Challenges

Financial institutions faced an increasing demand for producing new prediction models in an ever-changing ecosystem. The ability to develop new prediction models that were accurate and precise while maintaining their performance over time presented a challenge that required the right solution to address. Financial institutions and algo-trading firms utilized Qualitest’s automated, no-code solution for building quality prediction models across a variety of instruments.

Solution Stages

Qualitest’s machine learning solution for finance provided a prediction modeling environment that sustainably decreased the time to production while ensuring accurate and stable prediction models for different financial instruments. For most modeling tasks, the process had four main phases:

  • The first phase involved uploading the data and performing data exploration.
  • The second phase consisted of applying predictive algorithms that automatically created an optimized prediction model from over 840 options.
  • The third phase included analyzing prediction KPIs and validating the model.
  • The fourth phase involved implementing the solution into the company’s production environment.

Benefits

  • Fast, comprehensive, no-code guided machine learning solution
  • More than 40 machine learning algorithms with proprietary optimization engine for each
  • Proven results for modelling financial and algo-trading tasks
  • Point-and-Click Interface combined with the ability to implement models in a matter of minutes.

Use case – Optimizing Options trading model

Qualitest’s software tool was used to optimize options trading orders. A client provided Qualitest with thousands of options trades made by its trading desk. The objective was to identify the best entry points to minimize loss trades while maintaining a sufficient number of trading opportunities. Qualitest’s machine learning tool utilized more than 40 algorithms, each with its own unique set of variables and hyperparameters. In a short processing time of a few days, the tool found the best entry rules, ensuring a stable and accurate model that could be used for future trades.

Project Stages

We used the dataset provided by the client, which contained hundreds of explanatory variables from multiple sources. The main model for predicting trade success was built based on an ensemble of machine learning models. The next phase involved validating the model on new data that was unknown during its creation. The final ensemble model’s formula was fed into Qualitest’s production component for easy implementation on the client’s infrastructure.

  • Planning and data reprocessing by the company’s data analysts, guided by the Qualitest team, allowed for thorough exploration of the data and the creation of a unified model.
  • Running on-premises, the Qualitest solution provided 40 machine learning algorithms with automatic fine-tuning to identify the best combination of explanatory variables.
  • Delivered a stable and transparent prediction model that was ready for deployment in production.
  • Qualitest’s solution offered a variety of implementation methods and processes to address the constantly changing demand for data.

Endnotes

Qualitest’s AI-powered machine learning predictive analytics solution proved to be fast, easy to use, and accurate when applied to complex trading data. The solution enabled the company to increase trading revenues and automatically avoid risky trades.