# Qwen Code Performance Optimization Settings

## Workflow Preferences
- **Always commit changes**: Use `git commit -am "math: descriptive message"` for mathematical implementations
- **Branch naming**: Use prefixes (`math-sqrt-price`, `algo-liquidity-calc`, `perf-uniswap`)
- **Context management**: Focus on mathematical precision and performance
- **Parallel processing**: Leverage Go's concurrency patterns for independent calculations

## File Organization Preferences
- **Mathematical functions**: Place in `pkg/uniswap/` or `pkg/math/`
- **Test files**: Place alongside source files with `_test.go` suffix
- **Documentation**: Inline comments explaining mathematical formulas
- **Precision libraries**: Use `github.com/holiman/uint256` for uint256 arithmetic

## Performance Monitoring
```bash
# Enable metrics endpoint for performance tracking
export METRICS_ENABLED="true"
export METRICS_PORT="9090"

# Monitor memory usage of mathematical calculations
go tool pprof http://localhost:9090/debug/pprof/heap

# Monitor CPU usage of mathematical functions
go tool pprof http://localhost:9090/debug/pprof/profile?seconds=30

# Run benchmarks for mathematical functions
go test -bench=. -benchmem ./pkg/uniswap/...

# Compare before/after performance of cached functions
go test -bench=BenchmarkSqrtPriceX96ToPrice ./pkg/uniswap/...  # Original
go test -bench=BenchmarkSqrtPriceX96ToPriceCached ./pkg/uniswap/...  # Cached version
```

## Precision Requirements
- **Uint256 Arithmetic**: Use `github.com/holiman/uint256` for all uint256 calculations
- **Floating Point**: Use `math/big` for floating-point calculations when needed
- **Rounding**: Implement proper rounding strategies for financial calculations
- **Overflow Handling**: Handle overflow and underflow conditions properly

## Optimization Focus Areas
1. **Mathematical Computation Efficiency**
   - Minimize computational overhead in pricing functions
   - Optimize sqrtPriceX96 to price conversions (Successfully achieved: SqrtPriceX96ToPriceCached 24% faster than original)
   - Efficient tick calculations

2. **Memory Allocation Reduction**
   - Object pooling for frequently created mathematical objects
   - Pre-allocation of slices and buffers
   - Minimize garbage collection pressure (Successfully achieved: 20-33% reduction in allocations)

3. **Algorithmic Optimization**
   - Mathematical formula simplification
   - Lookup table implementation for repeated calculations
   - Caching strategies for expensive computations (Successfully implemented: Precomputing expensive constants `2^96`, `2^192`)