Remove wavelet denoising function and related tests; update requirements

- Deleted the `wavelet_denoising` function from `jesse/utils.py`, which was designed for signal processing using wavelet transforms.
- Removed the corresponding test case from `tests/test_utils.py` to reflect the function's removal.
- Updated `requirements.txt` to exclude the `PyWavelets` dependency, as it is no longer needed.
- These changes streamline the codebase by eliminating unused functionality and dependencies.

jesse/utils.py

@@ -313,31 +313,6 @@ def combinations_without_repeat(a: np.ndarray, n: int = 2) -> np.ndarray:
     return np.array(list(permutations(a, n)))
 
 
-def wavelet_denoising(raw: np.ndarray, wavelet='haar', level: int = 1, mode: str = 'symmetric',
-                      smoothing_factor: float = 0, threshold_mode: str = 'hard') -> np.ndarray:
-    """
-    deconstructs, thresholds then reconstructs
-    higher thresholds = less detailed reconstruction
-
-    Only consider haar, db, sym, coif wavelet basis functions, as these are relatively suitable for financial data
-    """
-    import pywt
-    # Deconstruct
-    coeff = pywt.wavedec(raw, wavelet, mode=mode)
-    # Mean absolute deviation of a signal
-    max_level = pywt.dwt_max_level(len(raw), wavelet)
-    level = min(level, max_level)
-    madev = np.mean(np.absolute(coeff[-level] - np.mean(coeff[-level])))
-    # The hardcored factor is explained here: https://en.wikipedia.org/wiki/Median_absolute_deviation
-    sigma = (1 / 0.67449) * madev * smoothing_factor
-    threshold = sigma * np.sqrt(2 * np.log(len(raw)))
-    coeff[1:] = (pywt.threshold(i, value=threshold, mode=threshold_mode) for i in coeff[1:])
-    signal = pywt.waverec(coeff, wavelet, mode=mode)
-    if len(signal) > len(raw):
-        signal = np.delete(signal, -1)
-    return signal
-
-
 def calculate_alpha_beta(returns1: np.ndarray, returns2: np.ndarray) -> tuple:
     # Add a constant to the independent variable (returns2)
     X = sm.add_constant(returns2)  # Independent variable

requirements.txt

@@ -9,7 +9,6 @@ psycopg2-binary~=2.9.9
 pydash~=6.0.0
 fnc~=0.5.3
 pytest~=6.2.5
-PyWavelets~=1.2.0
 requests~=2.32.0
 scipy~=1.15.0
 statsmodels~=0.14.4

tests/test_utils.py

@@ -176,12 +176,6 @@ def test_combinations_without_repeat():
                                                [3, 1]]))
 
 
-def test_wavelet_denoising():
-    candles = np.array(test_candles_19)
-    denoised = utils.wavelet_denoising(candles[:, 2], wavelet="sym4", level=1, mode='symmetric', smoothing_factor=2)
-    assert len(candles) == len(denoised)
-
-
 def test_timeframe_to_one_minutes():
     assert utils.timeframe_to_one_minutes("1m") == 1
     assert utils.timeframe_to_one_minutes("3m") == 3