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app.py

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+#importing libraries
+import streamlit as st
+import pandas as pd
+import yfinance as yf
+import pandas_datareader.data as web
+import datetime
+
+import capm_functions
+
+#Streamlit page configuration
+st.set_page_config(page_title="CAPM",
+                   page_icon="chart_with_upwards_trend",
+                   layout='wide')
+
+st.title("Capital Asset Pricing Model")
+
+# getting input from user
+try:
+    col1, col2 =st.columns([1,1])
+    with col1:
+        stocks_list = st.multiselect("Choose 4 stocks", ('TSLA','AAPL','NFLX','MSFT','MGM','AMZN','NVDA','GOOGL'),('TSLA','AAPL','AMZN','GOOGL'))
+
+    with col2:
+        year=st.number_input("Numbers of years",1,10)
+
+    #downloading data for SP500
+
+    end = datetime.date.today()
+    start = datetime.date(datetime.date.today().year-year, datetime.date.today().month, datetime.date.today().day)
+
+    SP500 = web.DataReader(['sp500'], 'fred',start,end)
+
+    stocks_df = pd.DataFrame()
+
+    for stock in stocks_list:
+        data = yf.download(stock, period=f'{year}y')
+        stocks_df[f'{stock}'] = data['Close']
+
+
+    stocks_df.reset_index(inplace=True)
+    SP500.reset_index(inplace=True)
+    SP500.columns = ['Date','SP500']
+    stocks_df['Date'] = stocks_df['Date'].astype('datetime64[ns]')
+    stocks_df['Date'] = stocks_df['Date'].apply(lambda x:str(x)[:10])
+    stocks_df['Date'] = pd.to_datetime(stocks_df['Date'])
+    stocks_df = pd.merge(stocks_df, SP500, on='Date', how='inner')
+
+    col1, col2 = st.columns([1,1])
+    with col1:
+        st.markdown('### Dataframe head')
+        st.dataframe(stocks_df.head(), use_container_width=True)
+
+    with col2:
+        st.markdown('### Dataframe tail')
+        st.dataframe(stocks_df.tail(), use_container_width=True)
+
+
+    col1, col2 = st.columns([1,1])
+    with col1:
+        st.markdown('### Price of all the Stocks')
+        st.plotly_chart(capm_functions.interactive_plot(stocks_df))
+
+
+    with col2:
+    
+        st.markdown('### Price of all the Stocks After Normalization')
+        st.plotly_chart(capm_functions.interactive_plot(capm_functions.normalize(stocks_df)))
+
+    stocks_daily_returns = capm_functions.daily_return(stocks_df)
+    #print(stocks_daily_returns.head())
+
+    beta = {}
+    alpha = {}
+
+    for i in stocks_daily_returns.columns:
+        if i !='Date' and i !='SP500':
+            b,a = capm_functions.calculate_beta(stocks_daily_returns,i)
+
+            beta[i]=b
+            alpha[i]=a
+    print(beta, alpha)
+
+    beta_df = pd.DataFrame(columns=['stock','Beta Value'])
+    beta_df['Stock'] = beta.keys()
+    beta_df['Beta value'] = [str(round(i,2)) for i in beta.values()]
+
+
+    with col1:
+        st.markdown('### Calculated Beta Value')
+        st.dataframe(beta_df, use_container_width=True)
+
+
+    rf = 0
+    rm = stocks_daily_returns['SP500'].mean()*252
+
+    return_df = pd.DataFrame()
+    return_value = []
+    for stock, value in beta.items():
+        return_value.append(str(round(rf+(value*(rf-rm)),2)))
+    return_df['Stock'] = stocks_list
+
+    return_df['Return value'] = return_value
+
+    with col2:
+        st.markdown('### Calculated Return using CAPM')
+
+        st.dataframe(return_df, use_container_width=True)
+except:
+    st.write('Please select valid Input')
+
+
+
+

capm_functions.py

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+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

requirements.txt

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+streamlit
+pandas
+yfinance
+pandas_datareader
+datetime
+plotly-express