Delete app.py.bak

app.py.bak

@@ -1,130 +0,0 @@
-import gradio as gr
-import yfinance as yf
-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
-from datetime import datetime
-import datetime
-
-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 with random weights
-    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
-
-def output_results(start_date, end_date, tickers_string):
-    tickers = tickers_string.split(',')
-    
-    # Get Stock Prices
-    stocks_df = yf.download(tickers, start=start_date, end=end_date)['Adj Close']
-    
-    # Plot Individual Stock Prices
-    fig_indiv_prices = 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_efficient_frontier = plot_efficient_frontier_and_max_sharpe(mu, S)
-
-    # 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()
-    
-    expected_annual_return, annual_volatility, sharpe_ratio = '{}%'.format((expected_annual_return*100).round(2)), \
-    '{}%'.format((annual_volatility*100).round(2)), \
-    '{}%'.format((sharpe_ratio*100).round(2))
-    
-    weights_df = pd.DataFrame.from_dict(weights, orient = 'index')
-    weights_df = weights_df.reset_index()
-    weights_df.columns = ['Tickers', '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')
-
-    return  fig_cum_returns_optimized, weights_df, fig_efficient_frontier, fig_corr,   \
-            expected_annual_return, annual_volatility, sharpe_ratio, fig_indiv_prices, fig_cum_returns
-
-
-with gr.Blocks() as app:
-    with gr.Row():
-        gr.HTML("<h1>Bohmian's Stock Portfolio Optimizer</h1>")
-    
-    with gr.Row():
-        start_date = gr.Textbox("2013-01-01", label="Start Date")
-        end_date = gr.Textbox(datetime.datetime.now().date(), label="End Date")
-    
-    with gr.Row():        
-        tickers_string = gr.Textbox("MA,META,V,AMZN,JPM,BA", 
-                                    label='Enter all stock tickers to be included in portfolio separated \
-                                    by commas WITHOUT spaces, e.g. "MA,META,V,AMZN,JPM,BA"')
-        btn = gr.Button("Get Optimized Portfolio")
-       
-    with gr.Row():
-        gr.HTML("<h3>Optimizied Portfolio Metrics</h3>")
-        
-    with gr.Row():
-        expected_annual_return = gr.Text(label="Expected Annual Return")
-        annual_volatility = gr.Text(label="Annual Volatility")
-        sharpe_ratio = gr.Text(label="Sharpe Ratio")            
-   
-    with gr.Row():        
-        fig_cum_returns_optimized = gr.Plot(label="Cumulative Returns of Optimized Portfolio (Starting Price of $100)")
-        weights_df = gr.DataFrame(label="Optimized Weights of Each Ticker")
-        
-    with gr.Row():
-        fig_efficient_frontier = gr.Plot(label="Efficient Frontier")
-        fig_corr = gr.Plot(label="Correlation between Stocks")
-    
-    with gr.Row():
-        fig_indiv_prices = gr.Plot(label="Price of Individual Stocks")
-        fig_cum_returns = gr.Plot(label="Cumulative Returns of Individual Stocks Starting with $100")
-
-    btn.click(fn=output_results, inputs=[start_date, end_date, tickers_string], 
-              outputs=[fig_cum_returns_optimized, weights_df, fig_efficient_frontier, fig_corr,   \
-            expected_annual_return, annual_volatility, sharpe_ratio, fig_indiv_prices, fig_cum_returns])
-
-app.launch()