capm_functions.py

import plotly.express as px
import numpy as np


#function to plot interactive plotely 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


#function to normalize the prices based on the initial price
def normalize(df_2):
    df = df_2.copy()
    for i in df.columns[1:]:
        df[i] = df[i]/df[i][0]
    return df


#functions 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
    return df_daily_return


#functions 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