diff --git a/README.md b/README.md
index 07b2308..e5711cb 100644
--- a/README.md
+++ b/README.md
@@ -118,7 +118,7 @@ $ pip install pandas_ta
Latest Version
--------------
-Best choice! Version: *0.3.42b*
+Best choice! Version: *0.3.43b*
* 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
@@ -219,7 +219,7 @@ Thanks for using **Pandas TA**!
_Thank you for your contributions!_
-
+ 
@@ -1034,7 +1034,7 @@ help(ta.sample)
## **New Indicators**
* _Bill Williams Alligator_ (**alligator**) attempts to identify if an asset is trending. See ```help(ta.alligator)```
-* _Cross Signals_ (**xsignals**) was created by Kevin Johnson. It is a wrapper of Trade Signals that returns Trends, Trades, Entries and Exits. Cross Signals are commonly used for **bbands**, **rsi**, **zscore** crossing some value either above or below two values at different times. See ```help(ta.xsignals)```
+* _Cross Signals_ (**xsignals**) is a wrapper of Trend Signals (```help(ta.tsignals)```) that returns Trends, Trades, Entries and Exits. Cross Signals are commonly used for **bbands**, **rsi**, **zscore** crossing some value either above or below two values at different times. See ```help(ta.xsignals)```
* _Directional Movement_ (**dm**) developed by J. Welles Wilder in 1978 attempts to determine which direction the price of an asset is moving. See ```help(ta.dm)```
* _Jurik Moving Average_ (**jma**) attempts to eliminate noise to see the "true" underlying activity. See: ```help(ta.jma)```
* _Smoothed Moving Average_ (**smma**) can be used to confirm trends and define areas of support and resistance. See: ```help(ta.smma)```
@@ -1069,7 +1069,7 @@ help(ta.sample)
# **Support**
Feeling generous, like the package or want to see it become more a mature package?
-* Donations help cover data and API costs so that other platform indicataors (like [TradingView](https://github.com/tradingview/)) are accurate and consistent.
+* Donations help cover data and API costs so platform indicataors (like [TradingView](https://github.com/tradingview/)) are accurate.
* I appreciate **ALL** of those that have bought me Coffee/Beer/Wine et al. I greatly appreciate it! 😎
diff --git a/pandas_ta/momentum/stoch.py b/pandas_ta/momentum/stoch.py
index e83537d..7f5654a 100644
--- a/pandas_ta/momentum/stoch.py
+++ b/pandas_ta/momentum/stoch.py
@@ -40,7 +40,7 @@ def stoch(high, low, close, k=None, d=None, smooth_k=None, mamode=None, talib=No
Returns:
pd.DataFrame: %K, %D columns.
"""
- # Validate arguments
+ # Validate
k = k if k and k > 0 else 14
d = d if d and d > 0 else 3
smooth_k = smooth_k if smooth_k and smooth_k > 0 else 3
@@ -54,7 +54,7 @@ def stoch(high, low, close, k=None, d=None, smooth_k=None, mamode=None, talib=No
if high is None or low is None or close is None: return
- # Calculate Result
+ # Calculate
if Imports["talib"] and mode_tal:
from talib import STOCH
stoch_ = STOCH(high, low, close, k, d, tal_ma(mamode), d, tal_ma(mamode))
@@ -74,7 +74,7 @@ def stoch(high, low, close, k=None, d=None, smooth_k=None, mamode=None, talib=No
stoch_k = stoch_k.shift(offset)
stoch_d = stoch_d.shift(offset)
- # Handle fills
+ # Fill
if "fillna" in kwargs:
stoch_k.fillna(kwargs["fillna"], inplace=True)
stoch_d.fillna(kwargs["fillna"], inplace=True)
@@ -82,14 +82,14 @@ def stoch(high, low, close, k=None, d=None, smooth_k=None, mamode=None, talib=No
stoch_k.fillna(method=kwargs["fill_method"], inplace=True)
stoch_d.fillna(method=kwargs["fill_method"], inplace=True)
- # Name and Categorize it
+ # Name and Category
_name = "STOCH"
_props = f"_{k}_{d}_{smooth_k}"
stoch_k.name = f"{_name}k{_props}"
stoch_d.name = f"{_name}d{_props}"
stoch_k.category = stoch_d.category = "momentum"
- # Prepare DataFrame to return
+ # Return DataFrame
data = {stoch_k.name: stoch_k, stoch_d.name: stoch_d}
df = DataFrame(data, index=close.index)
df.name = f"{_name}{_props}"
diff --git a/pandas_ta/momentum/stochf.py b/pandas_ta/momentum/stochf.py
index f5e536e..bace768 100644
--- a/pandas_ta/momentum/stochf.py
+++ b/pandas_ta/momentum/stochf.py
@@ -34,7 +34,7 @@ def stochf(high, low, close, k=None, d=None, mamode=None, talib=None, offset=Non
Returns:
pd.DataFrame: Fast %K, %D columns.
"""
- # Validate arguments
+ # Validate
k = k if k and k > 0 else 14
d = d if d and d > 0 else 3
_length = max(k, d)
@@ -47,7 +47,7 @@ def stochf(high, low, close, k=None, d=None, mamode=None, talib=None, offset=Non
if high is None or low is None or close is None: return
- # Calculate Result
+ # Calculate
if Imports["talib"] and mode_tal:
from talib import STOCHF
stochf_ = STOCHF(high, low, close, k, d, tal_ma(mamode))
@@ -65,7 +65,7 @@ def stochf(high, low, close, k=None, d=None, mamode=None, talib=None, offset=Non
stochf_k = stochf_k.shift(offset)
stochf_d = stochf_d.shift(offset)
- # Handle fills
+ # Fill
if "fillna" in kwargs:
stochf_k.fillna(kwargs["fillna"], inplace=True)
stochf_d.fillna(kwargs["fillna"], inplace=True)
@@ -73,14 +73,14 @@ def stochf(high, low, close, k=None, d=None, mamode=None, talib=None, offset=Non
stochf_k.fillna(method=kwargs["fill_method"], inplace=True)
stochf_d.fillna(method=kwargs["fill_method"], inplace=True)
- # Name and Categorize it
+ # Name and Category
_name = "STOCHF"
_props = f"_{k}_{d}"
stochf_k.name = f"{_name}k{_props}"
stochf_d.name = f"{_name}d{_props}"
stochf_k.category = stochf_d.category = "momentum"
- # Prepare DataFrame to return
+ # Return DataFrame
data = {stochf_k.name: stochf_k, stochf_d.name: stochf_d}
df = DataFrame(data, index=close.index)
df.name = f"{_name}{_props}"
diff --git a/pandas_ta/momentum/stochrsi.py b/pandas_ta/momentum/stochrsi.py
index eb545a3..23899e0 100644
--- a/pandas_ta/momentum/stochrsi.py
+++ b/pandas_ta/momentum/stochrsi.py
@@ -36,7 +36,7 @@ def stochrsi(close, length=None, rsi_length=None, k=None, d=None, mamode=None, o
Returns:
pd.DataFrame: RSI %K, RSI %D columns.
"""
- # Validate arguments
+ # Validate
length = length if length and length > 0 else 14
rsi_length = rsi_length if rsi_length and rsi_length > 0 else 14
k = k if k and k > 0 else 3
@@ -47,7 +47,7 @@ def stochrsi(close, length=None, rsi_length=None, k=None, d=None, mamode=None, o
if close is None: return
- # Calculate Result
+ # Calculate
rsi_ = rsi(close, length=rsi_length)
lowest_rsi = rsi_.rolling(length).min()
highest_rsi = rsi_.rolling(length).max()
@@ -63,7 +63,7 @@ def stochrsi(close, length=None, rsi_length=None, k=None, d=None, mamode=None, o
stochrsi_k = stochrsi_k.shift(offset)
stochrsi_d = stochrsi_d.shift(offset)
- # Handle fills
+ # Fill
if "fillna" in kwargs:
stochrsi_k.fillna(kwargs["fillna"], inplace=True)
stochrsi_d.fillna(kwargs["fillna"], inplace=True)
@@ -71,14 +71,14 @@ def stochrsi(close, length=None, rsi_length=None, k=None, d=None, mamode=None, o
stochrsi_k.fillna(method=kwargs["fill_method"], inplace=True)
stochrsi_d.fillna(method=kwargs["fill_method"], inplace=True)
- # Name and Categorize it
+ # Name and Categorize
_name = "STOCHRSI"
_props = f"_{length}_{rsi_length}_{k}_{d}"
stochrsi_k.name = f"{_name}k{_props}"
stochrsi_d.name = f"{_name}d{_props}"
stochrsi_k.category = stochrsi_d.category = "momentum"
- # Prepare DataFrame to return
+ # Return DataFrame
data = {stochrsi_k.name: stochrsi_k, stochrsi_d.name: stochrsi_d}
df = DataFrame(data)
df.name = f"{_name}{_props}"
diff --git a/setup.py b/setup.py
index 76c36da..d078075 100644
--- a/setup.py
+++ b/setup.py
@@ -19,7 +19,7 @@ setup(
"pandas_ta.volatility",
"pandas_ta.volume"
],
- version=".".join(("0", "3", "42b")),
+ version=".".join(("0", "3", "43b")),
description=long_description,
long_description=long_description,
author="Kevin Johnson",