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>
26 lines
1023 B
Markdown
26 lines
1023 B
Markdown
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.
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I need help with:
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1. Implementing efficient market scanning algorithms
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2. Calculating price impact of large swaps
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3. Detecting triangular arbitrage opportunities
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4. Estimating gas costs for arbitrage transactions
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5. Determining profitability after gas costs
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6. Implementing risk management strategies
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Please provide production-ready Go code that:
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- Implements efficient data structures for market data
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- Calculates arbitrage opportunities across multiple pools
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- Estimates gas costs accurately
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- Handles edge cases properly
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- Follows Go best practices
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- Is optimized for performance
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- Includes comprehensive comments
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The code should:
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- Work with Uniswap V3 pool data
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- Calculate price impact using liquidity and swap amounts
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- Identify profitable arbitrage paths
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- Estimate transaction costs
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- Filter opportunities based on minimum profit thresholds |