Merge branch 'pr/515' into development

pandas_ta/overlap/rma.py

@@ -39,8 +39,7 @@ def rma(
     alpha = (1.0 / length) if length > 0 else 0.5
     offset = v_offset(offset)
 
-    # Calculate
-    rma = close.ewm(alpha=alpha, min_periods=length).mean()
+    rma = close.ewm(alpha=alpha, adjust=False).mean()
 
     # Offset
     if offset != 0:

pandas_ta/volatility/atr.py

@@ -1,16 +1,25 @@
 # -*- coding: utf-8 -*-
+from numpy import nan
 from pandas import Series
 from pandas_ta._typing import DictLike, Int
 from pandas_ta.ma import ma
 from pandas_ta.maps import Imports
-from pandas_ta.utils import v_drift, v_mamode, v_offset
-from pandas_ta.utils import v_pos_default, v_series, v_talib
+from pandas_ta.utils import (
+    v_bool,
+    v_drift,
+    v_mamode,
+    v_offset,
+    v_pos_default,
+    v_series,
+    v_talib
+)
 from .true_range import true_range
 
 
 def atr(
     high: Series, low: Series, close: Series, length: Int = None,
-    mamode: str = None, talib: bool = None, drift: Int = None,
+    mamode: str = None, talib: bool = None,
+    prenan: bool = None, drift: Int = None,
     offset: Int = None, **kwargs: DictLike
 ) -> Series:
     """Average True Range (ATR)
@@ -29,6 +38,8 @@ def atr(
         mamode (str): See ``help(ta.ma)``. Default: 'rma'
         talib (bool): If TA Lib is installed and talib is True, Returns the
             TA Lib version. Default: True
+        prenan (bool): If True, behave like TA Lib with some initial nan
+            based on drift (typically 1). Default: False
         drift (int): The difference period. Default: 1
         offset (int): How many periods to offset the result. Default: 0
 
@@ -51,6 +62,7 @@ def atr(
 
     mamode = v_mamode(mamode, "rma")
     mode_tal = v_talib(talib)
+    prenan = v_bool(prenan, False)
     drift = v_drift(drift)
     offset = v_offset(offset)
 
@@ -60,8 +72,12 @@ def atr(
         atr = ATR(high, low, close, length)
     else:
         tr = true_range(
-            high=high, low=low, close=close, drift=drift, talib=mode_tal
+            high=high, low=low, close=close,
+            talib=mode_tal, prenan=prenan, drift=drift
         )
+        sma_nth = tr[0:length].mean()
+        tr[:length - 1] = nan
+        tr.iloc[length - 1] = sma_nth
         atr = ma(mamode, tr, length=length, talib=mode_tal)
 
     percent = kwargs.pop("percent", False)

pandas_ta/volatility/true_range.py

@@ -3,13 +3,13 @@ from numpy import nan
 from pandas import concat, Series
 from pandas_ta._typing import DictLike, Int
 from pandas_ta.maps import Imports
-from pandas_ta.utils import non_zero_range, v_drift, v_offset
-from pandas_ta.utils import v_series, v_talib
+from pandas_ta.utils import non_zero_range, v_bool, v_drift
+from pandas_ta.utils import v_offset, v_series, v_talib
 
 
 def true_range(
     high: Series, low: Series, close: Series,
-    talib: bool = None, drift: Int = None,
+    talib: bool = None, prenan: bool = None, drift: Int = None,
     offset: Int = None, **kwargs: DictLike
 ) -> Series:
     """True Range
@@ -26,6 +26,8 @@ def true_range(
         close (pd.Series): Series of 'close's
         talib (bool): If TA Lib is installed and talib is True, Returns
             the TA Lib version. Default: True
+        prenan (bool): If True, behave like TA Lib with some initial nan
+            based on drift (typically 1). Default: False
         drift (int): The shift period. Default: 1
         offset (int): How many periods to offset the result. Default: 0
 
@@ -41,6 +43,7 @@ def true_range(
     low = v_series(low)
     close = v_series(close)
     mode_tal = v_talib(talib)
+    prenan = v_bool(prenan, False)
     drift = v_drift(drift)
     offset = v_offset(offset)
 
@@ -54,7 +57,8 @@ def true_range(
         ranges = [hl_range, high - pc, pc - low]
         true_range = concat(ranges, axis=1)
         true_range = true_range.abs().max(axis=1)
-        true_range.iloc[:drift] = nan
+        if prenan:
+            true_range.iloc[:drift] = nan
 
     # Offset
     if offset != 0:

setup.py

@@ -42,6 +42,7 @@ setup(
         "Programming Language :: Python :: 3.7",
         "Programming Language :: Python :: 3.8",
         "Programming Language :: Python :: 3.9",
+        "Programming Language :: Python :: 3.10",
         "Operating System :: OS Independent",
         "License :: OSI Approved :: MIT License",
         "Natural Language :: English",
@@ -64,8 +65,8 @@ setup(
     extras_require={
         "full": [
             "alphaVantage-api", "matplotlib", "mplfinance", "numba", "polygon"
-            "scipy", "sklearn", "statsmodels", "stochastic", "ta-lib", "tqdm",
-            "vectorbt", "yfinance",
+            "python-dotenv", "scipy", "sklearn", "statsmodels", "stochastic",
+            "ta-lib", "tqdm", "vectorbt", "yfinance",
         ],
         "test": ["ta-lib"],
     },

tests/config.py

@@ -2,6 +2,7 @@
 import datetime
 from pathlib import Path
 
+from numpy import array
 from pandas import DataFrame, read_csv
 import pandas_datareader as pdr
 
@@ -17,6 +18,13 @@ CORRELATION: str = "corr"  # "sem"
 CORRELATION_THRESHOLD: IntFloat = 0.99  # Less than 0.99 is undesirable
 VERBOSE: bool = False
 
+welles_wilder_df = DataFrame({
+    "open": array([50, 50.7, 51.7, 52.5, 53.6, 54.4, 52.9, 52]),
+    "high": array([51.2, 51.8, 52.9, 53.7, 54.8, 54.4, 53.2, 52.7]),
+    "low": array([49.8, 50.3, 51.7, 52.3, 53.5, 52.9, 52, 52]),
+    "close": array([50.9, 51.5, 52.8, 53.5, 54.7, 53, 52, 52.2])
+})
+
 
 def error_analysis(
     df: DataFrame, kind: str, msg: str,

tests/test_indicator_overlap.py

@@ -6,7 +6,12 @@ from pandas import DataFrame, Series
 
 import talib as tal
 
-from .config import CORRELATION, CORRELATION_THRESHOLD, error_analysis, sample_data
+from .config import (
+    CORRELATION,
+    CORRELATION_THRESHOLD,
+    error_analysis,
+    sample_data,
+)
 from .context import pandas_ta