Vertex Capital: Custom Trading Platform

The Challenge

Vertex Capital Partners had outgrown their cobbled-together trading infrastructure. Their quantitative strategies required real-time data from multiple sources, but their existing system couldn't keep up. Backtesting was painfully slow, execution latency was costing alpha, and their analysts spent more time fighting tools than finding opportunities.

They needed a unified platform that could handle their current volume and scale with their growth—without the million-dollar price tag of enterprise solutions.

Our Approach

We designed a modular trading platform architecture that separated concerns cleanly:

• Data Layer: Real-time market data aggregation from 12 sources
• Analytics Engine: Custom indicator library with GPU-accelerated backtesting
• Execution Layer: Smart order routing with latency monitoring
• UI Layer: React-based trading interface with customizable workspaces

The key insight was that Vertex didn't need another generic platform—they needed infrastructure that could be extended by their own quants. We built APIs and SDKs that let their team add indicators and strategies without touching core platform code.

Technical Implementation

Data Pipeline

Market data flows through a high-performance ingestion pipeline built on Kafka. Each data source has a dedicated adapter that normalizes data into a common format. The system processes 2 million+ events per second during peak market hours.

// Simplified data flow architecture
interface MarketEvent {
  source: DataSource;
  symbol: string;
  timestamp: number;
  bid: number;
  ask: number;
  volume: number;
}

// Real-time aggregation with sub-millisecond latency
const pipeline = new DataPipeline({
  sources: ['bloomberg', 'refinitiv', 'polygon', 'binance', ...],
  buffer: 'kafka',
  processors: [normalize, dedupe, enrich, route],
  outputs: ['websocket', 'timescaledb', 'redis']
});

Backtesting Engine

The original backtesting system took 45 minutes to test a single strategy across one year of data. Our GPU-accelerated engine does the same in under 5 minutes. Multi-strategy portfolio tests that were previously overnight jobs now complete during lunch.

AI Signal Generation

We integrated machine learning models for pattern recognition and signal generation. The system continuously trains on market data and generates probability-weighted trade signals that complement Vertex's quantitative models.

Results

The new platform transformed Vertex's trading operations:

• Execution Speed: Order-to-fill latency reduced from 340ms to 89ms
• Research Velocity: Backtesting 10x faster, enabling more strategy iteration
• Data Coverage: Unified view across equities, options, crypto, and forex
• Team Efficiency: Analysts spend 70% less time on data wrangling

Six months after launch, Vertex's Sharpe ratio improved by 0.4—directly attributable to faster execution and better signal quality.

Client Testimonial

"We evaluated Bloomberg Terminal, Refinitiv Eikon, and several quant platforms. Nothing fit how we actually work. DolphyTech built exactly what we needed: fast, flexible, and maintainable. Our developers can extend it without calling for help. That's rare."

— Jennifer Walsh, CTO, Vertex Capital Partners