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https://github.com/aljazceru/lnflow.git
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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>
91 lines
2.1 KiB
Plaintext
91 lines
2.1 KiB
Plaintext
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# Improved charge-lnd configuration with advanced inbound fee support
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# This configuration demonstrates the enhanced capabilities over original charge-lnd
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[default]
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# Non-final policy that sets defaults
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final = false
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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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strategy = static
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[high-capacity-active]
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# High capacity channels that are active get revenue optimization
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chan.min_capacity = 5000000
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activity.level = high, medium
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strategy = revenue_max
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fee_ppm = 1500
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inbound_fee_ppm = -50
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enable_auto_rollback = true
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rollback_threshold = 0.2
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learning_enabled = true
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priority = 10
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[balance-drain-channels]
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# Channels with too much local balance - encourage outbound routing
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chan.min_ratio = 0.8
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strategy = balance_based
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inbound_fee_ppm = -100
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inbound_base_fee_msat = -500
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priority = 20
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[balance-preserve-channels]
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# Channels with low local balance - preserve liquidity
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chan.max_ratio = 0.2
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strategy = balance_based
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fee_ppm = 2000
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inbound_fee_ppm = 50
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priority = 20
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[flow-optimize-channels]
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# Channels with good flow patterns - optimize for revenue
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flow.7d.min = 1000000
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strategy = flow_based
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learning_enabled = true
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priority = 30
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[competitive-channels]
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# Channels where we compete with many alternatives
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network.min_alternatives = 5
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peer.fee_ratio.min = 0.5
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peer.fee_ratio.max = 1.5
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strategy = inbound_discount
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inbound_fee_ppm = -75
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priority = 40
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[premium-peers]
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# Special rates for high-value peers
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node.id = 033d8656219478701227199cbd6f670335c8d408a92ae88b962c49d4dc0e83e025
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strategy = static
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fee_ppm = 500
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inbound_fee_ppm = -25
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inbound_base_fee_msat = -200
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priority = 5
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[inactive-channels]
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# Inactive channels - aggressive activation strategy
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activity.level = inactive
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strategy = balance_based
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fee_ppm = 100
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inbound_fee_ppm = -200
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max_fee_ppm = 500
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priority = 50
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[discourage-routing]
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# Channels we want to discourage routing through
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chan.max_ratio = 0.1
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chan.min_capacity = 250000
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strategy = static
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base_fee_msat = 1000
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fee_ppm = 3000
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inbound_fee_ppm = 100
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priority = 90
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[catch-all]
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# Final policy for any unmatched channels
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strategy = static
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fee_ppm = 1000
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inbound_fee_ppm = 0
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priority = 100
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