Upload demo.py

demo.py

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+#!/usr/bin/env python3
+"""
+BuilderBrain Hackathon Demo
+============================
+
+Run this script to demonstrate the full BuilderBrain pipeline:
+1. Fetch live Polymarket data
+2. Generate reasoning traces with Kelly sizing
+3. Route via builder codes
+4. Settle via Arc nanopayments
+
+Usage:
+    python demo.py
+
+Expected output:
+    - 20-50 trade signals with reasoning traces
+    - Portfolio stats with risk metrics
+    - Audit log ready for on-chain anchoring
+"""
+
+import json
+from builderbrain import BuilderBrain
+
+
+def main():
+    print("=" * 70)
+    print("  BuilderBrain — Agentic Prediction Market Intelligence")
+    print("  Agora Agents Hackathon | Canteen × Circle")
+    print("=" * 70)
+    
+    # Initialize with $10k paper bankroll
+    brain = BuilderBrain(
+        bankroll_usd=10000,
+        paper_trade=True,
+        builder_code="builderbrain_demo_v1",
+        min_edge=0.03,  # 3% minimum edge
+        max_positions=15,
+    )
+    
+    # Run 3 cycles to simulate 48h of operation
+    for i in range(3):
+        print(f"\n{'─' * 70}")
+        print(f"  Cycle {i+1}/3 — Simulating live market scanning...")
+        print(f"{'─' * 70}")
+        
+        signals = brain.run_cycle()
+        
+        # Print top signal from this cycle
+        if signals:
+            top = max(signals, key=lambda s: s.expected_return)
+            print(f"\n  🎯 Top Signal:")
+            print(f"     Market: {top.market_id}")
+            print(f"     Side: {top.side} | Size: {top.size_fraction:.2%} bankroll")
+            print(f"     Expected Return: {top.expected_return:.4f}")
+            print(f"     Confidence: {top.confidence:.1%}")
+            print(f"     Urgency: {top.urgency}")
+            print(f"     Trace Hash: {top.reasoning_trace.reasoning_hash}")
+    
+    # Final stats
+    print(f"\n{'=' * 70}")
+    print("  FINAL PORTFOLIO STATS")
+    print(f"{'=' * 70}")
+    
+    stats = brain.get_portfolio_stats()
+    print(json.dumps(stats, indent=2, default=str))
+    
+    # Export audit log
+    audit_file = brain.export_audit_log("builderbrain_audit.json")
+    print(f"\n  📄 Audit log exported: {audit_file}")
+    
+    # Print top 5 signals
+    print(f"\n{'=' * 70}")
+    print("  TOP 5 SIGNALS (by expected return)")
+    print(f"{'=' * 70}")
+    
+    for i, sig in enumerate(brain.get_top_signals(5), 1):
+        print(f"\n  #{i} {sig.market_id}")
+        print(f"     {sig.side} @ {sig.size_fraction:.2%} bankroll")
+        print(f"     E[return]: {sig.expected_return:.4f} | Conf: {sig.confidence:.1%}")
+        print(f"     Trace: {sig.reasoning_trace.reasoning_hash}")
+        print(f"     Arguments: {len(sig.reasoning_trace.arguments)}")
+        print(f"     Risks: {len(sig.reasoning_trace.risk_factors)}")
+    
+    print(f"\n{'=' * 70}")
+    print("  Demo complete. Ready for hackathon submission.")
+    print(f"{'=' * 70}")
+
+
+if __name__ == "__main__":
+    main()