PhantomArbiter
Solana DEX arbitrage engine with multi-strategy detection and MEV protection
The Problem
Cryptocurrency arbitrage opportunities exist across Solana's decentralized exchanges (Jupiter, Raydium, Orca, Meteora), but MEV (Maximal Extractable Value) bots front-run profitable trades, capturing the arbitrage before it can be executed. Detection must happen in milliseconds across multiple DEX prices.
My Approach
- → Real-time WebSocket feeds aggregating price data from 4+ DEX liquidity pools
- → Multi-strategy detection engine identifying cross-DEX, triangle, and statistical arbitrage
- → JITO bundle protection to atomically execute trades against MEV front-running
- → Python asyncio for high-throughput data processing
- → TypeScript DEX bridge implementations for Jupiter, Raydium, Orca, Meteora
- → Rust bindings for performance-critical path calculations
Key Highlights
- 26+ commits (actively maintained, updated today)
- Real-time Solana blockchain data aggregation
- Multi-DEX strategy detection (cross, triangle, statistical arbitrage)
- Production-ready FastAPI backend with comprehensive docs
- Runs in paper-trading, monitoring, and live execution modes
How It Works
PhantomArbiter is my most actively-developed project—a real-time trading system operating on Solana’s blockchain at the millisecond scale required to compete with MEV bots.
The Problem Domain
On Solana, liquidity is fragmented across 4+ major decentralized exchanges. When prices diverge, arbitrage opportunities arise—buy low on one DEX, sell high on another. But MEV bots watch the mempool, see your transaction, insert their own before it, and steal the profit.
PhantomArbiter solves this by:
- Detecting opportunities in milliseconds
- Using JITO bundles to atomically execute (front-running proof)
- Supporting multiple strategies (not just one-off opportunities)
Architecture
Price Aggregation Layer: WebSocket connections to each DEX’s liquidity pool contracts. Updates stream in real-time; latency matters.
Strategy Engine: Analyzes price vectors across pools. Detects:
- Cross arbitrage: X → Y → Z at profit on single chain
- Triangle arbitrage: ABC cycles with profit loop
- Statistical: Mean reversion trades on stablecoin pairs
Execution Layer: Three modes:
- Paper: Simulate trades without capital (learn the system)
- Monitor: Detect opportunities but don’t execute (audit mode)
- Live: Actually execute trades via JITO bundles
Tech Stack Breakdown
Python 3.12+: Async processing, real-time market data, strategy logic. Uses asyncio for concurrent pool monitoring.
TypeScript: DEX integrations (Jupiter, Raydium, Orca, Meteora). Each bridge implements the same interface, allowing hot-swapping strategies.
Rust: Performance-critical path calculations (price vectors, profit thresholds). Compiled to WebAssembly for browser compatibility (future).
FastAPI: Production API with comprehensive OpenAPI docs. Exposes monitoring endpoints for real-time opportunity tracking.
Why This Matters
This project demonstrates ability to:
- Work with real financial systems (stakes are real)
- Operate at trading bot performance scales (milliseconds)
- Integrate multiple APIs and protocols coherently
- Maintain code quality while moving fast
- Understand blockchain and DeFi ecosystem deeply
Lessons Learned
The $4 loss on 400 trades wasn’t due to strategy flaws—it was execution. RPC latency and network conditions matter more than algorithm sophistication. With proper infrastructure (dedicated validator access, bundle relay redundancy), this system would be profitable. The technical foundation is solid; the economic barriers are practical.
This is production trading infrastructure. Not a toy.