Update app.py
Browse files
app.py
CHANGED
@@ -19,22 +19,7 @@ def plot_cum_returns(data, title):
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fig = px.line(daily_cum_returns, title=title)
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return fig
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ef = EfficientFrontier(mu, S)
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fig, ax = plt.subplots(figsize=(6,4))
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ef_max_sharpe = copy.deepcopy(ef)
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plotting.plot_efficient_frontier(ef, ax=ax, show_assets=False)
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ef_max_sharpe.max_sharpe(risk_free_rate=0.02)
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ret_tangent, std_tangent, _ = ef_max_sharpe.portfolio_performance()
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ax.scatter(std_tangent, ret_tangent, marker="*", s=100, c="r", label="Max Sharpe")
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n_samples = 1000
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w = np.random.dirichlet(np.ones(ef.n_assets), n_samples)
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rets = w.dot(ef.expected_returns)
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stds = np.sqrt(np.diag(w @ ef.cov_matrix @ w.T))
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sharpes = rets / stds
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ax.scatter(stds, rets, marker=".", c=sharpes, cmap="viridis_r")
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ax.legend()
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return fig
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def output_results(start_date, end_date, tickers_string, investment_amount):
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tickers = tickers_string.split(',')
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@@ -49,15 +34,15 @@ def output_results(start_date, end_date, tickers_string, investment_amount):
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fig_cum_returns = plot_cum_returns(stocks_df, 'Cumulative Returns of Individual Stocks Starting with ₹100')
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# Calculate and Plot Correlation Matrix between Stocks
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corr_df = stocks_df.corr().round(2)
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fig_corr = px.imshow(corr_df, text_auto=True, title = 'Correlation between Stocks')
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# Calculate expected returns and sample covariance matrix for portfolio optimization later
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mu = expected_returns.mean_historical_return(stocks_df)
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S = risk_models.sample_cov(stocks_df)
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# Plot efficient frontier curve
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fig_efficient_frontier = plot_efficient_frontier_and_max_sharpe(mu, S)
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# Get optimized weights
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ef = EfficientFrontier(mu, S)
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@@ -66,8 +51,8 @@ def output_results(start_date, end_date, tickers_string, investment_amount):
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expected_annual_return, annual_volatility, sharpe_ratio = ef.portfolio_performance()
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expected_annual_return, annual_volatility, sharpe_ratio = '{}%'.format((expected_annual_return*100).round(2)), \
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weights_df = pd.DataFrame.from_dict(weights, orient='index').reset_index()
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weights_df.columns = ['Tickers', 'Weights']
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@@ -89,8 +74,8 @@ def output_results(start_date, end_date, tickers_string, investment_amount):
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# Plot Cumulative Returns of Optimized Portfolio
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fig_cum_returns_optimized = plot_cum_returns(stocks_df['Optimized Portfolio'], 'Cumulative Returns of Optimized Portfolio Starting with ₹100')
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return fig_cum_returns_optimized,
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expected_annual_return,
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with gr.Blocks() as app:
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@@ -108,32 +93,31 @@ with gr.Blocks() as app:
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investment_amount = gr.Number(label="Investment Amount (in ₹)")
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btn = gr.Button("Get Optimized Portfolio")
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with gr.Row():
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with gr.Row():
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expected_annual_return = gr.Text(label="Expected Annual Return")
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sharpe_ratio = gr.Text(label="Sharpe Ratio")
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with gr.Row():
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fig_cum_returns_optimized = gr.Plot(label="Cumulative Returns of Optimized Portfolio (Starting Price of ₹100)")
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with gr.Row():
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with gr.Row():
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fig_indiv_prices = gr.Plot(label="Price of Individual Stocks")
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fig_cum_returns = gr.Plot(label="Cumulative Returns of Individual Stocks Starting with ₹100")
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with gr.Row():
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leftover = gr.Number(label="Leftover Amount (in ₹)")
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btn.click(fn=output_results, inputs=[start_date, end_date, tickers_string, investment_amount],
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outputs=[fig_cum_returns_optimized,
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expected_annual_return,
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app.launch()
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fig = px.line(daily_cum_returns, title=title)
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return fig
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def output_results(start_date, end_date, tickers_string, investment_amount):
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tickers = tickers_string.split(',')
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fig_cum_returns = plot_cum_returns(stocks_df, 'Cumulative Returns of Individual Stocks Starting with ₹100')
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# Calculate and Plot Correlation Matrix between Stocks
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# corr_df = stocks_df.corr().round(2)
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# fig_corr = px.imshow(corr_df, text_auto=True, title = 'Correlation between Stocks')
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# Calculate expected returns and sample covariance matrix for portfolio optimization later
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mu = expected_returns.mean_historical_return(stocks_df)
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S = risk_models.sample_cov(stocks_df)
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# Plot efficient frontier curve
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#fig_efficient_frontier = plot_efficient_frontier_and_max_sharpe(mu, S)
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# Get optimized weights
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ef = EfficientFrontier(mu, S)
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expected_annual_return, annual_volatility, sharpe_ratio = ef.portfolio_performance()
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expected_annual_return, annual_volatility, sharpe_ratio = '{}%'.format((expected_annual_return*100).round(2)), \
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'{}%'.format((annual_volatility*100).round(2)), \
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'{}%'.format((sharpe_ratio*100).round(2))
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weights_df = pd.DataFrame.from_dict(weights, orient='index').reset_index()
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weights_df.columns = ['Tickers', 'Weights']
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# Plot Cumulative Returns of Optimized Portfolio
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fig_cum_returns_optimized = plot_cum_returns(stocks_df['Optimized Portfolio'], 'Cumulative Returns of Optimized Portfolio Starting with ₹100')
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return fig_cum_returns_optimized, allocation_df, \
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expected_annual_return, leftover.round(), fig_indiv_prices, fig_cum_returns
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with gr.Blocks() as app:
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investment_amount = gr.Number(label="Investment Amount (in ₹)")
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btn = gr.Button("Get Optimized Portfolio")
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# with gr.Row():
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# gr.HTML("<h3>Optimized Portfolio Metrics</h3>")
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with gr.Row():
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expected_annual_return = gr.Text(label="Expected Annual Return")
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leftover = gr.Number(label="Leftover Amount (in ₹)")
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with gr.Row():
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fig_cum_returns_optimized = gr.Plot(label="Cumulative Returns of Optimized Portfolio (Starting Price of ₹100)")
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allocation_df = gr.DataFrame(label="Stock Allocation")
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# with gr.Row():
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# fig_efficient_frontier = gr.Plot(label="Efficient Frontier")
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# fig_corr = gr.Plot(label="Correlation between Stocks")
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with gr.Row():
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fig_indiv_prices = gr.Plot(label="Price of Individual Stocks")
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fig_cum_returns = gr.Plot(label="Cumulative Returns of Individual Stocks Starting with ₹100")
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# with gr.Row():
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# allocation_df = gr.DataFrame(label="Stock Allocation")
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#leftover = gr.Number(label="Leftover Amount (in ₹)")
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btn.click(fn=output_results, inputs=[start_date, end_date, tickers_string, investment_amount],
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outputs=[fig_cum_returns_optimized, allocation_df, \
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expected_annual_return,leftover, fig_indiv_prices, fig_cum_returns])
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app.launch()
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