Originally launched as a personal project to explore advancements in the latest technologies, the system later merged with my CQF final project on pairs trading, evolving into a fully-functional algorithmic trading solution.

Environment

• A set of statistical arbitrage algorithms based on daily trading of contracts for difference (CFDs) on the UK equities was developed.
• The main technology used is .Net 8. Real-time market data is collected by isolated, self-sufficient components and distributed across the system via RabbitMQ. It is consumed directly by trading algorithms as well as storage components that keep the latest snapshot in Redis and full historical trace in protobuf-encoded binary vault. Static data is stored in PostgreSQL database. Positions and order management are directly accessible from .Net-based strategy execution code that natively supports backtesting.
• Initial pair selection and analysis is implemented in C++ 23.
• Deep analytics and research, scripting and configuration is done in Python 3.12.
• Logging and monitoring is based on RabbitMQ and Elastic Stack.
• The system is deployed in a self-managed Kubernetes cluster.

Achievements

• Built an end-to-end algorithmic trading system from scratch. While I had previously contributed to various components of similar systems, this was my first experience leading the quantitative analysis and development from start to finish.
• Cross-asset design. Multiple asset classes are supported from the outset.
• Performance improvements. By rewriting some Python code in C++ and utilising multithreading I managed to reduce calculation time on initial pair selection and analysis stage from two days to just five minutes.

Contributed to the Fundamental Review of the Trading Book (FRTB) Internal Model Approach (IMA) project within the Regulatory Risk department. My primary responsibility was to ensure the accurate and timely delivery of Profit and Loss Attribution (PLA) reports, covering the bank's entire portfolio. Collaborated closely with quantitative analysts and the risk department to meet regulatory standards.

Environment

• Multi-asset risk engine covering all products traded across the bank's desks for Value at Risk (VaR) and Expected Shortfall (ES) calculations.
• C#, .Net Framework on Windows, and .Net Core on Linux as the main technology of the system and the underlying quant library.
• C++ for computation-intensive operations and interest rate models calibration.
• Python for scripting, initial data analysis and results reconciliation.

Achievements

• Enhanced PLA Reporting. Given PLA reports with the very basic functionality, I extended them to get close to regulatory requirements. I managed to make the calculation stable and fast, making sure ongoing migration of computation nodes from Windows-based in-house grid to Linux-based Azure cloud does not materially affect results.
• Automated day-to-day tasks. Build a set of tools for development, deployment, support and maintenance, reducing project's overall cost of running.
• Resolved long-standing technical issue. Diagnosed and fixed a problem that was randomly breaking all the risk calculations for about two years. It was a challenging problem with no one on the team able to find the root cause and fix it.

As a personal service IT consultancy company, I have been offering software engineering services to various clients within the financial sector through short-term and mid-term contracts.

Clients

• Crédit Agricole, electronic foreign exchange trading (Jetstream).
• BNP Paribas, commodity derivatives trading (Markets 360).
• Rokos Capital Management.
• Chicago Mercantile Exchange (CME), multi-asset trading platform (LIQUID).
• Royal Bank of Scotland (RBS), foreign exchange options trading.

Environment

I gained exposure to various asset classes, with the primary focus on FX and FX Options. From a technology perspective, my core skill set was centered around .NET development on the server and the GUI side. However, I frequently took on C++, Java and web development responsibilities, as I was often the sole team member with the necessary expertise in those technologies.

Achievements

• Led FX Options Integration. Took ownership and led the integration of an external FX options trading system with Crédit Agricole single-dealer platform. This expanded the product offering to include FX options for private clients in France, with an anticipated positive impact on the bank's profitability.
• Developed Early Termination for FX Forwards. Created an early termination solution for FX Forwards, providing Crédit Agricole clients with enhanced flexibility by allowing early realization of profit or loss from their positions.
• Prototyped a web-based FX Options trading front-end. Created a React-based FX Options trading interface prototype for Crédit Agricole.
• Added Fixed Income Support. Contributed to the transformation of BNP Paribas commodity derivatives platform into a cross-asset platform by adding support for fixed income products.
• Implemented Interest Rate Swaps portfolio optimisation. Utilised a dedicated quant library to develop a portfolio optimisation solution to help CME clients reduce the number of positions they hold with the exchange, lowering their transaction costs.
• Extended FX Options Offering. Expanded the range of exotic FX options available on RBS single-dealer platform, enhancing the bank’s competitive position in the FX Options market.

Contributed to the Investment Banking IT division within the Credit IT department, delivering services to trading desks globally, with a primary focus on London and New York operations.

Environment

Although primarily focused on the credit business, I gained exposure to equity and fixed income products. From a technology standpoint, we worked mainly with .NET, alongside some C++ and Excel/VBA.

Achievements

• Developed an intraday risk and PnL reporting system. It enabled traders to instantly assess their risk profile, eliminating the need to wait until the end of the trading day.
• Extended exotics offering. Expanded exotics trading capabilities by developing a pricing system for asset swaps and equity variance swaps, enabling the bank to capitalize on niche client demands.
• Established an offshore team. Built and led a development team in Moscow.