ADD more indictaors, BB,RSI and Z-score

engine.py

@@ -375,7 +375,39 @@ class DataEngine:
         prev_close = float(df.iloc[-2]['close'])
         change_pct = ((last_close - prev_close) / prev_close) * 100
         count = len(df)
+        # --- NEW INDICATORS START ---
+    
+        # Bollinger Bands %B (20-day)
+        bb_pct = "N/A"
+        if count >= 20:
+            from ta.volatility import BollingerBands
+            indicator_bb = BollingerBands(close=df['close'], window=20, window_dev=2)
+            m_avg = indicator_bb.bollinger_mavg().iloc[-1]
+            h_band = indicator_bb.bollinger_hband().iloc[-1]
+            l_band = indicator_bb.bollinger_lband().iloc[-1]
+            # Calculate %B: where is price relative to bands?
+            if (h_band - l_band) != 0:
+                bb_pct = round((last_close - l_band) / (h_band - l_band), 2)
 
+        # RSI (14-day)
+        rsi_val = "N/A"
+        if count >= 14:
+            from ta.momentum import RSIIndicator
+            rsi_val = round(RSIIndicator(close=df['close'], window=14).rsi().iloc[-1], 1)
+
+        # Z-Score Helper Function
+        def get_z_score(window):
+            if count >= window:
+                rolling_mean = df['close'].rolling(window=window).mean()
+                rolling_std = df['close'].rolling(window=window).std()
+                z = (last_close - rolling_mean.iloc[-1]) / rolling_std.iloc[-1]
+                return round(z, 2)
+            return "N/A"
+        
+        z60 = get_z_score(60)
+        z120 = get_z_score(120)
+        
+        # Existing EMA Logic (Distance % from EMA)
         def get_ema_offset(window):
             if count >= window:
                 from ta.trend import EMAIndicator
@@ -396,18 +428,22 @@ class DataEngine:
 
         return {
             "symbol": self.symbol,
-            "last_date": formatted_date, # <--- New field added
+            "last_date": formatted_date,
             "last_close": round(last_close, 2),
             "change_pct": round(change_pct, 2),
             "low_52": round(float(df.tail(252)['close'].min()), 2),
             "high_52": round(float(df.tail(252)['close'].max()), 2),
+            "bb_pct": bb_pct,
+            "rsi": rsi_val,
+            "z60": z60,
+            "z120": z120,
             "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"{int(k_val)}/{int(d_val)}" if k_val != "N/A" else "N/A"
         }
-
+    
 class StrategyEngine:
     """
     Handles financial strategy simulations and backtesting.