Files
quantumbotx/testing/test_atr_education.py
T
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

189 lines
6.9 KiB
Python

#!/usr/bin/env python3
"""
📚 Test ATR Education System
Validates the new educational features for ATR-based risk management
"""
import sys
import os
# Add the project root to the path
sys.path.insert(0, os.path.dirname(os.path.abspath(__file__)))
try:
from core.education.atr_education import (
ATREducationHelper,
get_atr_tutorial,
explain_atr_example,
validate_beginner_atr_settings
)
from core.strategies.beginner_defaults import (
get_atr_education_info,
explain_atr_for_beginners
)
print("✅ All ATR education imports successful!")
except Exception as e:
print(f"❌ Import error: {e}")
sys.exit(1)
def test_atr_education_system():
"""Test the ATR education system"""
print("\n📚 Testing ATR Education System")
print("=" * 60)
# Test 1: Basic education helper
print("\n1. 📖 ATR Education Helper:")
helper = ATREducationHelper()
tutorial = helper.get_beginner_tutorial()
print(f" 📚 Tutorial has {len(tutorial['steps'])} steps")
print(f" 💡 Key takeaways: {len(tutorial['key_takeaways'])}")
for i, step in enumerate(tutorial['steps'], 1):
print(f" Step {i}: {step['title']}")
# Test 2: Interactive examples
print("\n2. 🎯 Interactive Examples:")
test_scenarios = [
{'symbol': 'EURUSD', 'account': 10000, 'risk': 1.0, 'atr': 0.0050},
{'symbol': 'XAUUSD', 'account': 10000, 'risk': 2.0, 'atr': 15.0}, # Will be protected
{'symbol': 'BTCUSD', 'account': 5000, 'risk': 1.5, 'atr': 500.0}
]
for scenario in test_scenarios:
example = helper.get_interactive_example(
scenario['symbol'],
scenario['account'],
scenario['risk'],
scenario['atr']
)
print(f"\\n 📊 {scenario['symbol']} Example:")
print(f" Input Risk: {scenario['risk']}% → Actual: {example['risk_percent_actual']}%")
print(f" ATR: {scenario['atr']} → SL Distance: {example['sl_distance']:.2f}")
print(f" Lot Size: {example['lot_size']}")
print(f" Protection Active: {example['protection_active']}")
print(f" Risk-to-Reward: {example['risk_to_reward_ratio']}")
if example['protection_active']:
print(f" 🛡️ PROTECTION: System reduced risk for safety!")
# Test 3: Parameter validation
print("\n3. ⚙️ Parameter Validation:")
validation_tests = [
{'symbol': 'EURUSD', 'risk': 0.5, 'sl': 2.0, 'tp': 4.0, 'name': 'Conservative EURUSD'},
{'symbol': 'XAUUSD', 'risk': 3.0, 'sl': 3.0, 'tp': 5.0, 'name': 'Risky Gold (will warn)'},
{'symbol': 'BTCUSD', 'risk': 1.0, 'sl': 1.0, 'tp': 1.5, 'name': 'Poor risk-reward crypto'}
]
for test in validation_tests:
validation = helper.validate_beginner_parameters(
test['symbol'], test['risk'], test['sl'], test['tp']
)
print(f"\\n 🧪 {test['name']}:")
print(f" Safe for beginners: {validation['is_beginner_safe']}")
print(f" Will be protected: {validation['will_be_protected']}")
if validation['warnings']:
for warning in validation['warnings']:
print(f" ⚠️ {warning}")
if validation['suggestions']:
for suggestion in validation['suggestions']:
print(f" 💡 {suggestion}")
# Test 4: Integration with beginner defaults
print("\n4. 🔗 Integration with Beginner Defaults:")
atr_info = get_atr_education_info()
print(f" 📚 ATR concept explanations: {len(atr_info['concept_explanation']['detailed'])}")
print(f" 📊 Example markets: {list(atr_info['examples'].keys())}")
print(f" 🛡️ Protection features: {len(atr_info['protection_features'])}")
# Test specific symbol explanations
for symbol in ['EURUSD', 'XAUUSD']:
explanation = explain_atr_for_beginners(symbol)
print(f"\\n 📈 {symbol} Explanation:")
print(f" {explanation['example']['explanation']}")
print(f" Typical ATR: {explanation['example']['typical_atr']}")
print("\n🎉 All ATR education tests completed successfully!")
def demonstrate_atr_protection():
"""Demonstrate the ATR protection system in action"""
print("\n🛡️ ATR Protection System Demonstration")
print("=" * 60)
helper = ATREducationHelper()
# Show dangerous vs safe scenarios
scenarios = [
{
'name': 'Beginner Mistake (Before Protection)',
'symbol': 'XAUUSD',
'account': 10000,
'risk': 5.0, # Dangerous!
'atr': 20.0,
'description': 'What would happen without protection'
},
{
'name': 'System Protection (After)',
'symbol': 'XAUUSD',
'account': 10000,
'risk': 5.0, # Same input
'atr': 20.0,
'description': 'How the system saves the beginner'
}
]
for scenario in scenarios:
example = helper.get_interactive_example(
scenario['symbol'],
scenario['account'],
scenario['risk'],
scenario['atr']
)
print(f"\\n📊 {scenario['name']}:")
print(f" Account: ${scenario['account']:,}")
print(f" Desired Risk: {scenario['risk']}%")
print(f" ATR: ${scenario['atr']}")
print(f" 📉 Target Risk Amount: ${example['amount_to_risk_target']:.0f}")
print(f" 🛡️ Actual Risk Amount: ${example['actual_risk_amount']:.0f}")
if example['protection_active']:
savings = example['amount_to_risk_target'] - example['actual_risk_amount']
print(f" 💰 PROTECTION SAVED: ${savings:.0f}")
print(f" 🎯 System automatically reduced risk by {(savings/example['amount_to_risk_target']*100):.0f}%")
print(f"\\n 📝 Explanation:")
for exp in example['explanation']:
print(f" {exp}")
print("\\n✨ CONCLUSION:")
print(" Your ATR system is like having a professional trader watching over beginners!")
print(" It prevents the common mistakes that blow up accounts.")
if __name__ == "__main__":
print("📚 QuantumBotX ATR Education System Test")
print("=" * 60)
try:
test_atr_education_system()
demonstrate_atr_protection()
print("\\n" + "=" * 60)
print("🏆 SUCCESS! ATR education system is working perfectly!")
print("🎓 Your app now teaches beginners professional risk management!")
print("🛡️ Built-in protection prevents common beginner mistakes!")
print("=" * 60)
except Exception as e:
print(f"\\n❌ Error during testing: {e}")
import traceback
traceback.print_exc()