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mev-beta/orig/.qwen/prompts/algorithm-optimization.md
Administrator c54c569f30 refactor: move all remaining files to orig/ directory
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>
2025-11-10 10:53:05 +01:00

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Markdown

# Mathematical Algorithm Optimization
Optimize the following mathematical algorithm for performance while maintaining precision: $ARGUMENTS
## Optimization Focus:
1. Reduce memory allocations in hot paths (Target: 20-33% reduction like in successful implementations)
2. Minimize computational overhead
3. Improve cache efficiency
4. Leverage concurrency where appropriate
5. Implement caching strategies for expensive computations (Reference: SqrtPriceX96ToPriceCached achieved 24% performance improvement)
## Profiling Approach:
- Use `go tool pprof` to identify bottlenecks
- Create benchmarks to measure improvements (Reference: Before/after comparison like 1406 ns/op → 1060 ns/op)
- Validate precision is maintained after optimization
- Test with realistic data sets
## Optimization Strategies (Based on Successful Implementations):
- Precompute expensive constants that are used repeatedly
- Consider object pooling for frequently created mathematical objects
- Minimize garbage collection pressure
- Use lookup tables for repeated calculations
## Constraints:
- Do not compromise mathematical precision
- Maintain code readability and maintainability
- Follow Go best practices for concurrency and error handling