def scan_and_input_recent_prices(self, stock_sym_list, num_days_for_updates=10):
""" Another method to input the data to database. For shorter duration of the dates.
Function for storing the recent prices and set it to the databse.
Use with the YQL modules.
Args:
stock_sym_list (list): stock symbol list.
Kwargs:
num_days_for_updates: number of days to update. Cannot be set too large a date.
Default 10 days.
"""
w = YComDataExtr()
w.set_full_stocklist_to_retrieve(stock_sym_list)
w.set_hist_data_num_day_fr_current(num_days_for_updates)
w.get_all_hist_data()
## save to one particular funciton
# save to sql -- hist table
w.datatype_com_data_allstock_df.to_sql(
self.hist_data_tablename,
self.con,
flavor="sqlite",
schema=None,
if_exists="append",
index=True,
index_label=None,
chunksize=None,
dtype=None,
)
def scan_and_input_recent_prices(self,
stock_sym_list,
num_days_for_updates=10):
""" Another method to input the data to database. For shorter duration of the dates.
Function for storing the recent prices and set it to the databse.
Use with the YQL modules.
Args:
stock_sym_list (list): stock symbol list.
Kwargs:
num_days_for_updates: number of days to update. Cannot be set too large a date.
Default 10 days.
"""
w = YComDataExtr()
w.set_full_stocklist_to_retrieve(stock_sym_list)
w.set_hist_data_num_day_fr_current(num_days_for_updates)
w.get_all_hist_data()
## save to one particular funciton
#save to sql -- hist table
w.datatype_com_data_allstock_df.to_sql(self.hist_data_tablename,
self.con,
flavor='sqlite',
schema=None,
if_exists='append',
index=True,
index_label=None,
chunksize=None,
dtype=None)
#do not have the header --> add in header and convert to pandas data
con.close()
if selection == 3:
""" Divident data might not need to collect so often"""
""" Deal with YQL and extract recent data """
""" Hist price retrieve using YQL. """
file = r'c:\data\full_Dec29.csv'
full_stock_data_df = pandas.read_csv(file)
w = YComDataExtr()
w.set_full_stocklist_to_retrieve(list(full_stock_data_df['SYMBOL'])[:100])
w.get_all_hist_data()
print w.datatype_com_data_allstock_df.head() # str away use the pandas to sql version to send??
## Users parameters
hist_data_tablename = 'histprice'
divdnt_data_tablename = 'dividend'
## initialized the database
con = lite.connect(db_full_path)
## save to one particular funciton
#save to sql -- hist table
w.datatype_com_data_allstock_df.to_sql(hist_data_tablename, con, flavor='sqlite',
schema=None, if_exists='replace', index=True,
index_label=None, chunksize=None, dtype=None)
rows = cur.fetchall() # return list of tuples
# do not have the header --> add in header and convert to pandas data
con.close()
if selection == 3:
""" Divident data might not need to collect so often"""
""" Deal with YQL and extract recent data """
""" Hist price retrieve using YQL. """
file = r"c:\data\full_Dec29.csv"
full_stock_data_df = pandas.read_csv(file)
w = YComDataExtr()
w.set_full_stocklist_to_retrieve(list(full_stock_data_df["SYMBOL"])[:100])
w.get_all_hist_data()
print w.datatype_com_data_allstock_df.head() # str away use the pandas to sql version to send??
## Users parameters
hist_data_tablename = "histprice"
divdnt_data_tablename = "dividend"
## initialized the database
con = lite.connect(db_full_path)
## save to one particular funciton
# save to sql -- hist table
w.datatype_com_data_allstock_df.to_sql(
hist_data_tablename,
con,
flavor="sqlite",
