#!/usr/bin/env python
import os
import numpy as np
import py7zr
import shutil
import pandas as pd
import time
import multiprocessing
import re
def fun_time_l2(a,b):
if float(a)<=float(b) :
return 1
else:
return 0
def read_files(filename):#读文件内容
df1 = pd.DataFrame()
with open(filename, "r") as f:
listT = []
for line in f:
listT.append(line)
df1 = pd.DataFrame(listT)
index = df1.loc[(df1[0].str.contains("find"))].index
if index.isnull:
df1 = df1.drop(index=index)
df1 = pd.DataFrame(df1[0].str.strip())
df1 = pd.DataFrame(df1[0].str.split("\t", expand=True))
df1[3] = df1[1].astype("int") * df1[2].astype("int")
df1.columns = ["time", "price", "vol", "amount"]
vol_t = abs(df1["vol"].astype("long")).sum()
amount_t = abs(df1["amount"].astype("long")).sum()
df_f_xiao = df1[(df1["amount"].astype("int") < 0) & ((df1["amount"].astype("int") > -40000))]
df_f_zhong = df1[(df1["amount"].astype("int") <= -40000) & ((df1["amount"].astype("int") > -200000))]
df_f_da = df1[(df1["amount"].astype("int") <= - 200000) & ((df1["amount"].astype("int") > -1000000))]
df_f_te_da = df1[(df1["amount"].astype("int") <= - 1000000)]
f_xiao = df_f_xiao["amount"].astype("long").sum()
f_zhong = df_f_zhong["amount"].astype("long").sum()
f_da = df_f_da["amount"].astype("long").sum()
f_te_da = df_f_te_da["amount"].astype("long").sum()
df_z_xiao = df1[(df1["amount"].astype("int") > 0) & ((df1["amount"].astype("int") < 40000))]
df_z_zhong = df1[(df1["amount"].astype("int") >= 40000) & ((df1["amount"].astype("int") < 200000))]
df_z_da = df1[(df1["amount"].astype("int") >= 200000) & ((df1["amount"].astype("int") < 1000000))]
df_z_te_da = df1[(df1["amount"].astype("int") >= 1000000)]
z_xiao = df_z_xiao["amount"].astype("long").sum()
z_zhong = df_z_zhong["amount"].astype("long").sum()
z_da = df_z_da["amount"].astype("long").sum()
z_te_da = df_z_te_da["amount"].astype("long").sum()
# add 增加计算最小值
min_L = df1["price"].astype("int").min()
sum_V = abs(df1["vol"].astype("int")).sum()
min_2 = min_L * 1.02
df_min_2 = df1[(df1["price"].astype("int") < min_2)]
sum_min_2_v = abs(df_min_2["vol"].astype("long")).sum()
re_min_L2 = abs(sum_min_2_v) / sum_V * 100
# add time
df_min_3 = pd.DataFrame()
df_min_3["time"] = df_min_2["time"].str[:-2]
df_min_3 = df_min_3.drop_duplicates(subset = ['time'],keep = 'first',inplace = False)
time_l2 = len(df_min_3)
#add minute
df_time_all = pd.DataFrame()
df_time_all["time"] = df1["time"].str[:-2]
df_time_all["price"] = df1["price"].astype('int')
df_time_all["vol"] = df1["vol"].astype('long')
df_time_all["abs_vol"] = abs(df1["vol"].astype('long'))
df_time_all_only = df_time_all.drop_duplicates(subset=['time'], keep='first', inplace=False)
df_time_all_only = df_time_all_only.reset_index(drop=True)
df_list_return = pd.DataFrame()
b = {"vol_t": vol_t, "amount_t": amount_t, "z_xiao": z_xiao, "z_zhong": z_zhong, "z_da": z_da, "z_te_da": z_te_da,
"f_xiao": f_xiao, "f_zhong": f_zhong, "f_da": f_da, "f_te_da": f_te_da, "re_min_L2": re_min_L2,
'time_l2': time_l2}
df_list_return = df_list_return.append(b,ignore_index=True)
pd_read = pd.pivot_table(df_time_all, index='time',values = ['price','vol',"abs_vol"] , aggfunc = { 'price':np.mean,'vol':np.sum ,"abs_vol":np.sum})
pd_read = pd_read.reset_index()
pd_read["time_abs_vol"] = pd_read["time"] +".2"
pd_read["time_price"] = pd_read["time"] + ".1"
pd_read["time_vol"] = pd_read["time"] + ".3"
pd_r_1 = pd.DataFrame()
pd_r_1["value"] = pd_read["price"]
pd_r_1["time_price"] = pd_read["time_price"]
pd_r_1 = pd_r_1.set_index("time_price",drop = True)
pd_r_2 = pd.DataFrame()
pd_r_2["value"] = pd_read["abs_vol"]
pd_r_2["time_abs_vol"] = pd_read["time_abs_vol"]
pd_r_2 = pd_r_2.set_index("time_abs_vol", drop=True)
pd_r_3 = pd.DataFrame()
pd_r_3["value"] = pd_read["vol"]
pd_r_3["time_vol"] = pd_read["time_vol"]
pd_r_3 = pd_r_3.set_index("time_vol", drop=True)
pd_r_co = pd.DataFrame()
pd_r_co = pd_r_co.append(pd_r_1)
pd_r_co = pd_r_co.append(pd_r_2)
pd_r_co = pd_r_co.append(pd_r_3)
pd_r_co = pd_r_co.reset_index()
pd_r_co["index"] = pd_r_co["index"].astype('float')
pd_r_co = pd_r_co.sort_values(by = "index")
pd_r_co = pd_r_co.set_index("index", drop=True)
df_list_return = df_list_return.T
df_list_return.columns = ['value']
df_list_return = df_list_return.append(pd_r_co)
df_list_return = df_list_return.T
return df_list_return
def extract_files(filename):#提出7Z文件
with py7zr.SevenZipFile(filename, 'r') as archive:
allfiles = archive.getnames()#获取7Z文件内的子文件名
tempdir = allfiles[0].split("/")[0]#取7Z文件内文件夹名称
savedir =pathsave + str(tempdir)
if os.path.exists(savedir):
shutil.rmtree(savedir)#删除同名文件夹
os.mkdir(savedir)#重建文件夹
#archive.extract(pathsave,allfiles[0:3])#解压到文件夹
archive.extractall(pathsave)#解压到文件夹
#print(archive.extractall())
return savedir
def read_dirs(savedir):#读文件夹
files=np.array(os.listdir(savedir))
file_names = np.char.add(savedir + "\\",files)
return file_names
def sub_process(df_only_name1,q):
list_t1 = pd.DataFrame()
n_count = 0
for file in df_only_name1:
n_count = n_count + 1
#print("No. " ,n_count)
(filepath, tempfilename) = os.path.split(file)
(filename, extension) = os.path.splitext(tempfilename)
if not os.path.getsize(file): # 判断文件大小是否为0
print("file siz = 0")
print(file)
else:
list_t = pd.DataFrame()
list_t = read_files(file)
list_t.insert(0,"a_name",filename)
list_t1 = list_t1.append(list_t)
list_t1 = list_t1.reset_index(drop = True)#=============================================
q.put(list_t1,block = False)
exit(0)
if __name__ == '__main__':
#path = r'G:\datas of status\tick-by-tick trade' # 数据文件存放位置
path = r'G:\datas of status\2018-tick-by-tick trade'
pathsave = 'G:\\datas of status\\python codes\\' # 设定临时文件存放位置
pathTemp = 'G:\\datas of status\\python codes\\everyday_data\\temp'
listM = np.array(os.listdir(path)) # 获取月文件夹
print(listM)
listM = np.char.add(path + "\\", listM) # 获取月文件夹路径
#====================start work
m = 13 # 开始处理第几个文件夹(1~16,16=202004,15=202003)
do_num = 2
for n in range(do_num):
i = m - n #处理第几个文件夹(1~16)
print(listM[i])
listD = np.array(os.listdir(listM[i]))#获取一个文件夹下所有日文件全路径
print(listD)
listD = np.char.add(listM[i] + "\\",listD)#获取日文件全名
print(listD)
pdM_all = pd.DataFrame()
for filename in listD:
#for filename in listD:
# filename = listD[0]
print("=========")
print(time.strftime("%Y-%m-%d %H:%M:%S", time.localtime()))
npM = pd.DataFrame()
savedir = extract_files(filename)
#savedir = "G:\\datas of status\\python codes\\20160315"#如果处理单个文件（11111文件夹只存放一个文件包，上一行注释掉，不执行），则用此行，用完还原注释此行
print(time.strftime("%Y-%m-%d %H:%M:%S", time.localtime()))
savedir_t = re.sub("-", '', savedir)
findt = re.search("\d+$", savedir_t)
tempdir = findt.group()
#====================
file_names = read_dirs(savedir)
all_nums = len(file_names)
epochs = 3
step = int(all_nums/epochs)
process_list = []
datelist = []
q = multiprocessing.Queue(maxsize=epochs)
for i in range(epochs):
begin = i * step
end = begin + step
if i == epochs -1:
end = all_nums
df_only_name1 = file_names[begin:end]
tmp_process = multiprocessing.Process(target=sub_process, args=(df_only_name1, q))
process_list.append(tmp_process)
for process in process_list:
process.start()
#print("start",process)
while(q.qsize() != epochs):
#print(q.qsize(),"begin")
if(q.qsize()>=1):
#print(q.qsize())
time.sleep(3)
else:
time.sleep(5)
count = 0
time.sleep(1)
#exit(0)
while not q.empty():
list_g = q.get()
#print(list_g,"midle")
#print("hhaa",count )
count = count +1
npM = npM.append(list_g)
#print(npM)
#=======================
shutil.rmtree(savedir)
#npM.columns = list_columns1
#print(len(npM))
pdD_t = npM
pdD_t.insert(1, "date", tempdir, allow_duplicates=False)
#===========
#save_dfile = pathsave + "\\" + "everyday_data" + "\\" + pdD_t["date"][0] + ".csv"
save_dfile = pathsave + "\\" + "everyday_data" + "\\" + tempdir + ".csv"
# print(save_dfile)
pdD_t = pdD_t.sort_values(by=['time_l2'], ascending=True)
pdD_t.to_csv(save_dfile, sep=",", index=False, header=True)
pdM_all = pdM_all.append(pdD_t)
print(filename)
print(time.strftime("%Y-%m-%d %H:%M:%S", time.localtime()))
# print(pdM_all)
save_file = pathsave + pdM_all["date"][0].str[0:6] + ".csv"
save_file = save_file.reset_index(drop=True)
print(save_file[0])
# df.to_csv(‘/opt/births1880.csv’, index=False, header=False
# pdM_all = pdM_all.sort_values(by=['re_min_L2'], ascending=True)
pdM_all.to_csv(save_file[0], sep=",", index=False, header=True)
exit(0)
