Update app.py

app.py

@@ -9,22 +9,31 @@ from scipy.stats import norm
 # Define functions
 
 def fetch_earnings_data(ticker, limit=99):
-    msft = yf.Ticker(ticker)
-    earnings_dates = msft.get_earnings_dates(limit=limit)
-    earnings_dates.index = earnings_dates.index.tz_localize(None)
-    earnings_dates = earnings_dates.dropna(subset=['EPS Estimate'])
-    return earnings_dates
+    try:
+        msft = yf.Ticker(ticker)
+        earnings_dates = msft.get_earnings_dates(limit=limit)
+        earnings_dates.index = earnings_dates.index.tz_localize(None)
+        earnings_dates = earnings_dates.dropna(subset=['EPS Estimate'])
+        return earnings_dates
+    except Exception as e:
+        st.warning("There was an issue fetching earnings data. Please try again later.")
+        return pd.DataFrame()  # Return an empty DataFrame on failure
 
 def fetch_stock_data(ticker, start_date, end_date, buffer_days):
-    start_date = start_date - pd.Timedelta(days=buffer_days)
-    end_date = end_date + pd.Timedelta(days=buffer_days)
-    stock_data = yf.download(ticker, start=start_date, end=end_date)
-    stock_data.index = stock_data.index.tz_localize(None)
-    return stock_data
+    try:
+        start_date = start_date - pd.Timedelta(days=buffer_days)
+        end_date = end_date + pd.Timedelta(days=buffer_days)
+        stock_data = yf.download(ticker, start=start_date, end=end_date)
+        stock_data.index = stock_data.index.tz_localize(None)
+        return stock_data
+    except Exception as e:
+        st.warning("There was an issue fetching stock data. Please try again later.")
+        return pd.DataFrame()  # Return an empty DataFrame on failure
 
 def calculate_metrics(stock_data):
-    stock_data['Returns'] = stock_data['Close'].pct_change()
-    stock_data['20D Volatility'] = stock_data['Returns'].rolling(window=20).std()
+    if not stock_data.empty:
+        stock_data['Returns'] = stock_data['Close'].pct_change()
+        stock_data['20D Volatility'] = stock_data['Returns'].rolling(window=20).std()
     return stock_data
 
 def plot_stock_price_with_earnings(stock_data, earnings_dates, ticker):
@@ -484,120 +493,130 @@ with st.sidebar.expander("Analysis Parameters", expanded=True):
 
 
 if st.sidebar.button("Run Analysis"):
-    # Fetch data
-    earnings_dates = fetch_earnings_data(ticker)
-    current_time = pd.Timestamp.now().tz_localize(None)
-    future_eps_estimate = earnings_dates.loc[earnings_dates.index > current_time]
-    if not future_eps_estimate.empty:
-        future_eps_estimate = future_eps_estimate.iloc[0]['EPS Estimate']
-    else:
-        future_eps_estimate = None
-    stock_data = fetch_stock_data(ticker, earnings_dates.index.min(), earnings_dates.index.max(), buffer_days)
-    stock_data = calculate_metrics(stock_data)
-
-    latest_close_price = stock_data['Close'].iloc[-1]
-    upper_threshold = 1 + threshold_percentage
-    lower_threshold = 1 - threshold_percentage
-
-    # Normalize price movements around earnings dates
-    all_normalized_prices = []
-    for earning_date in earnings_dates.index:
-        start = earning_date - pd.Timedelta(days=pre_announcement_window)
-        end = earning_date + pd.Timedelta(days=post_announcement_window)
-        subset = stock_data.loc[start:end]['Close'].copy()
-        subset = ensure_window_size(subset, earning_date, pre_announcement_window, post_announcement_window)
-        subset.ffill(inplace=True)
-        subset.bfill(inplace=True)
-        subset = subset / subset[0]
-        all_normalized_prices.append(subset.tolist())
-
-    # Display earnings data before processing
-    st.subheader("Earnings Announcements Data")
-    st.dataframe(earnings_dates)
-
-    # Plot and display charts
-    st.subheader("Stock Price with Earnings Surprises")
-    st.markdown("This chart shows the stock price movements with markers indicating earnings surprises. "
-                "Positive earnings surprises are marked with green upward triangles, while negative surprises "
-                "are marked with red downward triangles. The size of the marker indicates the magnitude of the surprise.")
-    st.plotly_chart(plot_stock_price_with_earnings(stock_data, earnings_dates, ticker), use_container_width=True)
-
-    st.subheader("Normalized Price Movements Around Earnings Dates")
-    st.markdown("This plot shows the normalized price movements of the stock around earnings dates. "
-                "The prices are normalized to the price on the earnings date (Day 0). "
-                "We analyze the price behavior before and after the earnings announcement within a specified window. "
-                "The plot also calculates the probabilities of price movements exceeding given thresholds.")
-    st.latex(r"""
-        \text{Normalized Price} = \frac{\text{Stock Price}}{\text{Stock Price on Day 0}}
-    """)
-    st.markdown("To calculate the probabilities, we count the number of times the normalized prices exceed the upper threshold "
-                "or fall below the lower threshold. These counts are then divided by the total number of observations to get the probabilities.")
-    st.latex(r"""
-        \text{Probability Above} = \frac{\text{Count of Prices Above Upper Threshold}}{\text{Total Observations}}
-    """)
-    st.latex(r"""
-        \text{Probability Below} = \frac{\text{Count of Prices Below Lower Threshold}}{\text{Total Observations}}
-    """)
-    st.latex(r"""
-        \text{Probability Between} = 1 - \text{Probability Above} - \text{Probability Below}
-    """)
-    st.plotly_chart(plot_normalized_price_movements(stock_data, earnings_dates, ticker, pre_announcement_window, post_announcement_window, upper_threshold, lower_threshold), use_container_width=True)
-
-    st.subheader("Volatility Around Earnings Dates")
-    st.markdown("This plot shows the 20-day rolling volatility of the stock price around earnings dates. "
-                "Volatility is calculated as the standard deviation of daily returns over a 20-day window.")
-    st.latex(r"""
-        \sigma_{20D} = \sqrt{\frac{1}{19} \sum_{i=1}^{20} (R_i - \bar{R})^2}
-    """)
-    st.plotly_chart(plot_volatility_around_earnings(stock_data, earnings_dates), use_container_width=True)
-
-    st.subheader("Volume Around Earnings Dates")
-    st.markdown("This plot shows the trading volume changes around earnings dates. "
-                "We analyze the volume trends within a specified window around the earnings announcements.")
-    st.plotly_chart(plot_volume_around_earnings(stock_data, earnings_dates), use_container_width=True)
-
-    price_effects = earnings_dates.index.to_series().apply(compute_price_effect, stock_data=stock_data)
-    earnings_dates[['Price Before', 'Price On', 'Price After', 'Price Effect (%)']] = pd.DataFrame(price_effects.tolist(), index=earnings_dates.index)
-    earnings_dates.dropna(subset=['Price Before', 'Price On', 'Price After'], inplace=True)
-
-    st.subheader("Price Effects Around Earnings Dates")
-    st.markdown("This bar chart compares the stock prices before, on, and after the earnings dates. "
-                "It also shows the percentage change in price as the 'Price Effect' due to the earnings announcement.")
-    st.plotly_chart(plot_price_effects(earnings_dates), use_container_width=True)
-
-    st.subheader("Earnings Surprise vs. Price Effect")
-    st.markdown("This scatter plot shows the relationship between earnings surprise percentages and the resulting price effects. "
-                "A regression line is fitted to show the correlation between these two variables.")
-    st.latex(r"""
-        \text{Price Effect (\%)} = \beta_0 + \beta_1 \times \text{Surprise (\%)}
-    """)
-    st.plotly_chart(plot_surprise_vs_price_effect(earnings_dates), use_container_width=True)
-
-    st.subheader("Monte Carlo Simulation for Normalized Price Movements")
-    st.markdown("This plot shows the results of a Monte Carlo simulation for normalized price movements around earnings dates. "
-                "We simulate multiple price paths to estimate the probabilities of price movements exceeding given thresholds.")
-    window_days = list(range(-pre_announcement_window, post_announcement_window + 1))
-    st.plotly_chart(monte_carlo_normalized_prices(all_normalized_prices, upper_threshold, lower_threshold, latest_close_price, window_days, ticker), use_container_width=True)
-
-    up_target = latest_close_price * upper_threshold
-    down_target = latest_close_price * lower_threshold
-
-    st.subheader("Expected Price Range Based on Implied Volatility")
-    st.markdown("This plot shows the expected price range of the stock based on implied volatility over a specified period. "
-                "It uses the current stock price and implied volatility to estimate the upper and lower bounds.")
-    st.latex(r"""
-        \text{Upper Bound} = S_0 \times (1 + \sigma \sqrt{t})
-    """)
-    st.latex(r"""
-        \text{Lower Bound} = S_0 \times (1 - \sigma \sqrt{t})
-    """)
-    st.plotly_chart(plot_price_ranges(ticker, implied_volatility, days_until_earnings, up_target, down_target, stock_data), use_container_width=True)
+    try:
+        # Fetch data
+        earnings_dates = fetch_earnings_data(ticker)
+        current_time = pd.Timestamp.now().tz_localize(None)
+        future_eps_estimate = earnings_dates.loc[earnings_dates.index > current_time]
+        if not future_eps_estimate.empty:
+            future_eps_estimate = future_eps_estimate.iloc[0]['EPS Estimate']
+        else:
+            future_eps_estimate = None
 
-    st.subheader("Monte Carlo Simulation for Price Movements")
-    st.markdown("We simulate multiple price paths using the stock's implied volatility to estimate the probabilities of the stock price reaching given targets.")
-    st.markdown("Implied volatility (IV) is used to model the expected volatility of the stock's price. "
-                "The simulation generates random price paths based on the IV, the current stock price, and the time remaining until the earnings date.")
-    st.plotly_chart(monte_carlo_simulation(ticker, implied_volatility, days_until_earnings, up_target, down_target, stock_data, num_simulations), use_container_width=True)
+        stock_data = fetch_stock_data(ticker, earnings_dates.index.min(), earnings_dates.index.max(), buffer_days)
+        if stock_data.empty:
+            st.error("Failed to fetch stock data. Please try again later.")
+        else:
+            stock_data = calculate_metrics(stock_data)
+
+            latest_close_price = stock_data['Close'].iloc[-1]
+            upper_threshold = 1 + threshold_percentage
+            lower_threshold = 1 - threshold_percentage
+
+            # Normalize price movements around earnings dates
+            all_normalized_prices = []
+            for earning_date in earnings_dates.index:
+                start = earning_date - pd.Timedelta(days=pre_announcement_window)
+                end = earning_date + pd.Timedelta(days=post_announcement_window)
+                subset = stock_data.loc[start:end]['Close'].copy()
+                subset = ensure_window_size(subset, earning_date, pre_announcement_window, post_announcement_window)
+                subset.ffill(inplace=True)
+                subset.bfill(inplace=True)
+                subset = subset / subset[0]
+                all_normalized_prices.append(subset.tolist())
+
+            # Display earnings data before processing
+            st.subheader("Earnings Announcements Data")
+            st.dataframe(earnings_dates)
+
+            # Plot and display charts
+            st.subheader("Stock Price with Earnings Surprises")
+            st.markdown("This chart shows the stock price movements with markers indicating earnings surprises. "
+                        "Positive earnings surprises are marked with green upward triangles, while negative surprises "
+                        "are marked with red downward triangles. The size of the marker indicates the magnitude of the surprise.")
+            st.plotly_chart(plot_stock_price_with_earnings(stock_data, earnings_dates, ticker), use_container_width=True)
+
+            st.subheader("Normalized Price Movements Around Earnings Dates")
+            st.markdown("This plot shows the normalized price movements of the stock around earnings dates. "
+                        "The prices are normalized to the price on the earnings date (Day 0). "
+                        "We analyze the price behavior before and after the earnings announcement within a specified window. "
+                        "The plot also calculates the probabilities of price movements exceeding given thresholds.")
+            st.latex(r"""
+                \text{Normalized Price} = \frac{\text{Stock Price}}{\text{Stock Price on Day 0}}
+            """)
+            st.markdown("To calculate the probabilities, we count the number of times the normalized prices exceed the upper threshold "
+                        "or fall below the lower threshold. These counts are then divided by the total number of observations to get the probabilities.")
+            st.latex(r"""
+                \text{Probability Above} = \frac{\text{Count of Prices Above Upper Threshold}}{\text{Total Observations}}
+            """)
+            st.latex(r"""
+                \text{Probability Below} = \frac{\text{Count of Prices Below Lower Threshold}}{\text{Total Observations}}
+            """)
+            st.latex(r"""
+                \text{Probability Between} = 1 - \text{Probability Above} - \text{Probability Below}
+            """)
+            st.plotly_chart(plot_normalized_price_movements(stock_data, earnings_dates, ticker, pre_announcement_window, post_announcement_window, upper_threshold, lower_threshold), use_container_width=True)
+
+            st.subheader("Volatility Around Earnings Dates")
+            st.markdown("This plot shows the 20-day rolling volatility of the stock price around earnings dates. "
+                        "Volatility is calculated as the standard deviation of daily returns over a 20-day window.")
+            st.latex(r"""
+                \sigma_{20D} = \sqrt{\frac{1}{19} \sum_{i=1}^{20} (R_i - \bar{R})^2}
+            """)
+            st.plotly_chart(plot_volatility_around_earnings(stock_data, earnings_dates), use_container_width=True)
+
+            st.subheader("Volume Around Earnings Dates")
+            st.markdown("This plot shows the trading volume changes around earnings dates. "
+                        "We analyze the volume trends within a specified window around the earnings announcements.")
+            st.plotly_chart(plot_volume_around_earnings(stock_data, earnings_dates), use_container_width=True)
+
+            price_effects = earnings_dates.index.to_series().apply(compute_price_effect, stock_data=stock_data)
+            earnings_dates[['Price Before', 'Price On', 'Price After', 'Price Effect (%)']] = pd.DataFrame(price_effects.tolist(), index=earnings_dates.index)
+            earnings_dates.dropna(subset=['Price Before', 'Price On', 'Price After'], inplace=True)
+
+            st.subheader("Price Effects Around Earnings Dates")
+            st.markdown("This bar chart compares the stock prices before, on, and after the earnings dates. "
+                        "It also shows the percentage change in price as the 'Price Effect' due to the earnings announcement.")
+            st.plotly_chart(plot_price_effects(earnings_dates), use_container_width=True)
+
+            st.subheader("Earnings Surprise vs. Price Effect")
+            st.markdown("This scatter plot shows the relationship between earnings surprise percentages and the resulting price effects. "
+                        "A regression line is fitted to show the correlation between these two variables.")
+            st.latex(r"""
+                \text{Price Effect (\%)} = \beta_0 + \beta_1 \times \text{Surprise (\%)}
+            """)
+            st.plotly_chart(plot_surprise_vs_price_effect(earnings_dates), use_container_width=True)
+
+            st.subheader("Monte Carlo Simulation for Normalized Price Movements")
+            st.markdown("This plot shows the results of a Monte Carlo simulation for normalized price movements around earnings dates. "
+                        "We simulate multiple price paths to estimate the probabilities of price movements exceeding given thresholds.")
+            window_days = list(range(-pre_announcement_window, post_announcement_window + 1))
+            st.plotly_chart(monte_carlo_normalized_prices(all_normalized_prices, upper_threshold, lower_threshold, latest_close_price, window_days, ticker), use_container_width=True)
+
+            up_target = latest_close_price * upper_threshold
+            down_target = latest_close_price * lower_threshold
+
+            st.subheader("Expected Price Range Based on Implied Volatility")
+            st.markdown("This plot shows the expected price range of the stock based on implied volatility over a specified period. "
+                        "It uses the current stock price and implied volatility to estimate the upper and lower bounds.")
+            st.latex(r"""
+                \text{Upper Bound} = S_0 \times (1 + \sigma \sqrt{t})
+            """)
+            st.latex(r"""
+                \text{Lower Bound} = S_0 \times (1 - \sigma \sqrt{t})
+            """)
+            st.plotly_chart(plot_price_ranges(ticker, implied_volatility, days_until_earnings, up_target, down_target, stock_data), use_container_width=True)
+
+            st.subheader("Monte Carlo Simulation for Price Movements")
+            st.markdown("We simulate multiple price paths using the stock's implied volatility to estimate the probabilities of the stock price reaching given targets.")
+            st.markdown("Implied volatility (IV) is used to model the expected volatility of the stock's price. "
+                        "The simulation generates random price paths based on the IV, the current stock price, and the time remaining until the earnings date.")
+            st.plotly_chart(monte_carlo_simulation(ticker, implied_volatility, days_until_earnings, up_target, down_target, stock_data, num_simulations), use_container_width=True)
+
+    except Exception as e:
+        st.error("An error occurred while running the analysis. Please try again.")
+        # Optionally log the exception or print it to the console for debugging
+        # print(e)
 
 hide_streamlit_style = """
 <style>