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>
84 lines
2.1 KiB
Markdown
84 lines
2.1 KiB
Markdown
# Optimize Performance
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Optimize the performance of the MEV bot in the following area: $ARGUMENTS
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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
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go tool pprof http://localhost:9090/debug/pprof/profile?seconds=30
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# Memory profiling
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go tool pprof http://localhost:9090/debug/pprof/heap
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# Trace analysis
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go tool trace trace.out
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# Benchmark testing
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go test -bench=. -benchmem ./...
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```
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### 2. **Optimization Areas**
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#### **Concurrency Optimization**
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- Worker pool sizing and configuration
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- Channel buffer optimization
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- Goroutine pooling and reuse
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- Lock contention reduction
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- Context cancellation patterns
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#### **Memory Optimization**
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- Object pooling for frequent allocations
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- Buffer reuse patterns
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- Garbage collection tuning
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- Memory leak prevention
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- Slice and map pre-allocation
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#### **I/O Optimization**
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- Connection pooling for RPC calls
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- Request batching and pipelining
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- Caching frequently accessed data
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- Asynchronous processing patterns
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- Rate limiting optimization
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#### **Algorithm Optimization**
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- Uniswap math calculation efficiency
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- Event parsing performance
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- Data structure selection
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- Caching strategies
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- Indexing improvements
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### 3. **MEV Bot Specific Optimizations**
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#### **Transaction Processing Pipeline**
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- Parallel transaction processing
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- Event filtering optimization
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- Batch processing strategies
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- Pipeline stage optimization
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#### **Market Analysis**
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- Price calculation caching
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- Pool data caching
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- Historical data indexing
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- Real-time processing optimization
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#### **Arbitrage Detection**
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- Opportunity scanning efficiency
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- Profit calculation optimization
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- Market impact analysis speed
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- Cross-DEX comparison performance
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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 code readability and maintainability
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- Add performance tests for regressions
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- Document performance characteristics
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## Deliverables:
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- Performance benchmark results (before/after)
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- Optimized code with maintained functionality
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- Performance monitoring enhancements
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- Optimization documentation
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- Regression test suite |