def data_standardize_xbx(code):
# 1.读取万得的股票数据
stock_data = Functions.import_stock_data_xbx(code)
# 2.读取指数数据
index_data = Functions.import_index_data_xbx()
# 3.个股数据除权处理, 默认后复权，直接替换OLHC列
stock_data = Functions.cal_adjust_price(stock_data)
# 3.合并个股数据和指数数据
stock_data = Functions.merge_with_index_data(stock_data, index_data)
# 4.添加涨跌停列
stock_data = Functions.limit_up_down(stock_data)
return stock_data
def data_standardize_money163(stock_code, index_code='000001.SH'):
# 1.读取万得的股票数据
stock_data = Functions.import_stock_data_money163(stock_code)
# 2.读取指数数据
index_data = Functions.import_index_data_wande(index_code=index_code)
# 3.个股数据除权处理, 默认后复权，直接替换OLHC列
stock_data = Functions.cal_adjust_price(stock_data)
# 3.合并个股数据和指数数据
stock_data = Functions.merge_with_index_data(stock_data, index_data)
# 4.添加涨跌停列
stock_data = Functions.limit_up_down(stock_data)
return stock_data
def data_standardize_money163(df,
index_code='000001.SH',
return_type=1,
start_date='19890101'):
# 1.读取万得的股票数据
# stock_data = Functions.import_stock_data_money163(stock_code)
stock_data = df.copy()
# 2.读取指数数据
index_data = Functions.import_index_data_wande(index_code=index_code)
# 3.个股数据除权处理, 默认后复权，直接替换OLHC列
stock_data = Functions.cal_adjust_price(stock_data,
return_type=return_type)
# 3.合并个股数据和指数数据
stock_data = Functions.merge_with_index_data(stock_data, index_data)
# 4.添加涨跌停列
stock_data = Functions.limit_up_down(stock_data)
# 取得指定时间范围的数据
stock_data = stock_data[stock_data['date'] >= pd.to_datetime(start_date)]
return stock_data
def get_all_stock_data_wande(
input_path='C:/all_trading_data/data/input_data/stock_data_wande',
output_path='C:/all_trading_data/data/output_data/Going_Merry/',
save=True):
"""
# 获取指定文件夹中所有股票代码的list
:param input_path: 个股数据所在的文件夹
:param output_path: 结果输出所在的文件夹
:param save: 是否要保存数据
:return: all_stock_data
"""
all_stock_data = pd.DataFrame()
stock_list = Functions.get_stock_code_list_in_one_dir_wande(input_path)
for code in stock_list:
# stock_data = Functions.import_stock_data_wande(code)
# # 判断每天开盘是否涨停
# stock_data.ix[stock_data['open'] > stock_data['close'].shift(1) * 1.097, 'limit_up'] = 1
# stock_data['limit_up'].fillna(0, inplace=True)
# 获取标准化的个股数据，除权、指数合并、涨跌停列添加
stock_data = Data_standardize.data_standardize_wande(code)
all_stock_data = all_stock_data.append(stock_data, ignore_index=True)
date = time.strftime('%Y%m%d', time.localtime(time.time()))
# 输出一个进度过程，不然都不知道运行到哪里了
progess = ((stock_list.index(code) + 1.0) /
len(stock_list)) * 100.00 # 读取当前股票在list中的位置， 除以总的list长度
print 'stock %s in progress is %.2f%%' % (code, progess)
if save == True:
all_stock_data.to_hdf(output_path + 'all_stock_data_wande_' +
str(date) + '.h5',
key='all_stock',
mode='w')
# all_stock_data.to_csv('C:/all_trading_data/data/output_data/Going_Merry/all_stock_data_20171005.csv')
return all_stock_data
print 'all_stock_data has been finished.'
"""
import os
import warnings
import pandas as pd
from Basic_Functions import Functions
from Strategy_test import TA_strategy
from Performance_analysis import pf_analysis
from Performance_analysis import equity_cal
import matplotlib.pyplot as plt
warnings.filterwarnings("ignore")
pd.set_option('expand_frame_repr', False)
# 导入指数数据,作为benchmark
index_data = Functions.import_index_data_wande()
# 此处可以填入对应的code进行单个股票测试
stock_data = Functions.import_stock_data_wande('600000.SH')
stock_data = Functions.cal_adjust_price(stock_data,
adjust_type='adjust_back',
return_type=2)
# 和index合并
stock_data = Functions.merge_with_index_data(stock_data, index_data)
stock_data = stock_data[[
'date', 'code', 'open', 'high', 'low', 'close', 'change'
]]
concat = pd.DataFrame()
for i in range(14, 19):
"""
import os
import warnings
import pandas as pd
from Basic_Functions import Functions
from Strategy_test import TA_strategy
from Performance_analysis import pf_analysis
from Performance_analysis import equity_cal
warnings.filterwarnings("ignore")
pd.set_option('expand_frame_repr', False)
# 导入指数数据,作为benchmark
index_data = Functions.import_index_data_wande()
# 遍历数据文件夹中所有股票文件的文件名，得到股票代码列表
stock_code_list = Functions.get_stock_code_list_in_one_dir_wande()
# print len(stock_code_list)
# exit()
# ====数据准备
for code in stock_code_list[0:]: # 可以适当少选一点股票
stock_data = Functions.import_stock_data_wande(code,
other_columns=['成交量(股)'])
if len(stock_data) < 250: # 剔除发行时间小于约1年的数据
continue
print 'The progress is in %.2f%%.' % (stock_code_list.index(code) * 1.0 /
"""
import os
import warnings
import pandas as pd
from Basic_Functions import Functions
from Strategy_test import TA_strategy
from Performance_analysis import pf_analysis
from Performance_analysis import equity_cal
warnings.filterwarnings("ignore")
pd.set_option('expand_frame_repr', False)
# 导入指数数据,作为benchmark
index_data = Functions.import_index_data_wande()
# 遍历数据文件夹中所有股票文件的文件名，得到股票代码列表
stock_code_list = Functions.get_stock_code_list_in_one_dir_wande()
# print len(stock_code_list)
# exit()
# ====数据准备
for code in stock_code_list[0:]: # 可以适当少选一点股票
stock_data = Functions.import_stock_data_wande(code)
if len(stock_data) < 250: # 剔除发行时间小于约1年的数据
continue
print 'The progress is in %.2f%%.' % (stock_code_list.index(code) * 1.0 /
len(stock_code_list) * 100)
@contract: [email protected] @file: test_20171118.py @time: 2017/11/18 16:20 """ import pandas as pd from Basic_Functions import Functions from Basic_Functions import Data_standardize from Strategy_test import TA_strategy from Performance_analysis import equity_cal from Performance_analysis import pf_analysis import tushare as ts pd.set_option('expand_frame_repr', False) # 读取万得的非除权数据，然后进行数据标准化 stock_data = Functions.import_stock_data_wande('600000.SH') stock_data = Data_standardize.data_standardize_wande(stock_data, adjust_type='adjust_back', start_date='20050101') # stock_data.to_csv(Functions.out_put_path+'/600000.SH.csv') #读取xbx的数据 # stock_data = Functions.import_stock_data_xbx('sh600000') # stock_data = Data_standardize.data_standardize_xbx(stock_data, adjust_type='adjust_back', start_date='20081031') # stock_data.to_csv(Functions.out_put_path+'/sh600000.csv') #读取tushare的股票数据 # cons = ts.get_apis() # stock_data=ts.bar(code='600000', adj='hfq', conn=cons) # stock_data.to_csv(Functions.out_put_path+'/600000ts.csv') # print stock_data
数据：万得
每月调仓
每次10个
选股策略 市值因子
实际买卖方式：每月末选股，买入，持有一个月
"""
import pandas as pd
from Basic_Functions import Functions
import matplotlib.pyplot as plt
pd.set_option('expand_frame_repr', False)
# =====建立all_stock_data_monthly
all_stock_data_monthly = pd.DataFrame()
# 获得stock_list
stock_list = Functions.get_stock_code_list_in_one_dir_wande()
# print stock_list
#
# range = stock_list
# for code in range:
# # 获得每个股票的数据
# df = Functions.import_stock_data_wande(code)
# # 读取指数数据
# index_df = Functions.import_index_data_wande()
# # 个股与指数数据合并
# df = Functions.merge_with_index_data(df,index_df)
# # 截取20060101以后的数据
# df = df[df['date']>pd.to_datetime('20060101')]
# # 周期转化昵称月
# df_month = Functions.transfer_to_period_data(df)
pd.set_option('expand_frame_repr', False)
# ====1 测试个股数据导入
# 1.1 测试万得个股数据读取
# stock_data = Functions.import_stock_data_wande('000001.SZ')
# print stock_data['date']
# exit()
# 1.2 测试xbx个股数据读取
# stock_data = Functions.import_stock_data_xbx('sh600000')
# print stock_data
# exit()
# 1.3 测试money163个股数据读取
stock_data = Functions.import_stock_data_money163('000001')
# df['change'] = df['change'].astype('float')
print stock_data
# print df['change'].astype('float', inplace=True)
# print df['change'].astype('float')
exit()
# 2 测试指数数据导入
# 2.1 测试大盘数据读取 万得
# index_code='000001.SH'
# index_data = Functions.import_index_data_wande()
# print index_data['date']
# exit()
# 2.2 测试大盘数据读取 xbx
# index_code='sh000001'