618 lines
25 KiB
Python
618 lines
25 KiB
Python
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"""
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Enhanced Strategy Comparison Test Framework for 2025 Data
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Comprehensive testing for comparing original incremental strategies from cycles/IncStrategies
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with new implementations in IncrementalTrader/strategies using real 2025 data.
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Features:
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- Interactive plots using Plotly
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- CSV export of all signals
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- Detailed signal analysis
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- Performance comparison
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- Real 2025 data (Jan-Apr)
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"""
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import pandas as pd
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import numpy as np
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import plotly.graph_objects as go
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import plotly.subplots as sp
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from plotly.offline import plot
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from datetime import datetime
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import sys
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from pathlib import Path
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from typing import Dict, List, Tuple, Any
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import warnings
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warnings.filterwarnings('ignore')
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# Add project paths
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project_root = Path(__file__).parent.parent
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sys.path.insert(0, str(project_root))
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sys.path.insert(0, str(project_root / "cycles"))
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sys.path.insert(0, str(project_root / "IncrementalTrader"))
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# Import original strategies
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from cycles.IncStrategies.metatrend_strategy import IncMetaTrendStrategy
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from cycles.IncStrategies.random_strategy import IncRandomStrategy
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from cycles.IncStrategies.bbrs_incremental import BBRSIncrementalState
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# Import new strategies
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from IncrementalTrader.strategies.metatrend import MetaTrendStrategy
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from IncrementalTrader.strategies.random import RandomStrategy
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from IncrementalTrader.strategies.bbrs import BBRSStrategy
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class Enhanced2025StrategyComparison:
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"""Enhanced strategy comparison framework with interactive plots and CSV export."""
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def __init__(self, data_file: str = "data/temp_2025_data.csv"):
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"""Initialize the comparison framework."""
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self.data_file = data_file
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self.data = None
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self.results = {}
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# Create results directory
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self.results_dir = Path("test/results/strategies_2025")
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self.results_dir.mkdir(parents=True, exist_ok=True)
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print("Enhanced 2025 Strategy Comparison Framework")
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print("=" * 60)
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def load_data(self) -> None:
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"""Load and prepare 2025 data."""
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print(f"Loading data from {self.data_file}...")
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self.data = pd.read_csv(self.data_file)
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# Convert timestamp to datetime
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self.data['DateTime'] = pd.to_datetime(self.data['Timestamp'], unit='s')
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print(f"Data loaded: {len(self.data):,} rows")
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print(f"Date range: {self.data['DateTime'].iloc[0]} to {self.data['DateTime'].iloc[-1]}")
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print(f"Columns: {list(self.data.columns)}")
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def compare_metatrend_strategies(self) -> Dict[str, Any]:
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"""Compare IncMetaTrendStrategy vs MetaTrendStrategy with detailed analysis."""
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print("\n" + "="*80)
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print("COMPARING METATREND STRATEGIES - 2025 DATA")
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print("="*80)
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try:
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# Initialize strategies
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original_strategy = IncMetaTrendStrategy(weight=1.0, params={})
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new_strategy = MetaTrendStrategy(name="metatrend", weight=1.0, params={})
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# Track all signals and data
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signals_data = []
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price_data = []
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print("Processing data points...")
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# Process data
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for i, row in self.data.iterrows():
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if i % 10000 == 0:
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print(f"Processed {i:,} / {len(self.data):,} data points...")
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timestamp = row['DateTime']
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ohlcv_data = {
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'open': row['Open'],
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'high': row['High'],
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'low': row['Low'],
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'close': row['Close'],
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'volume': row['Volume']
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}
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# Update strategies
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original_strategy.update_minute_data(timestamp, ohlcv_data)
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new_strategy.process_data_point(timestamp, ohlcv_data)
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# Get signals
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orig_entry = original_strategy.get_entry_signal()
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new_entry = new_strategy.get_entry_signal()
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orig_exit = original_strategy.get_exit_signal()
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new_exit = new_strategy.get_exit_signal()
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# Determine combined signals
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orig_signal = "BUY" if orig_entry and orig_entry.signal_type == "ENTRY" else (
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"SELL" if orig_exit and orig_exit.signal_type == "EXIT" else "HOLD")
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new_signal = "BUY" if new_entry and new_entry.signal_type == "ENTRY" else (
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"SELL" if new_exit and new_exit.signal_type == "EXIT" else "HOLD")
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# Store data
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signals_data.append({
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'timestamp': timestamp,
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'price': row['Close'],
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'original_entry': orig_entry.signal_type if orig_entry else "HOLD",
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'new_entry': new_entry.signal_type if new_entry else "HOLD",
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'original_exit': orig_exit.signal_type if orig_exit else "HOLD",
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'new_exit': new_exit.signal_type if new_exit else "HOLD",
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'original_combined': orig_signal,
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'new_combined': new_signal,
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'signals_match': orig_signal == new_signal
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})
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price_data.append({
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'timestamp': timestamp,
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'open': row['Open'],
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'high': row['High'],
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'low': row['Low'],
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'close': row['Close'],
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'volume': row['Volume']
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})
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# Convert to DataFrame
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signals_df = pd.DataFrame(signals_data)
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price_df = pd.DataFrame(price_data)
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# Calculate statistics
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total_signals = len(signals_df)
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matching_signals = signals_df['signals_match'].sum()
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consistency = (matching_signals / total_signals) * 100
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# Signal distribution
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orig_signal_counts = signals_df['original_combined'].value_counts()
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new_signal_counts = signals_df['new_combined'].value_counts()
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# Save signals to CSV
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csv_file = self.results_dir / "metatrend_signals_2025.csv"
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signals_df.to_csv(csv_file, index=False, encoding='utf-8')
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# Create interactive plot
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self.create_interactive_plot(signals_df, price_df, "MetaTrend", "metatrend_2025")
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results = {
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'strategy': 'MetaTrend',
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'total_signals': total_signals,
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'matching_signals': matching_signals,
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'consistency_percentage': consistency,
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'original_signal_distribution': orig_signal_counts.to_dict(),
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'new_signal_distribution': new_signal_counts.to_dict(),
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'signals_dataframe': signals_df,
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'csv_file': str(csv_file)
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}
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print(f"✅ MetaTrend Strategy Comparison Complete")
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print(f" Signal Consistency: {consistency:.2f}%")
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print(f" Total Signals: {total_signals:,}")
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print(f" Matching Signals: {matching_signals:,}")
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print(f" CSV Saved: {csv_file}")
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return results
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except Exception as e:
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print(f"❌ Error in MetaTrend comparison: {str(e)}")
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import traceback
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traceback.print_exc()
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return {'error': str(e)}
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def compare_random_strategies(self) -> Dict[str, Any]:
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"""Compare IncRandomStrategy vs RandomStrategy with detailed analysis."""
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print("\n" + "="*80)
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print("COMPARING RANDOM STRATEGIES - 2025 DATA")
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print("="*80)
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try:
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# Initialize strategies with same seed for reproducibility
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original_strategy = IncRandomStrategy(weight=1.0, params={"random_seed": 42})
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new_strategy = RandomStrategy(name="random", weight=1.0, params={"random_seed": 42})
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# Track all signals and data
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signals_data = []
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print("Processing data points...")
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# Process data (use subset for Random strategy to speed up)
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subset_data = self.data.iloc[::10] # Every 10th point for Random strategy
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for i, row in subset_data.iterrows():
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if i % 1000 == 0:
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print(f"Processed {i:,} data points...")
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timestamp = row['DateTime']
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ohlcv_data = {
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'open': row['Open'],
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'high': row['High'],
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'low': row['Low'],
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'close': row['Close'],
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'volume': row['Volume']
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}
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# Update strategies
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original_strategy.update_minute_data(timestamp, ohlcv_data)
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new_strategy.process_data_point(timestamp, ohlcv_data)
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# Get signals
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orig_entry = original_strategy.get_entry_signal()
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new_entry = new_strategy.get_entry_signal()
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orig_exit = original_strategy.get_exit_signal()
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new_exit = new_strategy.get_exit_signal()
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# Determine combined signals
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orig_signal = "BUY" if orig_entry and orig_entry.signal_type == "ENTRY" else (
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"SELL" if orig_exit and orig_exit.signal_type == "EXIT" else "HOLD")
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new_signal = "BUY" if new_entry and new_entry.signal_type == "ENTRY" else (
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"SELL" if new_exit and new_exit.signal_type == "EXIT" else "HOLD")
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# Store data
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signals_data.append({
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'timestamp': timestamp,
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'price': row['Close'],
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'original_entry': orig_entry.signal_type if orig_entry else "HOLD",
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'new_entry': new_entry.signal_type if new_entry else "HOLD",
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'original_exit': orig_exit.signal_type if orig_exit else "HOLD",
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'new_exit': new_exit.signal_type if new_exit else "HOLD",
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'original_combined': orig_signal,
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'new_combined': new_signal,
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'signals_match': orig_signal == new_signal
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})
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# Convert to DataFrame
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signals_df = pd.DataFrame(signals_data)
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# Calculate statistics
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total_signals = len(signals_df)
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matching_signals = signals_df['signals_match'].sum()
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consistency = (matching_signals / total_signals) * 100
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# Save signals to CSV
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csv_file = self.results_dir / "random_signals_2025.csv"
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signals_df.to_csv(csv_file, index=False, encoding='utf-8')
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results = {
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'strategy': 'Random',
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'total_signals': total_signals,
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'matching_signals': matching_signals,
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'consistency_percentage': consistency,
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'signals_dataframe': signals_df,
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'csv_file': str(csv_file)
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}
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print(f"✅ Random Strategy Comparison Complete")
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print(f" Signal Consistency: {consistency:.2f}%")
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print(f" Total Signals: {total_signals:,}")
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print(f" CSV Saved: {csv_file}")
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return results
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except Exception as e:
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print(f"❌ Error in Random comparison: {str(e)}")
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import traceback
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traceback.print_exc()
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return {'error': str(e)}
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def compare_bbrs_strategies(self) -> Dict[str, Any]:
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"""Compare BBRSIncrementalState vs BBRSStrategy with detailed analysis."""
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print("\n" + "="*80)
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print("COMPARING BBRS STRATEGIES - 2025 DATA")
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print("="*80)
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try:
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# Initialize strategies
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bbrs_config = {
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"bb_period": 20,
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"bb_std": 2.0,
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"rsi_period": 14,
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"volume_ma_period": 20
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}
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original_strategy = BBRSIncrementalState(config=bbrs_config)
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new_strategy = BBRSStrategy(name="bbrs", weight=1.0, params=bbrs_config)
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# Track all signals and data
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signals_data = []
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print("Processing data points...")
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# Process data
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for i, row in self.data.iterrows():
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if i % 10000 == 0:
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print(f"Processed {i:,} / {len(self.data):,} data points...")
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timestamp = row['DateTime']
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ohlcv_data = {
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'open': row['Open'],
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'high': row['High'],
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'low': row['Low'],
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'close': row['Close'],
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'volume': row['Volume']
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}
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# Update strategies
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orig_result = original_strategy.update_minute_data(timestamp, ohlcv_data)
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new_strategy.process_data_point(timestamp, ohlcv_data)
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# Get signals - original returns signals in result, new uses methods
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if orig_result is not None:
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orig_buy = orig_result.get('buy_signal', False)
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orig_sell = orig_result.get('sell_signal', False)
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else:
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orig_buy = False
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orig_sell = False
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new_entry = new_strategy.get_entry_signal()
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new_exit = new_strategy.get_exit_signal()
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new_buy = new_entry and new_entry.signal_type == "ENTRY"
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new_sell = new_exit and new_exit.signal_type == "EXIT"
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# Determine combined signals
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orig_signal = "BUY" if orig_buy else ("SELL" if orig_sell else "HOLD")
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new_signal = "BUY" if new_buy else ("SELL" if new_sell else "HOLD")
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# Store data
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signals_data.append({
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'timestamp': timestamp,
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'price': row['Close'],
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'original_entry': "ENTRY" if orig_buy else "HOLD",
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'new_entry': new_entry.signal_type if new_entry else "HOLD",
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'original_exit': "EXIT" if orig_sell else "HOLD",
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'new_exit': new_exit.signal_type if new_exit else "HOLD",
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'original_combined': orig_signal,
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'new_combined': new_signal,
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'signals_match': orig_signal == new_signal
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})
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# Convert to DataFrame
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signals_df = pd.DataFrame(signals_data)
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# Calculate statistics
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total_signals = len(signals_df)
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matching_signals = signals_df['signals_match'].sum()
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consistency = (matching_signals / total_signals) * 100
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||
|
|
# Save signals to CSV
|
||
|
|
csv_file = self.results_dir / "bbrs_signals_2025.csv"
|
||
|
|
signals_df.to_csv(csv_file, index=False, encoding='utf-8')
|
||
|
|
|
||
|
|
# Create interactive plot
|
||
|
|
self.create_interactive_plot(signals_df, self.data, "BBRS", "bbrs_2025")
|
||
|
|
|
||
|
|
results = {
|
||
|
|
'strategy': 'BBRS',
|
||
|
|
'total_signals': total_signals,
|
||
|
|
'matching_signals': matching_signals,
|
||
|
|
'consistency_percentage': consistency,
|
||
|
|
'signals_dataframe': signals_df,
|
||
|
|
'csv_file': str(csv_file)
|
||
|
|
}
|
||
|
|
|
||
|
|
print(f"✅ BBRS Strategy Comparison Complete")
|
||
|
|
print(f" Signal Consistency: {consistency:.2f}%")
|
||
|
|
print(f" Total Signals: {total_signals:,}")
|
||
|
|
print(f" CSV Saved: {csv_file}")
|
||
|
|
|
||
|
|
return results
|
||
|
|
|
||
|
|
except Exception as e:
|
||
|
|
print(f"❌ Error in BBRS comparison: {str(e)}")
|
||
|
|
import traceback
|
||
|
|
traceback.print_exc()
|
||
|
|
return {'error': str(e)}
|
||
|
|
|
||
|
|
def create_interactive_plot(self, signals_df: pd.DataFrame, price_df: pd.DataFrame,
|
||
|
|
strategy_name: str, filename: str) -> None:
|
||
|
|
"""Create interactive Plotly chart with signals and price data."""
|
||
|
|
print(f"Creating interactive plot for {strategy_name}...")
|
||
|
|
|
||
|
|
# Create subplots
|
||
|
|
fig = sp.make_subplots(
|
||
|
|
rows=3, cols=1,
|
||
|
|
shared_xaxes=True,
|
||
|
|
vertical_spacing=0.05,
|
||
|
|
subplot_titles=(
|
||
|
|
f'{strategy_name} Strategy - Price & Signals',
|
||
|
|
'Signal Comparison',
|
||
|
|
'Signal Consistency'
|
||
|
|
),
|
||
|
|
row_heights=[0.6, 0.2, 0.2]
|
||
|
|
)
|
||
|
|
|
||
|
|
# Price chart with signals
|
||
|
|
fig.add_trace(
|
||
|
|
go.Scatter(
|
||
|
|
x=price_df['timestamp'],
|
||
|
|
y=price_df['close'],
|
||
|
|
mode='lines',
|
||
|
|
name='Price',
|
||
|
|
line=dict(color='blue', width=1)
|
||
|
|
),
|
||
|
|
row=1, col=1
|
||
|
|
)
|
||
|
|
|
||
|
|
# Add buy signals
|
||
|
|
buy_signals_orig = signals_df[signals_df['original_combined'] == 'BUY']
|
||
|
|
buy_signals_new = signals_df[signals_df['new_combined'] == 'BUY']
|
||
|
|
|
||
|
|
if len(buy_signals_orig) > 0:
|
||
|
|
fig.add_trace(
|
||
|
|
go.Scatter(
|
||
|
|
x=buy_signals_orig['timestamp'],
|
||
|
|
y=buy_signals_orig['price'],
|
||
|
|
mode='markers',
|
||
|
|
name='Original BUY',
|
||
|
|
marker=dict(color='green', size=8, symbol='triangle-up')
|
||
|
|
),
|
||
|
|
row=1, col=1
|
||
|
|
)
|
||
|
|
|
||
|
|
if len(buy_signals_new) > 0:
|
||
|
|
fig.add_trace(
|
||
|
|
go.Scatter(
|
||
|
|
x=buy_signals_new['timestamp'],
|
||
|
|
y=buy_signals_new['price'],
|
||
|
|
mode='markers',
|
||
|
|
name='New BUY',
|
||
|
|
marker=dict(color='lightgreen', size=6, symbol='triangle-up')
|
||
|
|
),
|
||
|
|
row=1, col=1
|
||
|
|
)
|
||
|
|
|
||
|
|
# Add sell signals
|
||
|
|
sell_signals_orig = signals_df[signals_df['original_combined'] == 'SELL']
|
||
|
|
sell_signals_new = signals_df[signals_df['new_combined'] == 'SELL']
|
||
|
|
|
||
|
|
if len(sell_signals_orig) > 0:
|
||
|
|
fig.add_trace(
|
||
|
|
go.Scatter(
|
||
|
|
x=sell_signals_orig['timestamp'],
|
||
|
|
y=sell_signals_orig['price'],
|
||
|
|
mode='markers',
|
||
|
|
name='Original SELL',
|
||
|
|
marker=dict(color='red', size=8, symbol='triangle-down')
|
||
|
|
),
|
||
|
|
row=1, col=1
|
||
|
|
)
|
||
|
|
|
||
|
|
if len(sell_signals_new) > 0:
|
||
|
|
fig.add_trace(
|
||
|
|
go.Scatter(
|
||
|
|
x=sell_signals_new['timestamp'],
|
||
|
|
y=sell_signals_new['price'],
|
||
|
|
mode='markers',
|
||
|
|
name='New SELL',
|
||
|
|
marker=dict(color='pink', size=6, symbol='triangle-down')
|
||
|
|
),
|
||
|
|
row=1, col=1
|
||
|
|
)
|
||
|
|
|
||
|
|
# Signal comparison chart
|
||
|
|
signal_mapping = {'HOLD': 0, 'BUY': 1, 'SELL': -1}
|
||
|
|
signals_df['original_numeric'] = signals_df['original_combined'].map(signal_mapping)
|
||
|
|
signals_df['new_numeric'] = signals_df['new_combined'].map(signal_mapping)
|
||
|
|
|
||
|
|
fig.add_trace(
|
||
|
|
go.Scatter(
|
||
|
|
x=signals_df['timestamp'],
|
||
|
|
y=signals_df['original_numeric'],
|
||
|
|
mode='lines',
|
||
|
|
name='Original Signals',
|
||
|
|
line=dict(color='blue', width=2)
|
||
|
|
),
|
||
|
|
row=2, col=1
|
||
|
|
)
|
||
|
|
|
||
|
|
fig.add_trace(
|
||
|
|
go.Scatter(
|
||
|
|
x=signals_df['timestamp'],
|
||
|
|
y=signals_df['new_numeric'],
|
||
|
|
mode='lines',
|
||
|
|
name='New Signals',
|
||
|
|
line=dict(color='red', width=1, dash='dash')
|
||
|
|
),
|
||
|
|
row=2, col=1
|
||
|
|
)
|
||
|
|
|
||
|
|
# Signal consistency chart
|
||
|
|
signals_df['consistency_numeric'] = signals_df['signals_match'].astype(int)
|
||
|
|
|
||
|
|
fig.add_trace(
|
||
|
|
go.Scatter(
|
||
|
|
x=signals_df['timestamp'],
|
||
|
|
y=signals_df['consistency_numeric'],
|
||
|
|
mode='lines',
|
||
|
|
name='Signal Match',
|
||
|
|
line=dict(color='green', width=1),
|
||
|
|
fill='tonexty'
|
||
|
|
),
|
||
|
|
row=3, col=1
|
||
|
|
)
|
||
|
|
|
||
|
|
# Update layout
|
||
|
|
fig.update_layout(
|
||
|
|
title=f'{strategy_name} Strategy Comparison - 2025 Data',
|
||
|
|
height=800,
|
||
|
|
showlegend=True,
|
||
|
|
hovermode='x unified'
|
||
|
|
)
|
||
|
|
|
||
|
|
# Update y-axes
|
||
|
|
fig.update_yaxes(title_text="Price ($)", row=1, col=1)
|
||
|
|
fig.update_yaxes(title_text="Signal", row=2, col=1, tickvals=[-1, 0, 1], ticktext=['SELL', 'HOLD', 'BUY'])
|
||
|
|
fig.update_yaxes(title_text="Match", row=3, col=1, tickvals=[0, 1], ticktext=['No', 'Yes'])
|
||
|
|
|
||
|
|
# Save interactive plot
|
||
|
|
html_file = self.results_dir / f"{filename}_interactive.html"
|
||
|
|
plot(fig, filename=str(html_file), auto_open=False)
|
||
|
|
|
||
|
|
print(f" Interactive plot saved: {html_file}")
|
||
|
|
|
||
|
|
def generate_comprehensive_report(self) -> None:
|
||
|
|
"""Generate comprehensive comparison report."""
|
||
|
|
print("\n" + "="*80)
|
||
|
|
print("GENERATING COMPREHENSIVE REPORT")
|
||
|
|
print("="*80)
|
||
|
|
|
||
|
|
report_file = self.results_dir / "comprehensive_strategy_comparison_2025.md"
|
||
|
|
|
||
|
|
with open(report_file, 'w', encoding='utf-8') as f:
|
||
|
|
f.write("# Comprehensive Strategy Comparison Report - 2025 Data\n\n")
|
||
|
|
f.write(f"**Generated**: {datetime.now().strftime('%Y-%m-%d %H:%M:%S')}\n\n")
|
||
|
|
f.write(f"**Data Period**: January 1, 2025 - April 30, 2025\n")
|
||
|
|
f.write(f"**Total Data Points**: {len(self.data):,} minute-level OHLCV records\n\n")
|
||
|
|
|
||
|
|
f.write("## Executive Summary\n\n")
|
||
|
|
f.write("This report compares the signal generation consistency between original incremental strategies ")
|
||
|
|
f.write("from `cycles/IncStrategies` and new implementations in `IncrementalTrader/strategies` ")
|
||
|
|
f.write("using real market data from 2025.\n\n")
|
||
|
|
|
||
|
|
f.write("## Strategy Comparison Results\n\n")
|
||
|
|
|
||
|
|
for strategy_name, results in self.results.items():
|
||
|
|
if 'error' not in results:
|
||
|
|
f.write(f"### {results['strategy']} Strategy\n\n")
|
||
|
|
f.write(f"- **Signal Consistency**: {results['consistency_percentage']:.2f}%\n")
|
||
|
|
f.write(f"- **Total Signals Compared**: {results['total_signals']:,}\n")
|
||
|
|
f.write(f"- **Matching Signals**: {results['matching_signals']:,}\n")
|
||
|
|
f.write(f"- **CSV Export**: `{results['csv_file']}`\n\n")
|
||
|
|
|
||
|
|
if 'original_signal_distribution' in results:
|
||
|
|
f.write("**Original Strategy Signal Distribution:**\n")
|
||
|
|
for signal, count in results['original_signal_distribution'].items():
|
||
|
|
f.write(f"- {signal}: {count:,}\n")
|
||
|
|
f.write("\n")
|
||
|
|
|
||
|
|
f.write("**New Strategy Signal Distribution:**\n")
|
||
|
|
for signal, count in results['new_signal_distribution'].items():
|
||
|
|
f.write(f"- {signal}: {count:,}\n")
|
||
|
|
f.write("\n")
|
||
|
|
|
||
|
|
f.write("## Files Generated\n\n")
|
||
|
|
f.write("### CSV Signal Exports\n")
|
||
|
|
for csv_file in self.results_dir.glob("*_signals_2025.csv"):
|
||
|
|
f.write(f"- `{csv_file.name}`: Complete signal history with timestamps\n")
|
||
|
|
|
||
|
|
f.write("\n### Interactive Plots\n")
|
||
|
|
for html_file in self.results_dir.glob("*_interactive.html"):
|
||
|
|
f.write(f"- `{html_file.name}`: Interactive Plotly visualization\n")
|
||
|
|
|
||
|
|
f.write("\n## Conclusion\n\n")
|
||
|
|
f.write("The strategy comparison validates the migration accuracy by comparing signal generation ")
|
||
|
|
f.write("between original and refactored implementations. High consistency percentages indicate ")
|
||
|
|
f.write("successful preservation of strategy behavior during the refactoring process.\n")
|
||
|
|
|
||
|
|
print(f"✅ Comprehensive report saved: {report_file}")
|
||
|
|
|
||
|
|
def run_all_comparisons(self) -> None:
|
||
|
|
"""Run all strategy comparisons."""
|
||
|
|
print("Starting comprehensive strategy comparison with 2025 data...")
|
||
|
|
|
||
|
|
# Load data
|
||
|
|
self.load_data()
|
||
|
|
|
||
|
|
# Run comparisons
|
||
|
|
self.results['metatrend'] = self.compare_metatrend_strategies()
|
||
|
|
self.results['random'] = self.compare_random_strategies()
|
||
|
|
self.results['bbrs'] = self.compare_bbrs_strategies()
|
||
|
|
|
||
|
|
# Generate report
|
||
|
|
self.generate_comprehensive_report()
|
||
|
|
|
||
|
|
print("\n" + "="*80)
|
||
|
|
print("ALL STRATEGY COMPARISONS COMPLETED")
|
||
|
|
print("="*80)
|
||
|
|
print(f"Results directory: {self.results_dir}")
|
||
|
|
print("Files generated:")
|
||
|
|
for file in sorted(self.results_dir.glob("*")):
|
||
|
|
print(f" - {file.name}")
|
||
|
|
|
||
|
|
if __name__ == "__main__":
|
||
|
|
# Run the enhanced comparison
|
||
|
|
comparison = Enhanced2025StrategyComparison()
|
||
|
|
comparison.run_all_comparisons()
|