Description:

At TRV Trading, we know that the financial market offers different opportunities to profit from trading according to the way it's behaving. For instance, when the market is very active (a lot of trading is going on), there are more opportunities to profit, when it's inactive, there are less. TRV uses trading models to execute trades in the financial market for profit, these trading models have parameters which determine how the trading is performed. The trading model parameters that cause the model to generate optimal profit in an active versus an inactive market are known to be different. Currently the setting of trading model parameters is performed manually by human traders. We want to have an automated system that autonomously and continuously adjusts trading model parameters to maximise profits in the current market conditions.

Type:

Undergraduate

Outcome:

• Designing and generating descriptive features of the behaviour of the financial market.
• Figuring out how to apply the model to the domain to change trading parameters as the market changes.
• Implementing the automated system, testing and determining its performance in a simulated trading environment.
• Experimenting with different ML models to improve performance.

Prerequisites

None

Specialisations

• Computer Systems Engineering
• Software Engineering

Categories

• AI & Machine learning

Supervisor

• Maryam Hemmati

Co-supervisor

Team

• Oliver Mitchell
• Rivah Somerville

Lab

Embedded Systems (405.760, Lab)