Improving risk-adjusted performance in high frequency trading using interval type-2 fuzzy logic

In this paper, we investigate the ability of higher order fuzzy systems to handle increased uncertainty, mostly induced by the market microstructure noise inherent in a high frequency trading (HFT) scenario. Whilst many former studies comparing type-1 and type-2 Fuzzy Logic Systems (FLSs) focus on error reduction or market direction accuracy, our interest is predominantly risk-adjusted performance and more in line with both trading practitioners and upcoming regulatory regimes. We propose an innovative approach to design an interval type-2 model which is based on a generalisation of the popular type-1 ANFIS model. The significance of this work stems from the contributions as a result of introducing type-2 fuzzy sets in intelligent trading algorithms, with the objective to improve the risk-adjusted performance with minimal increase in the design and computational complexity. Overall, the proposed ANFIS/T2 model scores significant performance improvements when compared to both standard ANFIS and Buy-and-Hold methods. As a further step, we identify a relationship between the increased trading performance benefits of the proposed type-2 model and higher levels of microstructure noise. The results resolve a desirable need for practitioners, researchers and regulators in the design of expert and intelligent systems for better management of risk in the field of HFT

Improving risk-adjusted performance in high-frequency trading: The role of fuzzy logic systems

In recent years, algorithmic and high-frequency trading have been the subject of increasing risk concerns. A general theme that we adopt in this thesis is that trading practitioners are predominantly interested in risk-adjusted performance. Likewise, regulators are demanding stricter risk controls. First, we scrutinise conventional AI model design approaches with the aim to increase the risk-adjusted trading performance of the proposed fuzzy logic models. We show that applying risk-return objective functions and accounting for transaction costs improve out-of-sample results. Our experiments identify that neuro-fuzzy models exhibit superior performance stability across multiple risk regimes when compared to popular neural network models identified in AI literature. Moreover, we propose an innovative ensemble model approach which combines multiple risk-adjusted objective functions and dynamically adapts risk- tolerance according to time-varying risk. Next, we extend our findings to the money management aspects of trading algorithms. We introduce an effective fuzzy logic approach which dynamically discriminates across different regions in the trend and volatility space. The model prioritises higher performing regions at an intraday level and adapts capital allocation policies with the objective to maximise global risk-adjusted performance. Finally, we explore trading improvements that can be attained by advancing our type-1 fuzzy logic ideas to higher order fuzzy systems in view of the increased noise (uncertainty) that is inherent in high-frequency data. We propose an innovative approach to design type-2 models with minimal increase in design and computational complexity. As a further step, we identify a relationship between the increased trading performance benefits of the proposed type-2 model and higher levels of trading frequencies. In conclusion, this thesis sets a framework for practitioners, researchers and regulators in the design of fuzzy logic systems for better management of risk in the field of algorithmic and high-frequency trading

Machine Learning for Financial Prediction Under Regime Change Using Technical Analysis: A Systematic Review

Recent crises, recessions and bubbles have stressed the non-stationary nature and the presence of drastic structural changes in the financial domain. The most recent literature suggests the use of conventional machine learning and statistical approaches in this context. Unfortunately, several of these techniques are unable or slow to adapt to changes in the price-generation process. This study aims to survey the relevant literature on Machine Learning for financial prediction under regime change employing a systematic approach. It reviews key papers with a special emphasis on technical analysis. The study discusses the growing number of contributions that are bridging the gap between two separate communities, one focused on data stream learning and the other on economic research. However, it also makes apparent that we are still in an early stage. The range of machine learning algorithms that have been tested in this domain is very wide, but the results of the study do not suggest that currently there is a specific technique that is clearly dominant

Colombian Energy Market: An approach of Anfis and Clustering Techniques to an Optimal Portfolio

This paper focuses on the study of a first approach to an optimal portfolio in the Colombian Energy Market using Artificial Intelligence. Specifically, ANFIS and Clustering techniques are applied. The methodology is implemented using the Matlab Toolboxes for clustering and FIS generation. Te results are presented, as well as the analysis of them. A first approximation to an optimal portfolio obtained with this methodology is shown. Consequently, some conclusions of the different techniques available for the same purpose are discussed. Finally the future work is proposed

Hybrid Product Cost Calculation Model as a Decision Support Tool

Cost calculation is of huge importance both for determining a rational and competitive price of the product and preparation of offers according to customer\u27s demand, where deadlines for sending offers are limited. In individual and small batch production, which is characterised by a wide product range, reduced quantities, and short delivery times, cost calculation in forming the price of the product according to customer requirements is of essential importance. Traditional methods of calculating the cost of products in these cases are inefficient, considering the number of offers that need to be made, timing, as well as their competitiveness in the market. For product cost calculation in individual and small batch production, it is necessary to apply modern, efficient methods and models based on the application of artificial intelligence. A wide range of products, which is characteristic of individual and small batch production in some companies, enables the development of modern costing models with the use of basic principles of group technology. The constructional and technological similarity of products enables the formation of groups of similar parts and appropriate group technological processes. Combining that with artificial intelligence, it is possible to develop appropriate cost calculation models. This paper presents a developed model for production cost calculation, based on the principles of group technology and adaptive neuralfuzzy networks (ANFIS)

A PSO Approach to Search for Adaptive Trading Rules in the EUA Futures Market

AbstractThe carbon emission futures markets become more and more important in worldwide. More and more counties begin to emphasize environmental protection in the economicdevelopment. Carbon emission trading has become an important part of the energy finance. How to make more profits in the carbon emission futures market is concern by more and more traders and scholars. This paper proposed an approach to search for optimal trading rules in the CO2 allowance futures markets. A group of different moving average trading rules with different weights are used to constitute an integrated trading rule. This is better than a single fixed moving average trading rule.Similarity of trading rules, a parameter we designed, is used to help select basic rules. The authors use static particle swarm optimization process to find the best weights distributions of the selected basic trading rules. After the initial weight distribution is determined, the weights of the basic trading rules will adjusted dynamically every day in the trading process using particle swarm optimization algorithms. Experiments using the EUA Futures Market price data were conducted to find out best adaptive trading rules in the carbon emission futures market. According to our results, it is not necessary to use two moving average trading rules that making same investment advice at a probability higher than 70%. The results show this approach have good performance in adjusting the weights according to the price changes. We found that the adaptive trading rules can help traders make profit in the EUA Futures Marketexcept extreme specialcircumstancesafter price change significantly. This approach might be helpful for traders to make scientificdecision in actual investments

Incremental Market Behavior Classification in Presence of Recurring Concepts

In recent years, the problem of concept drift has gained importance in the financial domain. The succession of manias, panics and crashes have stressed the non-stationary nature and the likelihood of drastic structural or concept changes in the markets. Traditional systems are unable or slow to adapt to these changes. Ensemble-based systems are widely known for their good results predicting both cyclic and non-stationary data such as stock prices. In this work, we propose RCARF (Recurring Concepts Adaptive Random Forests), an ensemble tree-based online classifier that handles recurring concepts explicitly. The algorithm extends the capabilities of a version of Random Forest for evolving data streams, adding on top a mechanism to store and handle a shared collection of inactive trees, called concept history, which holds memories of the way market operators reacted in similar circumstances. This works in conjunction with a decision strategy that reacts to drift by replacing active trees with the best available alternative: either a previously stored tree from the concept history or a newly trained background tree. Both mechanisms are designed to provide fast reaction times and are thus applicable to high-frequency data. The experimental validation of the algorithm is based on the prediction of price movement directions one second ahead in the SPDR (Standard & Poor's Depositary Receipts) S&P 500 Exchange-Traded Fund. RCARF is benchmarked against other popular methods from the incremental online machine learning literature and is able to achieve competitive results.This research was funded by the Spanish Ministry of Economy and Competitiveness under grant number ENE2014-56126-C2-2-R

Adaptive Algorithms For Classification On High-Frequency Data Streams: Application To Finance

Mención Internacional en el título de doctorIn recent years, the problem of concept drift has gained importance in the financial domain. The succession of manias, panics and crashes have stressed the nonstationary nature and the likelihood of drastic structural changes in financial markets. The most recent literature suggests the use of conventional machine learning and statistical approaches for this. However, these techniques are unable or slow to adapt to non-stationarities and may require re-training over time, which is computationally expensive and brings financial risks. This thesis proposes a set of adaptive algorithms to deal with high-frequency data streams and applies these to the financial domain. We present approaches to handle different types of concept drifts and perform predictions using up-to-date models. These mechanisms are designed to provide fast reaction times and are thus applicable to high-frequency data. The core experiments of this thesis are based on the prediction of the price movement direction at different intraday resolutions in the SPDR S&P 500 exchange-traded fund. The proposed algorithms are benchmarked against other popular methods from the data stream mining literature and achieve competitive results. We believe that this thesis opens good research prospects for financial forecasting during market instability and structural breaks. Results have shown that our proposed methods can improve prediction accuracy in many of these scenarios. Indeed, the results obtained are compatible with ideas against the efficient market hypothesis. However, we cannot claim that we can beat consistently buy and hold; therefore, we cannot reject it.Programa de Doctorado en Ciencia y Tecnología Informática por la Universidad Carlos III de MadridPresidente: Gustavo Recio Isasi.- Secretario: Pedro Isasi Viñuela.- Vocal: Sandra García Rodrígue