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quantumbotx/testing/test_xauusd.py
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Reynov Christian a24fa8637b 🚀 REVOLUTIONARY FEATURE: Indonesian AI Trading Mentor System
 CORE AI MENTOR SYSTEM:
  - Complete Indonesian language AI trading mentor
  - Real-time trading psychology analysis with cultural context
  - Emotional intelligence for Indonesian trading behavior
  - Personal feedback with Islamic context ('Alhamdulillah profit!')
  - Jakarta timezone optimization and BI rate awareness

 DATABASE INTEGRATION:
  - New trading_sessions, ai_mentor_reports, daily_trading_data tables
  - Real-time capture of trading data for AI analysis
  - Historical performance tracking and emotional state logging
  - Seamless integration with existing bot architecture

 WEB INTERFACE:
  - Beautiful Indonesian AI mentor dashboard
  - Interactive emotion selection with cultural sensitivity
  - Real-time feedback generation and instant AI consultation
  - Daily report generation with comprehensive analysis
  - Quick feedback modal for emotional check-ins

 TRADING BOT INTEGRATION:
  - Automatic trade logging for AI mentor analysis
  - Risk management scoring (1-10 scale)
  - Strategy performance correlation with emotional states
  - Stop loss and take profit usage tracking

 REVOLUTIONARY FEATURES:
  - First-ever Indonesian AI trading mentor in the world
  - Combines trading psychology with Islamic values
  - Market-specific guidance for Indonesian traders
  - Progressive learning path from beginner to expert
  - Cultural trading wisdom (Jakarta hours, Ramadan considerations)

IMPACT: This transforms QuantumBotX into the world's first culturally-aware
AI trading mentor specifically designed for Indonesian retail traders.

Indonesian beginners now have personal AI guidance in their native language
with full understanding of local market conditions and cultural context.
2025-08-26 09:02:03 +08:00

148 lines
5.1 KiB
Python

#!/usr/bin/env python3
"""
XAUUSD Backtesting Validator
Tests the fixes for gold trading position sizing and risk management
"""
import sys
import os
import pandas as pd
import numpy as np
# Add the project root to the path
sys.path.insert(0, os.path.dirname(os.path.abspath(__file__)))
def test_xauusd_pulse_sync():
"""Test Pulse Sync strategy on XAUUSD with conservative parameters"""
from core.backtesting.engine import run_backtest
print("🧪 Testing XAUUSD with Pulse Sync Strategy...")
# Create realistic XAUUSD test data
dates = pd.date_range('2023-01-01', periods=300, freq='h')
base_price = 1950.0
# Gold price movements
price_changes = np.random.randn(300) * 1.5 # Realistic gold volatility
prices = base_price + np.cumsum(price_changes)
df = pd.DataFrame({
'time': dates,
'open': prices,
'high': prices + np.random.uniform(0.5, 2.0, 300),
'low': prices - np.random.uniform(0.5, 2.0, 300),
'close': prices + np.random.uniform(-0.5, 0.5, 300),
'volume': np.random.randint(100, 1000, 300)
})
# Ensure OHLC integrity
df['high'] = df[['high', 'close', 'open']].max(axis=1)
df['low'] = df[['low', 'close', 'open']].min(axis=1)
print(f"📊 Created XAUUSD data: ${df['close'].min():.2f} - ${df['close'].max():.2f}")
# Test different parameter sets
test_cases = [
{'lot_size': 0.5, 'sl_pips': 1.0, 'tp_pips': 2.0, 'name': 'Conservative'},
{'lot_size': 1.0, 'sl_pips': 1.5, 'tp_pips': 3.0, 'name': 'Moderate'},
{'lot_size': 2.0, 'sl_pips': 2.0, 'tp_pips': 4.0, 'name': 'Aggressive (will be capped)'},
]
results = []
for test_case in test_cases:
params = {k: v for k, v in test_case.items() if k != 'name'}
name = test_case['name']
print(f"\\n📈 Testing {name}: Risk={params['lot_size']}%, SL={params['sl_pips']}x ATR")
try:
# Pass XAUUSD as symbol name for accurate detection
result = run_backtest('PULSE_SYNC', params, df, symbol_name='XAUUSD')
if 'error' in result:
print(f" ❌ Error: {result['error']}")
continue
# Extract key metrics
profit = result.get('total_profit_usd', 0)
trades = result.get('total_trades', 0)
final_capital = result.get('final_capital', 10000)
drawdown = result.get('max_drawdown_percent', 0)
win_rate = result.get('win_rate_percent', 0)
# Safety check
is_safe = (
abs(profit) < 25000 and # No extreme profits/losses
drawdown < 40 and # Reasonable drawdown
final_capital > 5000 # Account didn't blow up
)
status = "✅ SAFE" if is_safe else "⚠️ RISKY"
print(f" {status} Results:")
print(f" Profit: ${profit:,.2f}")
print(f" Trades: {trades}")
print(f" Final Capital: ${final_capital:,.2f}")
print(f" Max Drawdown: {drawdown:.2f}%")
print(f" Win Rate: {win_rate:.2f}%")
if not is_safe:
print(f" ⚠️ WARNING: Position sizing may still be too aggressive!")
results.append({
'name': name,
'params': params,
'result': result,
'is_safe': is_safe
})
except Exception as e:
print(f" ❌ Exception: {e}")
import traceback
traceback.print_exc()
return results
def main():
"""Main test function"""
print("🥇 XAUUSD Position Sizing Validator")
print("=" * 50)
try:
results = test_xauusd_pulse_sync()
print("\\n" + "=" * 50)
print("📊 VALIDATION SUMMARY")
print("=" * 50)
safe_count = sum(1 for r in results if r['is_safe'])
total_count = len(results)
print(f"Safe Results: {safe_count}/{total_count}")
if safe_count == total_count:
print("✅ ALL TESTS PASSED! XAUUSD position sizing is now safe.")
elif safe_count > 0:
print("🟡 Some tests passed. Position sizing improved but needs more work.")
else:
print("❌ All tests failed. Position sizing algorithm needs major fixes.")
print("\\n💡 XAUUSD Trading Recommendations:")
print(" • Use maximum 0.1 lot size for gold")
print(" • Keep risk below 1% per trade")
print(" • Use smaller ATR multipliers (1.0-1.5x)")
print(" • Monitor drawdown closely")
print(" • Consider using fixed lot sizes instead of dynamic sizing")
return safe_count > 0
except Exception as e:
print(f"❌ Validation failed: {e}")
import traceback
traceback.print_exc()
return False
if __name__ == "__main__":
success = main()
sys.exit(0 if success else 1)