Cloud computing platforms have emerged as the foundational paradigm for modern IT infrastructure. With their robust global deployment capabilities and centralized operations management, enterprises can confidently pursue worldwide business expansion while substantially reducing operational costs and achieving streamlined infrastructure management. Aligned with this transformative trend, quant trading firms as well as private equity firms are increasingly migrating their infrastructure to cloud platforms.
This repo consolidates best practices from cloud-native quantitative trading institutions, focusing on building research environments and data processing systems on the AWS platform. It aims to provide valuable references and actionable guidance for industry peers navigating their own cloud transformation journeys.
Key Components:
- Quant Trading serves as the primary actor initiating trading activities using algorithmic and quantitative strategies.
- Market Data Provider supplies real-time and historical market data to multiple participants through both direct feeds to quant trading firms and distribution channels to broker-dealers.
- Broker-Dealer acts as the central intermediary hub, receiving orders from quant trading firms and routing them to exchanges while maintaining connections to market surveillance systems.
- Exchange is the marketplace where actual trade execution occurs, receiving orders from broker-dealers and routing completed trades to settlement services.
- Custody, Clearing & Settlement handles all post-trade processes including asset custody, trade clearing, and final settlement of transactions.
- Market Surveillance monitors market activity for regulatory compliance, market manipulation detection, and trading pattern analysis, overseeing both quant trading activities and broker-dealer operations.
Workflow:
Transaction Flow
- Quant Trading → Broker-Dealer: Quantitative trading firms generate and submit buy/sell orders and trading instructions to their broker-dealers. This represents the core transactional order flow.
- Broker-Dealer → Exchange: Broker-dealer route client orders to exchanges for matching and execution in the public marketplace. This is the primary execution pathway where trades are actually executed.
- Exchange → Custody, Clearing & Settlement: Once trades are executed on the exchange, the trade details flow to custody, clearing, and settlement services for finalization.
- Broker-Dealer → Custody, Clearing & Settlement: Some trades (particularly OTC transactions) may settle directly through broker relationships, providing an alternative settlement pathway.
- Exchange → Market Surveillance: Trade execution data sent directly to surveillance systems for monitoring
Information Flow
- Market Data Provider → Quant Trading: Market data providers supply real-time prices, historical data, and market information to quant trading firms for algorithmic analysis and strategy development.
- Market Surveillance → Quant Trading: Market surveillance monitors quant trading patterns for potential manipulation, spoofing, or regulatory violations, providing oversight and compliance alerts.
Phase 1: Data Accessing & Pre-processing
The foundation of the system handles data acquisition and preparation:
- Data Accessing: Collects raw market data from exchanges, market data providers, and alternative data sources
- Data Pre-processing: Cleans, normalizes, and transforms data into usable formats, handling missing values, outliers, and standardization
This phase ensures high-quality, consistent data flows into the research environment.
Phase 2: Research
The core analytical phase where quantitative strategies are developed and validated:
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Data Exploration & Analysis: Initial statistical analysis, pattern discovery, feature engineering, and correlation studies to understand market dynamics.
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Data Modeling (the highlighted central component containing four specialized models):
- Alpha Modeling: Develops predictive signals that forecast asset returns and identify trading opportunities, representing excess returns above market benchmarks
- Risk Modeling: Quantifies portfolio risk through volatility estimation, Value-at-Risk (VaR), stress testing, and factor risk decomposition
- Transaction Cost Modeling: Estimates trading costs including market impact, bid-ask spreads, slippage, and commissions to ensure realistic profitability
- Portfolio Construction Modeling: Optimizes asset allocation by combining alpha signals while managing risk constraints, position limits, and capital allocation
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Back-testing: Validates strategies through historical simulation, evaluating performance metrics (Sharpe ratio, drawdown, turnover) and testing model robustness.
Phase 3: Model Serving & Execution
The production phase that operationalizes research models into live trading:
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A, R, T, P (four specialized execution modules): These mirror the research modeling components, likely representing real-time Alpha signal processing, Risk management monitoring, Transaction cost analysis, and Portfolio optimization in production.
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OMS, EMS (Order Management System & Execution Management System): The OMS manages order lifecycle, compliance checks, and position tracking, while the EMS handles actual order execution, smart order routing, and algorithmic execution strategies.
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Custody, Clearing & Settlement: Manages post-trade processing, asset custody, trade clearing, final settlement, and reconciliation with broker-dealers and exchanges.
🔗For detailed analysis of Quant Trading's Research Workload
- Inside the Black Box: A Quant Trading Research Architecture Analysis
- Technical Anaylsis of Quant Trading Research Workload
- Quant Alpha Modeling Strategy Review
This repo discusses Data Accessing and Research Modeling as well as how to achieve low latency on AWS
🌟 Data Accessing
- 🎯 CME MDP Multicast Data Access
- 🎯 Cross Region, Cross Account Large Historical Data Sync from Data Vendor
🌟 Research Modeling
🌟 Low Latency
See CONTRIBUTING for more information.
This library is licensed under the MIT-0 License. See the LICENSE file.
built with ❤️ by Shiyang Wei, Sr. Solutions Architect, AWS
