About

The Client is a US startup in the sphere of financial services.

challenge

The Client wanted to develop a trading system that was to recommend traders certain courses of action on US stock exchanges, such as NASDAQ and AMEX. Striving to make the recommendations as precise as possible, the Client was looking for a professional data science team.

Solution

VolgoTechnologies team of 20 data scientists examined publicly available research papers that dwelled on best practices in financial trading. We took the described patterns, such as a wave, an ascending scallop, a pennant, a descending triangle, and turned them into algorithms, as the Client’s trading system was to make well-considered decisions as the most successful traders would do.

The system was designed in a way that allowed a new pattern to be easily added. This flexibility made it possible to continuously improve the model and keep pace with the quickly changing stock market environment.

Our data scientists tuned the system so that it scanned the stock market data at different time intervals (for example, every minute and every five minutes). This allowed the system to better understand securities’ behavior as some securities showed various patterns on different interval scales.

Staging

Staging the Portfolio Management and Trading Automation Software Powered by Data Science involves a highly technical and strategic implementation process. The goal is to deliver a platform that leverages advanced data science techniques, machine learning models, and real-time data to automate portfolio management, optimize trading decisions, and improve overall financial performance.

Datawarehouse

Dataware House

Desktop Application

Results

The Client got a proprietary multi-user system and started offering it on the market as a ready-to-use solution for the financial industry. With data science, predictive and prescriptive analytics at its core, the system translated various patterns into precise trading recommendations and allowed users to efficiently manage their investment portfolios.

Technologies and Tools

Prediction and recommendation engine: C++. User application back end: Java.