The Nucleus

Deployment of a Smart Trading System for Intelligent Stock Trading

I. Ali, S.Z. Mahfooz, N.Q. Mehmood, M.N. Mehmood — Wed, 07 Dec 2022 00:00:00 +0500

In this article we evaluate the deployment of a smart trading system that exploits the features of different technical indicators for intelligent stock trading. Depending on their behaviors, these indicators help in trading under various market conditions. Our smart trading system uses a unified trading strategy that selects five indicators from three well-known categories referred as leading, lagging, and volatility indicators. The trading system looks for common trend signals from at least three indicators within a certain period of time. Collectively generated signals from the technical indicators are used to train a neural network model. The trained neural network model is then used to produce buy and sell signals for trading in stocks. The system is efficient and convenient to use for both individual traders and fund managers. We tested the model on actual data collected from Saudi Stock Exchange and New York Stock Exchange. The performance of the model was checked in terms of percentage returns. The results of the proposed trading model were compared with the benchmark trading strategy. The deployed smart trading system is efficient to produce significant returns over the longer and shorter timeframes.

Reliance of the Strength of a Sandstone on Petrographic Attributes: A Preliminary Study

M. Yar, A. Meraj, A. Basit, F. Naseem, M.A. Khan — Wed, 11 Jan 2023 00:00:00 +0500

For the present preliminary geotechnical investigation, sandstone from Dandot Formation of Permian age has been selected with eighteen samples that were collected for detailed petrographic analysis while three bulk samples were collected for geotechnical analysis. On the basis of grain size sandstone of Dandot Formation was divided into three parts. The lower part was mostly very fine, middle part was generally fine and upper part was of medium grain. Texturally and mineralogically the sandstone was sub-mature. Framework grains in the studied samples essentially consisted of variable amount of quartz (62 to 73%), feldspar (10 to 19%), and rock fragments (3 to 6%). Accessory minerals include muscovite, biotite, iron ore minerals, zircon and glauconite. The cement type in the samples was clayey ferruginous. The modal composition of the sandstone falls in the category of Arkose. The strength test including unconfined compressive strength, unconfined tensile strength, shear strength, specific gravity, and water absorption tests were employed on the rock samples to assess their geotechnical utilities. After evaluating these properties, the acquired test results indicated that the sandstone is very weak and hence cannot be used for construction purposes.

Spatio-Temporal Analysis of Land Use Change and Its Driving Factors in Layyah, Punjab, Pakistan

M. Sajid, M. Mohsin, M. Mobeen, A. Rehman, A. Rafique, M. Rauf, G. Ali — Fri, 27 Jan 2023 00:00:00 +0500

Specific objective of this study was to find out the distribution of various land use changes in District Layyah from 2000 to 2020 using geographic information system (GIS) and remote sensing (RS) techniques, and the forces or factors that lead to land use change. District Layyah has experienced remarkable land use and land cover (LULC) changes for the past three decades. Three Landsat satellite images i.e. thematic mapper (TM), Landsat enhanced thematic mapper plus (ETM+) and operational land imager (OLI)/ TIRS for the years 2000, 2010 and 2020 were acquired from USGS website in order to detect the land use changes. By using ERDAS Imagine software, the maximum likelihood classification was employed in order to classify the images. The spatial and spectral distribution of five land use types was made including i.e. Water, Built-up, Vegetation, Desert, Bare and Sparse land. Ground Truth points were noted and these points were used for the validation and classification of the images. This accuracy showed an overall accuracy rate of 85% with a Kappa coefficient of 0.9 which demonstrated the basic classification method because the images used in the research were highly good. Results showed that the rise was revealed in Vegetation, Built-up and Water land uses from the year 2000 to 2020. On the other side, the decrease in Bare and Sparse land and Desert land use was calculated. The main driving factors behind these LULC changes were found the growth in population, agro-technological advancement and various physical factors (e.g. availability of water and so on), resulting an increase in built-up area. Present research will be beneficial in understanding the most important land use changes to estimate the future change trends in various land use classes for policy making and land use management.