Files
mev-beta/orig/.qwen/prompts/uniswap-pricing.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

# Uniswap V3 Pricing Function Implementation
Implement the following Uniswap V3 pricing function with high precision: $ARGUMENTS
## Requirements:
1. Use `github.com/holiman/uint256` for all uint256 arithmetic
2. Ensure mathematical precision and numerical stability
3. Handle edge cases and boundary conditions
4. Include comprehensive test coverage
5. Provide performance benchmarks
## Implementation Guidelines:
- Follow the official Uniswap V3 whitepaper specifications
- Implement proper error handling for invalid inputs
- Document mathematical formulas and implementation decisions
- Optimize for performance while maintaining precision
- Consider caching strategies for expensive computations (Reference: SqrtPriceX96ToPriceCached achieved 24% performance improvement)
## Optimization Techniques (Based on Successful Implementations):
- Precompute expensive constants (e.g., `2^96`, `2^192`) to reduce computation time
- Minimize memory allocations in hot paths
- Consider object pooling for frequently created mathematical objects
- Use benchmarks to validate performance improvements