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hs300_make_data.py
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hs300_make_data.py
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# -*- coding: utf-8 -*-
import pandas as pd
import tushare as ts
import numpy as np
import copy
import time
import os
import json as js
def getOpts():
opts = {}
opts['datadir'] = './Data/HS300_total/'
opts['trade_index'] = './data_trade_index.xls'
opts['data_type'] = '/daily_data'
opts['datadest_excel'] = opts['datadir'] + '/data_excel'
opts['datadest_record'] = opts['datadir'] + opts['data_type'] + '/data_record_20'
# opts['start_time'] = '1990-12-19'
opts['start_time'] = '2005-01-01'
opts['end_time'] = '2018-06-12'
# trade file
if os.path.exists(opts['trade_index']):
Date_index = pd.read_excel(opts['trade_index'], sheet_name='sheet1')
else:
raise Exception('No trade day index file')
opts['Date_index'] = Date_index
return opts
# opts['datadir'] = 'K:/ZJU/Projects/DLstock/code/Data/hs300/data_excel/'
# opts['datadest_excel'] = 'K:/ZJU/Projects/Dlstock/code/Data/hs300/week_data/data_record_20/'
# hs_300 = pd.read_excel(opts['datadir'] + 'hs300index.xls', 'sheet1', na_values='NA')
# opts['datadir'] = 'M:/Dlstock/data/HS300_total/'
# opts['datadest_excel'] = opts['datadir'] + '/data_excel/'
# 旧版本
def from_D_data_get_20d_data(opts):
Date_index = opts['Date_index']
docs_num = 25
date_length = 20
for i in range(docs_num):
sheet1 = pd.read_excel(opts['datadest_excel'] + '/hs300_{}_D.xls'.format(i+1), 'sheet1')
new_sheet1 = sheet1.loc[:,['code','sheet']]
writer = pd.ExcelWriter(opts['datadest_excel'] + '/hs300_{}_20D_noflag.xls'.format(i+1))
new_sheet1.to_excel(writer, sheet_name = 'sheet1')
for j in range(len(sheet1)):
stock_code = '{:0>6}'.format(sheet1.iloc[j]['code'])
stock_index = sheet1.iloc[j]['sheet']
stock_data = pd.read_excel(opts['datadest_excel'] + '/hs300_{}_D.xls'.format(i+1), str(stock_index))
# stock_data_20d = pd.DataFrame(columns=['date', 'open', 'close', 'high', 'low', 'volume', 'code', 'flag_no_lost_data'])
stock_data_20d = pd.DataFrame(columns=['date', 'open', 'close', 'high', 'low', 'volume', 'code'])
data_length = len(stock_data) - date_length + 1
# new_data_series = []
for k in range(data_length):
date_start = stock_data.iloc[k]['date']
date_end = stock_data.iloc[k + date_length - 1]['date']
date = date_start + ',' + date_end
open = stock_data.iloc[k]['open']
close = stock_data.iloc[k + date_length - 1]['close']
high = np.max(np.array(stock_data.loc[k:k+date_length-1, 'high']))
low = np.min(np.array(stock_data.loc[k:k+date_length-1, 'low']))
volume = np.sum(np.array(stock_data.loc[k:k+date_length-1, 'volume']))
code = stock_code
# if np.where(Date_index == date_start)[0] + 19 == np.where(Date_index == date_end)[0]:
# flag_no_lost_data = 1
# else:
# flag_no_lost_data = 0
# new_data_series = [date, open, close, high, low, volume, code, flag_no_lost_data]
# stock_data_20d.loc[k] = new_data_series
if np.where(Date_index == date_start)[0] + 19 == np.where(Date_index == date_end)[0]:
new_data_series = [date, open, close, high, low, volume, code]
stock_data_20d.loc[k] = new_data_series
stock_data_20d.to_excel(writer, sheet_name = str(j + 1))
print('{}/{} done of doc {}'.format(j+1, len(sheet1), i+1))
writer.save()
writer.close()
def from_D_data_get_20d_data_with_date_index(opts):
Date_index = opts['Date_index']
Date_index = np.array(Date_index['calendarDate'])
docs_num = 25
date_length = 20
for i in range(docs_num):
sheet1 = pd.read_excel(opts['datadest_excel'] + '/hs300_{}_D.xls'.format(i+1), 'sheet1')
new_sheet1 = sheet1.loc[:,['code','sheet']]
for j in range(len(sheet1)):
new_sheet1.loc[j, 'code'] = '{:0>6}'.format(sheet1.loc[j, 'code'])
writer = pd.ExcelWriter(opts['datadest_excel'] + '/hs300_{}_20D_date_flag.xls'.format(i+1))
new_sheet1.to_excel(writer, sheet_name = 'sheet1')
for j in range(len(sheet1)):
stock_code = '{:0>6}'.format(sheet1.iloc[j]['code'])
stock_index = sheet1.iloc[j]['sheet']
stock_data = pd.read_excel(opts['datadest_excel'] + '/hs300_{}_D.xls'.format(i+1), str(stock_index))
# stock_data_20d = pd.DataFrame(columns=['date', 'open', 'close', 'high', 'low', 'volume', 'code', 'no_invalid_data', '20D_periods'])
data_length = len(stock_data) - date_length + 1
stock_date_index = np.array(stock_data['date'])
the_first_date = stock_date_index[0]
the_last_date = stock_date_index[-1]
the_first_date_index = np.where(Date_index == the_first_date)[0][0]
the_end_date_index = np.where(Date_index == the_last_date)[0][0] - date_length + 1
stock_data_20d = pd.DataFrame(index=[k for k in range(the_end_date_index - the_first_date_index + 1)],
columns=['date', 'open', 'close', 'high', 'low', 'volume', 'code', 'no_invalid_data', '20D_periods'])
# stock_data_20d.index = [k for k in range(the_end_date_index - the_first_date_index + 1)]
month_period = {}
valid_data_count = 0
for k in range(20):
month_period[k] = the_first_date_index + k
# new_data_series = []
for key in month_period.keys():
# start_date_index = month_period[key]
current_index = month_period[key]
while current_index <= the_end_date_index:
date_start = Date_index[current_index]
date_end = Date_index[current_index + 19]
valid_data_list = np.arange(len(stock_data))[np.array(stock_date_index >= date_start).astype(np.bool) & np.array(stock_date_index <= date_end).astype(np.bool)]
valid_data_len = len(valid_data_list)
if valid_data_len:
valid_data_count += 1
date_name = date_start + ',' + date_end
if valid_data_len > 20:
assert Exception('Error in original data')
elif valid_data_len == 20:
flag_no_invalid_data = 1
else:
flag_no_invalid_data = 0
valid_data_list.sort()
open_price = stock_data.iloc[valid_data_list[0]]['open']
close_price = stock_data.iloc[valid_data_list[-1]]['close']
high = np.max(np.array(stock_data['high'])[valid_data_list])
low = np.min(np.array(stock_data['low'])[valid_data_list])
volume = np.sum(np.array(stock_data['volume'])[valid_data_list])
code = stock_code
stock_data_20d.iloc[current_index - the_first_date_index] = [date_name, open_price, close_price, high, low, volume, code, flag_no_invalid_data, key]
else:
stock_data_20d.iloc[current_index - the_first_date_index] = ['0', 0, 0, 0, 0, 0, '0', 0, 0]
current_index += date_length
code_data_list = np.array(stock_data_20d['code'])
invalid_index_list = np.array(stock_data_20d.index)[code_data_list == '0']
stock_data_20d.drop(invalid_index_list, axis=0, inplace = True)
stock_data_20d.index = [m for m in range(len(stock_data_20d))]
assert len(stock_data_20d) == valid_data_count
stock_data_20d.to_excel(writer, sheet_name = str(j + 1))
print('{}/{} done of doc {}'.format(j+1, len(sheet1), i+1))
writer.save()
writer.close()
def data_for_every_stock(hs300, data_type, opts):
stock_list = hs300
for i in range(len(stock_list)):
stock_name = '{:0>6}'.format(stock_list[i])
stock_dir = os.path.join(opts['datadest_record'], stock_name)
if not os.path.exists(stock_dir):
os.makedirs(stock_dir)
stock_data = ts.get_k_data(stock_name, start=opts['start_time'], end=opts['end_time'], ktype=data_type, autype='qfq')
# time.sleep(0.5)
stock_data.to_excel(stock_dir + '/data.xls', sheet_name='sheet1')
# stock_data.to_csv(stock_dir + '/data.csv')
print('{}/{} done'.format(i, len(stock_list)))
def make_raw_data(hs_300, data_type, opts):
subset = 30
hs_300_length = len(hs_300)
subset_num = [hs_300_length // subset if hs_300_length % subset == 0 else hs_300_length // subset + 1][0]
if not os.path.exists(opts['datadest_excel']):
os.makedirs(opts['datadest_excel'])
for i in range(subset_num):
hs_code = hs_300[i*subset:np.min((hs_300_length, (i+1)*subset))]
hs_len = len(hs_code)
hs_index = [j for j in range(hs_len)]
hs300 = pd.DataFrame(columns=['code', 'sheet', 'amount'])
hs300['sheet'] = [j+1 for j in range(hs_len)]
hs300['code'] = ['{:0>6}'.format(hs_code[j]) for j in range(hs_len)]
writer = pd.ExcelWriter(opts['datadest_excel'] + 'hs300_{}_{}.xls'.format(i + 1, data_type))
# for i in range(hs_len):
# hs300['sheet'][i] = i + 1
# hs300.to_excel(writer, '0')
# 获取沪深300股票十年间的数据
st_dict = {}
st_num = []
for index in range(hs_len):
st_code = '{:0>6}'.format(hs_code[index])
st_data = ts.get_k_data(st_code, start=opts['start_time'], end=opts['end_time'], ktype=data_type, autype='qfq')
st_dict[index] = st_data
st_num.append(len(st_data))
# time.sleep(0.5)
print('{}/{} done'.format(i*subset + index, hs_300_length))
# 表格0中增加amount列
# for j in range(hs_len):
# hs300['amount'][j] = st_num[j]
hs300['amount'] = [st_num[j] for j in range(hs_len)]
hs300.to_excel(writer, sheet_name='sheet1')
for index in range(hs_len):
st_data = st_dict[index]
st_data.to_excel(writer, sheet_name=str(index + 1))
print('Load down')
writer.save()
writer.close()
# for i in range(subset_num):
# hs300 = copy.copy(hs_300[j*subset:(j+1)*subset])
# hs_index = [i for i in range(len(hs300))]
# hs300.index = hs_index
# hs_code = hs300['code']
# hs_name = hs300['name']
# hs_len = len(hs_code)
# # 重新建立表格数据,表格0中增加sheet列
# writer = pd.ExcelWriter(opts['datadir'] + 'hs300data_{}_{}.xls'.format(i+1, data_type))
# sh_index = pd.DataFrame([j+1 for j in range(hs_len)], columns=['sheet'])
# hs300['sheet'] = sh_index
# # for i in range(hs_len):
# # hs300['sheet'][i] = i + 1
# # hs300.to_excel(writer, '0')
#
# # 获取沪深300股票十年间的数据
# st_dict = {}
# st_num = []
# for index in range(hs_len):
# st_code = '{:0>6}'.format(hs_code[index])
# start_time = '2008-01-01'
# end_time = '2018-01-01'
# st_data = ts.get_k_data(st_code, start=start_time, end=end_time, ktype=data_type, autype='qfq')
# st_dict[index] = st_data
# st_num.append(len(st_data))
# # time.sleep(0.5)
# print(st_code, 'done')
#
# # 表格0中增加amount列
# st_amount = pd.DataFrame([j+1 for j in range(hs_len)], columns=['amount'])
# hs300['amount'] = st_amount
# for j in range(hs_len):
# hs300['amount'][j] = st_num[j]
# hs300.to_excel(writer, sheet_name='sheet1')
#
# for index in range(hs_len):
# st_data = st_dict[index]
# st_data.to_excel(writer, sheet_name=str(index+1))
# print('Load down')
# writer.save()
# writer.close()
if __name__ == '__main__':
opts = getOpts()
# from_D_data_get_20d_data(opts)
from_D_data_get_20d_data_with_date_index(opts)
with open(opts['datadir'] + 'hs300_code_total.json', 'r') as f:
hs_300 = js.load(f)
hs_300 = np.array(hs_300)
data_type = 'D' # 日K
# data_type = 'M' # 月K
# make_raw_data(hs_300, data_type, opts)
data_for_every_stock(hs_300, data_type, opts)