Machine learning for algorithmic trading

Kondratieva, Tatiana; Prianishnikova, L; Razveeva, Irina

doi:10.1051/e3sconf/202022401019

Cited by 7 publications

(7 citation statements)

References 8 publications

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“…Thus, patterns formed earlier are destroyed and new, better ones are formed, until the model error becomes digestible. This method has already been used by authors in paper 18 and observed in the works of other scientists 19 .…”

Section: Methodsmentioning

confidence: 99%

Processing of Polymers Stress Relaxation Curves Using Machine Learning Methods

Chepurnenko,

Kondratieva,

Al-Wali

2023

Baghdad Sci.J

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Currently, one of the topical areas of application of machine learning methods is the prediction of material characteristics. The aim of this work is to develop machine learning models for determining the rheological properties of polymers from experimental stress relaxation curves. The paper presents an overview of the main directions of metaheuristic approaches (local search, evolutionary algorithms) to solving combinatorial optimization problems. Metaheuristic algorithms for solving some important combinatorial optimization problems are described, with special emphasis on the construction of decision trees. A comparative analysis of algorithms for solving the regression problem in CatBoost Regressor has been carried out. The object of the study is the generated data sets obtained on the basis of theoretical stress relaxation curves. Tables of initial data for training models for all samples are presented, a statistical analysis of the characteristics of the initial data sets is carried out. The total number of numerical experiments for all samples was 346020 variations. When developing the models, CatBoost artificial intelligence methods were used, regularization methods (Weight Decay, Decoupled Weight Decay Regularization, Augmentation) were used to improve the accuracy of the model, and the Z-Score method was used to normalize the data. As a result of the study, intelligent models were developed to determine the rheological parameters of polymers included in the generalized non-linear Maxwell-Gurevich equation (initial relaxation viscosity, velocity modulus) using generated data sets for the EDT-10 epoxy binder as an example. Based on the results of testing the models, the quality of the models was assessed, graphs of forecasts for trainees and test samples, graphs of forecast errors were plotted. Intelligent models are based on the CatBoost algorithm and implemented in the Jupyter Notebook environment in Python. The constructed models have passed the quality assessment according to the following metrics: MAE, MSE, RMSE, MAPE. The maximum value of model error predictions was 0.86 for the MAPE metric, and the minimum value of model error predictions was 0.001 for the MSE metric. Model performance estimates obtained during testing are valid.

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Section: Methodsmentioning

confidence: 99%

Processing of Polymers Stress Relaxation Curves Using Machine Learning Methods

Chepurnenko,

Kondratieva,

Al-Wali

2023

Baghdad Sci.J

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“…Let us also note that the model-free character of the proposed PID scheme ( 1)-( 3) and (4) indicates the possibility to apply some suitable machine learning (ML) approaches to the corresponding optimal PID gains tuning problem. We refer to Bertsekas (2019); Bertsimas and Lo (1998); Jansen (2020) for the technical details related to the reinforcement learning (RL) approach and to some applications of the ML techniques in the modern AT.…”

Section: Model-free Pid-based Trading Algorithmmentioning

confidence: 99%

“…Note that various optimization techniques play an important role in modern financial engineering (see, for example, Azhmyakov et al 2022aAzhmyakov et al , 2022bBirge and Louveaux 2011;Hammel and Paul 2002;Jansen 2020;Lewis 1986;Liu and Wright 2015;Nemirovski et al 2009;Wets 1990;Zenios 1993;Ziemba and Vickson 1975 and the references therein). In our case, we examine two different data-driven optimization approaches to the PID gains optimization, namely, the regression-based technique and the stochastic optimization framework.…”

Section: Introductionmentioning

confidence: 99%

On a Data-Driven Optimization Approach to the PID-Based Algorithmic Trading

Azhmyakov,

Shirokov,

Dernov

et al. 2023

JRFM

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This paper proposes an optimal trading algorithm based on a novel application of conventional control engineering (CE). We consider a fundamental CE concept, namely, the feedback control, and apply it to algorithmic trading (AT). The concrete feedback control strategy is designed in a form of the celebrated proportional–integral–derivative (PID) model. The highly fluctuating nature of the modern financial markets has led to the adoption of a model-free realization of the generic PID framework. The control theoretical methodology we propose is combined with the advanced statistics for the historical market data. We obtain a specific log-normal probability distribution function (pdf) associated with the specific quantities associated with the available stock data. The empirical log-normal pdf mentioned above enables the necessary PID gains optimization. For this aim, we apply the data-driven optimization approaches and consider the corresponding Monte Carlo solution procedure. The optimized PID trading algorithm we propose is also studied in the Fourier analysis framework. This equivalent frequency domain representation involves a new concept in financial engineering, namely, the “stock market energy” concept. For the evaluation, we implement the proposed PID optimal trading algorithm and develop a Python-based prototype software. We finally apply the corresponding prototype software to a data set from the Binance BTC/USDT (Bitcoin/Tether) stock market. The experimental result illustrates the implementability of the proposed optimal PID trading scheme and also shows the effectiveness of the proposed CE methods in the modern AT.

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“…Upon reviewing the literature, however, it is clear that there is a complete lack of ensemble-based trading strategies. This lack may, of course, only be because of an absence of detailed descriptions of the working of trading strategies that are used by traders (owing to the generally avaricious nature of traders) and their need to maintain a competitive advantage, for it is well known that trading signal generation techniques experience market saturation once a trading methodology is publicly known and implemented [10]. The difficulty in this domain of research, therefore, is to make an impactful contribution to the literature without voiding its relevance and interfering with its subsequent mass adoption.…”

Section: Introductionmentioning

confidence: 99%

Heterogeneous Trading Strategy Ensembling for Intraday Trading Algorithms

Koegelenberg,

van Vuuren

2023

SAJIE

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Since the inception of algorithmic trading during the mid-1970s, considerable resources and time have been committed by the financial sector to the development of trading algorithms in the hope of obtaining a competitive advantage over human contenders. A plethora of trading algorithms has been proposed in the literature; each algorithm is unique in its design, but little emphasis has been placed on heterogeneous trading strategy ensembling. In this paper we propose a trading strategy ensemble method for combining three different domain-specific trading strategies: a deterministic strategy, a probabilistic strategy, and a machine-learning strategy. The objective of the trading strategy ensemble is to find an appropriate trade-off between the levels of return and the risk exposure of a trader. We implement our strategy across different historical forex currency pair data in a bid to validate the trading strategy ensemble, and we analyse the results by invoking appropriate return and risk performance measures.

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Machine learning for algorithmic trading

Cited by 7 publications

References 8 publications

Processing of Polymers Stress Relaxation Curves Using Machine Learning Methods

Processing of Polymers Stress Relaxation Curves Using Machine Learning Methods

On a Data-Driven Optimization Approach to the PID-Based Algorithmic Trading

Heterogeneous Trading Strategy Ensembling for Intraday Trading Algorithms

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