Notebooks/MAexp.py

import pandas as pd
import numpy as np
import yfinance as yf
import streamlit as st
import plotly.graph_objects as go
import time
from utilities import checker
import datetime

with open(r"../style/style.css") as css:
    st.markdown(f"<style>{css.read()}</style>", unsafe_allow_html=True)

st.markdown(
    "<h1 style='text-align: center;'><u>CapiPort</u></h1>", unsafe_allow_html=True
)

st.markdown(
    "<h5 style='text-align: center; color: gray;'>Your Portfolio Optimisation Tool</h5>",
    unsafe_allow_html=True,
)
st.header(
    "",
    divider="rainbow",
)

color = "Quest"
st.markdown(
    "<h1 style='text-align: center;'>🔍 Quest for financial excellence begins with meticulous portfolio optimization</u></h1>",
    unsafe_allow_html=True,
)

st.header(
    "",
    divider="rainbow",
)

list_df = pd.read_csv("../Data/Company List.csv")

company_name = list_df["Name"].to_list()
company_symbol = (list_df["Ticker"] + ".NS").to_list()

company_dict = dict()
company_symbol_dict = dict()

for CSymbol, CName in zip(company_symbol, company_name):
    company_dict[CName] = CSymbol

for CSymbol, CName in zip(company_symbol, company_name):
    company_symbol_dict[CSymbol] = CName

st.markdown(
    """ 
    <style>
        .big-font {
        font-size:20px;
        }
    </style>""",
    unsafe_allow_html=True,
)

st.markdown('<p class="big-font">Select Multiple Companies</p>', unsafe_allow_html=True)

com_sel_name = st.multiselect("", company_name, default=None)
com_sel_date = []

for i in com_sel_name:
    d = st.date_input(
        f"On which date did you invested in - {i}",
        value= pd.Timestamp('2021-01-01'),
        format="YYYY-MM-DD",
    )
    d = d - datetime.timedelta(days=3)
    com_sel_date.append(d)

com_sel = [company_dict[i] for i in com_sel_name]

num_tick = len(com_sel)


if num_tick > 1:
    com_data = pd.DataFrame()
    for cname, cdate in zip(com_sel, com_sel_date):
        stock_data_temp = yf.download(cname, start=cdate, end=pd.Timestamp.now().strftime('%Y-%m-%d'))['Low']
        stock_data_temp.name = cname
        com_data = pd.merge(com_data, stock_data_temp, how="outer", right_index=True, left_index=True)
    for i in com_data.columns:
        com_data.dropna(axis=1, how='all', inplace=True)
    # com_data.dropna(inplace=True)
    num_tick = len(com_data.columns)

    # Dataframe of the selected companies
    st.dataframe(com_data, use_container_width=True)

    # make a function to calculate moving averages from the dataframe com_data, store those moving averages in dictionary for respective company
    def moving_average(data, window):
        ma = {}
        for i in data.columns:
            ma[i] = data[i].rolling(window=window).mean().values[2]
        return ma

    moving_avg = moving_average(com_data, 3)
    MA_df = pd.DataFrame(moving_avg.items(), columns=['Company', 'Purchase Rate (MA)'])

    # calculate percentage return till present date from the moving average price of the stock
    def percentage_return(data, moving_avg):
        pr = {}
        for i in data.columns:
            pr[i] = f'{round(((data[i].values[-1] - moving_avg[i]) / moving_avg[i]) * 100,2) }%'
        return pr
    
    # make percentage return a dataframe from dictionary
    percentage_return = pd.DataFrame(percentage_return(com_data, moving_avg).items(), columns=['Company', 'Percentage Return'])

    #merge MA_df and percentage_return on "Company" columns
    MA_df = pd.merge(MA_df, percentage_return, on='Company')

    st.markdown(
            "<h5 style='text-align: center;'>Percent Returns & MA price</h5>",
            unsafe_allow_html=True,
        )

    st.write("<p style='text-align: center;'>**rate of purchase is moving average(MA) of 3 (t+2) days</p>", unsafe_allow_html=True) 
    st.dataframe(MA_df,use_container_width=True)

    if num_tick > 1:
        com_sel_name_temp = []
        for i in com_data.columns:
            com_sel_name_temp.append(company_symbol_dict[i])
        com_sel = com_data.columns.to_list()
        

        ## Log-Return of Company Dataset
        log_return = np.log(1 + com_data.pct_change())

        ## Generate Random Weights
        rand_weig = np.array(np.random.random(num_tick))

        ## Rebalancing Random Weights
        rebal_weig = rand_weig / np.sum(rand_weig)

        ## Calculate the Expected Returns, Annualize it by * 252.0
        exp_ret = np.sum((log_return.mean() * rebal_weig) * 252)

        ## Calculate the Expected Volatility, Annualize it by * 252.0
        exp_vol = np.sqrt(np.dot(rebal_weig.T, np.dot(log_return.cov() * 252, rebal_weig)))

        ## Calculate the Sharpe Ratio.
        sharpe_ratio = exp_ret / exp_vol

        # Put the weights into a data frame to see them better.
        weights_df = pd.DataFrame(
            data={
                "company_name": com_sel_name_temp,
                "random_weights": rand_weig,
                "rebalance_weights": rebal_weig,
            }
        )

        st.divider()

        st.markdown(
            "<h5 style='text-align: center;'>Random Portfolio Weights</h5>",
            unsafe_allow_html=True,
        )
        st.dataframe(weights_df, use_container_width=True)

        # Do the same with the other metrics.
        metrics_df = pd.DataFrame(
            data={
                "Expected Portfolio Returns": exp_ret,
                "Expected Portfolio Volatility": exp_vol,
                "Portfolio Sharpe Ratio": sharpe_ratio,
            },
            index=[0],
        )

        st.markdown(
            "<h5 style='text-align: center;'>Random Weights Metrics</h5>",
            unsafe_allow_html=True,
        )
        st.dataframe(metrics_df, use_container_width=True)

        st.divider()