Merge remote-tracking branch 'github-desktop-ryanrussell/development' into development

README.md

@@ -920,7 +920,7 @@ Back to [Contents](#contents)
 * _Jurik Moving Average_: **jma**
 * _Kaufman's Adaptive Moving Average_: **kama**
 * _Linear Regression_: **linreg**
-* _Ehler's MESA Adapative Moving Average_: **mama**
+* _Ehler's MESA Adaptive Moving Average_: **mama**
     * Includes: **fama**
 * _McGinley Dynamic_: **mcgd**
 * _Midpoint_: **midpoint**

examples/PandasTA_Study_Examples.ipynb

@@ -466,7 +466,7 @@
       "[+] Saving: /Users/kj/av_data/SPY_D.csv\n",
       "[+] Study: Common Price and Volume SMAs\n",
       "[i] Indicator arguments: {'timed': True, 'append': True}\n",
-      "[i] No mulitproccessing (cores = 0).\n"
+      "[i] No multiprocessing (cores = 0).\n"
      ]
     },
     {
@@ -502,7 +502,7 @@
       "[+] Saving: /Users/kj/av_data/IWM_D.csv\n",
       "[+] Study: Common Price and Volume SMAs\n",
       "[i] Indicator arguments: {'timed': True, 'append': True}\n",
-      "[i] No mulitproccessing (cores = 0).\n"
+      "[i] No multiprocessing (cores = 0).\n"
      ]
     },
     {

examples/example.ipynb

@@ -836,7 +836,7 @@
       "[i] Loaded SPY(7367, 34)\n",
       "[+] Study: Common Price and Volume SMAs\n",
       "[i] Indicator arguments: {'append': True}\n",
-      "[i] No mulitproccessing (cores = 0).\n"
+      "[i] No multiprocessing (cores = 0).\n"
      ]
     },
     {

pandas_ta/core.py

@@ -222,7 +222,7 @@ class AnalysisIndicators(object):
 
     @property
     def time_range(self) -> Float:
-        """Returns the time ranges of the DataFrame as a float. Default is in "years". help(ta.toal_time)"""
+        """Returns the time ranges of the DataFrame as a float. Default is in "years". help(ta.total_time)"""
         return total_time(self._df, self._time_range)
 
     @time_range.setter
@@ -717,9 +717,9 @@ class AnalysisIndicators(object):
         else:
             # Without multiprocessing:
             if verbose:
-                _col_msg = f"[i] No mulitproccessing (cores = 0)."
+                _col_msg = f"[i] No multiprocessing (cores = 0)."
                 if has_col_names:
-                    _col_msg = f"[i] No mulitproccessing support for 'col_names' option."
+                    _col_msg = f"[i] No multiprocessing support for 'col_names' option."
                 print(_col_msg)
 
             if mode["custom"]:

pandas_ta/custom.py

@@ -147,7 +147,7 @@ def import_dir(path: str, verbose: bool = True):
 
     >>> import_dir(ta_dir)
 
-    If your custom indicator(s) loaded succesfully then it should behave exactly
+    If your custom indicator(s) loaded successfully then it should behave exactly
     like all other native indicators in pandas_ta, including help functions.
     """
     # ensure that the passed directory exists / is readable

pandas_ta/momentum/inertia.py

@@ -25,7 +25,7 @@ def inertia(
 
     Inertia was developed by Donald Dorsey and was introduced his article
     in September, 1995. It is the Relative Vigor Index smoothed by the Least
-    Squares Moving Average. Postive Inertia when values are greater than 50,
+    Squares Moving Average. Positive Inertia when values are greater than 50,
     Negative Inertia otherwise.
 
     Sources:

pandas_ta/momentum/rsi.py

@@ -64,8 +64,8 @@ def rsi(
         negative = close.diff(drift)
         positive = negative.copy()
 
-        positive[positive < 0] = 0  # Make negatives 0 for the postive series
-        negative[negative > 0] = 0  # Make postives 0 for the negative series
+        positive[positive < 0] = 0  # Make negatives 0 for the positive series
+        negative[negative > 0] = 0  # Make positives 0 for the negative series
 
         positive_avg = rma(positive, length=length)
         negative_avg = rma(negative, length=length)

pandas_ta/momentum/stc.py

@@ -19,12 +19,12 @@ def stc(
     """Schaff Trend Cycle (STC)
 
     The Schaff Trend Cycle is an evolution of the popular MACD
-    incorportating two cascaded stochastic calculations with additional
+    incorporating two cascaded stochastic calculations with additional
     smoothing.
 
     The STC returns also the beginning MACD result as well as the result
     after the first stochastic including its smoothing. This implementation
-    has been extended for Pandas TA to also allow for separatly feeding any
+    has been extended for Pandas TA to also allow for separately feeding any
     other two moving Averages (as ma1 and ma2) or to skip this to feed an
     oscillator, based on which the Schaff Trend Cycle should be calculated.
 
@@ -55,9 +55,9 @@ def stc(
         offset (int): How many periods to offset the result. Default: 0
 
     Kwargs:
-        ma1: External MA (mandatory in conjuction with ma2)
-        ma2: External MA (mandatory in conjuction with ma1)
-        osc: External osillator
+        ma1: External MA (mandatory in conjunction with ma2)
+        ma2: External MA (mandatory in conjunction with ma1)
+        osc: External oscillator
         fillna (value, optional): pd.DataFrame.fillna(value)
         fill_method (value, optional): Type of fill method
 
@@ -94,14 +94,14 @@ def stc(
 
         if ma1 is None or ma2 is None:
             return
-        # According to external feeded series
+        # According to external feed series
         xmacd = ma1 - ma2
         pff, pf = schaff_tc(close, xmacd, tclength, factor)
     elif isinstance(osc, Series):
         osc = v_series(osc, _length)
         if osc is None:
             return
-        # According to feeded oscillator (should be ranging around 0 x-axis)
+        # According to feed oscillator (should be ranging around 0 x-axis)
         xmacd = osc
         pff, pf = schaff_tc(close, xmacd, tclength, factor)
     else:

pandas_ta/overlap/alligator.py

@@ -14,7 +14,7 @@ def alligator(
 
     The Alligator Indicator was developed by Bill Williams and combines
     moving averages with fractal geometry and the lines are meant to
-    resemeble an alligator opening and closing his mouth.. It attempts to
+    resemble an alligator opening and closing his mouth.. It attempts to
     identify if an asset is trending. It consists of 3 lines: the
     Alligator's Jaw, Teeth, and Lips. Each have different lookback periods
     and but require the user to offset the results; this is avoid data leaks

pandas_ta/overlap/jma.py

@@ -89,10 +89,10 @@ def jma(
         power = np_power(r_volty, pow1)
         alpha = np_power(beta, power)
 
-        # 1st stage - prelimimary smoothing by adaptive EMA
+        # 1st stage - preliminary smoothing by adaptive EMA
         ma1 = (1 - alpha) * price + alpha * ma1
 
-        # 2nd stage - one more prelimimary smoothing by Kalman filter
+        # 2nd stage - one more preliminary smoothing by Kalman filter
         det0 = (1 - beta) * (price - ma1) + beta * det0
         ma2 = ma1 + pr * det0
 

pandas_ta/overlap/mama.py

@@ -103,9 +103,9 @@ def mama(
     prenan: Int = None, talib: bool = None,
     offset: Int = None, **kwargs: DictLike
 ) -> Series:
-    """Ehler's MESA Adapative Moving Average (MAMA)
+    """Ehler's MESA Adaptive Moving Average (MAMA)
 
-    Ehler's MESA Adapative Moving Average (MAMA) aka the Mother of All Moving
+    Ehler's MESA Adaptive Moving Average (MAMA) aka the Mother of All Moving
     Averages attempts to adapt to the source's dynamic nature. The adapation
     is based on the rate change of phase as measured by the Hilbert
     Transform Discriminator. The advantage of this method of adaptation is

pandas_ta/overlap/ssf.py

@@ -53,7 +53,7 @@ def ssf(
 
     John F. Ehlers's solution to reduce lag and remove aliasing noise with
     his research in Aerospace analog filter design. This implementation had
-    two poles. Since SSF is a (Resursive) Digital Filter, the number of
+    two poles. Since SSF is a (Recursive) Digital Filter, the number of
     poles determine how many prior recursive SSF bars to include in the
     filter design.
 

pandas_ta/overlap/ssf3.py

@@ -40,7 +40,7 @@ def ssf3(
 
     John F. Ehlers's solution to reduce lag and remove aliasing noise
     with his research in aerospace analog filter design. This is
-    implementation has three poles. Since SSF is a (Resursive) Digital
+    implementation has three poles. Since SSF is a (Recursive) Digital
     Filter, the number of poles determine how many prior recursive SSF bars
     to include in the filter design.
 

pandas_ta/utils/_core.py

@@ -111,7 +111,7 @@ def unsigned_differences(series: Series, amount: Int = None,
 
     Default Example:
     series   = Series([3, 2, 2, 1, 1, 5, 6, 6, 7, 5, 3]) and returns
-    postive  = Series([0, 0, 0, 0, 0, 1, 1, 0, 1, 0, 0])
+    positive  = Series([0, 0, 0, 0, 0, 1, 1, 0, 1, 0, 0])
     negative = Series([0, 1, 0, 1, 0, 0, 0, 0, 0, 1, 1])
     """
     amount = int(amount) if amount is not None else 1

pandas_ta/utils/_time.py

@@ -46,7 +46,7 @@ def df_year_to_date(df: DataFrame) -> DataFrame:
 
 
 def final_time(stime: Float) -> str:
-    """Human readable elapsed time. Calculates the final time elasped since
+    """Human readable elapsed time. Calculates the final time elapsed since
     stime and returns a string with microseconds and seconds."""
     time_diff = perf_counter() - stime
     return f"{time_diff * 1000:2.4f} ms ({time_diff:2.4f} s)"

pandas_ta/utils/data/yahoofinance.py

@@ -436,7 +436,7 @@ def yf(ticker: str, **kwargs) -> DataFrame:
                     print(
                         f"[!] Best choice: update yfinance to the latest version.")
                     print(
-                        f"[!] Ignore if aleady patched. Some tickers do not have financials.")
+                        f"[!] Ignore if already patched. Some tickers do not have financials.")
                     print(
                         f"[!] Otherwise to enable Company Financials, see yfinance Issue #517 patch.")
                     print(

pandas_ta/volatility/atr.py

@@ -24,7 +24,7 @@ def atr(
 ) -> Series:
     """Average True Range (ATR)
 
-    Averge True Range is used to measure volatility, especially volatility
+    Average True Range is used to measure volatility, especially volatility
     caused by gaps or limit moves.
 
     Sources:

pandas_ta/volatility/massi.py

@@ -13,7 +13,7 @@ def massi(
     """Mass Index (MASSI)
 
     The Mass Index is a non-directional volatility indicator that
-    utilitizes the High-Low Range to identify trend reversals based on
+    utilizes the High-Low Range to identify trend reversals based on
     range expansions.
 
     Sources:

pandas_ta/volume/vwap.py

@@ -33,7 +33,7 @@ def vwap(
             https://pandas.pydata.org/pandas-docs/stable/user_guide/timeseries.html#timeseries-offset-aliases
             Default: "D".
         bands (list): List of deviations to be calculated. Calculates upper
-            and lower values given a postive list of ints or floats.
+            and lower values given a positive list of ints or floats.
             Default: []
         offset (int): How many periods to offset the result. Default: 0
 

tests/test_ext_indicator_cycles.py

@@ -5,7 +5,7 @@ from pandas import DataFrame
 from .config import sample_data
 
 
-class TestCylesExtension(TestCase):
+class TestCyclesExtension(TestCase):
     @classmethod
     def setUpClass(cls):
         cls.data = sample_data

tests/test_indicator_candle.py

@@ -79,7 +79,7 @@ class TestCandle(TestCase):
         self.assertEqual(result.name, "CDL_Z_30_1")
 
     def test_ha(self):
-        """Candle: Heiken Ashi"""
+        """Candle: Heikin Ashi"""
         result = pandas_ta.ha(self.open, self.high, self.low, self.close)
         self.assertIsInstance(result, DataFrame)
         self.assertEqual(result.name, "Heikin-Ashi")