Disclaimer: there is no guarantee that our investment technology will prevent losses to occur. In addition, we may have technological issues that can potentially lead to losses in our investments.

Trading Technology

Our proprietary trading technology supports the deployment of investment capital. Since our strategies are fully systematic, we developed most of our investment technology in-house in order to have control over execution quality and satisfy the requirements imposed by our trading algorithms.

In order to support our quantitative research process, our quantitative developers have developed internally automated tools that help our quantitative research team in the identification of new sources of alpha in big amounts of data in an effective and efficient way.

Disclaimer: these investment products involve substantial risks of loss.

Data Retrieval Technology

The first step in the deployment of a new quantitative investment strategy is the retrieval of data. While years ago data was in limited supply because of the lack of proper methods for storing and processing data, today the advancement of technology and the reduction in the cost of storage has massively increased the amount of data stored by organizations from all industries. We can say that the problem today is the opposite, in other words too much data is stored and often it cannot be effectively analyzed by human operators, leading to the new field of Big Data.

In order to support our team in the objective of discovering new sources of alpha, we take advantage of Big Data by looking at many different datasets from various fields. They include traditional historical financial time series data, fundamental data about companies and macroeconomic sectors, and more sophisticated alternative datasets.

Our quantitative developers continuously develop new technological tools to support the requirements of our data scientists to efficiently retrieve and store very large amounts of historical data.

Disclaimer: these investment products involve substantial risks of loss.

Data Cleaning Scripts

A common problem in dealing with data is that most of the time the datasets contain errors, or a not structured in the way required by the quantitative researchers and analysts, thus they need to be cleaned. While years ago, where the amount of data to by analyzed was limited and it could be done by a human operator, nowadays the vast amount of data recorded makes it impractical to manually clean the data at disposal and makes it necessary for organizations to adopt automated tools.

In order to support the vast amount of data that needs to be cleaned and checked for potential errors, our technology team has implemented various automated tools that help our quants run their analyses and reduce the occurrence of faulty data which could lead to biased backtesting and erroneous conclusions.

Analysis Tools

After the data has been cleaned, our quantitative researcher can start exploring the data using the scientific method to find patterns, formulate hypotheses, and test them within a rigorous research framework.

In order to run their analyses on dataset containing millions of records, our quants need advanced technological tools, which enable them to run sophisticated queries and statistical analyses on data with speed, to visualize patterns, and to discover relationships within disparate datasets.

Among the technology used in the analysis phase there are advanced statistical analyses methods, machine learning techniques, and sophisticated visualization and patter recognition tools.

Backtesting Technology

After our researchers believe that new sources of alpha have been found, they run backtests spanning long historical data to increase the probability of finding robust signals.

Since the backtesting phase is very time consuming due to the large amount of historical data on which to run the strategy, our quants need advanced algorithms and enough computing processing power in order to complete their tests as fast as possible.

In order to support the speed requirements imposed by the backtesting phase, our team of quantitative developers and system administrators check that fast testing tools are developed and that our researchers have the computing resources needed to run their tests quickly.

Disclaimer: these investment products involve substantial risks of loss.

Execution Algorithms

Once the hypotheses formulated by our researchers have been validated through statistical analyses and backtesting, the next step is to deploy the new investment strategies into production and execute them.

In order to connect to our prime brokers, exchanges, and custodians to execute our investment orders, our technology team of quant developers deploys execution and network algorithms that enable our funds to connect to our counterparties and execute our investment strategies. In addition, our team of system administrators and engineers checks that all the connectivity is in place and promptly intervenes in case of network and technological issues.

Our quantitative team has the objective of performing periodic research into market microstructure to deploy new execution algorithms that reduce our market impact and execution costs.

Monitoring Technology

Once a strategy is put into production and orders can be sent to the prime brokers and exchanges, our team monitors the investment strategies to make sure that everything is running properly and to promptly intervene when the situation dictates.

To facilitate the tasks of our trading team, our developers have the objective of developing new monitoring tools that help us in the monitornig of our investment strategies.

Operational Infrastructure

In order to run our investment strategies into production and to support the development of new investment strategies by our quantitative researcher, our technology team needs to periodically review our technological infrastructure and upgrade it when needed. Our live trading algorithms are run both on local premises and on-the-cloud in order to minimize the possibility of interruptions due to technological issues and in order to take advantage of a scalable computing power which can handle the requirements of our business.

Backup & Disaster Recovery Systems

Our technology team has the objective of reviewing our backup and disaster recovery system to check that it meets predefined service criteria set by the management, and in order to minimize the possibility of interruption in our investment operations due to technological issues, such as power outages, loss of internet and network connection, or server breakdowns.