*stock_data_list[ii])[self.volume_index])) self.plot(self.price_list,bench_mark_list,self.range_day,short_flag) #self.run(bench_mark_list) print "done\n"
def __init__(self): file_path = "input/HighLowPoint/user_data.xlsx" stock_code_list,stock_name_list,stock_buy_price_list,stock_sell_price_list = LoadUserData.load_user_data(file_path) stock_daily_data_list = LoadData.get_daily_stock_data(stock_code_list) self.fall_end_flag = [] self.rise_end_flag = [] print "begin running\n" self.run(stock_code_list,stock_buy_price_list,stock_sell_price_list)
def __init__(self):
file_path = "input/HighLowPoint/user_data.xlsx"
stock_code_list, stock_name_list, stock_buy_price_list, stock_sell_price_list = LoadUserData.load_user_data(
file_path)
stock_daily_data_list = LoadData.get_daily_stock_data(stock_code_list)
self.fall_end_flag = []
self.rise_end_flag = []
print "begin running\n"
self.run(stock_code_list, stock_buy_price_list, stock_sell_price_list)
def get_high_low_points(self, stock_code_list):
msg = '''<html> <tr>Trend inverse reminder from H&L Model</tr> <table width="300" border="1" bordercolor="black" cellspacing="1">'''
bar_size = [5, 30, 60]
column_num = [5, 9, 13]
threshold = [0.03, 0.05, 0.15]
send_email_flag = False
for ii in range(len(bar_size)):
if bar_size[ii] == 60:
stock_intraday_data = LoadData.get_daily_stock_data(
stock_code_list, "20100101")
msg = msg + ''' <tr><td rowspan="2">''' + ''' daily </td>'''
else:
stock_intraday_data = LoadData.get_intraday_stock_data(
stock_code_list, bar_size=bar_size[ii], delta_days=365)
msg = msg + ''' <tr><td rowspan="2">''' + str(
bar_size[ii]) + ''' min </td>'''
high_point_index_list, high_point_list, high_point_time_list, low_point_index_list, low_point_list, low_point_time_list = HighLowPoint.get_high_low_points_list(
stock_intraday_data, threshold[ii])
fall_end_list = []
rise_end_lsit = []
fall_end_msg = ""
rise_end_msg = ""
for jj in range(len(stock_code_list)):
self.write_data_to_file(column_num[ii], jj,
high_point_list[jj][-1],
high_point_time_list[jj][-1],
low_point_list[jj][-1],
low_point_time_list[jj][-1])
temp_msg_fall_end, temp_msg_rise_end = self.judge_trend_inverse(
high_point_list[jj], low_point_list[jj],
self.current_price[jj])
if temp_msg_fall_end == True:
fall_end_list.append(stock_code_list[jj])
send_email_flag = True
if temp_msg_rise_end == True:
rise_end_lsit.append(stock_code_list[jj])
send_email_flag = True
for item in fall_end_list:
fall_end_msg = fall_end_msg + item + " "
for item in rise_end_lsit:
rise_end_msg = rise_end_msg + item + " "
msg = msg + "<td>" + "Falling End" + "</td><td>" + fall_end_msg + "</td></tr><tr><td> Rising End </td><td>" + rise_end_msg + "</td></tr> \n"
msg = msg + " </table></html>"
print msg
if send_email_flag == True:
a = SendEmail.Send_Email("Trend inverse reminder from H&L Model",
msg)
print a.isSend
def __init__(self,
ma_days=200,
start_date="19950101",
init_days=60,
short_flag=0):
input_file_path = "../input/DeviationFromMA/user_data.xlsx"
self.user_stock_code_list, self.user_stock_name_list = self.load_excel(
input_file_path)
self.user_stock_data_list = LoadData.get_daily_stock_data(
self.user_stock_code_list, start_date)
self.short_flag = short_flag
self.trade_cost = 1 - 0.001
self.ma_days = ma_days
self.user_stock_price_data_list = []
self.user_stock_uniform_price_list = []
self.user_stock_datetime_list = []
self.user_stock_price_data_old_list = []
for ii in range(len(self.user_stock_code_list)):
self.user_stock_price_data_list.append(
(list(zip(*self.user_stock_data_list[ii])[4]))[init_days:])
self.user_stock_price_data_old_list.append(
list(zip(*self.user_stock_data_list[ii])[4]))
self.user_stock_datetime_list.append(
(list(zip(*self.user_stock_data_list[ii])[0]))[init_days:])
self.user_stock_uniform_price_list.append(
CalcIndex.Uniform_Price(self.user_stock_price_data_list[ii]))
self.price_ma_list = CalcIndex.Calc_MA(
self.user_stock_price_data_old_list, int(ma_days), int(init_days))
self.diviation_from_ma_list, self.diviation_from_ma_std_list = self.calc_diviation_from_ma(
ma_days)
self.ma_dict = {60: 10, 120: 20, 200: 60, 250: 60}
self.diviation_ma_list = CalcIndex.Calc_MA(self.diviation_from_ma_list,
self.ma_dict[self.ma_days])
#self.plot(ma_days)
#self.eveluate_index(ma_days)
long_stand_deviation = {60: -0.5, 120: -0.1, 200: -0.15, 250: -0.2}
short_stand_deviation = {60: 0.5, 120: 0.1, 200: 0.15, 250: 0.2}
self.back_test_info = self.back_test(long_stand_deviation[ma_days],
short_stand_deviation[ma_days])
self.plot_back_test(ma_days)
print "done."
def __init__(self): file_path = "input/user_data.xlsx" self.user_stock_code_list,self.user_stock_name_list,user_stock_buy_point_list,user_stock_sell_point_list = LoadUserData.load_user_data(file_path) self.daily_stock_data_all = LoadData.get_daily_stock_data(self.user_stock_code_list,"20150101") #存放收盘价 self.daily_stock_data = [] for ii in range(len(self.daily_stock_data_all)): temp = [] for jj in range(len(self.daily_stock_data_all[ii])): temp.append(self.daily_stock_data_all[ii][jj][4]) self.daily_stock_data.append(temp) #print self.daily_stock_data[0][-1],self.daily_stock_data[1][-1] self.reminder_info= [] self.run()
def __init__(self):
file_path = "input/user_data.xlsx"
self.user_stock_code_list, self.user_stock_name_list, user_stock_buy_point_list, user_stock_sell_point_list = LoadUserData.load_user_data(
file_path)
self.daily_stock_data_all = LoadData.get_daily_stock_data(
self.user_stock_code_list, "20150101")
#存放收盘价
self.daily_stock_data = []
for ii in range(len(self.daily_stock_data_all)):
temp = []
for jj in range(len(self.daily_stock_data_all[ii])):
temp.append(self.daily_stock_data_all[ii][jj][4])
self.daily_stock_data.append(temp)
#print self.daily_stock_data[0][-1],self.daily_stock_data[1][-1]
self.reminder_info = []
self.run()
def get_high_low_points(self,stock_code_list):
msg = '''<html> <tr>Trend inverse reminder from H&L Model</tr> <table width="300" border="1" bordercolor="black" cellspacing="1">'''
bar_size = [5,30,60]
column_num = [5,9,13]
threshold = [0.03,0.05,0.15]
send_email_flag = False
for ii in range(len(bar_size)):
if bar_size[ii]==60:
stock_intraday_data = LoadData.get_daily_stock_data(stock_code_list,"20100101")
msg = msg +''' <tr><td rowspan="2">''' + ''' daily </td>'''
else:
stock_intraday_data = LoadData.get_intraday_stock_data(stock_code_list,bar_size=bar_size[ii],delta_days = 365)
msg = msg +''' <tr><td rowspan="2">''' +str(bar_size[ii]) + ''' min </td>'''
high_point_index_list,high_point_list,high_point_time_list,low_point_index_list,low_point_list,low_point_time_list = HighLowPoint.get_high_low_points_list(stock_intraday_data,threshold[ii])
fall_end_list = []
rise_end_lsit = []
fall_end_msg = ""
rise_end_msg = ""
for jj in range(len(stock_code_list)):
self.write_data_to_file(column_num[ii],jj,high_point_list[jj][-1],high_point_time_list[jj][-1],low_point_list[jj][-1],low_point_time_list[jj][-1])
temp_msg_fall_end, temp_msg_rise_end = self.judge_trend_inverse(high_point_list[jj],low_point_list[jj],self.current_price[jj])
if temp_msg_fall_end ==True:
fall_end_list.append(stock_code_list[jj])
send_email_flag = True
if temp_msg_rise_end == True:
rise_end_lsit.append(stock_code_list[jj])
send_email_flag = True
for item in fall_end_list:
fall_end_msg = fall_end_msg + item + " "
for item in rise_end_lsit:
rise_end_msg = rise_end_msg + item +" "
msg = msg +"<td>" + "Falling End" + "</td><td>"+fall_end_msg +"</td></tr><tr><td> Rising End </td><td>"+rise_end_msg+"</td></tr> \n"
msg = msg +" </table></html>"
print msg
if send_email_flag == True:
a = SendEmail.Send_Email("Trend inverse reminder from H&L Model",msg)
print a.isSend
def __init__(self):
input_file_path = "input/PriceVolume/bench_mark.xlsx"
bench_mark_list, self.bench_mark_name_list = self.load_bench_mark_data(
input_file_path)
#bench_mark_list = ["000016.SH","000001.SH","000300.SH","399006.SZ","399905.SZ"]
#self.bench_mark_name_list = [u"上证50",u"上证综指",u"沪深300",u"创业板指",u"中证500"]
#bench_mark_list = ["0806.HK","QIHU.N"]
#self.bench_mark_name_list = [u"惠理",u"奇虎"]
stock_data_list = LoadData.get_daily_stock_data(
bench_mark_list, "20110101")
self.price_index = 4
self.volume_index = 5
self.trade_cost = 1 - 0.001
self.range_day = 1
self.regression_day = [2, 5]
# short flag = 1 表示可以做空
short_flag = 1
self.price_list = []
self.volume_list = []
for ii in range(len(bench_mark_list)):
self.price_list.append(
list(zip(*stock_data_list[ii])[self.price_index]))
self.volume_list.append(
list(zip(*stock_data_list[ii])[self.volume_index]))
self.plot(self.price_list, bench_mark_list, self.range_day, short_flag)
#self.run(bench_mark_list)
print "done\n"
def __init__(self,ma_days=200,start_date="19950101",init_days = 60,short_flag =0):
input_file_path = "../input/DeviationFromMA/user_data.xlsx"
self.user_stock_code_list,self.user_stock_name_list = self.load_excel(input_file_path)
self.user_stock_data_list = LoadData.get_daily_stock_data(self.user_stock_code_list,start_date)
self.short_flag = short_flag
self.trade_cost = 1-0.001
self.ma_days = ma_days
self.user_stock_price_data_list = []
self.user_stock_uniform_price_list =[]
self.user_stock_datetime_list = []
self.user_stock_price_data_old_list =[]
for ii in range(len(self.user_stock_code_list)):
self.user_stock_price_data_list.append((list(zip(*self.user_stock_data_list[ii])[4]))[init_days:])
self.user_stock_price_data_old_list.append(list(zip(*self.user_stock_data_list[ii])[4]))
self.user_stock_datetime_list.append((list(zip(*self.user_stock_data_list[ii])[0]))[init_days:])
self.user_stock_uniform_price_list.append(CalcIndex.Uniform_Price(self.user_stock_price_data_list[ii]))
self.price_ma_list = CalcIndex.Calc_MA(self.user_stock_price_data_old_list,int(ma_days),int(init_days))
self.diviation_from_ma_list,self.diviation_from_ma_std_list = self.calc_diviation_from_ma(ma_days)
self.ma_dict ={60:10,120:20,200:60,250:60}
self.diviation_ma_list = CalcIndex.Calc_MA(self.diviation_from_ma_list,self.ma_dict[self.ma_days])
#self.plot(ma_days)
#self.eveluate_index(ma_days)
long_stand_deviation = {60:-0.5,120:-0.1,200:-0.15,250:-0.2}
short_stand_deviation = {60:0.5,120:0.1,200:0.15,250:0.2}
self.back_test_info = self.back_test(long_stand_deviation[ma_days],short_stand_deviation[ma_days])
self.plot_back_test(ma_days)
print "done."
def creepy_data_from_wind_to_local(self):
for ii in range(len(self.user_stock_name_list)):
self.path_is_exist(self.user_stock_name_list[ii])
data_base_path = sys.path[0] + "\\" + "data" + "\\" + "data_base"
stock_fold_path = data_base_path + "\\" + self.user_stock_name_list[ii]
for data_type in self.data_type_list:
data_path = stock_fold_path + "\\" + data_type
latest_date = self.get_latest_date_in_fold(data_path)
print latest_date
temp_stock_name = []
temp_stock_name.append(self.user_stock_name_list[ii])
delta_day = (
datetime.now() - datetime(int(latest_date[0:4]), int(latest_date[4:6]), int(latest_date[6:8]))
).days
print latest_date, delta_day, self.user_stock_name_list[ii]
pos = self.user_stock_name_list[ii].split(".")[1]
print pos
if data_type == "daily":
stock_daily_data = LoadData.get_daily_stock_data(temp_stock_name, latest_date)
for jj in range(len(stock_daily_data[0])):
output_data_path = data_path + "\\" + stock_daily_data[0][jj][0] + ".txt"
WriteToFile.write_to_file(output_data_path, stock_daily_data[0][jj])
if pos != "SZ" and pos != "SH":
break
if float(delta_day) > 3 * 365:
delta_day = 1095
if data_type == "60min":
print "aaa"
stock_60_min_data = LoadData.get_intraday_stock_data(
temp_stock_name, bar_size=60, delta_days=delta_day
)
date_60_min_index = self.get_day_sperate_index(stock_60_min_data)
print stock_60_min_data
print date_60_min_index
for jj in range(len(date_60_min_index) - 1):
file_path = data_path + "\\" + stock_60_min_data[0][date_60_min_index[jj]][0][0:8] + ".txt"
WriteToFile.write_list_to_file(
file_path, stock_60_min_data[0][date_60_min_index[jj] : date_60_min_index[jj + 1]]
)
elif data_type == "30min":
stock_30_min_data = LoadData.get_intraday_stock_data(
temp_stock_name, bar_size=30, delta_days=delta_day
)
date_30_min_index = self.get_day_sperate_index(stock_30_min_data)
for jj in range(len(date_30_min_index) - 1):
file_path = data_path + "\\" + stock_30_min_data[0][date_30_min_index[jj]][0][0:8] + ".txt"
WriteToFile.write_list_to_file(
file_path, stock_30_min_data[0][date_30_min_index[jj] : date_30_min_index[jj + 1]]
)
elif data_type == "15min":
stock_15_min_data = LoadData.get_intraday_stock_data(
temp_stock_name, bar_size=15, delta_days=delta_day
)
date_15_min_index = self.get_day_sperate_index(stock_15_min_data)
for jj in range(len(date_15_min_index) - 1):
file_path = data_path + "\\" + stock_15_min_data[0][date_15_min_index[jj]][0][0:8] + ".txt"
WriteToFile.write_list_to_file(
file_path, stock_15_min_data[0][date_15_min_index[jj] : date_15_min_index[jj + 1]]
)
elif data_type == "5min":
stock_5_min_data = LoadData.get_intraday_stock_data(
temp_stock_name, bar_size=5, delta_days=delta_day
)
date_5_min_index = self.get_day_sperate_index(stock_5_min_data)
for jj in range(len(date_5_min_index) - 1):
file_path = data_path + "\\" + stock_5_min_data[0][date_5_min_index[jj]][0][0:8] + ".txt"
WriteToFile.write_list_to_file(
file_path, stock_5_min_data[0][date_5_min_index[jj] : date_5_min_index[jj + 1]]
)
def creepy_data_from_wind_to_local(self):
for ii in range(len(self.user_stock_name_list)):
self.path_is_exist(self.user_stock_name_list[ii])
data_base_path = sys.path[0] + "\\" + "data" + "\\" + "data_base"
stock_fold_path = data_base_path + "\\" + self.user_stock_name_list[
ii]
for data_type in self.data_type_list:
data_path = stock_fold_path + "\\" + data_type
latest_date = self.get_latest_date_in_fold(data_path)
print latest_date
temp_stock_name = []
temp_stock_name.append(self.user_stock_name_list[ii])
delta_day = (
datetime.now() -
datetime(int(latest_date[0:4]), int(latest_date[4:6]),
int(latest_date[6:8]))).days
print latest_date, delta_day, self.user_stock_name_list[ii]
pos = self.user_stock_name_list[ii].split(".")[1]
print pos
if data_type == "daily":
stock_daily_data = LoadData.get_daily_stock_data(
temp_stock_name, latest_date)
for jj in range(len(stock_daily_data[0])):
output_data_path = data_path + "\\" + stock_daily_data[
0][jj][0] + ".txt"
WriteToFile.write_to_file(output_data_path,
stock_daily_data[0][jj])
if pos != "SZ" and pos != "SH":
break
if float(delta_day) > 3 * 365:
delta_day = 1095
if data_type == "60min":
print "aaa"
stock_60_min_data = LoadData.get_intraday_stock_data(
temp_stock_name, bar_size=60, delta_days=delta_day)
date_60_min_index = self.get_day_sperate_index(
stock_60_min_data)
print stock_60_min_data
print date_60_min_index
for jj in range(len(date_60_min_index) - 1):
file_path = data_path + "\\" + stock_60_min_data[0][
date_60_min_index[jj]][0][0:8] + ".txt"
WriteToFile.write_list_to_file(
file_path, stock_60_min_data[0]
[date_60_min_index[jj]:date_60_min_index[jj + 1]])
elif data_type == "30min":
stock_30_min_data = LoadData.get_intraday_stock_data(
temp_stock_name, bar_size=30, delta_days=delta_day)
date_30_min_index = self.get_day_sperate_index(
stock_30_min_data)
for jj in range(len(date_30_min_index) - 1):
file_path = data_path + "\\" + stock_30_min_data[0][
date_30_min_index[jj]][0][0:8] + ".txt"
WriteToFile.write_list_to_file(
file_path, stock_30_min_data[0]
[date_30_min_index[jj]:date_30_min_index[jj + 1]])
elif data_type == "15min":
stock_15_min_data = LoadData.get_intraday_stock_data(
temp_stock_name, bar_size=15, delta_days=delta_day)
date_15_min_index = self.get_day_sperate_index(
stock_15_min_data)
for jj in range(len(date_15_min_index) - 1):
file_path = data_path + "\\" + stock_15_min_data[0][
date_15_min_index[jj]][0][0:8] + ".txt"
WriteToFile.write_list_to_file(
file_path, stock_15_min_data[0]
[date_15_min_index[jj]:date_15_min_index[jj + 1]])
elif data_type == "5min":
stock_5_min_data = LoadData.get_intraday_stock_data(
temp_stock_name, bar_size=5, delta_days=delta_day)
date_5_min_index = self.get_day_sperate_index(
stock_5_min_data)
for jj in range(len(date_5_min_index) - 1):
file_path = data_path + "\\" + stock_5_min_data[0][
date_5_min_index[jj]][0][0:8] + ".txt"
WriteToFile.write_list_to_file(
file_path, stock_5_min_data[0]
[date_5_min_index[jj]:date_5_min_index[jj + 1]])
# -*- coding: utf-8 -*- # File Name: main.py # Author: Changsheng Zhang # mail: [email protected] # Created Time: Tue Jul 7 10:09:08 2015 ######################################################################### import os import sys import strategy.test as test import data.LoadData as LoadData if __name__ == '__main__': b = LoadData.get_daily_stock_data(["600000.SH"]) print b[0][0]
