Create app.py

app.py

@@ -0,0 +1,125 @@
+import streamlit as st
+from pandas_datareader.data import DataReader
+from pypfopt.discrete_allocation import DiscreteAllocation, get_latest_prices
+from pypfopt import EfficientFrontier
+from pypfopt import risk_models
+from pypfopt import expected_returns
+from pypfopt import plotting
+import copy
+import numpy as np
+import pandas as pd
+import plotly.express as px
+import matplotlib.pyplot as plt
+import seaborn as sns
+from datetime import datetime
+from io import BytesIO
+
+def plot_cum_returns(data, title):    
+	daily_cum_returns = 1 + data.dropna().pct_change()
+	daily_cum_returns = daily_cum_returns.cumprod()*100
+	fig = px.line(daily_cum_returns, title=title)
+	return fig
+	
+def plot_efficient_frontier_and_max_sharpe(mu, S): 
+	# Optimize portfolio for max Sharpe ratio and plot it out with efficient frontier curve
+	ef = EfficientFrontier(mu, S)
+	fig, ax = plt.subplots(figsize=(6,4))
+	ef_max_sharpe = copy.deepcopy(ef)
+	plotting.plot_efficient_frontier(ef, ax=ax, show_assets=False)
+	# Find the max sharpe portfolio
+	ef_max_sharpe.max_sharpe(risk_free_rate=0.02)
+	ret_tangent, std_tangent, _ = ef_max_sharpe.portfolio_performance()
+	ax.scatter(std_tangent, ret_tangent, marker="*", s=100, c="r", label="Max Sharpe")
+	# Generate random portfolios
+	n_samples = 1000
+	w = np.random.dirichlet(np.ones(ef.n_assets), n_samples)
+	rets = w.dot(ef.expected_returns)
+	stds = np.sqrt(np.diag(w @ ef.cov_matrix @ w.T))
+	sharpes = rets / stds
+	ax.scatter(stds, rets, marker=".", c=sharpes, cmap="viridis_r")
+	# Output
+	ax.legend()
+	return fig
+
+st.set_page_config(page_title = "Bohmian's Stock Portfolio Optimizer", layout = "wide")
+st.header("Bohmian's Stock Portfolio Optimizer")
+
+col1, col2 = st.columns(2)
+
+with col1:
+	start_date = st.date_input("Start Date",datetime(2013, 1, 1))
+	
+with col2:
+	end_date = st.date_input("End Date") # it defaults to current date
+
+tickers_string = st.text_input('Enter all stock tickers to be included in portfolio separated by commas \
+								WITHOUT spaces, e.g. "MA,FB,V,AMZN,JPM,BA"', '').upper()
+tickers = tickers_string.split(',')
+
+try:
+	# Get Stock Prices using pandas_datareader Library	
+	stocks_df = DataReader(tickers, 'yahoo', start = start_date, end = end_date)['Adj Close']	
+	# Plot Individual Stock Prices
+	fig_price = px.line(stocks_df, title='Price of Individual Stocks')
+	# Plot Individual Cumulative Returns
+	fig_cum_returns = plot_cum_returns(stocks_df, 'Cumulative Returns of Individual Stocks Starting with $100')
+	# Calculatge and Plot Correlation Matrix between Stocks
+	corr_df = stocks_df.corr().round(2)
+	fig_corr = px.imshow(corr_df, text_auto=True, title = 'Correlation between Stocks')
+		
+	# Calculate expected returns and sample covariance matrix for portfolio optimization later
+	mu = expected_returns.mean_historical_return(stocks_df)
+	S = risk_models.sample_cov(stocks_df)
+	
+	# Plot efficient frontier curve
+	fig = plot_efficient_frontier_and_max_sharpe(mu, S)
+	fig_efficient_frontier = BytesIO()
+	fig.savefig(fig_efficient_frontier, format="png")
+	
+	# Get optimized weights
+	ef = EfficientFrontier(mu, S)
+	ef.max_sharpe(risk_free_rate=0.02)
+	weights = ef.clean_weights()
+	expected_annual_return, annual_volatility, sharpe_ratio = ef.portfolio_performance()
+	weights_df = pd.DataFrame.from_dict(weights, orient = 'index')
+	weights_df.columns = ['weights']
+	
+	# Calculate returns of portfolio with optimized weights
+	stocks_df['Optimized Portfolio'] = 0
+	for ticker, weight in weights.items():
+		stocks_df['Optimized Portfolio'] += stocks_df[ticker]*weight
+	
+	# Plot Cumulative Returns of Optimized Portfolio
+	fig_cum_returns_optimized = plot_cum_returns(stocks_df['Optimized Portfolio'], 'Cumulative Returns of Optimized Portfolio Starting with $100')
+	
+	# Display everything on Streamlit
+	st.subheader("Your Portfolio Consists of {} Stocks".format(tickers_string))	
+	st.plotly_chart(fig_cum_returns_optimized)
+	
+	st.subheader("Optimized Max Sharpe Portfolio Weights")
+	st.dataframe(weights_df)
+	
+	st.subheader("Optimized Max Sharpe Portfolio Performance")
+	st.image(fig_efficient_frontier)
+	
+	st.subheader('Expected annual return: {}%'.format((expected_annual_return*100).round(2)))
+	st.subheader('Annual volatility: {}%'.format((annual_volatility*100).round(2)))
+	st.subheader('Sharpe Ratio: {}'.format(sharpe_ratio.round(2)))
+	
+	st.plotly_chart(fig_corr) # fig_corr is not a plotly chart
+	st.plotly_chart(fig_price)
+	st.plotly_chart(fig_cum_returns)
+	
+
+	
+except:
+	st.write('Enter correct stock tickers to be included in portfolio separated\
+	by commas WITHOUT spaces, e.g. "MA,FB,V,AMZN,JPM,BA"and hit Enter.')	
+	
+hide_streamlit_style = """
+<style>
+#MainMenu {visibility: hidden;}
+footer {visibility: hidden;}
+</style>
+"""
+st.markdown(hide_streamlit_style, unsafe_allow_html=True)