Update app.py

app.py

@@ -82,7 +82,7 @@ class CacheManager:
                     df[col] = pd.to_datetime(df[col])
             # If the first column is the index, set it
             if 'Date' in df.columns and df.columns[0] == 'Date':
-                 df.set_index('Date', inplace=True)
+                df.set_index('Date', inplace=True)
             return df
         except Exception:
             return None
@@ -121,7 +121,7 @@ class StockAnalyzer:
         cache_file = f"{self.ticker}_sentiment.csv"
         df_daily = CacheManager.load_df(cache_file)
         if df_daily is not None and not self.force_refresh:
-            return df_daily, None # Detailed news_df not needed from cache
+            return df_daily, None  # Detailed news_df not needed from cache
 
         end_date = datetime.now()
         start_date = end_date - timedelta(days=Config.SENTIMENT_DAYS)
@@ -219,6 +219,8 @@ class StockAnalyzer:
     @staticmethod
     def _calculate_indicators(df: pd.DataFrame) -> pd.DataFrame:
         """Calculates a suite of technical indicators."""
+        df = df.copy()  # Avoid modifying original
+
         # RSI
         delta = df['Close'].diff()
         gain = (delta.where(delta > 0, 0)).rolling(14).mean()
@@ -238,9 +240,20 @@ class StockAnalyzer:
         df['SlowK'] = ((df['Close'] - ll) / (hh - ll)) * 100
         df['SlowD'] = df['SlowK'].rolling(3).mean()
 
-        # Chaikin Money Flow (CMF)
-        mfv = ((df['Close'] - df['Low']) - (df['High'] - df['Close'])) / (df['High'] - df['Low']) * df['Volume']
-        df['CMF'] = mfv.rolling(20).sum() / df['Volume'].rolling(20).sum()
+        # Chaikin Money Flow (CMF) — FIXED SECTION
+        price_range = df['High'] - df['Low']
+        # Avoid division by zero
+        price_range = price_range.replace(0, np.nan)
+        mfv = ((df['Close'] - df['Low']) - (df['High'] - df['Close'])) / price_range * df['Volume']
+
+        mfv_sum = mfv.rolling(20).sum()
+        vol_sum = df['Volume'].rolling(20).sum()
+
+        # Use .values to prevent pandas alignment errors
+        cmf_raw = mfv_sum.values / vol_sum.values
+        # Replace inf/-inf with NaN
+        cmf_clean = np.where(np.isfinite(cmf_raw), cmf_raw, np.nan)
+        df['CMF'] = cmf_clean
 
         # Signals (using stricter thresholds)
         df['RSI_Signal'] = np.where(df['RSI'] < 20, 1, np.where(df['RSI'] > 80, -1, 0))
@@ -248,6 +261,7 @@ class StockAnalyzer:
         df['Stochastic_Signal'] = np.where((df['SlowK'] < 15) & (df['SlowD'] < 15), 1, np.where((df['SlowK'] > 85) & (df['SlowD'] > 85), -1, 0))
         df['CMF_Signal'] = np.where(df['CMF'] < -0.25, 1, np.where(df['CMF'] > 0.25, -1, 0))
         df['Technical_Score'] = df[['RSI_Signal', 'BB_Signal', 'Stochastic_Signal', 'CMF_Signal']].sum(axis=1)
+
         return df
 
 # ============================================================================
@@ -342,9 +356,9 @@ class Plotter:
 # ============================================================================
 def run_full_analysis(tickers_str: str, time_range: str, show_bollinger: bool, force_refresh: bool, progress=gr.Progress()):
     """Main function triggered by the Gradio button."""
-    tickers = [t.strip().upper() for t in tickers_str.split(',') if t.strip()][:5] # Limit to 5 tickers
+    tickers = [t.strip().upper() for t in tickers_str.split(',') if t.strip()][:5]  # Limit to 5 tickers
     if not tickers:
-        return "Please enter at least one ticker.", None, gr.Accordion(visible=False)
+        return "Please enter at least one ticker.", None, gr.Column(visible=False)
 
     progress(0, desc="Starting analysis...")
     all_results = {}
@@ -353,14 +367,14 @@ def run_full_analysis(tickers_str: str, time_range: str, show_bollinger: bool, f
         try:
             analyzer = StockAnalyzer(ticker, force_refresh)
             if analyzer.tech_df.empty:
-                continue # Skip if no data
+                continue  # Skip if no data
             all_results[ticker] = analyzer
         except Exception as e:
             print(f"Error analyzing {ticker}: {e}")
             continue
 
     if not all_results:
-        return "Could not retrieve data for the entered tickers.", None, gr.Accordion(visible=False)
+        return "Could not retrieve data for the entered tickers.", None, gr.Column(visible=False)
 
     # 1. Create the comparative multi-ticker plot
     multi_plot = Plotter.create_multi_ticker_plot(
@@ -372,11 +386,13 @@ def run_full_analysis(tickers_str: str, time_range: str, show_bollinger: bool, f
     accordion_items = []
     for ticker, analyzer in all_results.items():
         # Summary Tab Content
+        current_price = analyzer.tech_df['Close'].iloc[-1] if not analyzer.tech_df.empty else 0.0
+        avg_sent = analyzer.sentiment_daily['avg_sentiment'].mean() if analyzer.sentiment_daily is not None else 0.0
         summary_md = f"""
         ### 🎯 Decision: **{analyzer.decision}** (Score: {analyzer.total_score}/6)
-        - **Current Price:** `${analyzer.tech_df['Close'].iloc[-1]:.2f}`
+        - **Current Price:** `${current_price:.2f}`
         - **Technical Score:** `{analyzer.scores['Technical']}`
-        - **Sentiment Score:** `{analyzer.scores['Sentiment']}` (Avg: {analyzer.sentiment_daily['avg_sentiment'].mean():.2f})
+        - **Sentiment Score:** `{analyzer.scores['Sentiment']}` (Avg: {avg_sent:.2f})
         - **Forecast Score:** `{analyzer.scores['Forecast']}` ({analyzer.forecast_pct:.1f}% change to `${analyzer.forecast_price:.2f}`)
         """
         gauge_plot = Plotter.create_decision_gauge(analyzer.decision, analyzer.total_score)
@@ -394,16 +410,22 @@ def run_full_analysis(tickers_str: str, time_range: str, show_bollinger: bool, f
         # Assemble the accordion item
         ticker_accordion = gr.Accordion(
             label=f"📊 {ticker} Analysis",
-            open=ticker == tickers[0] # Open the first one by default
+            open=ticker == tickers[0]  # Open the first one by default
         )
         with ticker_accordion:
             with gr.Tabs():
                 with gr.TabItem("📈 Summary & Decision"):
                     summary_col.render()
                 with gr.TabItem("😊 Sentiment Analysis"):
-                    gr.Plot(sentiment_plot).render() if isinstance(sentiment_plot, go.Figure) else sentiment_plot.render()
+                    if isinstance(sentiment_plot, go.Figure):
+                        gr.Plot(sentiment_plot).render()
+                    else:
+                        sentiment_plot.render()
                 with gr.TabItem("🔮 Forecast"):
-                    gr.Plot(forecast_plot).render() if isinstance(forecast_plot, go.Figure) else forecast_plot.render()
+                    if isinstance(forecast_plot, go.Figure):
+                        gr.Plot(forecast_plot).render()
+                    else:
+                        forecast_plot.render()
         accordion_items.append(ticker_accordion)
 
     progress(1, "Analysis complete!")
@@ -411,7 +433,7 @@ def run_full_analysis(tickers_str: str, time_range: str, show_bollinger: bool, f
 
 
 # --- Build the Gradio App ---
-with gr.Blocks(theme=gr.themes.Default(primary_hue="blue", secondary_hue="blue"), title="Stock Intelligence Dashboard") as demo:
+with gr.Blocks(theme=gr.themes.Default(primary_hue="black", secondary_hue="purple"), title="Stock Intelligence Dashboard") as demo:
     gr.Markdown("# 📊 Unified Stock Intelligence Dashboard")
     gr.Markdown("An advanced tool for technical, sentiment, and predictive analysis of stocks.")