Mukhtar-is / Python-for-Finance-Capital-Asset-Pricing-Model-CAPM-

This project's goal is to assist in comprehending and using the CAPM in practical situations. The relationship between systematic risk and expected return for assets, especially stocks, is described by the Capital Asset Pricing Model (CAPM), a fundamental idea in finance.

Python-for-Finance-Capital-Asset-Pricing-Model-CAPM

Overview

The main objective of this project is to assist in comprehending and using the CAPM in practical situations. The relationship between systematic risk and expected return for assets, especially stocks, is described by the Capital Asset Pricing Model (CAPM), a fundamental idea in finance. Using Python, Streamlit, and a number of financial libraries, this project enables users to compute and display important metrics, such as beta and alpha, for a stock portfolio in comparison to a benchmark index (such as the S&P 500).

Screenshots

# Function to plot interactive plot

def intrective_plot(df, title):
    fig = px.line(title=title, width=1000, height=600)
    for i in df.columns[1:]:
        fig.add_scatter(x=df["Date"], y= df[i], name=i)
        
        
    fig.show()

# Plot interactive chart
intrective_plot(df, "Stock prices")

# Plot normalized interactive chart
intrective_plot(stocks_df, "Normalized Stock prices")

Stock prices

Normalized Stock prices

Calculating Beta For All Stocks

for i in stock_daily_return.columns:
  
  if i != 'Date' and i != 'sp500':
    
    # Use plotly express to plot the scatter plot for every stock vs. the S&P500
    fig = px.scatter(stock_daily_return, x = 'sp500', y = i, title = i, width=1000, height=600)

    # Fit a straight line to the data and obtain beta and alpha
    b, a = np.polyfit(stock_daily_return['sp500'], stock_daily_return[i], 1)
    
    # Plot the straight line 
    fig.add_scatter(x = stock_daily_return['sp500'], y = b*stock_daily_return['sp500'] + a)
    fig.show()

Here some of the Outputs: Twitter Netflix Tesla