diff --git a/.gitignore b/.gitignore
index dc05d78..db9fdb3 100644
--- a/.gitignore
+++ b/.gitignore
@@ -143,4 +143,7 @@ data/TV_5min.csv
 data/tulip.csv
 examples/*.csv
 jnb/*.ipynb
-jnb/*.txt
\ No newline at end of file
+jnb/*.txt
+
+*.txt
+reza_ohlcv
\ No newline at end of file
diff --git a/README.md b/README.md
index 419321e..97beaa2 100644
--- a/README.md
+++ b/README.md
@@ -113,7 +113,7 @@ $ pip install pandas_ta
 
 Latest Version
 --------------
-Best choice! Version: *0.3.19b*
+Best choice! Version: *0.3.20b*
 * 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
@@ -958,6 +958,7 @@ print(pf.returns_stats())
 <br />
 
 ## **Breaking / Depreciated Indicators**
+* _Arnaud Legoux Moving Average_ (**alma**) New default ```length=9```. See ```help(ta.alma)```.
 * _Trend Return_ (**trend_return**) has been removed and replaced with **tsignals**. When given a trend Series like ```close > sma(close, 50)``` it returns the Trend, Trade Entries and Trade Exits of that trend to make it compatible with [**vectorbt**](https://github.com/polakowo/vectorbt) by setting ```asbool=True``` to get boolean Trade Entries and Exits. See ```help(ta.tsignals)```
 * _Zero Lag Moving Average_ (**zlma**) now using available Moving Averages from ```ta.ma```. See ```help(ta.zlma)``` and ```help(ta.ma)```.
 
diff --git a/pandas_ta/overlap/alma.py b/pandas_ta/overlap/alma.py
index 2107831..c6dfd04 100644
--- a/pandas_ta/overlap/alma.py
+++ b/pandas_ta/overlap/alma.py
@@ -1,4 +1,5 @@
 # -*- coding: utf-8 -*-
+from math import floor
 from numpy import exp as npExp
 from numpy import nan as npNaN
 from pandas import Series
@@ -8,8 +9,8 @@ from pandas_ta.utils import get_offset, verify_series
 def alma(close, length=None, sigma=None, distribution_offset=None, offset=None, **kwargs):
     """Indicator: Arnaud Legoux Moving Average (ALMA)"""
     # Validate Arguments
-    length = int(length) if length and length > 0 else 10
-    sigma = float(sigma) if sigma and sigma > 0 else 6.0
+    length = int(length) if isinstance(length, int) and length > 0 else 9
+    sigma = float(sigma) if isinstance(sigma, float) and sigma > 0 else 6.0
     distribution_offset = float(distribution_offset) if distribution_offset and distribution_offset > 0 else 0.85
     close = verify_series(close, length)
     offset = get_offset(offset)
@@ -67,6 +68,7 @@ in conjunction with smoothing to reduce noise.
 Implemented for Pandas TA by rengel8 based on the source provided below.
 
 Sources:
+    https://www.sierrachart.com/index.php?page=doc/StudiesReference.php&ID=475&Name=Moving_Average_-_Arnaud_Legoux
     https://www.prorealcode.com/prorealtime-indicators/alma-arnaud-legoux-moving-average/
 
 Calculation:
@@ -74,7 +76,7 @@ Calculation:
 
 Args:
     close (pd.Series): Series of 'close's
-    length (int): It's period, window size. Default: 10
+    length (int): It's period, window size. Default: 9
     sigma (float): Smoothing value. Default 6.0
     distribution_offset (float): Value to offset the distribution min 0
         (smoother), max 1 (more responsive). Default 0.85
diff --git a/pandas_ta/overlap/ssf.py b/pandas_ta/overlap/ssf.py
index 88742d7..692f65b 100644
--- a/pandas_ta/overlap/ssf.py
+++ b/pandas_ta/overlap/ssf.py
@@ -10,8 +10,8 @@ from pandas_ta.utils import get_offset, verify_series
 def ssf(close, length=None, poles=None, offset=None, **kwargs):
     """Indicator: Ehler's Super Smoother Filter (SSF)"""
     # Validate Arguments
-    length = int(length) if length and length > 0 else 10
-    poles = int(poles) if poles in [2, 3] else 2
+    length = int(length) if isinstance(length, int) and length > 0 else 10
+    poles = int(poles) if isinstance(poles, int) and poles in [2, 3] else 2
     close = verify_series(close, length)
     offset = get_offset(offset)
 
@@ -32,11 +32,9 @@ def ssf(close, length=None, poles=None, offset=None, **kwargs):
         c2 = c0 + b0 # e^(-2x) + 2e^(-x)*cos(3^(.5) * x)
         c1 = 1 - c2 - c3 - c4
 
-        for i in range(0, m):
+        for i in range(poles, m):
             ssf.iloc[i] = c1 * close.iloc[i] + c2 * ssf.iloc[i - 1] + c3 * ssf.iloc[i - 2] + c4 * ssf.iloc[i - 3]
 
-        ssf.iloc[:3] = npNaN
-
     else: # poles == 2
         x = npPi * npSqrt(2) / length # x = PI * 2^(.5) / n
         a0 = npExp(-x) # e^(-x)
@@ -44,10 +42,10 @@ def ssf(close, length=None, poles=None, offset=None, **kwargs):
         b1 = 2 * a0 * npCos(x) # 2e^(-x)*cos(x)
         c1 = 1 - a1 - b1 # e^(-2x) - 2e^(-x)*cos(x) + 1
 
-        for i in range(0, m):
+        for i in range(poles, m):
             ssf.iloc[i] = c1 * close.iloc[i] + b1 * ssf.iloc[i - 1] + a1 * ssf.iloc[i - 2]
 
-        ssf.iloc[:2] = npNaN
+    ssf.iloc[:length] = npNaN
 
     # Offset
     if offset != 0:
diff --git a/pandas_ta/statistics/stdev.py b/pandas_ta/statistics/stdev.py
index 6047101..f0e6780 100644
--- a/pandas_ta/statistics/stdev.py
+++ b/pandas_ta/statistics/stdev.py
@@ -8,7 +8,7 @@ from pandas_ta.utils import get_offset, verify_series
 def stdev(close, length=None, ddof=None, talib=None, offset=None, **kwargs):
     """Indicator: Standard Deviation"""
     # Validate Arguments
-    length = int(length) if length and length > 0 else 30
+    length = int(length) if isinstance(length, int) and length > 0 else 30
     ddof = int(ddof) if isinstance(ddof, int) and ddof >= 0 and ddof < length else 1
     close = verify_series(close, length)
     offset = get_offset(offset)
diff --git a/pandas_ta/statistics/variance.py b/pandas_ta/statistics/variance.py
index 60f54f4..c964c9f 100644
--- a/pandas_ta/statistics/variance.py
+++ b/pandas_ta/statistics/variance.py
@@ -6,7 +6,7 @@ from pandas_ta.utils import get_offset, verify_series
 def variance(close, length=None, ddof=None, talib=None, offset=None, **kwargs):
     """Indicator: Variance"""
     # Validate Arguments
-    length = int(length) if length and length > 1 else 30
+    length = int(length) if isinstance(length, int) and length > 1 else 30
     ddof = int(ddof) if isinstance(ddof, int) and ddof >= 0 and ddof < length else 1
     min_periods = int(kwargs["min_periods"]) if "min_periods" in kwargs and kwargs["min_periods"] is not None else length
     close = verify_series(close, max(length, min_periods))
diff --git a/setup.py b/setup.py
index fbaf0a6..ddfb6c3 100644
--- a/setup.py
+++ b/setup.py
@@ -19,7 +19,7 @@ setup(
         "pandas_ta.volatility",
         "pandas_ta.volume"
     ],
-    version=".".join(("0", "3", "19b")),
+    version=".".join(("0", "3", "20b")),
     description=long_description,
     long_description=long_description,
     author="Kevin Johnson",
diff --git a/tests/test_ext_indicator_overlap_ext.py b/tests/test_ext_indicator_overlap_ext.py
index 514af45..80990d5 100644
--- a/tests/test_ext_indicator_overlap_ext.py
+++ b/tests/test_ext_indicator_overlap_ext.py
@@ -26,7 +26,7 @@ class TestOverlapExtension(TestCase):
     def test_alma_ext(self):
         self.data.ta.alma(append=True)
         self.assertIsInstance(self.data, DataFrame)
-        self.assertEqual(self.data.columns[-1], "ALMA_10_6.0_0.85")
+        self.assertEqual(self.data.columns[-1], "ALMA_9_6.0_0.85")
 
     def test_dema_ext(self):
         self.data.ta.dema(append=True)
diff --git a/tests/test_indicator_overlap.py b/tests/test_indicator_overlap.py
index 4ec0cb0..f2d7592 100644
--- a/tests/test_indicator_overlap.py
+++ b/tests/test_indicator_overlap.py
@@ -42,7 +42,7 @@ class TestOverlap(TestCase):
     def test_alma(self):
         result = pandas_ta.alma(self.close)# , length=None, sigma=None, distribution_offset=)
         self.assertIsInstance(result, Series)
-        self.assertEqual(result.name, "ALMA_10_6.0_0.85")
+        self.assertEqual(result.name, "ALMA_9_6.0_0.85")
 
     def test_dema(self):
         result = pandas_ta.dema(self.close, talib=False)