import pandas as pd import numpy as np import yfinance as yf import streamlit as st import plotly.graph_objects as go with open(r"style/style.css") as css: st.markdown(f"", unsafe_allow_html=True) st.markdown( "

CapiPort

", unsafe_allow_html=True ) st.markdown( "
Your Portfolio Optimisation Tool
", unsafe_allow_html=True, ) st.header( "", divider="rainbow", ) color = "Quest" st.markdown( "

🔍 Quest for financial excellence begins with meticulous portfolio optimization

", 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( """ """, unsafe_allow_html=True, ) st.markdown('

Select Multiple Companies

', unsafe_allow_html=True) com_sel_name = st.multiselect("", 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="1900-01-01", end="2024-03-08")["Adj Close"] 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) 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() com_sel_name.sort() 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_temp, "random_weights": rand_weig, "rebalance_weights": rebal_weig, } ) st.divider() st.markdown( "
Random Portfolio Weights
", 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( "
Random Weights Metrics
", unsafe_allow_html=True, ) st.dataframe(metrics_df, use_container_width=True) st.divider() ## Let's get started with Monte Carlo Simulations ## How many times should we run Monte Carlo num_of_port = 8000 ## 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("\n\n") st.markdown( "

Simulation Results

", unsafe_allow_html=True, ) 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_temp, "random_weights": max_sharpe_ratio["Portfolio Weights"], } ) st.markdown( "
Portfolio with Max Sharpe Ratio
", unsafe_allow_html=True, ) 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_temp, "random_weights": min_volatility["Portfolio Weights"], } ) st.markdown( "
Portfolio with Min Volatility
", unsafe_allow_html=True, ) st.dataframe(min_volatility, use_container_width=True) st.dataframe(min_volatility_weights_df, use_container_width=True) st.divider() st.markdown("

Plotting

", unsafe_allow_html=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 / Volatility"), 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)