ENH tos_stdevall initial code in dev

README.md

@@ -56,7 +56,7 @@ _Pandas Technical Analysis_ (**Pandas TA**) is an easy to use library that lever
     * [Momentum](#momentum-40)
     * [Overlap](#overlap-32)
     * [Performance](#performance-3)
-    * [Statistics](#statistics-9)
+    * [Statistics](#statistics-10)
     * [Trend](#trend-18)
     * [Utility](#utility-5)
     * [Volatility](#volatility-14)
@@ -110,7 +110,7 @@ $ pip install pandas_ta
 
 Latest Version
 --------------
-Best choice! Version: *0.2.92b*
+Best choice! Version: *0.2.93b*
 ```sh
 $ pip install -U git+https://github.com/twopirllc/pandas-ta
 ```
@@ -774,7 +774,7 @@ Use parameter: cumulative=**True** for cumulative results.
 | ![Example Cumulative Percent Return](/images/SPY_CumulativePercentReturn.png) |
 <br/>
 
-### **Statistics** (9)
+### **Statistics** (10)
 
 * _Entropy_: **entropy**
 * _Kurtosis_: **kurtosis**

pandas_ta/__init__.py

@@ -65,6 +65,7 @@ Category = {
     # Statistics
     "statistics": [
         "entropy", "kurtosis", "mad", "median", "quantile", "skew", "stdev",
+        # "tos_stdevall",
         "variance", "zscore"
     ],
     # Trend

pandas_ta/core.py

@@ -1365,6 +1365,11 @@ class AnalysisIndicators(BasePandasObject):
         result = stdev(close=close, length=length, offset=offset, **kwargs)
         return self._post_process(result, **kwargs)
 
+    # def tos_stdevall(self, length=None, stds=None, offset=None, **kwargs):
+    #     close = self._get_column(kwargs.pop("close", "close"))
+    #     result = tos_stdevall(close=close, length=length, stds=stds, offset=offset, **kwargs)
+    #     return self._post_process(result, **kwargs)
+
     def variance(self, length=None, offset=None, **kwargs):
         close = self._get_column(kwargs.pop("close", "close"))
         result = variance(close=close, length=length, offset=offset, **kwargs)

pandas_ta/statistics/__init__.py

@@ -6,5 +6,6 @@ from .median import median
 from .quantile import quantile
 from .skew import skew
 from .stdev import stdev
+from .tos_stdevall import tos_stdevall
 from .variance import variance
 from .zscore import zscore

pandas_ta/statistics/tos_stdevall.py

@@ -0,0 +1,104 @@
+# -*- coding: utf-8 -*-
+from numpy import array as npArray
+from numpy import arange as npArange
+from numpy import polyfit as npPolyfit
+from numpy import std as npStd
+from pandas import DataFrame, DatetimeIndex, Series
+# from pandas_ta import Imports
+from .stdev import stdev as stdev
+from pandas_ta.utils import get_offset, verify_series
+
+def tos_stdevall(close, length=None, stds=None, ddof=None, offset=None, **kwargs):
+    """Indicator: TD Ameritrade's Think or Swim Standard Deviation All"""
+    # Validate Arguments
+    stds = stds if isinstance(stds, list) and len(stds) > 0 else [1, 2, 3]
+    if min(stds) <= 0: return
+    if not all(i < j for i, j in zip(stds, stds[1:])):
+        stds = stds[::-1]
+    ddof = int(ddof) if ddof and ddof >= 0 and ddof < length else 1
+    offset = get_offset(offset)
+
+    if length is None:
+        length = close.size
+        _props = f"STDEVALL"
+    else:
+        length = int(length) if length and length > 2 else 30
+        close = close.iloc[-length:]
+        _props = f"STDEVALL_{length}"
+
+    close = verify_series(close, length)
+
+    if close is None: return
+
+    # Calculate Result
+    if isinstance(close.index, DatetimeIndex):
+        close_ = npArray(close)
+        np_index = npArange(length)
+        m, b = npPolyfit(np_index, close_, 1)
+        lr_ = m * np_index + b
+    else:
+        m, b = npPolyfit(close.index, close, 1)
+        lr_ = m * close.index + b
+
+    lr = Series(lr_, index=close.index)
+    stdevall = stdev(Series(close), length=length, ddof=ddof)
+    # std = npStd(close, ddof=ddof)
+
+    # Name and Categorize it
+    df = DataFrame({f"{_props}_LR": lr}, index=close.index)
+    for i in stds:
+        df[f"{_props}_L_{i}"] = lr - i * stdevall.iloc[-1]
+        df[f"{_props}_U_{i}"] = lr + i * stdevall.iloc[-1]
+        df[f"{_props}_L_{i}"].name = df[f"{_props}_U_{i}"].name = f"{_props}"
+        df[f"{_props}_L_{i}"].category = df[f"{_props}_U_{i}"].category = "statistics"
+
+    # Offset
+    if offset != 0:
+        df = df.shift(offset)
+
+    # Handle fills
+    if "fillna" in kwargs:
+        df.fillna(kwargs["fillna"], inplace=True)
+    if "fill_method" in kwargs:
+        df.fillna(method=kwargs["fill_method"], inplace=True)
+
+    # Prepare DataFrame to return
+    df.name = f"{_props}"
+    df.category = "statistics"
+
+    return df
+
+
+tos_stdevall.__doc__ = \
+"""TD Ameritrade's Think or Swim Standard Deviation All (TOS_STDEV)
+
+**UNDER DEVELOPMENT**
+
+A port of TD Ameritrade's Think or Swim Standard Deviation All indicator which
+returns the standard deviation of data for the entire plot or for the interval
+of the last bars defined by the length parameter.
+
+Sources:
+    https://tlc.thinkorswim.com/center/reference/thinkScript/Functions/Statistical/StDevAll
+
+Calculation:
+    Default Inputs:
+        length=30
+    VAR = Variance
+    STDEV = variance(close, length).apply(np.sqrt)
+
+Args:
+    close (pd.Series): Series of 'close's
+    length (int): It's period. Default: 30
+    ddof (int): Delta Degrees of Freedom.
+                The divisor used in calculations is N - ddof,
+                where N represents the number of elements. Default: 1
+    offset (int): How many periods to offset the result. Default: 0
+
+Kwargs:
+    fillna (value, optional): pd.DataFrame.fillna(value)
+    fill_method (value, optional): Type of fill method
+
+Returns:
+    pd.Series: New feature generated.
+"""

setup.py

@@ -19,7 +19,7 @@ setup(
         "pandas_ta.volatility",
         "pandas_ta.volume"
     ],
-    version=".".join(("0", "2", "92b")),
+    version=".".join(("0", "2", "93b")),
     description=long_description,
     long_description=long_description,
     author="Kevin Johnson",

tests/test_ext_indicator_momentum.py

@@ -197,8 +197,7 @@ class TestMomentumExtension(TestCase):
 
         self.data.ta.squeeze(tr=False, append=True)
         self.assertIsInstance(self.data, DataFrame)
-        self.assertEqual(
-            list(self.data.columns[-4:]),
+        self.assertEqual(list(self.data.columns[-4:]),
             ["SQZ_ON", "SQZ_OFF", "SQZ_NO", "SQZhlr_20_2.0_20_1.5"]
         )
 

tests/test_ext_indicator_statistics.py

@@ -1,7 +1,7 @@
 from .config import sample_data
 from .context import pandas_ta
 
-from unittest import TestCase
+from unittest import skip, TestCase
 from pandas import DataFrame
 
 
@@ -53,6 +53,17 @@ class TestStatisticsExtension(TestCase):
         self.assertIsInstance(self.data, DataFrame)
         self.assertEqual(self.data.columns[-1], "STDEV_30")
 
+    @skip
+    def test_tos_stdevall_ext(self):
+        self.data.ta.tos_stdevall(append=True)
+        self.assertIsInstance(self.data, DataFrame)
+        self.assertEqual(list(self.data.columns[-7:]), [
+            "STDEVALL_LR",
+            "STDEVALL_L_1", "STDEVALL_U_1",
+            "STDEVALL_L_2", "STDEVALL_U_2",
+            "STDEVALL_L_3", "STDEVALL_U_3"
+        ])
+
     def test_variance_ext(self):
         self.data.ta.variance(append=True)
         self.assertIsInstance(self.data, DataFrame)

tests/test_indicator_statistics.py

@@ -1,9 +1,9 @@
 from .config import error_analysis, sample_data, CORRELATION, CORRELATION_THRESHOLD, VERBOSE
 from .context import pandas_ta
 
-from unittest import TestCase, skip
+from unittest import skip, TestCase
 import pandas.testing as pdt
-from pandas import Series
+from pandas import DataFrame, Series
 
 import talib as tal
 
@@ -79,6 +79,23 @@ class TestStatistics(TestCase):
             except Exception as ex:
                 error_analysis(result, CORRELATION, ex)
 
+    @skip
+    def test_tos_sdtevall(self):
+        result = pandas_ta.tos_stdevall(self.close)
+        self.assertIsInstance(result, DataFrame)
+        self.assertEqual(result.name, "STDEVALL")
+        self.assertEqual(len(result.columns), 7)
+
+        result = pandas_ta.tos_stdevall(self.close, length=30)
+        self.assertIsInstance(result, DataFrame)
+        self.assertEqual(result.name, "STDEVALL_30")
+        self.assertEqual(len(result.columns), 7)
+
+        result = pandas_ta.tos_stdevall(self.close, length=30, stds=[1, 2])
+        self.assertIsInstance(result, DataFrame)
+        self.assertEqual(result.name, "STDEVALL_30")
+        self.assertEqual(len(result.columns), 5)
+
     def test_variance(self):
         result = pandas_ta.variance(self.close)
         self.assertIsInstance(result, Series)