Add up_and_down_trends indicator

pyindicators/__init__.py

@@ -6,6 +6,7 @@
     has_values_above_threshold, has_values_below_threshold, is_down_trend, \
     is_up_trend
 from .exceptions import PyIndicatorException
+from .date_range import DateRange
 
 __all__ = [
     'sma',
@@ -35,4 +36,5 @@
     'PyIndicatorException',
     'is_down_trend',
     'is_up_trend',
+    'DateRange',
 ]

pyindicators/date_range.py

@@ -0,0 +1,56 @@
+from datetime import datetime
+from typing import Union
+
+
+class DateRange:
+    """
+    DateRange class. This class is used to define a date range and the name of
+    the range. Also, it can be used to store trading metadata such as
+    classification of the trend (Up or Down).
+    """
+
+    def __init__(
+        self,
+        start_date: datetime,
+        end_date: datetime,
+        name: str,
+        up_trend: bool = False,
+        down_trend: bool = False
+    ):
+        self.start_date = start_date
+        self.end_date = end_date
+        self.name = name
+        self._up_trend = up_trend
+        self._down_trend = down_trend
+
+    @property
+    def up_trend(self) -> Union[bool, None]:
+
+        if self._up_trend and not self._down_trend:
+            return True
+        else:
+            return None
+
+    @up_trend.setter
+    def up_trend(self, value: bool):
+        self._up_trend = value
+
+    @property
+    def down_trend(self) -> Union[bool, None]:
+
+        if self._down_trend and not self._up_trend:
+            return True
+        else:
+            return None
+
+    @down_trend.setter
+    def down_trend(self, value: bool):
+        self._down_trend = value
+
+    def __str__(self):
+        return f"DateRange({self.start_date}, {self.end_date}, {self.name})"
+
+    def __repr__(self):
+        return f"DateRange(Name: {self.name} " + \
+            f"Start date: {self.start_date} " + \
+            f"End date: {self.end_date})"

pyindicators/indicators/__init__.py

@@ -14,6 +14,7 @@
     has_values_below_threshold
 from .is_down_trend import is_down_trend
 from .is_up_trend import is_up_trend
+from .up_and_down_trends import up_and_downtrends
 
 __all__ = [
     'sma',
@@ -42,4 +43,5 @@
     'has_values_below_threshold',
     'is_down_trend',
     'is_up_trend',
+    'up_and_downtrends'
 ]

pyindicators/indicators/up_and_down_trends.py

@@ -0,0 +1,135 @@
+from typing import Union, List
+from datetime import timedelta
+
+from pandas import DataFrame as PdDataFrame
+from polars import DataFrame as PlDataFrame
+import pandas as pd
+
+from .exponential_moving_average import ema
+from .utils import is_above
+from pyindicators.date_range import DateRange
+from pyindicators.exceptions import PyIndicatorException
+
+
+def up_and_downtrends(
+    data: Union[PdDataFrame, PlDataFrame]
+) -> List[DateRange]:
+    """
+    Function to get the up and down trends of a pandas dataframe.
+
+    Params:
+        data: pd.Dataframe - instance of pandas Dateframe
+        containing OHLCV data.
+
+    Returns:
+        List of date ranges that with up_trend and down_trend
+        flags specified.
+    """
+
+    # Check if the data is larger then 200 data points
+    if len(data) < 200:
+        raise PyIndicatorException(
+            "The data must be larger than 200 data " +
+            "points to determine up and down trends."
+        )
+
+    if isinstance(data, PlDataFrame):
+        # Convert Polars DataFrame to Pandas DataFrame
+        data = data.to_pandas()
+
+    selection = data.copy()
+    selection = ema(
+        selection,
+        source_column="Close",
+        period=50,
+        result_column="SMA_Close_50"
+    )
+    selection = ema(
+        selection,
+        source_column="Close",
+        period=200,
+        result_column="SMA_Close_200"
+    )
+
+    # Make selections based on the trend
+    current_trend = None
+    start_date_range = selection.index[0]
+    date_ranges = []
+
+    for idx, row in enumerate(selection.itertuples(index=True), start=1):
+        selected_rows = selection.iloc[:idx]
+
+        # Check if last row is null for the SMA_50 and SMA_200
+        if pd.isnull(selected_rows["SMA_Close_50"].iloc[-1]) \
+                or pd.isnull(selected_rows["SMA_Close_200"].iloc[-1]):
+            continue
+
+        if is_above(
+            selected_rows,
+            fast_column="SMA_Close_50",
+            slow_column="SMA_Close_200"
+        ):
+            if current_trend != 'Up':
+
+                if current_trend is not None:
+                    end_date = selection.loc[
+                        row.Index - timedelta(days=1)
+                    ].name
+                    date_ranges.append(
+                        DateRange(
+                            start_date=start_date_range,
+                            end_date=end_date,
+                            name=current_trend,
+                            down_trend=True
+                        )
+                    )
+                    start_date_range = row.Index
+                    current_trend = 'Up'
+                else:
+                    current_trend = 'Up'
+                    start_date_range = row.Index
+        else:
+
+            if current_trend != 'Down':
+
+                if current_trend is not None:
+                    end_date = selection.loc[
+                        row.Index - timedelta(days=1)
+                    ].name
+                    date_ranges.append(
+                        DateRange(
+                            start_date=start_date_range,
+                            end_date=end_date,
+                            name=current_trend,
+                            up_trend=True
+                        )
+                    )
+                    start_date_range = row.Index
+                    current_trend = 'Down'
+                else:
+                    current_trend = 'Down'
+                    start_date_range = row.Index
+
+    if current_trend is not None:
+        end_date = selection.index[-1]
+
+        if current_trend == 'Up':
+            date_ranges.append(
+                DateRange(
+                    start_date=start_date_range,
+                    end_date=end_date,
+                    name=current_trend,
+                    up_trend=True
+                )
+            )
+        else:
+            date_ranges.append(
+                DateRange(
+                    start_date=start_date_range,
+                    end_date=end_date,
+                    name=current_trend,
+                    down_trend=True
+                )
+            )
+
+    return date_ranges