capm_functions.py

import numpy as np
import plotly.express as px

#function to plot interactive plotly chart

def interactive_plot(df):
    fig = px.line()
    for i in df.columns[1:]:
        fig.add_scatter(x = df['Date'], y =df[i], name = i) #creates one by one for every stock
    fig.update_layout(width =450, margin =dict(l=20,r=20,t= 50,b= 20),legend = dict(orientation ='h', yanchor ='bottom',y=1.02, xanchor= 'right',x=1))
    return fig


#function to normalize the prices based on initial price
def normalize(df_2):
    df = df_2.copy()
    for i in df.columns[1:]:   #skips the date column
        df[i]=df[i]/df[i][0]   #divides all the prices of tesla by its first price
    return df

#function to calculate daily returns

def daily_return(df):
    df_daily_return = df.copy()
    for i in df.columns[1:]:
        for j in range(1,len(df)):
            df_daily_return[i][j] = (( df[i][j] - df[i][j-1] ) / df[i][j-1])*100
        df_daily_return[i][0] = 0 #first column must be zero
    return df_daily_return


#function to calculate beta
def calculate_beta(stocks_daily_return, stock):
    rm = stocks_daily_return['sp500'].mean()*252

    b , a = np.polyfit(stocks_daily_return['sp500'], stocks_daily_return[stock],1)
    return b,a