Generating Moving Average Trading Rules on the Oil Futures Market with Genetic Algorithms

The crude oil futures market plays a critical role in energy finance. To gain greater investment return, scholars and traders use technical indicators when selecting trading strategies in oil futures market. In this paper, the authors used moving average prices of oil futures with genetic algorithms to generate profitable trading rules. We defined individuals with different combinations of period lengths and calculation methods as moving average trading rules and used genetic algorithms to search for the suitable lengths of moving average periods and the appropriate calculation methods. The authors used daily crude oil prices of NYMEX futures from 1983 to 2013 to evaluate and select moving average rules. We compared the generated trading rules with the buy-and-hold (BH) strategy to determine whether generated moving average trading rules can obtain excess returns in the crude oil futures market. Through 420 experiments, we determine that the generated trading rules help traders make profits when there are obvious price fluctuations. Generated trading rules can realize excess returns when price falls and experiences significant fluctuations, while BH strategy is better when price increases or is smooth with few fluctuations. The results can help traders choose better strategies in different circumstances

Nouveaux estimateurs pour l’optimisation des systèmes automatisés de négociation d’options

RÉSUMÉ : Les options sont des produits financiers modernes permettant une approche stratégique à l’investissement. Les possibilités qui s’offrent à l’investisseur avec l’utilisation de ces produits sont vastes. Certains investisseurs les utilisent de manière subjective pour prendre position dans le marché et d’autres développent des stratégies plus systématiques afin d’enrichir leur portefeuille. L’objectif de ce mémoire est d’améliorer le rendement composé annuellement, ajusté au risque, des systèmes automatisés de négociation d’options comparativement aux méthodes usuelles. Deux contributions principales ont été faites pour y arriver. Premièrement, nous avons développé un estimateur de la moyenne de gain, conditionnelle à un ensemble d’information, d’une stratégie quelconque d’options. Deuxièmement, nous avons formulé un nouveau problème d’optimisation à variables discrètes pour la sélection des paramètres des stratégies. De plus, nous avons utilisé un algorithme de résolution avec des propriétés de convergence. Une contribution secondaire a été le développement d’un algorithme pour la liquidation des stratégies exploitant un ensemble d’information. Les performances de notre système ont été simulées en utilisant des données historiques et un ensemble d’information standard. Les résultats de ces simulations montrent que notre système mène à des rendements supérieurs aux systèmes usuels avec un niveau de risque comparable, dans certains cas. Les meilleurs résultats sont obtenus lorsque l’on utilise seulement des options sur indice. Les simulations utilisant des options sur action et indice mènent à des rendements moins élevés. De plus, notre algorithme de liquidation permet de réduire le risque du système en gardant un rendement relativement stable. Le mémoire se déroulera comme suit. Nous allons débuter en introduisant tous les concepts importants reliés à la négociation d’options et aux marchés financiers. Ensuite, nous ferons une revue de littérature exhaustive des systèmes de négociation automatiques d’options existants. Nous discuterons des lacunes de ces systèmes ainsi que des liens avec le nôtre. Puis, nous effectuerons le développement théorique des éléments de notre système de négociation. Finalement, nous expliquerons comment nous avons mis en place et simulé notre système et nous présenterons les résultats.----------ABSTRACT : Option trading has grown in popularity over the last few decades. These financial products can be used to create a vast array of different trading strategies. A lot of investors use options to take positions, in financial markets, expressing their opinion. Other investors develop systematic methods of investing using options. The aim of this master’s thesis is to improve the annually compounded return, adjusted to risk, of systematic option trading systems compared to common option trading methods. To achieve this goal, two main contributions were made. First, we developed an estimator of the mean, conditional to an information set, of the gain of any option strategy. Second, we formulated a new discrete optimisation problem for the selection of strategy parameters and solved it using a converging algorithm. We also developed an algorithm based on our estimator to liquidate existing positions. The performance of our automated trading system was simulated using a historical dataset and a common information set. Results show that the annually compounded return of our system is higher than common methods, with similar risk characteristics, in certain situations. Simulations that only included index options produced the best results, while those including stock options had a lesser performance. Furthermore, our results suggest that the use of an algorithm for liquidation of existing positions tends to reduce the risk while keeping similar returns. This master’s thesis will be divided in four sections. In the first section, we will introduce every necessary concept relating to option trading and financial markets. The second section will present an exhaustive literature review of existing methods in automated option trading. We will concurrently analyze the existing literature and outline the difference between existing systems and ours. In the third section, we will lay out the mathematics behind our system. In the last section, we will detail the way in which the theoretical system was implemented and display the results of the simulations

Adaptive fuzzy system for algorithmic trading : interpolative Boolean approach

Тема овог рада je адаптивни фази систем за алгоритамско трговање. Систем је развијен коришћењем интерполативног Буловог приступа фази моделовању, анализи података и управљању. Предложени приступ укључује интерполативне логичке моделе за фази препознавање ценовних образаца на тржишту, логички ДуПонт метод за аутоматизовану анализу профитабилности предузећа, интерполативни фази контролер за управљање трговањем и генетски алгоритам за обучавање интерполативног фази контролера ради откривања стратегија. Интерполативни Булов приступ, заснован на интерполативној Буловој алгебри, превазилази проблем неконзистентности фази логике. Конструисани адаптивни фази систем може самостално, из података, да открије успешне стратегије, примени их за алгоритамско трговање и адаптира у случају пада њихових перформанси. Успешност система тестирана је на подацима са америчког тржишта акција, међународног девизног тржишта и тржишта криптовалута.The topic of this thesis is adaptive fuzzy system for algorithmic trading. The system is developed using interpolative Boolean approach for fuzzy modeling, data analysis and control. The proposed approach includes interpolative logical models for fuzzy recognition of price patterns in market data, logical DuPont method for automated analysis of company’s profitability, interpolative fuzzy controller for trading and a genetic algorithm for extracting trading strategies by training interpolative fuzzy controller. Interpolative Boolean approach, based on interpolative Boolean agebra, solves the problem of fuzzy logic’s inconsistency with Boolean axioms. The proposed system can independently discover successful trading strategies from data, apply them for algorithmic trading and adapt in the case of performance deterioration. The system was tested on historical data from US equity, foreign exchange market and cryptocurrency market

A Corpus Driven Computational Intelligence Framework for Deception Detection in Financial Text

Financial fraud rampages onwards seemingly uncontained. The annual cost of fraud in the UK is estimated to be as high as £193bn a year [1] . From a data science perspective and hitherto less explored this thesis demonstrates how the use of linguistic features to drive data mining algorithms can aid in unravelling fraud. To this end, the spotlight is turned on Financial Statement Fraud (FSF), known to be the costliest type of fraud [2]. A new corpus of 6.3 million words is composed of102 annual reports/10-K (narrative sections) from firms formally indicted for FSF juxtaposed with 306 non-fraud firms of similar size and industrial grouping. Differently from other similar studies, this thesis uniquely takes a wide angled view and extracts a range of features of different categories from the corpus. These linguistic correlates of deception are uncovered using a variety of techniques and tools. Corpus linguistics methodology is applied to extract keywords and to examine linguistic structure. N-grams are extracted to draw out collocations. Readability measurement in financial text is advanced through the extraction of new indices that probe the text at a deeper level. Cognitive and perceptual processes are also picked out. Tone, intention and liquidity are gauged using customised word lists. Linguistic ratios are derived from grammatical constructs and word categories. An attempt is also made to determine ‘what’ was said as opposed to ‘how’. Further a new module is developed to condense synonyms into concepts. Lastly frequency counts from keywords unearthed from a previous content analysis study on financial narrative are also used. These features are then used to drive machine learning based classification and clustering algorithms to determine if they aid in discriminating a fraud from a non-fraud firm. The results derived from the battery of models built typically exceed classification accuracy of 70%. The above process is amalgamated into a framework. The process outlined, driven by empirical data demonstrates in a practical way how linguistic analysis could aid in fraud detection and also constitutes a unique contribution made to deception detection studies

Digital Platforms and Algorithmic Subjectivities

Algorithms are a form of productive power – so how may we conceptualise the newly merged terrains of social life, economy and self in a world of digital platforms? How do multiple self-quantifying practices interact with questions of class, race and gender? This edited collection considers algorithms at work – for what purposes encoded data about behaviour, attitudes, dispositions, relationships and preferences are deployed – and black box control, platform society theory and the formation of subjectivities. It details technological structures and lived experience of algorithms and the operation of platforms in areas such as crypto-finance, production, surveillance, welfare, activism in pandemic times. Finally, it asks if platform cooperativism, collaborative design and neomutualism offer new visions. Even as problems with labour and in society mount, subjectivities and counter subjectivities here produced appear as conscious participants of change and not so much the servants of algorithmic control and dominant platforms