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Aljaz Ceru
8b6fd8b89d
Advanced Lightning Network channel fee optimization system with: ✅ Intelligent inbound fee strategies (beyond charge-lnd) ✅ Automatic rollback protection for safety ✅ Machine learning optimization from historical data ✅ High-performance gRPC + REST API support ✅ Enterprise-grade security with method whitelisting ✅ Complete charge-lnd compatibility Features: - Policy-based fee management with advanced strategies - Balance-based and flow-based optimization algorithms - Revenue maximization focus vs simple rule-based approaches - Comprehensive security analysis and hardening - Professional repository structure with proper documentation - Full test coverage and example configurations Architecture: - Modern Python project structure with pyproject.toml - Secure gRPC integration with REST API fallback - Modular design: API clients, policy engine, strategies - SQLite database for experiment tracking - Shell script automation for common tasks Security: - Method whitelisting for LND operations - Runtime validation of all gRPC calls - No fund movement capabilities - fee management only - Comprehensive security audit completed - Production-ready with enterprise standards 🤖 Generated with Claude Code Co-Authored-By: Claude <noreply@anthropic.com>
25 lines
526 B
Plaintext
25 lines
526 B
Plaintext
# Simple test configuration compatible with both charge-lnd and our implementation
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# This is for API integration testing only
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[default]
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strategy = static
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base_fee_msat = 1000
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fee_ppm = 1000
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time_lock_delta = 80
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[high-capacity-test]
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# Test basic matching and fee setting
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chan.min_capacity = 5000000
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strategy = static
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base_fee_msat = 1500
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fee_ppm = 1200
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inbound_fee_ppm = -50
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priority = 10
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[balance-test]
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# Test balance-based matching
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chan.min_ratio = 0.7
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strategy = static
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fee_ppm = 800
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inbound_fee_ppm = -25
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priority = 20 |