def dataloader(stock_name,
start='1995-01-01 01:00:00',
end='2018-12-31 04:00:00',
verbose=True):
my_share = share.Share(stock_name)
symbol_data = None
try:
symbol_data = my_share.get_historical(share.PERIOD_TYPE_YEAR, 30,
share.FREQUENCY_TYPE_DAY, 1)
if verbose:
print(stock_name + " has been downloaded")
except YahooFinanceError as e:
print(e.message)
sys.exit(1)
df = pd.DataFrame(symbol_data)
df['timestamp'] = [
datetime.fromtimestamp(t / 1000) for t in df['timestamp']
]
# test = df[(df['timestamp'] >= '2016-1-1 01:00:00') & (df['timestamp'] <= '2018-12-31 04:00:00')]
return df[(df['timestamp'] >= start) & (df['timestamp'] <= end)]
def gethourlydata(name):
my_share = share.Share(name)
symbol_data = None
try:
symbol_data = my_share.get_historical(share.PERIOD_TYPE_DAY, 30,
share.FREQUENCY_TYPE_MINUTE, 60)
except YahooFinanceError as e:
print(e.message)
sys.exit(1)
goodhoursspot = []
for i in range(len(symbol_data['timestamp'])):
strdata = str(
datetime.fromtimestamp(symbol_data['timestamp'][i] / 1000))
checker = strdata[14]
if checker == '3':
goodhoursspot.append(i)
# print(goodhoursspot)
# print(symbol_data['open'][5])
goodopenprices = []
goodhighprices = []
goodlowprices = []
goodcloseprices = []
goodhours = []
goodvolumes = []
for i in goodhoursspot:
# print(str(datetime.fromtimestamp(symbol_data['timestamp'][i] / 1000)))
goodopenprices.append(symbol_data['open'][i])
goodhighprices.append(symbol_data['high'][i])
goodlowprices.append(symbol_data['low'][i])
goodcloseprices.append(symbol_data['close'][i])
goodhours.append(symbol_data['timestamp'][i])
goodvolumes.append(symbol_data['volume'][i])
# print(goodopenprices, goodhighprices, goodlowprices, goodcloseprices)
return goodopenprices, goodhighprices, goodlowprices, goodcloseprices, goodhours, name, goodvolumes
def __init__(self, ticker, adj=True):
self.ticker = ticker
self.closing_price = []
_data = None
_share = share.Share(self.ticker)
_data = _share.get_historical(share.PERIOD_TYPE_YEAR, 2,
share.FREQUENCY_TYPE_MONTH, 1, adj)
if not _data:
raise ValueError("No data on stock {}".format(ticker))
for i in range(0, len(_data["timestamp"])):
date = datetime.fromtimestamp(int(_data["timestamp"][i]) / 1000)
if date >= datetime(2018, 6, 1) and date <= datetime(2019, 6, 2):
self.closing_price.append(_data["close"][i])
self.returns = []
for i in range(1, len(self.closing_price)):
if not self.closing_price[i] or not self.closing_price[i - 1]:
self.returns.append(0)
else:
self.returns.append(self.closing_price[i] /
self.closing_price[i - 1] - 1)
self.average = np.mean(self.get_returns())
self.stddev = np.std(self.get_returns(), ddof=1) * np.sqrt(12)
self.var = self.stddev**2
def __getLastPrices(share_name, days=1):
"""
This function returns last values of a share
Parameters
----------
share_name: str
Share name in yahoo format
Returns
-------
A pandas with last share values and TA
"""
my_share = share.Share(share_name)
symbol_data = None
try:
symbol_data = my_share.get_historical(share.PERIOD_TYPE_DAY, days,
share.FREQUENCY_TYPE_DAY, 1)
except YahooFinanceError as e:
print(e.message)
finally:
if symbol_data is None:
return None
else:
df = pd.DataFrame(symbol_data)
return df
def Obtain_price(symbol):
my_share = share.Share(symbol)
symbol_data = None
try:
symbol_data = my_share.get_historical(share.PERIOD_TYPE_DAY,
50,
share.FREQUENCY_TYPE_HOUR,
1)
except YahooFinanceError as e:
print(e.message)
sys.exit(1)
openrate = symbol_data['open']
closerate = symbol_data['close']
highest = symbol_data['high']
lowest = symbol_data['low']
# plt.scatter(newTime, y, label= "dots", color= "green", marker= ".", s=30)
# plt.xlabel('Epoch Time')
# plt.ylabel('Open Rate')
# plt.title("Stock Price")
# plt.legend()
# plt.show()
# return [datelist[len(datelist)-1], y[len(y)-1]]
return openrate[len(openrate)-1], closerate[len(closerate)-1], highest[len(highest)-1], lowest[len(lowest)-1]
def getStockData(Co_info):
stockData = dict()
errorIdx = []
for i in range(len(Co_info)):
#企業指定
my_share = share.Share(Co_info[i][0] + '.T')
tmpData = None
tmpDf = None
try:
#取得期間、周期
tmpData = my_share.get_historical(share.PERIOD_TYPE_MONTH, 120,
share.FREQUENCY_TYPE_WEEK, 1)
tmpData["timestamp"] = [
datetime.fromtimestamp(i / 1000) for i in tmpData["timestamp"]
]
tmpDf = pd.DataFrame(data=tmpData)
tmpDf["code"] = Co_info[i][0]
stockData[Co_info[i][0]] = tmpDf
print(
datetime.now(pytz.timezone('Asia/Tokyo')).strftime('%T'), i,
Co_info[i][0], Co_info[i][1])
time.sleep(5)
except Exception as e:
print(
datetime.now(pytz.timezone('Asia/Tokyo')).strftime('%T'), i, e)
errorIdx.append(i)
time.sleep(10)
return stockData, errorIdx
def get5minData(symbol,period,outname):
my_share = share.Share(symbol)
symbol_data = None
try:
symbol_data = my_share.get_historical(share.PERIOD_TYPE_DAY,
period,
share.FREQUENCY_TYPE_MINUTE,
5)
except YahooFinanceError as e:
print(e.message)
sys.exit(1)
# %%
datalist=[]
dates = list(symbol_data["timestamp"])
opens = list(symbol_data["open"])
highs = list(symbol_data["high"])
lows = list(symbol_data["low"])
closes = list(symbol_data["close"])
volumes = list(symbol_data["volume"])
for i in range(len(dates)):
d = [dates[i],spfloat(opens,i),spfloat(highs,i),spfloat(lows,i),spfloat(closes,i),spfloat(volumes,i)]
d[0]=datetime.datetime.fromtimestamp(d[0]/1000)
datalist.append(d)
final_df = pd.DataFrame(datalist, columns=['date','open','high','low','close','volume'])
final_df.set_index('date',inplace = True)
# %%
final_df.to_csv(outname+'.csv')
def Obtain_price(company_symbol):
my_share = share.Share(company_symbol)
symbol_data = None
try:
symbol_data = my_share.get_historical(share.PERIOD_TYPE_DAY, 50,
share.FREQUENCY_TYPE_HOUR, 1)
except YahooFinanceError as e:
print(e.message)
sys.exit(1)
x = symbol_data['timestamp']
newTime = [z / 1000 for z in x]
datelist = []
for x in newTime:
datelist.append(str(time.strftime('%d-%m-%Y', time.localtime(x))))
del symbol_data['volume']
symbol_data['timestamp'] = newTime
save_output(symbol_data, "{}.csv".format(company_symbol))
openrate = symbol_data['open']
closerate = symbol_data['close']
highest = symbol_data['high']
lowest = symbol_data['low']
lastest_open_rate = openrate[len(openrate) - 1]
latest_close_rate = closerate[len(closerate) - 1]
highest_price_for_today = highest[len(highest) - 1]
lowest_price_for_today = lowest[len(lowest) - 1]
return lastest_open_rate, latest_close_rate, highest_price_for_today, lowest_price_for_today
def get_single_stock_yahoo(ticker):
stock = share.Share(ticker)
# result_lst = stock.get_historical('2016-01-01', '2021-03-10')
# ticker_arr, date_arr, open_arr, high_arr, low_arr, close_arr, adj_arr, volume_arr = [], [], [], [], [], [], [], []
# for result in result_lst:
# date_arr.append(result['Date'])
# open_arr.append(result['Open'])
# high_arr.append(result['High'])
# low_arr.append(result['Low'])
# close_arr.append(result['Close'])
# adj_arr.append(result['Adj_Close'])
# volume_arr.append(result['Volume'])
# ticker_arr.append(ticker)
# return pd.DataFrame.from_dict({"Ticker": ticker_arr, "Date": date_arr, "Open": open_arr, "High": high_arr, "Low": low_arr, "Close": close_arr, "Adj.Close": adj_arr, "Volume": volume_arr})
url = f"https://query1.finance.yahoo.com/v8/finance/chart/{ticker}?symbol={ticker}&period1=1457568000&period2=1615334400&interval=1d&includePrePost=true&events=div%7Csplit%7Cearn&lang=en-US®ion=US&crumb=t5QZMhgytYZ&corsDomain=finance.yahoo.com"
# data = stock.get_historical(share.PERIOD_TYPE_DAY, 1250, share.FREQUENCY_TYPE_DAY, 1)
data = requests.get(url).json()
data = data["chart"]["result"][0]
date_arr = [
datetime.fromtimestamp(x).strftime("%Y-%m-%d")
for x in data["timestamp"]
]
data = data["indicators"]["quote"][0]
data["date"] = date_arr
data["ticker"] = [ticker] * len(date_arr)
data.pop("timestamp", None)
return pd.DataFrame.from_dict(data)
def lineear_regression(symbol):
my_share = share.Share(symbol)
symbol_data = None
try:
symbol_data = my_share.get_historical(share.PERIOD_TYPE_YEAR,
15,
share.FREQUENCY_TYPE_DAY,
1)
except YahooFinanceError as e:
print(e.message)
sys.exit(1)
x = symbol_data['timestamp']
newTime = [z / 1000 for z in x]
datelist = []
for x in newTime:
datelist.append(str(time.strftime('%Y-%m-%d', time.localtime(x))))
tempopenrate = symbol_data['open']
openrate = [round(x,2) for x in tempopenrate]
tempcloserate = symbol_data['close']
closerate = [round(x,4) for x in tempopenrate]
volume = symbol_data['volume']
low = symbol_data['low']
high = symbol_data['high']
return datelist,openrate, closerate, low, high
def get_price(symbol):
my_share = share.Share(symbol)
symbol_data = None
try:
symbol_data = my_share.get_historical(share.PERIOD_TYPE_DAY,
50,
share.FREQUENCY_TYPE_HOUR,
1)
except YahooFinanceError as e:
print(e.message)
sys.exit(1)
x = symbol_data['timestamp']
newTime = [z / 1000 for z in x]
datelist = []
for x in newTime:
datelist.append(str(time.strftime('%d-%m-%Y', time.localtime(x))))
tempopenrate = symbol_data['open']
openrate = [round(x,2) for x in tempopenrate]
# plt.scatter(newTime, y, label= "dots", color= "green", marker= ".", s=30)
# plt.xlabel('Epoch Time')
# plt.ylabel('Open Rate')
# plt.title("Stock Price")
# plt.legend()
# plt.show()
return datelist, openrate
def main(code, company):
code = str(code)
my_share = share.Share(code + '.T')
symbol_data = None
try:
symbol_data = my_share.get_historical(share.PERIOD_TYPE_YEAR, 2,
share.FREQUENCY_TYPE_DAY, 1)
df = pd.DataFrame(symbol_data.values(), index=symbol_data.keys()).T
df.timestamp = pd.to_datetime(df.timestamp, unit='ms')
df.index = pd.DatetimeIndex(df.timestamp, name='timestamp')
except YahooFinanceError as e:
print(e.message)
sys.exit(1)
after_covid19 = df[(df.index >= dt.datetime(2020, 11, 1)) & (df.index <= dt.datetime(2021, 4, 30))]
after_covid19 = round(after_covid19.close.mean())
before_covid19 = df[(df.index >= dt.datetime(2019, 6, 1)) & (df.index <= dt.datetime(2019, 12, 31))]
before_covid19 = round(before_covid19.close.mean())
ratio = round(after_covid19 / before_covid19, 2)
print("code: {}. company: {}".format(code, company))
print(before_covid19, after_covid19, ratio)
stock_df.loc[i] = [code, company, before_covid19, after_covid19, ratio]
def get_data():
global old_time, now_time, old_price, now_price, change_rate, data_volume, corona_date, corona_price, corona_time, corona_rate
my_share = share.Share(row_str + '.T')
symbol_data = None
try:
symbol_data = my_share.get_historical(share.PERIOD_TYPE_YEAR, 20,
share.FREQUENCY_TYPE_MONTH, 1)
#time.sleep(1)
date = symbol_data["timestamp"]
old_date = date[0]
now_date = date[-1]
corona_date = date[-6]
old_time = datetime.utcfromtimestamp(old_date / 1000)
now_time = datetime.utcfromtimestamp(now_date / 1000)
corona_time = datetime.utcfromtimestamp(corona_date / 1000)
price = symbol_data["close"]
old_price = price[0]
now_price = price[-1]
corona_price = price[-6]
change_rate = '{:.2f}'.format(now_price / old_price)
corona_rate = '{:.2f}'.format(corona_price / old_price)
data_volume = len(date)
except YahooFinanceError as e:
print(e.message)
sys.exit(1)
def handle_one_stock(ticker, index):
my_share = share.Share(ticker)
symbol_data = None
try:
symbol_data_1y = my_share.get_historical(share.PERIOD_TYPE_YEAR, 1,
share.FREQUENCY_TYPE_DAY, 1)
symbol_data_2y = my_share.get_historical(share.PERIOD_TYPE_YEAR, 2,
share.FREQUENCY_TYPE_DAY, 1)
symbol_data_5y = my_share.get_historical(share.PERIOD_TYPE_YEAR, 5,
share.FREQUENCY_TYPE_DAY, 1)
### Convert from ms to min ###
time_1y = pd.Series(symbol_data_1y['timestamp'])
time_2y = pd.Series(symbol_data_2y['timestamp'])
time_5y = pd.Series(symbol_data_5y['timestamp'])
open_1y = pd.Series(symbol_data_1y['open'])
open_1y = open_1y.fillna(method='ffill')
open_1y = open_1y.interpolate(method='nearest')
open_2y = pd.Series(symbol_data_2y['open'])
open_2y = open_2y.fillna(method='ffill')
open_2y = open_2y.interpolate(method='nearest')
open_5y = pd.Series(symbol_data_5y['open'])
open_5y = open_5y.fillna(method='ffill')
open_5y = open_5y.interpolate(method='nearest')
close_1y = pd.Series(symbol_data_1y['close'])
close_1y = open_1y.fillna(method='ffill')
close_1y = open_1y.interpolate(method='nearest')
close_2y = pd.Series(symbol_data_2y['close'])
close_2y = open_2y.fillna(method='ffill')
close_2y = open_2y.interpolate(method='nearest')
close_5y = pd.Series(symbol_data_5y['close'])
close_5y = open_5y.fillna(method='ffill')
close_5y = open_5y.interpolate(method='nearest')
except YahooFinanceError as e:
print(e.message)
sys.exit(1)
open_1y = np.array(open_1y.tolist())
open_2y = np.array(open_2y.tolist())
open_5y = np.array(open_5y.tolist())
close_1y = np.array(close_1y.tolist())
close_2y = np.array(close_2y.tolist())
close_5y = np.array(close_5y.tolist())
output = {
'time_1y': time_1y,
'time_2y': time_2y,
'time_5y': time_5y,
'open_1y': open_1y,
'open_2y': open_2y,
'open_5y': open_5y,
'close_1y': close_1y,
'close_2y': close_2y,
'close_5y': close_5y
}
return output
def get_share_info_list(info_list, period_day, frequency_day):
share_list = []
symbol_data_list = []
for code in info_list:
share_list.append(share.Share(str(code) + '.T'))
for share_info in share_list:
symbol_data_list.append(
get_share_info(share_info, period_day, frequency_day))
return symbol_data_list
def download_symbol_data(selected_symbol, selected_period, selected_freqtype,
period_number, selected_freqnum):
my_share = share.Share(selected_symbol)
if selected_period == 'day':
period_type = share.PERIOD_TYPE_DAY
elif selected_period == 'week':
period_type = share.PERIOD_TYPE_WEEK
elif selected_period == 'month':
period_type = share.PERIOD_TYPE_MONTH
elif selected_period == 'year':
period_type = share.PERIOD_TYPE_YEAR
if selected_freqtype == 'min':
freqtype = share.FREQUENCY_TYPE_MINUTE
elif selected_freqtype == 'hour':
freqtype = share.FREQUENCY_TYPE_HOUR
elif selected_freqtype == 'day':
freqtype = share.FREQUENCY_TYPE_DAY
elif selected_freqtype == 'week':
freqtype = share.FREQUENCY_TYPE_WEEK
elif selected_freqtype == 'month':
freqtype = share.FREQUENCY_TYPE_MONTH
print(freqtype)
if selected_freqnum == '1m':
freqnum = 1
elif selected_freqnum == '2m':
freqnum = 3
elif selected_freqnum == '5m':
freqnum = 5
elif selected_freqnum == '15m':
freqnum = 15
elif selected_freqnum == '30m':
freqnum = 30
elif selected_freqnum == '60m':
freqnum = 60
elif selected_freqnum == '90m':
freqnum = 90
elif selected_freqnum == '1h':
freqnum = 1
elif selected_freqnum == '1d':
freqnum = 1
elif selected_freqnum == '5d':
freqnum = 5
elif selected_freqnum == '1w':
freqnum = 1
elif selected_freqnum == '1mo':
freqnum = 1
elif selected_freqnum == '3mo':
freqnum = 3
result = None
result = my_share.get_historical(period_type, int(period_number), freqtype,
freqnum)
return result
def company_stock(period_type, period, company_code):
my_share = share.Share(company_code + '.T')
symbol_data = None
try:
symbol_data = my_share.get_historical(share.PERIOD_TYPE_DAY,
period_type,
share.FREQUENCY_TYPE_MINUTE,
period)
except YahooFinanceError as e:
logger.error(e.message)
sys.exit(1)
from datetime import datetime, timezone, timedelta
date = symbol_data["timestamp"]
old_datetime = datetime.fromtimestamp(date[0]/1000)
now_datetime = datetime.fromtimestamp(date[len(date) - 1]/1000)
old_date = old_datetime.strftime('%Y-%m-%d')
now_date = now_datetime.strftime('%Y-%m-%d')
old_time = old_datetime.strftime('%H:%M:%S')
now_time = now_datetime.strftime('%H:%M:%S')
price = symbol_data["close"]
old_price = price[0]
now_price = price[len(date) - 1]
logger.info(str(old_time) + "の時の株価： " + str(old_price))
logger.info(str(now_time) + "の時の株価： " + str(now_price))
body = [
[old_date, old_time, str(old_price)],
[now_date, now_time, str(now_price)]
]
df = pd.DataFrame(symbol_data.values(), index=symbol_data.keys()).T
df.timestamp = pd.to_datetime(df.timestamp, unit='ms')
df.index = pd.DatetimeIndex(df.timestamp, name='timestamp').tz_localize('UTC').tz_convert('Asia/Tokyo')
plt.figure(figsize=(10 ,5))
plt.title(company_code, color='black', size=15, loc='center') # title(タイトル, 線の色, 背景色, フォントサイズ, タイトル位置)
plt.plot(df.index, price, label='close', color='blue')
# plt.show()
return body
# plt.figure(figsize=(10 ,5))
# import time
# company_stock(2, 1, '9201.T')
def getdailydata(name):
stockname = name
my_share = share.Share(stockname)
symbol_data = None
try:
symbol_data = my_share.get_historical(share.PERIOD_TYPE_MONTH,3,share.FREQUENCY_TYPE_DAY,1)
except YahooFinanceError as e:
print(e.message)
return('bad')
#print(len(symbol_data['open']))
return('cool')
def getdailydata(name):
my_share = share.Share(name)
symbol_data = None
try:
symbol_data = my_share.get_historical(share.PERIOD_TYPE_MONTH, 12, share.FREQUENCY_TYPE_DAY, 1)
except YahooFinanceError as e:
print(e.message)
return 'bad'
# print(len(symbol_data['open']))
return (symbol_data['open'], symbol_data['high'], symbol_data['low'],
symbol_data['close'], symbol_data['timestamp'], name, symbol_data['volume'])
def get_historical(self, stock_name):
my_share = share.Share(stock_name)
symbol_data = None
try:
symbol_data = my_share.get_historical(share.PERIOD_TYPE_DAY, 0.01,
share.FREQUENCY_TYPE_MINUTE,
1)
except YahooFinanceError as e:
print(e.message)
sys.exit(1)
return pd.DataFrame.from_dict(symbol_data)
def open_file(stock):
# stock_data = []
stock_share = share.Share(stock)
symbol_data = None
try:
symbol_data = stock_share.get_historical(share.PERIOD_TYPE_YEAR, 500,
share.FREQUENCY_TYPE_DAY, 1)
except YahooFinanceError as e:
print(e.message)
sys.exit(1)
return transform_symbol_data(symbol_data)
def get_data(self,ticker):
self.ticker = ticker
if isinstance(self.ticker,str):
print('took_str_option')
self.my_share = share.Share(self.ticker)
self.data = self.my_share.get_historical(share.PERIOD_TYPE_DAY,
100,
share.FREQUENCY_TYPE_MINUTE,
30)
self.result = list(zip(self.data['timestamp'],self.data['open']))
self.result = [(dt.datetime.fromtimestamp(x[0]/1000),x[1]) for x in self.result]
return self.result
elif isinstance(self.ticker,list):
print('took_list_option')
self.my_share = share.Share(self.ticker)
self.data = self.my_share.get_historical(share.PERIOD_TYPE_DAY,
1000,
share.FREQUENCY_TYPE_MINUTE,
50)
self.result = list(zip(self.data['timestamp'],self.data['open']))
self.result = [(dt.datetime.fromtimestamp(x[0]/1000),x[1]) for x in self.result]
return self.result
def getData(dayLen=7):
stock = share.Share("TSLA")
weekData = dict(
stock.get_historical(share.PERIOD_TYPE_DAY, dayLen,
share.FREQUENCY_TYPE_DAY, 1))
for x in range(len(weekData["timestamp"])):
timestamp = int(str(int(int((weekData["timestamp"][x])) / 1000)))
dt_object = datetime.fromtimestamp(timestamp)
dt_object = str(dt_object)
dt_object = dt_object[:10]
someDict = weekData
weekData["timestamp"][x] = dt_object
return dict(weekData)
def main(args):
# default grab the last 75 days
import datetime
if args.csv:
import pandas as pd
data = pd.read_csv(args.csv)
else:
today = datetime.date.today()
ticker = share.Share(args.ticker)
data = ticker.get_historical(share.PERIOD_TYPE_DAY,args.start,share.FREQUENCY_TYPE_MINUTE,int(60/args.frequency))
torch.save({
'inputs':get_inputs(data),
'targets':get_targets(data)
},args.output_file)
def fetch_yahoo():
my_share = share.Share('^HSI')
symbol_data = None
try:
symbol_data = my_share.get_historical(share.PERIOD_TYPE_DAY, 0,
share.FREQUENCY_TYPE_DAY, 5)
except YahooFinanceError as e:
print(e.message)
sys.exit(1)
new_x = {'Open': [], 'High': [], 'Low': []}
new_x['Open'].append(symbol_data.get('open')[0])
new_x['High'].append(symbol_data.get('high')[0])
new_x['Low'].append(symbol_data.get('low')[0])
return new_x
def get_ticker_data_1(ticker):
my_share = share.Share(ticker)
try:
data = my_share.get_historical(share.PERIOD_TYPE_DAY,
100,
share.FREQUENCY_TYPE_MINUTE,
30)
if data is not None:
res = list(zip(data['timestamp'],data['open'],data['high'],data['low'],data['close']))
res = [(dt.datetime.fromtimestamp(x[0]/1000),x[1]) for x in res]
df = pd.DataFrame(res,columns=['time','open','high','low','close'])
return df
else:
print('No Data from YahooFinance')
return None
except exceptions.YahooFinanceError:
pass
def getdailydata(name):
my_share = share.Share(name)
symbol_data = None
try:
symbol_data = my_share.get_historical(share.PERIOD_TYPE_MONTH, 10, share.FREQUENCY_TYPE_DAY, 1)
except YahooFinanceError as e:
print(e.message)
return 'bad'
# print(len(symbol_data['open']))
try:
if not symbol_data['open']:
return 'bad'
return (symbol_data['open'], symbol_data['high'], symbol_data['low'],
symbol_data['close'], symbol_data['timestamp'], name, symbol_data['volume'])
except TypeError:
print(" non subscriptable: " + str(name), end=' ')
return 'bad'
def getfourhourdata(name):
my_share = share.Share(name)
symbol_data = None
try:
symbol_data = my_share.get_historical(share.PERIOD_TYPE_DAY, 30,
share.FREQUENCY_TYPE_MINUTE, 60)
except YahooFinanceError as e:
print(e.message)
return 'bad'
goodhoursspot = []
for i in range(len(symbol_data['timestamp'])):
strdata = str(
datetime.fromtimestamp(symbol_data['timestamp'][i] / 1000))
checker = strdata[14]
if checker == '3':
goodhoursspot.append(i)
# print(goodhoursspot)
# print(symbol_data['open'][5])
goodopenprices = []
goodhighprices = []
goodlowprices = []
goodcloseprices = []
goodhours = []
goodvolumes = []
keep = 0
for i in goodhoursspot:
if keep == 7:
keep = 0
if keep == 0 or keep == 4:
if keep == 0:
high = max(symbol_data['high'][i:i + 4])
low = min(symbol_data['low'][i:i + 4])
if keep == 4:
high = max(symbol_data['high'][i:i + 3])
low = min(symbol_data['low'][i:i + 3])
# print(str(datetime.fromtimestamp(symbol_data['timestamp'][i] / 1000)))
goodopenprices.append(symbol_data['open'][i])
goodhighprices.append(high)
goodlowprices.append(low)
goodcloseprices.append(symbol_data['close'][i])
goodhours.append(symbol_data['timestamp'][i])
goodvolumes.append(symbol_data['volume'][i])
keep += 1
# print(goodopenprices, goodhighprices, goodlowprices, goodcloseprices)
return goodopenprices, goodhighprices, goodlowprices, goodcloseprices, goodhours, name, goodvolumes
def get_ticker_data(ticker):
my_share = share.Share(ticker)
try:
data = my_share.get_historical(share.PERIOD_TYPE_DAY, 100,
share.FREQUENCY_TYPE_MINUTE, 30)
if data is not None:
res = list(zip(data['timestamp'], data['open']))
res = [(dt.datetime.fromtimestamp(x[0] / 1000), x[1]) for x in res]
df = pd.DataFrame.from_dict({
'value': [x[1] for x in res],
'time': [x[0] for x in res]
})
return df
else:
print('No Data from YahooFinance')
return None
except exceptions.YahooFinanceError:
pass
def get_stock_data(ticker):
stock = share.Share(ticker)
stock_info = stock.get_historical(share.PERIOD_TYPE_DAY, 1, share.FREQUENCY_TYPE_DAY, 1)
open_value = None
close_value = None
try:
for key, value in stock_info.items():
if key == 'open':
open_value = value[0]
if key == 'close':
close_value = value[0]
current = close_value
change = ((close_value - open_value) / open_value) * 100
current_rounded = math.ceil(current * 100)/100
change_rounded = math.ceil(change * 100)/100
return {'ticker': ticker, 'current': current_rounded, 'change': change_rounded}
except AttributeError:
return {'ticker': ticker, 'current': '-', 'change': '-'}