我正在使用Python从Yahoo下载股票数据。下载代码放在 csv_util.py 中。
调用脚本是 mapper.py 。导入csv_util脚本后，出现以下错误：
Traceback (most recent call last):
('Lines:', [])
File "C:/Users/laurel.ts/Desktop/predictstock/mapper.py", line 56, in <module>
download_file_ticker(ticker,ref_ticker)
File "C:/Users/laurel.ts/Desktop/predictstock/mapper.py", line 53, in download_file_ticker
csv_util.download_csv_file_web(ticker,ref_ticker)
TypeError: unbound method download_csv_file_web() must be called with csv_util instance as first argument (got str instance instead)
以下是代码：
mapper.py
###### Mapper.py ######
import time
import sys
# New imports
import stock_predict_main_app
import predict_stock.csv_util
from predict_stock.csv_util import csv_util
predict_date = '03112016'
ticker = 'ARVIND.NS'
ref_ticker = 'MSFT'
input_default_values = {'numdays2predict': 2,
'simulations': 10,
'historicaldatalen': 0,
'tickersymbol': 'ARVIND.NS',
# 'tickersymbol': 'MSFT',
'stockdate2predict': predict_date,
'downloadstock': 1,
'plotshow': 0,
'industrytype': 'itindustry'}
# Pass ticker symbol and date
def pass_ticker_date(predict_date, input_default_values):
with open('tickerList.txt') as f:
lines = f.read().splitlines()
print(type(input_default_values))
tickersymbol = input_default_values["tickersymbol"]
print("Lines:", lines)
for tickersymbol in lines:
print("tickersymbol:", tickersymbol)
stock_predict_main_app.test_predict(tickersymbol)
# Download the file based on the ticker symbol
def download_file_ticker(ticker,ref_ticker):
# ticker= input_default_values["tickersymbol"]
# ref_ticker = input_default_values["tickersymbol"]
csv_util.download_csv_file_web(ticker,ref_ticker)
pass_ticker_date(predict_date, input_default_values)
download_file_ticker(ticker,ref_ticker)
的 csv_util.py
import logging
import csv
import urllib
import datetime
import numpy as np
import pandas as pd
import sys
import os
from collections import defaultdict
###custom local modules###
from datetime_util import datetime_util
from loggingpy import loggingpy
global stock_input_file,stock_input_ref_file
global loggingpy_obj,datetime_util_obj
global actual_stock_price, output_stock_price
class csv_util:
def __init__(self,actual_stock_price,output_stock_price,download_flag):
print("Class anme: __init__",self.__class__.__name__)
self.stock_input_file=""
self.stock_input_ref_file = ""
self.actual_stock_price = actual_stock_price
self.output_stock_price = output_stock_price
self.download_flag=download_flag
self.datetime_util_obj = datetime_util()
self.loggingpy_obj = loggingpy()
#datetime_util_obj = self.datetime_util_obj
#loggingpy_obj=self.loggingpy_obj
'''
METHOD: prepare_actual_data2writecsv
'''
def prepare_actual_data2writecsv(self, predict_date_wise_data_wd, predict_datewise_data_wod):
logging.info("<<prepare_actual_data2writecsv")
temp_date_ary = []
temp_date_ary = self.actual_stock_price['date']
temp_closeprice_ary = self.actual_stock_price['closeprice']
temp_dailyreturn_ary = self.actual_stock_price['dailyreturn']
# predicted_date_array = sorted(temp_predicted_values_wd.keys(),reverse=True)
# remove last element of array or appenda dummy 0 to daily returns
temp_date_ary.pop()
temp_closeprice_ary.pop()
# temp_dailyreturn_ary.append(0)
self.loggingpy_obj.log_func({'temp_date_ary': temp_date_ary, 'temp_closeprice_ary': temp_closeprice_ary,
'temp_dailyreturn_ary': temp_dailyreturn_ary})
np_column_ary = np.column_stack((temp_date_ary, temp_closeprice_ary, temp_dailyreturn_ary))
num_rows, num_columns = np_column_ary.shape
logging.info("np_column_ary:%s,Rowsdata %s,ColumnData %s", np_column_ary.size, np_column_ary[:][0],
np_column_ary[:, 0])
logging.info("NumRows:%d,Num Columns:%s", num_rows, num_columns)
counter = 0
for i in range(0, num_rows):
counter += 1
temp_temp_row_data = []
temp_row_data = np_column_ary[:][i]
temp_temp_row_data = list(temp_row_data)
temp_rowdate = temp_row_data[0]
logging.debug("[%d],Length:[%d],type:[%s],Date:%s,Rowsdata:%s", i, len(temp_row_data), type(temp_row_data),
temp_rowdate, temp_row_data)
predict_date_wise_data_wd[temp_rowdate] = (map(str, temp_temp_row_data))
predict_datewise_data_wod[temp_rowdate] = (map(str, temp_temp_row_data))
logging.info(">>prepare_actual_data2writecsv")
'''
METHOD: prepare_data2writecsv
'''
def prep_predicted_data2writecsv(self, predict_date_wise_data_wd, predict_datewise_data_wod):
logging.info("<<prep_predicted_data2writecsv")
temp_predicted_values_wd = self.actual_stock_price['predicted_vals_with_drift']
temp_predicted_values_wod = self.actual_stock_price['predicted_vals_without_drift']
self.actual_stock_price['meanwithdrift'] = []
self.actual_stock_price['meanwithoutdrift'] = []
temp_var = temp_predicted_values_wd.keys()
predicted_date_array = self.datetime_util_obj.sort_datettime_list(temp_var, False)
for eack_key in predicted_date_array:
logging.debug("WD:eack key:%s", eack_key)
temp_string_val_wd = []
temp_string_val_wod = []
temp_string_val_wd = temp_predicted_values_wd[eack_key]
temp_string_val_wod = temp_predicted_values_wod[eack_key]
mean_wd = np.mean(temp_string_val_wd)
mean_wod = np.mean(temp_string_val_wod)
# Store mean in global variable
self.actual_stock_price['meanwithdrift'].append(mean_wd)
self.actual_stock_price['meanwithoutdrift'].append(mean_wod)
logging.debug("meanwithdrift:%s,meanwithoutdrift:%s", mean_wd, mean_wod)
logging.debug("temp_string_val_wd:len:%d,type:%s", len(temp_string_val_wd), type(temp_string_val_wd))
logging.debug("temp_string_val_wd:len:%d,type:%s", len(temp_string_val_wod), type(temp_string_val_wod))
temp_string_wd = []
temp_string_wod = []
if not predict_datewise_data_wod.has_key(eack_key):
predict_datewise_data_wod[eack_key] = []
predict_date_wise_data_wd[eack_key] = []
temp_string_wd = [eack_key, "", ""]
temp_string_wod = [eack_key, "", ""]
temp_string_wd.append(mean_wd)
temp_string_wod.append(mean_wod)
temp_string_wd.extend(temp_string_val_wd)
temp_string_wod.extend(temp_string_val_wod)
logging.debug("temp_string_wd:len:%d,type:%s,Data:%s", len(temp_string_wd), type(temp_string_wd),
temp_string_wd)
logging.debug("temp_string_wod:len:%d,type:%s,Data:%s", len(temp_string_wod), type(temp_string_wod),
temp_string_wod)
predict_date_wise_data_wd[eack_key].extend(temp_string_wd)
predict_datewise_data_wod[eack_key].extend(temp_string_wod)
#self.loggingpy_obj.log_func({"temp_string_wd": temp_string_wd, "temp_string_wod": temp_string_wod})
logging.info(">>prepare_data2writecsv")
'''
METHOD: write2csvfile
Writes given data to the given csv absolute path filename
Input arguments: filename to be written, data to be written
'''
def write2csvfile(self,file_name,local_rows_data):
logging.info("<<:write2csvfile")
#output_data_path=self.loggingpy_obj.output_data_path
#os.chdir(output_data_path)
with open(file_name, 'w') as csv_fw:
out_csv_writer = csv.writer(csv_fw, lineterminator='\n')
out_csv_writer.writerows(local_rows_data)
logging.info("csv file[%s]writing :Sucess",file_name)
logging.info(">>:write2csvfile")
'''
# Prepare header list of columns to write to csv file
# Write predicted values to csv file
predicted_data_wod_date_val_ary
'''
#@classmethod
#def write2csv_file(ticker_symbol):
def write2csv_file(self,ticker_symbol):
logging.info("<<:write2csv_file")
datetime_stamp=datetime.datetime.now().strftime("%Y%m%d_%H%M")
file_name="output_prediction_with_drift"+ticker_symbol+"_"+datetime_stamp+".csv"
file_name_wod = "output_prediction_without_drift" + ticker_symbol + "_" + datetime_stamp + ".csv"
file_name=self.loggingpy_obj.output_data_path + file_name
file_name_wod = self.loggingpy_obj.output_data_path + file_name_wod
column_headers=self.output_stock_price['column_headers']
#Prepare header list of columns to write to csv file;column_headers is a global variable
column_headers.insert(0,'Date')
column_headers.insert(1, 'Actual Prices')
column_headers.insert(2, 'Daily Return')
column_headers.insert(3, 'Mean')
logging.info("column_headers,len:%s,type:%s,data:%s", len(column_headers), type(column_headers), column_headers)
logging.info("self:column_headers,len:%s", len(self.output_stock_price['column_headers']))
predict_date_wise_data_wd = {}
predict_datewise_data_wod = {}
self.prepare_actual_data2writecsv(predict_date_wise_data_wd, predict_datewise_data_wod)
self.loggingpy_obj.log_func(
{"Before:predict data_wd": predict_date_wise_data_wd, "predict data_wod": predict_datewise_data_wod})
self.prep_predicted_data2writecsv(predict_date_wise_data_wd,predict_datewise_data_wod)
self.loggingpy_obj.log_func({"After:pred data_wd": predict_date_wise_data_wd, "pred data_wod": predict_datewise_data_wod})
temp_new_data_ary=predict_date_wise_data_wd.keys()
sorted_temp_new_data_ary = self.datetime_util_obj.sort_datettime_list(temp_new_data_ary,True)
self.loggingpy_obj.log_func({"sorted_temp_new_data_ary":sorted_temp_new_data_ary})
data2write2csv_wd = [column_headers]
data2write2csv_wod = [column_headers]
counter=1
# add headers
for each_key in sorted_temp_new_data_ary:
counter+=1
data2write2csv_wd.insert(counter, predict_date_wise_data_wd[each_key])
data2write2csv_wod.insert(counter,predict_datewise_data_wod[each_key])
self.write2csvfile(file_name, data2write2csv_wd)
self.write2csvfile(file_name_wod, data2write2csv_wod)
logging.debug("data2write2csv_wd:%s", repr(data2write2csv_wd))
logging.info("<<:write2csv_file")
#sys.exit()
# ######################### END OF METHOD write2csv_file ################################
'''
METHOD: read_csv_file
'''
#@classmethod
def read_csv_file(self,file_name,ref_data_flag):
logging.debug("<<:read_csv_file")
logging.info("file_name,%s,",file_name)
if not os.path.exists(file_name):
logging.critical("File not found:Check!"+file_name)
sys.exit(2)
#actual_stock_price=self.actual_stock_price
logging.info("file_name,%s,", file_name)
data = pd.read_csv(file_name,parse_dates=False, infer_datetime_format=False,date_parser=None)
logging.info("self:::::::::::::%s",repr(self.datetime_util_obj));
logging.info("data columns,len:%d,type:%s,data:%s,",len(data.columns),type(data.columns),data.columns)
logging.info(",data.columns:%s",data.columns.values)
datetime_list_dmy = self.datetime_util_obj.convert2datettime_list(data['Date'].tolist())
#actual_stock_price=defaultdict();
actual_stock_price = self.actual_stock_price
if ref_data_flag == 1:
actual_stock_price['ref_data']={}
actual_stock_price['ref_data']['date'] = datetime_list_dmy
actual_stock_price['ref_data']['closeprice'] = data['Close'].tolist()
else:
actual_stock_price['date'] = datetime_list_dmy
actual_stock_price['closeprice'] = data['Close'].tolist()
self.loggingpy_obj.log_func({"datetime_list_dmy": datetime_list_dmy})
#logging.debug("repr self asp:%s",repr(self.actual_stock_price))
del data
logging.debug(">>:read_csv_file")
'''
METHOD: download_csv_file_web
Download stock data from web yahoofinance
'''
def download_csv_file_web(self,ticker_symbol,ref_ticker_symbol):
logging.debug("<<:download_csv_file_web")
input_data_path=self.loggingpy_obj.input_data_path
logging.info("input_data_path:%s:",input_data_path)
yahoo_url="http://real-chart.finance.yahoo.com/table.csv?s="
base_url=yahoo_url + ticker_symbol
base_url_ref = yahoo_url + ref_ticker_symbol
#datetime_stamp = datetime.datetime.now().strftime("%Y%m%d_%H%M")
datetime_stamp = datetime.datetime.now().strftime("%Y%m%d")
global stock_input_file,stock_input_ref_file
#print("File found1:", stock_input_file)
if self.download_flag == 1:
file_name = "stock_input_" + ticker_symbol + "_" + datetime_stamp + ".csv"
ref_file_name = "stock_ref_input_" + ref_ticker_symbol + "_" + datetime_stamp + ".csv"
stock_input_file = input_data_path + "\\" + file_name
stock_input_ref_file = input_data_path + "\\" + ref_file_name
self.download_file(stock_input_file,base_url)
self.download_file(stock_input_ref_file, base_url_ref)
else:
file_name = "stock_input_" + ticker_symbol + ".csv"
ref_file_name = "stock_ref_input_" + ref_ticker_symbol + ".csv"
stock_input_file = input_data_path + "\\" + file_name
stock_input_ref_file = input_data_path + "\\" + ref_file_name
if os.path.isfile(stock_input_file) and os.path.isfile(stock_input_ref_file):
logging.info("File found 3 :[%s],[%s]",stock_input_file,stock_input_ref_file)
print("File found3:",stock_input_file,stock_input_ref_file)
else:
print("File not found4:", stock_input_file,stock_input_ref_file)
logging.critical("File not found4![%s] or [%s]",stock_input_file,stock_input_ref_file)
sys.exit(2)
print("File found5:", stock_input_file,stock_input_ref_file)
logging.info("stock_input_file 5 :%s,base_url:%s,ref file name:[%s]", stock_input_file, base_url,stock_input_ref_file)
self.stock_input_file=stock_input_file
self.stock_input_ref_file=stock_input_ref_file
#sys.exit(2)
logging.debug(">>:download_csv_file_web")
'''
METHOD: download_file
Downlaod stock data from web yahoofinance
'''
def download_file(self,file_name,base_url):
logging.debug("<<:download_file")
try:
logging.info("Try Reading [:%s]",base_url)
status = urllib.urlretrieve(base_url, file_name)
logging.info("Status:%s", status)
urllib.urlcleanup()
if os.path.exists(file_name):
logging.info ("File exists, File download success!File"+file_name)
else:
logging.critical("File downloaded DOE NOT EXIST, exit.."+file_name)
sys.exit(2)
except urllib.ContentTooShortError as ctse:
print("File download: Failed, found some error")
logging.critical("File donwlaod failed from url:%s",base_url)
sys.exit(2)
#logfile_handle.write(ctse.content)
logging.debug(">>:download_file")
def download_read_csv_file(self,ticker_symbol, industry_type_ts):
logging.debug("<<:download_read_csv_file")
ref_data_flag=0
self.download_csv_file_web(ticker_symbol, industry_type_ts)
stock_input_file = self.stock_input_file
stock_input_ref_file = self.stock_input_ref_file
# download_csv_file_web("GLD")
ref_data_flag = 0
self.read_csv_file(stock_input_file, 0)
self.read_csv_file(stock_input_ref_file, 1)
#self.loggingpy_obj.log_func({"csv_util:actual_stock_price":self.actual_stock_price})
logging.debug(">>:download_read_csv_file")
的 loggingpy.py
import logging
import datetime
import os
import sys
global current_dir_path, input_data_path, output_data_path
class loggingpy:
def __init__(self):
loglevel=logging.INFO
self.loglevel=loglevel
self.log_config()
print("loggingpy - log __init__")
def log_config(self):
global current_dir_path, input_data_path, output_data_path
current_dir_path = os.getcwd()
input_data_path = current_dir_path + "\..\stock_data"
output_data_path = current_dir_path + "\..\stock_data"
if not os.path.exists(output_data_path):
os.mkdir(output_data_path)
print("current_dir_path:" + current_dir_path + ",input_data_path:" + input_data_path,
",output_data_path:" + output_data_path)
main_date_time_stamp = datetime.datetime.now().strftime("%Y%m%d_%H%M")
logfile = output_data_path + "\log_" + main_date_time_stamp + ".log"
#logging.basicConfig(stream=sys.stdout,level=logging.DEBUG, format='%(levelname)s:%(message)s')
logging.basicConfig(stream=sys.stdout, level=logging.INFO)
#logging.basicConfig(stream=sys.stdout, level=self.loglevel)
#logging.basicConfig(filename=logfile, level=logging.INFO, format='%(levelname)s:%(message)s')
#console=logging.StreamHandler().setLevel(logging.DEBUG)
#logging.getLogger('abcd').addHandler(console)
self.input_data_path = input_data_path
self.output_data_path = output_data_path
self.current_dir_path = current_dir_path
logging.info("Logging test %s","HELLO TEST")
logging.info("current_dir_path:%s,input_data_path:%s,output_data_path:%s", current_dir_path,input_data_path,output_data_path)
def log_fn(self,temp_list):
for i in range(0, len(temp_list)):
log_level_set=logging.getLogger().getEffectiveLevel()
#print("log_level_set",log_level_set,logging.INFO,logging.DEBUG)
if log_level_set==logging.INFO:
logging.info(":len:%d,Type:%s", len(temp_list[i]), type(temp_list[i]))
if log_level_set == logging.DEBUG:
logging.debug("len:%d,Type:%sData:%s", len(temp_list[i]), type(temp_list[i]),temp_list[i])
def log_info(self,msg):
logging.info(msg)
def log_func(self,templog):
log_level_set = logging.getLogger().getEffectiveLevel()
#log_string_info = "Name:%s,len:%d,type:%s"
#log_string_debug = "Name:%s,len:%d,type:%s,Data:%s"
log_string_info = "Name:{0},len:{1},type:{2}"
log_string_debug = "Name:{0},len:{1},type:{2},Data:%{3}"
for var_name,var_value in templog.iteritems():
if log_level_set == logging.INFO:
#logging.info(""+log_string_info,var_name, len(var_value),type(var_value))
logging.info(log_string_info.format(var_name, len(var_value), type(var_value)))
if log_level_set == logging.DEBUG:
#logging.debug(""+log_string_debug,var_name, len(var_value), type(var_name),var_value)
logging.debug(log_string_debug.format(var_name, len(var_value), type(var_name), var_value))
错误的原因是什么？