IJPCTG-ABMCoinTossX

Semi-asynchronous agent-based market simulations with a matching engine

IJPCTG-HawkesCoinTossX

Point process market simulation using the CoinTossX matching engine.

FinancialModels

A collection of simple models of financial markets implemented in Julia

Adaptive-Farmer-Joshi-Agent-Based-Model-of-Financial-Markets

The project explores the calibration and simulation of the Farmer and Joshi (2002) agent-based model of financial markets using the method of moments along with a genetic algorithm and a Nelder-Mead with threshold accepting algorithm. The model is used for understanding daily trading decisions made from closing auction to closing auction in equity markets, as it attempts to model financial market behaviour without the inclusion of agent adaptation. However, our attempt at calibrating the model has limited success in replicating important stylized facts observed in financial markets, similar to what has been found in other calibration experiments of the model. This leads us to extend the Farmer-Joshi model to include agent adaptation using a Brock-Hommes (1998) approach to strategy fitness based on trading strategy profitability. The adaptive Farmer-Joshi model allows trading agents to switch between strategies, favouring strategies that have been more profitable over some period of time determined by a free-parameter determining the profit monitoring time-horizon.

Recommender-Systems

Recommender systems are software tools and techniques that provide suggestions for items to be of use to a user. The collaborative filtering approach evaluates items using the opinions or ratings of other users. Alternatively, the content-based approach works by learning the items’ features to match the user’s preferences and interests. The code found in this repository implements several collaborative filtering and content-based methods: K-nearest neighbours, hierarchical clustering, association rule mining, ordinal logistic regression, classification trees, TF-IDF, and matrix factorisation.