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

A Hierarchial Neural Network Implementation for Forecasting

In this paper, a hierarchical neural network architecture for forecasting time series is presented. The architecture is composed of two hierarchical levels using a maximum likelihood competitive learning algorithm. The first level of the system has three experts each using backpropagation and a gating network to partition the input space in order to map the input vectors to the output vectors. The second level of the hierarchical network has an expert using fuzzy ART for producing the correct trend coming from the first level. The experiments show that the resulting network is capable of forecasting the changes in the input and identifying the trends correctl

Evaluation and Optimization Study on a Hybrid EOR Technique Named as Chemical-Alternating-Foam Floods

A novel EOR method called Chemical-Alternating-Foam (CAF) floods was developed to overcome the drawbacks of the conventional foam flooding, e.g., insufficient amount of in-situ foams and severe foam collapse and surfactant retention. CAF flooding showed greater capability to reduce the brine permeability compared to the continuous foam flooding. The overall oil recovery of the CAF flooding was 10-15% higher compared to that of the conventional foam flooding with the same amount of CO2 chemicals injected. For the continuous CO2 foam flooding, the best displacement performance was obtained at the 60% water cut, while the CAF floods yielded the most amount of incremental oil at the 98% water cut. Under the experimental condition, the optimal foam quality, foam/chemical slug size ratio and cycle number for the proposed CAF floods were selected as 80%, 1:1 and 3, respectively. The proposed hybrid process is a viable and effective method significantly strengthening the conventional foam flooding

A new approach for the prediction of ash fusion temperatures: A case study using Turkish lignites

Prediction of ash fusion temperatures by using the chemical composition of the ash has previously been conducted only with linear correlations. In this study, a new technique is presented for predicting the fusibility temperatures of ash. Non-linear correlations are developed by using the chemical composition of ash (eight oxides) and coal parameters (ash content, specific gravity, Hardgrove index and mineral matter content). Regression analyses are conducted using information for Turkish lignites. Regression coefficients and variances of non-linear and linear correlations are compared. The results show that the non-linear correlations are superior to linear correlations for estimating ash fusion temperatures

Estimating Flow Patterns and Frictional Pressure Losses of Two-Phase Fluids in Horizontal Wellbores Using Artificial Neural Networks

Underbalanced drilling achieved by gasified fluids is a very commonly used technique in many petroleum-engineering applications. This study estimates the flow patterns and frictional pressure losses of two-phase fluids flowing through horizontal annular geometries using artificial neural networks rather than using conventional mechanistic models. Experimental data is collected from experiments conducted at METU-PETE Flow Loop as well as data from literature in order to train the artificial neural networks. Flow is characterized using superficial Reynolds numbers for both liquid and gas phase for simplicity. The results showed that artificial neural networks could estimate flow patterns with an accuracy of 5%, and frictional pressure losses with an error less than 30%. It is also observed that proper selection of artificial neural networks is important for accurate estimations