Update data_processor.py

data_processor.py

@@ -10,12 +10,13 @@ class DataProcessor:
     def get_market_data(self, ticker="GC=F", interval="1d"):
         """Fetch market data from Yahoo Finance for a given ticker"""
         try:
+            # FIX: Gunakan string interval yang didukung langsung oleh Yahoo/yfinance
             interval_map = {
                 "5m": "5m",
                 "15m": "15m",
                 "30m": "30m",
-                "1h": "60m",
-                "4h": "240m",
+                "1h": "1h",
+                "4h": "4h",
                 "1d": "1d",
                 "1wk": "1wk",
                 "1mo": "1mo",
@@ -34,10 +35,15 @@ class DataProcessor:
                 period = "max"
             
             ticker_obj = yf.Ticker(ticker)
+            # Mengatasi masalah data kripto yang mungkin tidak tersedia dalam periode lama
+            if ticker == "BTC-USD" and period == "max":
+                period = "5y" 
+                
             df = ticker_obj.history(interval=yf_interval, period=period)
             
             if df.empty:
-                raise ValueError(f"No data retrieved from Yahoo Finance for {ticker}")
+                # Perbarui pesan error untuk memperjelas log
+                raise ValueError(f"No data retrieved from Yahoo Finance for {ticker} at interval {interval}. Check if ticker/interval is supported.")
             
             df.columns = [col.capitalize() for col in df.columns]
             
@@ -48,41 +54,33 @@ class DataProcessor:
             return pd.DataFrame()
     
     def calculate_indicators(self, df):
-        """Calculate technical indicators"""
         if df.empty:
             return df
         
-        # Simple Moving Averages (5, 20 as requested)
         df['SMA_5'] = df['Close'].rolling(window=5).mean()
         df['SMA_20'] = df['Close'].rolling(window=20).mean()
         
-        # Exponential Moving Averages
         df['EMA_12'] = df['Close'].ewm(span=12, adjust=False).mean()
         df['EMA_26'] = df['Close'].ewm(span=26, adjust=False).mean()
         
-        # MACD (12, 26, 9)
         df['MACD'] = df['EMA_12'] - df['EMA_26']
         df['MACD_signal'] = df['MACD'].ewm(span=9, adjust=False).mean()
         df['MACD_histogram'] = df['MACD'] - df['MACD_signal']
         
-        # Split histogram into positive and negative for plotting
         df['MACD_bar_positive'] = df['MACD_histogram'].where(df['MACD_histogram'] > 0, 0)
         df['MACD_bar_negative'] = df['MACD_histogram'].where(df['MACD_histogram'] < 0, 0)
         
-        # RSI
         delta = df['Close'].diff()
         gain = (delta.where(delta > 0, 0)).rolling(window=14).mean()
         loss = (-delta.where(delta < 0, 0)).rolling(window=14).mean()
         rs = gain / loss
         df['RSI'] = 100 - (100 / (1 + rs))
         
-        # Bollinger Bands
         df['BB_middle'] = df['Close'].rolling(window=20).mean()
         bb_std = df['Close'].rolling(window=20).std()
         df['BB_upper'] = df['BB_middle'] + (bb_std * 2)
         df['BB_lower'] = df['BB_middle'] - (bb_std * 2)
         
-        # Average True Range (ATR)
         high_low = df['High'] - df['Low']
         high_close = np.abs(df['High'] - df['Close'].shift())
         low_close = np.abs(df['Low'] - df['Close'].shift())
@@ -90,23 +88,20 @@ class DataProcessor:
         true_range = ranges.max(axis=1)
         df['ATR'] = true_range.rolling(window=14).mean()
         
-        # Volume indicators
         df['Volume_SMA'] = df['Volume'].rolling(window=20).mean()
         df['Volume_ratio'] = df['Volume'] / df['Volume_SMA']
         
-        # Stochastic Oscillator (14, 3)
         low_14 = df['Low'].rolling(window=14).min()
         high_14 = df['High'].rolling(window=14).max()
         df['%K'] = 100 * (df['Close'] - low_14) / (high_14 - low_14)
         df['%D'] = df['%K'].rolling(window=3).mean()
         df['%SD'] = df['%D'].rolling(window=3).mean()
-        df['UL'] = 70  # Upper limit
-        df['DL'] = 30  # Lower limit
+        df['UL'] = 70
+        df['DL'] = 30
         
         return df
     
     def get_fundamental_data(self, ticker="GC=F"):
-        """Get fundamental gold market data (now generalized/mocked)"""
         try:
             if ticker == "BTC-USD":
                 fundamentals = {
@@ -135,7 +130,6 @@ class DataProcessor:
             return {"Error": str(e)}
     
     def prepare_for_chronos(self, df, lookback=100):
-        """Prepare data for Chronos model"""
         if df.empty or len(df) < lookback:
             return None