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

enginev0.py

@@ -0,0 +1,98 @@
+import pandas as pd
+import requests
+import os
+from datetime import datetime
+from ta.trend import EMAIndicator
+from ta.momentum import StochasticOscillator
+
+class DataEngine:
+    def __init__(self, symbol, url):
+        self.symbol = symbol
+        self.url = url
+        self.cache_dir = "data_cache"
+        if not os.path.exists(self.cache_dir):
+            os.makedirs(self.cache_dir)
+        self.file_path = os.path.join(self.cache_dir, f"{self.symbol}.csv")
+
+    def fetch_data(self):
+        # 1. Try to load existing local data
+        local_df = pd.DataFrame()
+        if os.path.exists(self.file_path):
+            try:
+                local_df = pd.read_csv(self.file_path, parse_dates=['date'])
+                # If data is fresh (from today), return it immediately
+                if not local_df.empty and local_df['date'].max().date() >= datetime.today().date():
+                    return local_df
+            except Exception as e:
+                print(f"Cache read error for {self.symbol}: {e}")
+
+        # 2. Fetch new data from URL
+        try:
+            headers = {'User-Agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36'}
+            response = requests.get(self.url, headers=headers, timeout=10)
+            response.raise_for_status()
+            json_data = response.json()
+            
+            # Parsing Logic (Handles JPM and generic JSON)
+            if isinstance(json_data, dict) and "historicalNAVList" in json_data:
+                new_df = pd.DataFrame(json_data["historicalNAVList"])
+                new_df = new_df.rename(columns={'navPrice': 'close'})
+            else:
+                new_df = pd.DataFrame(json_data)
+            
+            new_df['date'] = pd.to_datetime(new_df['date'])
+            
+            # 3. Merge and Save
+            if not local_df.empty:
+                # Combine, drop duplicates (keep newest), and sort
+                df = pd.concat([local_df, new_df]).drop_duplicates(subset=['date'], keep='last')
+            else:
+                df = new_df
+
+            df = df.sort_values('date').dropna(subset=['close'])
+            df.to_csv(self.file_path, index=False)
+            return df
+
+        except Exception as e:
+            print(f"Network error for {self.symbol}: {e}")
+            return local_df if not local_df.empty else None
+
+    def calculate_table_metrics(self, df):
+        if df is None or df.empty or len(df) < 2:
+            return None
+        
+        last_close = float(df.iloc[-1]['close'])
+        prev_close = float(df.iloc[-2]['close'])
+        change_pct = ((last_close - prev_close) / prev_close) * 100
+        
+        count = len(df)
+        df_52 = df.tail(min(count, 252))
+        
+        # EMA Calculations (Returns % offset from Price)
+        def get_ema_offset(window):
+            if count >= window:
+                ema = EMAIndicator(close=df['close'], window=window).ema_indicator().iloc[-1]
+                return round(((last_close / ema) * 100) - 100, 1)
+            return "N/A"
+
+        # Stochastic Logic
+        k_val = d_val = "N/A"
+        if count >= 14:
+            # Note: Using rolling close as proxy for High/Low since many URLs only provide Close
+            rolling_high = df['close'].rolling(window=14).max()
+            rolling_low = df['close'].rolling(window=14).min()
+            stoch = StochasticOscillator(high=rolling_high, low=rolling_low, close=df['close'], window=14, smooth_window=3)
+            k_val = round(stoch.stoch().iloc[-1], 0)
+            d_val = round(stoch.stoch_signal().iloc[-1], 0)
+
+        return {
+            "last_close": round(last_close, 2),
+            "change_pct": round(change_pct, 2),
+            "low_52": round(float(df_52['close'].min()), 2),
+            "high_52": round(float(df_52['close'].max()), 2),
+            "last_ema20": get_ema_offset(20),
+            "last_ema50": get_ema_offset(50),
+            "last_ema100": get_ema_offset(100),
+            "last_ema200": get_ema_offset(200),
+            "kd_values": f"{k_val}/{d_val}" if k_val != "N/A" else "N/A"
+        }