Add is_up_trend and is_down_trend indicators

Author: MDUYN

README.md

@@ -24,7 +24,7 @@ pip install pyindicators
 
 * Native Python implementation, no external dependencies needed except for Polars or Pandas
 * Dataframe first approach, with support for both pandas dataframes and polars dataframes
-* Supports python version 3.9 and above.
+* Supports python version 3.10 and above.
 * [Trend indicators](#trend-indicators)
   * [Weighted Moving Average (WMA)](#weighted-moving-average-wma)
   * [Simple Moving Average (SMA)](#simple-moving-average-sma)
@@ -40,6 +40,8 @@ pip install pyindicators
   * [Is Crossover](#is-crossover)
   * [Crossunder](#crossunder)
   * [Is Crossunder](#is-crossunder)
+  * [Is Downtrend](#is-downtrend)
+  * [Is Uptrend](#is-uptrend)
 
 ## Indicators
 
@@ -615,3 +617,66 @@ if is_crossunder(
 if is_crossunder(pd_df, crossover_column="Crossunder_EMA", number_of_data_points=3):
     print("Crossunder detected in Pandas DataFrame in the last 3 data points")
 ```
+
+#### Is Downtrend
+
+The is_downtrend function is used to determine if a downtrend occurred in the last N data points. It returns a boolean value indicating if a downtrend occurred in the last N data points. The function can be used to check for downtrends in a DataFrame that was previously calculated using the crossover function.
+
+```python
+
+def is_down_trend(
+    data: Union[PdDataFrame, PlDataFrame],
+    use_death_cross: bool = True,
+) -> bool:
+```
+
+Example
+
+```python
+from polars import DataFrame as plDataFrame
+from pandas import DataFrame as pdDataFrame
+
+from investing_algorithm_framework import CSVOHLCVMarketDataSource
+from pyindicators import is_down_trend
+
+# For this example the investing algorithm framework is used for dataframe creation,
+csv_path = "./tests/test_data/OHLCV_BTC-EUR_BINANCE_15m_2023-12-01:00:00_2023-12-25:00:00.csv"
+data_source = CSVOHLCVMarketDataSource(csv_file_path=csv_path)
+
+pl_df = data_source.get_data()
+pd_df = data_source.get_data(pandas=True)
+
+print(is_down_trend(pl_df))
+print(is_down_trend(pd_df))
+```
+
+#### Is Uptrend
+
+The is_up_trend function is used to determine if an uptrend occurred in the last N data points. It returns a boolean value indicating if an uptrend occurred in the last N data points. The function can be used to check for uptrends in a DataFrame that was previously calculated using the crossover function.
+
+```python
+def is_up_trend(
+    data: Union[PdDataFrame, PlDataFrame],
+    use_golden_cross: bool = True,
+) -> bool:
+```
+
+Example
+
+```python
+from polars import DataFrame as plDataFrame
+from pandas import DataFrame as pdDataFrame
+
+from investing_algorithm_framework import CSVOHLCVMarketDataSource
+from pyindicators import is_up_trend
+
+# For this example the investing algorithm framework is used for dataframe creation,
+csv_path = "./tests/test_data/OHLCV_BTC-EUR_BINANCE_15m_2023-12-01:00:00_2023-12-25:00:00.csv"
+data_source = CSVOHLCVMarketDataSource(csv_file_path=csv_path)
+
+pl_df = data_source.get_data()
+pd_df = data_source.get_data(pandas=True)
+
+print(is_up_trend(pl_df))
+print(is_up_trend(pd_df))
+```

pyindicators/__init__.py

@@ -3,7 +3,8 @@
     get_peaks, is_divergence, is_lower_low_detected, \
     is_below, is_above, get_slope, has_any_higher_then_threshold, \
     has_slope_above_threshold, has_any_lower_then_threshold, \
-    has_values_above_threshold, has_values_below_threshold
+    has_values_above_threshold, has_values_below_threshold, is_down_trend, \
+    is_up_trend
 from .exceptions import PyIndicatorException
 
 __all__ = [
@@ -32,4 +33,6 @@
     'has_values_above_threshold',
     'has_values_below_threshold',
     'PyIndicatorException',
+    'is_down_trend',
+    'is_up_trend',
 ]

pyindicators/indicators/__init__.py

@@ -12,6 +12,8 @@
     has_slope_above_threshold, has_any_lower_then_threshold, \
     has_slope_below_threshold, has_values_above_threshold, \
     has_values_below_threshold
+from .is_down_trend import is_down_trend
+from .is_up_trend import is_up_trend
 
 __all__ = [
     'sma',
@@ -38,4 +40,6 @@
     'has_slope_below_threshold',
     'has_values_above_threshold',
     'has_values_below_threshold',
+    'is_down_trend',
+    'is_up_trend',
 ]

pyindicators/indicators/exponential_moving_average.py

@@ -33,6 +33,14 @@ def ema(
             f"only contains {len(data)} data points."
         )
 
+    # Check if source_column exists in the DataFrame
+    if source_column not in data.columns:
+        raise PyIndicatorException(
+            f"The source column '{source_column}' does not "
+            "exist in the DataFrame."
+        )
+
+
     if result_column is None:
         result_column = f"ema_{source_column}_{period}"
 

pyindicators/indicators/is_down_trend.py

@@ -0,0 +1,70 @@
+from typing import Union
+
+from pandas import DataFrame as PdDataFrame
+from polars import DataFrame as PlDataFrame
+
+from .exponential_moving_average import ema
+
+
+def is_down_trend(
+    data: Union[PdDataFrame, PlDataFrame],
+    use_death_cross: bool = True,
+) -> bool:
+    """
+    Check if the market is in a downtrend using various indicators.
+
+    The function decides whether it is a downtrend based on a set of
+    weighted indicators.
+
+    If the value of the sum of the indicators is greater than 0.5,
+    it is considered a downtrend.
+
+    Args:
+        data: Pandas or Polars DataFrame
+        use_death_cross: If True, use the death cross indicator
+            to check for a downtrend
+
+    Returns:
+        bool: True if the market is in a downtrend, False otherwise
+    """
+
+    weights = {
+        "death_cross": {}
+    }
+
+    source_data = data.copy()
+
+    if use_death_cross:
+        source_data = ema(
+            source_data,
+            source_column="Close",
+            period=50,
+            result_column="EMA_CLOSE_50"
+        )
+        source_data = ema(
+            source_data,
+            source_column="Close",
+            period=200,
+            result_column="EMA_CLOSE_200"
+        )
+        death_cross = source_data["EMA_CLOSE_50"].iloc[-1] \
+            < source_data["EMA_CLOSE_200"].iloc[-1]
+
+        if death_cross:
+            weights["death_cross"]["value"] = 1
+        else:
+            weights["death_cross"]["value"] = 0
+
+        weights["death_cross"]["weight"] = 1
+
+    # Calculate the weighted sum of the indicators
+    weighted_sum = 0
+
+    for indicator, values in weights.items():
+        weighted_sum += values["value"] * values["weight"]
+
+    # Check if the weighted sum is greater than 0.5
+    if weighted_sum > 0.5:
+        return True
+    else:
+        return False

pyindicators/indicators/is_up_trend.py

@@ -0,0 +1,71 @@
+from typing import Union
+
+from pandas import DataFrame as PdDataFrame
+from polars import DataFrame as PlDataFrame
+
+from .exponential_moving_average import ema
+
+
+def is_up_trend(
+    data: Union[PdDataFrame, PlDataFrame],
+    use_golden_cross: bool = True,
+) -> bool:
+    """
+    Check if the market is in a uptrend using various indicators.
+
+    The function decides whether it is a uptrend based on a set of
+    weighted indicators.
+
+    If the value of the sum of the indicators is greater than 0.5,
+    it is considered a uptrend.
+
+    Args:
+        data: Pandas or Polars DataFrame
+        use_golden_cross: If True, use the golden cross indicator
+            to check for a golden cross
+
+    Returns:
+        bool: True if the market is in a uptrend, False otherwise
+    """
+
+    weights = {
+        "golden_cross": {}
+    }
+
+    source_data = data.copy()
+
+    if use_golden_cross:
+        # Check for a golden cross
+        source_data = ema(
+            source_data,
+            source_column="Close",
+            period=50,
+            result_column="EMA_CLOSE_50"
+        )
+        source_data = ema(
+            source_data,
+            source_column="Close",
+            period=200,
+            result_column="EMA_CLOSE_200"
+        )
+        golden_cross = source_data["EMA_CLOSE_50"].iloc[-1] \
+            < source_data["EMA_CLOSE_200"].iloc[-1]
+
+        if golden_cross:
+            weights["golden_cross"]["value"] = 1
+        else:
+            weights["golden_cross"]["value"] = 0
+
+        weights["golden_cross"]["weight"] = 1
+
+    # Calculate the weighted sum of the indicators
+    weighted_sum = 0
+
+    for indicator, values in weights.items():
+        weighted_sum += values["value"] * values["weight"]
+
+    # Check if the weighted sum is greater than 0.5
+    if weighted_sum > 0.5:
+        return True
+    else:
+        return False