ENH PERF #774 #771 #526 exhc TST stdev variance DOC speedtest

.gitignore

@@ -114,6 +114,7 @@ env/**
 .vscode/**
 
 # zed settings
+.zed
 .zed/**
 
 # Original Source (Dev)

examples/ni.py

@@ -36,7 +36,6 @@ def ni(
 
     Kwargs:
         fillna (value, optional): pd.DataFrame.fillna(value)
-        fill_method (value, optional): Type of fill method
 
     Returns:
         pd.Series: New feature generated.
@@ -65,8 +64,6 @@ def ni(
     # Handle fills
     if "fillna" in kwargs:
         ni.fillna(kwargs["fillna"], inplace=True)
-    if "fill_method" in kwargs:
-        ni.fillna(method=kwargs["fill_method"], inplace=True)
 
     # Name and Categorize it
     ni.name = f"ni_{length}"
@@ -80,4 +77,4 @@ def ni(
 def ni_method(self, length=None, offset=None, **kwargs):
     close = self._get_column(kwargs.pop("close", "close"))
     result = ni(close=close, length=length, offset=offset, **kwargs)
-    return self._post_process(result, **kwargs)
+    return self._post_process(result, **kwargs)

pandas_ta/__init__.py

@@ -30,7 +30,8 @@
 from pandas_ta.volatility import __all__ as volatility_all
 from pandas_ta.volume import __all__ as volume_all
 
-# Common Averages useful for Indicators with a mamode argument, like ta.adx()
+# Common Averages useful for Indicators
+# with a mamode argument, like ta.adx()
 from pandas_ta.ma import ma
 
 # Custom External Directory Commands. See help(import_dir)

pandas_ta/_typing.py

@@ -14,6 +14,7 @@
 from sys import float_info as sflt
 
 
+
 # Generic types
 T = TypeVar("T")
 

pandas_ta/candles/cdl_doji.py

@@ -7,6 +7,7 @@
 from pandas_ta.utils import v_scalar, v_series
 
 
+
 def cdl_doji(
     open_: Series, high: Series, low: Series, close: Series,
     length: Int = None, factor: IntFloat = None, scalar: IntFloat = None,
@@ -37,7 +38,6 @@ def cdl_doji(
             the length if less than a percentage of it's high-low range.
             Default: False
         fillna (value, optional): pd.DataFrame.fillna(value)
-        fill_method (value, optional): Type of fill method
 
     Returns:
         pd.Series: CDL_DOJI column.
@@ -75,8 +75,6 @@ def cdl_doji(
     # Fill
     if "fillna" in kwargs:
         doji.fillna(kwargs["fillna"], inplace=True)
-    if "fill_method" in kwargs:
-        doji.fillna(method=kwargs["fill_method"], inplace=True)
 
     # Name and Category
     doji.name = f"CDL_DOJI_{length}_{0.01 * factor}"

pandas_ta/candles/cdl_inside.py

@@ -6,6 +6,7 @@
 from pandas_ta.utils import v_bool, v_offset, v_offset, v_scalar, v_series
 
 
+
 @njit
 def np_cdl_inside(high, low):
     hdiff = where(high - roll(high, 1) < 0, 1, 0)
@@ -43,7 +44,6 @@ def cdl_inside(
 
     Kwargs:
         fillna (value, optional): pd.DataFrame.fillna(value)
-        fill_method (value, optional): Type of fill method
 
     Returns:
         pd.Series: New feature
@@ -76,9 +76,6 @@ def cdl_inside(
     # Fill
     if "fillna" in kwargs:
         inside.fillna(kwargs["fillna"], inplace=True)
-    if "fill_method" in kwargs:
-        inside.fillna(method=kwargs["fill_method"], inplace=True)
-
     # Name and Category
     inside.name = f"CDL_INSIDE"
     inside.category = "candles"

pandas_ta/candles/cdl_pattern.py

@@ -6,6 +6,7 @@
 from pandas_ta.candles import cdl_doji, cdl_inside
 
 
+
 ALL_PATTERNS = [
     "2crows", "3blackcrows", "3inside", "3linestrike", "3outside",
     "3starsinsouth", "3whitesoldiers", "abandonedbaby", "advanceblock",
@@ -58,8 +59,6 @@ def cdl_pattern(
 
     Kwargs:
         fillna (value, optional): pd.DataFrame.fillna(value)
-        fill_method (value, optional): Type of fill method
-
     Returns:
         pd.DataFrame: one column for each pattern.
     """
@@ -117,10 +116,6 @@ def cdl_pattern(
             # Fill
             if "fillna" in kwargs:
                 pattern_result.fillna(kwargs["fillna"], inplace=True)
-            if "fill_method" in kwargs:
-                pattern_result.fillna(
-                    method=kwargs["fill_method"], inplace=True)
-
             result[f"CDL_{n.upper()}"] = pattern_result
 
     if len(result) == 0:

pandas_ta/candles/cdl_z.py

@@ -5,6 +5,7 @@
 from pandas_ta.utils import v_bool, v_offset, v_pos_default, v_series
 
 
+
 def cdl_z(
     open_: Series, high: Series, low: Series, close: Series,
     length: Int = None, full: bool = None, ddof: Int = None,
@@ -30,7 +31,6 @@ def cdl_z(
             the length if less than a percentage of it's high-low range.
             Default: False
         fillna (value, optional): pd.DataFrame.fillna(value)
-        fill_method (value, optional): Type of fill method
 
     Returns:
         pd.Series: CDL_DOJI column.
@@ -78,8 +78,6 @@ def cdl_z(
     # Fill
     if "fillna" in kwargs:
         df.fillna(kwargs["fillna"], inplace=True)
-    if "fill_method" in kwargs:
-        df.fillna(method=kwargs["fill_method"], inplace=True)
 
     # Name and Category
     df.name = f"CDL_Z{_props}"

pandas_ta/candles/ha.py

@@ -6,6 +6,7 @@
 from pandas_ta.utils import v_offset, v_series
 
 
+
 @njit
 def np_ha(np_open, np_high, np_low, np_close):
     ha_close = 0.25 * (np_open + np_high + np_low + np_close)
@@ -49,7 +50,6 @@ def ha(
 
     Kwargs:
         fillna (value, optional): pd.DataFrame.fillna(value)
-        fill_method (value, optional): Type of fill method
 
     Returns:
         pd.DataFrame: ha_open, ha_high,ha_low, ha_close columns.
@@ -82,8 +82,6 @@ def ha(
     # Fill
     if "fillna" in kwargs:
         df.fillna(kwargs["fillna"], inplace=True)
-    if "fill_method" in kwargs:
-        df.fillna(method=kwargs["fill_method"], inplace=True)
 
     # Name and Category
     df.name = "Heikin-Ashi"

pandas_ta/core.py

@@ -568,7 +568,8 @@ def study(self, *args: Args, **kwargs: DictLike) -> dataclass:
                 Default: Number of cores of the OS
             exclude (list): List of indicator names to exclude. Some are
                 excluded by default for various reasons; they require additional
-                sources, performance (td_seq), not a time series chart (vp) etc.
+                sources (tsignals, xsignals), performance, , not a time series
+                chart (vp) etc.
             name (str): Select all indicators or indicators by
                 Category such as: "candles", "cycles", "momentum", "overlap",
                 "performance", "statistics", "trend", "volatility", "volume", or
@@ -962,6 +963,11 @@ def eri(self, length=None, offset=None, **kwargs: DictLike):
         result = eri(high=high, low=low, close=close, length=length, offset=offset, **kwargs)
         return self._post_process(result, **kwargs)
 
+    def exhc(self, length=None, cap=None, asint=None, show_all=None, nozeros=None, offset=None, **kwargs: DictLike):
+        close = self._get_column(kwargs.pop("close", "close"))
+        result = exhc(close=close, length=length, cap=cap, asint=asint, show_all=show_all, nozeros=nozeros, offset=offset, **kwargs)
+        return self._post_process(result, **kwargs)
+
     def fisher(self, length=None, signal=None, offset=None, **kwargs: DictLike):
         high = self._get_column(kwargs.pop("high", "high"))
         low = self._get_column(kwargs.pop("low", "low"))
@@ -1110,11 +1116,6 @@ def stochrsi(self, length=None, rsi_length=None, k=None, d=None, mamode=None, of
                           mamode=mamode, offset=offset, **kwargs)
         return self._post_process(result, **kwargs)
 
-    def td_seq(self, asint=None, offset=None, show_all=None, **kwargs: DictLike):
-        close = self._get_column(kwargs.pop("close", "close"))
-        result = td_seq(close=close, asint=asint, offset=offset, show_all=show_all, **kwargs)
-        return self._post_process(result, **kwargs)
-
     def tmo(self, tmo_length=None, calc_length=None, smooth_length=None, mamode=None, compute_momentum=False, normalize_signal=False, offset=None, **kwargs: DictLike):
         open_ = self._get_column(kwargs.pop("open", "open"))
         close = self._get_column(kwargs.pop("close", "close"))
@@ -1501,9 +1502,9 @@ def dpo(self, length=None, centered=True, offset=None, **kwargs: DictLike):
         result = dpo(close=close, length=length, centered=centered, offset=offset, **kwargs)
         return self._post_process(result, **kwargs)
 
-    def ht_trendline(self, offset=None, **kwargs: DictLike):
+    def ht_trendline(self, talib=None, prenan=None, offset=None, **kwargs: DictLike):
         close = self._get_column(kwargs.pop("close", "close"))
-        result = ht_trendline(close=close, offset=offset)
+        result = ht_trendline(close=close, talib=talib, prenan=prenan, offset=offset, **kwargs)
         return self._post_process(result, **kwargs)
 
     def increasing(self, length=None, strict=None, asint=None, offset=None, **kwargs: DictLike):

pandas_ta/custom.py

@@ -10,6 +10,7 @@
 from pandas_ta._typing import DictLike
 
 
+
 def bind(name: str, f: types.FunctionType, method: types.MethodType = None):
     """
     Helper function to bind the function and class method defined in a custom

pandas_ta/cycles/ebsw.py

@@ -5,6 +5,7 @@
 from pandas_ta.utils import v_offset, v_pos_default, v_series
 
 
+
 def ebsw(
     close: Series, length: Int = None, bars: Int = None,
     initial_version: bool = False,
@@ -45,8 +46,6 @@ def ebsw(
 
     Kwargs:
         fillna (value, optional): pd.DataFrame.fillna(value)
-        fill_method (value, optional): Type of fill method
-
     Returns:
         pd.Series: New feature generated.
     """
@@ -146,9 +145,6 @@ def ebsw(
     # Fill
     if "fillna" in kwargs:
         ebsw.fillna(kwargs["fillna"], inplace=True)
-    if "fill_method" in kwargs:
-        ebsw.fillna(method=kwargs["fill_method"], inplace=True)
-
     # Name and Category
     ebsw.name = f"EBSW_{length}_{bars}"
     ebsw.category = "cycles"

pandas_ta/cycles/reflex.py

@@ -6,6 +6,7 @@
 from pandas_ta.utils import v_offset, v_pos_default, v_series
 
 
+
 @njit
 def np_reflex(x, n, k, alpha, pi, sqrt2):
     m, ratio = x.size, 2 * sqrt2 / k
@@ -73,8 +74,6 @@ def reflex(
 
     Kwargs:
         fillna (value, optional): pd.DataFrame.fillna(value)
-        fill_method (value, optional): Type of fill method
-
     Returns:
         pd.Series: New feature generated.
     """
@@ -105,9 +104,6 @@ def reflex(
     # Fill
     if "fillna" in kwargs:
         result.fillna(kwargs["fillna"], inplace=True)
-    if "fill_method" in kwargs:
-        result.fillna(method=kwargs["fill_method"], inplace=True)
-
     # Name and Category
     result.name = f"REFLEX_{length}_{smooth}_{alpha}"
     result.category = "cycles"

pandas_ta/ma.py

@@ -20,6 +20,7 @@
 from pandas_ta.overlap.wma import wma
 
 
+
 def ma(name: str = None, source: Series = None, **kwargs: DictLike) -> Series:
     """Simple MA Utility for easier MA selection
 
@@ -69,4 +70,4 @@ def ma(name: str = None, source: Series = None, **kwargs: DictLike) -> Series:
     elif name == "trima": return trima(source, **kwargs)
     elif name == "vidya": return vidya(source, **kwargs)
     elif name == "wma": return wma(source, **kwargs)
-    else: return ema(source, **kwargs)
+    else: return ema(source, **kwargs)

pandas_ta/maps.py

@@ -5,7 +5,6 @@
 
 from pandas_ta._typing import Dict, IntFloat, ListStr
 
-
 _dist = get_distribution("pandas_ta")
 try:
     # Normalize case for Windows systems
@@ -49,10 +48,11 @@
     # Momentum
     "momentum": [
         "ao", "apo", "bias", "bop", "brar", "cci", "cfo", "cg", "cmo",
-        "coppock", "crsi", "cti", "er", "eri", "fisher", "inertia", "kdj", "kst",
-        "macd", "mom", "pgo", "ppo", "psl", "qqe", "roc", "rsi", "rsx",
-        "rvgi", "slope", "smi", "squeeze", "squeeze_pro", "stc", "stoch",
-        "stochf", "stochrsi", "td_seq", "tmo", "trix", "tsi", "uo", "willr"
+        "coppock", "crsi", "cti", "er", "eri", "exhc", "fisher", "inertia",
+        "kdj", "kst", "macd", "mom", "pgo", "ppo", "psl", "qqe", "roc",
+        "rsi", "rsx", "rvgi", "slope", "smi", "squeeze", "squeeze_pro",
+        "stc", "stoch", "stochf", "stochrsi", "tmo", "trix", "tsi", "uo",
+        "willr"
     ],
     # Overlap
     "overlap": [

pandas_ta/momentum/__init__.py

@@ -14,6 +14,7 @@
 from .dm import dm
 from .er import er
 from .eri import eri
+from .exhc import exhc
 from .fisher import fisher
 from .inertia import inertia
 from .kdj import kdj
@@ -36,7 +37,6 @@
 from .stoch import stoch
 from .stochf import stochf
 from .stochrsi import stochrsi
-from .td_seq import td_seq
 from .tmo import tmo
 from .trix import trix
 from .tsi import tsi
@@ -59,6 +59,7 @@
     "dm",
     "er",
     "eri",
+    "exhc",
     "fisher",
     "inertia",
     "kdj",
@@ -81,7 +82,6 @@
     "stoch",
     "stochf",
     "stochrsi",
-    "td_seq",
     "tmo",
     "trix",
     "tsi",

pandas_ta/momentum/ao.py

@@ -5,6 +5,7 @@
 from pandas_ta.utils import v_offset, v_pos_default, v_series
 
 
+
 def ao(
     high: Series, low: Series, fast: Int = None, slow: Int = None,
     offset: Int = None, **kwargs: DictLike
@@ -28,7 +29,6 @@ def ao(
 
     Kwargs:
         fillna (value, optional): pd.DataFrame.fillna(value)
-        fill_method (value, optional): Type of fill method
 
     Returns:
         pd.Series: New feature generated.
@@ -60,9 +60,6 @@ def ao(
     # Fill
     if "fillna" in kwargs:
         ao.fillna(kwargs["fillna"], inplace=True)
-    if "fill_method" in kwargs:
-        ao.fillna(method=kwargs["fill_method"], inplace=True)
-
     # Name and Category
     ao.name = f"AO_{fast}_{slow}"
     ao.category = "momentum"