def sectors():
params = [
{
'q': 'XLF',
'x': 'NYSEARCA',
},
{
'q': '.INX',
'x': 'INDEXSP'
}
]
comparables = get_prices_time_data(params, period="10Y", interval="86400")
index = ['XLF', '.INX']
for name in index:
comparables.drop(labels=[
"{}_Open".format(name),
"{}_High".format(name),
"{}_Low".format(name),
"{}_Volume".format(name),
],
axis=1, inplace=True
)
comparables.to_csv('Sectors.csv')
def goog_pull(request_again):
if request_again == True:
tickers = update_ticks()
else:
with open('SP500quotes.pickle', 'rb') as f:
tickers = pickle.load(f)
if not os.path.exists('stock_dfs'):
os.makedirs('stock_dfs')
else:
print('Directory Exists')
period = "5Y"
interval = "86400"
for stock in tickers:
if not exists('SP500/{}.csv'.format(stock)):
try:
param = [
{
'q': stock,
'x': "INDEXSP",
},
]
ticker = get_prices_time_data(param, period=period, interval=interval)
drop_labs = [
'{}_Open'.format(stock),
'{}_High'.format(stock),
'{}_Low'.format(stock),
'{}_Volume'.format(stock),
]
ticker.drop(drop_labs, axis=1, inplace=True)
ticker.rename(columns={'{}_Close'.format(stock):stock}, inplace=True)
ticker.to_csv('stock_dfs/{}.csv'.format(stock))
print("Adding {} to stock_dfs directory".format(stock))
except:
print("Unable to read URL for: {}".format(stock))
def example_3(self):
params = [
# Dow Jones
{
'q': ".DJI",
'x': "INDEXDJX",
},
# NYSE COMPOSITE (DJ)
{
'q': "NYA",
'x': "INDEXNYSEGIS",
},
# S&P 500
{
'q': ".INX",
'x': "INDEXSP",
}
]
period = "1Y"
interval = 60 * 30 # 30 minutes
# get open, high, low, close, volume time data (return pandas dataframe)
df = get_prices_time_data(params, period, interval)
print(df)
# ... ... ... ... ... ...
params = [
# Dow Jones
{
'q': ".DJI",
'x': "INDEXDJX",
},
# NYSE COMPOSITE (DJ)
{
'q': "NYA",
'x': "INDEXNYSEGIS",
},
# S&P 500
{
'q': ".INX",
'x': "INDEXSP",
}
]
period = "1Y"
interval = 60 * 30 # 30 minutes
# get open, high, low, close, volume time data (return pandas dataframe)
df = get_prices_time_data(params, period, interval)
print(df)
# .DJI_Open .DJI_High .DJI_Low .DJI_Close .DJI_Volume \
# 2016-07-19 23:00:00 18503.12 18542.13 18495.11 18522.47 0
# 2016-07-19 23:30:00 18522.44 18553.30 18509.25 18546.27 0
# 2016-07-20 00:00:00 18546.20 18549.59 18519.77 18539.93 0
# 2016-07-20 00:30:00 18540.24 18549.80 18526.99 18534.18 0
# 2016-07-20 01:00:00 18534.05 18540.38 18507.34 18516.41 0
# ... ... ... ... ... ...