Update app.py

app.py

@@ -10,20 +10,33 @@ import logging
 logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(levelname)s - %(message)s')
 logger = logging.getLogger(__name__)
 
+# Global variable to store the full results
+full_results = None
+output_file_path = None
+
 def run_crypto_analysis(symbols, interval):
     end_date = datetime.today().strftime("%Y-%m-%d")
     start_date = (datetime.today() - timedelta(days=365*5)).strftime("%Y-%m-%d")
     if symbols:
         symbols_list = [symbol.strip().upper() for symbol in symbols.split(",")]
     else:
-        symbols_list = get_top_crypto_symbols()[:10]  # Limit to top 10 for faster processing
+        symbols_list = get_top_crypto_symbols()[:100]  # Analyze top 100 cryptocurrencies
+    
+    logger.info(f"Analyzing {len(symbols_list)} cryptocurrencies")
     
     all_data = []
     for symbol in symbols_list:
-        data = analyze_crypto(symbol, start_date, end_date, interval)
-        if data is not None and not data.empty:
-            data['Symbol'] = symbol
-            all_data.append(data)
+        try:
+            data = analyze_crypto(symbol, start_date, end_date, interval)
+            if data is not None and not data.empty:
+                data['Symbol'] = symbol
+                all_data.append(data)
+            else:
+                logger.warning(f"No data returned for cryptocurrency: {symbol}")
+        except Exception as e:
+            logger.error(f"Error analyzing cryptocurrency {symbol}: {str(e)}")
+    
+    logger.info(f"Crypto analysis complete. Data available for {len(all_data)} cryptocurrencies")
     
     if all_data:
         combined_data = pd.concat(all_data)
@@ -37,10 +50,10 @@ def run_crypto_analysis(symbols, interval):
         os.makedirs("output", exist_ok=True)
         combined_data.to_csv(output_file, index=False)
         logger.info(f"Crypto analysis complete. Output saved to {output_file}")
-        return combined_data.head(15), output_file
+        return combined_data, output_file
     else:
-        logger.warning("No data available for the selected crypto parameters.")
-        return "No data available for the selected parameters.", None
+        logger.warning("No data available for any of the selected crypto parameters.")
+        return pd.DataFrame(), None
 
 def run_asset_analysis(symbols, interval):
     end_date = datetime.today().strftime("%Y-%m-%d")
@@ -48,14 +61,23 @@ def run_asset_analysis(symbols, interval):
     if symbols:
         symbols_list = [symbol.strip().upper() for symbol in symbols.split(",")]
     else:
-        symbols_list = get_sp500_tickers()[:10]  # Limit to top 10 for faster processing
+        symbols_list = get_sp500_tickers()
+    
+    logger.info(f"Analyzing {len(symbols_list)} symbols")
     
     all_data = []
     for symbol in symbols_list:
-        data = analyze_asset(symbol, start_date, end_date, interval, asset_type='stock')
-        if data is not None and not data.empty:
-            data['Symbol'] = symbol
-            all_data.append(data)
+        try:
+            data = analyze_asset(symbol, start_date, end_date, interval, asset_type='stock')
+            if data is not None and not data.empty:
+                data['Symbol'] = symbol
+                all_data.append(data)
+            else:
+                logger.warning(f"No data returned for symbol: {symbol}")
+        except Exception as e:
+            logger.error(f"Error analyzing symbol {symbol}: {str(e)}")
+    
+    logger.info(f"Analysis complete. Data available for {len(all_data)} symbols")
     
     if all_data:
         combined_data = pd.concat(all_data)
@@ -69,47 +91,117 @@ def run_asset_analysis(symbols, interval):
         os.makedirs("output", exist_ok=True)
         combined_data.to_csv(output_file, index=False)
         logger.info(f"Asset analysis complete. Output saved to {output_file}")
-        return combined_data.head(15), output_file
+        return combined_data, output_file
     else:
-        logger.warning("No data available for the selected asset parameters.")
-        return "No data available for the selected parameters.", None
+        logger.warning("No data available for any of the selected asset parameters.")
+        return pd.DataFrame(), None
 
-def main_interface(analysis_type, symbols, interval):
-    logger.info(f"Main interface called with: analysis_type={analysis_type}, symbols={symbols}, interval={interval}")
+def filter_latest_signals(df, signal_column):
+    if df.empty:
+        logger.warning("Empty dataframe passed to filter_latest_signals")
+        return df
+    
+    logger.info(f"Filtering signals. Input shape: {df.shape}")
+    
+    # Group by Symbol and get the latest date for each
+    latest_dates = df.groupby('Symbol')['Date'].max().reset_index()
+    
+    # Merge with the original dataframe to get the latest signals
+    latest_signals = pd.merge(df, latest_dates, on=['Symbol', 'Date'])
+    
+    logger.info(f"Latest signals shape after merge: {latest_signals.shape}")
+    
+    # Filter for the chosen signal
+    if signal_column != 'All':
+        latest_signals = latest_signals[latest_signals[signal_column] != '']
+        logger.info(f"Filtered for {signal_column}. Shape: {latest_signals.shape}")
+    else:
+        # Remove rows where all signal columns are empty
+        signal_columns = ['Signal_1x', 'Signal_2x', 'Signal_3x', 'VuManchu_Signal']
+        latest_signals = latest_signals[latest_signals[signal_columns].ne('').any(axis=1)]
+        logger.info(f"Removed rows with all empty signals. Shape: {latest_signals.shape}")
+    
+    result = latest_signals.sort_values('Date', ascending=False)
+    logger.info(f"Final filtered result shape: {result.shape}")
+    return result
+
+def generate_signals(analysis_type, symbols, interval):
+    global full_results, output_file_path
+    logger.info(f"Generating signals: analysis_type={analysis_type}, symbols={symbols}, interval={interval}")
     try:
         if analysis_type == "Cryptocurrency":
-            result, file = run_crypto_analysis(symbols, interval)
+            full_results, output_file_path = run_crypto_analysis(symbols, interval)
         else:
-            result, file = run_asset_analysis(symbols, interval)
+            full_results, output_file_path = run_asset_analysis(symbols, interval)
         
-        if isinstance(result, pd.DataFrame):
-            return result, file
+        if isinstance(full_results, pd.DataFrame) and not full_results.empty:
+            logger.info(f"Analysis result shape: {full_results.shape}")
+            filtered_result = filter_latest_signals(full_results, 'All')
+            logger.info(f"Filtered result shape: {filtered_result.shape}")
+            return filtered_result, output_file_path
         else:
-            return result, None
+            logger.warning("No data available from analysis")
+            return "No data available for the selected parameters.", None
     except Exception as e:
-        logger.error(f"An error occurred in main_interface: {e}")
+        logger.error(f"An error occurred in generate_signals: {e}")
         return f"An error occurred: {str(e)}", None
 
+def apply_filter(signal_filter):
+    global full_results
+    if full_results is None or full_results.empty:
+        return "No data available. Please generate signals first.", None
+    
+    filtered_result = filter_latest_signals(full_results, signal_filter)
+    return filtered_result, output_file_path
+
 with gr.Blocks() as iface:
     gr.Markdown("# VuManchu Trading Signals Analysis")
-    gr.Markdown("Perform technical analysis on cryptocurrencies or stocks using the VuManchu swing trading strategy and SuperTrend indicators. Select the analysis type, input desired symbols or use the defaults, choose the time interval, and view or download the generated trading signals. The sample shows the 15 most recent entries.")
-    gr.Markdown("Research Project & Not Financial Advice")
-                
+    
+    gr.Markdown("""
+    ## Legal Disclaimer
+
+    **IMPORTANT: Please read this disclaimer carefully before using this tool.**
+
+    This VuManchu Trading Signals Analysis tool is provided for educational and informational purposes only. It does not constitute financial advice, trading advice, or any other type of professional advice. The creators and distributors of this tool are not financial advisors and do not purport to provide any financial or investment guidance.
+
+    The information and signals generated by this tool are based on historical data and technical analysis techniques. Past performance is not indicative of future results. The financial markets are inherently risky, and all trading and investment decisions carry the risk of loss.
+
+    By using this tool, you acknowledge and agree that:
+
+    1. You are solely responsible for any trading or investment decisions you make.
+    2. The creators and distributors of this tool are not liable for any losses or damages resulting from your use of, or reliance on, the information provided.
+    3. You should always conduct your own research and due diligence before making any financial decisions.
+    4. You should consult with a qualified financial advisor before making any investment or trading decisions.
+
+    Use of this tool constitutes acceptance of this disclaimer and an acknowledgment of the inherent risks associated with trading and investing.
+    """)
+    
+    gr.Markdown("Perform technical analysis on cryptocurrencies or stocks using the VuManchu swing trading strategy and SuperTrend indicators. Select the analysis type, input desired symbols or use the defaults, choose the time interval, and view or download the generated trading signals. The table shows the latest trading signals for each symbol, excluding rows with no signals.")
+    
     with gr.Row():
         analysis_type = gr.Radio(["Cryptocurrency", "Asset"], label="Select Analysis Type")
         symbols = gr.Textbox(label="Enter symbols (comma-separated) or leave blank for default", placeholder="e.g., BTC,ETH,ADA or AAPL,MSFT,GOOGL")
         interval = gr.Radio(["1d", "1wk"], label="Select Time Interval")
     
-    submit_button = gr.Button("Generate Signals")
+    with gr.Row():
+        signal_filter = gr.Dropdown(["All", "Signal_1x", "Signal_2x", "Signal_3x", "VuManchu_Signal"], label="Filter by Latest Signal", value="All")
+    
+    generate_button = gr.Button("Generate Signals")
     
-    output_dataframe = gr.Dataframe(label="Sample of Latest Trading Signals: High to Low Risk ordered")
+    output_dataframe = gr.Dataframe(label="Latest Trading Signals")
     output_file = gr.File(label="Download Full Signals CSV")
     
-    submit_button.click(
-        main_interface,
+    generate_button.click(
+        generate_signals,
         inputs=[analysis_type, symbols, interval],
         outputs=[output_dataframe, output_file]
     )
+    
+    signal_filter.change(
+        apply_filter,
+        inputs=[signal_filter],
+        outputs=[output_dataframe, output_file]
+    )
 
 if __name__ == "__main__":
     logger.info("Starting Gradio interface")