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>

orig/.qwen/prompts/algorithm-optimization.md

@@ -0,0 +1,27 @@
+# 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