if len(df) > 0 and 'ticker' in df.columns:
proc_all_tickers = list(df['ticker'].unique()) + all_tickers
proc_all_tickers = set(proc_all_tickers)
print('Processing: %s' % len(proc_all_tickers))
iticker = 0
for ticker in proc_all_tickers:
if debug: print(ticker, iticker)
iticker += 1
sys.stdout.flush()
# if really high up on the list, then reprocess
# if not loaded, then let's compute stuff
if len(df_store_data[(df_store_data['ticker'] == ticker)]) == 0:
# checking if it is shortable and tradeable:
try:
aapl_asset = api.get_asset(ticker)
#pass
#print(aapl_asset) # this is info about it being tradeable
#print(aapl_asset.shortable)
except (alpaca_trade_api.rest.APIError,
requests.exceptions.HTTPError, ValueError,
urllib3.exceptions.ProtocolError, ConnectionResetError,
urllib3.exceptions.ProtocolError, ConnectionResetError,
requests.exceptions.ConnectionError,
requests.exceptions.ReadTimeout) as e:
print("Testing multiple exceptions for alpaca api. {}".
format(e.args[-1]))
continue
hour_prices_thirty = []
minute_prices_thirty = []
today = datetime.datetime.now(tz=est)
my_days = []
for i in range(0, 10):
new_date = today + datetime.timedelta(-1 * i)
if new_date.weekday() < 5:
my_days += [new_date]
st.write('The current ticker is %s and number of days checked: %i' %
(today, len(my_days)))
#st.table(pd.DataFrame(list(api.get_asset(title)._raw.items())))
doRelativeToSpyAll = st.checkbox('Show relative to SPY', key='relTitleSpy')
doEarnings = st.checkbox('Show earnings', key='do earnings')
if title != 'Select':
# Print a table of stock information on Alpaca
st.json(api.get_asset(title)._raw)
# Collect figures
fig_table, figs_minute_price = generateFigure(api, title)
# Plot!
for fig_minute_price in figs_minute_price:
st.plotly_chart(fig_minute_price)
mean_figs = generateMeanRevFigure(api, sqlcursor, ts, title,
doRelativeToSpyAll)
st.markdown('Number of entries: %s' % len(mean_figs))
for mean_fig in mean_figs:
st.plotly_chart(mean_fig)
if st.button("Show table"):