Update app.py

app.py

@@ -5,6 +5,7 @@ import numpy as np
 import pandas as pd
 import matplotlib.pyplot as plt
 from matplotlib.backends.backend_agg import FigureCanvasAgg as FigureCanvas
+import time
 
 # Polymarket GraphQL API endpoint
 POLYMARKET_API = "https://api.polymarket.com/graphql"
@@ -40,7 +41,7 @@ def fetch_polymarket_data(search_term="S&P"):
         for market in markets:
             node = market["node"]
             outcomes = node["outcomes"]
-            if len(outcomes) >= 2:  # Ensure there's at least Yes/No or similar
+            if len(outcomes) >= 2:
                 return {
                     "question": node["question"],
                     "outcomes": {outcome["name"]: float(outcome["price"]) for outcome in outcomes}
@@ -49,21 +50,27 @@ def fetch_polymarket_data(search_term="S&P"):
     except Exception as e:
         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 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, None
-    except Exception as e:
-        return None, None, None, f"Error fetching data for ticker '{ticker}': {str(e)}"
+# Function to fetch Yahoo Finance data with retry
+def fetch_yahoo_data(ticker, retries=3, delay=2):
+    for attempt in range(retries):
+        try:
+            stock = yf.download(ticker, period="1y", auto_adjust=False, progress=False)
+            if stock.empty or len(stock) < 2:
+                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, None
+        except Exception as e:
+            error_msg = f"Attempt {attempt + 1}/{retries} failed for ticker '{ticker}': {str(e)}"
+            if attempt < retries - 1:
+                time.sleep(delay)  # Wait before retrying
+            else:
+                return None, None, None, error_msg
+    return None, None, None, f"Failed to fetch data for '{ticker}' after {retries} attempts."
 
 # Monte Carlo Simulation with GBM
 def monte_carlo_simulation(S0, mu, sigma, T, N, sims, risk_factor, pm_data):
@@ -102,7 +109,7 @@ def run_simulation(investment, ticker, horizon, num_sims, risk_factor):
     if mu is None:
         return None, error_msg
     
-    pm_data = fetch_polymarket_data("S&P")  # Search for S&P-related markets
+    pm_data = fetch_polymarket_data("S&P")
     
     # Run Monte Carlo simulation
     sim_paths = monte_carlo_simulation(S0, mu, sigma, horizon, num_sims, num_sims, risk_factor, pm_data)