DEP removed mp property ENH new last_run, time_range and to_utc properties

README.md

@@ -424,6 +424,13 @@ df.ta.cores
 df.ta.datetime_ordered
 ```
 
+## **last_run**
+
+```python
+# Returns the time Pandas TA was last run as a string.
+df.ta.last_run
+```
+
 ## **reverse**
 
 ```python
@@ -448,6 +455,25 @@ bothhl2 = df.ta.hl2(prefix="pre", suffix="post")
 print(bothhl2.name)  # "pre_HL2_post"
 ```
 
+## **time_range**
+
+```python
+# Returns the time range of the DataFrame as a float.
+# By default, it returns the time in "years"
+df.ta.time_range
+
+# Available time_ranges include: "years", "months", "weeks", "days", "hours", "minutes". "seconds"
+df.ta.time_range = "days"
+df.ta.time_range # prints DataFrame time in "days" as float
+```
+
+## **to_utc**
+
+```python
+# Sets the DataFrame index to UTC format.
+df.ta.to_utc
+```
+
 <br/><br/>
 
 # **Indicators** (_by Category_)
@@ -687,7 +713,7 @@ result = ta.cagr(df.close)
     * **Moving Average Choices**: dema, ema, fwma, hma, linreg, midpoint, pwma, rma, sinwma, sma, swma, t3, tema, trima, vidya, wma, zlma.
 * An _experimental_ and independent __Watchlist__ Class located in the [Examples](https://github.com/twopirllc/pandas-ta/tree/master/examples/watchlist.py) Directory that can be used in conjunction with the new __Strategy__ Class.
 * _Linear Regression_ (**linear_regression**) is a new utility method for Simple Linear Regression using _Numpy_ or _Scikit Learn_'s implementation.
-* Added utility/convience function, ```to_utc```, to convert the DataFrame index to UTC. See: ```help(ta.to_utc)```
+* Added utility/convience function, ```to_utc```, to convert the DataFrame index to UTC. See: ```help(ta.to_utc)``` **Now** as a Pandas TA DataFrame Property to easily convert the DataFrame index to UTC.
 
 <br />
 

pandas_ta/core.py

@@ -50,8 +50,8 @@ class Strategy:
     ta: List = field(default_factory=list)  # Required.
     # Helpful. More descriptive version or notes or w/e.
     description: str = "TA Description"
-    # Optional. May change type later to datetime
-    created: str = datetime.now().strftime("%m/%d/%Y, %H:%M:%S")
+    # Optional. Gets Exchange Time and Local Time execution time
+    created: str = get_time(to_string=True)
 
     def __post_init__(self):
         has_name = True
@@ -234,8 +234,8 @@ class AnalysisIndicators(BasePandasObject):
 
     _adjusted = None
     _cores = cpu_count()
-    _mp = False
     _time_range = "years"
+    _last_run = get_time(to_string=True)
 
     # DataFrame Behavioral Methods
     def __call__(
@@ -253,6 +253,7 @@ class AnalysisIndicators(BasePandasObject):
 
                 # Run the indicator
                 result = fn(**kwargs)  # = getattr(self, kind)(**kwargs)
+                self._last_run = get_time(to_string=True) # Save when it completed it's run
 
                 if timed:
                     result.timed = final_time(stime)
@@ -294,17 +295,9 @@ class AnalysisIndicators(BasePandasObject):
             self._cores = cpus
 
     @property
-    def mp(self) -> bool:
-        """property: df.ta.mp"""
-        return self._mp
-
-    @mp.setter
-    def mp(self, value: bool) -> None:
-        """property: df.ta.mp = False (Default)"""
-        if value is not None and isinstance(value, bool):
-            self._mp = value
-        else:
-            self._mp = False
+    def last_run(self) -> str:
+        """Returns the time when the DataFrame was last run."""
+        return self._last_run
 
     # Public Get DataFrame Properties
     @property
@@ -326,18 +319,23 @@ class AnalysisIndicators(BasePandasObject):
         return self._df.iloc[::-1]
 
     @property
-    def time_range(self) -> str:
-        """"""
+    def time_range(self) -> float:
+        """Returns the time ranges of the DataFrame as a float. Default is in "years". help(ta.toal_time)"""
         return total_time(self._df, self._time_range)
 
     @time_range.setter
     def time_range(self, value: str) -> None:
-        """property: df.ta.mp = False (Default)"""
+        """property: df.ta.time_range = "years" (Default)"""
         if value is not None and isinstance(value, str):
             self._time_range = value
         else:
             self._time_range = "years"
 
+    @property
+    def to_utc(self) -> None:
+        """Sets the DataFrame index to UTC format"""
+        self._df = to_utc(self._df)
+
     @property
     def version(self) -> str:
         """Returns the version."""
@@ -540,9 +538,10 @@ class AnalysisIndicators(BasePandasObject):
             "categories",
             "cores",
             "datetime_ordered",
-            "mp",
+            "last_run",
             "reverse",
             "time_range",
+            "to_utc",
             "version",
         ]
 
@@ -671,7 +670,7 @@ class AnalysisIndicators(BasePandasObject):
             # Some magic to optimize chunksize for speed based on total ta indicators
             _chunksize = mp_chunksize - 1 if mp_chunksize > _total_ta else int(npLog10(_total_ta)) + 1
             if verbose:
-                print(f"[i] Multiprocessing: {_chunksize} chunks over {cpu_count()} cores for {_total_ta} indicators.")
+                print(f"[i] Multiprocessing {_total_ta} indicators with {_chunksize} chunks over {self.cores}/{cpu_count()} cpus.")
 
             results = None
             if mode["custom"]:
@@ -697,6 +696,7 @@ class AnalysisIndicators(BasePandasObject):
 
             pool.close()
             pool.join()
+            self._last_run = get_time(to_string=True)
 
         else:
             # Without multiprocessing:
@@ -718,14 +718,12 @@ class AnalysisIndicators(BasePandasObject):
         # DataFrame
         [self._post_process(r, **kwargs) for r in results]
 
-        if timed:
-            ftime = final_time(stime)
-
         if verbose:
             print(f"[i] Total indicators: {len(ta)}")
             print(f"[i] Columns added: {len(self._df.columns) - initial_column_count}")
+            print(f"[i] Last Run: {self._last_run}")
         if timed:
-            print(f"[i] Runtime: {ftime}")
+            print(f"[i] Runtime: {final_time(stime)}")
 
     # Public DataFrame Methods: Indicators and Utilities
     # Candles

pandas_ta/utils/_time.py

@@ -19,10 +19,6 @@ def df_dates(df: DataFrame, dates: Tuple[str, list] = None) -> DataFrame:
 
 def df_month_to_date(df: DataFrame) -> DataFrame:
     """Yields the Month-to-Date (MTD) DataFrame"""
-    # if df.empty: print("[X] Month-to-Date not in range"); return
-    # print(df.shape)
-    # print(type(df))
-    # print(df)
     return df[df.index >= Timestamp.now().strftime("%Y-%m-01")]
 
 

setup.py

@@ -18,7 +18,7 @@ setup(
         "pandas_ta.volatility",
         "pandas_ta.volume"
     ],
-    version=".".join(("0", "2", "50b")),
+    version=".".join(("0", "2", "51b")),
     description=long_description,
     long_description=long_description,
     author="Kevin Johnson",