Algorithmic trading

The field of automated/algorithmic trading has been developing rapidly. All companies with trading departments are slowly but steadily moving to automated algorithmic trading. This transformation requires knowledge of previously irrelevant subjects like: market microstructure, communication protocols (FIX, ITCH, OUCH), trading strategies and many more.

Specific area’s of expertise include:

  • System and architecture design of an automated trading system

  • Implementation of an automated execution system

  • Design and implementation of

      • Risk management specifically for automated trading

      • Complex trading strategies

      • A framework for historical and operational backtesting

      • Emulators for trading related components

  • Consultancy with the goal to

      • Explain the basics of trading strategies; stock pricing, options pricing, spread trading, market making, arbitrage and statistical arbitrage

      • Spread knowledge on the design, development and operational aspects of automated trading systems

      • Troubleshoot errors in automated trading systems

      • Analyze logs and other information to identify bugs and other shortcomings

Rather than developing everything from scratch, we consider literature in this field to lay the foundations on which to build.

Generative Models

Mathematical Simulations, Generative Machine Learning

Generative Models achieve the opposite of Calculation Models. A generative model can create complex objects and data from a small dimensional input. Recently, we observe a surge in the development and application of generative models.

Some simple general examples:

  • Using a starting price and volatility to generate a stock price movement

  • Generate/predict the movements of crowds, by modelling individuals as particles that moving according to a spatial swarming model

  • A machine learning (GAN) model which generates ultra-realistic pictures

Calculation Models

Mathematical Modelling, Data Science, Machine Learning

Calculation models can be used to solve complex problems. Typically these models use input data for calculation, estimation or classification purposes.

Often a calculation model must be developed for a specific use-case. Depending on the use case we will ensure to apply the most suitable methodology:

  • Mathematical Modelling - White box model with limited amounts of data

  • Data Science - White box model with large amounts of data

  • Machine Learning - Black/Grey box model with large amounts of data

Software Engineering

We see software engineering as a way to empower organisations to optimize their core business in a digital world. We can design, develop and maintain software systems.

Even though it’s a challenge, we always aim to deliver software of high quality. We achieve this by proper design, implementation according to recognized industry standards, automated testing and thorough documentation.