BUG #327 ENH #336 MAINT minor refactoring TST kvo update

README.md

@@ -114,7 +114,7 @@ $ pip install pandas_ta
 
 Latest Version
 --------------
-Best choice! Version: *0.3.05b*
+Best choice! Version: *0.3.06b*
 * Includes all fixes and updates between **pypi** and what is covered in this README.
 ```sh
 $ pip install -U git+https://github.com/twopirllc/pandas-ta
@@ -987,11 +987,13 @@ of the last bars defined by the length parameter. See ```help(ta.tos_stdevall)``
 * _Chande Kroll Stop_ (**cksp**): Added ```tvmode``` with default ```True```. When ```tvmode=False```, **cksp** implements “The New Technical Trader” with default values. See ```help(ta.cksp)```.
 * _Decreasing_ (**decreasing**): New argument ```strict``` checks if the series is continuously decreasing over period ```length``` with a faster calculation. Default: ```False```. The ```percent``` argument has also been added with default None. See ```help(ta.decreasing)```.
 * _Increasing_ (**increasing**): New argument ```strict``` checks if the series is continuously increasing over period ```length``` with a faster calculation. Default: ```False```. The ```percent``` argument has also been added with default None. See ```help(ta.increasing)```.
+* _Klinger Volume Oscillator_ (**kvo**): Implements TradingView's Klinger Volume Oscillator verion.  See ```help(ta.kvo)```.
+* _Moving Average Convergence Divergence_ (**macd**): New argument ```asmode``` enables AS version of MACD. Default is False.  See ```help(ta.macd)```.
 * _Parabolic Stop and Reverse_ (**psar**): Bug fix and adjustment to match TradingView's ```sar```. New argument ```af0``` to initialize the Acceleration Factor. See ```help(ta.psar)```.
 * _Percentage Price Oscillator_ (**ppo**): Included new argument ```mamode``` as an option. Default is **sma** to match TA Lib. See ```help(ta.ppo)```.
 * _Volume Profile_ (**vp**): Calculation improvements. See [Pull Request #320](https://github.com/twopirllc/pandas-ta/pull/320) See ```help(ta.vp)```.
-* _Volume Weighted Moving Average_ (**vwma**): Fixed bug in DataFrame Extension call. See ```help(ta.vwma)```.
 * _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. **Requires** the DataFrame index to be a DatetimeIndex. See ```help(ta.vwap)```.
+* _Volume Weighted Moving Average_ (**vwma**): Fixed bug in DataFrame Extension call. See ```help(ta.vwma)```.
 * _Z Score_ (**zscore**): Changed return column name from ```Z_length``` to ```ZS_length```. See ```help(ta.zscore)```.
 
 <br />

pandas_ta/momentum/macd.py

@@ -18,6 +18,8 @@ def macd(close, fast=None, slow=None, signal=None, offset=None, **kwargs):
 
     if close is None: return
 
+    as_mode = kwargs.setdefault("asmode", False)
+
     # Calculate Result
     if Imports["talib"]:
         from talib import MACD
@@ -30,6 +32,11 @@ def macd(close, fast=None, slow=None, signal=None, offset=None, **kwargs):
         signalma = ema(close=macd.loc[macd.first_valid_index():,], length=signal)
         histogram = macd - signalma
 
+    if as_mode:
+        macd = macd - signalma
+        signalma = ema(close=macd.loc[macd.first_valid_index():,], length=signal)
+        histogram = macd - signalma
+
     # Offset
     if offset != 0:
         macd = macd.shift(offset)
@@ -47,16 +54,17 @@ def macd(close, fast=None, slow=None, signal=None, offset=None, **kwargs):
         signalma.fillna(method=kwargs["fill_method"], inplace=True)
 
     # Name and Categorize it
+    _asmode = "AS" if as_mode else ""
     _props = f"_{fast}_{slow}_{signal}"
-    macd.name = f"MACD{_props}"
-    histogram.name = f"MACDh{_props}"
-    signalma.name = f"MACDs{_props}"
+    macd.name = f"MACD{_asmode}{_props}"
+    histogram.name = f"MACD{_asmode}h{_props}"
+    signalma.name = f"MACD{_asmode}s{_props}"
     macd.category = histogram.category = signalma.category = "momentum"
 
     # Prepare DataFrame to return
     data = {macd.name: macd, histogram.name: histogram, signalma.name: signalma}
     df = DataFrame(data)
-    df.name = f"MACD{_props}"
+    df.name = f"MACD{_asmode}{_props}"
     df.category = macd.category
 
     signal_indicators = kwargs.pop("signal_indicators", False)
@@ -105,6 +113,7 @@ the difference of MACD and Signal.
 
 Sources:
     https://www.tradingview.com/wiki/MACD_(Moving_Average_Convergence/Divergence)
+    AS Mode: https://tr.tradingview.com/script/YFlKXHnP/
 
 Calculation:
     Default Inputs:
@@ -114,6 +123,11 @@ Calculation:
     Signal = EMA(MACD, signal)
     Histogram = MACD - Signal
 
+    if asmode:
+        MACD = MACD - Signal
+        Signal = EMA(MACD, signal)
+        Histogram = MACD - Signal
+
 Args:
     close (pd.Series): Series of 'close's
     fast (int): The short period. Default: 12
@@ -122,6 +136,8 @@ Args:
     offset (int): How many periods to offset the result. Default: 0
 
 Kwargs:
+    asmode (value, optional): When True, enables AS version of MACD.
+        Default: False
     fillna (value, optional): pd.DataFrame.fillna(value)
     fill_method (value, optional): Type of fill method
 

pandas_ta/utils/data/yahoofinance.py

@@ -87,7 +87,14 @@ def yf(ticker: str, **kwargs):
 
         # Ticker Info & Chart History
         yfd = yfra.Ticker(ticker)
-        df = yfd.history(period=period, interval=interval, proxy=proxy, **kwargs)
+
+        try:
+            df = yfd.history(period=period, interval=interval, proxy=proxy, **kwargs)
+        except:
+            if yfra.__version__ == "0.1.60":
+                print(f"[!] If history is not downloading, see yfinance Issue #760 by user djl0.")
+                print(f"[!] https://github.com/ranaroussi/yfinance/issues/760#issuecomment-877355832")
+                return
 
         if df.empty: return
         df.name = ticker

pandas_ta/volume/kvo.py

@@ -1,18 +1,17 @@
 # -*- coding: utf-8 -*-
-from numpy import where as npWhere
 from pandas import DataFrame
 from pandas_ta.overlap import hlc3, ma
-from pandas_ta.utils import get_drift, get_offset, non_zero_range, verify_series
+from pandas_ta.utils import get_drift, get_offset, signed_series, verify_series
 
 
-def kvo(high, low, close, volume, fast=None, slow=None, length_sig=None, mamode=None, drift=None, offset=None, **kwargs):
+def kvo(high, low, close, volume, fast=None, slow=None, signal=None, mamode=None, drift=None, offset=None, **kwargs):
     """Indicator: Klinger Volume Oscillator (KVO)"""
     # Validate arguments
     fast = int(fast) if fast and fast > 0 else 34
     slow = int(slow) if slow and slow > 0 else 55
-    length_sig = int(length_sig) if length_sig and length_sig > 0 else 13
+    signal = int(signal) if signal and signal > 0 else 13
     mamode = mamode.lower() if mamode and isinstance(mamode, str) else "ema"
-    _length = max(fast, slow, length_sig)
+    _length = max(fast, slow, signal)
     high = verify_series(high, _length)
     low = verify_series(low, _length)
     close = verify_series(close, _length)
@@ -23,19 +22,10 @@ def kvo(high, low, close, volume, fast=None, slow=None, length_sig=None, mamode=
     if high is None or low is None or close is None or volume is None: return
 
     # Calculate Result
-    mom = hlc3(high, low, close).diff(drift)
-    trend = npWhere(mom > 0, 1, 0) + npWhere(mom < 0, -1, 0)
-    dm = non_zero_range(high, low)
-
-    m = high.size
-    cm = [0] * m
-    for i in range(1, m):
-        cm[i] = (cm[i - 1] + dm[i]) if trend[i] == trend[i - 1] else (dm[i - 1] + dm[i])
-
-    vf = 100 * volume * trend * abs(2 * dm / cm - 1)
-
-    kvo = ma(mamode, vf, length=fast) - ma(mamode, vf, length=slow)
-    kvo_signal = ma(mamode, kvo, length=length_sig)
+    signed_volume = volume * signed_series(hlc3(high, low, close), 1)
+    sv = signed_volume.loc[signed_volume.first_valid_index():,]
+    kvo = ma(mamode, sv, length=fast) - ma(mamode, sv, length=slow)
+    kvo_signal = ma(mamode, kvo.loc[kvo.first_valid_index():,], length=signal)
 
     # Offset
     if offset != 0:
@@ -51,17 +41,18 @@ def kvo(high, low, close, volume, fast=None, slow=None, length_sig=None, mamode=
         kvo_signal.fillna(method=kwargs["fill_method"], inplace=True)
 
     # Name and Categorize it
-    kvo.name = f"KVO_{fast}_{slow}"
-    kvo_signal.name = f"KVOSig_{length_sig}"
+    _props = f"_{fast}_{slow}_{signal}"
+    kvo.name = f"KVO{_props}"
+    kvo_signal.name = f"KVOs{_props}"
     kvo.category = kvo_signal.category = "volume"
 
     # Prepare DataFrame to return
     data = {kvo.name: kvo, kvo_signal.name: kvo_signal}
-    kvoandsig = DataFrame(data)
-    kvoandsig.name = f"KVO_{fast}_{slow}_{length_sig}"
-    kvoandsig.category = kvo.category
+    df = DataFrame(data)
+    df.name = f"KVO{_props}"
+    df.category = kvo.category
 
-    return kvoandsig
+    return df
 
 
 kvo.__doc__ = \
@@ -71,23 +62,17 @@ This indicator was developed by Stephen J. Klinger. It is designed to predict
 price reversals in a market by comparing volume to price.
 
 Sources:
-    https://www.tradingview.com/script/Qnn7ymRK-Klinger-Volume-Oscillator/
+    https://www.investopedia.com/terms/k/klingeroscillator.asp
     https://www.daytrading.com/klinger-volume-oscillator
 
 Calculation:
     Default Inputs:
-        fast=34, slow=55, length_sig=13, drift=1
-    MOM = HLC3.diff(drift)
-    NEG_TREND = -1 if MOM < 0 else 0
-    POS_TREND =  1 if MOM > 0 else 0
-    TREND = POS_TREND + NEG_TREND
-    DM = high - low
-    CM = [CMt-1 + DMt if TRENDt == TRENDt-1 else DMt-1 + DMt]
-
-    vf = 100 * volume * TREND * abs(2 * dm / cm - 1)
-    kvo = ema(vf, fast) - ema(vf, slow)
-    kvo_signal = ema(kvo, length_sig)
+        fast=34, slow=55, signal=13, drift=1
+    EMA = Exponential Moving Average
 
+    SV = volume * signed_series(HLC3, 1)
+    KVO = EMA(SV, fast) - EMA(SV, slow)
+    Signal = EMA(KVO, signal)
 
 Args:
     high (pd.Series): Series of 'high's
@@ -105,5 +90,5 @@ Kwargs:
     fill_method (value, optional): Type of fill method
 
 Returns:
-    pd.DataFrame: kvo and kvo_signal columns.
+    pd.DataFrame: KVO and Signal columns.
 """

pandas_ta/volume/nvi.py

@@ -17,7 +17,7 @@ def nvi(close, volume, length=None, initial=None, offset=None, **kwargs):
 
     # Calculate Result
     roc_ = roc(close=close, length=length)
-    signed_volume = signed_series(volume, initial=1)
+    signed_volume = signed_series(volume, 1)
     nvi = signed_volume[signed_volume < 0].abs() * roc_
     nvi.fillna(0, inplace=True)
     nvi.iloc[0] = initial

pandas_ta/volume/pvi.py

@@ -16,9 +16,8 @@ def pvi(close, volume, length=None, initial=None, offset=None, **kwargs):
     if close is None or volume is None: return
 
     # Calculate Result
-    roc_ = roc(close=close, length=length)
-    signed_volume = signed_series(volume, initial=1)
-    pvi = signed_volume[signed_volume > 0].abs() * roc_
+    signed_volume = signed_series(volume, 1)
+    pvi = roc(close=close, length=length) * signed_volume[signed_volume > 0].abs()
     pvi.fillna(0, inplace=True)
     pvi.iloc[0] = initial
     pvi = pvi.cumsum()

pandas_ta/volume/pvol.py

@@ -11,10 +11,9 @@ def pvol(close, volume, offset=None, **kwargs):
     signed = kwargs.pop("signed", False)
 
     # Calculate Result
+    pvol = close * volume
     if signed:
-        pvol = signed_series(close, 1) * close * volume
-    else:
-        pvol = close * volume
+         pvol *= signed_series(close, 1)
 
     # Offset
     if offset != 0:

pandas_ta/volume/vp.py

@@ -16,10 +16,10 @@ def vp(close, volume, width=None, **kwargs):
     if close is None or volume is None: return
 
     # Setup
-    signed_price = signed_series(close, initial=1)
-    pos_volume = signed_price[signed_price > 0] * volume
+    signed_price = signed_series(close, 1)
+    pos_volume = volume * signed_price[signed_price > 0]
     pos_volume.name = volume.name
-    neg_volume = signed_price[signed_price < 0] * -volume
+    neg_volume = -volume * signed_price[signed_price < 0]
     neg_volume.name = volume.name
     vp = concat([close, pos_volume, neg_volume], axis=1)
 

setup.py

@@ -19,7 +19,7 @@ setup(
         "pandas_ta.volatility",
         "pandas_ta.volume"
     ],
-    version=".".join(("0", "3", "05b")),
+    version=".".join(("0", "3", "06b")),
     description=long_description,
     long_description=long_description,
     author="Kevin Johnson",

tests/test_ext_indicator_volume.py

@@ -63,7 +63,7 @@ class TestVolumeExtension(TestCase):
     def test_kvo_ext(self):
         self.data.ta.kvo(append=True)
         self.assertIsInstance(self.data, DataFrame)
-        self.assertEqual(self.data.columns[-1], "KVOSig_13")
+        self.assertEqual(list(self.data.columns[-2:]), ["KVO_34_55_13", "KVOs_34_55_13"])
 
     def test_mfi_ext(self):
         self.data.ta.mfi(append=True)

tests/test_indicator_momentum.py

@@ -243,6 +243,11 @@ class TestMomentum(TestCase):
             except Exception as ex:
                 error_analysis(result.iloc[:, 2], CORRELATION, ex, newline=False)
 
+    def test_macdas(self):
+        result = pandas_ta.macd(self.close, asmode=True)
+        self.assertIsInstance(result, DataFrame)
+        self.assertEqual(result.name, "MACDAS_12_26_9")
+
     def test_mom(self):
         result = pandas_ta.mom(self.close)
         self.assertIsInstance(result, Series)