Machine learning models for algorithmic trading

A complex trading environment required analytical models capable of identifying patterns in market data and supporting the development of algorithmic trading strategies. The challenge was to investigate how different machine-learning approaches could be applied to the problem, and determine how the resulting models performed against the relevant modelling objectives.

Algorithmic trading presents a challenging modelling environment where large volumes of data, changing market conditions and multiple influencing factors need to be considered. Rather than relying on a single modelling technique, the engagement required experimentation across different machine-learning algorithms.

Priscient Ideas provided machine-learning and data science expertise, contributing to the development and evaluation of models for algorithmic trading.

1. 01

   Defined the modelling objectives.

2. 02

   Prepared and structured relevant datasets.

3. 03

   Explored different machine-learning algorithms.

4. 04

   Developed multiple modelling approaches.

5. 05

   Tested and compared model outputs.

6. 06

   Analysed model behaviour and performance.

7. 07

   Iterated the approaches based on analytical findings.

8. 08

   Considered the practical application of model outputs within an algorithmic trading context.

• Multiple machine-learning models developed and evaluated using different algorithms, providing an analytical basis for exploring algorithmic approaches to trading.

The engagement produced a range of machine-learning approaches that could be evaluated for their suitability to an algorithmic trading application. It also demonstrated how advanced analytics and machine learning can be applied to complex, data-intensive problems within the energy and commodities environment.

• Python
• Machine learning
• Data science
• Algorithm development
• Statistical analysis
• Energy analytics