CAPM_functions.py

import plotly.express as px
import numpy as np

#  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)
    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
def normalize(df_2):
    df=df_2.copy()
    for i in df.columns[1:]:
        df[i]=df[i]/df[i][0]
    return df
# function to calculate daily return
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
    return df_daily_return

# function to create 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