Update app.py

app.py

@@ -11,54 +11,59 @@ POLYMARKET_API = "https://api.polymarket.com/graphql"
 
 # Function to fetch Polymarket data
 def fetch_polymarket_data(search_term="S&P"):
-    query = """
-    query {
-      markets(first: 5, searchTerm: "%s") {
-        edges {
-          node {
-            id
-            question
-            outcomes {
-              name
-              price
+    try:
+        query = """
+        query {
+          markets(first: 5, searchTerm: "%s") {
+            edges {
+              node {
+                id
+                question
+                outcomes {
+                  name
+                  price
+                }
+              }
             }
           }
         }
-      }
-    }
-    """ % search_term
-    response = requests.post(POLYMARKET_API, json={"query": query})
-    if response.status_code != 200:
+        """ % search_term
+        response = requests.post(POLYMARKET_API, json={"query": query}, timeout=10)
+        if response.status_code != 200:
+            return None
+        data = response.json()
+        markets = data["data"]["markets"]["edges"]
+        if not markets:
+            return None
+        
+        # Parse the first relevant market
+        for market in markets:
+            node = market["node"]
+            outcomes = node["outcomes"]
+            if len(outcomes) >= 2:  # Ensure there's at least Yes/No or similar
+                return {
+                    "question": node["question"],
+                    "outcomes": {outcome["name"]: float(outcome["price"]) for outcome in outcomes}
+                }
         return None
-    data = response.json()
-    markets = data["data"]["markets"]["edges"]
-    if not markets:
+    except Exception as e:
         return None
-    
-    # Parse the first relevant market
-    for market in markets:
-        node = market["node"]
-        outcomes = node["outcomes"]
-        if len(outcomes) >= 2:  # Ensure there's at least Yes/No or similar
-            return {
-                "question": node["question"],
-                "outcomes": {outcome["name"]: float(outcome["price"]) for outcome in outcomes}
-            }
-    return None
 
 # Function to fetch Yahoo Finance data and calculate drift/volatility
 def fetch_yahoo_data(ticker):
     try:
         stock = yf.download(ticker, period="1y", auto_adjust=False, progress=False)
-        if stock.empty:
-            return None, None, None
-        daily_returns = stock["Close"].pct_change().dropna()  # Use raw Close prices
+        if stock.empty or len(stock) < 2:  # Check for empty or insufficient data
+            return None, None, None, f"No data returned for ticker '{ticker}'. It may be invalid or lack sufficient history."
+        daily_returns = stock["Close"].pct_change().dropna()
+        if daily_returns.empty:
+            return None, None, None, f"No valid returns calculated for ticker '{ticker}'. Insufficient price data."
         mu = daily_returns.mean() * 252  # Annualized drift
         sigma = daily_returns.std() * np.sqrt(252)  # Annualized volatility
         last_price = stock["Close"][-1]  # Use most recent unadjusted Close
-        return mu, sigma, last_price
-    except Exception:
-        return None, None, None
+        return mu, sigma, last_price, None
+    except Exception as e:
+        return None, None, None, f"Error fetching data for ticker '{ticker}': {str(e)}"
 
 # Monte Carlo Simulation with GBM
 def monte_carlo_simulation(S0, mu, sigma, T, N, sims, risk_factor, pm_data):
@@ -88,10 +93,14 @@ def monte_carlo_simulation(S0, mu, sigma, T, N, sims, risk_factor, pm_data):
 
 # Main simulation function
 def run_simulation(investment, ticker, horizon, num_sims, risk_factor):
+    # Validate inputs
+    if not ticker or investment <= 0 or horizon <= 0 or num_sims <= 0:
+        return None, "Please provide valid inputs: positive investment, horizon, and simulations, and a ticker."
+
     # Fetch data
-    mu, sigma, S0 = fetch_yahoo_data(ticker)
+    mu, sigma, S0, error_msg = fetch_yahoo_data(ticker)
     if mu is None:
-        return None, "Invalid ticker or no data available from Yahoo Finance."
+        return None, error_msg
     
     pm_data = fetch_polymarket_data("S&P")  # Search for S&P-related markets
     
@@ -116,14 +125,15 @@ def run_simulation(investment, ticker, horizon, num_sims, risk_factor):
     # Prepare summary text
     summary = f"Market Data (Yahoo Finance):\n"
     summary += f"- Drift (μ): {mu:.4f} (based on unadjusted Close)\n"
-    summary += f"- Volatility (σ): {sigma:.4f}\n\n"
+    summary += f"- Volatility (σ): {sigma:.4f}\n"
+    summary += f"- Last Close Price: ${S0:.2f}\n\n"
     if pm_data:
         summary += f"Polymarket Data:\n- Question: {pm_data['question']}\n"
         for outcome, prob in pm_data["outcomes"].items():
             summary += f"- {outcome}: {prob*100:.1f}%\n"
     else:
-        summary += "Polymarket Data: No relevant market found.\n"
-    summary += f"\nSimulation Results:\n"
+        summary += "Polymarket Data: No relevant market found or API unavailable.\n"
+    summary += f"\nSimulation Results ({num_sims} runs):\n"
     summary += f"- Mean Final Value: ${mean_val:.2f}\n"
     summary += f"- Min Final Value: ${min_val:.2f}\n"
     summary += f"- Max Final Value: ${max_val:.2f}\n"
@@ -147,7 +157,7 @@ with gr.Blocks(title="Investment Simulation Platform") as demo:
         
         with gr.Column():
             plot_output = gr.Plot(label="Final Value Distribution")
-            text_output = gr.Textbox(label="Simulation Summary", lines=10)
+            text_output = gr.Textbox(label="Simulation Summary", lines=12)
     
     submit_btn.click(
         fn=run_simulation,