Add supertrend

Author: MDUYN

README.md

@@ -29,8 +29,8 @@ pip install pyindicators
   * [Weighted Moving Average (WMA)](#weighted-moving-average-wma)
   * [Simple Moving Average (SMA)](#simple-moving-average-sma)
   * [Exponential Moving Average (EMA)](#exponential-moving-average-ema)
+  * [SuperTrend](#supertrend)
   * [SuperTrend Clustering](#supertrend-clustering)
-  * [SuperTrend Basic](#supertrend-basic)
 * [Momentum and Oscillators](#momentum-and-oscillators)
   * [Moving Average Convergence Divergence (MACD)](#moving-average-convergence-divergence-macd)
   * [Relative Strength Index (RSI)](#relative-strength-index-rsi)
@@ -910,6 +910,50 @@ pd_df.tail(10)
 
 Indicators that combine trend detection with adaptive trailing stops.
 
+#### SuperTrend
+
+The SuperTrend indicator uses a fixed ATR multiplier factor to create a trend-following trailing stop. When the price is above the SuperTrend line the trend is bullish; when below, bearish. Trend changes generate buy/sell signals.
+
+```python
+def supertrend(
+    data: Union[PdDataFrame, PlDataFrame],
+    atr_length: int = 10,
+    factor: float = 3.0
+) -> Union[PdDataFrame, PlDataFrame]:
+```
+
+Returns the following columns:
+- `supertrend`: The SuperTrend trailing stop value
+- `supertrend_trend`: Current trend (1=bullish, 0=bearish)
+- `supertrend_upper`: Upper band
+- `supertrend_lower`: Lower band
+- `supertrend_signal`: 1=buy signal, -1=sell signal, 0=no signal
+
+Example
+
+```python
+from investing_algorithm_framework import download
+
+from pyindicators import supertrend
+
+pd_df = download(
+    symbol="btc/eur",
+    market="binance",
+    time_frame="1d",
+    start_date="2023-12-01",
+    end_date="2023-12-25",
+    pandas=True,
+    save=True,
+    storage_path="./data"
+)
+
+# Calculate SuperTrend
+pd_df = supertrend(pd_df, atr_length=10, factor=3.0)
+pd_df.tail(10)
+```
+
+![SUPERTREND](https://github.com/coding-kitties/PyIndicators/blob/main/static/images/indicators/supertrend.png)
+
 #### SuperTrend Clustering
 
 The SuperTrend Clustering indicator uses K-means clustering to optimize the ATR multiplier factor for the SuperTrend calculation. It computes multiple SuperTrend variations with different factors, evaluates their performance, and clusters them into "best", "average", and "worst" groups. The best-performing factor is then used to generate an adaptive trailing stop with buy/sell signals.
@@ -976,48 +1020,6 @@ pd_df.tail(10)
 
 ![SUPERTREND_CLUSTERING](https://github.com/coding-kitties/PyIndicators/blob/main/static/images/indicators/supertrend_clustering.png)
 
-#### SuperTrend Basic
-
-The basic SuperTrend indicator uses a fixed ATR multiplier factor to create a trend-following trailing stop. When the price is above the SuperTrend line the trend is bullish; when below, bearish. Trend changes generate buy/sell signals.
-
-```python
-def supertrend_basic(
-    data: Union[PdDataFrame, PlDataFrame],
-    atr_length: int = 10,
-    factor: float = 3.0
-) -> Union[PdDataFrame, PlDataFrame]:
-```
-
-Returns the following columns:
-- `supertrend`: The SuperTrend trailing stop value
-- `supertrend_trend`: Current trend (1=bullish, 0=bearish)
-- `supertrend_upper`: Upper band
-- `supertrend_lower`: Lower band
-- `supertrend_signal`: 1=buy signal, -1=sell signal, 0=no signal
-
-Example
-
-```python
-from investing_algorithm_framework import download
-
-from pyindicators import supertrend_basic
-
-pd_df = download(
-    symbol="btc/eur",
-    market="binance",
-    time_frame="1d",
-    start_date="2023-12-01",
-    end_date="2023-12-25",
-    pandas=True,
-    save=True,
-    storage_path="./data"
-)
-
-# Calculate basic SuperTrend
-pd_df = supertrend_basic(pd_df, atr_length=10, factor=3.0)
-pd_df.tail(10)
-```
-
 ### Support and Resistance
 
 Indicators that help identify potential support and resistance levels in the market.

pyindicators/__init__.py

@@ -19,7 +19,7 @@
     market_structure_choch_bos, choch_bos_signal, get_choch_bos_stats,
     momentum_confluence, momentum_confluence_signal,
     get_momentum_confluence_stats,
-    supertrend_clustering, supertrend_basic, supertrend_signal,
+    supertrend_clustering, supertrend, supertrend_signal,
     get_supertrend_stats,
     nadaraya_watson_envelope
 )
@@ -106,7 +106,7 @@ def get_version():
     'momentum_confluence_signal',
     'get_momentum_confluence_stats',
     'supertrend_clustering',
-    'supertrend_basic',
+    'supertrend',
     'supertrend_signal',
     'get_supertrend_stats',
     'nadaraya_watson_envelope'

pyindicators/indicators/__init__.py

@@ -41,7 +41,7 @@
     get_momentum_confluence_stats
 )
 from .supertrend import (
-    supertrend_clustering, supertrend_basic, supertrend_signal,
+    supertrend_clustering, supertrend, supertrend_signal,
     get_supertrend_stats
 )
 from .nadaraya_watson_envelope import nadaraya_watson_envelope
@@ -111,7 +111,7 @@
     'momentum_confluence_signal',
     'get_momentum_confluence_stats',
     'supertrend_clustering',
-    'supertrend_basic',
+    'supertrend',
     'supertrend_signal',
     'get_supertrend_stats',
     'nadaraya_watson_envelope'

pyindicators/indicators/supertrend.py

@@ -380,7 +380,7 @@ def supertrend_clustering(
     return df
 
 
-def supertrend_basic(
+def supertrend(
     data: Union[PdDataFrame, PlDataFrame],
     atr_length: int = 10,
     factor: float = 3.0

static/images/indicators/supertrend.png

@@ -2,7 +2,7 @@
 import pandas as pd
 import numpy as np
 
-from pyindicators import supertrend_ai, supertrend_basic, get_supertrend_stats
+from pyindicators import supertrend_ai, supertrend, get_supertrend_stats
 
 
 class TestSuperTrend(unittest.TestCase):
@@ -31,14 +31,14 @@ def setUp(self):
             'Close': close
         })
 
-    def test_supertrend_basic_returns_dataframe(self):
-        """Test that supertrend_basic returns a DataFrame."""
-        result = supertrend_basic(self.df.copy())
+    def test_supertrend_returns_dataframe(self):
+        """Test that supertrend returns a DataFrame."""
+        result = supertrend(self.df.copy())
         self.assertIsInstance(result, pd.DataFrame)
 
-    def test_supertrend_basic_columns(self):
-        """Test that supertrend_basic adds expected columns."""
-        result = supertrend_basic(self.df.copy())
+    def test_supertrend_columns(self):
+        """Test that supertrend adds expected columns."""
+        result = supertrend(self.df.copy())
 
         expected_columns = [
             'supertrend', 'supertrend_trend',
@@ -47,23 +47,23 @@ def test_supertrend_basic_columns(self):
         for col in expected_columns:
             self.assertIn(col, result.columns)
 
-    def test_supertrend_basic_trend_values(self):
+    def test_supertrend_trend_values(self):
         """Test that trend values are 0 or 1."""
-        result = supertrend_basic(self.df.copy())
+        result = supertrend(self.df.copy())
         unique_trends = result['supertrend_trend'].unique()
         for trend in unique_trends:
             self.assertIn(trend, [0, 1])
 
-    def test_supertrend_basic_signal_values(self):
+    def test_supertrend_signal_values(self):
         """Test that signal values are -1, 0, or 1."""
-        result = supertrend_basic(self.df.copy())
+        result = supertrend(self.df.copy())
         unique_signals = result['supertrend_signal'].unique()
         for signal in unique_signals:
             self.assertIn(signal, [-1, 0, 1])
 
-    def test_supertrend_basic_custom_parameters(self):
-        """Test supertrend_basic with custom parameters."""
-        result = supertrend_basic(self.df.copy(), atr_length=14, factor=2.0)
+    def test_supertrend_custom_parameters(self):
+        """Test supertrend with custom parameters."""
+        result = supertrend(self.df.copy(), atr_length=14, factor=2.0)
         self.assertIn('supertrend', result.columns)
 
     def test_supertrend_ai_returns_dataframe(self):
@@ -115,7 +115,7 @@ def test_supertrend_ai_invalid_factor_range(self):
 
     def test_get_supertrend_stats(self):
         """Test statistics function."""
-        result = supertrend_basic(self.df.copy())
+        result = supertrend(self.df.copy())
         stats = get_supertrend_stats(result)
 
         self.assertIn('buy_signals', stats)
@@ -133,7 +133,7 @@ def test_get_supertrend_stats_ai(self):
 
     def test_supertrend_upper_lower_relationship(self):
         """Test that upper band is always above lower band."""
-        result = supertrend_basic(self.df.copy())
+        result = supertrend(self.df.copy())
 
         # After initialization period
         for i in range(10, len(result)):
@@ -145,12 +145,12 @@ def test_supertrend_upper_lower_relationship(self):
     def test_supertrend_with_minimal_data(self):
         """Test SuperTrend with minimal data."""
         minimal_df = self.df.head(30).copy()
-        result = supertrend_basic(minimal_df)
+        result = supertrend(minimal_df)
         self.assertEqual(len(result), 30)
 
     def test_supertrend_signal_on_trend_change(self):
         """Test that signals occur when trend changes."""
-        result = supertrend_basic(self.df.copy())
+        result = supertrend(self.df.copy())
 
         # Find where trend changes
         trend_changes = result['supertrend_trend'].diff().abs()
@@ -166,4 +166,3 @@ def test_supertrend_signal_on_trend_change(self):
 
 if __name__ == '__main__':
     unittest.main()
-