If the market is crashing and everyone is afraid, then you should buy stocks.
This script downloads and saves the current CNN Fear and Greed Index at user-defined time intervals. At critical values it sends information mails, which warn you of a market overheating or if there is particularly big panic on the markets.
https://money.cnn.com/data/fear-and-greed/
The fear and greed index is a contrarian index of sorts. It is based on the premise that excessive fear can result in stocks trading well below their intrinsic values, and that unbridled greed can result in stocks being bid up far above what they should be worth.
CNN examines seven different factors to establish how much fear and greed there is in the market. They are:
Stock Price Momentum: Measuring the Standard & Poor's 500 Index (S&P 500) versus its 125-day moving average (MA)
Stock Price Strength: Calculating the number of stocks hitting 52-week highs versus those hitting 52-week lows on the New York Stock Exchange (NYSE)
Stock Price Breadth: Analyzing trading volumes in rising stocks against declining stocks
Put and Call Options: How much do put options lag behind call options, signifying greed, or surpass them, indicating fear
Junk Bond Demand: Gauging appetite for higher risk strategies by measuring the spread between yields on investment-grade bonds and junk bonds
Market Volatility: CNN measures the Chicago Board Options Exchange Volatility Index (VIX), concentrating on a 50-day MA
Safe Haven Demand: The difference in returns for stocks versus treasuries
copy attached python script on your raspberry pi or copy this text into a python file
#!/usr/bin/python
import time
import datetime
import numpy as np
from bs4 import BeautifulSoup
import requests
import re
from email.mime.text import MIMEText
from email.mime.application import MIMEApplication
from email.mime.multipart import MIMEMultipart
from email.mime.base import MIMEBase
from email import encoders
from smtplib import SMTP
import smtplib
import sys
#TIME
UnixTime = int(time.time()) # get current unix epoch time
currentdate = datetime.datetime.fromtimestamp(UnixTime).strftime('%Y-%m-%d') # get current date
#Find Fear and Greed Value in Sourcecode
URL = 'https://money.cnn.com/data/fear-and-greed/'
response = requests.get(URL).text
soup = BeautifulSoup(response, 'html.parser')
soup2 = str(soup)
ind1=soup2.find('; Greed Now: ') # Show the position in the string where the first search indicator is located
ind2=soup2.find('; Greed Previous Close:') # Show the position in the string where the second search indicator is located
FnG = soup2[ind1:ind2] # Store only the range between first and second indicator
FnG = re.findall(r'\d+', FnG) # find a digits
FnG = int(FnG[0])
FnG_newrow = ([UnixTime, int(datetime.datetime.fromtimestamp(UnixTime).strftime('%Y')), int(datetime.datetime.fromtimestamp(UnixTime).strftime('%m')), int(datetime.datetime.fromtimestamp(UnixTime).strftime('%d')), int(datetime.datetime.fromtimestamp(UnixTime).strftime('%H' + "00")), FnG])
#csv load
#If there is no existing list, then a NumPy list must be created for the first time
#FnG_csv = np.zeros((1,5))
Path = "/home/pi/Dokumente/Programme/Fear_and_Greed/Fear_and_Greed.csv"
FnG_csv = np.loadtxt(Path, delimiter=",")
FnG_csv = np.flipud(FnG_csv)
FnG_csv = np.vstack([FnG_csv, FnG_newrow])
FnG_csv = np.flipud(FnG_csv)
#csv save
fmt = fmt = '%d', '%d', '%d', '%d','%d', '%1.2f'
np.savetxt(Path, FnG_csv, delimiter=",", fmt=fmt)
MailTrigger = 0
if FnG > 90:
State = "Overheated"
MailTrigger = 1
print(State)
elif FnG < 5:
State = "Buying-opportunity"
MailTrigger = 1
print(State)
if MailTrigger == 1:
fromaddr = 'OutputMailAccount' #(eg. peter_pan@gmail.com)
toaddr = 'TargetMailAccound' #(eg. mice@gmail.com)
msg = MIMEMultipart()
msg['From'] = fromaddr
msg['To'] = toaddr
msg['Subject'] = currentdate + "_FnG: " + State
body = ''
msg.attach(MIMEText(body, 'plain'))
server = smtplib.SMTP('smtp.gmail.com', 587)
server.starttls()
server.login(fromaddr, 'OutputMailAccountPassword') #(e.g. 123123)
text = msg.as_string()
server.sendmail(fromaddr, toaddr, text)
server.quit()| UNIX EPOCH | YEAR | MONTH | DAY | HOUR | Fear and Greed Index |
|---|---|---|---|---|---|
| 1613484062 | 2021 | 2 | 16 | 1500 | 58 |
| 1613480463 | 2021 | 2 | 16 | 1400 | 58 |
| 1613476863 | 2021 | 2 | 16 | 1300 | 63 |
Go on your terminal and enter crontab -e and enter this command
1 * * * * /usr/bin/python3 /home/pi/Dokumente/Programme/Fear_and_Greed/20_12_30_Fear_and_Greed.pyThis script will be executed every hour and one minute
Important: You need to install dropbox on your raspberry pi (cf. https://pimylifeup.com/raspberry-pi-dropbox/)
3 * * * * /home/pi/Dropbox-Uploader/dropbox_uploader.sh upload /home/pi/Dokumente/Programme/Fear_and_Greed/Fear_and_Greed.csvThis script will be executed every hour and 3 minutes. The time delay is due to the fact that in case the Python script takes longer to retrieve and save the data, the latest data will still be uploaded to the cloud.
After the data is downloaded regularly, it can also be visualized using the following script
#!/usr/bin/python
from pandas_datareader import data as pdr
import yfinance as yf
import matplotlib.pyplot as plt
from datetime import date
from dateutil.relativedelta import relativedelta
import pandas as pd
import numpy as np
import os, sys
import time
import os
from os import makedirs
import shutil
from currency_converter import CurrencyConverter
import quandl as ql
import datetime
import numpy as np
import matplotlib.pyplot as plt
import pandas as pd
import quandl as ql
from datetime import date
import time
import datetime
import yfinance as yf
%matplotlib widget
%matplotlib inline
import pandas as pd
filename = "*******************/Fear_and_Greed.csv"
df = pd.read_csv(filename, sep=',')
df.columns = ['UNIX', 'Jahr', 'Monat', 'Tag','Uhrzeit', 'FnG']
df = df.iloc[::-1]
average_duration = 20
df['FnG_average']=df['FnG'].rolling(average_duration).mean()
fig, ax1 = plt.subplots(figsize=(20,12))
ax1.grid(color='black', linestyle='--', linewidth=0.05)
color = 'tab:red'
ax1.set_xlabel('date')
ax1.set_ylabel('Fear and Greed', color=color)
ax1.plot(pd.to_datetime(df['UNIX'],unit='s'), df.FnG, label='FnG', color=color)
ax1.plot(pd.to_datetime(df['UNIX'],unit='s'), df.FnG_average, label='FnG_average', color='b')
ax1.tick_params(axis='y', labelcolor=color)
ax1.set_ylim(0,100)
ax1.axhspan(0, 10, alpha=0.3, color='#32FF00')
ax1.axhspan(10, 15, alpha=0.3, color='#FFF700')
ax1.axhspan(100, 90, alpha=0.3, color='#FF1300')
fig.tight_layout() # otherwise the right y-label is slightly clipped
Here you can see the data of the last 45 days. Critical areas are marked in colors. The market is overheated as soon as it is in the purple area. A good buying opportunity arises on the basis of backtests when there is great market panic, visible in the red area.