def update_price_data(path,
ticker,
_start_date='2009-10-01',
_end_date=str(datetime.datetime.now())[:10]):
file = path + ticker + '.h5'
start_date_DT = datetime.datetime.strptime(
pd.read_hdf(file, 'start_date').iloc[-1], '%Y-%m-%d')
end_date_DT = datetime.datetime.strptime(
pd.read_hdf(file, 'end_date').iloc[-1], '%Y-%m-%d')
_start_date_DT = datetime.datetime.strptime(_start_date, '%Y-%m-%d')
_end_date_DT = datetime.datetime.strptime(_end_date, '%Y-%m-%d')
retrieved_at = pd.read_hdf(file, 'retrieved_at')
updated_at = str(datetime.datetime.now())[:10]
sort_by_date_ascending = pd.read_hdf(file,
'sort_by_date_ascending').iloc[-1]
freq = pd.read_hdf(file, 'freq').iloc[-1]
df = pd.read_hdf(file, 'df')
if _start_date_DT < start_date_DT:
yf = YahooFinancials(ticker)
END = str(start_date_DT - datetime.timedelta(days=1))[:10]
price_data = clean_stock_data(
yf.get_historical_stock_data(_start_date, END,
freq)[ticker]['prices'])
pd_price_data = pd.DataFrame(price_data)
print("The downloaded price data has shape" + str(pd_price_data.shape))
df = df.append(pd_price_data, ignore_index=True)
if _end_date_DT > end_date_DT:
yf = YahooFinancials(ticker)
START = str(end_date_DT + datetime.timedelta(days=1))[:10]
price_data = clean_stock_data(
yf.get_historical_stock_data(START, _end_date,
freq)[ticker]['prices'])
pd_price_data = pd.DataFrame(price_data)
print("The downloaded price data has shape" + str(pd_price_data.shape))
df = df.append(pd_price_data, ignore_index=True)
if sort_by_date_ascending:
df.sort_values('date', ascending=True, inplace=True)
df.reset_index(inplace=True, drop=True)
df.to_hdf(file, key='df', mode='w')
pd.Series(df['formatted_date'].iloc[0]).to_hdf(file, key='start_date')
pd.Series(df['formatted_date'].iloc[-1]).to_hdf(file, key='end_date')
pd.Series(freq).to_hdf(file, key='freq')
pd.Series(sort_by_date_ascending).to_hdf(file,
key='sort_by_date_ascending')
pd.Series(retrieved_at).to_hdf(file, key='retrieved_at')
pd.Series(updated_at).to_hdf(file, key='updated_at')
updated_at = str(datetime.datetime.now())[:10]
pd.Series(updated_at).to_hdf(file, key='updated_at')
print(str(ticker) + "'s" + " price data is updated")
def getStockInfoList(ticker_symbol_list):
stock = YahooFinancials(ticker_symbol_list)
stock_info = stock.get_stock_price_data()
return_info = dict()
result = dict()
weeks_ago = (datetime.datetime.today() -
datetime.timedelta(days=14)).strftime('%Y-%m-%d')
tomorrow = (datetime.datetime.today() +
datetime.timedelta(days=1)).strftime('%Y-%m-%d')
stock_price = stock.get_historical_stock_data(weeks_ago, tomorrow, "daily")
for i in range(len(ticker_symbol_list)):
fullname = stock_info[ticker_symbol_list[i]]['longName']
return_info['symbol'] = ticker_symbol_list[i]
return_info['company'] = fullname
return_info['quantity'] = 0
for j in range(5):
return_info.setdefault('dates', []).append(stock_price[
ticker_symbol_list[i]]['prices'][j]['formatted_date'])
return_info.setdefault('prices', []).append(
stock_price[ticker_symbol_list[i]]['prices'][j]['close'])
result.setdefault('stocks', []).append(return_info)
return_info = {}
return result
def download_yahoo(ticker: str,
base_dir: str = default_data_dir) -> pd.DataFrame:
try:
yf = YahooFinancials(ticker)
data = yf.get_historical_stock_data(dt_str(start_date),
dt_str(end_date), 'daily')
except Exception as err:
print(f'Unable to read data for {ticker}: {err}')
return pd.DataFrame({})
if data.get(ticker) is None or data[ticker].get('prices') is None or \
data[ticker].get('timeZone') is None or len(data[ticker]['prices']) == 0:
print(f'Yahoo: no data for {ticker}')
return pd.DataFrame({})
prices = {}
for rec in sorted(data[ticker]['prices'], key=lambda r: r['date']):
if rec.get('type') is None:
date = datetime.strptime(rec['formatted_date'], '%Y-%m-%d')
dic_with_prices(prices, ticker, date, rec['open'], rec['high'],
rec['low'], rec['close'], rec['volume'])
frame = pd.DataFrame.from_dict(
prices,
orient='index',
columns=['Open', 'High', 'Low', 'Close', 'Volume'])
save_csv(base_dir, ticker, frame, 'yahoo')
def download_price_data(path, ticker, freq, start_date, end_date,
sort_by_date_ascending):
yf = YahooFinancials(ticker)
price_data = clean_stock_data(
yf.get_historical_stock_data(start_date, end_date,
freq)[ticker]['prices'])
pd_price_data = pd.DataFrame(price_data)
print("The downloaded price data has shape" + str(pd_price_data.shape))
retrieved_at = updated_at = str(datetime.datetime.now())[:10]
if sort_by_date_ascending:
pd_price_data.sort_values('date', ascending=True, inplace=True)
pd_price_data.reset_index(inplace=True, drop=True)
file = path + ticker + '.h5'
pd_price_data.to_hdf(file, key='df', mode='w')
print(str(ticker) + "'s" + " price data is saved.")
pd.Series(ticker).to_hdf(file, key='ticker')
pd.Series(freq).to_hdf(file, key='freq')
pd.Series(pd_price_data['formatted_date'].iloc[0]).to_hdf(file,
key='start_date')
pd.Series(pd_price_data['formatted_date'].iloc[-1]).to_hdf(file,
key='end_date')
pd.Series(sort_by_date_ascending).to_hdf(file,
key='sort_by_date_ascending')
pd.Series(start_date).to_hdf(file, key='start_date')
pd.Series(end_date).to_hdf(file, key='end_date')
pd.Series(retrieved_at).to_hdf(file, key='retrieved_at')
pd.Series(updated_at).to_hdf(file, key='updated_at')
end_date = (datetime.date.today()).strftime('%Y-%m-%d')
print(end_date)
new_tickers = all_tickers
attempt = 0
drop_list = []
while len(new_tickers) != 0 and attempt <= 5:
print("-----------------")
print("attempt number ", attempt)
print("-----------------")
new_tickers = [j for j in new_tickers if j not in drop_list]
for i in range(len(new_tickers)):
try:
new_financial = YahooFinancials(new_tickers[i])
print(
type(
new_financial.get_historical_stock_data(
beg_date, end_date, "daily")))
new_json = new_financial.get_historical_stock_data(
beg_date, end_date, "daily")
new_json_field = new_json[new_tickers[i]]['prices']
new_dataframe = pd.DataFrame(new_json_field)[[
"formatted_date", "adjclose"
]]
new_dataframe.set_index("formatted_date", inplace=True)
first_dataframe = new_dataframe[~new_dataframe.index.duplicated(
keep='first')]
all_dataframe[new_tickers[i]] = first_dataframe["adjclose"]
drop_list.append(new_tickers[i])
except:
print(new_tickers[i], " :failed to fetch data...retrying")
continue
attempt += 1
'''
ticker = 'AAPL'
yahoo_financials = YahooFinancials(ticker)
stock_info={
'marketCap': 0 ,
'price_c':0,
'i_rate':0,
'long_debt':0,
'short_debt':0,
'ROE':0,
'num_shares':0,
}
stock_summary = yahoo_financials.get_stock_summary_data()
stock_info['num_shares']=stock_summary[ticker]['marketCap']/stock_summary[ticker]['previousClose']
stock_info['price_c']=172.23
stock_info['marketCap']=stock_info['price_c']*stock_info['num_shares']
balance_sheet_data_qt = yahoo_financials.get_financial_stmts('quarterly', 'balance')
income_statement_data_qt = yahoo_financials.get_financial_stmts('quarterly', 'income')
all_statement_data_qt = yahoo_financials.get_financial_stmts('quarterly', ['income', 'cash', 'balance'])
earnings_data = yahoo_financials.get_stock_earnings_data()
net_income = yahoo_financials.get_net_income()
historical_stock_prices = yahoo_financials.get_historical_stock_data('2018-01-01', '2018-04-20', 'daily')
all_tickers = ["AAPL", "MSFT", "CSCO", "AMZN", "INTC"]
# extracting stock data (historical close price) for the stocks identified
close_prices = pd.DataFrame()
end_date = (datetime.date.today()).strftime('%Y-%m-%d')
beg_date = (datetime.date.today() -
datetime.timedelta(1825)).strftime('%Y-%m-%d')
cp_tickers = all_tickers
attempt = 0
drop = []
while len(cp_tickers) != 0 and attempt <= 5:
print("-----------------")
print("attempt number ", attempt)
print("-----------------")
cp_tickers = [j for j in cp_tickers if j not in drop]
for i in range(len(cp_tickers)):
try:
yahoo_financials = YahooFinancials(cp_tickers[i])
json_obj = yahoo_financials.get_historical_stock_data(
beg_date, end_date, "daily")
ohlv = json_obj[cp_tickers[i]]['prices']
temp = pd.DataFrame(ohlv)[["formatted_date", "adjclose"]]
temp.set_index("formatted_date", inplace=True)
temp2 = temp[~temp.index.duplicated(keep='first')]
close_prices[cp_tickers[i]] = temp2["adjclose"]
drop.append(cp_tickers[i])
except:
print(cp_tickers[i], " :failed to fetch data...retrying")
continue
attempt += 1
new_list = []
for rec in stock_data_list:
if 'type' not in rec.keys():
new_list.append(rec)
return new_list
# Construct yahoo financials objects for data extraction
aapl_financials = YahooFinancials(ticker)
mfst_financials = YahooFinancials(ticker2)
intl_financials = YahooFinancials(ticker3)
index_financials = YahooFinancials(index)
# Clean returned stock history data and remove dividend events from price history
daily_aapl_data = clean_stock_data(
aapl_financials.get_historical_stock_data(start_date, end_date,
freq)[ticker]['prices'])
daily_msft_data = clean_stock_data(
mfst_financials.get_historical_stock_data(start_date, end_date,
freq)[ticker2]['prices'])
daily_intl_data = clean_stock_data(
intl_financials.get_historical_stock_data(start_date, end_date,
freq)[ticker3]['prices'])
daily_index_data = index_financials.get_historical_stock_data(
start_date, end_date, freq)[index]['prices']
stock_hist_data_list = [{
'NDX': daily_index_data
}, {
'AAPL': daily_aapl_data
}, {
'MSFT': daily_msft_data
}, {
from yahoofinancials import YahooFinancials
import yahoofinancials as yf
yahoo_financials = YahooFinancials('AAPL')
print(yahoo_financials)
#
# tech_stocks = ['AAPL', 'MSFT', 'INTC']
# yahoo_financials_tech = YahooFinancials(tech_stocks)
# tech_cash_flow_data_an = yahoo_financials_tech.get_financial_stmts('annual', 'cash')
# for s in tech_cash_flow_data_an["cashflowStatementHistory"]:
# for date in tech_cash_flow_data_an["cashflowStatementHistory"][s][0]:
# print(s, date, tech_cash_flow_data_an["cashflowStatementHistory"][s][0][date])
yahoo_financials.get_historical_stock_data("2017-09-10", "2017-10-10",
"monthly")
new_list = []
for rec in stock_data_list:
if 'type' not in rec.keys():
new_list.append(rec)
return new_list
# Construct yahoo financials objects for data extraction
aapl_financials = YahooFinancials(ticker)
mfst_financials = YahooFinancials(ticker2)
intl_financials = YahooFinancials(ticker3)
index_financials = YahooFinancials(index)
# Clean returned stock history data and remove dividend events from price history
daily_aapl_data = clean_stock_data(
aapl_financials.get_historical_stock_data(start_date, end_date,
freq)[ticker]['prices'])
daily_msft_data = clean_stock_data(
mfst_financials.get_historical_stock_data(start_date, end_date,
freq)[ticker2]['prices'])
daily_intl_data = clean_stock_data(
intl_financials.get_historical_stock_data(start_date, end_date,
freq)[ticker3]['prices'])
daily_index_data = index_financials.get_historical_stock_data(
start_date, end_date, freq)[index]['prices']
stock_hist_data_list = [{
'NDX': daily_index_data
}, {
'AAPL': daily_aapl_data
}, {
'MSFT': daily_msft_data
}, {
#extracts data through webscraping and not an API
from yahoofinancials import YahooFinancials #https://github.com/JECSand/yahoofinancials
ticker = 'AMZN'
yahoo_financials = YahooFinancials(ticker) #new object
historic_stock_prices = yahoo_financials.get_historical_stock_data(
'2018-01-26', '2019-01-26', 'daily')
print(historic_stock_prices)