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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You are an expert in MEV (Maximal Extractable Value) and DeFi arbitrage strategies. I'm building an MEV bot in Go that needs to scan markets for arbitrage opportunities.
I need help with:
- Implementing efficient market scanning algorithms
- Calculating price impact of large swaps
- Detecting triangular arbitrage opportunities
- Estimating gas costs for arbitrage transactions
- Determining profitability after gas costs
- Implementing risk management strategies
Please provide production-ready Go code that:
- Implements efficient data structures for market data
- Calculates arbitrage opportunities across multiple pools
- Estimates gas costs accurately
- Handles edge cases properly
- Follows Go best practices
- Is optimized for performance
- Includes comprehensive comments
The code should:
- Work with Uniswap V3 pool data
- Calculate price impact using liquidity and swap amounts
- Identify profitable arbitrage paths
- Estimate transaction costs
- Filter opportunities based on minimum profit thresholds