text:
- Penelope Walsh
- 2 years ago
- Views:
1 PARALLEL ARCHITECTURES TO IMPROVE A GA BASED REAL-TIME SYSTEM FOR TRADING THE STOCK MARKET. IVÁN CONTRERAS FERNÁNDEZ - DÁVILA MÁSTER EN INVESTIGACIÓN EN INFORMÁTICA. FACULTAD DE INFORMÁTICA UNIVERSIDAD COMPLUTENSE DE MADRID Trabajo Fin Máster en Ingeniería de la Industria June, 20th 2011 Score:9,5 Directors: Jose Ignacio Hidalgo Perez (UCM) Laura Núñez-Letamendia (IE Business School)
2 Autorización de difusión Iván Contreras Fernández - Dávila June, 20th 2011 El abajo rmante, matriculado en el Máster en Investigación en Informática de la Facultad de Informática, autoriza a la Universidad Complutense de Madrid (UCM) a difundir y utilizar con nes académicos, no comerciales y mencionando expresamente a su autor el presente Trabajo Fin de Máster: "PARALLEL ARCHITECTURES TO IMPROVE A GA BASED REAL-TIME SYSTEM FOR TRADING THE STOCK MARKET", realizado durante el curso académico bajo la dirección de Jose Ignacio Hidalgo Perez y con la colaboración externa de dirección de Laura Nuñez Letamendi del I.E, y a la Biblioteca de la UCM a depositarlo en el Archivo Institucional E-Prints Complutense con el objeto de incrementar la difusión, uso e impacto del trabajo en Internet y garantizar su preservación y acceso a largo plazo.
3 Resumen en castellano La investigación y el desarrollo de sistemas de trading son cada vez más frecuentes ya que pueden alcanzar un alto potencial en la predicción de los movimientos bursatiles. El uso de estos sistemas permite manejar una enorme cantidad de datos relacionados con factores que afectan directamente al rendimiento de las inversiones (variables macroeconómicas, información de las compañías, indicadores industriales, variables de mercado, etc.), además evita las reacciones psicológicas asociadas a la inversión en los mercados nancieros. Los movimientos de los mercados bursátiles son continuos a lo largo de cada día, lo que reclama que los sistemas de trading deban ser apoyados por motores de analisis muy potentes, ya que la cantidad de datos necesarios para hacer frente a unas buenas predicciones crece, mientras que el tiempo de respuesta se acorta. Numerosos estudios documentan el uso de algoritmos genéticos (AG) como eje principal de los sistemas de trading. Los resultados experimentales proporcionados en este documento muestran diferentes formás de combinar el uso de AG y sistemas de paralelización. La paralelización mediantes las técnicas propuestas proporcionan una cuantiosa aceleración en la potencia y la capacidad de búsqueda de los AG para este tipo de aplicaciones nancieras. Por otra parte, el analisis previo a la paralelización nos permite implementar mejoras para las anteriores aproximaciones de AG. Respecto a los resultados de inversión, se pueden demostrar un 870% de ganancias para el S&P 500 en un plazo de 10 años ( ), cuando la ganancia media del índice S&P 500 en el mismo período fue de 273%. Palabras clave Boinc, computación distribuida, sistema de trading, algoritmo genético, Jacket, GPU
4 Abstract Research and development of trading systems are becoming more frequent as they can reach a high potential in the prediction of market movements. The use of these systems allows to manage a huge amount of data related to the factors aecting investments performance (macroeconomic variables, company information, industrial indicators, market variables, etc.) while avoids the psychological reactions of traders when they invest in - nancial markets. The movements in the stock markets are continuous throughout each day, which requires that trading systems should be supported by very powerful engines, since the amount of data to deal with grows while the respond time required to support trades gets shorter. Numerous studies document the use of genetic algorithms (GA) as the engine driving mechanical trading systems. The experimental results provided in this paper show dierent ways of combining the use of AG and parallelization systems. Parallelization using the proposed techniques provide a substantial acceleration in the power and capacity of the GA search for this type of nancial applications. Moreover, a previous analysis of the paralellization allows us to implement improvements to the previous approaches AG. With regard to investment results, we demonstrate a 870% of earnings for the S&P 500 over a period of 10 years ( ), when the average gain in the S&P 500 over the same period was 273%. Keywords Boinc, grid computing,trading system, genetic algorithm, Jacket, GPU
5 Contents Index List of Figures List of Tables Thanks Dedication i iii v vi vii 1 Introduction 1 2 Trading System Formulation of the investment problem Sample data Genetic algorithm Two types of nancial analysis Fundamental analysis Technical analysis Parallelization with a grid system Introducction to BOINC The BOINC architecture BOINC server Server like a virtual machine Independent BOINC server BOINC projects Project structure Congure a project Applications, versions and platforms Jobs and templates Wrapper BOINC database BOINC client: Inputs and outputs Modications in the code Parallelization tasks Input pre-process and output post-process Conguration i
6 3.7.3 Matlab and BOINC Experimental results Metrics Execution Time analysis of the algorithm in the grid Parallelization with a computer graphic card CUDA architecture Execution time analysis in CPU Jacket Parallelization Tasks Modications in the code Experimental Results Metrics Execution Time analysis of the algorithm in the GPU Analysis of the execution time evolution Taking advantage of the parallelization for trading the stock market Conclusions Grid computing versus GPU computing Performance Final conclusions Publications Bibliography 70 A Install a Boinc server step by step 71 A.1 Introduction A.2 Install the Boinc sources A.3 Project-specic conguration B Boinc project step by step 75 B.1 Create a Boinc project C Create a executable for Matlab 80 ii
7 List of Figures 2.1 Typical genetic algorithm work ow Fundamental Trading Rules Technical Trading Rules Typical dierences between fundamental and technical analysis Example of a technical indicator MACD The typical BOINC project structure The typical BOINC project structure Wrapper operation Inputs and outputs BOINC client structure Detail of EMCO Remote Shutdown screenshot Detail of BOINC Viewer screenshot Execution times in the grid (y- axis) for 500 generations and dierent number of individuals (x- axis) Speed-up in the grid General architecture CPU-GPU GeForce GTX 280 GPU Parallel Computing Architecture Detail of a Thread Procesing Cluster (TPC) Genetic algorithm execution in the CPU. It has been run for a company with 5000 individuals; 500 generations and the roulette wheel selection algorithm. Total time = 2266 seconds Typical genetic algorithm work ow Execution time analysis of the genetic algorithm in the CPU. (One company, 5000 individuals, 500 generations and tournament selection algorithm). Total Time = 648 seconds Genetic algorithm execution in the GPU. It has been run for a company of 5000 individuals, 500 generation and the tournament selection algorithm Total Time= 66 seconds Execution times (y- axis) for 500 generations and dierent number of individuals (x- axis) Speed-up on the GPU (y-axis)with variable number of individuals (x-axis) and 500 generations Execution times (y-axis) for 100 generations and dierent number of individuals (x-axis) Speed-up : Speed-up on the GPU (y-axis) with variable number of individuals(xaxis) and 100 generations iii
8 4.12 Execution of the GA in the GPU. It is run for 10 companies, with individuals, 500 generations and tournament selection algorithm. Total time =109 seconds, partial time for company =10.9 seconds Execution times comparative (y- axis) for 500 generations and dierent number of individuals (x- axis) Speed-up on the dierents platforms (y-axis)with variable number of individuals (x-axis) and 500 generations iv
9 List of Tables 2.1 Formulation of indicators used for fundamental analysis Formulation of indicators used for technical analysis Main characteristics of the Server used in the experiments Main characteristics of the grid used in the experiments CPU Architectures used for comparison Main characteristics of the GPUs used in the experiments Returns obtained by the trading systems for the companies of the S&P 500 with available data during the period v
10 Agradecimientos Principalmente, quiero agradecer la preocupación y paciencia que han tenido conmigo durante la realización de este trabajo, mis tutores: Jose Ignacio Y Laura. Gracias por vuestra ayuda y por compartir vuestros conocimientos. vi
11 Dedicatoria Este trabajo está dedicado completamente a mis padres. Gracias por estar siempre ahi. vii
12 Chapter 1 Introduction The growing availability of data for nancial markets and companies, together with the increasing complexity of socioeconomic and nancial environment, tragically illustrated by the nancial crisis, makes it more dicult the decision making process for real time investments in stock markets. Complexity in this sense derives from the diculty of the assets valuation process due that each type of asset requires addressing its valuation with a separate approach to be able to modeling the factors aecting its performance in order to anticipate its risk adjusted return. The huge number of potential interrelated factors and their changing time patterns, aecting nancial assets, make the investment process remains a challenge. In the past years interest in mechanical or automatic trading systems has spread due to several factors: (i) the explosive amount of information available for companies and markets that makes it hard the analysis of more than a few stocks or assets (ii) the advance of inexpensive computing power (iii) the tremendous expansion of the globalization process symbolized for instance by the tremendous development of the hedge funds industry, a class of funds which invest in any kind of assets around the World (stocks, indexes, bonds, commodities, currencies, etc.) 1
13 (iv) the attempt to avoid the psychological aspects that biases the investment process (known in the literature as behavioral nance (32) Mechanical trading systems are systems based on rules that use market, business or macroeconomic information embedded in algorithms that look for the best combination of these rules to drive the stock trades in an attempt of obtaining the maximum possible return for a period. Mechanical trading systems have evolved from very simple If-then algorithms to more sophisticated models that use methods like articial intelligence, chaos theory, fractals, evolutionary algorithms, nonlinear stochastic representations, econo-physics models, etc., which are ultimately based on market, fundamental or macroeconomic data. In particular previous studies document the use of Genetic Algorithms to design and optimize automatic trading systems for the Stock Market (see 2; 27; 26; 19; 28). Furthermore, the investment industry has now begun to use GAs and other kinds of evolutionary algorithms to build automatic trading systems. Not only the biggest investment rms have these kind of systems, but smaller rms are also beginning to access GA tools through marketed software packages. One example of these software packages is GeneHunter, marketed by MBAWare an American Company (http://www.mbaware.com) located in Arlington, Virginia in the Washington DC metro area, with more than 2500 customers in 40 countries. The rm states on its website that GeneHunter is a powerful software solution for optimization problems which utilizes a state-of-the-art genetic algorithm methodology pointing out that GeneHunter has been used to generate rules to predict a rise in the NYSE index. This is just one example of an increasing number of GA-based software for investment management. In (28) the authors describe a trading system designed with GAs that use dierent kind of rules with market, macroeconomic and companies information, which is applied to trade, in a daily base, the companies belonging to the S&P 500 index. One of the diculties the authors claim is the computational time required for training the trading system with daily data of stocks prices. This restriction is even more critical when we take into account that the majority of traders invest in an intra-day base (what means that the investment 2
14 positions are canceled during the same day). Therefore, the operative in nancial markets seems to recommend the use of an even shorter frequency in data when training and applying mechanical trading systems. However, the focus on intra day instead daily data cause some diculties for the design and application of these trading systems because of the shorter time of respond we require, since it is not possible to wait for hours to obtain the investment decision result provided by the mechanical trading system when the investments have to be done continuously during the day. The necessity of speed up the GA process, to get in time good results for this kind of trading applications, as well as its possibilities of parallelization pushed us to think about new ways of implementing these applications. In this work we propose to combine the use of two dierents parallel computer architectures to speed up the functioning of a GA used to design trading systems to invest in stocks. First, we have used a corporative grid, and later, a graphics device, both plataforms have been used for to obtain a prot in computation time. We can found lot of cases in the literature where they used this plataforms for to optimize time. Experimental Results show how the combination of the GA and the parallel architectures allows us to obtain solutions for real time (or intra-day data) investment decision. It is very dicult to have access to intra-day data since the commercial databases for stock prices and stock exchange markets do not provide frequency data inferior to that of daily prices. The way to accede to intra-day data is through an investment bank, and usually they do not like to publish results obtained in joined research projects. Nevertheless since the only dierent between a trading-system based on intra-day data versus daily data is the respond time required, that when dealing with intra-day data should be rather shorter, we apply our trading systems to daily data, because of the mentioned diculty of accede to intra-day data. We show how we can use and congure the GA on the parallel architectures in order to analyze results for dierent companies at once. The rest of the document is organized as follows. 3
15 In the next chapter, Chapter 2, we formulate the investment problem describing the proposed trading system to cope with it. Furthermore, the Chapter 4 introduces with some detail the implementation of the original GA for the developed application of a mechanical trading system. In Chapter 3 we explain the parallelization in the Boinc platform. We take a look to the general achitecture of Boinc and later we carry out several test of performance. Chapter 4 describes the general parallel architecture, in this case the Cuda architecture. Moreover we show the parallelization process with high detail. In the last sections of this chapter we execute the parallized program in the computer graphic card and we show the experimental results. Finally, we summarize and conclude in Chapter 5, where we show both parallel architectures in opposition. In addition, in the last pages we can nd several appendixes where we explain details for some parts of the work for develop this document. 4
16 Chapter 2 Trading System 2.1 Formulation of the investment problem The investment problem is dened as to maximize return (or risk adjusted return) for a specic time period when investing long or short-sell in a nancial asset (in our case a stock). Since the performance of investment decisions in stock markets is inuenced by a wideness of factors of dierent type: political, macroeconomic, regulatory, local, international, etc. that are uncertain, there is not a single and perfect rule with a specic parameter or threshold value that can be used to maximize future returns, but a broad set of potential rules which combine indicators, representing dierent factors and driven by a range of values in their parameters. Therefore, the investment problem consists rst of nding the best combination of indicators and second of ne-tuning the parameters for these indicators to obtain the maximum return when applied to the investment decision making in a stock. Thus, the input set of variables for a trading system consists of the indicators to be used as investment criteria and the parameters being the threshold values for these indicators, while the output is the return obtained by the trading system this way dened. 2.2 Sample data We use diferents data for the trading system, the initial sample of rms comprises all companies included in the S&P 500 Index Constituent List for at least two quarters during the 5
17 period January 1986 to December 2006, with the exception of those belonging to the nance, insurance, and real estate industries. This gives us a total of 834 companies. The program need between 5 and 10 years of enterprise information to ne-tune the fundamental trading system. Firms with less than consecutively 20 quarters non-missing value before the listing year are deleted from the list. The nal sample size is 599 companies and the sample period ranges from 1976 Q Q4, among which only 24 companies survive throughout the full period. Finally the data provide observations for quarterly fundamental data and observations for daily technical data. The source of the data is from Compustat and CRSP databases. 2.3 Genetic algorithm A genetic algorithm (GA) is a search heuristic that mimics the process of natural evolution. As dened by Koza (see 33), "The genetic algorithm simulates Darwinian evolutionary processes and naturally occurring genetic operations on chromosomes"..."the genetic algorithm is a highly parallel mathematical algorithm that transforms a set (population) of individual mathematical objects, each with an associated tness value, into a new population using operators patterned after the Darwinian principle of reproduction and survival of the ttest and after naturally ocurring genetic operations...". This heuristic is routinely used to generate useful solutions to optimization and search problems. Genetic algorithms belong to the larger class of evolutionary algorithms, which generate solutions to optimization problems using techniques inspired by natural evolution, such as inheritance, mutation, selection, and crossover. Figure 2.1 represents the mainly work ow in the genetics algorithms. The functioning of the GA methodology is driven by dierent parameters: the crossover and mutation percents, the scaling of tness function in the reproduction process, the initial population and 6
18 Figure 2.1: Typical genetic algorithm work ow the numbers of generations. We considered important to mention that these parameters depend on a particular problem, this mean that the good results for a combination of a specic values cannot be extrapolated to others genetic algorithm problems. As it is well known, the design of a GA involves some key factors that will depend heavily on the characteristics of the problem at hand. One of them is the chromosome encoding used for representing the solutions by means of some type of code (binary, real, etc.). For codifying the chromosomes we adopted the following approach: Our GA is used to select the threshold values of the 4 indicators that comprise the trading systems or investment rules (see 2.2, 2.3), and we use binary code chains like these: [ , , 0110, ] where the rst 8 genes correspond to the threshold value of the short selling and long positions for the company multiple price ratio preselected indicator, the next 8 match to those of the company improvement or eciency preselected ratio, the next 4 to those of the leverage indicator (this type of indicator is only employed to generate short selling signals, that is why the chain for the parameters only contains 4 genes instead of 8) 7
19 Figure 2.2: Fundamental Trading Rules and the last 8 correspond to those of the company growth selected indicator. Hence, our chromosomes of trading systems comprise 28 binary genes giving a total search space of 2 28 = possible combinations, implying that the analysis of more than 2.5 millions of potential combinations of indicators with dierent threshold values would reach only 1% of the full search space. For selection we use the roulette wheel (16) method initially (on Boinc system) and tournament selection (on the GPU). Crossover and mutation are implemented as usual based on one-point crossover and mutation. The GA choice of threshold values is driven by the value of the tness function (the accumulated return obtained computed as described in section 2.4.1) when applying the trading systems to the sample data. Although this method has given promising results, the main problem of the initial approximation is the execution time. In this sense with a sequential execution of the GA we are not able to apply our proposal to real time problems with intra-day data. In the following sections we explain how we can approach this problem by using several parallel implementations. For this purpose we made rst an analysis of the execution time for the whole 8
20 Figure 2.3: Technical Trading Rules program and, based on the results, we implemented several important changes not only in the structure of the program, but also in the genetic operators. 2.4 Two types of nancial analysis When the objective of the analysis is to determine what stock to buy and at what price, there are two basic methodologies: fundamental analysis and technical analysis. Investors can use any or all of these dierent but somewhat complementary methods for stock picking. For example many fundamental investors use technicals for deciding entry and exit points. Many technical investors use the analysis fundamental to limit their universe of possible stock to good companies. Figure 2.4 shows the typical dierence in a middle term investment. The Y axis represent the price of a company in a stock market, and the X axis represents the price evolution in a year. The red arrows represents the possible signals of sell and buy that we could have done in operations using the technical analysis. On other hand, if we had used the fundamental 9
21 Figure 2.4: Typical dierences between fundamental and technical analysis. analysis, we would have done the green marks as signals of sell and buy. When the arrows point down means "sell" and when these arrows are pointing upward means "buy". As we can observe in the Figure 2.4, the fundamental analysis obtains less benet, to change for a less intervention in the process of investments, that mean, more freedom for the investors Fundamental analysis Fundamental analysis maintains that markets may misprice a security in the short run but that the "correct" price will eventually be reached. Prots can be made by trading the mispriced security and then waiting for the market to recognize its "mistake" and reprice the security. Fundamental analysis of a business involves analyzing its nancial statements and health, its management and competitive advantages, and its competitors and markets. When applied to futures and forex, it focuses on the overall state of the economy, interest rates, production, earnings, and management. Millions of indicators can be used, thus the rst step to solve the investment problem is to select the indicators to be included in the decision making trading system. In this document 10
22 we follow Jiang et al. (2009) who carried out an exhaustive analysis of the investment literature to pre-select the set of indicators to be used in their trading systems driven by a GA: price-earnings ratio, price-cash ow ratio, price-book value ratio; price-earnings ratio (PER), price-book value ratio (PBV), price-cash ow ratio (PCF), debt over book equity (D/BE), sales growth (SG), net income growth (NIG), cash ow growth (CFG), return on assets (ROA), turnover growth (TOG), and prot margin growth (PMG). All these indicators have been reported to be useful in the literature on investments (see 3; 5; 9; 11; 15; 14; 31, among others). The formulation for their initial set of indicator is presented in Table 2.1. For a broader description see the original paper by these authors (19). To compute these indicators (19) obtained from the COMPUSTAT database, quarterly data on nine items from the companies nancial statements (COMPUSTAT codes in parenthesis) for the period January 1986 to December 2006: total assets (ATQ) total common ordinary equity (CEQQ) cash and short-term investments (CHEQ) debt in current liabilities (DLCQ) total long-term debt (DLTTQ) total depreciation and amortization (DPQ) net income / loss (NIQ) total revenue (REVTQ) common shares outstanding (CSHOQ). Prices (PRCCQ) were obtained from the Center for Research in Security Prices (CRSP) on a quarterly basis and were adjusted by stock splits and stocks dividends. To ensure 11
23 Table 2.1: Formulation of indicators used for fundamental analysis Indicators representing Company Price Multiples Name Description Formula P ER t Price Earning Ratio P RCCQ t CSHOQ t NIQ t 1 P BV t Price Book Value P RCCQ t CSHOQ t CEQQ t 1 P CF t Price Cash Flow P RCCQ t CSHOQ t NIQ t 1 +DP Q t 1 Indicators representing Company Leverage Name Description Formula D t 1 /BE t 1 Debt / Book Value of Common Equity DLT T Q t 1 +DLCQ t 1 CHEQ t 1 CEQQ t 1 Indicators representing Company Growth Name Description Formula SG t 1 NIG t 1 CF G t 1 Growth in Sales Growth in Net Income Growth in Cash Flow REV T Q t 1 REV T Q t 2 1 NIQ t 1 NIQ t 2 1 NIQt 1+DP Q t 1 NIQ t 2 +DP Q t 2 1 Indicators representing Company Improvement or Eciency Name Description Formula P MG t 1 T OG t 1 ROA t 1 Growth in Prot Margin Growth in Turnover Ratio Return on Asset NIQ t 1 /REV T Q t 1 NIQ t 2 /REV T Q t 2 1 REV T Q t 1 /AT Q t 1 REV T Q t 0 /AT Q t 0 1 NIQ t 1 AT Q t 1 12
24 that the items used to compute the indicators at quarter t were known to the market as of quarter t, they used the data on quarter t-1 for all of them, except for the common shares outstanding and prices. Therefore nancial statement data for period t-1 was matched with prices and common shares outstanding data at time t. Since the above 10 indicators can be clustered around 4 classes that represent similar features of the Company performance, Jiang used a GA to pre-select one indicator from each cluster to signal the long and short selling investment decisions, allowing to choose dierent indicators for long trades than for short selling trades. The nal selected indicators for short positions were: PCF, D/BE, SG and TOG while the indicators used for long positions were PBV, SG and ROA (they did not use the leverage indicator for investing long). Parameters The above mentioned indicators are applied to the investment process related to a threshold or parameter value. For instance, the PER can be used as follows: take a long position in the company stock if the PER is below 10 / invest short in the company stock if the PER is above 20. Therefore, it is necessary to ne-tune the parameter for each one of the indicators and for both positions long and short-sell, within a range that can be dened following the investors practices. The trading system signals are triggered by the comparison between the indicators value (obtained with the company information) for every day of the trading period and the threshold value selected to maximize return. Consequently, the diculty of solving the investment problem dened above depends on the number of indicators included in the trading system and the range allowed for the parameters to be used as threshold values for these indicators. Notice that we face a combinatorial optimization hard problem that is growing exponentially with both indicators and parameters range, being explosive when using a high number or range for both variables. 13
Architecture bits. (Chromosome) (Evolved chromosome) Downloading. Downloading PLD. GA operation Architecture bits
A Pattern Recognition System Using Evolvable Hardware Masaya Iwata 1 Isamu Kajitani 2 Hitoshi Yamada 2 Hitoshi Iba 1 Tetsuya Higuchi 1 1 1-1-4,Umezono,Tsukuba,Ibaraki,305,Japan Electrotechnical LaboratoryMore information
DIPLOMADO DE JAVA - OCA TABLA DE CONTENIDO INTRODUCCION... 3 ESTRUCTURA DEL DIPLOMADO... 4 Nivel I:... 4 Fundamentals of the Java Programming Language Java SE 7... 4 Introducing the Java Technology...More information
INTELIGENCIA DE NEGOCIO CON SQL SERVER Este curso de Microsoft e-learning está orientado a preparar a los alumnos en el desarrollo de soluciones de Business Intelligence con SQL Server. El curso constaMore information
Tai Kam Fong, Jackie. Master of Science in E-Commerce Technology
Trend Following Algorithms in Automated Stock Market Trading by Tai Kam Fong, Jackie Master of Science in E-Commerce Technology 2011 Faculty of Science and Technology University of Macau Trend FollowingMore information
Extracted Templates. Postgres database: results
Natural Language Processing and Expert System Techniques for Equity Derivatives Trading: the IE-Expert System Marco Costantino Laboratory for Natural Language Engineering Department of Computer ScienceMore information
Pivot Trading the FOREX Markets
Pivot Trading the FOREX Markets A report by Jim White April, 2004 Pivot Research & Trading Co. 3203 Provence Place Thousand Oaks, Ca. 91362 Phone: 805-493-4221 FAX: 805-493-4349 Email: firstname.lastname@example.com information
Algorithmic Trading Session 1 Introduction. Oliver Steinki, CFA, FRM
Algorithmic Trading Session 1 Introduction Oliver Steinki, CFA, FRM Outline An Introduction to Algorithmic Trading Definition, Research Areas, Relevance and Applications General Trading Overview GoalsMore information
VMware Server 2.0 Essentials. Virtualization Deployment and Management
VMware Server 2.0 Essentials Virtualization Deployment and Management . This PDF is provided for personal use only. Unauthorized use, reproduction and/or distribution strictly prohibited. All rights reserved.More information
TYPES OF FINANCIAL RATIOS In the previous articles we discussed how to invest in the stock market and unit trusts. When investing in the stock market an investor should have a clear understanding aboutMore information
Memory Allocation Technique for Segregated Free List Based on Genetic Algorithm
Journal of Al-Nahrain University Vol.15 (2), June, 2012, pp.161-168 Science Memory Allocation Technique for Segregated Free List Based on Genetic Algorithm Manal F. Younis Computer Department, CollegeMore information
Comments on Draft OECD/IOPS Good Practices on Pension Fund s Use of Alternative Investments and Derivatives
Comments on Draft OECD/IOPS Good Practices on Pension Fund s Use of Alternative Investments and Derivatives This document includes comments from various FIAP members, belonging to different countries.More information
Research on a Heuristic GA-Based Decision Support System for Rice in Heilongjiang Province
Research on a Heuristic GA-Based Decision Support System for Rice in Heilongjiang Province Ran Cao 1,1, Yushu Yang 1, Wei Guo 1, 1 Engineering college of Northeast Agricultural University, Haerbin, ChinaMore information
Managing large sound databases using Mpeg7
Max Jacob 1 1 Institut de Recherche et Coordination Acoustique/Musique (IRCAM), place Igor Stravinsky 1, 75003, Paris, France Correspondence should be addressed to Max Jacob (email@example.com) ABSTRACTMore information
Mihai Horia Zaharia, Florin Leon, Dan Galea (3) A Simulator for Load Balancing Analysis in Distributed Systems in A. Valachi, D. Galea, A. M. Florea, M. Craus (eds.) - Tehnologii informationale, EdituraMore information
A Comparison of Genotype Representations to Acquire Stock Trading Strategy Using Genetic Algorithms
2009 International Conference on Adaptive and Intelligent Systems A Comparison of Genotype Representations to Acquire Stock Trading Strategy Using Genetic Algorithms Kazuhiro Matsui Dept. of Computer ScienceMore information
GLAS: framework to improve assessment in educational videogames
GLAS: framework to improve assessment in educational videogames Ángel Serrano Laguna MÁSTER EN INVESTIGACIÓN EN INFORMÁTICA. FACULTAD DE INFORMÁTICA UNIVERSIDAD COMPLUTESNE DE MADRID Trabajo Fin MásterMore information
- An Essential Building Block for Stable and Reliable Compute Clusters
Ferdinand Geier ParTec Cluster Competence Center GmbH, V. 1.4, March 2005 Cluster Middleware - An Essential Building Block for Stable and Reliable Compute Clusters Contents: Compute Clusters a Real AlternativeMore information
SiteCelerate white paper
SiteCelerate white paper Arahe Solutions SITECELERATE OVERVIEW As enterprises increases their investment in Web applications, Portal and websites and as usage of these applications increase, performanceMore information
Programming models for heterogeneous computing. Manuel Ujaldón Nvidia CUDA Fellow and A/Prof. Computer Architecture Department University of Malaga
Programming models for heterogeneous computing Manuel Ujaldón Nvidia CUDA Fellow and A/Prof. Computer Architecture Department University of Malaga Talk outline [30 slides] 1. Introduction [5 slides] 2.More information
FUNDAMENTALS OF HEALTHCARE FINANCE. Online Appendix B Financial Analysis Ratios
3/27/09 FUNDAMENTALS OF HEALTHCARE FINANCE Online Appendix B Financial Analysis Ratios Introduction In Chapter 13 of Fundamentals of Healthcare Finance, we indicated that financial ratio analysis is aMore information
Goldberg, D. E. (1989). Genetic algorithms in search, optimization, and machine learning. Reading, MA:
is another objective that the GA could optimize. The approach used here is also adaptable. On any particular project, the designer can congure the GA to focus on optimizing certain constraints (such asMore information
How Can Metaheuristics Help Software Engineers
and Software How Can Help Software Engineers Enrique Alba firstname.lastname@example.com http://www.lcc.uma.es/~eat Universidad de Málaga, ESPAÑA Enrique Alba How Can Help Software Engineers of 8 and Software What s aMore information
Enhancing Cloud-based Servers by GPU/CPU Virtualization Management
Enhancing Cloud-based Servers by GPU/CPU Virtualiz Management Tin-Yu Wu 1, Wei-Tsong Lee 2, Chien-Yu Duan 2 Department of Computer Science and Inform Engineering, Nal Ilan University, Taiwan, ROC 1 DepartmentMore information
Stream Processing on GPUs Using Distributed Multimedia Middleware
Stream Processing on GPUs Using Distributed Multimedia Middleware Michael Repplinger 1,2, and Philipp Slusallek 1,2 1 Computer Graphics Lab, Saarland University, Saarbrücken, Germany 2 German ResearchMore information
GUL-UC3M Jornadas Técnicas
GUL-UC3M Jornadas Técnicas Global Open Source Enterprises My experience in Openbravo 14 de Noviembre de 2008 Agenda Introduction to Openbravo Dynamics of Open Source Openbravo Community Services My takeawaysMore information
Sympa, un gestor de listas de distribución para las universidades
Sympa, un gestor de listas de distribución para las universidades PONENCIAS Sympa, a mailing list software for universities S. Aumont y O. Salaün Resumen Este artículo describe las funcionalidades de Sympa,More information
VMware vsphere with Operations Management: Fast Track
VMware vsphere with Operations Management: Fast Track Duración: 5 Días Código del Curso: VSOMFT Método de Impartición: Curso Cerrado (In-Company) Temario: Curso impartido directamente por VMware This intensive,More information
Wildix Management System (WMS) White Paper
Wildix Management System (WMS) White Paper February 2007 Author: Giuseppe Innamorato Wildix Management System White Paper Status: Draft 0.1 Page 1 Index: 1. Management Summary...3 2. Document purpose...3More information
Overlapping ETF: Pair trading between two gold stocks
MPRA Munich Personal RePEc Archive Overlapping ETF: Pair trading between two gold stocks Peter N Bell and Brian Lui and Alex Brekke University of Victoria 1. April 2012 Online at http://mpra.ub.uni-muenchen.de/39534/More information
Bilateral Exposures and Systemic Solvency Risk
Bilateral Exposures and Systemic Solvency Risk C., GOURIEROUX (1), J.C., HEAM (2), and A., MONFORT (3) (1) CREST, and University of Toronto (2) CREST, and Autorité de Contrôle Prudentiel et de RésolutionMore information
Visión general de la integración con asanetwork
Visión general de la integración con asanetwork Este documento se ha preparado parar dar una visión general del flujo de trabajo de asanetwork y de las tareas a realizar por los programadores del SistemaMore information
A Robust Method for Solving Transcendental Equations
www.ijcsi.org 413 A Robust Method for Solving Transcendental Equations Md. Golam Moazzam, Amita Chakraborty and Md. Al-Amin Bhuiyan Department of Computer Science and Engineering, Jahangirnagar University,More information
Hadoop and Map-Reduce. Swati Gore
Hadoop and Map-Reduce Swati Gore Contents Why Hadoop? Hadoop Overview Hadoop Architecture Working Description Fault Tolerance Limitations Why Map-Reduce not MPI Distributed sort Why Hadoop? Existing DataMore information
Data processing goes big
Test report: Integration Big Data Edition Data processing goes big Dr. Götz Güttich Integration is a powerful set of tools to access, transform, move and synchronize data. With more than 450 connectors,More information
BROKER SERVICES AND PLATFORM A broker is an individual who executes buy and sell orders and get commission in the form of SPREAD (I will talk about SPREAD in the subsequent lessons). You trade throughMore information
Advanced analytics at your hands
2.3 Advanced analytics at your hands Neural Designer is the most powerful predictive analytics software. It uses innovative neural networks techniques to provide data scientists with results in a way previouslyMore information
Review on Financial Forecasting using Neural Network and Data Mining Technique
ORIENTAL JOURNAL OF COMPUTER SCIENCE & TECHNOLOGY An International Open Free Access, Peer Reviewed Research Journal Published By: Oriental Scientific Publishing Co., India. www.computerscijournal.org ISSN:More information
FXCM Metatrader4. User Guide to the No Dealing Desk Platform. 1 P a g e
FXCM Metatrader4 User Guide to the No Dealing Desk Platform 1 P a g e Table of Content Benefits of FXCM Metatrader 4...4 Expert Advisor Use...4 Metatrader 4 versus Trading Station II...4 Expert AdvisorMore information
Chapter Seven STOCK SELECTION
Chapter Seven STOCK SELECTION 1. Introduction The purpose of Part Two is to examine the patterns of each of the main Dow Jones sectors and establish relationships between the relative strength line ofMore information
Keywords revenue management, yield management, genetic algorithm, airline reservation
Volume 4, Issue 1, January 2014 ISSN: 2277 128X International Journal of Advanced Research in Computer Science and Software Engineering Research Paper Available online at: www.ijarcsse.com A Revenue ManagementMore information
Chapter 2.3. Technical Analysis: Technical Indicators
Chapter 2.3 Technical Analysis: Technical Indicators 0 TECHNICAL ANALYSIS: TECHNICAL INDICATORS Charts always have a story to tell. However, from time to time those charts may be speaking a language youMore information
Control of a variety of structures and idioms; occasional errors may occur, but
AP SPANISH LANGUAGE 2012 PRESENTATIONAL WRITING SCORING GUIDELINES SCORE DESCRIPTION TASK COMPLETION TOPIC DEVELOPMENT LANGUAGE USE 5 Demonstrates excellence 4 Demonstrates command 3 Demonstrates competenceMore information
BEAR: A person who believes that the price of a particular security or the market as a whole will go lower.
Trading Terms ARBITRAGE: The simultaneous purchase and sale of identical or equivalent financial instruments in order to benefit from a discrepancy in their price relationship. More generally, it refersMore information
The Lattice Project: A Multi-Model Grid Computing System. Center for Bioinformatics and Computational Biology University of Maryland
The Lattice Project: A Multi-Model Grid Computing System Center for Bioinformatics and Computational Biology University of Maryland Parallel Computing PARALLEL COMPUTING a form of computation in whichMore information
Efficiency of Web Based SAX XML Distributed Processing
Efficiency of Web Based SAX XML Distributed Processing R. Eggen Computer and Information Sciences Department University of North Florida Jacksonville, FL, USA A. Basic Computer and Information SciencesMore information
SAM XFile. Trial Installation Guide Linux. Snell OD is in the process of being rebranded SAM XFile
SAM XFile Trial Installation Guide Linux Snell OD is in the process of being rebranded SAM XFile Version History Table 1: Version Table Date Version Released by Reason for Change 10/07/2014 1.0 Andy GingellMore information
Communication and connectivity the ideal solution for integrated system management and data integrity
the ideal solution for integrated system management and data integrity The solution for Management solutions > Data centre > Emergency applications > Offices > Service industries > Industry > TelecommunicationsMore information
A Genetic Algorithm Processor Based on Redundant Binary Numbers (GAPBRBN)
ISSN: 2278 1323 All Rights Reserved 2014 IJARCET 3910 A Genetic Algorithm Processor Based on Redundant Binary Numbers (GAPBRBN) Miss: KIRTI JOSHI Abstract A Genetic Algorithm (GA) is an intelligent searchMore information
Management of VMware ESXi. on HP ProLiant Servers
Management of VMware ESXi on W H I T E P A P E R Table of Contents Introduction................................................................ 3 HP Systems Insight Manager.................................................More information
Students will become familiar with the Brandeis Datastream installation as the primary source of pricing, financial and economic data.
BUS 211f (1) Information Management: Financial Data in a Quantitative Investment Framework Spring 2004 Fridays 9:10am noon Lemberg Academic Center, Room 54 Prof. Hugh Lagan Crowther C (781) 640-3354 firstname.lastname@example.com information
ISSN: 2319-5967 ISO 9001:2008 Certified International Journal of Engineering Science and Innovative Technology (IJESIT) Volume 2, Issue 3, May 2013
Transistor Level Fault Finding in VLSI Circuits using Genetic Algorithm Lalit A. Patel, Sarman K. Hadia CSPIT, CHARUSAT, Changa., CSPIT, CHARUSAT, Changa Abstract This paper presents, genetic based algorithmMore information
DIPLOMADO EN BASE DE DATOS OBJETIVOS Preparan al Estudiante en el uso de las tecnologías de base de datos OLTP y OLAP, con conocimientos generales en todas las bases de datos y especialización en SQL serverMore information
Analysis of GPU Parallel Computing based on Matlab
Analysis of GPU Parallel Computing based on Matlab Mingzhe Wang, Bo Wang, Qiu He, Xiuxiu Liu, Kunshuai Zhu (School of Computer and Control Engineering, University of Chinese Academy of Sciences, Huairou,More information
Web Hosting Analysis Tool for Service Providers
Web Hosting Analysis Tool for Service Providers Ludmila Cherkasova, Mohan DeSouza 1, Jobin James 1 Computer Systems and Technology Laboratory HPL-1999-150 November, 1999 E-mail: {cherkasova,mdesouza,email@example.com information
2010-2011 Assessment for Master s Degree Program Fall 2010 - Spring 2011 Computer Science Dept. Texas A&M University - Commerce
2010-2011 Assessment for Master s Degree Program Fall 2010 - Spring 2011 Computer Science Dept. Texas A&M University - Commerce Program Objective #1 (PO1):Students will be able to demonstrate a broad knowledgeMore information
Neural Network and Genetic Algorithm Based Trading Systems. Donn S. Fishbein, MD, PhD Neuroquant.com
Neural Network and Genetic Algorithm Based Trading Systems Donn S. Fishbein, MD, PhD Neuroquant.com Consider the challenge of constructing a financial market trading system using commonly available technicalMore information
OPERATION MANUAL. MV-410RGB Layout Editor. Version 2.1- higher
OPERATION MANUAL MV-410RGB Layout Editor Version 2.1- higher Table of Contents 1. Setup... 1 1-1. Overview... 1 1-2. System Requirements... 1 1-3. Operation Flow... 1 1-4. Installing MV-410RGB LayoutMore information
Review on Financial Forecasting using Neural Network and Data Mining Technique
Global Journal of Computer Science and Technology Neural & Artificial Intelligence Volume 12 Issue 11 Version 1.0 Year 2012 Type: Double Blind Peer Reviewed International Research Journal Publisher: GlobalMore information
Surperformance Ratings
User guide www.4-traders.com Table of contents 1 General explanation... 4 2 - Description of fundamental criterion... 4 Investor Rating... 4 Trading Rating... 4 Growth (Revenue)... 4 Valuation... 5 EPSMore information
CE 504 Computational Hydrology Computational Environments and Tools Fritz R. Fiedler
CE 504 Computational Hydrology Computational Environments and Tools Fritz R. Fiedler 1) Operating systems a) Windows b) Unix and Linux c) Macintosh 2) Data manipulation tools a) Text Editors b) SpreadsheetsMore information
Certification Program on Corporate Treasury Management
Certification Program on Corporate Treasury Management Introduction to corporate treasury management Introduction to corporate treasury management: relevance, scope, approach and issues Understanding treasuryMore information
Boosting Long Term Evolution (LTE) Application Performance with Intel System Studio
Case Study Intel Boosting Long Term Evolution (LTE) Application Performance with Intel System Studio Challenge: Deliver high performance code for time-critical tasks in LTE wireless communication applications.More information
SCADA Questions and Answers
SCADA Questions and Answers By Dr. Jay Park SCADA System Evaluation Questions Revision 4, October 1, 2007 Table of Contents SCADA System Evaluation Questions... 1 Revision 4, October 1, 2007... 1 Architecture...More information
Leon Wilson Trading Success
Leon Wilson Trading Success A talk given by Mary de la Lande to the BullCharts User Group Meeting on 13 February 2008 1. Who is Leon Wilson? His Books and where to get them. 2. Book 1: The Business ofMore information
HEALTHCARE FINANCE: AN INTRODUCTION TO ACCOUNTING AND FINANCIAL MANAGEMENT. Online Appendix A Financial Ratios
HEALTHCARE FINANCE: AN INTRODUCTION TO ACCOUNTING AND FINANCIAL MANAGEMENT Online Appendix A Financial Ratios INTRODUCTION In Chapter 17, we indicated that ratio analysis is a technique commonly used toMore information
Network Attached Storage. Jinfeng Yang Oct/19/2015
Network Attached Storage Jinfeng Yang Oct/19/2015 Outline Part A 1. What is the Network Attached Storage (NAS)? 2. What are the applications of NAS? 3. The benefits of NAS. 4. NAS s performance (ReliabilityMore information
CS Standards Crosswalk: CSTA K-12 Computer Science Standards and Oracle Java Programming (2014)
CS Standards Crosswalk: CSTA K-12 Computer Science Standards and Oracle Java Programming (2014) CSTA Website Oracle Website Oracle Contact http://csta.acm.org/curriculum/sub/k12standards.html https://academy.oracle.com/oa-web-introcs-curriculum.htmlMore information
Introduction to grid technologies, parallel and cloud computing. Alaa Osama Allam Saida Saad Mohamed Mohamed Ibrahim Gaber
Introduction to grid technologies, parallel and cloud computing Alaa Osama Allam Saida Saad Mohamed Mohamed Ibrahim Gaber OUTLINES Grid Computing Parallel programming technologies (MPI- Open MP-Cuda )More information
ORACLE FINANCIAL ANALYTICS KEY FEATURES & BENEFITS FOR BUSINESS USERS Receive intra-period information on income statement, cash flow, and balance sheet condition without having to perform consolidationsMore information
Sales Management Main Features
Sales Management Main Features Optional Subject (4 th Businesss Administration) Second Semester 4,5 ECTS Language: English Professor: Noelia Sánchez Casado e-mail: email@example.com Objectives DescriptionMore information
irods and Metadata survey Version 0.1 Date March Abhijeet Kodgire firstname.lastname@example.com 25th
irods and Metadata survey Version 0.1 Date 25th March Purpose Survey of Status Complete Author Abhijeet Kodgire firstname.lastname@example.com Table of Contents 1 Abstract... 3 2 Categories and Subject Descriptors...More information
Overlapping Data Transfer With Application Execution on Clusters
Overlapping Data Transfer With Application Execution on Clusters Karen L. Reid and Michael Stumm firstname.lastname@example.com email@example.com Department of Computer Science Department of Electrical and ComputerMore information
HPC Wales Skills Academy Course Catalogue 2015
HPC Wales Skills Academy Course Catalogue 2015 Overview The HPC Wales Skills Academy provides a variety of courses and workshops aimed at building skills in High Performance Computing (HPC). Our coursesMore information
Information and Communications Technology Courses at a Glance
Information and Communications Technology Courses at a Glance Level 1 Courses ICT121 Introduction to Computer Systems Architecture This is an introductory course on the architecture of modern computerMore information
Problem 1 (Issuance and Repurchase in a Modigliani-Miller World)
Problem 1 (Issuance and Repurchase in a Modigliani-Miller World) A rm has outstanding debt with a market value of $100 million. The rm also has 15 million shares outstanding with a market value of $10More information
DELL. Virtual Desktop Infrastructure Study END-TO-END COMPUTING. Dell Enterprise Solutions Engineering
DELL Virtual Desktop Infrastructure Study END-TO-END COMPUTING Dell Enterprise Solutions Engineering 1 THIS WHITE PAPER IS FOR INFORMATIONAL PURPOSES ONLY, AND MAY CONTAIN TYPOGRAPHICAL ERRORS AND TECHNICALMore information
Converged, Real-time Analytics Enabling Faster Decision Making and New Business Opportunities
Technology Insight Paper Converged, Real-time Analytics Enabling Faster Decision Making and New Business Opportunities By John Webster February 2015 Enabling you to make the best technology decisions EnablingMore information
Virtualized Security: The Next Generation of Consolidation
Virtualization. Consolidation. Simplification. Choice. WHITE PAPER Virtualized Security: The Next Generation of Consolidation Virtualized Security: The Next Generation of Consolidation As we approach theMore information
Purpose of Selling Stocks Short JANUARY 2007 NUMBER 5
An Overview of Short Stock Selling An effective short stock selling strategy provides an important hedge to a long portfolio and allows hedge fund managers to reduce sector and portfolio beta. Short sellingMore information
Cluster, Grid, Cloud Concepts
Cluster, Grid, Cloud Concepts Kalaiselvan.K Contents Section 1: Cluster Section 2: Grid Section 3: Cloud Cluster An Overview Need for a Cluster Cluster categorizations A computer cluster is a group ofMore information
Catálogo de cursos plataforma elearning Microsoft Imagine Academy: Microsoft SQL Server y Visual Studio
Catálogo de cursos plataforma elearning Microsoft Imagine Academy: Microsoft SQL Server y Visual Studio Academic Visual Studio Library Curso Nombre del curso Idioma 2263 Clinic 2263: Exam Preparation forMore information
Structure and Main Features of the RIT Market Simulator Application
Build 1.00 Structure and Main Features of the RIT Market Simulator Application Overview The Rotman Interactive Trader is a market-simulator that provides students with a hands-on approach to learning finance.More information
SpreadSheet Inside. Xenomorph White Paper. Spreadsheet flexibility, database consistency
SpreadSheet Inside Spreadsheet flexibility, database consistency This paper illustrates how the TimeScape SpreadSheet Inside can bring unstructured spreadsheet data and complex calculations within a centralisedMore information
Supervisors: Mr.K.Rotter/ Mr.S.Mondal Author: Mr.P.Adams
PLC Application Exercise 7 Four Floor Elevator 1. Aim You will develop your problem solving skills by programming a real life engineering problem. This application exercise will bring together what youMore information
Distributed Optimization of Fiber Optic Network Layout using MATLAB. R. Pfarrhofer, M. Kelz, P. Bachhiesl, H. Stögner, and A. Uhl
Distributed Optimization of Fiber Optic Network Layout using MATLAB R. Pfarrhofer, M. Kelz, P. Bachhiesl, H. Stögner, and A. Uhl firstname.lastname@example.com R. Pfarrhofer, M. Kelz, P. Bachhiesl, H. Stögner, andMore information
:Introducing Star-P. The Open Platform for Parallel Application Development. Yoel Jacobsen E&M Computing LTD email@example.com
:Introducing Star-P The Open Platform for Parallel Application Development Yoel Jacobsen E&M Computing LTD email@example.com The case for VHLLs Functional / applicative / very high-level languages allowMore information
Experience with Server Self Service Center (S3C)
Experience with Server Self Service Center (S3C) Juraj Sucik, Sebastian Bukowiec IT Department, CERN, CH-1211 Genève 23, Switzerland E-mail: email@example.com, email@example.com Abstract. CERNMore information
Microsoft SQL Server 2008 R2 Enterprise Edition and Microsoft SharePoint Server 2010
Microsoft SQL Server 2008 R2 Enterprise Edition and Microsoft SharePoint Server 2010 Better Together Writer: Bill Baer, Technical Product Manager, SharePoint Product Group Technical Reviewers: Steve Peschka,More information
Alpha Cut based Novel Selection for Genetic Algorithm
Alpha Cut based Novel for Genetic Algorithm Rakesh Kumar Professor Girdhar Gopal Research Scholar Rajesh Kumar Assistant Professor ABSTRACT Genetic algorithm (GA) has several genetic operators that canMore information
CS 3530 Operating Systems. L02 OS Intro Part 1 Dr. Ken Hoganson
CS 3530 Operating Systems L02 OS Intro Part 1 Dr. Ken Hoganson Chapter 1 Basic Concepts of Operating Systems Computer Systems A computer system consists of two basic types of components: Hardware components,More information
CVA Desk in the Bank
CVA Desk in the Bank Implementation Counterparty Credit Risk represents the risk that the counterparty of an OTC will default before the maturity of the contract and therefore will not make the remainingMore information
Fundamentals of Futures and Options (a summary)
Fundamentals of Futures and Options (a summary) Roger G. Clarke, Harindra de Silva, CFA, and Steven Thorley, CFA Published 2013 by the Research Foundation of CFA Institute Summary prepared by Roger G.More information
About This Document 3. Integration and Automation Capabilities 4. Command-Line Interface (CLI) 8. API RPC Protocol 9.
Parallels Panel Contents About This Document 3 Integration and Automation Capabilities 4 Command-Line Interface (CLI) 8 API RPC Protocol 9 Event Handlers 11 Panel Notifications 13 APS Packages 14 C H AMore information
NIST/ITL CSD Biometric Conformance Test Software on Apache Hadoop. September 2014. National Institute of Standards and Technology (NIST)
NIST/ITL CSD Biometric Conformance Test Software on Apache Hadoop September 2014 Dylan Yaga NIST/ITL CSD Lead Software Designer Fernando Podio NIST/ITL CSD Project Manager National Institute of StandardsMore information
15-418 Final Project Report. Trading Platform Server
15-418 Final Project Report Yinghao Wang email@example.com May 8, 214 Trading Platform Server Executive Summary The final project will implement a trading platform server that provides back-end supportMore information
Bank Customers (Credit) Rating System Based On Expert System and ANN
Bank Customers (Credit) Rating System Based On Expert System and ANN Project Review Yingzhen Li Abstract The precise rating of customers has a decisive impact on loan business. We constructed the BP network,More information
Práctica 1: PL 1a: Entorno de programación MathWorks: Simulink
Práctica 1: PL 1a: Entorno de programación MathWorks: Simulink 1 Objetivo... 3 Introducción Simulink... 3 Open the Simulink Library Browser... 3 Create a New Simulink Model... 4 Simulink Examples... 4More information
50399AE Diseño de soluciones Business Intelligence con Microsoft SQL Server 2008
50399AE Diseño de soluciones Business Intelligence con Microsoft SQL Server 2008 Fabricante: Indra Grupo: Inteligencia de Negocios Subgrupo: SQL Server 2008 - ETL - AS - RS Formación: Indra Horas: 25 IntroducciónMore information
White Paper Electronic Trading- Algorithmic & High Frequency Trading. PENINSULA STRATEGY, Namir Hamid
White Paper Electronic Trading- Algorithmic & High Frequency Trading PENINSULA STRATEGY, Namir Hamid AUG 2011 Table Of Contents EXECUTIVE SUMMARY...3 Overview... 3 Background... 3 HIGH FREQUENCY ALGORITHMICMore information
ValueCharts TradeStation
ValueCharts TradeStation ValueCharts TradeStation indicator suite can be located in the TradeStation Strategy Network under MicroQuantSM products. Free trial subscription periods are available for allMore information


text:
Why a vendor course approval process?
Not all virtual courses are created the same. With virtual providers proliferating and rigorous national and State-Board approved standards, the array of options can be overwhelming and, for some schools, difficult to navigate. The North Carolina Vendor Approval Process is intended to ensure access to a variety of high-quality, standards-based courses from vendors and providers all over the country, while lowering costs by eliminating the need for each school or LEA to separately evaluate virtual course products.
NC General Assembly S.L. 2011-145 states is section 7.22 (h)(2), the following:
The Director of NCVPS shall continue to ensure the following:
- Course quality standards are established and met.
- All e-learning opportunities other than virtual charter schools offered by State-funded entities to public school students are consolidated under the NCVPS program, eliminating course duplication.
- All courses offered through NCVPS are aligned to the North Carolina Standard Course of Study.
SBE Policy # GCS-M-001 ("Course for Credit") Section 4 states the following:
An e-learning course qualifies for course credit if it meets the following requirements:
4.1 Any K-12 e-learning course taken for credit towards a diploma must first be approved for credit by the NC Virtual Public School (NCVPS).
4.2 E-learning courses offering instruction in courses included within the NC Standard Course of Study must meet the Standard Course of Study competency goals and objectives. E-learning courses offered for AP or IB credit must align with the governing bodies of the AP and IB programs.
4.3 E-learning courses offering instruction in courses not included in the Standard Course of Study curriculum must have rigor, depth and breadth comparable to courses included in the Standard Course of Study. When determining whether an e-learning course has such rigor, depth and breadth, the NCVPS shall consider whether the course meets the Southern Regional Education Board (SREB) and/or International Association for K-12 Online Learning (iNACOL) criteria for awarding credit or is offered for credit toward a degree by a UNC system institution, an institution in the North Carolina Community College System, or a regionally accredited college.
The goal of theNC Vendor Approval Process is not to limit choices, but to consolidate as many high-quality courses as possible into a menu of options for schools and LEAs. NCVPS invites LEAs and schools to identify virtual learning courses necessary to meet the needs of their students. Through partnership with LEAs and schools, NCVPS can better serve the needs of all students and the state may achieve some benefit through cost consolidation and state-level agreements.
The Review Process
All K-12 e-learning courses must go through the approval process. This process only applies to teacher-led online courses which are taken for full credit either because the student is taking the course for the first time or has failed the course previously and needs to retake the entire course. By teacher-led, we mean that the Vendor provides the teacher to deliver the instruction for the course. Courses taken for credit recovery where the student may only retake portions of the course are not subject to this approval process.
In addition, courses that do not have any corresponding NC State Standards are not eligible for review. Courses are approved for five years.
Vendor Approval for AP Courses
If a Vendor can demonstrate that each Advanced Placement (AP) course syllabus has been approved by the College Board then the course will not need to go through a Phase 1 (Course content alignment to State standards and evidence that teacher is certified to teach in North Carolina) review and will proceed directly to a Phase 2 Review (full review). Note: If you have had a previous course approved already, then the respective course moves to a Phase 3 review.
To successfully review the AP course at Phase 1, NCVPS needs the following two pieces of information submitted with documentation for Course and Teacher Quality.
- For Course Quality: Documentation that supports the claim that the syllabus has been approved by the College Board. Make reference to this documentation in the Course Quality Document for A2 and A3. Then upload the documentation verifying that the course is certified and approved by the College Board to the respective AP course folder.
- For Teacher Quality: Submit documentation that your teacher for the respective AP course has participated in an in-depth Professional Development offered for the specific AP course. The College Board holds these events all over the country in either a week long or weekend long experience. You can add that to the Teacher Quality documentation you submit for A3 and A4 for Teacher Quality and then upload that documentation in the respective AP course folder.
Additional Resources
- To determine whether a course is eligible for review, please follow this Decision Tree.
- Frequently Asked Questions about the NC Vendor Course Approval Process.
- View this NC Vendor Course Approval Process infographic to see the process "At a Glance."
Submitting a Course for Review:
To submit a course for review, follow these steps:
- Vendors need to complete this Request Form for each course to be approved
- Once the application has been received, Don Lourcey, an NCVPS staff member, will contact the vendor to provide information on where all evaluation artifacts should be submitted.
- Vendors will submit an artifact for each item on the following checklists to the location supplied based on letter "b" above.
- Upon receipt of all artifacts, the items will be reviewed and results reported to the vendor within 120 days.
The Submission and Review Cycle:
The North Carolina Virtual Public School accepts Vendor courses for review between July 1st-April 15th of each fiscal year. Courses submitted after April 15th will be held for review until the start of the following fiscal year on July 1st.
If you have any questions, please contact Don Lourcey by e-mail at firstname.lastname@example.com.
- Approved Vendor Courses
- Catalogue
- Calendars
- Credit Recovery
- Middle School
- High School Courses for Middle School Students
- Flex Learning
- OCS Blended Learning
- Supporting Blended Learning
- Physical Education with NCVPS
- Professional Learning
- Shared Courses
- Technology Requirements
- Textbooks
- Unique Courses
- Vendor Approval for AP Courses
- Vendor Course Approval
- Visual Arts Courses

subset: commoncrawl


text:
Editors' Note: You must be 17 years old to download this application. The "Download Now" link directs you to the iTunes App Store, where you must continue the download process. You must have iTunes installed in order to open the link, and you must have an active iTunes account to download the application. This download may not be available in some countries.
Sponsored Products
|Entertainment Software
|Music Software
Publisher's Description+
From Zaggle: The highest rated FREE MUSIC Downloader!!
What's new in this version:
* more music sites
read more +


text:
Kim Kardashian's Bikini Throwback, Lindsay Lohan Strips Down, Selena Gomez's New Album: A Roundup
Hey Look, Kim Kardashian Is In a Bikini Again
The reality star is once again remembering the good 'ol days by sharing photos of — what else? — her bikini body. See the bootylicious beach pics on Huffington Post.
Lindsay Lohan Strips Down During Yacht Trip
LiLo is making the most out of her Italian vacation by taking her pals on a yachting trip. Fox News has the photos.
Selena Gomez Is Already Asking People to Write Songs for Her New Album
On the eve of Star Dance's one year anniversary, SelGo has (unofficially) confirmed that she is working on new material for its highly-anticipated follow-up. Idolator has the details.
Former Destiny's Child Member Arrested for Disorderly Conduct
Farrah Franklin was arrested in Myrtle Beach, S.C., over the weekend after the police found her loitering on a neighbor's lawn. Starpulse has the full story.
Justin Bieber Risks Probation Violation After Another Run-In With Cops
The singer, who is currently serving two years probation for egging his neighbor's house, is risking jail time if he doesn't stop his partying ways. Find out why Bieber is on a path to prison on Gossip Cop.
Yes, A Prince George Look-a-Like Actually Exists
Does this baby look like the royal spawn of Kate Middleton and Prince William to you? See for yourself on The Frisky.


text:
Submitted by jerryterrizo1017 • August 21, 2013 jerseyshorebeaches.webs.com
After a long hard working week, it's time to get relax in the weekend. Isn't it? New Jersey is one of the places where you can enjoy your weekend.
- Category: Travel
- Tags: beaches on the jersey shore, jersey shore beaches


text:
Looking for an eSata raid 1 unit with or without HDDs. So far I have found the owc Guardian MAXimus. This will be for additional backup storage capacity.
I forsee needed a SAS raid 1 unit in the future (maybe as early as next year).
Any suggestions?
4 Replies
Feb 17, 2012 at 10:21 UTC
Niagara Technology Group (NTG) is an IT service provider.
SAS RAID enclosure? Not sure what you are envisioning. That is what we normally call a SAN. Like an HP P4000.
Feb 17, 2012 at 10:48 UTC
Then I am misundertanding SAN (Storage area network) , and SAS (serial attachened scsi). I thought one is a network and the other is a computer bus. I did make the assumption that SAS, like scsi before it, had an external option. I notice that sata has an esata option, which I find very useful.
I just assumed that the days of external scsi raid units are gone, and now we would be able to purchase external sas raid units. I do remember back in the days of Netware 3.12 and 4.1 we would connect via external scsi, raid towers to our servers. HP and a few other companies made these scsi raid enclosures.
With our current equipment, I think an external SAS raid 1 unit might be all we need to add additional storage to a server, though I might be wrong.
Doing a very brief google, looks like HP calls these Smart Arrays or storage enclosures. Like maybe the old HP StorageWorks Model 4314T or HP 60 Modular Smart Array. Again, quick googling, so I may have gotten all this info wrong.
I was hoping for something simpler. Maybe no one uses these kind of devices anymore.
Feb 18, 2012 at 9:48 UTC
Niagara Technology Group (NTG) is an IT service provider.
All networks are a form of computer bus. That is why both SAS adapters and iSCSI are known as "host bus adapaters." HBA is the correct term for your SAS. SAS can have switches too. And the traditionally popular SAN technology is Fibre Channel which is, like SAS, both a drive connection technology and a network technology.
You can certainly purchase an external SAS JBOD array, and depending on how you hook it up it will be called DAS or SAN. But it is all grey areas in terms of naming when using SAS.
The HP MSA is likely what you are looking for. Just do straight SAS from your computer to the MSA and you'll be all set.
Be aware that this is not a RAID enclosure, though, this is a JBOD enclosure. That is... the RAID card driving this is in your server, not in the enclosure. If you were to move the RAID card into the enclosure then people would generally call it a SAN (this HP P4000 or Dell Equalogic.)
So it is the wording of the question that often determines if someone would call is DAS or SAN. Your original question wanted a RAID 1 enclosure (so people would often say SAN) but if you would be happy with the MSA and the RAID card in your server then we generally say DAS.
Technically most SAN and DAS are both. Even an external USB hard drive is technically a SAN. SAN encompasses basically any block storage device connected over a techworking technology and basically any connection technology today does networking (USB, FireWire, Fibre Channel, SAS, Ethernet, Inifiband, etc.)
Feb 18, 2012 at 1:31 UTC
Doing RAID within your DAS is really kind of silly if you ask me, until you start getting into PB scale storage virtualization (One SAN fronting other SANs).
This discussion has been inactive for over a year.
You may get a better answer to your question by starting a new discussion.


text:
The quake, with a preliminary magnitude of 5.7, struck near the town of Karliova in rural Bingol province, 560 miles southeast of Ankara, the Kandilli Observatory said.
Five of the injured were in serious condition while others were treated at a hospital for minor injuries and released.
The quake caused several homes to collapse and damaged at least 63 others in neighboring villages, according to the prime minister's office and local officials. Most homes that collapsed were previously damaged in a March earthquake, Bingol Gov. Vehbi Avuc said.
"We are happy that there were no deaths," Avuc said, but he added that the quake "has caused quite a panic and we are trying to provide psychological support."
Officials sent 1,000 tents and 2,000 blankets to the area and closed schools for two days.
The quake occurred at 10:41 a.m. and was felt in the neighboring provinces of Erzurum, Mus and Tunceli, the Anatolia news agency reported. At least 10 aftershocks, measuring between 2.9 and 3.5, shook the region, Kandilli said.
Quakes are frequent in Turkey, much of which lies atop the active North Anatolian fault. Two devastating earthquakes killed about 18,000 people in northwestern Turkey in 1999.
In 2003, an earthquake measuring 6.4 magnitude collapsed a school dormitory in Bingol province, killing 83 children. The collapse was blamed on poor construction.
A magnitude-6.4 quake killed 177 people in Bingol in 1995.


text:
I have a client who has deep horizontal ridges in her nails. Her cuticle area is kind of swollen and irritated also. I'm thinking Beau's lines, but I just want to see what an expert has to say.
This client has a chronic paronychia with resultant nail dystrophy and possibly a habit tic deformity. You are right to have noticed that the cuticle is swollen or boggy. That is because the cuticle barrier has been compromised and when that occurs water can enter the nail unit. Once moisture accumulates, yeast will take up residence because yeast thrives in warm moist environments. The cuticle directly overlies the nail matrix and that is why the nail is growing in abnormally — because the matrix is what grows into the actual nail plate. Therefore any inflammation or trauma in the area of the cuticle is going to potentially result in a damaged nail. Your client's cuticle is being compromised somehow. I would ask her if she pushes back on it with her index finger (habit tic). It is also possible that the cuticle is being removed. I always tell my patients that the cuticle is like the grout in your bathtub. It prevents water from seeping into the cracks. The cuticle is an important and amazing protective structure. Your client should not touch her cuticles and she should see a dermatologist. She will likely need a prescription medication to treat the current issue. The beauty of this diagnosis is that if treated properly, the nail will grow in normally.
— Dana Stern, M.D. is a board-certified dermatologist specializing in nails. She works in private practice in New York City.
InternatioNAILS: Germany20 photos
Cosmoprof North America 201758 photos
Like so many other technicians listed here, what makes Moore (owner of Just Nails, Erlanger, Ky.) influential is her attitude of "one person...
Learn More
Is it normal that I'm in pain after doing a couple clients' nails?
How do you handle a client who constantly tries to correct you or tell you what to do?
What are reasonable terms for a non-compete contract?
How do I announce to my clients that I will be moving to a new salon?
The new products I'm using are adding time to each appointment. How do I adjust my schedule?
Featured Products & Promotions | Advertisement
How big is the U.S. nail business? $7.3 billion. What's the average service price for a manicure? Dig into our decades' deep research archives.
View All
VietSalon is a Vietnamese-language magazine and the sister publication to NAILS. Click the link below to sign up for a FREE one-year subscription.
Get a free preview issue and a Free Gift
Subscribe Today!


text:
Connecting Families to Senior Living
Talk to a local advisor for FREE
Talk to a local Advisor about this community
Contact this community at:
(866) 333-2174
99 Pointe Dr
Facility License: 5105512
Caretypes Provided: Alzheimer's Memory Care
Pricing is available for the following rooms:
*Prices quoted are monthly rental charges and are provided by the communities themselves. Actual prices may differ due to one-time fees, timing and care services required. Speak with your Senior Living Advisor to learn more.
Our apologies. Pricing is currently unavailable for this particular community.
Northbrook Inn, part of the Koelsch Senior Communities, and family owned since 1958 is located in the village of Northbrook. The single-story memory care community in the Pointe Drive neighborhood is considered by many to be a retiree's dream neighborhood as it is peaceful and quiet, close to medical facilities, with a vibrant mix of educated seniors, a high safety rating, and is one of the most retiree-friendly areas in IL. If your loved one is just in the beginning stages of Alzheimer's, Dementia, Memory Care, or is needing more one-on-one care, Northbrook Inn will be the perfect environment. Trained professional staff and licensed nurses will be onsite 24 hours a day, 7 days a week to meet the specific needs of each person and remain committed to bringing dignity, respect and the highest standards of service to all of our 'family members'. Whether for a short respite stay or for a lifetime, our family will take care of your family. Along with excellence in care, Northbrook Inn will offer full meals, vibrant mental and physical activities 7 days a week, scheduled social outings on a wheelchair-accessible bus, and regular family engagement opportunities. With over half a century of experience caring for seniors, you can be certain the Koelsch family will take great care of yours! We understand the challenges that families who are caring for loved ones with memory loss. Northbrook Inn will offer in-house support groups for family-caregivers as well as facilitate strategic relationships in the community to inspire our 'family' of residents.
Schedule an appointment with an Advisor
out of 6 reviews
Northbrook Inn Memory Care Community
"I have to thank you once again for your support through this
process. The biggest help was understanding the levels of care
available and most appropriate for my parents. The
facilities you identified helped us streamline the list of possible
locations within the geographic area my parents were looking
Kathleen O., Found Assisted Living in Shiloh, IL
"I am so grateful for the help and support my adviser
provided during this emotional transition. He was supportive of me
and my family and provided the guidance we needed to select the
correct place for my parents. He asked the right questions
which helped us to find the right place."
Katherine S., Found Assisted Living in Arlington Heights, IL
From what I know my loved one is doing well with her move to this community. It is a nice community, and we are pleased with her move here. It is a nice facility for her, and her needs. We know sh... more...
Call Now (866) 333-2174
When: 7/20/2017 at 10 am until 11 am
Where: Northbrook Inn
Support groups for caregivers caring for those with memory loss. Groups led by Terry Rasmussen, Bereavement Coordinator for Heartland Hospice. We'll care for your loved one with memory loss while you attend the group meeting. Pre-registration is required.
Contact: Courtney Bayron, at 224-261-8352
Get the latest tips, news, and advice on aging and caregiving.
A Place for Mom is the largest assisted living referral service. We are paid by our participating communities, therefore our service is offered at no charge to families.
Copyright ©2017 A Place for Mom, Inc. All rights reserved.