FST Graduate Research Topic


Research Topic: Financial Trading: Algorithmic Trading Agent
Suggested By: Harris Wang
Last Update: September 28th, 2025

Constrained Object Hierarchies (COH) provides a neuroscience-inspired theoretical framework for modeling Artificial General Intelligence (AGI). As a flexible and robust foundation, COH supports the representation of general intelligent systems across diverse domains.


Formally, a COH is defined as a 9-tuple:


O = (C, A, M, N, E, I, T, G, D)


Where:



  • C: Components – sub-objects organized hierarchically

  • A: Attributes – state variables describing object properties

  • M: Methods – executable actions associated with components

  • N: Neural Components – adaptive models for learning and inference

  • E: Embedding Neural Component – semantic representation of the COH

  • I: Identity Constraints – rules ensuring object integrity

  • T: Trigger Constraints – event-condition-action rules for dynamic behavior

  • G: Goal Constraints – optimization objectives guiding intelligent behavior

  • D: Constraint Daemons – real-time monitors enforcing system constraints





Based on the COH framework, the modelling process and result of Algorithmic Trading Agent in Financial Trading are as follows:


System Description and Significance: Algorithmic trading systems automate financial market decisions using quantitative models and real-time data analysis. These systems are significant for their ability to execute trades at speeds and frequencies impossible for human traders, providing liquidity to markets while requiring sophisticated risk management to prevent catastrophic failures.


Analysis and Design: The system must process vast amounts of market data, assess risk in real-time, and execute trades while adhering to regulatory and risk constraints. Neural components are essential for predicting price movements and classifying market regimes, while identity constraints enforce fundamental risk management principles that cannot be violated.


Modelling Solution:



  • C: {MarketDataFeed, RiskModel, ExecutionEngine}

  • A: portfolio_value, asset_prices, volatility, position_size

  • M: execute_buy_order(), execute_sell_order(), hedge_position()

  • N: PriceDirectionPredictor, MarketRegimeClassifier

  • E: Embedding of current market sentiment and regime (bullish, volatile, etc.)

  • I: Value_at_Risk (VaR) must not exceed predefined limit (risk_limit)

  • T: IF stop_loss_price is hit THEN execute_sell_order IMMEDIATELY

  • G: Maximize_risk-adjusted_returns

  • D: VolatilitySpikeDaemon monitoring real-time volatility to protect against breach of I:risk_limit


The COH model captures the delicate balance between aggressive profit-seeking and conservative risk management through its constraint hierarchy, with neural components providing predictive edge while identity constraints maintaining safety boundaries.





You may choose this topic and pursue one of the following routes:


📝 Essay



  • Survey existing methodologies for modeling, designing, and implementing similar intelligent systems.

  • Include:

    • Literature synthesis

    • COH framework analysis

    • Critical evaluation of competing approaches




🛠️ Project



  • Design and implement the system using the COH model.

  • Include:

    • System architecture and design

    • Development and testing

    • Demonstration of core functionality




🎓 Thesis



  • Explore COH’s theoretical foundations in depth.

  • Include:

    • Proposed enhancements or extensions

    • Implementation and evaluation

    • Development of an advanced intelligent system grounded in COH principles