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>
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Mathematical Algorithm Optimization
Optimize the following mathematical algorithm for performance while maintaining precision: $ARGUMENTS
Optimization Focus:
- Reduce memory allocations in hot paths (Target: 20-33% reduction like in successful implementations)
- Minimize computational overhead
- Improve cache efficiency
- Leverage concurrency where appropriate
- Implement caching strategies for expensive computations (Reference: SqrtPriceX96ToPriceCached achieved 24% performance improvement)
Profiling Approach:
- Use
go tool pprofto 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