base version only KD indicator with jpm, ft yahoo as sources

appv0.py

@@ -0,0 +1,88 @@
+from flask import Flask, render_template, jsonify
+from engine import DataEngine
+import concurrent.futures
+from flask_caching import Cache
+import csv
+import os
+import logging
+
+app = Flask(__name__)
+
+# Configure caching
+cache = Cache(app, config={'CACHE_TYPE': 'SimpleCache'})
+
+# Configure logging
+logging.basicConfig(level=logging.DEBUG, format='%(asctime)s - %(levelname)s - %(message)s')
+
+# Function to load instruments from a CSV file
+def load_instruments_from_csv(file_path):
+    instruments = []
+    try:
+        abs_path = os.path.join(os.path.dirname(__file__), file_path)
+        # 'utf-8-sig' handles the hidden characters Excel often adds
+        with open(abs_path, mode='r', encoding='utf-8-sig') as csvfile:
+            # Clean spaces from column names automatically
+            reader = csv.DictReader(csvfile)
+            reader.fieldnames = [name.strip().lower() for name in reader.fieldnames]
+            
+            for row in reader:
+                symbol = row.get('symbol', '').strip()
+                cusip = row.get('cusip', '').strip()
+                
+                if symbol and cusip:
+                    url = f"https://am.jpmorgan.com/FundsMarketingHandler/historicalData?cusip={cusip}&country=hk&role=per&userLoggedIn=false&language=en&version=6.9.0_1684"
+                    instruments.append({"symbol": symbol, "url": url})
+        
+        logging.info(f"Successfully loaded {len(instruments)} instruments.")
+    except Exception as e:
+        logging.error(f"Error reading CSV file: {e}")
+    return instruments
+
+# Load instruments from CSV
+URL_CONFIG = load_instruments_from_csv('instruments.csv')
+
+# Log the contents of URL_CONFIG to verify instruments are loaded
+logging.debug(f"Loaded instruments: {URL_CONFIG}")
+
+@cache.memoize(timeout=86400)  # Cache results for 24 hours
+def fetch_and_calculate(config):
+    try:
+        logging.debug(f"Fetching data for: {config['symbol']}")
+        engine = DataEngine(config['url'])
+        df = engine.fetch_data()
+        if df is None or df.empty:
+            logging.warning(f"No data fetched for: {config['symbol']}")
+            return None
+        
+        metrics = engine.calculate_table_metrics(df)
+        if metrics:
+            metrics['symbol'] = config['symbol']
+            logging.debug(f"Metrics calculated for {config['symbol']}: {metrics}")
+            return metrics
+        logging.warning(f"Metrics calculation failed for: {config['symbol']}")
+        return None
+    except Exception as e:
+        logging.error(f"Error processing {config['symbol']}: {e}")
+        return None
+
+@app.route('/')
+def index():
+    return render_template('index.html')
+
+@app.route('/api/summary')
+def get_summary():
+    results = []
+    # Use ThreadPoolExecutor for faster parallel fetching
+    with concurrent.futures.ThreadPoolExecutor(max_workers=5) as executor:
+        future_to_config = {executor.submit(fetch_and_calculate, cfg): cfg for cfg in URL_CONFIG}
+        for future in concurrent.futures.as_completed(future_to_config):
+            res = future.result()
+            if res:
+                results.append(res)
+    
+    # Sort by symbol name
+    results.sort(key=lambda x: x['symbol'])
+    return jsonify(results)
+
+if __name__ == '__main__':
+    app.run(debug=True, host='0.0.0.0', port=5000)  # Changed port back to 5000