import pandas as pd
import numpy as np
import yfinance as yf
import streamlit as st
import plotly.graph_objects as go

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()

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

com_sel_name = st.multiselect('Select Multiple Companies', company_name, default = None)
com_sel = [company_dict[i] for i in com_sel_name]

num_tick = len(com_sel)

if num_tick > 1:

    com_data = yf.download(com_sel, start="2019-03-01", end="2024-03-01")['Adj Close']
    com_data.dropna(inplace = True)

    st.dataframe(com_data, use_container_width=True)

    ## 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 * 247.0
    exp_ret = np.sum((log_return.mean() * rebal_weig) * 247)

    ## Calculate the Expected Volatility, Annualize it by * 247.0
    exp_vol = np.sqrt(
        np.dot(
            rebal_weig.T,
            np.dot(
                log_return.cov() * 247,
                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,
        'random_weights': rand_weig,
        'rebalance_weights': rebal_weig
    })

    st.write('PORTFOLIO WEIGHTS:')
    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.write('PORTFOLIO METRICS:')
    st.dataframe(metrics_df, use_container_width=True)

    ## Let's get started with Monte Carlo Simulations

    ## How many times should we run Monte Carlo
    num_of_port = 5000

    ## Create an Array to store the weights as they are generated
    all_weights = np.zeros((num_of_port, num_tick))

    ## Create an Array to store the returns as they are generated
    ret_arr = np.zeros(num_of_port)

    ## Create an Array to store the volatilities as they are generated
    vol_arr = np.zeros(num_of_port)

    ## Create an Array to store the Sharpe Ratios as they are generated
    sharpe_arr = np.zeros(num_of_port)

    ## Let's start the Monte Carlo Simulation

    for ind in range(num_of_port):
        ## Let's first Calculate the Weights
        weig = np.array(np.random.random(num_tick))
        weig = weig / np.sum(weig)

        ## Append the Weights to Weigths array
        all_weights[ind, :] = weig

        ## Calculate and Append the Expected Log Returns to Returns Array
        ret_arr[ind] = np.sum((log_return.mean() * weig) * 247)

        ## Calculate and Append the Volatility to the Volatitlity Array
        vol_arr[ind] = np.sqrt(
            np.dot(weig.T, np.dot(log_return.cov() * 247, weig))
        )

        ## Calculate and Append the Sharpe Ratio to Sharpe Ratio Array
        sharpe_arr[ind] = ret_arr[ind] / vol_arr[ind]

    ## Let's create a Data Frame with Weights, Returns, Volatitlity, and the Sharpe Ratio
    sim_data = [ret_arr, vol_arr, sharpe_arr, all_weights]

    ## Create a Data Frame using above, then Transpose it
    sim_df = pd.DataFrame(data=sim_data).T

    ## Give the columns in Simulation Data Proper Names
    sim_df.columns = [
        'Returns',
        'Volatility',
        'Sharpe Ratio',
        'Portfolio Weights'
    ]

    ## Make sure the Data Types are correct in the Data Frame
    sim_df = sim_df.infer_objects()

    # Print out the results.
    st.write('SIMULATIONS RESULT:')
    st.dataframe(sim_df.head(), use_container_width=True)

    # Return the Max Sharpe Ratio from the run.
    max_sharpe_ratio = sim_df.loc[sim_df['Sharpe Ratio'].idxmax()]

    # Return the Min Volatility from the run.
    min_volatility = sim_df.loc[sim_df['Volatility'].idxmin()]

    max_sharpe_weights_df = pd.DataFrame(data={
        'company_name': com_sel_name,
        'random_weights': max_sharpe_ratio["Portfolio Weights"],
    })

    st.write('MAX SHARPE RATIO:')
    st.dataframe(max_sharpe_ratio, use_container_width=True)
    st.dataframe(max_sharpe_weights_df, use_container_width=True)

    min_volatility_weights_df = pd.DataFrame(data={
        'company_name': com_sel_name,
        'random_weights': min_volatility["Portfolio Weights"],
    })

    st.write('MIN VOLATILITY:')
    st.dataframe(min_volatility, use_container_width=True)
    st.dataframe(min_volatility_weights_df, use_container_width=True)

    fig = go.Figure(data=go.Scatter(
        x=sim_df['Volatility'],
        y=sim_df['Returns'],
        mode='markers',
        marker=dict(
            color=sim_df['Sharpe Ratio'],
            colorscale='RdYlBu',
            size=10
        )
    ))

    # Add color bar
    fig.update_layout(
        coloraxis_colorbar=dict(
            title='Sharpe Ratio'
        )
    )

    # Add title and axis labels
    fig.update_layout(
        title='Portfolio Returns Vs. Risk',
        xaxis=dict(title='Standard Deviation'),
        yaxis=dict(title='Returns')
    )

    # Plot the Max Sharpe Ratio, using a `Red Star`.
    fig.add_trace(go.Scatter(
        x=[max_sharpe_ratio[1]],
        y=[max_sharpe_ratio[0]],
        mode='markers',
        marker=dict(
            color='red',
            symbol='star',
            size=20
        ),
        name='Max Sharpe Ratio'
    ))

    # Plot the Min Volatility, using a `Blue Star`.
    fig.add_trace(go.Scatter(
        x=[min_volatility[1]],
        y=[min_volatility[0]],
        mode='markers',
        marker=dict(
            color='blue',
            symbol='star',
            size=20
        ),
        name='Min Volatility'
    ))

    st.plotly_chart(fig, use_container_width=True)