Trading Technologies

Our proprietary trading technology supports the placement of investment capital.

Since our strategies are completely systematic, we have developed most of our investment technologies in-house. This makes it possible to control the quality of execution and meet the requirements of our trading algorithms.

To support our quantitative research process, our quantitative analysts have developed internal automated tools to help our quantitative research team quickly and efficiently identify new sources of alpha in large data sets.

Data retrieval technology (data mining technology)

The first step in deploying a new quantitative investment strategy is data acquisition.

For example, a few years ago, there was a limited amount of data due to the lack of proper methods for storing and processing data. Today, advances in technology and declining storage costs have dramatically increased the amount of data stored by organizations across all industries.

This trend has caused new challenges in the form of storing vast amounts of data, which often can not be effectively analyzed by people-operators. This, in turn, is driving the development of a new field of big data.

To support our team in their search for new sources of alpha testing, we use big data, browsing multiple datasets from different areas. They include data such as traditional historical financial time series data, fundamental company data, macroeconomic sector data, and more complex alternative datasets.

Our quantitative developers are constantly creating new technology tools to support the demands of our data scientists. This contributes to efficient extraction and storage of very large amounts of historical data.

Data cleaning scripts

Popular problems with datasets are that they may have bugs or be unstructured to the requirements of quantitative researchers and analysts, so data needs to be validated and cleaned.

Several years ago, the amount of data for analysis was not so huge and the operator could easily process the information received.

Our technology team has implemented various automated tools to support big data that need to be cleaned up and оchecked for potential errors. These instruments help our quanta to analyze and reduce the appearance of erroneous data that can lead to biased testing on historical data and erroneous conclusions.

Analysis tools

Once the data is cleansed, our quantitative researcher begins to explore the data using the scientific method of finding patterns, formulating hypotheses and testing them within a rigorous research framework.

Our quanta need advanced technological tools to analyze a dataset of millions of records. This allows us to quickly execute complex queries, do statistical analysis of data, visualize patterns, and discover relationships in disparate datasets.

At the analysis stage, a huge number of technologies are used, which can also include advanced statistical analysis methods, machine learning methods, as well as sophisticated visualization and pattern recognition tools.

History testing technology

After discovering new sources of alpha signal, our researchers conduct backtests that span long historical data to increase the likelihood of finding reliable signals.

The testing stage takes a lot of time due to a large amount of historical data on which the strategy can be run. Our quanta require advanced algorithms and sufficient processing power to complete their tests as quickly as possible.

In order to meet the required speeds during the backtesting phase, our development and sysadmin team verifies the availability of rapid test tools and the computational resources needed to run tests quickly.

Disclaimer: These investment products come with a significant risk of loss.

Execution algorithms

After the hypotheses formulated by our researchers have been confirmed using statistical analysis and testing on historical data, the next step is to introduce new investment strategies into production and implement them.

Our technical team of quantitative analysis developers deploys executive and network algorithms which allow our agents to connect to our counterparts and to fulfil our investment strategies. This in turn facilitates our close relationship with primary brokers, exchanges and custodians for the implementation of our investment orders.

Besides, our team of system administrators and engineers regularly checks for all connections and immediately responds to any network and technology problems.

The main goal of the quantitative analysis team is to conduct periodic market microstructure studies to implement new execution algorithms that reduce our market impact and execution costs.

Monitoring technology

After launching a strategy into production and sending orders to primary brokers and exchanges, our team monitors investment strategies.

This approach helps us make sure everything is working as expected and enables us to intervene promptly when the situation calls for it.

To facilitate the tasks of our trading team, our developers aim to develop new monitoring tools that will help us monitor our investment strategies.

Operating infrastructure

To launch investment strategies into production and support their new developments with a quantitative research specialist, our technology team must periodically check the technological infrastructure and, if necessary, update it.

Our live trading algorithms work both locally and in the cloud. This approach can meet the needs of our business, help minimize disruption due to technical problems, and take advantage of scalable computing power.

Backup and disaster recovery systems

The FOBs technology group aims to test our backup and disaster recovery system.

This enables us to ensure that the system meets predefined service criteria and minimizes the possibility of interruption to our investment operations due to technological problems such as power outages, loss of Internet and network connectivity, or server failure.

Investment strategies

Investment strategies leverage our core competencies in quantitative research. When developing a new investment product, we aim to find innovative investment products that can deliver alpha quality over a long period.

Disclaimer: These investment products come with a significant risk of loss.

Strategies for a wide range of market opportunities

In pursuit of successful investments, there is no chance of developing new ideas.

Our team members have over 25 years of experience, strive to maximize value, apply their talents in fundamental research and advanced quantitative analysis to bring their ideas to market. We strive to quickly and purposefully seize opportunities with the greatest potential, operate in a culture of excellence, and build on long-term relationships with thousands of companies and institutions.

FOBS’s investment strategies focus on all major asset classes in the global capital markets. We use our capital to generate a market-leading return on investment across a wide range of investment strategies.