tested and updated supertrand indicators to give us the same result as in original strategy

cycles/IncStrategies/indicators/supertrend.py

@@ -65,12 +65,12 @@ class SupertrendState(OHLCIndicatorState):
         
         # State variables
         self.previous_close = None
-        self.previous_trend = 1  # Start with uptrend assumption
+        self.previous_trend = None  # Don't assume initial trend, let first calculation determine it
         self.final_upper_band = None
         self.final_lower_band = None
         
         # Current values
-        self.current_trend = 1
+        self.current_trend = None
         self.current_supertrend = None
         
         self.is_initialized = True
@@ -123,10 +123,11 @@ class SupertrendState(OHLCIndicatorState):
         
         # Determine trend
         if self.previous_close is None:
-            # First calculation
-            trend = 1 if close > final_lower_band else -1
+            # First calculation - match original logic
+            # If close <= upper_band, trend is -1 (downtrend), else trend is 1 (uptrend)
+            trend = -1 if close <= basic_upper_band else 1
         else:
-            # Trend logic
+            # Trend logic for subsequent calculations
             if self.previous_trend == 1 and close <= final_lower_band:
                 trend = -1
             elif self.previous_trend == -1 and close >= final_upper_band:
@@ -174,10 +175,10 @@ class SupertrendState(OHLCIndicatorState):
         """Reset Supertrend state to initial conditions."""
         self.atr_state.reset()
         self.previous_close = None
-        self.previous_trend = 1
+        self.previous_trend = None
         self.final_upper_band = None
         self.final_lower_band = None
-        self.current_trend = 1
+        self.current_trend = None
         self.current_supertrend = None
         self.values_received = 0
         self._current_values = {}
@@ -198,9 +199,9 @@ class SupertrendState(OHLCIndicatorState):
         Get current trend direction.
         
         Returns:
-            Current trend: +1 for uptrend, -1 for downtrend
+            Current trend: +1 for uptrend, -1 for downtrend, 0 if not initialized
         """
-        return self.current_trend
+        return self.current_trend if self.current_trend is not None else 0
     
     def get_current_supertrend_value(self) -> Optional[float]:
         """

test_metatrend_comparison.py

@@ -0,0 +1,796 @@
+"""
+MetaTrend Strategy Comparison Test
+
+This test verifies that our incremental indicators produce identical results
+to the original DefaultStrategy (metatrend strategy) implementation.
+
+The test compares:
+1. Individual Supertrend indicators (3 different parameter sets)
+2. Meta-trend calculation (agreement between all 3 Supertrends)
+3. Entry/exit signal generation
+4. Overall strategy behavior
+
+Test ensures our incremental implementation is mathematically equivalent
+to the original batch calculation approach.
+"""
+
+import pandas as pd
+import numpy as np
+import logging
+from typing import Dict, List, Tuple
+import os
+import sys
+
+# Add project root to path
+sys.path.append(os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
+
+from cycles.strategies.default_strategy import DefaultStrategy
+from cycles.IncStrategies.indicators.supertrend import SupertrendState, SupertrendCollection
+from cycles.Analysis.supertrend import Supertrends
+from cycles.backtest import Backtest
+
+# Configure logging
+logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(levelname)s - %(message)s')
+logger = logging.getLogger(__name__)
+
+
+class MetaTrendComparisonTest:
+    """
+    Comprehensive test suite for comparing original and incremental MetaTrend implementations.
+    """
+    
+    def __init__(self):
+        """Initialize the test suite."""
+        self.test_data = None
+        self.original_results = None
+        self.incremental_results = None
+        
+        # Supertrend parameters from original implementation
+        self.supertrend_params = [
+            {"period": 12, "multiplier": 3.0},
+            {"period": 10, "multiplier": 1.0},
+            {"period": 11, "multiplier": 2.0}
+        ]
+    
+    def load_test_data(self, symbol: str = "BTCUSDT", limit: int = 500) -> pd.DataFrame:
+        """
+        Load test data for comparison.
+        
+        Args:
+            symbol: Trading symbol to load
+            limit: Number of data points to load
+            
+        Returns:
+            DataFrame with OHLCV data
+        """
+        logger.info(f"Loading test data for {symbol} (limit: {limit})")
+        
+        try:
+            # Try to load from existing data files
+            data_file = f"data/{symbol}_1m.csv"
+            if os.path.exists(data_file):
+                df = pd.read_csv(data_file)
+                df['timestamp'] = pd.to_datetime(df['timestamp'])
+                df.set_index('timestamp', inplace=True)
+                df = df.tail(limit)
+                logger.info(f"Loaded {len(df)} data points from {data_file}")
+            else:
+                # Generate synthetic data for testing if no real data available
+                logger.warning(f"No data file found at {data_file}, generating synthetic data")
+                df = self._generate_synthetic_data(limit)
+            
+            # Ensure required columns
+            required_cols = ['open', 'high', 'low', 'close', 'volume']
+            for col in required_cols:
+                if col not in df.columns:
+                    if col == 'volume':
+                        df['volume'] = 1000.0  # Default volume
+                    else:
+                        raise ValueError(f"Missing required column: {col}")
+            
+            # Reset index to get timestamp as column for incremental processing
+            df_with_timestamp = df.reset_index()
+            
+            self.test_data = df_with_timestamp
+            return df_with_timestamp
+            
+        except Exception as e:
+            logger.error(f"Failed to load test data: {e}")
+            # Fallback to synthetic data
+            df = self._generate_synthetic_data(limit)
+            df_with_timestamp = df.reset_index()
+            self.test_data = df_with_timestamp
+            return df_with_timestamp
+    
+    def _generate_synthetic_data(self, length: int) -> pd.DataFrame:
+        """Generate synthetic OHLCV data for testing."""
+        logger.info(f"Generating {length} synthetic data points")
+        
+        np.random.seed(42)  # For reproducible results
+        
+        # Generate price series with trend and noise
+        base_price = 50000.0
+        trend = np.linspace(0, 0.1, length)  # Slight upward trend
+        noise = np.random.normal(0, 0.02, length)  # 2% volatility
+        
+        close_prices = base_price * (1 + trend + noise.cumsum() * 0.1)
+        
+        # Generate OHLC from close prices
+        data = []
+        timestamps = pd.date_range(start='2024-01-01', periods=length, freq='1min')
+        
+        for i in range(length):
+            close = close_prices[i]
+            volatility = close * 0.01  # 1% intraday volatility
+            
+            high = close + np.random.uniform(0, volatility)
+            low = close - np.random.uniform(0, volatility)
+            open_price = low + np.random.uniform(0, high - low)
+            
+            # Ensure OHLC relationships
+            high = max(high, open_price, close)
+            low = min(low, open_price, close)
+            
+            data.append({
+                'timestamp': timestamps[i],
+                'open': open_price,
+                'high': high,
+                'low': low,
+                'close': close,
+                'volume': np.random.uniform(100, 1000)
+            })
+        
+        df = pd.DataFrame(data)
+        # Set timestamp as index for compatibility with original strategy
+        df.set_index('timestamp', inplace=True)
+        return df
+    
+    def test_original_strategy(self) -> Dict:
+        """
+        Test the original DefaultStrategy implementation.
+        
+        Returns:
+            Dictionary with original strategy results
+        """
+        logger.info("Testing original DefaultStrategy implementation...")
+        
+        try:
+            # Create indexed DataFrame for original strategy (needs DatetimeIndex)
+            indexed_data = self.test_data.set_index('timestamp')
+            
+            # The original strategy limits data to 200 points for performance
+            # We need to account for this in our comparison
+            if len(indexed_data) > 200:
+                original_data_used = indexed_data.tail(200)
+                logger.info(f"Original strategy will use last {len(original_data_used)} points of {len(indexed_data)} total points")
+            else:
+                original_data_used = indexed_data
+            
+            # Create a minimal backtest instance for strategy initialization
+            class MockBacktester:
+                def __init__(self, df):
+                    self.original_df = df
+                    self.min1_df = df
+                    self.strategies = {}
+            
+            backtester = MockBacktester(original_data_used)
+            
+            # Initialize original strategy
+            strategy = DefaultStrategy(weight=1.0, params={
+                "stop_loss_pct": 0.03,
+                "timeframe": "1min"  # Use 1min since our test data is 1min
+            })
+            
+            # Initialize strategy (this calculates meta-trend)
+            strategy.initialize(backtester)
+            
+            # Extract results
+            if hasattr(strategy, 'meta_trend') and strategy.meta_trend is not None:
+                meta_trend = strategy.meta_trend
+                trends = None  # Individual trends not directly available from strategy
+            else:
+                # Fallback: calculate manually using original Supertrends class
+                logger.info("Strategy meta_trend not available, calculating manually...")
+                supertrends = Supertrends(original_data_used, verbose=False)
+                supertrend_results_list = supertrends.calculate_supertrend_indicators()
+                
+                # Extract trend arrays
+                trends = [st['results']['trend'] for st in supertrend_results_list]
+                trends_arr = np.stack(trends, axis=1)
+                
+                # Calculate meta-trend
+                meta_trend = np.where(
+                    (trends_arr[:,0] == trends_arr[:,1]) & (trends_arr[:,1] == trends_arr[:,2]), 
+                    trends_arr[:,0], 
+                    0
+                )
+            
+            # Generate signals
+            entry_signals = []
+            exit_signals = []
+            
+            for i in range(1, len(meta_trend)):
+                # Entry signal: meta-trend changes from != 1 to == 1
+                if meta_trend[i-1] != 1 and meta_trend[i] == 1:
+                    entry_signals.append(i)
+                
+                # Exit signal: meta-trend changes to -1
+                if meta_trend[i-1] != -1 and meta_trend[i] == -1:
+                    exit_signals.append(i)
+            
+            self.original_results = {
+                'meta_trend': meta_trend,
+                'entry_signals': entry_signals,
+                'exit_signals': exit_signals,
+                'individual_trends': trends,
+                'data_start_index': len(self.test_data) - len(original_data_used)  # Track where original data starts
+            }
+            
+            logger.info(f"Original strategy: {len(entry_signals)} entry signals, {len(exit_signals)} exit signals")
+            logger.info(f"Meta-trend length: {len(meta_trend)}, unique values: {np.unique(meta_trend)}")
+            return self.original_results
+            
+        except Exception as e:
+            logger.error(f"Original strategy test failed: {e}")
+            import traceback
+            traceback.print_exc()
+            raise
+    
+    def test_incremental_indicators(self) -> Dict:
+        """
+        Test the incremental indicators implementation.
+        
+        Returns:
+            Dictionary with incremental results
+        """
+        logger.info("Testing incremental indicators implementation...")
+        
+        try:
+            # Create SupertrendCollection with same parameters as original
+            supertrend_configs = [
+                (params["period"], params["multiplier"]) 
+                for params in self.supertrend_params
+            ]
+            
+            collection = SupertrendCollection(supertrend_configs)
+            
+            # Determine data range to match original strategy
+            data_start_index = self.original_results.get('data_start_index', 0)
+            test_data_subset = self.test_data.iloc[data_start_index:]
+            
+            logger.info(f"Processing incremental indicators on {len(test_data_subset)} points (starting from index {data_start_index})")
+            
+            # Process data incrementally
+            meta_trends = []
+            individual_trends_list = []
+            
+            for _, row in test_data_subset.iterrows():
+                ohlc = {
+                    'open': row['open'],
+                    'high': row['high'],
+                    'low': row['low'],
+                    'close': row['close']
+                }
+                
+                result = collection.update(ohlc)
+                meta_trends.append(result['meta_trend'])
+                individual_trends_list.append(result['trends'])
+            
+            meta_trend = np.array(meta_trends)
+            individual_trends = np.array(individual_trends_list)
+            
+            # Generate signals
+            entry_signals = []
+            exit_signals = []
+            
+            for i in range(1, len(meta_trend)):
+                # Entry signal: meta-trend changes from != 1 to == 1
+                if meta_trend[i-1] != 1 and meta_trend[i] == 1:
+                    entry_signals.append(i)
+                
+                # Exit signal: meta-trend changes to -1
+                if meta_trend[i-1] != -1 and meta_trend[i] == -1:
+                    exit_signals.append(i)
+            
+            self.incremental_results = {
+                'meta_trend': meta_trend,
+                'entry_signals': entry_signals,
+                'exit_signals': exit_signals,
+                'individual_trends': individual_trends
+            }
+            
+            logger.info(f"Incremental indicators: {len(entry_signals)} entry signals, {len(exit_signals)} exit signals")
+            return self.incremental_results
+            
+        except Exception as e:
+            logger.error(f"Incremental indicators test failed: {e}")
+            raise
+    
+    def compare_results(self) -> Dict[str, bool]:
+        """
+        Compare original and incremental results.
+        
+        Returns:
+            Dictionary with comparison results
+        """
+        logger.info("Comparing original vs incremental results...")
+        
+        if self.original_results is None or self.incremental_results is None:
+            raise ValueError("Must run both tests before comparison")
+        
+        comparison = {}
+        
+        # Compare meta-trend arrays
+        orig_meta = self.original_results['meta_trend']
+        inc_meta = self.incremental_results['meta_trend']
+        
+        # Handle length differences (original might be shorter due to initialization)
+        min_length = min(len(orig_meta), len(inc_meta))
+        orig_meta_trimmed = orig_meta[-min_length:]
+        inc_meta_trimmed = inc_meta[-min_length:]
+        
+        meta_trend_match = np.array_equal(orig_meta_trimmed, inc_meta_trimmed)
+        comparison['meta_trend_match'] = meta_trend_match
+        
+        if not meta_trend_match:
+            # Find differences
+            diff_indices = np.where(orig_meta_trimmed != inc_meta_trimmed)[0]
+            logger.warning(f"Meta-trend differences at indices: {diff_indices[:10]}...")  # Show first 10
+            
+            # Show some examples
+            for i in diff_indices[:5]:
+                logger.warning(f"Index {i}: Original={orig_meta_trimmed[i]}, Incremental={inc_meta_trimmed[i]}")
+        
+        # Compare individual trends if available
+        if (self.original_results['individual_trends'] is not None and 
+            self.incremental_results['individual_trends'] is not None):
+            
+            orig_trends = self.original_results['individual_trends']
+            inc_trends = self.incremental_results['individual_trends']
+            
+            # Trim to same length
+            orig_trends_trimmed = orig_trends[-min_length:]
+            inc_trends_trimmed = inc_trends[-min_length:]
+            
+            individual_trends_match = np.array_equal(orig_trends_trimmed, inc_trends_trimmed)
+            comparison['individual_trends_match'] = individual_trends_match
+            
+            if not individual_trends_match:
+                logger.warning("Individual trends do not match")
+                # Check each Supertrend separately
+                for st_idx in range(3):
+                    st_match = np.array_equal(orig_trends_trimmed[:, st_idx], inc_trends_trimmed[:, st_idx])
+                    comparison[f'supertrend_{st_idx}_match'] = st_match
+                    if not st_match:
+                        diff_indices = np.where(orig_trends_trimmed[:, st_idx] != inc_trends_trimmed[:, st_idx])[0]
+                        logger.warning(f"Supertrend {st_idx} differences at indices: {diff_indices[:5]}...")
+        
+        # Compare signals
+        orig_entry = set(self.original_results['entry_signals'])
+        inc_entry = set(self.incremental_results['entry_signals'])
+        entry_signals_match = orig_entry == inc_entry
+        comparison['entry_signals_match'] = entry_signals_match
+        
+        if not entry_signals_match:
+            logger.warning(f"Entry signals differ: Original={orig_entry}, Incremental={inc_entry}")
+        
+        orig_exit = set(self.original_results['exit_signals'])
+        inc_exit = set(self.incremental_results['exit_signals'])
+        exit_signals_match = orig_exit == inc_exit
+        comparison['exit_signals_match'] = exit_signals_match
+        
+        if not exit_signals_match:
+            logger.warning(f"Exit signals differ: Original={orig_exit}, Incremental={inc_exit}")
+        
+        # Overall match
+        comparison['overall_match'] = all([
+            meta_trend_match,
+            entry_signals_match,
+            exit_signals_match
+        ])
+        
+        return comparison
+    
+    def save_detailed_comparison(self, filename: str = "metatrend_comparison.csv"):
+        """Save detailed comparison data to CSV for analysis."""
+        if self.original_results is None or self.incremental_results is None:
+            logger.warning("No results to save")
+            return
+        
+        # Prepare comparison DataFrame
+        orig_meta = self.original_results['meta_trend']
+        inc_meta = self.incremental_results['meta_trend']
+        
+        min_length = min(len(orig_meta), len(inc_meta))
+        
+        # Get the correct data range for timestamps and prices
+        data_start_index = self.original_results.get('data_start_index', 0)
+        comparison_data = self.test_data.iloc[data_start_index:data_start_index + min_length]
+        
+        comparison_df = pd.DataFrame({
+            'timestamp': comparison_data['timestamp'].values,
+            'close': comparison_data['close'].values,
+            'original_meta_trend': orig_meta[:min_length],
+            'incremental_meta_trend': inc_meta[:min_length],
+            'meta_trend_match': orig_meta[:min_length] == inc_meta[:min_length]
+        })
+        
+        # Add individual trends if available
+        if (self.original_results['individual_trends'] is not None and 
+            self.incremental_results['individual_trends'] is not None):
+            
+            orig_trends = self.original_results['individual_trends'][:min_length]
+            inc_trends = self.incremental_results['individual_trends'][:min_length]
+            
+            for i in range(3):
+                comparison_df[f'original_st{i}_trend'] = orig_trends[:, i]
+                comparison_df[f'incremental_st{i}_trend'] = inc_trends[:, i]
+                comparison_df[f'st{i}_trend_match'] = orig_trends[:, i] == inc_trends[:, i]
+        
+        # Save to results directory
+        os.makedirs("results", exist_ok=True)
+        filepath = os.path.join("results", filename)
+        comparison_df.to_csv(filepath, index=False)
+        logger.info(f"Detailed comparison saved to {filepath}")
+    
+    def save_trend_changes_analysis(self, filename_prefix: str = "trend_changes"):
+        """Save detailed trend changes analysis for manual comparison."""
+        if self.original_results is None or self.incremental_results is None:
+            logger.warning("No results to save")
+            return
+        
+        # Get the correct data range
+        data_start_index = self.original_results.get('data_start_index', 0)
+        orig_meta = self.original_results['meta_trend']
+        inc_meta = self.incremental_results['meta_trend']
+        min_length = min(len(orig_meta), len(inc_meta))
+        comparison_data = self.test_data.iloc[data_start_index:data_start_index + min_length]
+        
+        # Analyze original trend changes
+        original_changes = []
+        for i in range(1, len(orig_meta)):
+            if orig_meta[i] != orig_meta[i-1]:
+                original_changes.append({
+                    'index': i,
+                    'timestamp': comparison_data.iloc[i]['timestamp'],
+                    'close_price': comparison_data.iloc[i]['close'],
+                    'prev_trend': orig_meta[i-1],
+                    'new_trend': orig_meta[i],
+                    'change_type': self._get_change_type(orig_meta[i-1], orig_meta[i])
+                })
+        
+        # Analyze incremental trend changes
+        incremental_changes = []
+        for i in range(1, len(inc_meta)):
+            if inc_meta[i] != inc_meta[i-1]:
+                incremental_changes.append({
+                    'index': i,
+                    'timestamp': comparison_data.iloc[i]['timestamp'],
+                    'close_price': comparison_data.iloc[i]['close'],
+                    'prev_trend': inc_meta[i-1],
+                    'new_trend': inc_meta[i],
+                    'change_type': self._get_change_type(inc_meta[i-1], inc_meta[i])
+                })
+        
+        # Save original trend changes
+        os.makedirs("results", exist_ok=True)
+        original_df = pd.DataFrame(original_changes)
+        original_file = os.path.join("results", f"{filename_prefix}_original.csv")
+        original_df.to_csv(original_file, index=False)
+        logger.info(f"Original trend changes saved to {original_file} ({len(original_changes)} changes)")
+        
+        # Save incremental trend changes
+        incremental_df = pd.DataFrame(incremental_changes)
+        incremental_file = os.path.join("results", f"{filename_prefix}_incremental.csv")
+        incremental_df.to_csv(incremental_file, index=False)
+        logger.info(f"Incremental trend changes saved to {incremental_file} ({len(incremental_changes)} changes)")
+        
+        # Create side-by-side comparison
+        comparison_changes = []
+        max_changes = max(len(original_changes), len(incremental_changes))
+        
+        for i in range(max_changes):
+            orig_change = original_changes[i] if i < len(original_changes) else {}
+            inc_change = incremental_changes[i] if i < len(incremental_changes) else {}
+            
+            comparison_changes.append({
+                'change_num': i + 1,
+                'orig_index': orig_change.get('index', ''),
+                'orig_timestamp': orig_change.get('timestamp', ''),
+                'orig_close': orig_change.get('close_price', ''),
+                'orig_prev_trend': orig_change.get('prev_trend', ''),
+                'orig_new_trend': orig_change.get('new_trend', ''),
+                'orig_change_type': orig_change.get('change_type', ''),
+                'inc_index': inc_change.get('index', ''),
+                'inc_timestamp': inc_change.get('timestamp', ''),
+                'inc_close': inc_change.get('close_price', ''),
+                'inc_prev_trend': inc_change.get('prev_trend', ''),
+                'inc_new_trend': inc_change.get('new_trend', ''),
+                'inc_change_type': inc_change.get('change_type', ''),
+                'match': (orig_change.get('index') == inc_change.get('index') and 
+                         orig_change.get('new_trend') == inc_change.get('new_trend')) if orig_change and inc_change else False
+            })
+        
+        comparison_df = pd.DataFrame(comparison_changes)
+        comparison_file = os.path.join("results", f"{filename_prefix}_comparison.csv")
+        comparison_df.to_csv(comparison_file, index=False)
+        logger.info(f"Side-by-side comparison saved to {comparison_file}")
+        
+        # Create summary statistics
+        summary = {
+            'original_total_changes': len(original_changes),
+            'incremental_total_changes': len(incremental_changes),
+            'original_entry_signals': len([c for c in original_changes if c['change_type'] == 'ENTRY']),
+            'incremental_entry_signals': len([c for c in incremental_changes if c['change_type'] == 'ENTRY']),
+            'original_exit_signals': len([c for c in original_changes if c['change_type'] == 'EXIT']),
+            'incremental_exit_signals': len([c for c in incremental_changes if c['change_type'] == 'EXIT']),
+            'original_to_neutral': len([c for c in original_changes if c['new_trend'] == 0]),
+            'incremental_to_neutral': len([c for c in incremental_changes if c['new_trend'] == 0]),
+            'matching_changes': len([c for c in comparison_changes if c['match']]),
+            'total_comparison_points': max_changes
+        }
+        
+        summary_file = os.path.join("results", f"{filename_prefix}_summary.json")
+        import json
+        with open(summary_file, 'w') as f:
+            json.dump(summary, f, indent=2)
+        logger.info(f"Summary statistics saved to {summary_file}")
+        
+        return {
+            'original_changes': original_changes,
+            'incremental_changes': incremental_changes,
+            'summary': summary
+        }
+    
+    def _get_change_type(self, prev_trend: float, new_trend: float) -> str:
+        """Classify the type of trend change."""
+        if prev_trend != 1 and new_trend == 1:
+            return 'ENTRY'
+        elif prev_trend != -1 and new_trend == -1:
+            return 'EXIT'
+        elif new_trend == 0:
+            return 'TO_NEUTRAL'
+        elif prev_trend == 0 and new_trend != 0:
+            return 'FROM_NEUTRAL'
+        else:
+            return 'OTHER'
+    
+    def save_individual_supertrend_analysis(self, filename_prefix: str = "supertrend_individual"):
+        """Save detailed analysis of individual Supertrend indicators."""
+        if (self.original_results is None or self.incremental_results is None or
+            self.original_results['individual_trends'] is None or 
+            self.incremental_results['individual_trends'] is None):
+            logger.warning("Individual trends data not available")
+            return
+        
+        data_start_index = self.original_results.get('data_start_index', 0)
+        orig_trends = self.original_results['individual_trends']
+        inc_trends = self.incremental_results['individual_trends']
+        min_length = min(len(orig_trends), len(inc_trends))
+        comparison_data = self.test_data.iloc[data_start_index:data_start_index + min_length]
+        
+        # Analyze each Supertrend indicator separately
+        for st_idx in range(3):
+            st_params = self.supertrend_params[st_idx]
+            st_name = f"ST{st_idx}_P{st_params['period']}_M{st_params['multiplier']}"
+            
+            # Original Supertrend changes
+            orig_st_changes = []
+            for i in range(1, len(orig_trends)):
+                if orig_trends[i, st_idx] != orig_trends[i-1, st_idx]:
+                    orig_st_changes.append({
+                        'index': i,
+                        'timestamp': comparison_data.iloc[i]['timestamp'],
+                        'close_price': comparison_data.iloc[i]['close'],
+                        'prev_trend': orig_trends[i-1, st_idx],
+                        'new_trend': orig_trends[i, st_idx],
+                        'change_type': 'UP' if orig_trends[i, st_idx] == 1 else 'DOWN'
+                    })
+            
+            # Incremental Supertrend changes
+            inc_st_changes = []
+            for i in range(1, len(inc_trends)):
+                if inc_trends[i, st_idx] != inc_trends[i-1, st_idx]:
+                    inc_st_changes.append({
+                        'index': i,
+                        'timestamp': comparison_data.iloc[i]['timestamp'],
+                        'close_price': comparison_data.iloc[i]['close'],
+                        'prev_trend': inc_trends[i-1, st_idx],
+                        'new_trend': inc_trends[i, st_idx],
+                        'change_type': 'UP' if inc_trends[i, st_idx] == 1 else 'DOWN'
+                    })
+            
+            # Save individual Supertrend analysis
+            os.makedirs("results", exist_ok=True)
+            
+            # Original
+            orig_df = pd.DataFrame(orig_st_changes)
+            orig_file = os.path.join("results", f"{filename_prefix}_{st_name}_original.csv")
+            orig_df.to_csv(orig_file, index=False)
+            
+            # Incremental
+            inc_df = pd.DataFrame(inc_st_changes)
+            inc_file = os.path.join("results", f"{filename_prefix}_{st_name}_incremental.csv")
+            inc_df.to_csv(inc_file, index=False)
+            
+            logger.info(f"Supertrend {st_idx} analysis: Original={len(orig_st_changes)} changes, Incremental={len(inc_st_changes)} changes")
+    
+    def save_full_timeline_data(self, filename: str = "full_timeline_comparison.csv"):
+        """Save complete timeline data with all values for manual analysis."""
+        if self.original_results is None or self.incremental_results is None:
+            logger.warning("No results to save")
+            return
+        
+        data_start_index = self.original_results.get('data_start_index', 0)
+        orig_meta = self.original_results['meta_trend']
+        inc_meta = self.incremental_results['meta_trend']
+        min_length = min(len(orig_meta), len(inc_meta))
+        comparison_data = self.test_data.iloc[data_start_index:data_start_index + min_length]
+        
+        # Create comprehensive timeline
+        timeline_data = []
+        for i in range(min_length):
+            row_data = {
+                'index': i,
+                'timestamp': comparison_data.iloc[i]['timestamp'],
+                'open': comparison_data.iloc[i]['open'],
+                'high': comparison_data.iloc[i]['high'],
+                'low': comparison_data.iloc[i]['low'],
+                'close': comparison_data.iloc[i]['close'],
+                'original_meta_trend': orig_meta[i],
+                'incremental_meta_trend': inc_meta[i],
+                'meta_trend_match': orig_meta[i] == inc_meta[i],
+                'meta_trend_diff': abs(orig_meta[i] - inc_meta[i])
+            }
+            
+            # Add individual Supertrend data if available
+            if (self.original_results['individual_trends'] is not None and 
+                self.incremental_results['individual_trends'] is not None):
+                
+                orig_trends = self.original_results['individual_trends']
+                inc_trends = self.incremental_results['individual_trends']
+                
+                for st_idx in range(3):
+                    st_params = self.supertrend_params[st_idx]
+                    prefix = f"ST{st_idx}_P{st_params['period']}_M{st_params['multiplier']}"
+                    
+                    row_data[f'{prefix}_orig'] = orig_trends[i, st_idx]
+                    row_data[f'{prefix}_inc'] = inc_trends[i, st_idx]
+                    row_data[f'{prefix}_match'] = orig_trends[i, st_idx] == inc_trends[i, st_idx]
+            
+            # Mark trend changes
+            if i > 0:
+                row_data['orig_meta_changed'] = orig_meta[i] != orig_meta[i-1]
+                row_data['inc_meta_changed'] = inc_meta[i] != inc_meta[i-1]
+                row_data['orig_change_type'] = self._get_change_type(orig_meta[i-1], orig_meta[i]) if orig_meta[i] != orig_meta[i-1] else ''
+                row_data['inc_change_type'] = self._get_change_type(inc_meta[i-1], inc_meta[i]) if inc_meta[i] != inc_meta[i-1] else ''
+            else:
+                row_data['orig_meta_changed'] = False
+                row_data['inc_meta_changed'] = False
+                row_data['orig_change_type'] = ''
+                row_data['inc_change_type'] = ''
+            
+            timeline_data.append(row_data)
+        
+        # Save timeline data
+        os.makedirs("results", exist_ok=True)
+        timeline_df = pd.DataFrame(timeline_data)
+        filepath = os.path.join("results", filename)
+        timeline_df.to_csv(filepath, index=False)
+        logger.info(f"Full timeline comparison saved to {filepath} ({len(timeline_data)} rows)")
+        
+        return timeline_df
+    
+    def run_full_test(self, symbol: str = "BTCUSDT", limit: int = 500) -> bool:
+        """
+        Run the complete comparison test.
+        
+        Args:
+            symbol: Trading symbol to test
+            limit: Number of data points to test
+            
+        Returns:
+            True if all tests pass, False otherwise
+        """
+        logger.info("=" * 60)
+        logger.info("STARTING METATREND STRATEGY COMPARISON TEST")
+        logger.info("=" * 60)
+        
+        try:
+            # Load test data
+            self.load_test_data(symbol, limit)
+            logger.info(f"Test data loaded: {len(self.test_data)} points")
+            
+            # Test original strategy
+            logger.info("\n" + "-" * 40)
+            logger.info("TESTING ORIGINAL STRATEGY")
+            logger.info("-" * 40)
+            self.test_original_strategy()
+            
+            # Test incremental indicators
+            logger.info("\n" + "-" * 40)
+            logger.info("TESTING INCREMENTAL INDICATORS")
+            logger.info("-" * 40)
+            self.test_incremental_indicators()
+            
+            # Compare results
+            logger.info("\n" + "-" * 40)
+            logger.info("COMPARING RESULTS")
+            logger.info("-" * 40)
+            comparison = self.compare_results()
+            
+            # Save detailed comparison
+            self.save_detailed_comparison()
+            
+            # Save trend changes analysis
+            self.save_trend_changes_analysis()
+            
+            # Save individual supertrend analysis
+            self.save_individual_supertrend_analysis()
+            
+            # Save full timeline data
+            self.save_full_timeline_data()
+            
+            # Print results
+            logger.info("\n" + "=" * 60)
+            logger.info("COMPARISON RESULTS")
+            logger.info("=" * 60)
+            
+            for key, value in comparison.items():
+                status = "✅ PASS" if value else "❌ FAIL"
+                logger.info(f"{key}: {status}")
+            
+            overall_pass = comparison.get('overall_match', False)
+            
+            if overall_pass:
+                logger.info("\n🎉 ALL TESTS PASSED! Incremental indicators match original strategy.")
+            else:
+                logger.error("\n❌ TESTS FAILED! Incremental indicators do not match original strategy.")
+            
+            return overall_pass
+            
+        except Exception as e:
+            logger.error(f"Test failed with error: {e}")
+            import traceback
+            traceback.print_exc()
+            return False
+
+
+def main():
+    """Run the MetaTrend comparison test."""
+    test = MetaTrendComparisonTest()
+    
+    # Run test with different data sizes
+    test_cases = [
+        ("BTCUSDT", 200),   # Small test
+        ("BTCUSDT", 500),   # Medium test
+        ("BTCUSDT", 1000),  # Large test
+    ]
+    
+    all_passed = True
+    
+    for symbol, limit in test_cases:
+        logger.info(f"\n{'='*80}")
+        logger.info(f"RUNNING TEST CASE: {symbol} with {limit} data points")
+        logger.info(f"{'='*80}")
+        
+        passed = test.run_full_test(symbol, limit)
+        all_passed = all_passed and passed
+        
+        if not passed:
+            logger.error(f"Test case {symbol}:{limit} FAILED")
+            break
+        else:
+            logger.info(f"Test case {symbol}:{limit} PASSED")
+    
+    if all_passed:
+        logger.info("\n🎉 ALL TEST CASES PASSED!")
+    else:
+        logger.error("\n❌ SOME TEST CASES FAILED!")
+    
+    return all_passed
+
+
+if __name__ == "__main__":
+    success = main()
+    sys.exit(0 if success else 1)