Financial Trading Model with Stock Bar Chart Image Time Series with Deep Convolutional Neural Networks

Even though computational intelligence techniques have been extensively utilized in financial trading systems, almost all developed models use the time series data for price prediction or identifying buy-sell points. However, in this study we decided to use 2-D stock bar chart images directly without introducing any additional time series associated with the underlying stock. We propose a novel algorithmic trading model CNN-BI (Convolutional Neural Network with Bar Images) using a 2-D Convolutional Neural Network. We generated 2-D images of sliding windows of 30-day bar charts for Dow 30 stocks and trained a deep Convolutional Neural Network (CNN) model for our algorithmic trading model. We tested our model separately between 2007-2012 and 2012-2017 for representing different market conditions. The results indicate that the model was able to outperform Buy and Hold strategy, especially in trendless or bear markets. Since this is a preliminary study and probably one of the first attempts using such an unconventional approach, there is always potential for improvement. Overall, the results are promising and the model might be integrated as part of an ensemble trading model combined with different strategies.Comment: accepted to be published in Intelligent Automation and Soft Computing journa

Estimation of Hardgrove grindability index of Turkish coals by neural networks

In this research, different techniques for the estimation of coal HGI values are studied. Data from 163 sub-bituminous coals from Turkey are used by featuring I I coal parameters, which include proximate analysis, group maceral analysis and rank. Nonlinear regression and neural network techniques are used for predicting the HGI values for the specified coal parameters. Results indicate that a hybrid network which is a combination of 4 separate neural networks gave the most accurate HGI prediction and all of the neural network models, outperformed non-linear regression in the estimation process

Support Vector Regression and Computational Fluid Dynamics Modeling of Newtonian and Non-Newtonian Fluids in Annulus With Pipe Rotation

The estimation of the pressure losses inside annulus during pipe rotation is one of the main concerns in various engineering professions. Pipe rotation is a considerable parameter affecting pressure losses in annulus during drilling. In this study, pressure losses of Newtonian and non-Newtonian fluids flowing through concentric horizontal annulus are predicted using computational fluid dynamics (CFD) and support vector regression (SVR). SVR and CFD results are compared with experimental data obtained from literature. The comparisons show that CFD model could predict frictional pressure gradient with an average absolute percent error less than 3.48% for Newtonian fluids and 19.5% for non-Newtonian fluids. SVR could predict frictional pressure gradient with an average absolute percent error less than 5.09% for Newtonian fluids and 5.98% for non-Newtonian fluids

Hole-Cleaning Performance of Gasified Drilling Fluids in Horizontal Well Sections

This study aims to investigate the hole-cleaning process during the flow of a drilling fluid consisting of a gas and a liquid phase through a horizontal annulus. Experiments have been conducted using the Middle East Technical University (METU) multiphase flow loop under a wide range of air- and water-flow rates while introducing cuttings into the annulus for different amounts. Data have been collected for steady-state conditions (i.e., liquid, gas, and cuttings injection rates are stabilized). Collected data include flow rates of liquid and gas phases, frictional pressure drop inside the test section, local pressures at different locations in the flow loop, and high-speed digital images for identification of solid, liquid, and gas distribution inside the wellbore. Digital image-processing techniques are applied on the recorded images for volumetric phase distribution inside the test section, which are in dynamic condition. The effects of liquid and gas phases are investigated on cuttings-transport behavior under different flow conditions. Observations showed that the major contribution for carrying the cuttings along the wellbore is the liquid phase. However, as the gas-flow rate is increased, the flow area left for the liquid phase dramatically decreases, which leads to an increase in the local velocity of the liquid phase causing the cuttings to be dragged and moved, or a significant erosion on the cuttings bed. Therefore, increase in the flow rate of gas phase causes an improvement in the cuttings transport although the liquid-phase flow rate is kept constant. On the basis of the experimental observations, a mechanistic model that estimates the total cuttings concentration and frictional pressure loss inside the wellbore is introduced for gasified fluids flowing through a horizontal annulus. The model estimations are in good agreement with the measurements obtained from the experiments. By using the model, minimum liquid- and gas-flow rates can be identified for having an acceptable cuttings concentration inside the wellbore as well as a preferably low frictional pressure drop. Thus, the information obtained from this study is applicable to any underbalanced drilling operation conducted with gas/liquid mixtures, for optimization of flow rates for liquid and gas phases to transport the cuttings in the horizontal sections in an effective way with a reasonably low frictional pressure loss

A New Model to Determine the Two-phase Drilling Fluid Behavior Through Horizontal Eccentric Annular Geometry, Part A: Flow Pattern Identification and Liquid Hold-up Estimation

Flow patterns, liquid holdup, and frictional pressure gradient are three importance parameters to study the multiphase drilling fluid behavior. Although two-phase fluid flow is studied in detail for pipes, there exists a lack of information about aerated fluid flow behavior inside a wellbore. This study aims to identify the flow patterns of gasified fluids flowing inside a horizontal annulus, and to develop a method for measurement of liquid holdup by using the image processing techniques. Experiments have been conducted at Middle East Technical University (METU) Multiphase Flow Loop using air-water mixtures with various in-situ flow velocities. A digital high-speed camera is used for recording each test dynamically for the identification of flow patterns and the measurement of liquid holdup

A New Model to Determine the Two-phase Drilling Fluid Behaviors through Horizontal Eccentric Annular Geometry, Part B: Frictional Pressure Losses Estimation

Drilling with aerated muds is becoming more often used in underbalanced drilling operations. One of the major challenges that has to be faced in such operations is the estimation of the physical behavior of aerated fluids inside the annulus. In this study, experiments have been conducted at METU Multiphase Flow Loop using air-water mixtures with various in-situ flow velocities of 0-120 and 0-10 ft/s, respectively. This study aims to develop a model to estimate the frictional pressure losses for two-phase flow through horizontal eccentric annular geometry. In order to estimate the frictional pressure losses, three different methods were developed: (i) definition of new friction factors by using experimental data; (ii) modification of Lockhart-Martinelli pressure loss correction factor; and (iii) modification of Beggs and Brill model by changing the equation constants. The comparison of the developed models with experimental data has shown that frictional pressure losses can be estimated with a reasonable accuracy

Pressure drop estimation in horizontal annuli for liquid-gas 2 phase flow: Comparison of mechanistic models and computational intelligence techniques

Frictional pressure loss calculations and estimating the performance of cuttings transport during underbalanced drilling operations are more difficult due to the characteristics of multi-phase fluid flow inside the wellbore. In directional or horizontal wellbores, such calculations are becoming more complicated due to the inclined wellbore sections, since gravitational force components are required to be considered properly. Even though there are numerous studies performed on pressure drop estimation for multiphase flow in inclined pipes, not as many studies have been conducted for multiphase flow in annular geometries with eccentricity. In this study, the frictional pressure losses are examined thoroughly for liquid-gas multiphase flow in horizontal eccentric annulus. Pressure drop measurements for different liquid and gas flow rates are recorded. Using the experimental data, a mechanistic model based on the modification of Lockhart and Martinelli [18] is developed. Additionally, 4 different computational intelligence techniques (nearest neighbor, regression trees, multilayer perceptron and Support Vector Machines - SVM) are modeled and developed for pressure drop estimation. The results indicate that both mechanistic model and computational intelligence techniques estimated the frictional pressure losses successfully for the given flow conditions, when compared with the experimental results. It is also noted that the computational intelligence techniques performed slightly better than the mechanistic model. (C) 2014 Elsevier Ltd. All rights reserved