Nija

GMIG Quick Start Guide

🚀 Get Started in 5 Minutes

1. Installation

GMIG is already included in NIJA. No additional installation required!

# Verify installation
python -c "from bot.gmig import GMIGEngine; print('✓ GMIG installed')"

2. Optional: Setup FRED API (Recommended)

For enhanced central bank data:

# Get free API key from: https://fred.stlouisfed.org/docs/api/api_key.html

# Add to .env file
echo "FRED_API_KEY=your_api_key_here" >> .env

3. Run Your First Analysis

from bot.gmig import GMIGEngine

# Initialize GMIG
gmig = GMIGEngine()

# Run full analysis
report = gmig.run_full_analysis()

# View results
print(f"Regime: {report['macro_regime']['regime']}")
print(f"Signal: {report['positioning_signals']['primary_signal']}")
print(f"Alert: {report['crisis_assessment']['alert_level']}")

4. Run Tests

# Test the entire GMIG system
python test_gmig.py

Expected output:

Tests Passed: 6/6
✓ PASS  Central Bank Monitor
✓ PASS  Interest Rate Analyzer
✓ PASS  Yield Curve Modeler
✓ PASS  Liquidity Stress Detector
✓ PASS  Crisis Warning System
✓ PASS  GMIG Engine (Full)

🎉 ALL TESTS PASSED - GMIG is operational!

5. Integrate with Your Trading Bot

from bot.gmig import GMIGEngine
from bot.trading_strategy import TradingStrategy

# Initialize both systems
gmig = GMIGEngine()
strategy = TradingStrategy()

# Get macro intelligence
macro_report = gmig.run_full_analysis()

# Adjust trading based on macro regime
regime = macro_report['macro_regime']['regime']
risk_adj = macro_report['risk_adjustments']

# Apply risk adjustments
if regime == 'crisis':
    # Emergency defensive mode
    strategy.stop_all_new_trades()
    strategy.reduce_positions(multiplier=0.20)
elif regime == 'pre_recession':
    # Defensive mode
    strategy.reduce_positions(multiplier=0.50)
elif regime == 'risk_on':
    # Aggressive mode
    strategy.increase_exposure(multiplier=1.20)

# Apply position size adjustments
position_multiplier = risk_adj['position_size_multiplier']
strategy.set_position_size_multiplier(position_multiplier)

📊 Common Use Cases

Use Case 1: Daily Macro Check

from bot.gmig import GMIGEngine
import schedule

gmig = GMIGEngine()

def daily_macro_check():
    """Run daily at market open"""
    report = gmig.run_full_analysis()

    # Log to file or database
    with open('gmig_daily_log.txt', 'a') as f:
        f.write(f"{report['timestamp']}: {report['summary']}\n")

    # Send alert if crisis warning
    if report['crisis_assessment']['alert_level'] in ['orange', 'red']:
        send_alert(f"⚠️ GMIG Alert: {report['summary']['key_recommendation']}")

# Schedule daily at 9:00 AM
schedule.every().day.at("09:00").do(daily_macro_check)

Use Case 2: Crisis Monitoring

from bot.gmig import GMIGEngine
import time

gmig = GMIGEngine()

def monitor_crisis():
    """Monitor for crisis signals every 5 minutes"""
    while True:
        crisis_check = gmig.run_crisis_check()

        if crisis_check['action_required']:
            print(f"🚨 ALERT: {crisis_check['alert_level']}")
            print(f"Crisis Probability: {crisis_check['crisis_probability']:.1%}")
            # Take defensive action

        time.sleep(300)  # 5 minutes

# Run in background
import threading
monitor_thread = threading.Thread(target=monitor_crisis, daemon=True)
monitor_thread.start()

Use Case 3: Component-Specific Analysis

from bot.gmig import YieldCurveAIModeler, LiquidityStressDetector

# Just yield curve analysis
yc = YieldCurveAIModeler()
yc_data = yc.analyze_curve()

if yc_data['recession_probability'] > 0.50:
    print("⚠️ High recession probability - reduce risk")

# Just liquidity monitoring
liquidity = LiquidityStressDetector()
liq_data = liquidity.detect_stress()

if liq_data['overall_stress_level'] == 'red':
    print("🚨 Liquidity crisis - emergency defensive")

🎯 Understanding Outputs

Macro Regimes

Regime	What It Means	Your Action
crisis	Financial crisis active	Move to cash/safe havens
pre_recession	Recession likely in 6-18mo	Reduce risk, increase cash
risk_off	Market stress elevated	Defensive positioning
tightening	Central banks raising rates	Cautious, favor value
easing	Central banks cutting rates	Bullish for risk assets
risk_on	Favorable conditions	Aggressive positioning
transitional	Mixed signals	Balanced approach

Alert Levels

Level	Crisis Prob	Action
🟢 Green	< 20%	Normal operations
🟡 Yellow	20-40%	Increase monitoring
🟠 Orange	40-60%	Reduce positions 50%
🔴 Red	> 60%	Emergency defensive

Positioning Signals

Signal	Meaning	Position Size
maximum_defensive	Crisis mode	20% of normal
reduce_risk	High risk	50% of normal
defensive	Elevated risk	70% of normal
cautious	Moderate risk	75% of normal
neutral	Balanced	100% (normal)
bullish	Favorable	120% of normal
aggressive	Strong conditions	150% of normal

🔧 Configuration Options

Engine Modes

# Full mode (all features, default)
gmig = GMIGEngine(config={'mode': 'full'})

# Essential mode (core features only, faster)
gmig = GMIGEngine(config={'mode': 'essential'})

# Crisis-only mode (just crisis detection)
gmig = GMIGEngine(config={'mode': 'crisis_only'})

Intelligence Levels

# Ultra (most advanced, default)
gmig = GMIGEngine(config={'intelligence_level': 'ultra'})

# Advanced (good balance)
gmig = GMIGEngine(config={'intelligence_level': 'advanced'})

# Standard (faster, less sophisticated)
gmig = GMIGEngine(config={'intelligence_level': 'standard'})

Update Frequency

gmig = GMIGEngine(config={
    'update_frequency_minutes': 15,      # Full analysis every 15 min
    'crisis_check_frequency_minutes': 5  # Crisis check every 5 min
})

📈 Performance Tips

1. Run Full Analysis Sparingly: Every 15-60 minutes is sufficient
2. Use Crisis Checks for Monitoring: Every 5 minutes for real-time alerts
3. Cache Results: Store reports and reuse for a few minutes
4. Parallel Components: Individual components can run in parallel
5. FRED API: Set up for better central bank data quality

🎓 Learning Path

Beginner (Day 1)

1. Run test_gmig.py to verify setup
2. Run simple analysis and understand output
3. Review GMIG_DOCUMENTATION.md overview

Intermediate (Week 1)

1. Integrate crisis checks into trading bot
2. Set up daily macro analysis
3. Study historical regimes and outcomes

Advanced (Month 1)

1. Customize risk adjustments based on regime
2. Build multi-account orchestration
3. Create investor-grade reporting

Expert (Month 3+)

1. Add custom indicators
2. Tune AI models for your markets
3. Build predictive regime transitions

🆘 Troubleshooting

“No FRED API key” warning

• Optional: Get free key at https://fred.stlouisfed.org/docs/api/api_key.html
• Add to .env: FRED_API_KEY=your_key
• System works without it (uses cached/simulated data)

Analysis taking too long

• Use mode='essential' for faster analysis
• Reduce update frequency
• Use crisis checks instead of full analysis

Unexpected regime detection

• Check individual component outputs
• Review confidence scores
• Compare to market conditions
• Regimes can change quickly in volatile markets

📞 Next Steps

1. Read Full Documentation: GMIG_DOCUMENTATION.md
2. Study Examples: Review test cases in test_gmig.py
3. Integrate: Add to your trading strategy
4. Monitor: Track regime changes and outcomes
5. Optimize: Tune based on your risk tolerance

💡 Pro Tips

• Act on Red Alerts: They’re rare but critical
• Log Everything: Track regime changes and performance
• Backtest Regimes: Study historical regime transitions
• Combine with MMIN: Use both for complete intelligence
• Trust the System: GMIG uses proven academic research

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Ready to start? Run python test_gmig.py now! 🚀