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EN
This study presents a method for testing high-frequency trading (HFT) for algorithms on GPUs using kernel parallelization, code vectorization, and multidimensional matrices. The research evaluates HFT strategies within algorithmic cryptocurrency trading in volatile market conditions, particularly during the COVID-19 pandemic. The study's objective is to provide an efficient and comprehensive approach to assessing the efficiency and profitability of HFT strategies. The results show that the method effectively evaluates the efficiency and profitability of HFT strategies, as demonstrated by the Sharp ratio of 2.29 and the Sortino ratio of 2.88. The authors suggest that further study on HFT testing methods could be conducted using a tool that directly connects to electronic marketplaces, enabling real-time receipt of high-frequency trading data and simulation of trade decisions. Finally, the study introduces a novel method for testing HFT algorithms on GPUs, offering promising results in assessing the efficiency and profitability of HFT strategies during volatile market conditions.
EN
The paper studies the problem of computing the parameters for investment strategies. Proposed is an innovative modification of Particle Swarm Optimization algorithm for discrete and continuous data. The article shows how discrete and continuous version of the algorithm can be combined in order to achieve the best results. Moreover, the presented algorithm is expanded by a multi-swarm mechanism which allows to achieve better results in a fixed time. The proposed algorithm was tested on a simple investment strategy, based on one of the well known indicators Rate of Change (further referred as ROC) that uses a mixture of discrete and continuous parameters. All the tests were performed on a data gathered from one of the most important of currency pairs — EURUSD.
EN
The paper verifies usefulness of the high frequency trading model developed by Marco Avellaneda and Sasha Stoikov, used in simulation of turnover with futures contract securities of one of agricultural commodities on the selected commodity stock exchange. Accuracy of provided signals of purchase and sale signals was verified on authentic quotations – the futures contract for coffee prices of the London Stock Exchange. Results of ten subsequent session days was analysed in detail. Quality of the assumed investment algorithm was determined with the use of stock exchange ratios: Information Ratio and Maximum Drawdown. A short discussion was conducted, which compared a standard investing method and the analysed model of algorithmic trading. In conclusion, all most important statements and conclusions were made, which confirmed usefulness of the HFT model developed by Marco Avellaneda and Sasha Stoikov for turnover of futures contract securities for agricultural commodities.
PL
W pracy sprawdzono przydatność modelu szybkiego kupna i sprzedaży (High Frequency Trading) Marco Avellanedy i Sashy Stoikov'a, użytego w symulacji obrotu walorami kontraktu terminowego na towar pochodzenia rolniczego na wybranej giełdzie towarowej. Zbadano trafność podawanych sygnałów transakcji kupna i sprzedaży na autentycznych notowaniach - kontrakt terminowy na ceny kawy londyńskiej giełdy papierów wartościowych (London Stock Exchange). Szczegółowo zanalizowano wyniki dziesięciu kolejnych dni sesyjnych. Jakość przyjętego algorytmu inwestycyjnego określono za pomocą wskaźników giełdowych: Information Ratio oraz Maximum Drawdown. Przeprowadzono krótką dyskusję porównującą standardową metodę inwestowania oraz analizowany model handlu algorytmicznego. Na zakończenie zebrano najważniejsze stwierdzenia i wyciągnięto wnioski potwierdzające przydatność modelu HFT Marco Avellanedy i Sashy Stoikov'a do obrotu walorami kontraktów terminowych na towary pochodzenia rolniczego o dużej płynności oraz możliwość jego praktycznego zastosowania.
EN
In this work, the possibility of assessing traditional investment strategy based on the pivot points for using with other than the commonly used criterion is examined. The authors attempted to apply the Matthews Correlation Coefficient (further reffered as MCC) criterion based on a confusion matrix when assessing the strategy to include more factors than the traditional criteria (such as profit, profit vs. Risk, Sharpe ratio, Calmar ratio) and to express these factors by one number. The criterion based on a confusion matrix is, in authors beliefs, unique in this application and gives a fairly valuable estimation of trading strategy. An example of several strategies tested on EURUSD 1h time series in selected intervals in the years 2012-2013 is considered. Among these strategies there is a simple strategy based on the concept of pivot points levels and more complex derivative strategies, based on the vector of optimized values of certain parameters. These strategies are evaluated using both traditional criteria and modification of MCC proposed by the authors.
5
Content available remote Rough Set Based Reasoning About Changes
EN
We consider several issues related to reasoning about changes in systems interacting with the environment by sensors. In particular, we discuss challenging problems of reasoning about changes in hierarchical modeling and approximation of transition functions or trajectories. This paper can also be treated as a step toward developing rough calculus.
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