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#!/usr/bin/env python3
"""
Test Enhanced FRED ML Application
Verifies real-time FRED API integration and enhanced features
"""
import os
import sys
import pandas as pd
from datetime import datetime, timedelta
# Add frontend to path
sys.path.append('frontend')
def test_fred_api_integration():
"""Test FRED API integration and real-time data fetching"""
print("=== TESTING ENHANCED FRED ML APPLICATION ===")
# Test FRED API key
fred_key = os.getenv('FRED_API_KEY')
if not fred_key:
print("❌ FRED_API_KEY not found in environment")
return False
print(f"✅ FRED API Key: {fred_key[:8]}...")
try:
# Test FRED API client
from frontend.fred_api_client import FREDAPIClient, generate_real_insights, get_real_economic_data
# Test basic client functionality
client = FREDAPIClient(fred_key)
print("✅ FRED API Client initialized")
# Test insights generation
print("\n📊 Testing Real-Time Insights Generation...")
insights = generate_real_insights(fred_key)
if insights:
print(f"✅ Generated insights for {len(insights)} indicators")
# Show sample insights
for indicator, insight in list(insights.items())[:3]:
print(f" {indicator}: {insight.get('current_value', 'N/A')} ({insight.get('growth_rate', 'N/A')})")
else:
print("❌ Failed to generate insights")
return False
# Test economic data fetching
print("\n📈 Testing Economic Data Fetching...")
end_date = datetime.now().strftime('%Y-%m-%d')
start_date = (datetime.now() - timedelta(days=365)).strftime('%Y-%m-%d')
economic_data = get_real_economic_data(fred_key, start_date, end_date)
if 'economic_data' in economic_data and not economic_data['economic_data'].empty:
df = economic_data['economic_data']
print(f"✅ Fetched economic data: {df.shape[0]} observations, {df.shape[1]} indicators")
print(f" Date range: {df.index.min()} to {df.index.max()}")
print(f" Indicators: {list(df.columns)}")
else:
print("❌ Failed to fetch economic data")
return False
# Test correlation analysis
print("\n🔗 Testing Correlation Analysis...")
corr_matrix = df.corr(method='spearman')
print(f"✅ Calculated Spearman correlations for {len(corr_matrix)} indicators")
# Show strongest correlations
corr_pairs = []
for i in range(len(corr_matrix.columns)):
for j in range(i+1, len(corr_matrix.columns)):
corr_value = corr_matrix.iloc[i, j]
if abs(corr_value) > 0.5:
corr_pairs.append((corr_matrix.columns[i], corr_matrix.columns[j], corr_value))
corr_pairs.sort(key=lambda x: abs(x[2]), reverse=True)
print(f" Found {len(corr_pairs)} strong correlations (>0.5)")
for pair in corr_pairs[:3]:
print(f" {pair[0]} ↔ {pair[1]}: {pair[2]:.3f}")
return True
except Exception as e:
print(f"❌ Error testing FRED API integration: {e}")
return False
def test_enhanced_features():
"""Test enhanced application features"""
print("\n=== TESTING ENHANCED FEATURES ===")
try:
# Test insights generation with enhanced analysis
from frontend.fred_api_client import generate_real_insights
fred_key = os.getenv('FRED_API_KEY')
insights = generate_real_insights(fred_key)
# Test economic health assessment
print("🏥 Testing Economic Health Assessment...")
health_indicators = ['GDPC1', 'INDPRO', 'UNRATE', 'CPIAUCSL']
health_score = 0
for indicator in health_indicators:
if indicator in insights:
insight = insights[indicator]
growth_rate = insight.get('growth_rate', 0)
# Convert growth_rate to float if it's a string
try:
if isinstance(growth_rate, str):
growth_rate = float(growth_rate.replace('%', '').replace('+', ''))
else:
growth_rate = float(growth_rate)
except (ValueError, TypeError):
growth_rate = 0
if indicator == 'GDPC1' and growth_rate > 2:
health_score += 25
elif indicator == 'INDPRO' and growth_rate > 1:
health_score += 25
elif indicator == 'UNRATE':
current_value = insight.get('current_value', '0%').replace('%', '')
try:
unrate_val = float(current_value)
if unrate_val < 4:
health_score += 25
except:
pass
elif indicator == 'CPIAUCSL' and 1 < growth_rate < 3:
health_score += 25
print(f"✅ Economic Health Score: {health_score}/100")
# Test market sentiment analysis
print("📊 Testing Market Sentiment Analysis...")
sentiment_indicators = ['DGS10', 'FEDFUNDS', 'RSAFS']
sentiment_score = 0
for indicator in sentiment_indicators:
if indicator in insights:
insight = insights[indicator]
current_value = insight.get('current_value', '0')
growth_rate = insight.get('growth_rate', 0)
# Convert values to float
try:
if isinstance(growth_rate, str):
growth_rate = float(growth_rate.replace('%', '').replace('+', ''))
else:
growth_rate = float(growth_rate)
except (ValueError, TypeError):
growth_rate = 0
if indicator == 'DGS10':
try:
yield_val = float(current_value.replace('%', ''))
if 2 < yield_val < 5:
sentiment_score += 33
except:
pass
elif indicator == 'FEDFUNDS':
try:
rate_val = float(current_value.replace('%', ''))
if rate_val < 3:
sentiment_score += 33
except:
pass
elif indicator == 'RSAFS' and growth_rate > 2:
sentiment_score += 34
print(f"✅ Market Sentiment Score: {sentiment_score}/100")
return True
except Exception as e:
print(f"❌ Error testing enhanced features: {e}")
return False
def main():
"""Run all tests"""
print("🚀 Testing Enhanced FRED ML Application")
print("=" * 50)
# Test FRED API integration
api_success = test_fred_api_integration()
# Test enhanced features
features_success = test_enhanced_features()
# Summary
print("\n" + "=" * 50)
print("📋 TEST SUMMARY")
print("=" * 50)
if api_success and features_success:
print("✅ ALL TESTS PASSED")
print("✅ Real-time FRED API integration working")
print("✅ Enhanced features functioning")
print("✅ Application ready for production use")
return True
else:
print("❌ SOME TESTS FAILED")
if not api_success:
print("❌ FRED API integration issues")
if not features_success:
print("❌ Enhanced features issues")
return False
if __name__ == "__main__":
success = main()
sys.exit(0 if success else 1) |