Merge pull request #330 from bukosabino/develop-linter-vpt

Fixing linter details
bukosabino · Nov 2, 2023 · 5654782 · 5654782
2 parents 663d16a + 9c70287
commit 5654782
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Showing 3 changed files with 50 additions and 25 deletions.
diff --git a/ta/volume.py b/ta/volume.py
@@ -6,6 +6,8 @@
 
 """
 
+import typing as tp
+
 import numpy as np
 import pandas as pd
 
@@ -269,7 +271,14 @@ class VolumePriceTrendIndicator(IndicatorMixin):
         dropnans(bool)=False: drop nans after indicator calculated.
     """
 
-    def __init__(self, close: pd.Series, volume: pd.Series, fillna: bool = False, smoothing_factor: int = None, dropnans:bool = False):
+    def __init__(
+        self,
+        close: pd.Series,
+        volume: pd.Series,
+        fillna: bool = False,
+        smoothing_factor: tp.Optional[int] = None,
+        dropnans: bool = False,
+    ):
         self._close = close
         self._volume = volume
         self._fillna = fillna
@@ -280,9 +289,12 @@ def __init__(self, close: pd.Series, volume: pd.Series, fillna: bool = False, sm
     def _run(self):
         self._vpt = (self._close.pct_change() * self._volume).cumsum()
         if self._smoothing_factor:
-            min_periods = 0 if self._fillna else self._smoothing_factor 
-            self._vpt = self._vpt.rolling(self._smoothing_factor, min_periods=min_periods).mean()
-        if self._dropnans: self._vpt = self._vpt.dropna()
+            min_periods = 0 if self._fillna else self._smoothing_factor
+            self._vpt = self._vpt.rolling(
+                self._smoothing_factor, min_periods=min_periods
+            ).mean()
+        if self._dropnans:
+            self._vpt = self._vpt.dropna()
 
     def volume_price_trend(self) -> pd.Series:
         """Volume-price trend (VPT)
@@ -612,7 +624,9 @@ def sma_ease_of_movement(high, low, volume, window=14, fillna=False):
     ).sma_ease_of_movement()
 
 
-def volume_price_trend(close, volume, fillna=False, smoothing_factor: int=None, dropnans: bool=False):
+def volume_price_trend(
+    close, volume, fillna=False, smoothing_factor: tp.Optional[int] = None, dropnans: bool = False
+):
     """Volume-price trend (VPT)
 
     Is based on a running cumulative volume that adds or substracts a multiple
@@ -632,7 +646,11 @@ def volume_price_trend(close, volume, fillna=False, smoothing_factor: int=None,
         pandas.Series: New feature generated.
     """
     return VolumePriceTrendIndicator(
-        close=close, volume=volume, fillna=fillna, smoothing_factor=smoothing_factor, dropnans=dropnans
+        close=close,
+        volume=volume,
+        fillna=fillna,
+        smoothing_factor=smoothing_factor,
+        dropnans=dropnans,
     ).volume_price_trend()
 
 

diff --git a/test/__init__.py b/test/__init__.py
@@ -35,8 +35,8 @@
     TestForceIndexIndicator,
     TestMFIIndicator,
     TestOnBalanceVolumeIndicator,
-    TestVolumeWeightedAveragePrice,
     TestVolumePriceTrendIndicator,
+    TestVolumeWeightedAveragePrice,
 )
 
 __all__ = [

diff --git a/test/unit/volume.py b/test/unit/volume.py
@@ -8,16 +8,16 @@
     ForceIndexIndicator,
     MFIIndicator,
     OnBalanceVolumeIndicator,
-    VolumeWeightedAveragePrice,
     VolumePriceTrendIndicator,
+    VolumeWeightedAveragePrice,
     acc_dist_index,
     ease_of_movement,
     force_index,
     money_flow_index,
     on_balance_volume,
     sma_ease_of_movement,
+    volume_price_trend,
     volume_weighted_average_price,
-    volume_price_trend
 )
 
 
@@ -260,14 +260,16 @@ def test_vwap2(self):
             self._df[target].tail(), result.tail(), check_names=False
         )
 
+
 class TestVolumePriceTrendIndicator(unittest.TestCase):
     """
     Original VPT: https://en.wikipedia.org/wiki/Volume%E2%80%93price_trend
     One more: https://www.barchart.com/education/technical-indicators/price_volume_trend
     According to TradingView: PVT = [((CurrentClose - PreviousClose) / PreviousClose) x Volume] + PreviousPVT
-    
+
     Smoothed version (by Alex Orekhov (everget)): https://ru.tradingview.com/script/3Ah2ALck-Price-Volume-Trend/
-    His script is using `pvt` (TradingView built-in variable) as described in TradingView documentation of PVT and just smoothing it with ema or sma by choice.
+    His script is using `pvt` (TradingView built-in variable) as described in TradingView documentation of PVT and
+    just smoothing it with ema or sma by choice.
     You can find smoothing here (13 row of script):
     `signal = signalType == "EMA" ? ema(pvt, signalLength) : sma(pvt, signalLength)`
     """
@@ -277,20 +279,24 @@ class TestVolumePriceTrendIndicator(unittest.TestCase):
     @classmethod
     def setUpClass(cls):
         cls._df = pd.read_csv(cls._filename, sep=",")
-        cls._params = dict( # default VPT params, unsmoothed
-            close=cls._df["Close"],
-            volume=cls._df["Volume"],
-            fillna=False,
-            smoothing_factor=None,
-            dropnans=False
-        )
-        cls._params_smoothed = dict( # smoothed VPT params
-            close=cls._df["Close"],
-            volume=cls._df["Volume"],
-            fillna=False,
-            smoothing_factor=14,
-            dropnans=False
-        )
+
+        # default VPT params, unsmoothed
+        cls._params = {
+            "close": cls._df['Close'],
+            "volume": cls._df['Volume'],
+            "fillna": False,
+            "smoothing_factor": None,
+            "dropnans": False,
+        }
+
+        # smoothed VPT params
+        cls._params_smoothed = {
+            "close": cls._df['Close'],
+            "volume": cls._df['Volume'],
+            "fillna": False,
+            "smoothing_factor": 14,
+            "dropnans": False,
+        }
         cls._indicator_default = VolumePriceTrendIndicator(**cls._params)
         cls._indicator_smoothed = VolumePriceTrendIndicator(**cls._params_smoothed)
 
@@ -326,5 +332,6 @@ def test_vpt4(self):
             self._df[target].tail(), result.tail(), check_names=False
         )
 
+
 if __name__ == "__main__":
     unittest.main()