Project Milestone Complete: Infrastructure Ready for Arbitrage Scanner, Planner, and Executor

TL;DR

• Infrastructure Phase Complete: 85% of infrastructure built and production-ready with full observability stack
• Quote Aggregator Service Validated: gRPC streaming achieves 94/100 production readiness score with 90.3% local quote win rate and sub-100ms latency
• Financial Feasibility: 70-75% probability of achieving $1k/month target within 3-4 months
• Next Phase: Arbitrage scanner/planner/executor in TypeScript, serving as prototypes for future high-performance Rust implementation

Table of Contents

1. Milestone Achievement
2. Quote Aggregator Service Assessment
3. Project Status Analysis
4. TypeScript as Prototype Strategy
5. Next Phase: Trading Logic
6. Conclusion

Milestone Achievement

After months of development, the Solana HFT Trading System has reached a critical milestone: infrastructure is production-ready.

[Phase 1: Infrastructure]     █████████████░░░ 85%
[Phase 2: Quote Engine]       █████████████░░░ 85%
[Phase 3: Trading Logic]      ████░░░░░░░░░░░░ 25%
[Phase 4: Optimization]       ██░░░░░░░░░░░░░░ 10%
[OVERALL]                     █████████░░░░░░░ 55%

What’s Built and Working

Go Codebase (~62,000 lines of production code across 206 files):

Full Breakdown:

Infrastructure (21 Docker services):

• Event Streaming: NATS JetStream with 6 configured streams
• Observability: Grafana LGTM+ stack (Prometheus, Loki, Tempo, Mimir, Pyroscope)
• Database: PostgreSQL + TimescaleDB
• Caching: Redis with pub/sub
• Networking: Traefik reverse proxy

Quote Aggregator Service Assessment

The Quote Aggregator Service has been thoroughly tested and optimized, achieving a 94/100 production readiness score.

Performance Summary

Reliability Metrics

Token Pair Readiness

The SOL/USDC pairs are now production-ready with 100% oracle alignment, representing the primary trading pairs for initial arbitrage operations.

Confidence Scoring System

The aggregator uses a multi-factor confidence scoring algorithm:

confidenceScore = poolAgeFactor
                × routeFactor
                × oracleFactor
                × providerFactor
                × slippageFactor
                × providerUptime

Winner selection prioritizes outputAmount × confidenceScore, ensuring both price quality and execution reliability.

Project Status Analysis

A comprehensive feasibility assessment has been completed, incorporating:

• 5 years of Solana development experience
• 3 working prototype systems
• Measured 2-10ms local quote performance (10-30x faster than Jupiter API)
• Industry state-of-the-art analysis

Feasibility Assessment

Probability Distribution

Industry Position

INSTITUTIONAL ═══════════════════════════════════════════ $10k-100k+/month
                                                            (Unreachable)
PROFESSIONAL  ═══════════════════════════════════════════ $5k-20k/month
                                                            (Possible with upgrades)
SEMI-PRO      ═══════════════════════[YOU ARE HERE]══════ $1k-5k/month
                                      (Current target)
RETAIL ADV    ═══════════════════════════════════════════ $200-1k/month
                                                            (Fallback position)

The system sits in the Semi-Professional tier, competitive in LST arbitrage and long-tail niches that institutional players ignore due to:

1. Lower absolute dollar returns (not worth their overhead)
2. Smaller position sizes (can’t deploy $10M)
3. Niche market knowledge required

TypeScript as Prototype Strategy

The next phase will implement the arbitrage scanner, planner, and executor in TypeScript. This is a deliberate strategic decision.

Why TypeScript First

1. Rapid Iteration

• Faster development cycles for algorithm refinement
• Easy debugging and testing of trading logic
• Familiar ecosystem for financial calculations

2. Prototype Validation

• Test strategies with real market data before optimization
• Validate arbitrage detection algorithms
• Prove profitability before committing to Rust rewrite

3. Risk Reduction

• Lower development cost for unproven strategies
• Easier to modify and experiment
• Quick feedback loop for strategy tuning

The Path to Rust

Once TypeScript prototypes prove profitable:

┌─────────────────────┐
│   TypeScript MVP    │  ← Current focus
│   (3-4 months)      │
│   • Scanner         │
│   • Planner         │
│   • Executor        │
│   • Validate $1k/mo │
└─────────┬───────────┘
          │
          ▼ Profitable?
┌─────────────────────┐
│   Rust Rewrite      │  ← Future optimization
│   (2-3 months)      │
│   • Sub-10ms latency│
│   • Zero-copy events│
│   • Hot path only   │
└─────────────────────┘

The existing Rust infrastructure (RPC proxy, observability) remains in place. Only the performance-critical hot path will be rewritten once strategies are validated.

Next Phase: Trading Logic

Critical Path Items

Scanner Service

The scanner service will:

• Consume market.swap_route.* events from Quote Aggregator
• Detect arbitrage opportunities (price spreads > threshold)
• Publish opportunity.arbitrage events to NATS

Key Algorithm:

// Simplified arbitrage detection
function detectArbitrage(localQuote: Quote, externalQuote: Quote): boolean {
  const spread = Math.abs(localQuote.price - externalQuote.price) / localQuote.price;
  const fees = estimateFees(localQuote, externalQuote);
  return spread > fees + MIN_PROFIT_THRESHOLD;
}

Planner Service

The planner service will:

• Consume opportunity.* events from Scanner
• Validate opportunities (liquidity, slippage, timing)
• Build execution plans with risk parameters
• Publish execution.plan.* events to NATS

Validation Checklist:

• Pool liquidity sufficient for trade size
• Slippage within acceptable bounds
• Opportunity still valid (not stale)
• Risk limits not exceeded

Executor Service

The executor service will:

• Consume execution.plan.* events from Planner
• Build and simulate transactions
• Submit via Jito bundles for MEV protection
• Track execution results

Execution Flow:

Plan → Simulate → Build Transaction → Submit Bundle → Confirm → Report

Risk Management (Critical)

Essential Controls:

• Position Limits: Max trade size based on capital
• Daily Loss Cap: Stop trading after X% daily loss
• Kill Switch: Immediate stop on anomaly detection
• Exposure Tracking: Real-time capital at risk monitoring

Conclusion

This milestone represents a significant achievement: production-grade infrastructure ready to support trading operations. The foundation is solid:

• Quote Engine: 10 DEX protocols, <100ms latency, 90%+ accuracy
• Event Backbone: NATS JetStream with 6 streams, FlatBuffers serialization
• Observability: Full Grafana LGTM+ stack, 31+ metrics, comprehensive dashboards
• Integration: gRPC streaming validated with TypeScript client

The shift from infrastructure to trading logic is now appropriate. With 85% of infrastructure complete, 80% of effort should focus on the money-making components:

1. Month 1: Implement scanner/planner/executor in TypeScript
2. Month 2: Paper trading validation and risk management
3. Month 3: Small capital testing ($100-200)
4. Month 4: Scale to $1k/month target

The TypeScript implementation serves as a prototype, allowing rapid iteration on trading strategies. Once profitable, performance-critical paths can be optimized in Rust.

Bottom Line: The infrastructure investment has paid off. Now it’s time to build the engine.

Related Posts

• Quote Services Evolution: Local Enhancements, External Integration, and Aggregator Architecture - Quote service ecosystem details
• Pool Discovery Refactored: Bug Fixes and Comprehensive Testing - Pool discovery foundation
• Architecture Deep Dive: Building a Sub-500ms Solana HFT System - System architecture overview

Technical Documentation

• Project Status and Feasibility Analysis
• Quote Aggregator Service Assessment
• Industry State-of-the-Art Analysis

Connect

• GitHub: guidebee/solana-trading-system
• LinkedIn: James Shen

This is post #23 in the Solana Trading System development series. With infrastructure reaching 85% completion and the Quote Aggregator Service validated for production use, the project is ready to shift focus to the trading logic phase. The arbitrage scanner, planner, and executor will be developed in TypeScript as prototypes, validating strategies before potential Rust optimization for maximum performance.