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>
76 lines
2.8 KiB
TOML
76 lines
2.8 KiB
TOML
name = "optimize-math"
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description = "Optimize Mathematical Performance - Optimize the performance of mathematical functions in the MEV bot"
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category = "optimization"
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parameters = [
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{ name = "function", type = "string", description = "The mathematical function to optimize" }
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]
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[command]
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shell = '''
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echo "# Optimize Mathematical Performance
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Optimize the performance of the following mathematical function in the MEV bot: ${function}
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## Performance Optimization Strategy:
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### 1. **Profiling and Measurement**
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```bash
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# CPU profiling for mathematical functions
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go tool pprof http://localhost:9090/debug/pprof/profile?seconds=30
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# Memory profiling for mathematical calculations
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go tool pprof http://localhost:9090/debug/pprof/heap
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# Benchmark testing for mathematical functions
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go test -bench=. -benchmem ./pkg/uniswap/...
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```
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### 2. **Optimization Areas**
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#### **Precision Handling Optimization**
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- Uint256 arithmetic optimization
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- Object pooling for frequent calculations
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- Minimize memory allocations in hot paths (Target: 20-33% reduction like in successful implementations)
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- Efficient conversion between data types
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#### **Algorithm Optimization**
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- Mathematical formula simplification
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- Lookup table implementation for repeated calculations
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- Caching strategies for expensive computations (Reference: SqrtPriceX96ToPriceCached achieved 24% performance improvement)
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- Parallel processing opportunities
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#### **Memory Optimization**
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- Pre-allocation of slices and buffers
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- Object pooling for mathematical objects
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- Minimize garbage collection pressure (Target: 20-33% reduction like in successful implementations)
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- Efficient data structure selection
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### 3. **MEV Bot Specific Optimizations**
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#### **Uniswap V3 Pricing Functions**
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- sqrtPriceX96 to price conversion optimization
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- Tick calculation performance improvements
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- Liquidity-based calculation efficiency
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- Price impact computation optimization
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#### **Arbitrage Calculations**
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- Profit calculation optimization
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- Cross-pool comparison performance
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- Gas estimation accuracy and speed
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- Multi-hop arbitrage efficiency
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## Implementation Guidelines:
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- Measure before optimizing (baseline metrics)
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- Focus on bottlenecks identified through profiling
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- Maintain mathematical precision while improving performance
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- Add performance tests for regressions
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- Document optimization strategies and results
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- Consider caching strategies for expensive computations (Reference: Precomputing expensive constants like `2^96`, `2^192` achieved 19-24% performance improvements)
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## Deliverables:
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- Performance benchmark results (before/after) - Reference: SqrtPriceX96ToPriceCached: 1406 ns/op → 1060 ns/op (24% faster)
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- Optimized code with maintained precision
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- Performance monitoring enhancements
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- Optimization documentation
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- Regression test suite"
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''' |