Automated Algorithmic Trading for Cryptocurrencies

The proposed project work is totally supported and easy yet effective strategy named as Martingale. An automatic system which only requires only some pre-coded instructions to execute trades on variety of market variables starting from asset price to trading volume. The strategy along with each cryptocurrency, the benchmark against which the algorithm is tested is that the market’s performance. Returns are compared with the buying and so multiplying the trade volume at each loss and different scenarios are analysed to work out the chance related to the buying compared with an algorithmic strategy. Results are going to be in love with the market’s actual trends and also with some alternate possible trends to check all market scenarios. An internet interface will accompany the presentation allowing the users to check the strategies by entering their parameters and instantly seeing the results

Modeling the Business Environment of an Energy Holding in the Formation of a Financial Strategy

This article consists of the development of a set of methodological provisions concerning the identification of the features of the influence of the business environment on the effectiveness of the implementation of the company’s financial strategy and the development of a system for its adaptation to the conditions of a dynamic external environment. The purpose of this article is to build an economic and mathematical model to identify the main elements of the business environment that affect the company’s strategy, the formation of methods for evaluating the effectiveness of the implementation of a financial strategy taking into account such influence. The author’s contribution consists in the development of an effective financial algorithmic strategy of the energy holding, considering the influence of the environmental factors. Hypothesis: the use of mathematical models of the business environment will increase the efficiency of energy holding management in the field of finance and investments. The scientific novelty of this article lies in the development of an algorithm that allows for obtaining an integral assessment of the impact of external and internal factors of the energy holding’s business environment on its financial strategy using taxonomy methods, multidimensional statistical analysis and cluster and discriminant models. Results: the authors have developed a model of the influence of the energy holding’s business space, which allows improving the interaction of financial flows within the holding and obtaining an optimal distribution of financial resources, taking into consideration the dynamic factors of the company’s external environment.

STRATEGY OF AUTOMATED CREATION OF MULTICOMPONENT SOFTWARE TOOLS BASED ON OPTIMIZATION OF KNOWLEDGE SOURCES

В статье описывается алгоритмическая стратегия, способствующая созданию многокомпонентных программных средств. Представлено определение задачи идентификации набора компонентов, математическая формулировка, процедура решения и некоторые результаты вычислений. The article describes an algorithmic strategy that contributes to the creation of multicomponent software tools. The definition of the problem of identifying a set of components, a mathematical formulation, a solution procedure and some calculation results are presented.

MULTI-PHACET - MULTIdimensional clinical phenotyping of hospitalised acute COPD ExacerbaTions

BackgroundThe generic term “exacerbation” does not reflect the heterogeneity of acute exacerbations of COPD (AECOPD). We utilised a novel algorithmic strategy to profile exacerbation phenotypes based on underlying aetiologies.MethodsPatients hospitalised for AECOPD (n=146) were investigated for aetiological contributors summarised in a mnemonic acronym ABCDEFGX (A=Airway virus, B=Bacterial, C=Coinfection, D=Depression/anxiety, E=Eosinophils, F=Failure (cardiac), G=General environment, X=Unknown). Results from clinical investigations were combined to construct AECOPD phenotypes. Relationships to clinical outcomes were examined for both composite phenotypes and their specific aetiological components. Aetiologies identified at exacerbation were reassessed at outpatient follow-up.ResultsHospitalised AECOPDs were remarkably diverse, with 26 distinct phenotypes identified. Multiple aetiologies were common (70%) and unidentifiable aetiology rare (4.1%). If viruses were detected (29.5%), patients had longer hospitalisation (7.7±5.6 versus 6.0±3.9 days, p=0.03) despite fewer “frequent exacerbators” (9.3% versus 37%, p=0.001) and lower mortality at 1 year (p=0.03). If bacterial infection was found (40.4%), patients were commonly “frequent exacerbators” (44% versus 18.4%, p=0.001). Eosinophilic exacerbations (28%) were associated with lower pH (7.32±0.06 versus 7.36±0.09, p=0.04), higher PvCO2 (53.7±10.5 versus 48.8±12.8, p=0.04), greater NIV usage (34.1% versus 18.1%) but shorter hospitalisation (4[3–5] versus 6[4–9] days, p<0.001) and lower infection rates (41.4% versus 80.9%, p<0.0001). Cardiac dysfunction and severe anxiety/depression were common in both infective and non-infective exacerbations. Characteristics identified at exacerbation often persisted after recovery.ConclusionsHospitalised AECOPDs have numerous causes, often in combination, that converge in complex, multi-faceted phenotypes. Clinically important differences in outcomes suggest that a phenotyping strategy based on aetiologies can enhance AECOPD management.

Application Research of File Fingerprint Identification Detection Based on a Network Security Protection System

A DLP (data loss prevention) system usually arranges network monitors at the network boundary to perform network traffic capture, file parsing, and strategy matching procedures. Strategy matching is a key process to prevent corporate secret-related documents from leaking. This paper adopts the document fingerprint similarity detection method based on the SimHash principle and customizes the KbS (Keyword-based SimHash) fingerprint, PbS (Paragraph-based SimHash) fingerprint, and SoP (SimHash of Paragraph) fingerprint, three different feature extraction SimHash algorithms for strategy matching to detect. The parsed unstructured data is stored as a file type in.txt format, and then a file fingerprint is generated. Matching the established sensitive document library to calculate the Hamming distance between the fingerprints, the Hamming distance values under different modification degrees are summarized. The experimental results reveal that the hybrid algorithmic strategy matching rules with different levels and accuracy are established. This paper has a reference role for the leakage prevention research of enterprise sensitive data.

Statistical analysis of small twitter data collection to identify dengue outbreaks

This study presents an algorithmic strategy to analyze a small set of social network information to monitor the dengue disease. Previous studies have achieved similar results based on large datasets of Twitter microblogs. In this study, we successfully map dengue cases using a small data collection of tweets from a medium-size city. A set of modules were constructed to collect, categorize, and display dengue-related tweets. We compared the collected tweets with real data from confirmed dengue cases. We showed a significant correlation between the number of confirmed dengue cases and the number of dengue-related tweets, even considering such a small dataset. The results of this approach may be relevant in public health policies.

In Field Detection of Downy Mildew Symptoms with Proximal Colour Imaging

This paper proposes to study the potentialities of on-board colour imaging for the in-field detection of a textbook case disease: the grapevine downy mildew. It introduces an algorithmic strategy for the detection of various forms of foliar symptoms on proximal high-resolution images. The proposed strategy is based on structure–colour representations and probabilistic models of grapevine tissues. It operates in three steps: (i) Formulating descriptors to extract the characteristic and discriminating properties of each class. They combine the Local Structure Tensors (LST) with colorimetric statistics calculated in pixel’s neighbourhood. (ii) Modelling the statistical distributions of these descriptors in each class. To account for the specific nature of LSTs, the descriptors are mapped in the Log-Euclidean space. In this space, the classes of interest can be modelled with mixtures of multivariate Gaussian distributions. (iii) Assigning each pixel to one of the classes according to its suitability to their models. The decision method is based on a “seed growth segmentation” process. This step exploits statistical criteria derived from the probabilistic model. The resulting processing chain reliably detects downy mildew symptoms and estimates the area of the affected tissues. A leave-one-out cross-validation is conducted on a dataset constituted of a hundred independent images of grapevines affected only by downy mildew and/or abiotic stresses. The proposed method achieves an extensive and accurate recovery of foliar symptoms, with on average, a 83% pixel-wise precision and a 76% pixel-wise recall.

VERSO: a comprehensive framework for the inference of robust phylogenies and the quantification of intra-host genomic diversity of viral samples

SummaryWe introduce VERSO, a two-step framework for the characterization of viral evolution from sequencing data of viral genomes, which improves over phylogenomic approaches for consensus sequences. VERSO exploits an efficient algorithmic strategy to return robust phylogenies from clonal variant profiles, also in conditions of sampling limitations. It then leverages variant frequency patterns to characterize the intra-host genomic diversity of samples, revealing undetected infection chains and pinpointing variants likely involved in homoplasies. On simulations, VERSO outperforms state-of-the-art tools for phylogenetic inference. Notably, the application to 6726 Amplicon and RNA-seq samples refines the estimation of SARS-CoV-2 evolution, while co-occurrence patterns of minor variants unveil undetected infection paths, which are validated with contact tracing data. Finally, the analysis of SARS-CoV-2 mutational landscape uncovers a temporal increase of overall genomic diversity, and highlights variants transiting from minor to clonal state and homoplastic variants, some of which falling on the spike gene. Available at: https://github.com/BIMIB-DISCo/VERSO.

ELECTOR: evaluator for long reads correction methods

The error rates of third-generation sequencing data have been capped &gt;5%, mainly containing insertions and deletions. Thereby, an increasing number of diverse long reads correction methods have been proposed. The quality of the correction has huge impacts on downstream processes. Therefore, developing methods allowing to evaluate error correction tools with precise and reliable statistics is a crucial need. These evaluation methods rely on costly alignments to evaluate the quality of the corrected reads. Thus, key features must allow the fast comparison of different tools, and scale to the increasing length of the long reads. Our tool, ELECTOR, evaluates long reads correction and is directly compatible with a wide range of error correction tools. As it is based on multiple sequence alignment, we introduce a new algorithmic strategy for alignment segmentation, which enables us to scale to large instances using reasonable resources. To our knowledge, we provide the unique method that allows producing reproducible correction benchmarks on the latest ultra-long reads (&gt;100 k bases). It is also faster than the current state-of-the-art on other datasets and provides a wider set of metrics to assess the read quality improvement after correction. ELECTOR is available on GitHub (https://github.com/kamimrcht/ELECTOR) and Bioconda.