Gaussian process-based algorithmic trading strategy identification

Abstract

Many market participants now employ algorithmic trading, commonly defined as the use of computer algorithms, to automatically make certain trading decisions, submit orders and manage those orders after submission. Identifying and understanding the impact of algorithmic trading on financial markets has become a critical issue for market operators and regulators. Advanced data feeds and audit trail information from market operators now allow for the full observation of market participants’ actions. A key question is the extent to which it is possible to understand and characterize the behaviour of individual participants from observations of trading actions. In this paper, we consider the basic problems of categorizing and recognizing traders (or, equivalently, trading algorithms) on the basis of observed limit orders. These problems are of interest to regulators engaged in strategy identification for the purposes of fraud detection and policy development. Methods have been suggested in the literature for describing trader behaviour using classification rules defined over a feature space consisting of summary trading statistics of volume and inventory, along with derived variables that reflect the consistency of buying or selling behaviour. Our principal contribution is to suggest an entirely different feature space that is constructed by inferring key parameters of a sequential optimization model that we take as a surrogate for the decision-making process of the traders. In particular, we model trader behaviour in terms of a Markov decision process. We infer the reward (or objective) function for this process from observations of trading actions using a process from machine learning known as inverse reinforcement learning (IRL). The reward functions learned through IRL then constitute a feature space that can be the basis for supervised learning (for classification or recognition of traders) or unsupervised learning (for categorization of traders). Making use of a real-world data-set from the E-Mini futures contract, we compare two principal IRL variants, linear IRL and Gaussian Process IRL, against a method based on summary trading statistics. Results suggest that IRL-based feature spaces support accurate classification and meaningful clustering. Further, we argue that, because they attempt to learn traders’ underlying value propositions under different market conditions, the IRL methods are more informative and robust than the summary statistic-based approach and are well suited for discovering new behaviour patterns of market participants

Benchmark dataset for mid‐price forecasting of limit order book data with machine learning methods

“…Kim (2001) uses input/output hidden Markov models (IOHMMs) and reinforcement learning (RL) in order to identify the order flow distribution and market-making strategies, respectively. Yang et al (2015) apply apprenticeship learning 17 methods, like linear inverse reinforcement learning (LIRL) and Gaussian process IRL (GPIRL), to recognize traders or algorithmic trades 14 The Dow Jones Industrial Average (DJIA) is the price-weighted average of the 30 largest, publicly owned US companies. 15 S&P 500 is the index that provides a summary of the overall market by tracking some of the 500 top stocks in US stock market.…”

Decision analytics and machine learning in economic and financial systems

“…Qiao and Beling 2011). Yang et al (2015) applied that approach to algorithmic trading, modeling the trading strategies in terms of an MDP and then learning trader behavior in the space of reward functions learned through inverse reinforcement learning. Empirical results on E-Mini S&P 500 futures market show that the machine learning-based approach provides significant and consistent improvement on previous rule-based classification methods.…”

Latent Segmentation of Stock Trading Strategies Using Multi-Modal Imitation Learning

“…As mentioned previously, financial markets consist of stock orders with various objectives, and these objectives are not self-evident from trading data alone. Since previous studies were seemingly able to classify trading strategies Yang et al (2015), we should be able to achieve higher prediction accuracy by modeling each strategy class. In this study, we assume latent segmentation of traders Cohen and Ramaswamy (1998); Swait (1994) and that all traders belong to a unique segment at each time, that each trader may drift between segments, but cannot belong to more than one segment simultaneously.…”