ENH reverse and datetime_ordered properties

.gitignore

@@ -116,7 +116,6 @@ env/**
 pandas_ta/_wrapper.py
 
 # twopirllc stuff
-IBclient/
 data/datas.csv
 data/SPY_5min.csv
 data/SPY_1min.csv

LICENSE

@@ -1,6 +1,6 @@
 The MIT License (MIT)
 
-Copyright (c) Microsoft Corporation
+Copyright (c) 2020 pandas-ta
 
 Permission is hereby granted, free of charge, to any person obtaining a copy
 of this software and associated documentation files (the "Software"), to deal

README.md

@@ -1,7 +1,7 @@
 # Technical Analysis Library in Python 3.7
 ![Example Chart](/images/TA_Chart.png)
 
-Technical Analysis (TA) is an easy to use library that is built upon Python's Pandas library with more than 80 Indicators.  These indicators are comminly used for financial time series datasets with columns or labels similar to: datetime, open, high, low, close, volume, et al.  Many commonly used indicators are included, such as: _Moving Average Convergence Divergence_ (*MACD*), _Hull Exponential Moving Average_ (*HMA*), _Bollinger Bands_ (*BBANDS*), _On-Balance Volume_ (*OBV*), _Aroon Oscillator_ (*AROON*) and more.
+Technical Analysis (TA) is an easy to use library that is built upon Python's Pandas library with more than 85 Indicators.  These indicators are comminly used for financial time series datasets with columns or labels similar to: datetime, open, high, low, close, volume, et al.  Many commonly used indicators are included, such as: _Moving Average Convergence Divergence_ (*MACD*), _Hull Exponential Moving Average_ (*HMA*), _Bollinger Bands_ (*BBANDS*), _On-Balance Volume_ (*OBV*), _Aroon Oscillator_ (*AROON*) and more.
 
 This version contains both the orignal code branch as well as a newly refactored branch with the option to use [Pandas DataFrame Extension](https://pandas.pydata.org/pandas-docs/stable/extending.html) mode. 
 All the indicators return a named Series or a DataFrame in uppercase underscore parameter format.  For example, MACD(fast=12, slow=26, signal=9) will return a DataFrame with columns: ['MACD_12_26_9', 'MACDH_12_26_9', 'MACDS_12_26_9'].
@@ -9,12 +9,11 @@ All the indicators return a named Series or a DataFrame in uppercase underscore
 
 ## Features
 
-* Over 80 indicators.
+* Has 89+ indicators.
 * Example Jupyter Notebook under the examples directory.
 * Abbreviated Indicator names as listed below.
 * *Extended Pandas DataFrame* as 'ta'.  See examples below.
-* Parameter names are more consistent.
-* Refactoring indicators into categories similar to [TA-lib](https://github.com/mrjbq7/ta-lib/tree/master/docs/func_groups).
+* Categories similar to [TA-lib](https://github.com/mrjbq7/ta-lib/tree/master/docs/func_groups).
 
 
 ## Recent Changes
@@ -86,7 +85,20 @@ help(pd.DataFrame().ta.log_return)
 ```
 
 
-## New ta DataFrame Property: *adjusted*
+## New DataFrame Properties: *reverse* & *datetime_ordered*
+
+```python
+# The 'reverse' is a helper property that returns the DataFrame
+# in reverse order
+df = df.ta.reverse
+
+# The 'datetime_ordered' property returns True if the DataFrame
+# index is of Pandas datetime64 and df.index[0] < df.index[-1]
+# Otherwise it return False
+time_series_in_order = df.ta.datetime_ordered
+```
+
+## DataFrame Property: *adjusted*
 
 ```python
 # Set ta to default to an adjusted column, 'adj_close', overriding default 'close'
@@ -153,7 +165,7 @@ df.ta.adjusted = None
 * _T3 Moving Average_: **t3**
 * _Triple Exponential Moving Average_: **tema**
 * _Triangular Moving Average_: **trima**
-* _Volume Weighted Average Price_: **vwap**
+* _Volume Weighted Average Price_: **vwap** 
 * _Volume Weighted Moving Average_: **vwma**
 * _Weighted Moving Average_: **wma**
 * _Zero Lag Moving Average_: **zlma**
@@ -248,6 +260,9 @@ Use parameter: cumulative=**True** for cumulative results.
 | ![Example OBV](/images/SPY_OBV.png) |
 
 
+# Contributors
+* [allahyarzadeh](https://github.com/allahyarzadeh)
+
 
 # Inspiration
 * TradingView: http://www.tradingview.com

pandas_ta/core.py

@@ -140,6 +140,18 @@ class AnalysisIndicators(BasePandasObject):
         else:
             self._adjusted = None
 
+    @property
+    def datetime_ordered(self) -> bool:
+        """Returns true if the index is a datetime and ordered else False."""
+        index_is_datetime = pd.api.types.is_datetime64_any_dtype(self._df.index)
+        ordered = self._df.index[0] < self._df.index[-1]
+        return True if index_is_datetime and ordered else False
+
+    @property
+    def reverse(self) -> pd.DataFrame:
+        """Reverses the DataFrame"""
+        return self._df.iloc[::-1]
+
     def _append(self, result=None, **kwargs):
         """Appends a Pandas Series or DataFrame columns to self._df."""
         if 'append' in kwargs and kwargs['append']:
@@ -219,7 +231,7 @@ class AnalysisIndicators(BasePandasObject):
         """Indicator list"""
         header = f"pandas.ta - Technical Analysis Indicators"
         helper_methods = ['indicators', 'constants']  # Public non-indicator methods
-        ta_properties = ['adjusted']
+        ta_properties = ['adjusted', 'datetime_ordered', 'reverse']
         exclude_methods = kwargs.pop('exclude', None)
         as_list = kwargs.pop('as_list', False)
         ta_indicators = list((x for x in dir(pd.DataFrame().ta) if not x.startswith('_') and not x.endswith('_')))

setup.py

@@ -6,7 +6,7 @@ long_description = "An easy to use Python 3 Pandas Extension with 80+ Technical
 setup(
     name ="pandas_ta",
     packages =['pandas_ta', 'pandas_ta.momentum', 'pandas_ta.overlap', 'pandas_ta.performance', 'pandas_ta.statistics', 'pandas_ta.trend', 'pandas_ta.volatility', 'pandas_ta.volume'],
-    version ="0.1.40b",
+    version ="0.1.41b",
     description =long_description,
     long_description =long_description,
     author ="Kevin Johnson",

tests/config.py

@@ -1,5 +1,4 @@
 import os
-from random import choice
 from pandas import read_csv
 
 VERBOSE = True
@@ -10,7 +9,13 @@ INFO = f"[i]"
 CORRELATION = 'corr'  #'sem'
 CORRELATION_THRESHOLD = 0.99  # Less than 0.99 is undesirable
 
-sample_data = read_csv(f"data/SPY_D.csv", index_col=0, parse_dates=True, infer_datetime_format=False, keep_date_col=True)
+sample_data = read_csv(
+        f"data/SPY_D.csv",
+        index_col=0,
+        parse_dates=True,
+        infer_datetime_format=False,
+        keep_date_col=True
+    )
 
 def error_analysis(df, kind, msg, icon=INFO, newline=True):
     if VERBOSE:

tests/test_indicator_momentum.py

@@ -34,6 +34,31 @@ class TestMomentum(TestCase):
     def tearDown(self): pass
     
 
+    def test_datetime_ordered(self):
+        # Test if datetime64 index and ordered
+        result = self.data.ta.datetime_ordered
+        self.assertTrue(result)
+
+        # Test if not ordered
+        original = self.data.copy()
+        reversal = original.ta.reverse
+        result = reversal.ta.datetime_ordered
+        self.assertFalse(result)
+
+        # Test a non-datetime64 index
+        original = self.data.copy()
+        original.reset_index(inplace=True)
+        result = original.ta.datetime_ordered
+        self.assertFalse(result)
+
+    def test_reverse(self):
+        original = self.data.copy()
+        result = original.ta.reverse
+
+        # Check if first and last time are reversed
+        self.assertEqual(result.index[-1], original.index[0])
+        self.assertEqual(result.index[0], original.index[-1])
+
     def test_ao(self):
         result = pandas_ta.ao(self.high, self.low)
         self.assertIsInstance(result, Series)