Fix tests

Author: MDUYN

README.md

@@ -846,7 +846,7 @@ pd_df.tail(10)
 
 #### Nadaraya-Watson Envelope (NWE)
 
-The Nadaraya-Watson Envelope uses Gaussian kernel regression to create a smoothed price estimate, then adds an envelope based on the mean absolute error (MAE) scaled by a multiplier. This is a non-repainting (endpoint) implementation inspired by the TradingView "Nadaraya-Watson Envelope [LuxAlgo]" indicator. It is useful for identifying overbought/oversold zones and mean-reversion opportunities.
+The Nadaraya-Watson Envelope uses Gaussian kernel regression to create a smoothed price estimate, then adds an envelope based on the mean absolute error (MAE) scaled by a multiplier. This is a non-repainting (endpoint) implementation. It is useful for identifying overbought/oversold zones and mean-reversion opportunities.
 
 Calculation:
 - Kernel weights: `w(i) = exp(-i² / (2 × h²))` for `i = 0..lookback-1`
@@ -958,8 +958,6 @@ pd_df.tail(10)
 
 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.
 
-Based on the LuxAlgo SuperTrend AI indicator concept.
-
 ```python
 def supertrend_clustering(
     data: Union[PdDataFrame, PlDataFrame],

tests/indicators/test_supertrend.py

@@ -2,7 +2,7 @@
 import pandas as pd
 import numpy as np
 
-from pyindicators import supertrend_ai, supertrend, get_supertrend_stats
+from pyindicators import supertrend_clustering, supertrend, get_supertrend_stats
 
 
 class TestSuperTrend(unittest.TestCase):
@@ -66,14 +66,14 @@ def test_supertrend_custom_parameters(self):
         result = supertrend(self.df.copy(), atr_length=14, factor=2.0)
         self.assertIn('supertrend', result.columns)
 
-    def test_supertrend_ai_returns_dataframe(self):
-        """Test that supertrend_ai returns a DataFrame."""
-        result = supertrend_ai(self.df.copy(), max_data=100)
+    def test_supertrend_clustering_returns_dataframe(self):
+        """Test that supertrend_clustering returns a DataFrame."""
+        result = supertrend_clustering(self.df.copy(), max_data=100)
         self.assertIsInstance(result, pd.DataFrame)
 
-    def test_supertrend_ai_columns(self):
-        """Test that supertrend_ai adds expected columns."""
-        result = supertrend_ai(self.df.copy(), max_data=100)
+    def test_supertrend_clustering_columns(self):
+        """Test that supertrend_clustering adds expected columns."""
+        result = supertrend_clustering(self.df.copy(), max_data=100)
 
         expected_columns = [
             'supertrend', 'supertrend_trend', 'supertrend_ama',
@@ -82,10 +82,10 @@ def test_supertrend_ai_columns(self):
         for col in expected_columns:
             self.assertIn(col, result.columns)
 
-    def test_supertrend_ai_factor_range(self):
-        """Test that supertrend_ai uses factors within specified range."""
+    def test_supertrend_clustering_factor_range(self):
+        """Test that supertrend_clustering uses factors within specified range."""
         min_mult, max_mult = 1.0, 3.0
-        result = supertrend_ai(
+        result = supertrend_clustering(
             self.df.copy(),
             min_mult=min_mult,
             max_mult=max_mult,
@@ -98,20 +98,20 @@ def test_supertrend_ai_factor_range(self):
             self.assertGreaterEqual(factor, min_mult - 0.1)
             self.assertLessEqual(factor, max_mult + 0.1)
 
-    def test_supertrend_ai_cluster_options(self):
-        """Test supertrend_ai with different cluster options."""
+    def test_supertrend_clustering_cluster_options(self):
+        """Test supertrend_clustering with different cluster options."""
         for cluster in ['best', 'average', 'worst']:
-            result = supertrend_ai(
+            result = supertrend_clustering(
                 self.df.copy(),
                 from_cluster=cluster,
                 max_data=100
             )
             self.assertIn('supertrend', result.columns)
 
-    def test_supertrend_ai_invalid_factor_range(self):
+    def test_supertrend_clustering_invalid_factor_range(self):
         """Test that invalid factor range raises error."""
         with self.assertRaises(ValueError):
-            supertrend_ai(self.df.copy(), min_mult=5.0, max_mult=1.0)
+            supertrend_clustering(self.df.copy(), min_mult=5.0, max_mult=1.0)
 
     def test_get_supertrend_stats(self):
         """Test statistics function."""
@@ -123,9 +123,9 @@ def test_get_supertrend_stats(self):
         self.assertIn('current_trend', stats)
         self.assertIn(stats['current_trend'], ['bullish', 'bearish'])
 
-    def test_get_supertrend_stats_ai(self):
-        """Test statistics for AI version includes additional fields."""
-        result = supertrend_ai(self.df.copy(), max_data=100)
+    def test_get_supertrend_stats_clustering(self):
+        """Test statistics for clustering version includes additional fields."""
+        result = supertrend_clustering(self.df.copy(), max_data=100)
         stats = get_supertrend_stats(result)
 
         self.assertIn('avg_factor', stats)