Update utils.py

utils.py

@@ -7,6 +7,33 @@ import plotly.graph_objects as go
 from plotly.subplots import make_subplots
 import spaces
 
+def clean_value(v, default_val=0.0):
+    """
+    Mengonversi tipe NumPy dan float yang tidak valid (NaN/Inf) 
+    ke nilai yang aman untuk JSON.
+    """
+    if v is None:
+        return default_val
+    
+    # Periksa apakah itu float (Python atau NumPy) dan apakah itu NaN atau Inf
+    if isinstance(v, (float, np.float64, np.float32)) and (np.isnan(v) or np.isinf(v)):
+        return default_val
+    
+    # Konversi int NumPy ke int Python
+    if isinstance(v, (np.int64, np.int32)):
+        return int(v)
+    
+    # Konversi float NumPy ke float Python
+    if isinstance(v, (np.float64, np.float32)):
+        return float(v)
+
+    # Jika sudah merupakan tipe Python standar, kembalikan apa adanya
+    if isinstance(v, (int, float)):
+        return v
+
+    # Fallback untuk tipe lain (misalnya, jika sudah string)
+    return v
+
 def get_indonesian_stocks():
     return {
         "BBCA.JK": "Bank Central Asia",
@@ -40,7 +67,10 @@ def calculate_technical_indicators(data):
         rs = gain / loss
         rsi = 100 - (100 / (1 + rs))
         return rsi
-    indicators['rsi'] = {'current': calculate_rsi(data['Close']).iloc[-1], 'values': calculate_rsi(data['Close'])}
+    
+    rsi_val = calculate_rsi(data['Close']).iloc[-1]
+    indicators['rsi'] = {'current': clean_value(rsi_val), 'values': calculate_rsi(data['Close'])}
+    
     def calculate_macd(prices, fast=12, slow=26, signal=9):
         exp1 = prices.ewm(span=fast).mean()
         exp2 = prices.ewm(span=slow).mean()
@@ -48,30 +78,71 @@ def calculate_technical_indicators(data):
         signal_line = macd.ewm(span=signal).mean()
         histogram = macd - signal_line
         return macd, signal_line, histogram
+    
     macd, signal_line, histogram = calculate_macd(data['Close'])
-    indicators['macd'] = {'macd': macd.iloc[-1], 'signal': signal_line.iloc[-1], 'histogram': histogram.iloc[-1], 'signal_text': 'BUY' if histogram.iloc[-1] > 0 else 'SELL', 'macd_values': macd, 'signal_values': signal_line}
+    macd_hist_val = histogram.iloc[-1]
+    indicators['macd'] = {
+        'macd': clean_value(macd.iloc[-1]), 
+        'signal': clean_value(signal_line.iloc[-1]), 
+        'histogram': clean_value(macd_hist_val), 
+        'signal_text': 'BUY' if macd_hist_val > 0 else 'SELL', 
+        'macd_values': macd, 
+        'signal_values': signal_line
+    }
+    
     def calculate_bollinger_bands(prices, period=20, std_dev=2):
         sma = prices.rolling(window=period).mean()
         std = prices.rolling(window=period).std()
         upper_band = sma + (std * std_dev)
         lower_band = sma - (std * std_dev)
         return upper_band, sma, lower_band
+    
     upper, middle, lower = calculate_bollinger_bands(data['Close'])
     current_price = data['Close'].iloc[-1]
-    bb_position = (current_price - lower.iloc[-1]) / (upper.iloc[-1] - lower.iloc[-1])
+    
+    bb_upper_val = upper.iloc[-1]
+    bb_lower_val = lower.iloc[-1]
+    
+    # Hindari pembagian dengan nol jika upper == lower
+    if (bb_upper_val - bb_lower_val) == 0:
+        bb_position_val = 0.5 # Default ke tengah
+    else:
+        bb_position_val = (current_price - bb_lower_val) / (bb_upper_val - bb_lower_val)
+
     indicators['bollinger'] = {
-        'upper': upper.iloc[-1],
-        'middle': middle.iloc[-1],
-        'lower': lower.iloc[-1],
+        'upper': clean_value(bb_upper_val),
+        'middle': clean_value(middle.iloc[-1]),
+        'lower': clean_value(bb_lower_val),
         'upper_values': upper,
         'middle_values': middle,
         'lower_values': lower,
-        'position': 'UPPER' if bb_position > 0.8 else 'LOWER' if bb_position < 0.2 else 'MIDDLE'
+        'position': 'UPPER' if bb_position_val > 0.8 else 'LOWER' if bb_position_val < 0.2 else 'MIDDLE'
     }
+    
     sma_20_series = data['Close'].rolling(20).mean()
     sma_50_series = data['Close'].rolling(50).mean()
-    indicators['moving_averages'] = {'sma_20': sma_20_series.iloc[-1], 'sma_50': sma_50_series.iloc[-1], 'sma_200': data['Close'].rolling(200).mean().iloc[-1], 'ema_12': data['Close'].ewm(span=12).mean().iloc[-1], 'ema_26': data['Close'].ewm(span=26).mean().iloc[-1], 'sma_20_values': sma_20_series, 'sma_50_values': sma_50_series}
-    indicators['volume'] = {'current': data['Volume'].iloc[-1], 'avg_20': data['Volume'].rolling(20).mean().iloc[-1], 'ratio': data['Volume'].iloc[-1] / data['Volume'].rolling(20).mean().iloc[-1]}
+    
+    indicators['moving_averages'] = {
+        'sma_20': clean_value(sma_20_series.iloc[-1]), 
+        'sma_50': clean_value(sma_50_series.iloc[-1]), 
+        'sma_200': clean_value(data['Close'].rolling(200).mean().iloc[-1]), 
+        'ema_12': clean_value(data['Close'].ewm(span=12).mean().iloc[-1]), 
+        'ema_26': clean_value(data['Close'].ewm(span=26).mean().iloc[-1]), 
+        'sma_20_values': sma_20_series, 
+        'sma_50_values': sma_50_series
+    }
+    
+    vol_current = data['Volume'].iloc[-1]
+    vol_avg_20 = data['Volume'].rolling(20).mean().iloc[-1]
+    
+    # Hindari pembagian dengan nol jika vol_avg_20 adalah 0
+    vol_ratio = vol_current / vol_avg_20 if vol_avg_20 > 0 else 0.0
+    
+    indicators['volume'] = {
+        'current': clean_value(vol_current, 0), 
+        'avg_20': clean_value(vol_avg_20, 0), 
+        'ratio': clean_value(vol_ratio)
+    }
     return indicators
 
 def generate_trading_signals(data, indicators):
@@ -80,7 +151,8 @@ def generate_trading_signals(data, indicators):
     buy_signals = 0
     sell_signals = 0
     signal_details = []
-    rsi = indicators['rsi']['current']
+    
+    rsi = indicators['rsi']['current'] # Sudah di-clean dari fungsi sebelumnya
     if rsi < 30:
         buy_signals += 1
         signal_details.append(f"✅ RSI ({rsi:.1f}) - Oversold - BUY signal")
@@ -89,13 +161,15 @@ def generate_trading_signals(data, indicators):
         signal_details.append(f"❌ RSI ({rsi:.1f}) - Overbought - SELL signal")
     else:
         signal_details.append(f"⚪ RSI ({rsi:.1f}) - Neutral")
-    macd_hist = indicators['macd']['histogram']
+        
+    macd_hist = indicators['macd']['histogram'] # Sudah di-clean
     if macd_hist > 0:
         buy_signals += 1
         signal_details.append(f"✅ MACD Histogram ({macd_hist:.4f}) - Positive - BUY signal")
     else:
         sell_signals += 1
         signal_details.append(f"❌ MACD Histogram ({macd_hist:.4f}) - Negative - SELL signal")
+        
     bb_position = indicators['bollinger']['position']
     if bb_position == 'LOWER':
         buy_signals += 1
@@ -105,8 +179,9 @@ def generate_trading_signals(data, indicators):
         signal_details.append(f"❌ Bollinger Bands - Near upper band - SELL signal")
     else:
         signal_details.append("⚪ Bollinger Bands - Middle position")
-    sma_20 = indicators['moving_averages']['sma_20']
-    sma_50 = indicators['moving_averages']['sma_50']
+        
+    sma_20 = indicators['moving_averages']['sma_20'] # Sudah di-clean
+    sma_50 = indicators['moving_averages']['sma_50'] # Sudah di-clean
     if current_price > sma_20 > sma_50:
         buy_signals += 1
         signal_details.append(f"✅ Price above MA(20,50) - Bullish - BUY signal")
@@ -115,7 +190,8 @@ def generate_trading_signals(data, indicators):
         signal_details.append(f"❌ Price below MA(20,50) - Bearish - SELL signal")
     else:
         signal_details.append("⚪ Moving Averages - Mixed signals")
-    volume_ratio = indicators['volume']['ratio']
+        
+    volume_ratio = indicators['volume']['ratio'] # Sudah di-clean
     if volume_ratio > 1.5:
         buy_signals += 0.5
         signal_details.append(f"✅ High volume ({volume_ratio:.1f}x avg) - Strengthens BUY signal")
@@ -124,12 +200,24 @@ def generate_trading_signals(data, indicators):
         signal_details.append(f"❌ Low volume ({volume_ratio:.1f}x avg) - Weakens SELL signal")
     else:
         signal_details.append(f"⚪ Normal volume ({volume_ratio:.1f}x avg)")
+        
     total_signals = buy_signals + sell_signals
     signal_strength = (buy_signals / max(total_signals, 1)) * 100
     overall_signal = "BUY" if buy_signals > sell_signals else "SELL" if sell_signals > buy_signals else "HOLD"
+    
     recent_high = data['High'].tail(20).max()
     recent_low = data['Low'].tail(20).min()
-    signals = {'overall': overall_signal, 'strength': signal_strength, 'details': '\n'.join(signal_details), 'support': recent_low, 'resistance': recent_high, 'stop_loss': recent_low * 0.95 if overall_signal == "BUY" else recent_high * 1.05}
+    
+    stop_loss_val = recent_low * 0.95 if overall_signal == "BUY" else recent_high * 1.05
+    
+    signals = {
+        'overall': overall_signal, 
+        'strength': clean_value(signal_strength), 
+        'details': '\n'.join(signal_details), 
+        'support': clean_value(recent_low), 
+        'resistance': clean_value(recent_high), 
+        'stop_loss': clean_value(stop_loss_val)
+    }
     return signals
 
 def get_fundamental_data(stock):
@@ -142,15 +230,17 @@ def get_fundamental_data(stock):
         raw_pe = info.get('forwardPE', 0)
         raw_div_yield = info.get('dividendYield', 0) * 100 if info.get('dividendYield') else 0
         raw_volume = history['Volume'].iloc[-1] if not history.empty else 0
+        
+        market_cap_clean = clean_value(raw_market_cap, 0)
 
         fundamental_info = {
             'name': info.get('longName', 'N/A'), 
-            'current_price': float(raw_price), 
-            'market_cap': int(raw_market_cap), 
-            'pe_ratio': float(raw_pe), 
-            'dividend_yield': float(raw_div_yield), 
-            'volume': int(raw_volume), 
-            'info': f"Sector: {info.get('sector', 'N/A')}\nIndustry: {info.get('industry', 'N/A')}\nMarket Cap: {int(raw_market_cap):,}\n52 Week High: {info.get('fiftyTwoWeekHigh', 'N/A')}\n52 Week Low: {info.get('fiftyTwoWeekLow', 'N/A')}\nBeta: {info.get('beta', 'N/A')}\nEPS: {info.get('forwardEps', 'N/A')}\nBook Value: {info.get('bookValue', 'N/A')}\nPrice to Book: {info.get('priceToBook', 'N/A')}"
+            'current_price': clean_value(raw_price), 
+            'market_cap': market_cap_clean,
+            'pe_ratio': clean_value(raw_pe), 
+            'dividend_yield': clean_value(raw_div_yield), 
+            'volume': clean_value(raw_volume, 0), 
+            'info': f"Sector: {info.get('sector', 'N/A')}\nIndustry: {info.get('industry', 'N/A')}\nMarket Cap: {int(market_cap_clean):,}\n52 Week High: {info.get('fiftyTwoWeekHigh', 'N/A')}\n52 Week Low: {info.get('fiftyTwoWeekLow', 'N/A')}\nBeta: {info.get('beta', 'N/A')}\nEPS: {info.get('forwardEps', 'N/A')}\nBook Value: {info.get('bookValue', 'N/A')}\nPrice to Book: {info.get('priceToBook', 'N/A')}"
         }
         return fundamental_info
     except:
@@ -180,13 +270,29 @@ def predict_prices(data, model=None, tokenizer=None, prediction_days=30):
         if forecast_np.ndim > 1:
             mean_forecast = forecast_np.mean(axis=tuple(range(forecast_np.ndim - 1)))
         else:
-            mean_forecast = forecast_np
+            mean_forecast = mean_forecast
+            
         last_price = prices[-1]
         predicted_high = float(np.max(mean_forecast))
         predicted_low = float(np.min(mean_forecast))
         predicted_mean = float(np.mean(mean_forecast))
+        
+        # Hindari pembagian dengan nol
         change_pct = ((predicted_mean - last_price) / last_price) * 100 if last_price != 0 else 0
-        return {'values': mean_forecast, 'dates': pd.date_range(start=data.index[-1] + timedelta(days=1), periods=len(mean_forecast), freq='D'), 'high_30d': predicted_high, 'low_30d': predicted_low, 'mean_30d': predicted_mean, 'change_pct': change_pct, 'summary': f"AI Model: Amazon Chronos-Bolt (Base)\nPredicted High: {predicted_high:.2f}\nPredicted Low: {predicted_low:.2f}\nExpected Change: {change_pct:.2f}%"}
+        
+        high_clean = clean_value(predicted_high)
+        low_clean = clean_value(predicted_low)
+        change_clean = clean_value(change_pct)
+
+        return {
+            'values': mean_forecast, 
+            'dates': pd.date_range(start=data.index[-1] + timedelta(days=1), periods=len(mean_forecast), freq='D'), 
+            'high_30d': high_clean,
+            'low_30d': low_clean,
+            'mean_30d': clean_value(predicted_mean), 
+            'change_pct': change_clean,
+            'summary': f"AI Model: Amazon Chronos-Bolt (Base)\nPredicted High: {high_clean:.2f}\nPredicted Low: {low_clean:.2f}\nExpected Change: {change_clean:.2f}%"
+        }
     except Exception as e:
         print(f"Error in prediction: {e}")
         return {'values': [], 'dates': [], 'high_30d': 0, 'low_30d': 0, 'mean_30d': 0, 'change_pct': 0, 'summary': f'Model error: {e}'}