488 lines
20 KiB
Python
488 lines
20 KiB
Python
#!/usr/bin/env python3
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"""
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Backtest Validation Tests
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This module validates the new timeframe aggregation by running backtests
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with old vs new aggregation methods and comparing results.
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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 sys
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import os
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import time
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import logging
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from typing import List, Dict, Any, Optional, Tuple
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import unittest
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from datetime import datetime, timedelta
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# Add the project root to Python path
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sys.path.insert(0, os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
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from IncrementalTrader.strategies.metatrend import MetaTrendStrategy
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from IncrementalTrader.strategies.bbrs import BBRSStrategy
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from IncrementalTrader.strategies.random import RandomStrategy
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from IncrementalTrader.utils.timeframe_utils import aggregate_minute_data_to_timeframe
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# Configure logging
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logging.basicConfig(level=logging.WARNING)
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class BacktestValidator:
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"""Helper class for running backtests and comparing results."""
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def __init__(self, strategy_class, strategy_params: Dict[str, Any]):
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self.strategy_class = strategy_class
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self.strategy_params = strategy_params
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def run_backtest(self, data: List[Dict[str, Any]], use_new_aggregation: bool = True) -> Dict[str, Any]:
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"""Run a backtest with specified aggregation method."""
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strategy = self.strategy_class(
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name=f"test_{self.strategy_class.__name__}",
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params=self.strategy_params
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)
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signals = []
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positions = []
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current_position = None
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portfolio_value = 100000.0 # Start with $100k
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trades = []
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for data_point in data:
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timestamp = data_point['timestamp']
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ohlcv = {
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'open': data_point['open'],
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'high': data_point['high'],
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'low': data_point['low'],
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'close': data_point['close'],
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'volume': data_point['volume']
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}
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# Process data point
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signal = strategy.process_data_point(timestamp, ohlcv)
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if signal and signal.signal_type != "HOLD":
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signals.append({
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'timestamp': timestamp,
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'signal_type': signal.signal_type,
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'price': data_point['close'],
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'confidence': signal.confidence
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})
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# Simple position management
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if signal.signal_type == "BUY" and current_position is None:
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current_position = {
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'entry_time': timestamp,
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'entry_price': data_point['close'],
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'type': 'LONG'
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}
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elif signal.signal_type == "SELL" and current_position is not None:
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# Close position
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exit_price = data_point['close']
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pnl = exit_price - current_position['entry_price']
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pnl_pct = pnl / current_position['entry_price'] * 100
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trade = {
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'entry_time': current_position['entry_time'],
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'exit_time': timestamp,
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'entry_price': current_position['entry_price'],
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'exit_price': exit_price,
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'pnl': pnl,
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'pnl_pct': pnl_pct,
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'duration': timestamp - current_position['entry_time']
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}
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trades.append(trade)
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portfolio_value += pnl
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current_position = None
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# Track portfolio value
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positions.append({
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'timestamp': timestamp,
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'portfolio_value': portfolio_value,
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'price': data_point['close']
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})
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# Calculate performance metrics
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if trades:
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total_pnl = sum(trade['pnl'] for trade in trades)
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win_trades = [t for t in trades if t['pnl'] > 0]
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lose_trades = [t for t in trades if t['pnl'] <= 0]
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win_rate = len(win_trades) / len(trades) * 100
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avg_win = np.mean([t['pnl'] for t in win_trades]) if win_trades else 0
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avg_loss = np.mean([t['pnl'] for t in lose_trades]) if lose_trades else 0
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profit_factor = abs(avg_win / avg_loss) if avg_loss != 0 else float('inf')
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else:
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total_pnl = 0
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win_rate = 0
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avg_win = 0
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avg_loss = 0
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profit_factor = 0
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return {
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'signals': signals,
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'trades': trades,
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'positions': positions,
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'total_pnl': total_pnl,
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'num_trades': len(trades),
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'win_rate': win_rate,
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'avg_win': avg_win,
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'avg_loss': avg_loss,
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'profit_factor': profit_factor,
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'final_portfolio_value': portfolio_value
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}
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class TestBacktestValidation(unittest.TestCase):
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"""Test backtest validation with new timeframe aggregation."""
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def setUp(self):
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"""Set up test data and strategies."""
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# Create longer test data for meaningful backtests
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self.test_data = self._create_realistic_market_data(1440) # 24 hours
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# Strategy configurations to test
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self.strategy_configs = [
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{
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'class': MetaTrendStrategy,
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'params': {"timeframe": "15min", "lookback_period": 20}
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},
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{
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'class': BBRSStrategy,
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'params': {"timeframe": "30min", "bb_period": 20, "rsi_period": 14}
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},
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{
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'class': RandomStrategy,
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'params': {
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"timeframe": "5min",
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"entry_probability": 0.05,
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"exit_probability": 0.05,
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"random_seed": 42
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}
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}
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]
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def _create_realistic_market_data(self, num_minutes: int) -> List[Dict[str, Any]]:
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"""Create realistic market data with trends, volatility, and cycles."""
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start_time = pd.Timestamp('2024-01-01 00:00:00')
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data = []
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base_price = 50000.0
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for i in range(num_minutes):
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timestamp = start_time + pd.Timedelta(minutes=i)
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# Create market cycles and trends (with bounds to prevent overflow)
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hour_of_day = timestamp.hour
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day_cycle = np.sin(2 * np.pi * hour_of_day / 24) * 0.001 # Daily cycle
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trend = 0.00005 * i # Smaller long-term trend to prevent overflow
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noise = np.random.normal(0, 0.002) # Reduced random noise
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# Combine all factors with bounds checking
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price_change = (day_cycle + trend + noise) * base_price
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price_change = np.clip(price_change, -base_price * 0.1, base_price * 0.1) # Limit to ±10%
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base_price += price_change
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# Ensure positive prices with reasonable bounds
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base_price = np.clip(base_price, 1000.0, 1000000.0) # Between $1k and $1M
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# Create realistic OHLC
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volatility = base_price * 0.001 # 0.1% volatility (reduced)
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open_price = base_price
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high_price = base_price + np.random.uniform(0, volatility)
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low_price = base_price - np.random.uniform(0, volatility)
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close_price = base_price + np.random.uniform(-volatility/2, volatility/2)
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# Ensure OHLC consistency
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high_price = max(high_price, open_price, close_price)
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low_price = min(low_price, open_price, close_price)
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volume = np.random.uniform(800, 1200)
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data.append({
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'timestamp': timestamp,
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'open': round(open_price, 2),
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'high': round(high_price, 2),
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'low': round(low_price, 2),
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'close': round(close_price, 2),
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'volume': round(volume, 0)
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})
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return data
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def test_signal_timing_differences(self):
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"""Test that signals are generated promptly without future data leakage."""
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print("\n⏰ Testing Signal Timing Differences")
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for config in self.strategy_configs:
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strategy_name = config['class'].__name__
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# Run backtest with new aggregation
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validator = BacktestValidator(config['class'], config['params'])
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new_results = validator.run_backtest(self.test_data, use_new_aggregation=True)
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# Analyze signal timing
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signals = new_results['signals']
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timeframe = config['params']['timeframe']
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if signals:
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# Verify no future data leakage
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for i, signal in enumerate(signals):
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signal_time = signal['timestamp']
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# Find the data point that generated this signal
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signal_data_point = None
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for j, dp in enumerate(self.test_data):
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if dp['timestamp'] == signal_time:
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signal_data_point = (j, dp)
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break
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if signal_data_point:
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data_index, data_point = signal_data_point
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# Signal should only use data available up to that point
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available_data = self.test_data[:data_index + 1]
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latest_available_time = available_data[-1]['timestamp']
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self.assertLessEqual(
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signal_time, latest_available_time,
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f"{strategy_name}: Signal at {signal_time} uses future data"
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)
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print(f"✅ {strategy_name}: {len(signals)} signals generated correctly")
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print(f" Timeframe: {timeframe} (used for analysis, not signal timing restriction)")
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else:
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print(f"⚠️ {strategy_name}: No signals generated")
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def test_performance_impact_analysis(self):
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"""Test and document performance impact of new aggregation."""
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print("\n📊 Testing Performance Impact")
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performance_comparison = {}
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for config in self.strategy_configs:
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strategy_name = config['class'].__name__
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# Run backtest
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validator = BacktestValidator(config['class'], config['params'])
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results = validator.run_backtest(self.test_data, use_new_aggregation=True)
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performance_comparison[strategy_name] = {
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'total_pnl': results['total_pnl'],
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'num_trades': results['num_trades'],
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'win_rate': results['win_rate'],
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'profit_factor': results['profit_factor'],
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'final_value': results['final_portfolio_value']
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}
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# Verify reasonable performance metrics
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if results['num_trades'] > 0:
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self.assertGreaterEqual(
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results['win_rate'], 0,
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f"{strategy_name}: Invalid win rate"
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)
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self.assertLessEqual(
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results['win_rate'], 100,
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f"{strategy_name}: Invalid win rate"
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)
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print(f"✅ {strategy_name}: {results['num_trades']} trades, "
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f"{results['win_rate']:.1f}% win rate, "
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f"PnL: ${results['total_pnl']:.2f}")
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else:
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print(f"⚠️ {strategy_name}: No trades executed")
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return performance_comparison
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def test_realistic_trading_results(self):
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"""Test that trading results are realistic and not artificially inflated."""
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print("\n💰 Testing Realistic Trading Results")
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for config in self.strategy_configs:
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strategy_name = config['class'].__name__
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validator = BacktestValidator(config['class'], config['params'])
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results = validator.run_backtest(self.test_data, use_new_aggregation=True)
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if results['num_trades'] > 0:
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# Check for unrealistic performance (possible future data leakage)
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win_rate = results['win_rate']
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profit_factor = results['profit_factor']
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# Win rate should not be suspiciously high
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self.assertLess(
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win_rate, 90, # No strategy should win >90% of trades
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f"{strategy_name}: Suspiciously high win rate {win_rate:.1f}% - possible future data leakage"
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)
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# Profit factor should be reasonable
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if profit_factor != float('inf'):
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self.assertLess(
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profit_factor, 10, # Profit factor >10 is suspicious
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f"{strategy_name}: Suspiciously high profit factor {profit_factor:.2f}"
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)
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# Total PnL should not be unrealistically high
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total_return_pct = (results['final_portfolio_value'] - 100000) / 100000 * 100
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self.assertLess(
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abs(total_return_pct), 50, # No more than 50% return in 24 hours
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f"{strategy_name}: Unrealistic return {total_return_pct:.1f}% in 24 hours"
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)
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print(f"✅ {strategy_name}: Realistic performance - "
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f"{win_rate:.1f}% win rate, "
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f"{total_return_pct:.2f}% return")
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else:
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print(f"⚠️ {strategy_name}: No trades to validate")
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def test_no_future_data_in_backtests(self):
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"""Test that backtests don't use future data."""
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print("\n🔮 Testing No Future Data Usage in Backtests")
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for config in self.strategy_configs:
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strategy_name = config['class'].__name__
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validator = BacktestValidator(config['class'], config['params'])
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results = validator.run_backtest(self.test_data, use_new_aggregation=True)
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# Check signal timestamps
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for signal in results['signals']:
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signal_time = signal['timestamp']
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# Find the data point that generated this signal
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data_at_signal = None
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for dp in self.test_data:
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if dp['timestamp'] == signal_time:
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data_at_signal = dp
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break
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if data_at_signal:
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# Signal should be generated at or before the data timestamp
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self.assertLessEqual(
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signal_time, data_at_signal['timestamp'],
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f"{strategy_name}: Signal at {signal_time} uses future data"
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)
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print(f"✅ {strategy_name}: {len(results['signals'])} signals verified - no future data usage")
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def test_aggregation_consistency(self):
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"""Test that aggregation is consistent across multiple runs."""
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print("\n🔄 Testing Aggregation Consistency")
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# Test with MetaTrend strategy
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config = self.strategy_configs[0] # MetaTrend
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validator = BacktestValidator(config['class'], config['params'])
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# Run multiple backtests
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results1 = validator.run_backtest(self.test_data, use_new_aggregation=True)
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results2 = validator.run_backtest(self.test_data, use_new_aggregation=True)
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# Results should be identical (deterministic)
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self.assertEqual(
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len(results1['signals']), len(results2['signals']),
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"Inconsistent number of signals across runs"
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)
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# Compare signal timestamps and types
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for i, (sig1, sig2) in enumerate(zip(results1['signals'], results2['signals'])):
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self.assertEqual(
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sig1['timestamp'], sig2['timestamp'],
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f"Signal {i} timestamp mismatch"
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)
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self.assertEqual(
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sig1['signal_type'], sig2['signal_type'],
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f"Signal {i} type mismatch"
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)
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print(f"✅ Aggregation consistent: {len(results1['signals'])} signals identical across runs")
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def test_memory_efficiency_in_backtests(self):
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"""Test memory efficiency during long backtests."""
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print("\n💾 Testing Memory Efficiency in Backtests")
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import psutil
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import gc
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process = psutil.Process()
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initial_memory = process.memory_info().rss / 1024 / 1024 # MB
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# Create longer dataset
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long_data = self._create_realistic_market_data(4320) # 3 days
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config = self.strategy_configs[0] # MetaTrend
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validator = BacktestValidator(config['class'], config['params'])
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# Run backtest and monitor memory
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memory_samples = []
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# Process in chunks to monitor memory
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chunk_size = 500
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for i in range(0, len(long_data), chunk_size):
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chunk = long_data[i:i+chunk_size]
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validator.run_backtest(chunk, use_new_aggregation=True)
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gc.collect()
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current_memory = process.memory_info().rss / 1024 / 1024 # MB
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memory_samples.append(current_memory - initial_memory)
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# Memory should not grow unbounded
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max_memory_increase = max(memory_samples)
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final_memory_increase = memory_samples[-1]
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self.assertLess(
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max_memory_increase, 100, # Less than 100MB increase
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f"Memory usage too high: {max_memory_increase:.2f}MB"
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)
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print(f"✅ Memory efficient: max increase {max_memory_increase:.2f}MB, "
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f"final increase {final_memory_increase:.2f}MB")
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def run_backtest_validation():
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"""Run all backtest validation tests."""
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print("🚀 Phase 3 Task 3.2: Backtest Validation Tests")
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print("=" * 70)
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# Create test suite
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suite = unittest.TestLoader().loadTestsFromTestCase(TestBacktestValidation)
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# Run tests with detailed output
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runner = unittest.TextTestRunner(verbosity=2, stream=sys.stdout)
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result = runner.run(suite)
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# Summary
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print(f"\n🎯 Backtest Validation Results:")
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print(f" Tests run: {result.testsRun}")
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print(f" Failures: {len(result.failures)}")
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print(f" Errors: {len(result.errors)}")
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if result.failures:
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print(f"\n❌ Failures:")
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for test, traceback in result.failures:
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print(f" - {test}: {traceback}")
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if result.errors:
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print(f"\n❌ Errors:")
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for test, traceback in result.errors:
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print(f" - {test}: {traceback}")
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success = len(result.failures) == 0 and len(result.errors) == 0
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if success:
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print(f"\n✅ All backtest validation tests PASSED!")
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print(f"🔧 Verified:")
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print(f" - Signal timing differences")
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print(f" - Performance impact analysis")
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print(f" - Realistic trading results")
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print(f" - No future data usage")
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print(f" - Aggregation consistency")
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print(f" - Memory efficiency")
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else:
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print(f"\n❌ Some backtest validation tests FAILED")
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return success
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if __name__ == "__main__":
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success = run_backtest_validation()
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sys.exit(0 if success else 1) |