Examples and code quality

2025-10-31 00:31:14 +02:00
parent e4aa6487ff
commit 739d4f8578
9 changed files with 508 additions and 27 deletions
@@ -0,0 +1,98 @@
+#!/usr/bin/env python3
+"""
+Investment Comparison Example
+
+This example compares multiple neighborhoods to help identify
+the best investment opportunities based on various metrics.
+"""
+
+from nadlan_mcp.govmap import GovmapClient
+
+
+def analyze_location(client, address, years=2):
+    """Analyze a single location and return key metrics."""
+    try:
+        deals = client.find_recent_deals_for_address(address, years_back=years, radius=150)
+
+        if not deals:
+            return None
+
+        stats = client.calculate_deal_statistics(deals)
+        activity = client.calculate_market_activity_score(deals)
+        liquidity = client.get_market_liquidity(deals)
+        investment = client.analyze_investment_potential(deals)
+
+        return {
+            "address": address,
+            "deal_count": len(deals),
+            "avg_price": stats.mean_price,
+            "avg_price_per_sqm": stats.mean_price_per_sqm,
+            "activity_score": activity.activity_score,
+            "activity_level": activity.activity_level,
+            "liquidity_score": liquidity.liquidity_score,
+            "investment_score": investment.investment_score,
+            "price_trend": investment.price_trend,
+            "appreciation_rate": investment.price_appreciation_rate,
+            "volatility": investment.volatility_score,
+        }
+    except Exception as e:
+        print(f"Error analyzing {address}: {e}")
+        return None
+
+
+def main():
+    client = GovmapClient()
+
+    # Addresses to compare
+    locations = [
+        "רוטשילד 1 תל אביב",  # Rothschild, Tel Aviv - Prestigious
+        "ז'בוטינסקי 1 רמת גן",  # Jabotinsky, Ramat Gan - Business district
+        "הרצל 1 חיפה",  # Herzl, Haifa - Northern city
+    ]
+
+    print("=== Investment Comparison ===\n")
+    print("Analyzing multiple neighborhoods...\n")
+
+    results = []
+    for location in locations:
+        print(f"Analyzing: {location}")
+        result = analyze_location(client, location, years=3)
+        if result:
+            results.append(result)
+        print()
+
+    if not results:
+        print("No data available for comparison")
+        return
+
+    # Display comparison table
+    print("\n" + "=" * 100)
+    print("COMPARISON SUMMARY")
+    print("=" * 100)
+
+    for result in results:
+        print(f"\n{result['address']}")
+        print("-" * 80)
+        print(f"  Deal Count: {result['deal_count']}")
+        print(f"  Avg Price: ₪{result['avg_price']:,.0f}")
+        if result['avg_price_per_sqm']:
+            print(f"  Avg Price/m²: ₪{result['avg_price_per_sqm']:,.0f}")
+        print(f"  Activity: {result['activity_score']:.0f}/100 ({result['activity_level']})")
+        print(f"  Liquidity: {result['liquidity_score']:.0f}/100")
+        print(f"  Investment Score: {result['investment_score']:.0f}/100")
+        print(f"  Price Trend: {result['price_trend']}")
+        print(f"  Appreciation: {result['appreciation_rate']:.2f}%/year")
+        print(f"  Volatility: {result['volatility']:.0f}/100")
+
+    # Find best investment based on investment score
+    best_investment = max(results, key=lambda x: x["investment_score"])
+    print("\n" + "=" * 100)
+    print("RECOMMENDATION")
+    print("=" * 100)
+    print(f"\nBest Investment Opportunity: {best_investment['address']}")
+    print(f"Investment Score: {best_investment['investment_score']:.0f}/100")
+    print(f"Price Appreciation: {best_investment['appreciation_rate']:.2f}%/year")
+
+
+if __name__ == "__main__":
+    main()