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| import gradio as gr | |
| # Define stocks with their events and impacts | |
| stocks = { | |
| "AAPL": { | |
| "events": [ | |
| {"name": "New iPhone release", "impact_if_happens": 0.10, "impact_if_not": -0.05}, | |
| {"name": "Positive earnings report", "impact_if_happens": 0.08, "impact_if_not": -0.03}, | |
| ] | |
| }, | |
| "TSLA": { | |
| "events": [ | |
| {"name": "Achieves production target", "impact_if_happens": 0.15, "impact_if_not": -0.10}, | |
| {"name": "New model announcement", "impact_if_happens": 0.12, "impact_if_not": -0.02}, | |
| ] | |
| }, | |
| "GOOG": { | |
| "events": [ | |
| {"name": "AI breakthrough", "impact_if_happens": 0.20, "impact_if_not": -0.05}, | |
| {"name": "Regulatory approval", "impact_if_happens": 0.05, "impact_if_not": -0.15}, | |
| ] | |
| }, | |
| } | |
| def calculate_portfolio(selected_stocks, total_investment, | |
| aapl_event1_prob, aapl_event2_prob, | |
| tsla_event1_prob, tsla_event2_prob, | |
| goog_event1_prob, goog_event2_prob): | |
| # Check if any stocks are selected | |
| if not selected_stocks: | |
| return "Please select at least one stock." | |
| # Map user-input probabilities to stocks (convert from percentage to decimal) | |
| probs = { | |
| "AAPL": [aapl_event1_prob / 100, aapl_event2_prob / 100], | |
| "TSLA": [tsla_event1_prob / 100, tsla_event2_prob / 100], | |
| "GOOG": [goog_event1_prob / 100, goog_event2_prob / 100], | |
| } | |
| # Calculate expected return for each selected stock | |
| expected_returns = {} | |
| for stock in selected_stocks: | |
| events = stocks[stock]["events"] | |
| exp_return = 0 | |
| for i, event in enumerate(events): | |
| p = probs[stock][i] | |
| impact_happens = event["impact_if_happens"] | |
| impact_not = event["impact_if_not"] | |
| exp_return += p * impact_happens + (1 - p) * impact_not | |
| expected_returns[stock] = exp_return | |
| # Calculate portfolio metrics assuming equal investment | |
| num_stocks = len(selected_stocks) | |
| investment_per_stock = total_investment / num_stocks | |
| portfolio_expected_return = sum(expected_returns[stock] for stock in selected_stocks) / num_stocks | |
| expected_future_value = sum(investment_per_stock * (1 + expected_returns[stock]) for stock in selected_stocks) | |
| # Format the output | |
| output = "Expected Returns:\n" | |
| for stock in selected_stocks: | |
| output += f"{stock}: {expected_returns[stock]*100:.2f}%\n" | |
| output += f"\nPortfolio Expected Return: {portfolio_expected_return*100:.2f}%\n" | |
| output += f"Expected Future Value: ${expected_future_value:.2f}" | |
| return output | |
| # Define Gradio inputs | |
| selected_stocks = gr.CheckboxGroup(choices=["AAPL", "TSLA", "GOOG"], label="Select Stocks") | |
| total_investment = gr.Number(label="Total Investment ($)", value=10000) | |
| # Probability sliders for each event | |
| aapl_event1_prob = gr.Slider(0, 100, label="AAPL: Probability of New iPhone release (%)", value=50) | |
| aapl_event2_prob = gr.Slider(0, 100, label="AAPL: Probability of Positive earnings report (%)", value=50) | |
| tsla_event1_prob = gr.Slider(0, 100, label="TSLA: Probability of Achieving production target (%)", value=50) | |
| tsla_event2_prob = gr.Slider(0, 100, label="TSLA: Probability of New model announcement (%)", value=50) | |
| goog_event1_prob = gr.Slider(0, 100, label="GOOG: Probability of AI breakthrough (%)", value=50) | |
| goog_event2_prob = gr.Slider(0, 100, label="GOOG: Probability of Regulatory approval (%)", value=50) | |
| # Define output | |
| output = gr.Textbox(label="Results") | |
| # Create Gradio interface | |
| iface = gr.Interface( | |
| fn=calculate_portfolio, | |
| inputs=[selected_stocks, total_investment, | |
| aapl_event1_prob, aapl_event2_prob, | |
| tsla_event1_prob, tsla_event2_prob, | |
| goog_event1_prob, goog_event2_prob], | |
| outputs=output, | |
| title="Investment Decision Analysis POC", | |
| description="Select stocks and adjust event probabilities to analyze expected returns based on simulated prediction market data. Event impacts are predefined. Click 'Submit' to see results." | |
| ) | |
| # Launch the app | |
| if __name__ == "__main__": | |
| iface.launch() |