closing_price_today = contract.history(period="1d",
interval="15m")["Close"]
closing_price_daily = closing_price_daily.append(
closing_price_today[-1:])
#closing_price_daily = closing_price_daily.apply(lambda x: round(x,2))
#print(closing_price_daily[-14:])
rsi = RSI(closing_price_daily, timeperiod=14)
print('%s RSI: %.2f' % (symbol, rsi[-1]))
# if we should trigger an alert and thresholds are met:
if symbol in triggerAlert:
if triggerAlert[symbol] == True and (rsi[-1] <= rsiLow):
triggerAlert[symbol] = False
notification('%s < %d RSI [14,1d] (%.2f)' %
(symbol, rsiLow, rsi[-1]))
elif triggerAlert[symbol] == True and (rsi[-1] >= rsiHigh):
notification('%s > %d RSI [14,1d] (%.2f)' %
(symbol, rsiHigh, rsi[-1]))
elif triggerAlert[symbol] == False and (rsi[-1] > rsiLow) and (
rsi[-1] < rsiHigh):
triggerAlert[symbol] = True
else:
if rsi[-1] <= rsiLow:
triggerAlert[symbol] = False
notification('%s < %d RSI [14,1d] (%.2f)' %
(symbol, rsiLow, rsi[-1]))
elif rsi[-1] >= rsiHigh:
triggerAlert[symbol] = False
notification('%s > %d RSI [14,1d] (%.2f)' %
(symbol, rsiHigh, rsi[-1]))
'DGS3MO'),
'oneWeekAgo':
getValueFromCSV(csv, dates['oneWeekAgo'].strftime('%Y-%m-%d'), 'DATE',
'DGS3MO'),
'yesterday':
getValueFromCSV(csv, dates['yesterday'].strftime('%Y-%m-%d'), 'DATE',
'DGS3MO'),
'today':
getValueFromCSV(csv, dates['today'].strftime('%Y-%m-%d'), 'DATE',
'DGS3MO'),
}
# Send tweet
tweet = f"US 3-Month Treasury Rate - {lastDate.strftime('%Y-%m-%d')}: {values['today']}%\n\
1d: {values['yesterday']}% ({((values['today']-values['yesterday'])/values['yesterday'])*100:.1f}%)\n\
1w: {values['oneWeekAgo']}% ({((values['today']-values['oneWeekAgo'])/values['oneWeekAgo'])*100:.1f}%)\n\
2w: {values['twoWeeksAgo']}% ({((values['today']-values['twoWeeksAgo'])/values['twoWeeksAgo'])*100:.1f}%)\n\
1m: {values['oneMonthAgo']}% ({((values['today']-values['oneMonthAgo'])/values['oneMonthAgo'])*100:.1f}%)\n\
3m: {values['threeMonthsAgo']}% ({((values['today']-values['threeMonthsAgo'])/values['threeMonthsAgo'])*100:.1f}%)\n\
3m: {values['sixMonthsAgo']}% ({((values['today']-values['sixMonthsAgo'])/values['sixMonthsAgo'])*100:.1f}%)\n\
1y: {values['oneYearAgo']}% ({((values['today']-values['oneYearAgo'])/values['oneYearAgo'])*100:.1f}%)\n\
2y: {values['twoYearsAgo']}% ({((values['today']-values['twoYearsAgo'])/values['twoYearsAgo'])*100:.1f}%)"
notification(tweet)
else:
print('Not updated, no new data, last data is ' +
lastAvailableDate.strftime('%Y-%m-%d %H:%M:%S'))
# Update save file
savePickle(saveFile, lastDate)
impliedMovePuts, callVolume, putVolume, putCallRatio
]
#impliedMove.append([expiry,daysToExpiry,callsIV,putsIV,impliedMoveCalls,impliedMovePuts])
print(impliedMove)
# Put/Call Ratio for All Expiries
putCallRatioAllExpiryDates = putVolumeAllExpiryDates / callVolumeAllExpiryDates
print('Call volume: %d' % callVolumeAllExpiryDates)
print('Put volume: %d' % putVolumeAllExpiryDates)
print('Put/Call Ratio: %.4f' % putCallRatioAllExpiryDates)
# Send to Twitter
if putCallRatioAlert[
ticker] == True and putCallRatioAllExpiryDates > putCallRatioHigh:
notification('%s > %.2f PUT/CALL RATIO' % (ticker, putCallRatioHigh))
putCallRatioAlert[ticker] = False
elif putCallRatioAlert[
ticker] == True and putCallRatioAllExpiryDates < putCallRatioLow:
notification('%s < %.2f PUT/CALL RATIO' % (ticker, putCallRatioLow))
putCallRatioAlert[ticker] = False
elif putCallRatioAlert[
ticker] == False and putCallRatioLow < putCallRatioAllExpiryDates and putCallRatioAllExpiryDates > putCallRatioHigh:
putCallRatioAlert[ticker] = True
# Check if implied moves are greater than threshold.
for key, val in impliedMoveThresholds.items():
# Get the row index that is the least amount of days away from '7 days away', '30 days away', etc
# https://stackoverflow.com/questions/30112202/how-do-i-find-the-closest-values-in-a-pandas-series-to-an-input-number
index = impliedMove.iloc[(impliedMove['daysToExpiry'] -
