Create app.py

app.py

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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()