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tdx_mongodb_operation.py
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tdx_mongodb_operation.py
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import os, warnings, threading
import numpy as np
import pandas as pd
from sys import path
from pymongo import MongoClient
from concurrent import futures
class tdx_mongodb_operation(object):
def __init__(self,*arg,**kwarg):
self._IP = arg[0]
self._PORT = arg[1]
self._dataFrame = kwarg["dataframe"]
self._databaseName = kwarg["database"]
def conn_mongodb(self,collectionName):
self._Conn = MongoClient(self._IP, self._PORT)
self._mydb = self._Conn[self._databaseName]
collection = self._mydb.get_collection(collectionName)
return collection
def output_symbol_list(self,file_path):
symbol_info_list = []
for root, dirs, files in os.walk(file_path):
for file in files:
if os.path.splitext(file)[1] == '.txt':
symbol_info_list.append([os.path.join(root, file),file.replace('L8.txt','')])
return symbol_info_list
def gen_data_from_txt(self,symbol_path):
raw_data = []; Date = []; Time = []
Open = []; High = []; Low = []; Close = []
Volumn = []; OpenInterest = []
for line in open(symbol_path):
row = line.split(',')
if row[0].isdigit():
Date.append(row[0])
Time.append(row[1])
Open.append(row[2])
High.append(row[3])
Low.append(row[4])
Close.append(row[5])
Volumn.append(row[6])
OpenInterest.append(row[7])
raw_data = pd.DataFrame({'Date' : np.int64(Date),
'Time' : np.int32(Time),
'Open' : np.double(Open),
'High' : np.double(High),
'Low' : np.double(Low),
'Close' : np.double(Close),
'Volumn' : np.int64(Volumn),
'OpenInterest' : np.int64(OpenInterest)},
columns=['Date','Time','Open',
'High','Low','Close',
'Volumn','OpenInterest'])
return raw_data
def transfrom(self,latest_raw_data):
if self._dataFrame == 5:
print(' data frame: 5 mins ')
i = 0
while i < len(latest_raw_data):
if latest_raw_data.iloc[i,1] == 1500:
k = i + 1
t = []
date = latest_raw_data.iloc[i,0]
while k < len(latest_raw_data) and latest_raw_data.iloc[k,1] != 905:
if latest_raw_data.iloc[k,1] >= 2105 and latest_raw_data.iloc[k,1] <= 2355:
t.append(k)
k += 1
if len(t) != 0:
for j in range(len(t)):
latest_raw_data.iloc[t[j],0] = date
i = k
else:
i += 1
if i == len(latest_raw_data) - 1:
latest_raw_data.iloc[i,0] = latest_raw_data.iloc[i-1,0]
print(' have transfromed trading-date to action-date. ')
elif self._dataFrame == 1:
print(' data frame: 1 mins ')
i = 0
while i < len(latest_raw_data):
if latest_raw_data.iloc[i,1] == 1500:
k = i + 1
t = []
date = latest_raw_data.iloc[i,0]
while k < len(latest_raw_data) and latest_raw_data.iloc[k,1] != 901:
if latest_raw_data.iloc[k,1] >= 2101 and latest_raw_data.iloc[k,1] <= 2359:
t.append(k)
k += 1
if len(t) != 0:
for j in range(len(t)):
latest_raw_data.iloc[t[j],0] = date
i = k
else:
i += 1
if i == len(latest_raw_data) - 1:
latest_raw_data.iloc[i,0] = latest_raw_data.iloc[i-1,0]
print(' have transfromed trading-date to action-date. ')
latest_transformed_data = latest_raw_data
return latest_transformed_data
def cut(self,latest_transformed_data):
print(' date length:' + str(len(latest_transformed_data)))
t5 = []; t = 1; k = 1; u = 1
while t == k:
for i in range(len(latest_transformed_data)):
if (i+1) % 5 == 0:
t5.append(latest_transformed_data.iloc[i,1])
if t5[-1] % 5 != 0:
idx = i - np.where(latest_transformed_data.iloc[range(i,i-3,-1),1] - \
latest_transformed_data.iloc[range(i-1,i-4,-1),1] != 1)
latest_transformed_data.drop([idx],inplace=True) #inplace=True直接改变内存的值
latest_transformed_data = latest_transformed_data.reset_index(drop=True) #删除原来索引，重新建立从0开始的索引
print(' have cleared redundant data ' + str(u) + ', and data length is ' + \
str(len(latest_transformed_data)) + ' right now. ')
u += 1
break
if i == len(latest_transformed_data) - 1:
k += 1
print(' redundant data not exist anymore. ')
processed_data = latest_transformed_data
return processed_data
def data_processing(self,symbol_path):
latest_raw_data = self.gen_data_from_txt(symbol_path)
# 1.transfrom trading-day to action-day
latest_transformed_data = self.transfrom(latest_raw_data)
# 2.clear redundant data
if self._dataFrame == 5:
processed_data = self.cut(latest_transformed_data)
else:
processed_data = latest_transformed_data
return processed_data
def extract_info(self,tag_list,collectionName):
collection = self.conn_mongodb(collectionName)
tag_data = []
for tag in tag_list:
exec(tag + " = collection.distinct('" + tag + "')")
exec("tag_data.append(" + tag + ")")
return tag_data
def insert_to_database(self,symbol_path,symbol):
collection = self.conn_mongodb(symbol)
latest_processed_data = self.data_processing(symbol_path)
# 1.extract specific info from db
date_distinct_list = self.extract_info(['Date'],symbol)
if date_distinct_list[0] == []:
for i in range(len(latest_processed_data)):
data = {'_id' : str(i),
'Date' : str(latest_processed_data.iloc[i,0]),
'Time' : str(latest_processed_data.iloc[i,1]),
'Open' : str(latest_processed_data.iloc[i,2]),
'High' : str(latest_processed_data.iloc[i,3]),
'Low' : str(latest_processed_data.iloc[i,4]),
'Close' : str(latest_processed_data.iloc[i,5]),
'Volumn' : str(latest_processed_data.iloc[i,6]),
'OpenInterest' : str(latest_processed_data.iloc[i,7])}
collection.insert_one(data)
print(' * finish inserting ' + symbol + ' data. ')
else:
# 2.duplicate removal and insert latest data to database
date = np.int64(date_distinct_list[0])
last_date = str(np.max(date))
data = collection.find({'Date':last_date}) #return Object
last_time = max([int(k["Time"]) for k in data])
latest_processed_data_1 = latest_processed_data[latest_processed_data.Date==int(last_date)]
start_insert_ind = latest_processed_data_1[latest_processed_data_1.Time==last_time].index[0]
for i in range(len(latest_processed_data)):
if i > start_insert_ind:
data = {'_id' : str(i),
'Date' : str(latest_processed_data.iloc[i,0]),
'Time' : str(latest_processed_data.iloc[i,1]),
'Open' : str(latest_processed_data.iloc[i,2]),
'High' : str(latest_processed_data.iloc[i,3]),
'Low' : str(latest_processed_data.iloc[i,4]),
'Close' : str(latest_processed_data.iloc[i,5]),
'Volumn' : str(latest_processed_data.iloc[i,6]),
'OpenInterest' : str(latest_processed_data.iloc[i,7])}
collection.insert_one(data)
print(' * finish removing duplication and inserting latest ' + symbol + ' data. ')
def multi_thread_run(self,max_threads_num,file_path):
symbol_info_list = self.output_symbol_list(file_path)
#symbol_info[0] is symbol path; symbol_info[1] is symbol name
with futures.ThreadPoolExecutor(max_workers=max_threads_num) as executor:
future_to_symbol = {executor.submit(self.insert_to_database,symbol_info[0],symbol_info[1]) : \
ind for ind, symbol_info in enumerate(symbol_info_list)}
if __name__ == '__main__':
Data_Info_Dist = {"dataframe":[1,5],"databasename":["futures_1min_data","futures_5min_data"],\
"filepath":["D:\\Quant_Python\\tongdaxin_data\\1min_txt","D:\\Quant_Python\\tongdaxin_data\\5min_txt"]}
for i in range(len(Data_Info_Dist["dataframe"])):
tmo = tdx_mongodb_operation("localhost",27017,dataframe=Data_Info_Dist["dataframe"][i],\
database=Data_Info_Dist["databasename"][i])
tmo.multi_thread_run(max_threads_num=4,file_path=Data_Info_Dist["filepath"][i])