ENH BUG #38 vwap anchor argument fix

Makefile

@@ -28,5 +28,4 @@ test_ta:
 	python -m unittest -v -f tests/test_indicator_*.py
 
 test_utils:
-	python -m unittest -v -f tests/test_utils.py
-	python -m unittest -v -f tests/test_utils_metrics.py
+	python -m unittest -v -f tests/test_utils.py

README.md

@@ -672,6 +672,7 @@ article in the June, 1994 issue of Technical Analysis of Stocks & Commodities Ma
 * _Decreasing_ (**decreasing**): New argument ```strict``` checks if the series is continuously decreasing over period ```length```. Default: ```False```. See ```help(ta.decreasing)```.
 * _Increasing_ (**increasing**): New argument ```strict``` checks if the series is continuously increasing over period ```length```. Default: ```False```. See ```help(ta.increasing)```.
 * _Trend Return_ (**trend_return**): Returns a DataFrame now instead of Series with pertinenet trade info for a _trend_. An example can be found in the [AI Example Notebook](https://github.com/twopirllc/pandas-ta/tree/master/examples/AIExample.ipynb). The notebook is still a work in progress and open to colloboration.
+* _Volume Weighted Average Price_ (**vwap**) Added a new parameter called ```anchor```. Default: "D" for "Daily". See [Timeseries Offset Aliases](https://pandas.pydata.org/pandas-docs/stable/user_guide/timeseries.html#timeseries-offset-aliases) for additional options.
 
 <br />
 

pandas_ta/core.py

@@ -1104,7 +1104,7 @@ class AnalysisIndicators(BasePandasObject):
         result = vidya(close=close, length=length, offset=offset, **kwargs)
         return self._post_process(result, **kwargs)
 
-    def vwap(self, offset=None, **kwargs):
+    def vwap(self, anchor=None, offset=None, **kwargs):
         high = self._get_column(kwargs.pop("high", "high"))
         low = self._get_column(kwargs.pop("low", "low"))
         close = self._get_column(kwargs.pop("close", "close"))
@@ -1113,7 +1113,7 @@ class AnalysisIndicators(BasePandasObject):
         if not self.datetime_ordered:
             volume.index = self._df.index
 
-        result = vwap(high=high, low=low, close=close, volume=volume, offset=offset, **kwargs)
+        result = vwap(high=high, low=low, close=close, volume=volume, anchor=anchor, offset=offset, **kwargs)
         return self._post_process(result, **kwargs)
 
     def vwma(self, volume=None, length=None, offset=None, **kwargs):

pandas_ta/overlap/vwap.py

@@ -2,33 +2,33 @@
 from .hlc3 import hlc3
 from pandas_ta.utils import get_offset, is_datetime_ordered, verify_series
 
-def vwap(high, low, close, volume, offset=None, **kwargs):
+def vwap(high, low, close, volume, anchor=None, offset=None, **kwargs):
     """Indicator: Volume Weighted Average Price (VWAP)"""
     # Validate Arguments
     high = verify_series(high)
     low = verify_series(low)
     close = verify_series(close)
     volume = verify_series(volume)
+    anchor = anchor.upper() if anchor and isinstance(anchor, str) and len(anchor) >= 1 else "D"
     offset = get_offset(offset)
 
     typical_price = hlc3(high=high, low=low, close=close)
-
     if not is_datetime_ordered(volume):
         print(f"[!] VWAP volume series is not datetime ordered. Results may not be as expected.")
     if not is_datetime_ordered(typical_price):
         print(f"[!] VWAP price series is not datetime ordered. Results may not be as expected.")
 
     # Calculate Result
-    weighted_price = typical_price * volume
-    vwap  = weighted_price.groupby(weighted_price.index.to_period("d")).cumsum()
-    vwap /= volume.groupby(volume.index.to_period("d")).cumsum()
+    wp = typical_price * volume
+    vwap  = wp.groupby(wp.index.to_period(anchor)).cumsum()
+    vwap /= volume.groupby(volume.index.to_period(anchor)).cumsum()
 
     # Offset
     if offset != 0:
         vwap = vwap.shift(offset)
 
     # Name & Category
-    vwap.name = "VWAP"
+    vwap.name = f"VWAP_{anchor}"
     vwap.category = "overlap"
 
     return vwap
@@ -56,7 +56,11 @@ Args:
     low (pd.Series): Series of 'low's
     close (pd.Series): Series of 'close's
     volume (pd.Series): Series of 'volume's
-    offset (int): How many periods to offset the result.  Default: 0
+    anchor (str): How to anchor VWAP. Depending on the index values, it will
+        implement various Timeseries Offset Aliases as listed here:
+        https://pandas.pydata.org/pandas-docs/stable/user_guide/timeseries.html#timeseries-offset-aliases
+        Default: "D".
+    offset (int): How many periods to offset the result. Default: 0
 
 Kwargs:
     fillna (value, optional): pd.DataFrame.fillna(value)

setup.py

@@ -17,7 +17,7 @@ setup(
         "pandas_ta.volatility",
         "pandas_ta.volume"
     ],
-    version=".".join(("0", "2", "37b")),
+    version=".".join(("0", "2", "38b")),
     description=long_description,
     long_description=long_description,
     author="Kevin Johnson",

tests/test_ext_indicator_overlap_ext.py

@@ -150,7 +150,7 @@ class TestOverlapExtension(TestCase):
     def test_vwap_ext(self):
         self.data.ta.vwap(append=True)
         self.assertIsInstance(self.data, DataFrame)
-        self.assertEqual(self.data.columns[-1], "VWAP")
+        self.assertEqual(self.data.columns[-1], "VWAP_D")
 
     def test_vwma_ext(self):
         self.data.ta.vwma(append=True)

tests/test_indicator_overlap.py

@@ -331,7 +331,7 @@ class TestOverlap(TestCase):
     def test_vwap(self):
         result = pandas_ta.vwap(self.high, self.low, self.close, self.volume)
         self.assertIsInstance(result, Series)
-        self.assertEqual(result.name, "VWAP")
+        self.assertEqual(result.name, "VWAP_D")
 
     def test_vwma(self):
         result = pandas_ta.vwma(self.close, self.volume)