MAINT pvo vwap vwma refactor DOC readme ENH extra val

README.md

@@ -145,8 +145,9 @@ Pandas TA is used by Applications and Services like
 
 <br/>
 
-[Open BB](https://openbb.co/) (previously Gamestonk Terminal)
+[Open BB](https://openbb.co/)
 -------------------
+#### Previously **Gamestonk Terminal**
 > OpenBB is a leading open source investment analysis company.
 We represent millions of investors who want to leverage state-of-the-art data science and machine learning technologies to make sense of raw unrefined data. Our mission is to make investment research effective, powerful and accessible to everyone.
 
@@ -158,7 +159,7 @@ We represent millions of investors who want to leverage state-of-the-art data sc
 
 <br/>
 
-[VectorBT Pro & Open Source](https://vectorbt.pro/)
+[VectorBT Pro](https://vectorbt.pro/)
 -------------------
 > vectorbt PRO is the next-generation engine for backtesting, algorithmic trading, and research. It's a high-performance, actively-developed, commercial successor to the vectorbt library, one of the world's most innovative open-source backtesting engines. The PRO version extends the standard library with new impressive features and useful enhancements for professionals.
 
@@ -280,7 +281,7 @@ Back to [Contents](#contents)
 
 # **Issues and Contributions**
 
-Contributions, feedback, and bug squashing are integral to the success of this library. If something you can fix, _please_ do. Your contributon helps everyone!
+Contributions, feedback, and bug squashing are integral to the success of this library. If you see something you can fix, _please_ do. Your contributon helps us all!
 * :stop_sign: _Please_ **DO NOT** email me personally with Pandas TA Bugs, Issues or Feature Requests that are best handled with Github [Issues](https://github.com/twopirllc/pandas-ta/issues).
 
 <br/>
@@ -289,7 +290,7 @@ Contributions, feedback, and bug squashing are integral to the success of this l
 --------------------------------------
 1. Some bugs and features may already be be fixed or implemented in either the [Latest Version](#latest-version) or the the [Development Version](#development-version). _Please_ try them first.
 1. If the _Latest_ or _Development_ Versions do not resolve the bug or address the Issue, try searching both _Open_ and _Closed_ Issues **before** opening a new Issue.
-1. When you creating a new Issue, please be as **detailed** as possible **with** reproducible code, links if any, applicable screenshots, errors, logs, and data samples.
+1. When creating a new Issue, please be as **detailed** as possible **with** reproducible code, links if any, applicable screenshots, errors, logs, and data samples.
     * You **will** be asked again for skipping form questions.
     * Do you have correlation analysis to back your claim?
 
@@ -853,7 +854,7 @@ Back to [Contents](#contents)
 
 <br/>
 
-### **Momentum** (42)
+### **Momentum** (41)
 * _Awesome Oscillator_: **ao**
 * _Absolute Price Oscillator_: **apo**
 * _Bias_: **bias**
@@ -878,7 +879,6 @@ Back to [Contents](#contents)
 * _Pretty Good Oscillator_: **pgo**
 * _Percentage Price Oscillator_: **ppo**
 * _Psychological Line_: **psl**
-* _Percentage Volume Oscillator_: **pvo**
 * _Quantitative Qualitative Estimation_: **qqe**
 * _Rate of Change_: **roc**
 * _Relative Strength Index_: **rsi**
@@ -910,7 +910,7 @@ Back to [Contents](#contents)
 
 <br/>
 
-### **Overlap** (37)
+### **Overlap** (35)
 
 * _Bill Williams Alligator_: **alligator**
 * _Arnaud Legoux Moving Average_: **alma**
@@ -948,9 +948,6 @@ Back to [Contents](#contents)
 * _Triple Exponential Moving Average_: **tema**
 * _Triangular Moving Average_: **trima**
 * _Variable Index Dynamic Average_: **vidya**
-* _Volume Weighted Average Price_: **vwap**
-    * **Requires** the DataFrame index to be a DatetimeIndex
-* _Volume Weighted Moving Average_: **vwma**
 * _Weighted Closing Price_: **wcp**
 * _Weighted Moving Average_: **wma**
 * _Zero Lag Moving Average_: **zlma**
@@ -1084,7 +1081,7 @@ Back to [Contents](#contents)
 
 <br/>
 
-### **Volume** (16)
+### **Volume** (19)
 
 * _Accumulation/Distribution Index_: **ad**
 * _Accumulation/Distribution Oscillator_: **adosc**
@@ -1097,10 +1094,14 @@ Back to [Contents](#contents)
 * _Negative Volume Index_: **nvi**
 * _On-Balance Volume_: **obv**
 * _Positive Volume Index_: **pvi**
+* _Percentage Volume Oscillator_: **pvo**
 * _Price-Volume_: **pvol**
 * _Price Volume Rank_: **pvr**
 * _Price Volume Trend_: **pvt**
 * _Volume Profile_: **vp**
+* _Volume Weighted Average Price_: **vwap**
+    * **Requires** the DataFrame index to be a DatetimeIndex
+* _Volume Weighted Moving Average_: **vwma**
 * _Worden Brothers Time Segmented Value_: **wb_tsv**
 
 <br/>
@@ -1114,7 +1115,7 @@ Back to [Contents](#contents)
 <br/>
 
 # **Backtesting**
-While Pandas TA is not a backtesting application, Pandas TA does provide _two_ methods to help generate trading signals for backtesting purposes: **Trend Signals** (```ta.tsignals()```) and **Cross Signals** (```ta.xsignals()```). Both Signal methods return a DataFrame with columns for the Trend, Trades, Entries and Exits.
+While Pandas TA is not a backtesting application, it does provide _two_ trend methods that generate trading signals for backtesting purposes: **Trend Signals** (```ta.tsignals()```) and **Cross Signals** (```ta.xsignals()```). Both Signal methods return a DataFrame with columns for the signal's Trend, Trades, Entries and Exits.
 
 A simple manual backtest using **Trend Signals** can be found in the [TA Analysis Notebook](https://github.com/twopirllc/pandas-ta/blob/main/examples/TA_Analysis.ipynb) starting at _Trend Creation_ cell.
 
@@ -1123,14 +1124,14 @@ A simple manual backtest using **Trend Signals** can be found in the [TA Analysi
 Trend Signals
 -------------
 * Useful for signals based on trends or **states**.
-* Examples
+* _Examples_
     * **Golden Cross**: ```df.ta.sma(length=50) > df.ta.sma(length=200)```
     * **Positive MACD Histogram**: ```df.ta.macd().iloc[:,1] > 0```
 
 Cross Signals
 -------------
 * Useful for Signal Crossings or **events**.
-* Examples
+* _Examples_
     * RSI crosses above 30 and then below 70
     * ZSCORE crosses above -2 and then below 2.
 
@@ -1144,14 +1145,11 @@ _Ideally_ a backtesting application like [**vectorbt**](https://polakowo.io/vect
 Trend Signal Example
 --------------------
 ```python
-import pandas as pd
 import pandas_ta as ta
 import vectorbt as vbt
 
-df = pd.DataFrame()
-
 # requires 'yfinance' installed
-df = df.ta.ticker("AAPL", timed=True)
+df = ta.df.ta.ticker("AAPL", timed=True)
 
 # Create the "Golden Cross"
 df["GC"] = df.ta.sma(50, append=True) > df.ta.sma(200, append=True)
@@ -1167,6 +1165,31 @@ print(pf.stats())
 print(pf.returns_stats())
 ```
 
+<br/>
+
+Cross Signal Example
+--------------------
+```python
+import pandas_ta as ta
+import vectorbt as vbt
+
+# requires 'yfinance' installed
+df = ta.df.ta.ticker("AAPL", timed=True)
+
+# Signal when RSI crosses above 30 and later below 70
+rsi = df.ta.rsi(append=True)
+
+# Create Cross Signals
+rsi_long = ta.xsignals(rsi, 20, 80, above=True)
+
+# Create the Signals Portfolio
+pf = vbt.Portfolio.from_signals(df.Close, entries=rsi_long.TS_Entries, exits=rsi_long.TS_Exits, freq="D", init_cash=100_000, fees=0.0025, slippage=0.0025)
+
+# Print Portfolio Stats and Return Stats
+print(pf.stats())
+print(pf.returns_stats())
+```
+
 Back to [Contents](#contents)
 
 <br/>

pandas_ta/candles/cdl_pattern.py

@@ -64,13 +64,16 @@ def cdl_pattern(
         pd.DataFrame: one column for each pattern.
     """
     # Validate Arguments
-    open_ = v_series(open_)
-    high = v_series(high)
-    low = v_series(low)
-    close = v_series(close)
+    open_ = v_series(open_, 1)
+    high = v_series(high, 1)
+    low = v_series(low, 1)
+    close = v_series(close, 1)
     offset = v_offset(offset)
     scalar = v_scalar(scalar, 100)
 
+    if open_ is None or high is None or low is None or close is None:
+        return
+
     # Patterns that implemented in pandas-ta
     pta_patterns = {"doji": cdl_doji, "inside": cdl_inside}
 

pandas_ta/candles/ha.py

@@ -37,12 +37,15 @@ def ha(
         pd.DataFrame: ha_open, ha_high,ha_low, ha_close columns.
     """
     # Validate
-    open_ = v_series(open_)
-    high = v_series(high)
-    low = v_series(low)
-    close = v_series(close)
+    open_ = v_series(open_, 1)
+    high = v_series(high, 1)
+    low = v_series(low, 1)
+    close = v_series(close, 1)
     offset = v_offset(offset)
 
+    if open_ is None or high is None or low is None or close is None:
+        return
+
     # Calculate
     m = close.size
     df = DataFrame({

pandas_ta/core.py

@@ -607,6 +607,8 @@ class AnalysisIndicators(object):
 
         # If All or a Category, exclude user list if any
         user_excluded = kwargs.pop("exclude", [])
+        if isinstance(user_excluded, str) and len(user_excluded) > 1:
+            user_excluded = [user_excluded]
         if mode["all"] or mode["category"]:
             excluded += user_excluded
 
@@ -634,7 +636,8 @@ class AnalysisIndicators(object):
             if "length" in kwds and kwds["length"] > self._df.shape[0]:
                 _ = True
             if _: removal.append(kwds)
-        if len(removal) > 0: [ta.remove(x) for x in removal]
+        if len(removal) > 0:
+            [ta.remove(x) for x in removal]
 
         verbose = kwargs.pop("verbose", False)
         if verbose:
@@ -1028,11 +1031,6 @@ class AnalysisIndicators(object):
         result = psl(close=close, open_=open_, length=length, scalar=scalar, drift=drift, offset=offset, **kwargs)
         return self._post_process(result, **kwargs)
 
-    def pvo(self, fast=None, slow=None, signal=None, scalar=None, offset: Int = None, **kwargs: DictLike):
-        volume = self._get_column(kwargs.pop("volume", "volume"))
-        result = pvo(volume=volume, fast=fast, slow=slow, signal=signal, scalar=scalar, offset=offset, **kwargs)
-        return self._post_process(result, **kwargs)
-
     def qqe(self, length=None, smooth=None, factor=None, mamode=None, offset: Int = None, **kwargs: DictLike):
         close = self._get_column(kwargs.pop("close", "close"))
         result = qqe(close=close, length=length, smooth=smooth, factor=factor, mamode=mamode, offset=offset, **kwargs)
@@ -1330,24 +1328,6 @@ class AnalysisIndicators(object):
         result = vidya(close=close, length=length, offset=offset, **kwargs)
         return self._post_process(result, **kwargs)
 
-    def vwap(self, anchor=None, offset: Int = None, **kwargs: DictLike):
-        high = self._get_column(kwargs.pop("high", "high"))
-        low = self._get_column(kwargs.pop("low", "low"))
-        close = self._get_column(kwargs.pop("close", "close"))
-        volume = self._get_column(kwargs.pop("volume", "volume"))
-
-        if not self.datetime_ordered():
-            volume.index = self._df.index
-
-        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: Int = None, **kwargs: DictLike):
-        close = self._get_column(kwargs.pop("close", "close"))
-        volume = self._get_column(kwargs.pop("volume", "volume"))
-        result = vwma(close=close, volume=volume, length=length, offset=offset, **kwargs)
-        return self._post_process(result, **kwargs)
-
     def wcp(self, offset: Int = None, **kwargs: DictLike):
         high = self._get_column(kwargs.pop("high", "high"))
         low = self._get_column(kwargs.pop("low", "low"))
@@ -1768,6 +1748,11 @@ class AnalysisIndicators(object):
         result = pvi(close=close, volume=volume, length=length, initial=initial, signed=signed, offset=offset, **kwargs)
         return self._post_process(result, **kwargs)
 
+    def pvo(self, fast=None, slow=None, signal=None, scalar=None, offset: Int = None, **kwargs: DictLike):
+        volume = self._get_column(kwargs.pop("volume", "volume"))
+        result = pvo(volume=volume, fast=fast, slow=slow, signal=signal, scalar=scalar, offset=offset, **kwargs)
+        return self._post_process(result, **kwargs)
+
     def pvol(self, volume=None, offset: Int = None, **kwargs: DictLike):
         close = self._get_column(kwargs.pop("close", "close"))
         volume = self._get_column(kwargs.pop("volume", "volume"))
@@ -1786,6 +1771,24 @@ class AnalysisIndicators(object):
         result = pvt(close=close, volume=volume, offset=offset, **kwargs)
         return self._post_process(result, **kwargs)
 
+    def vwap(self, anchor=None, offset: Int = None, **kwargs: DictLike):
+        high = self._get_column(kwargs.pop("high", "high"))
+        low = self._get_column(kwargs.pop("low", "low"))
+        close = self._get_column(kwargs.pop("close", "close"))
+        volume = self._get_column(kwargs.pop("volume", "volume"))
+
+        if not self.datetime_ordered():
+            volume.index = self._df.index
+
+        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: Int = None, **kwargs: DictLike):
+        close = self._get_column(kwargs.pop("close", "close"))
+        volume = self._get_column(kwargs.pop("volume", "volume"))
+        result = vwma(close=close, volume=volume, length=length, offset=offset, **kwargs)
+        return self._post_process(result, **kwargs)
+
     def wb_tsv(self, length=None, signal=None, offset: Int = None, **kwargs: DictLike):
         close = self._get_column(kwargs.pop("close", "close"))
         volume = self._get_column(kwargs.pop("volume", "volume"))

pandas_ta/maps.py

@@ -49,9 +49,9 @@ Category: Dict[str, ListStr] = {
     "momentum": [
         "ao", "apo", "bias", "bop", "brar", "cci", "cfo", "cg", "cmo",
         "coppock", "cti", "er", "eri", "fisher", "inertia", "kdj", "kst",
-        "macd", "mom", "pgo", "ppo", "psl", "pvo", "qqe", "roc", "rsi",
-        "rsx", "rvgi", "slope", "smi", "squeeze", "squeeze_pro", "stc",
-        "stoch", "stochf", "stochrsi", "td_seq", "trix", "tsi", "uo", "willr"
+        "macd", "mom", "pgo", "ppo", "psl", "qqe", "roc", "rsi", "rsx",
+        "rvgi", "slope", "smi", "squeeze", "squeeze_pro", "stc", "stoch",
+        "stochf", "stochrsi", "td_seq", "trix", "tsi", "uo", "willr"
     ],
     # Overlap
     "overlap": [
@@ -59,7 +59,7 @@ Category: Dict[str, ListStr] = {
         "hma", "hwma", "ichimoku", "jma", "kama", "linreg", "mama",
         "mcgd", "midpoint", "midprice", "ohlc4", "pwma", "rma", "sinwma",
         "sma", "smma", "ssf", "ssf3", "supertrend", "swma", "t3", "tema",
-        "trima", "vidya", "vwap", "vwma", "wcp", "wma", "zlma"
+        "trima", "vidya", "wcp", "wma", "zlma"
     ],
     # Performance
     "performance": ["log_return", "percent_return"],
@@ -87,7 +87,7 @@ Category: Dict[str, ListStr] = {
     # Note: "vp" or "Volume Profile" is excluded since it does not return a Time Series
     "volume": [
         "ad", "adosc", "aobv", "cmf", "efi", "eom", "kvo", "mfi", "nvi",
-        "obv", "pvi", "pvol", "pvr", "pvt", "wb_tsv"
+        "obv", "pvi", "pvo", "pvol", "pvr", "pvt", "vwap", "vwma", "wb_tsv"
     ],
 }
 

pandas_ta/momentum/__init__.py

@@ -22,7 +22,6 @@ from .mom import mom
 from .pgo import pgo
 from .ppo import ppo
 from .psl import psl
-from .pvo import pvo
 from .qqe import qqe
 from .roc import roc
 from .rsi import rsi

pandas_ta/momentum/bop.py

@@ -50,7 +50,7 @@ def bop(
     offset = v_offset(offset)
 
     # Calculate
-    if Imports["talib"] and mode_tal:
+    if Imports["talib"] and mode_tal and close.size:
         from talib import BOP
         bop = BOP(open_, high, low, close)
     else:

pandas_ta/momentum/dm.py

@@ -47,8 +47,8 @@ def dm(
     """
     # Validate
     length = v_pos_default(length, 14)
-    high = v_series(high)
-    low = v_series(low)
+    high = v_series(high, length)
+    low = v_series(low, length)
 
     if high is None or low is None:
         return
@@ -58,7 +58,7 @@ def dm(
     drift = v_drift(drift)
     offset = v_offset(offset)
 
-    if Imports["talib"] and mode_tal:
+    if Imports["talib"] and mode_tal and high.size and low.size:
         from talib import MINUS_DM, PLUS_DM
         pos = PLUS_DM(high, low, length)
         neg = MINUS_DM(high, low, length)

pandas_ta/momentum/mom.py

@@ -1,8 +1,27 @@
 # -*- coding: utf-8 -*-
+# from numpy.ma import diff as np_ma_diff
 from pandas import Series
-from pandas_ta._typing import DictLike, Int
+from pandas_ta._typing import Array, DictLike, Int
 from pandas_ta.maps import Imports
-from pandas_ta.utils import v_offset, v_pos_default, v_series, v_talib
+from pandas_ta.utils import (
+    # np_prepend,
+    v_offset,
+    v_pos_default,
+    v_series,
+    v_talib
+)
+
+
+try:
+    from numba import njit
+except ImportError:
+    def njit(_): return _
+
+
+# Mockup
+# @njit
+# def np_mom(x: Array, n: Int):
+#     return np_prepend(np_ma_diff(x, n), n)
 
 
 def mom(

pandas_ta/momentum/roc.py

@@ -1,15 +1,29 @@
 # -*- coding: utf-8 -*-
 from pandas import Series
-from pandas_ta._typing import DictLike, Int, IntFloat
+from pandas_ta._typing import Array, DictLike, Int, IntFloat
 from pandas_ta.maps import Imports
 from pandas_ta.utils import (
+    np_shift,
     v_offset,
     v_pos_default,
     v_scalar,
     v_series,
     v_talib
 )
-from .mom import mom
+from .mom import mom#, np_mom
+
+
+# try:
+#     from numba import njit
+# except ImportError:
+#     def njit(_): return _
+
+
+# Mockup
+# @njit
+# def np_roc(x: Array, n: Int, k: IntFloat):
+#     result = k * np_mom(x, n) / np_shift(x, n)
+#     return result
 
 
 def roc(

pandas_ta/overlap/__init__.py

@@ -31,8 +31,6 @@ from .t3 import t3
 from .tema import tema
 from .trima import trima
 from .vidya import vidya
-from .vwap import vwap
-from .vwma import vwma
 from .wcp import wcp
 from .wma import wma
 from .zlma import zlma

pandas_ta/overlap/hl2.py

@@ -31,6 +31,9 @@ def hl2(
     low = v_series(low)
     offset = v_offset(offset)
 
+    if high is None or low is None:
+        return
+
     # Calculate
     avg = 0.5 * (high.values + low.values)
     hl2 = Series(avg, index=high.index)

pandas_ta/overlap/hlc3.py

@@ -35,8 +35,11 @@ def hlc3(
     mode_tal = v_talib(talib)
     offset = v_offset(offset)
 
+    if high is None or low is None or close is None:
+        return
+
     # Calculate
-    if Imports["talib"] and mode_tal:
+    if Imports["talib"] and mode_tal and close.size:
         from talib import TYPPRICE
         hlc3 = TYPPRICE(high, low, close)
     else:

pandas_ta/utils/_numba.py

@@ -17,7 +17,7 @@ def np_prepend(x: Array, n: Int, value: IntFloat = nan) -> Array:
 
 
 @njit
-def np_roll(x: Array, n: Int, fn = None) -> Array:
+def np_rolling(x: Array, n: Int, fn = None) -> Array:
     """Like Pandas Rolling Window. x.rolling(n).fn()"""
     m = x.size
     result = zeros_like(x, dtype=float)

pandas_ta/volatility/hwc.py

@@ -41,7 +41,7 @@ def hwc(
         pd.DataFrame: HWM (Mid), HWU (Upper), HWL (Lower) columns.
     """
     # Validate
-    close = v_series(close)
+    close = v_series(close, 1)
     scalar = v_pos_default(scalar, 1)
     channels = v_bool(channels, False)
     na = v_pos_default(na, 0.2)
@@ -50,6 +50,9 @@ def hwc(
     nd = v_pos_default(nd, 0.1)
     offset = v_offset(offset)
 
+    if close is None:
+        return
+
     # Calculate Result
     last_a = last_v = last_var = 0
     last_f = last_price = last_result = close[0]

pandas_ta/volume/__init__.py

@@ -10,8 +10,11 @@ from .mfi import mfi
 from .nvi import nvi
 from .obv import obv
 from .pvi import pvi
+from .pvo import pvo
 from .pvol import pvol
 from .pvr import pvr
 from .pvt import pvt
 from .vp import vp
+from .vwap import vwap
+from .vwma import vwma
 from .wb_tsv import wb_tsv

pandas_ta/volume/ad.py

@@ -44,7 +44,7 @@ def ad(
     offset = v_offset(offset)
 
     # Calculate
-    if Imports["talib"] and mode_tal:
+    if Imports["talib"] and mode_tal and volume.size:
         from talib import AD
         ad = AD(high, low, close, volume)
     else:

pandas_ta/volume/nvi.py

@@ -35,8 +35,8 @@ def nvi(
     """
     # Validate
     length = v_pos_default(length, 1)
-    close = v_series(close, length)
-    volume = v_series(volume, length)
+    close = v_series(close, length + 1)
+    volume = v_series(volume, length + 1)
 
     if close is None or volume is None:
         return

pandas_ta/volume/pvi.py

@@ -34,8 +34,8 @@ def pvi(
     """
     # Validate
     length = v_pos_default(length, 1)
-    close = v_series(close, length)
-    volume = v_series(volume, length)
+    close = v_series(close, length + 1)
+    volume = v_series(volume, length + 1)
 
     if close is None or volume is None:
         return

pandas_ta/volume/vwap.py

@@ -47,10 +47,11 @@ def vwap(
         pd.DataFrame: New feature generated.
     """
     # Validate
-    high = v_series(high)
-    low = v_series(low)
-    close = v_series(close)
-    volume = v_series(volume)
+    _length = 1
+    high = v_series(high, _length)
+    low = v_series(low, _length)
+    close = v_series(close, _length)
+    volume = v_series(volume, _length)
     bands = v_list(bands)
     offset = v_offset(offset)
 
@@ -60,7 +61,8 @@ def vwap(
         anchor = "D"
 
     typical_price = hlc3(high=high, low=low, close=close)
-    if not v_datetime_ordered(volume) or not v_datetime_ordered(typical_price):
+    if not v_datetime_ordered(volume) or \
+        not v_datetime_ordered(typical_price):
         print("[!] VWAP requires a datetime ordered index.")
         return
 

pandas_ta/volume/wb_tsv.py

@@ -1,4 +1,5 @@
 # -*- coding: utf-8 -*-
+from numpy import isnan
 from pandas import DataFrame, Series
 from pandas_ta._typing import DictLike, Int
 from pandas_ta.ma import ma
@@ -52,7 +53,7 @@ def wb_tsv(
     # Validate
     length = v_pos_default(length, 18)
     signal = v_pos_default(signal, 10)
-    _length = max(length, signal) - 2
+    _length = max(length, signal) + 1
     close = v_series(close, _length)
 
     if close is None:
@@ -69,6 +70,8 @@ def wb_tsv(
     cvd = signed_volume * close.diff(drift)
 
     tsv = cvd.rolling(length).sum()
+    if all(isnan(tsv)):
+        return  # Emergency Break
     signal_ = ma(mamode, tsv, length=signal)
     ratio = tsv / signal_
 

tests/test_study.py

@@ -112,7 +112,7 @@ class TestStudyMethods(TestCase):
         self.category = "Candles"
         self.data.ta.study(pandas_ta.AllStudy, verbose=verbose, timed=timed_test)
 
-    # @skipUnless(verbose, "verbose mode only")
+    @skipUnless(verbose, "verbose mode only")
     def test_all_without_append(self):
         """Study: All sans append"""
         self.category = "All: Sans append"
@@ -230,10 +230,18 @@ class TestStudyMethods(TestCase):
         )
         self.data.ta.study(custom, verbose=verbose, timed=timed_test)
 
+    def test_custom_ohlc(self):
+        """Custom E: OHLC"""
+        self.category = "Custom E: OHLC"
+
+        self.data.rename(columns={"volume": "_v"}, inplace=True)
+        self.data.ta.study(exclude=pandas_ta.Category["volume"], verbose=verbose, timed=timed_test)
+        self.data.rename(columns={"_v": "volume"}, inplace=True)
+
     # @skip
-    def test_custom_e(self):
-        """Custom E"""
-        self.category = "Custom E"
+    def test_custom_study_with_signals(self):
+        """Custom F: Custom Study with Signals"""
+        self.category = "Custom F: Custom Study with Signals"
 
         amat_logret_ta = [
             {"kind": "amat", "fast": 20, "slow": 50 },  # 2