Share Value Post-IPO

gistfile1.txt

'''
This script will tell you the stock price of a stock following its ipo, normalized for value
'''

import quandl
import matplotlib.pyplot as plt
import pandas
import yfinance as yf

quandl.ApiConfig.api_key = '' #put your key here

lowestPointFromDay ={}

def normalize_data(currStock, df):
    # df on input should contain only one column with the price data (plus dataframe index)
    min = df.min()
    max = df.max()
    x = df

    # time series normalization part
    # y will be a column in a dataframe
    y = (x - min) / (max - min)

    return y

def createChart(currStock):
    msft = yf.Ticker(currStock)

    # get historical market data
    Apple_EOD = msft.history(period="max")

    aapl = Apple_EOD[["Close"]]
    aapl = aapl.reset_index()
    aapl = aapl.iloc[1:]
    #number of days after start to prune
    aapl = aapl.drop(aapl.index[720:aapl.size])
    #normalize the first chunk of days since ipo
    aapl["Norm"] = normalize_data(currStock, aapl['Close'])

    #find smallest day
    appl_smallest = aapl.nsmallest(1, 'Norm')
    lowestPointFromDay[currStock] = appl_smallest

    plt.plot( aapl["Norm"], label=currStock)



stocks = ['AAPL', 'MSFT', 'UBER', 'TWTR', 'TEAM', 'WORK', 'PINS', 'DRII', 'BKNG','EXPE', 'FB']

for stock in stocks:
    createChart(stock)

for key,value in lowestPointFromDay.items():
    print(key + ":" + str(value))

plt.legend(loc="upper right")
plt.xlabel('Days Since IPO')
plt.ylabel('Normalized')
plt.axvspan(90, 180, color='red', alpha=0.5)
plt.show()