Technology Stack in Quantitative Trading

Systematic trading is a technology-driven business. The tools, infrastructure, and data pipelines determine how quickly ideas can be tested, how reliably strategies can be deployed, and how efficiently capital can be scaled. This section describes how modern trading technology works in general and how it works at QSG.

Many firms rely on internal software because off-the-shelf tools cannot handle the combination of research, simulation, portfolio management, execution, and real-time risk that systematic trading requires. Internal platforms allow firms to integrate data, models, execution systems, and monitoring into a single environment.

At QSG, we built our own software stack for traders. The original goal was to bring technologists and traders together by giving traders a platform they could use to automate ideas without needing to become software engineers. That goal still exists, but the platform has evolved to support both intuitive use and highly complex execution.

The result is a system that handles:

• research and simulation
• model development
• backtesting with real data and real assumptions
• production deployment
• real-time monitoring and risk
   

This combination allows us to move from research to production quickly with confidence in the underlying systems and data. It also allows us to back traders and strategies faster because the infrastructure is standardized and predictable.

The traditional gap between "research" and "engineering" slows down many firms. We designed the system to reduce that gap. Parts of the stack are simple enough for traders with no coding background to use, while the underlying framework supports complex strategies and execution logic for those who need more control.

We also maintain a full development team focused on the evolution of our tech stack. 

Execution quality is critical for systematic firms. Many strategies require low-latency execution, precise order handling, and robust connectivity. We have infrastructure in multiple colocation facilities so that we can route orders efficiently and interact with exchanges and brokers without unnecessary delay.

Execution systems involve:

• smart order routing
• order types and venues
• exchange-specific protocols
• risk checks and throttles
• failover and redundancy
• monitoring and alerts
   

These systems require both software and hardware engineering, as well as a deep understanding of market microstructure.

Data is the raw material of quantitative research. Firms use a mix of traditional market data and alternative data depending on the strategies they pursue.

Traditional financial data includes:

• trades and quotes (TAQ)
• order book data
• exchange feeds
• end-of-day pricing and corporate actions
• reference data and security master data
• index and ETF data
• futures and options data
• macroeconomic releases
• earnings reports and filings
   

Alternative data can include:

• web scraping
• credit card transaction data
• consumer behavior
• satellite and geospatial data
• NLP on filings and transcripts
• news sentiment
• supply chain and shipping data
• ESG and climate data
• job postings and hiring trends
   

We work with a variety of data vendors across traditional and alternative categories. The important point is that the stack must integrate, clean, normalize, and align these datasets so they can be used reliably in research and production.

Collecting data is not enough. It must be made usable. Data engineering includes:

• ingestion and scheduling
• schema design
• cleaning and standardization
• corporate action adjustments
• timestamp alignment
• handling missing fields or anomalies
• validating vendor changes and revisions
   

Simulation is a separate layer that models how strategies would have performed historically. This is not just running a backtest. It involves:

• using realistic assumptions about slippage and impact
• modeling order book conditions
• simulating latency and fill probabilities
• accounting for liquidity and fragmentation
• incorporating exchange-specific behavior
   

Good simulation gives traders information they can trust. Poor simulation creates unrealistic expectations that fail in live trading.

Technology does not generate alpha directly. It enables traders and researchers to explore ideas, test hypotheses, and deploy strategies in real markets. The best trading environments are the ones where:

• research and production are unified
• ideas can move quickly without breaking
• data is reliable and auditable
• execution is robust and observable
• failures can be diagnosed and fixed
• traders and technologists collaborate
   

The QSG platform was built with these principles in mind. It continues to evolve as strategies evolve, markets evolve, and new opportunities appear.