diff --git a/jesse/indicators/keltner.py b/jesse/indicators/keltner.py
index 2883afde2..8769a2e89 100644
--- a/jesse/indicators/keltner.py
+++ b/jesse/indicators/keltner.py
@@ -1,45 +1,60 @@
 from collections import namedtuple
-
 import numpy as np
-
+from numba import njit
 from jesse.helpers import get_candle_source, slice_candles
 from jesse.indicators.ma import ma
 
 KeltnerChannel = namedtuple('KeltnerChannel', ['upperband', 'middleband', 'lowerband'])
 
 
+@njit
 def _atr(high: np.ndarray, low: np.ndarray, close: np.ndarray, period: int) -> np.ndarray:
-    tr = np.empty_like(high)
+    """
+    Calculate ATR using Numba
+    """
+    n = len(close)
+    tr = np.empty(n)
+    atr_vals = np.full(n, np.nan)
+    
+    # Calculate True Range
     tr[0] = high[0] - low[0]
-    # Compute true range for the rest of the candles using vectorized operations
-    tr[1:] = np.maximum(
-        np.maximum(high[1:] - low[1:], np.abs(high[1:] - close[:-1])),
-        np.abs(low[1:] - close[:-1])
-    )
-
-    atr_vals = np.empty_like(tr, dtype=float)
-    # Not enough data for ATR in the first period-1 candles
-    atr_vals[:period-1] = float('nan')
-    # The first ATR value is a simple average
+    for i in range(1, n):
+        hl = high[i] - low[i]
+        hc = abs(high[i] - close[i-1])
+        lc = abs(low[i] - close[i-1])
+        tr[i] = max(max(hl, hc), lc)
+    
+    if n < period:
+        return atr_vals
+        
+    # First ATR value is the simple average of the first 'period' true ranges
     atr_vals[period-1] = np.mean(tr[:period])
-
-    # Wilder's smoothing method for subsequent values using vectorized operation with lfilter
-    if len(tr) > period:
-        alpha = 1 / period
-        from scipy.signal import lfilter
-        A0 = atr_vals[period - 1]  # initial ATR from simple average
-        x = tr[period:]
-        # Set the initial condition such that y[0] = (1 - alpha)*A0 + alpha*tr[period]
-        zi = [(1 - alpha) * A0]
-        y, _ = lfilter([alpha], [1, -(1 - alpha)], x, zi=zi)
-        atr_vals[period:] = y
+    
+    # Calculate subsequent ATR values using Wilder's smoothing
+    for i in range(period, n):
+        atr_vals[i] = (atr_vals[i-1] * (period - 1) + tr[i]) / period
+        
     return atr_vals
 
 
-def keltner(candles: np.ndarray, period: int = 20, multiplier: float = 2, matype: int = 1, source_type: str = "close",
-            sequential: bool = False) -> KeltnerChannel:
+@njit
+def _calculate_keltner(source: np.ndarray, high: np.ndarray, low: np.ndarray, close: np.ndarray, 
+                      ma_values: np.ndarray, period: int, multiplier: float) -> tuple:
     """
-    Keltner Channels
+    Core Keltner Channel calculation using Numba
+    """
+    atr_vals = _atr(high, low, close, period)
+    
+    up = ma_values + atr_vals * multiplier
+    low = ma_values - atr_vals * multiplier
+    
+    return up, ma_values, low
+
+
+def keltner(candles: np.ndarray, period: int = 20, multiplier: float = 2, matype: int = 1, 
+           source_type: str = "close", sequential: bool = False) -> KeltnerChannel:
+    """
+    Keltner Channels using Numba for optimization
 
     :param candles: np.ndarray
     :param period: int - default: 20
@@ -50,16 +65,20 @@ def keltner(candles: np.ndarray, period: int = 20, multiplier: float = 2, matype
 
     :return: KeltnerChannel(upperband, middleband, lowerband)
     """
-
     candles = slice_candles(candles, sequential)
 
     source = get_candle_source(candles, source_type=source_type)
-    e = ma(source, period=period, matype=matype, sequential=True)
-    a = _atr(candles[:, 3], candles[:, 4], candles[:, 2], period)
-
-    up = e + a * multiplier
-    mid = e
-    low = e - a * multiplier
+    ma_values = ma(source, period=period, matype=matype, sequential=True)
+    
+    up, mid, low = _calculate_keltner(
+        source, 
+        candles[:, 3],  # high
+        candles[:, 4],  # low
+        candles[:, 2],  # close
+        ma_values,
+        period,
+        multiplier
+    )
 
     if sequential:
         return KeltnerChannel(up, mid, low)