Return_CAPM.py

import datetime as dt
import streamlit as st
import pandas as pd
import yfinance as yf
import pandas_datareader.data as pdr
import CAPM_charts as graphs
import price_normalize as pn
import returns


st.set_page_config(page_title="CAPM",
                   page_icon=":moneybag:",layout="wide")

st.title("Capital Asset Pricing Model 💰")
st.markdown('###### Made with 💙 by Tanmay Chakrabarty | [↗ to github](https://github.com/tanmaychk) | [↗ to Portfolio website](https://tanmaychk.netlify.app)')
st.caption("The Capital Asset Pricing Model (CAPM) describes the relationship between systematic risk, or the general perils of investing, and expected return for assets, particularly stocks.It is a finance model that establishes a linear relationship between the required return on an investment and risk.")
st.subheader("assumed risk free return = 4.5%")
#Inputs

col1,col2=st.columns([1,1])
col3,col4 = st.columns([1,1])
with col1:
    stock_list = st.multiselect("Choose stocks by ticker",('AAPL', 'MSFT', 'AMZN', 'NVDA', 'GOOGL', 'TSLA', 'META', 'GOOG', 'BRK', 'UNH', 'XOM', 'JNJ', 'JPM', 'V', 'LLY', 'AVGO', 'PG', 'MA', 'HD', 'MRK', 'CVX', 'PEP', 'COST', 'ABBV', 'KO', 'ADBE', 'WMT', 'MCD', 'CSCO', 'PFE', 'CRM', 'TMO', 'BAC', 'NFLX', 'ACN', 'A','DE', 'GS', 'ELV', 'LMT', 'AXP', 'BLK', 'SYK', 'BKNG', 'MDLZ', 'ADI', 'TJX', 'GILD', 'MMC', 'ADP', 'VRTX', 'AMT', 'C', 'CVS', 'LRCX', 'SCHW', 'CI', 'MO', 'ZTS', 'TMUS', 'ETN', 'CB', 'FI'),('AAPL','TSLA','AMZN','GOOGL'))
with col2:
    year = st.number_input("Years of investment",1,10)

#download s&p data form fred using pdr
try:
    end=dt.date.today()
    start = dt.date(end.year-year,end.month,end.day)

    sp500_data= pdr.DataReader(['sp500'],'fred',start,end)

    #new data frame to store the close data according to date
    stocks_df = pd.DataFrame()

    for stock in stock_list:
        data = yf.download(stock,period=f'{year}y')
        stocks_df[f'{stock}'] = data['Close']


    #Preparing for Transformation
    stocks_df.reset_index(inplace=True)
    sp500_data.reset_index(inplace=True)
    sp500_data.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"])


    #merging data
    stocks_df = pd.merge(stocks_df,sp500_data, on ='Date', how ="inner")

    #show dataframe head
    with col1:  
        st.markdown("### Stock Prices (Head)")
        st.dataframe(stocks_df.head(),use_container_width=True,width=2000,hide_index=True)
    with col2: 
        st.markdown("### Stock Prices (Tail) ")
        st.dataframe(stocks_df.tail(),use_container_width=True,hide_index=True)
    #show graph normalized graph comparison
    with col1: 
        st.markdown("### Normalized Prices")
        st.caption("prices being normalized over initial stock prices")
        stocks_df_normalized = pn.normalized(stocks_df)
        st.plotly_chart(graphs.interactive_plot(stocks_df_normalized))

    #daily returns and CAPM returns
    stocks_daily_return = returns.daily_returns(stocks_df)

    #calculate beta

    beta={}
    alpha={}

    for i in stocks_daily_return.columns:
        if i != 'Date' and i !='sp500':
            b,a = returns.beta(stocks_daily_return,i)
            beta[i]=b
            alpha[i]=a

    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 col3:  
        st.markdown("### Calculated risk")
        st.caption("risk of market is considered as 1")
        st.dataframe(beta_df,use_container_width=True,hide_index=True)

    risk_free_asset = 4.5
    market_return = stocks_daily_return["sp500"].mean()*252  #252 is the number of active trading days

    return_df = pd.DataFrame()
    return_value = []
    for stock, value in beta.items():
        return_value.append(str(round(risk_free_asset+(value*(market_return-risk_free_asset)),2)))
    return_df['Stock'] = stock_list

    return_df['Return Value'] = return_value

    with col4:
        st.markdown("### Calculated return using CAPM")
        st.caption("the risk-free rate + the beta (or risk) of the investment * the expected return on the market - the risk free rate")
        st.dataframe(return_df,use_container_width=True,hide_index=True)
except:
    st.write("Please selct atleast 1 stock")


hide_default_format = """
       <style>
       #MainMenu {visibility: hidden; }
       footer {visibility: hidden;}
       </style>
       """
st.markdown(hide_default_format, unsafe_allow_html=True)