btc-trading/scripts/test_ma44_performance.py

#!/usr/bin/env python3
"""
Performance Test Script for MA44 Strategy
Tests backtesting performance on Synology DS218+ with 6GB RAM

Usage:
    python test_ma44_performance.py [--days DAYS] [--interval INTERVAL]
    
Example:
    python test_ma44_performance.py --days 7 --interval 1m
"""

import asyncio
import argparse
import time
import sys
import os
from datetime import datetime, timedelta, timezone

# Add src to path
sys.path.insert(0, os.path.join(os.path.dirname(__file__), '..', 'src'))

from data_collector.database import DatabaseManager
from data_collector.indicator_engine import IndicatorEngine, IndicatorConfig
from data_collector.brain import Brain
from data_collector.backtester import Backtester


async def run_performance_test(days: int = 7, interval: str = "1m"):
    """Run MA44 backtest and measure performance"""
    
    print("=" * 70)
    print(f"PERFORMANCE TEST: MA44 Strategy")
    print(f"Timeframe: {interval}")
    print(f"Period: Last {days} days")
    print(f"Hardware: Synology DS218+ (6GB RAM)")
    print("=" * 70)
    print()
    
    # Database connection (adjust these if needed)
    db = DatabaseManager(
        host=os.getenv('DB_HOST', 'localhost'),
        port=int(os.getenv('DB_PORT', 5432)),
        database=os.getenv('DB_NAME', 'btc_data'),
        user=os.getenv('DB_USER', 'btc_bot'),
        password=os.getenv('DB_PASSWORD', '')
    )
    
    try:
        await db.connect()
        print("✓ Database connected")
        
        # Calculate date range
        end_date = datetime.now(timezone.utc)
        start_date = end_date - timedelta(days=days)
        
        print(f"✓ Date range: {start_date.date()} to {end_date.date()}")
        print(f"✓ Symbol: BTC")
        print(f"✓ Strategy: MA44 (44-period SMA)")
        print()
        
        # Check data availability
        async with db.acquire() as conn:
            count = await conn.fetchval("""
                SELECT COUNT(*) FROM candles 
                WHERE symbol = 'BTC' 
                AND interval = $1 
                AND time >= $2 
                AND time <= $3
            """, interval, start_date, end_date)
            
            print(f"📊 Data points: {count:,} {interval} candles")
            
            if count == 0:
                print("❌ ERROR: No data found for this period!")
                print(f"   Run: python -m data_collector.backfill --days {days} --intervals {interval}")
                return
            
            print(f"   (Expected: ~{count * int(interval.replace('m','').replace('h','').replace('d',''))} minutes of data)")
            print()
        
        # Setup indicator configuration
        indicator_configs = [
            IndicatorConfig("ma44", "sma", 44, [interval])
        ]
        
        engine = IndicatorEngine(db, indicator_configs)
        brain = Brain(db, engine)
        backtester = Backtester(db, engine, brain)
        
        print("⚙️  Running backtest...")
        print("-" * 70)
        
        # Measure execution time
        start_time = time.time()
        
        await backtester.run("BTC", [interval], start_date, end_date)
        
        end_time = time.time()
        execution_time = end_time - start_time
        
        print("-" * 70)
        print()
        
        # Fetch results from database
        async with db.acquire() as conn:
            latest_backtest = await conn.fetchrow("""
                SELECT id, strategy, start_time, end_time, intervals, results, created_at
                FROM backtest_runs
                WHERE strategy LIKE '%ma44%'
                ORDER BY created_at DESC
                LIMIT 1
            """)
            
            if latest_backtest and latest_backtest['results']:
                import json
                results = json.loads(latest_backtest['results'])
                
                print("📈 RESULTS:")
                print("=" * 70)
                print(f"  Total Trades:     {results.get('total_trades', 'N/A')}")
                print(f"  Win Rate:         {results.get('win_rate', 0):.1f}%")
                print(f"  Win Count:        {results.get('win_count', 0)}")
                print(f"  Loss Count:       {results.get('loss_count', 0)}")
                print(f"  Total P&L:        ${results.get('total_pnl', 0):.2f}")
                print(f"  P&L Percent:      {results.get('total_pnl_pct', 0):.2f}%")
                print(f"  Initial Balance:  ${results.get('initial_balance', 1000):.2f}")
                print(f"  Final Balance:    ${results.get('final_balance', 1000):.2f}")
                print(f"  Max Drawdown:     {results.get('max_drawdown', 0):.2f}%")
                print()
                print("⏱️  PERFORMANCE:")
                print(f"  Execution Time:   {execution_time:.2f} seconds")
                print(f"  Candles/Second:   {count / execution_time:.0f}")
                print(f"  Backtest ID:      {latest_backtest['id']}")
                print()
                
                # Performance assessment
                if execution_time < 30:
                    print("✅ PERFORMANCE: Excellent (< 30s)")
                elif execution_time < 60:
                    print("✅ PERFORMANCE: Good (< 60s)")
                elif execution_time < 300:
                    print("⚠️  PERFORMANCE: Acceptable (1-5 min)")
                else:
                    print("❌ PERFORMANCE: Slow (> 5 min) - Consider shorter periods or higher TFs")
                    
                print()
                print("💡 RECOMMENDATIONS:")
                if execution_time > 60:
                    print("  • For faster results, use higher timeframes (15m, 1h, 4h)")
                    print("  • Or reduce date range (< 7 days)")
                else:
                    print("  • Hardware is sufficient for this workload")
                    print("  • Can handle larger date ranges or multiple timeframes")
                    
            else:
                print("❌ ERROR: No results found in database!")
                print("   The backtest may have failed. Check server logs.")
                
    except Exception as e:
        print(f"\n❌ ERROR: {e}")
        import traceback
        traceback.print_exc()
        
    finally:
        await db.disconnect()
        print()
        print("=" * 70)
        print("Test completed")
        print("=" * 70)


def main():
    parser = argparse.ArgumentParser(description='Test MA44 backtest performance')
    parser.add_argument('--days', type=int, default=7,
                       help='Number of days to backtest (default: 7)')
    parser.add_argument('--interval', type=str, default='1m',
                       help='Candle interval (default: 1m)')
    
    args = parser.parse_args()
    
    # Run the async test
    asyncio.run(run_performance_test(args.days, args.interval))


if __name__ == "__main__":
    main()