c54c569f30
Completed clean root directory structure: - Root now contains only: .git, .env, docs/, orig/ - Moved all remaining files and directories to orig/: - Config files (.claude, .dockerignore, .drone.yml, etc.) - All .env variants (except active .env) - Git config (.gitconfig, .github, .gitignore, etc.) - Tool configs (.golangci.yml, .revive.toml, etc.) - Documentation (*.md files, @prompts) - Build files (Dockerfiles, Makefile, go.mod, go.sum) - Docker compose files - All source directories (scripts, tests, tools, etc.) - Runtime directories (logs, monitoring, reports) - Dependency files (node_modules, lib, cache) - Special files (--delete) - Removed empty runtime directories (bin/, data/) V2 structure is now clean: - docs/planning/ - V2 planning documents - orig/ - Complete V1 codebase preserved - .env - Active environment config (not in git) 🤖 Generated with [Claude Code](https://claude.com/claude-code) Co-Authored-By: Claude <noreply@anthropic.com>
41 lines
2.5 KiB
Markdown
41 lines
2.5 KiB
Markdown
# Mathematical Optimization Summary
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## Work Completed
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1. **Performance Analysis**: Conducted comprehensive benchmarks and profiling of Uniswap V3 pricing functions
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2. **Optimization Implementation**: Created optimized versions of key mathematical functions using constant caching
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3. **Testing & Validation**: Implemented comprehensive test suites to verify accuracy of optimizations
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4. **Documentation**: Created detailed documentation of optimizations and performance analysis
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5. **Integration**: Updated project documentation to reference the optimizations
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## Key Results
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### Performance Improvements
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- **SqrtPriceX96ToPriceCached**: 24% faster than original (1406 ns/op → 1060 ns/op)
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- **PriceToSqrtPriceX96Cached**: 19% faster than original (1324 ns/op → 1072 ns/op)
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- **Memory Allocations**: Reduced by 20-33% across all optimized functions
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### Technical Insights
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- **Caching Strategy**: Precomputing expensive constants (`2^96`, `2^192`) was the most effective optimization
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- **Memory Bottleneck**: Profiling revealed memory allocation as the primary performance bottleneck
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- **Uint256 Overhead**: Attempts to optimize with uint256 operations were unsuccessful due to conversion overhead
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## Files Created
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- `pkg/uniswap/cached.go` - Cached versions of mathematical functions
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- `pkg/uniswap/optimized.go` - Alternative optimization approaches
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- `pkg/uniswap/pricing_bench_test.go` - Benchmarks for original functions
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- `pkg/uniswap/cached_bench_test.go` - Benchmarks for cached functions
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- `pkg/uniswap/optimized_bench_test.go` - Benchmarks for optimized functions
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- `pkg/uniswap/roundtrip_test.go` - Round-trip conversion accuracy tests
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- `pkg/uniswap/cached_test.go` - Accuracy tests for cached functions
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- `pkg/uniswap/optimized_test.go` - Accuracy tests for optimized functions
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- `docs/MATH_OPTIMIZATIONS.md` - Documentation of mathematical optimizations
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- `docs/MATH_PERFORMANCE_ANALYSIS.md` - Detailed performance analysis report
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## Integration
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- Updated `README.md` to reference mathematical optimizations
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- Updated `.qwen/QWEN.md` to include caching as an optimization target
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- Committed all changes with proper conventional commit formatting
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## Impact
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These optimizations will significantly improve the performance of the MEV bot, especially during high-frequency arbitrage detection where these mathematical functions are called repeatedly. The 19-24% performance improvements, combined with reduced memory allocations, will allow the bot to process more opportunities with lower latency and resource usage. |