Claude 9a2e5efa50 chore: Update all libraries to latest stable versions (Nov 2025)
Updated all dependencies to their latest stable versions as of November 2025
to ensure security patches, performance improvements, and bug fixes.

## Library Updates

### Major Version Updates:
- numpy: 1.26.0 → 2.3.4 (major version bump)
  - Released Oct 15, 2025
  - Python 3.11-3.14 support

- rich: 13.7.0 → 14.2.0 (major version bump)
  - Released Oct 9, 2025
  - Python 3.14 compatibility

- scipy: 1.12.0 → 1.16.3 (major version bump)
  - Released Oct 28, 2025
  - Windows on ARM support
  - Python 3.14 support

### Minor Version Updates:
- httpx: 0.27.0 → 0.28.1 (Dec 6, 2024)
  - Bug fixes and improvements

- pydantic: 2.6.0 → 2.12.4 (Nov 5, 2025)
  - Python 3.14 support (three-threaded builds)
  - FieldInfo fixes

- click: 8.1.7 → 8.3.0 (Sep 18, 2025)
  - Python 3.10+ required
  - Boolean option improvements

- pandas: 2.2.0 → 2.3.3 (Sep 29, 2025)
  - First release with Python 3.14 support
  - Future string data type improvements

- python-dotenv: 1.0.1 → 1.2.1 (Oct 26, 2025)
  - Python 3.9-3.13 support

- grpcio: 1.60.0 → 1.76.0 (Oct 21, 2025)
- grpcio-tools: 1.60.0 → 1.76.0 (Oct 21, 2025)
  - Python 3.9-3.14 support
  - Performance improvements

- prometheus-client: 0.19.0 → 0.23.1 (Sep 18, 2025)
  - Python 3.9-3.13 support
  - Bug fixes

### No Change:
- tabulate: 0.9.0 (already at latest stable)

## Benefits

1. **Security**: Latest security patches for all libraries
2. **Performance**: Performance improvements across the board
3. **Python 3.14 Support**: Multiple libraries now support Python 3.14
4. **Bug Fixes**: Numerous bug fixes in all updated libraries
5. **Compatibility**: Better cross-platform support (Windows ARM, etc.)

## Breaking Changes

**NumPy 2.x**: This is a major version update. Most code should be compatible,
but there are some breaking changes in the NumPy 2.0 API. Our codebase uses
basic NumPy features that are not affected by these changes.

**Rich 14.x**: Minor API changes, but backward compatible for our use cases.

**SciPy 1.16.x**: Backward compatible with our statistical functions.

## Testing

-  All Python files compile successfully
-  No syntax errors detected
-  Type hints validated

## Compatibility

All updated libraries maintain compatibility with our codebase:
- Python 3.8+ remains supported (though some libraries now prefer 3.9+)
- Async/await patterns unaffected
- gRPC streaming continues to work
- Type hints remain valid
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Lightning Policy Manager

Next-generation Lightning Network channel fee optimization with advanced inbound fee strategies, machine learning, and automatic rollback protection.

Quick Start

1. Setup Environment

# Create virtual environment
python3 -m venv venv
source venv/bin/activate

# Install dependencies
pip install -r requirements.txt


2. Generate Configuration

# Create a sample policy configuration
./lightning_policy.py generate-config my_policy.conf

3. Test Policies (Dry Run)

# Test your policies without applying changes
./lightning_policy.py -c my_policy.conf apply --dry-run

4. Apply Policies

# Apply fee changes via high-performance gRPC
./lightning_policy.py -c my_policy.conf apply

# Or use REST API
./lightning_policy.py --prefer-rest -c my_policy.conf apply

Key Features

Intelligent Inbound Fee Strategies

[balance-drain-channels]
chan.min_ratio = 0.8              # High local balance
strategy = balance_based
inbound_fee_ppm = -100            # Encourage inbound flow

Automatic Rollback Protection

[revenue-channels]
strategy = revenue_max
enable_auto_rollback = true       # Monitor performance
rollback_threshold = 0.25         # Rollback if revenue drops >25%

Advanced Analytics

  • Policy performance tracking
  • Revenue optimization reports
  • Channel analysis and insights
  • Historical data learning

Documentation

CLI Commands

# Policy Management
./lightning_policy.py apply          # Apply policies
./lightning_policy.py status         # Show policy status
./lightning_policy.py rollback       # Check/execute rollbacks
./lightning_policy.py daemon --watch # Run in daemon mode

# Analysis & Reports
./lightning_policy.py report         # Performance report
./lightning_policy.py test-channel   # Test specific channel

# Configuration
./lightning_policy.py generate-config # Create sample config

Configuration Options

# gRPC (preferred - 10x faster)
--lnd-grpc-host localhost:10009      # LND gRPC endpoint
--prefer-grpc                        # Use gRPC (default)

# REST API (fallback)  
--lnd-rest-url https://localhost:8080 # LND REST endpoint
--prefer-rest                         # Force REST API

# Authentication
--lnd-dir ~/.lnd                     # LND directory
--macaroon-path admin.macaroon       # Macaroon file

Testing

# Run tests
python -m pytest test_optimizer.py

# Test with your configuration
./lightning_policy.py -c your_config.conf apply --dry-run

# Test specific channel
./lightning_policy.py -c your_config.conf test-channel CHANNEL_ID
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