'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)
growth_estimate = re.findall('-?[0-9]+.[0-9]', estimate)[0]
date_of_estimate_str = re.findall('—.*', estimate)[0]
date_of_estimate_str = date_of_estimate_str.replace('—','')
# Check if this estimate is newer than last available estimate
# If so, post on Twitter and update CSV
date_of_estimate = datetime.strptime(date_of_estimate_str, '%B %d, %Y')
if lastAvailableDate < date_of_estimate:
tweet = f"US GDP Economy Now - {date_of_estimate_str}\n\
Next qtr: {growth_estimate}%"
notification(tweet)
if os.path.isfile('economynow.csv'):
with open('economynow.csv', 'a') as f:
writer = csv.writer(f)
writer.writerow([date_of_estimate, growth_estimate])
else:
with open('economynow.csv', 'w', newline='') as f:
writer = csv.writer(f)
writer.writerow(["date","growth_estimate"])
writer.writerow([date_of_estimate,growth_estimate])
# Update save file with latest estimate date
savePickle(saveFile, date_of_estimate)
else:
print('Not updated, no new data, last available data is ' + lastAvailableDate.strftime('%Y-%m-%d %H:%M:%S'))
tweet = f"Smart/Dumb Money Confidence - {datetime.strftime(max(df['date']), '%Y-%m-%d')}\n\
Smart: {thisMth['smart']}\n\n\
1 mth ago: {lastMth['smart']} (MoM: {(float(lastMth['smart']) - float(thisMth['smart'])) / float(thisMth['smart']) * 100:.1f}%)\n\
1 qtr ago: {lastQtr['smart']} (QoQ: {(float(lastQtr['smart']) - float(thisMth['smart'])) / float(thisMth['smart']) * 100:.1f}%)\n\
1 yr ago: {lastYr['smart']} (YoY: {(float(lastYr['smart']) - float(thisMth['smart'])) / float(thisMth['smart']) * 100:.1f}%)\n\n\
Dumb: {thisMth['dumb']}\n\n\
1 mth ago: {lastMth['dumb']} (MoM: {(float(lastMth['dumb']) - float(thisMth['dumb'])) / float(thisMth['dumb']) * 100:.1f}%)\n\
1 qtr ago: {lastQtr['dumb']} (QoQ: {(float(lastQtr['dumb']) - float(thisMth['dumb'])) / float(thisMth['dumb']) * 100:.1f}%)\n\
1 yr ago: {lastYr['dumb']} (YoY: {(float(lastYr['dumb']) - float(thisMth['dumb'])) / float(thisMth['dumb']) * 100:.1f}%)"
notification(tweet)
if os.path.isfile('smart_money_confidence.csv'):
with open('smart_money_confidence.csv', 'w') as f:
writer = csv.writer(f)
writer.writerow(thisMth)
else:
with open('smart_money_confidence.csv', 'w', newline='') as f:
writer = csv.writer(f)
writer.writerow(['date', 'smart', 'spy', 'dumb'])
writer.writerows(df)
else:
print('Not updated, no new data, last available data is ' +
lastAvailableDate.strftime('%Y-%m-%d %H:%M:%S'))
savePickle(saveFile, max(df['date']))
print(df)
#print(date_of_sentiments) For debugging
#print(sentiments)
if lastAvailableDate < max(date_of_sentiments):
tweet = f"NDR Crowd Sentiment - {max(date_of_sentiments).strftime('%Y-%m-%d')}\n\
5-day MA: {sentimentAvg:.3f}\n\n"
# Add data from each day to twee
max_length = max(len(date_of_sentiments),len(sentiments))
for i in range(max_length):
tweet += f"{date_of_sentiments[i]}: {sentiments[i]:.3f}\n"
notification(tweet)
with open('ndr_crowd_sentiment.csv','w') as f:
writer = csv.writer(f)
if os.path.isfile('ndr_crowd_sentiment.csv'):
writer.writerow(['date', 'sentiment'])
for i in range(max_length-1, -1, -1):
writer.writerow([date_of_sentiments[i], sentiments[i]])
savePickle(saveFile, max(date_of_sentiments))
else:
print('Not updated, no new data, last available data is ' + lastAvailableDate.strftime('%Y-%m-%d %H:%M:%S'))
1 yr ago: {lastYearData[1]:.2f} (YoY: {(float(newestData[1]) - float(lastYearData[1])) / float(lastYearData[1]) * 100:.1f}%)'
notification(tweet)
# Write CSV file
if os.path.exists('cass_intermodal_price_index.csv'):
df = pd.read_csv('cass_intermodal_price_index.csv')
for month in responseJson['dataset'][
'data']: # if there is a datapoint ("month") that is not in csv, add it
if month[0] not in df.index:
df.loc[month[0]] = [month[1]]
df.to_csv('cass_intermodal_price_index.csv')
else:
# add all datapoints in json to csv
index = []
value = []
for month in responseJson['dataset']['data']:
index.append(month[0])
value.append(month[1])
df = pd.DataFrame(index=index, columns=['value'])
df['value'] = value
df.to_csv('cass_intermodal_price_index.csv')
else:
print('Not updated, no new data, last available data is ' +
lastAvailableDate.strftime('%Y-%m-%d %H:%M:%S'))
savePickle(saveFile, newestAvailableDate)
