Merge pull request #434 from kakulukia/fixed-beta-indicator

Fixed beta indicator
jesse-ai · Apr 13, 2024 · 666a8bc · 666a8bc
2 parents 200085e + bed8465
commit 666a8bc
Show file tree

Hide file tree

Showing 5 changed files with 2,663 additions and 2,055 deletions.
diff --git a/jesse/indicators/bandpass.py b/jesse/indicators/bandpass.py
@@ -26,7 +26,6 @@ def bandpass(candles: np.ndarray, period: int = 20, bandwidth: float = 0.3,  sou
 
     source = get_candle_source(candles, source_type=source_type)
 
-
     hp = high_pass_fast(source, 4 * period / bandwidth)
 
     beta = np.cos(2 * np.pi / period)

diff --git a/jesse/indicators/beta.py b/jesse/indicators/beta.py
@@ -6,18 +6,20 @@
 from jesse.helpers import slice_candles
 
 
-def beta(candles: np.ndarray, period: int = 5, sequential: bool = False) -> Union[float, np.ndarray]:
+def beta(candles: np.ndarray, benchmark_candles: np.ndarray, period: int = 5, sequential: bool = False) -> Union[float, np.ndarray]:
     """
-    BETA - Beta
+    BETA - compares the given candles close price to its benchmark (should be in the same time frame)
 
     :param candles: np.ndarray
+    :param benchmark_candles: np.ndarray
     :param period: int - default: 5
     :param sequential: bool - default: False
 
     :return: float | np.ndarray
     """
     candles = slice_candles(candles, sequential)
+    benchmark_candles = slice_candles(benchmark_candles, sequential)
 
-    res = talib.BETA(candles[:, 3], candles[:, 4], timeperiod=period)
+    res = talib.BETA(candles[:, 2], benchmark_candles[:, 2], timeperiod=period)
 
     return res if sequential else res[-1]
diff --git a/jesse/indicators/ttm_trend.py b/jesse/indicators/ttm_trend.py
@@ -7,7 +7,7 @@
 
 
 def ttm_trend(candles: np.ndarray, period: int = 5, source_type: str = "hl2", sequential: bool = False) -> Union[
-    float, np.ndarray]:
+        bool, np.ndarray]:
     """
     TTM Trend
 
@@ -23,6 +23,6 @@ def ttm_trend(candles: np.ndarray, period: int = 5, source_type: str = "hl2", se
     source = get_candle_source(candles, source_type=source_type)
     swv = sliding_window_view(source, window_shape=period)
     trend_avg = np.mean(swv, axis=-1)
-    res = np.greater(candles[:, 2], same_length(source,trend_avg))
+    res = np.greater(candles[:, 2], same_length(source, trend_avg))
 
     return res if sequential else res[-1]