Using Reddit Stealing College Essay Topics

Utilizing Reddit Stealing College Essay TopicsAre you chipping away at a class task that includes Reddit taking school exposition subjects? Numerous understudies are utilizing this webpage to have online conversation gatherings and conversations about a wide assortment of subjects. In any case, numerous individuals are doing it without comprehending what they are doing wrong.Before beginning your task, ensure that you know the entirety of the subjects that are out there. There are numerous spots where you can get great points to utilize, so take as much time as necessary and discover them.Many of the great spots for subjects for a school exposition incorporate CollegeHumor, Ask.com, Cnet, etc. These are only a couple of the destinations that you can use to get great themes to work with. In the event that you need to be innovative, you can likewise get the points from TV and motion pictures to help get you started.It is significant that you join a portion of the online conversation bu nches that are out there. So as to make your task effective, you have to show that you can be a functioning member in the gathering. You likewise need to give individuals the points that they will be keen on with the goal that they will have the option to react to your work.For example, in the event that you go to the CollegeHumor site, you can discover subjects that you can utilize. In any case, if you somehow happened to post these points on the discussions, they would in all likelihood not be utilized. Nonetheless, you can see themes that are going as supportive to those in the gathering and ensure that you post the topics.The subsequent stage is to ensure that you are offering the point in a valuable manner. On the off chance that you post something that is impolite or mean, it could make a few people leave the gathering and that isn't the result that you need to have.As you experience the way toward utilizing your subjects, ensure that you center around the themes that the gath ering can profit by. You likewise need to ensure that you are pondering what subjects you need to post that may show others the discussion. Attempt to discover themes that will help get you further in the gathering and give a greater amount of an instructive experience.Make sure that you don't post any substance that is hostile or pestering to the individuals in the discussion. This will help guarantee that your school exposition themes are elegantly composed and will build your odds of progress.

Untold Stories on Salem Witch Trials Essay Topics That You Really Need to Read

<h1> Untold Stories on Salem Witch Trials Essay Topics That You Really Need to Read</h1> <h2>The Most Popular Salem Witch Trials Essay Topics </h2> <p>LinkedIn is actually the most notable employment and profession organizing site. Start with the presentation. Because of broad utilization of Social Media. </p> <p>We give our consistent customers with limits and higher predominant assistance. Remember to see the huge Fish Games Forums in the event that you discover you need more assistance. Modified school paper help is offered to understudies who wish to improve their evaluations and don't have the opportunity to achieve scholastic article. It's similarly as if you were getting help from an outsider to fix your PC, or pay a mentor to expand your composing abilities. </p> <p>With the War on Terror, there's a lot of neurosis. The main way an individual could escape from allegation was admit or set the fault on someone else. In the occasion the individual blamed for the wrongdoing is extremely blameworthy the discipline must fit the wrongdoing as referenced in the eighth Amendment. The touch test took a shot at the idea that casualties of magic would have an excellent response to physical contact by utilizing their scalawag. </p> <p>You get a chance to form into an extraordinary understudy! These preliminaries occurred in a settlement named Salem, which was a piece of the Massachusetts Bay Colony at that point. 1 instance of this is the occasion of Julius and Ethel Rosenberg. In Salem puritans where the main people in this area. </p> <h2> A Secret Weapon for Salem Witch Trials Essay Topics</h2> <p>Quakers, for instance, were obvious objectives. Be that as it may, most of them were older women. For example ladies were viewed as docile to men. </p> <p>The ones that perform black magic are thought of as witches. She speculates that the starting point of the peculiar real torments previously saw in the charmed young ladies might be the result of ergot harming. The people who state they're in certainty witches get the opportunity to live, and the individuals who don't get hanged or perhaps squeezed to death under a lot of stones. That is right, some motivation behind why individuals are sentenced to death demonstrated truly insignificant. </p> <p>There might be different impediments which make it inconceivable that you adapt to a task completely all alone. The preliminaries were a technique to keep the severe social chain of importance. At the point when you needn't bother with these snags to impact the bore of your work, you require proficient help from an assistance you may trust. </p> <p>Puritan pioneers began to stress they were losing charge of the network and wished to dodge change in the exacting social progressive system. The world should resolve issues until they get excessively enormous and to set the prerequisites of huge gatherings before those of themselves. Other than political occasions, there's a captivating subset of the crowd attitude that is fascinating to share inside this nation. All the outcome of the group mindset. </p> <p>Then dependent on the subject you pick, you can compose a postulation articulation on it. Furthermore, you will be in a situation to browse three one of a kind sizes of burgers. Obviously, there are special cases. </p> <p>We lost the chance to characterize the legitimate wording of the issue, on the grounds that the open needed equity managed before due training. How about we investigate some of those misinterpretations. Like his kindred Puritans, he's excessively worried about the vibe of goodness. There isn't a simple answer to that question. </p> <p>Practicing black magic was a way to discharge disappointments in the specific furrowed puritan culture. At last, it's imperative to know that while numerous cutting edge Pagans refer to the Salem preliminaries as an outline of strict bigotry, right now, black magic wasn't viewed as a religion at all. Strict bigotry had a significant impact in the development of the Salem black magic preliminaries. </p> <p>The loss of black magic would profess to watch a spirit or form of the person who had harrowed them. You're thinking about this in altogether the off base way, Abigail expressed. </p> <p>We are here so as to help you in making your scholarly life effective. Tituba's admission wasn't the sole thing that brought about madness prompting the start of the Salem Witch Trials. Any individual who restricted or had an issue with the congregation was viewed as a witch. </p> <h2> A Secret Weapon for Salem Witch Trials Essay Topics </h2> <p>By the end of the schedule year, 3 youngsters were executed. The day subsequent to Thanksgiving is another immortal representation of the group attitude that is connected to the Christmas Season. Following two or three minutes, Abigail expressed. </p> <p>The most notable of these is the swimming test that incorporates restricting the man or lady and tossing them in a waterway. Better spend that chance to read for tests, particularly once you have a lot of stuff to cover. 1 thing we should likewise comprehend each and every individual who kicked the bucket were conventional people. License the knowledge leak in and make you a little piece more astute, 1 hour at a second. </p>

Creative Writing Jobs - How To Find A Professorship Essay Writing Service

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Nursing Informatics Research Paper Topics

<h1>Nursing Informatics Research Paper Topics</h1><p>The goal of the nursing informatics look into paper themes is to furnish medical caretakers with an exhaustive and all around examined outline of the field. The point gives the fundamental components and the general extent of the nursing informatics zone. It is for the most part developed from the examinations and explores of the people that have accomplished their work in the field of the nursing informatics.</p><p></p><p>The nursing informatics look into paper points are required perusing for graduate nursing programs. The target of these themes is to give nurture a point by point portrayal of the basic ideas that are fundamental to the field. The medical attendants at that point can all the more effectively find wellsprings of extra research on this theme on the off chance that they need to search for additional information on these subjects.</p><p></p><p>These th emes have away from of the wording that is utilized with regards to explicit information and subtleties in the field. The creators of these subjects give essential data about an assortment of wellbeing points that are imperative to the field. The themes diagram the significant wordings utilized and furthermore the general phrasing that is utilized by the medical caretakers. The points likewise characterize a portion of the terms that are normally utilized in the field.</p><p></p><p>These themes contain explicit data about the clinical subjects, for example, how to manage drugs and perform techniques. The themes additionally present the phrasing that is regularly utilized in clinical research. There are different subjects that are just quickly portrayed that address progressively specialized issues and techniques for care in the field.</p><p></p><p>These themes might be introduced in a few different ways, contingent upon the investigati on. The most straightforward arrangement for introducing the points is by utilizing a slide introduction position. The introduction is normally joined by pictures, charts, and outlines to help make the substance progressively reasonable to the audience.</p><p></p><p>Some of the points are isolated into significant classes, for example, medicine. The subjects incorporate clinical pharmacology, which is worried about the organization of medicine. This incorporates the estimation of medications and the viewpoints identified with the variables that make them effective.</p><p></p><p>Some of the nursing informatics inquire about paper subjects that are particularly helpful to nursing understudies, incorporate the essentials of verifiable occasions, incorporating those throughout the entire existence of the United States. A portion of the themes spread the historical backdrop of geriatrics, which incorporates issues, for example, guaranteeing that the correct patients are fittingly thought about. The subjects likewise talk about the techniques for revival and how they are identified with the consideration of the elderly.</p><p></p><p>The points that are utilized in undergrad nursing programs additionally are ordinarily utilized in nursing graduate projects. The creators of these subjects center around the ebb and flow investigate in the field of nursing informatics. They give the essentials of the various wordings that are utilized in the field, while giving data on how the emergency clinics keep up and improve the frameworks inside the human services sector.</p>

Stock trading using computational intelligence - Free Essay Example

Sample details Pages: 30 Words: 8973 Downloads: 3 Date added: 2017/06/26 Category Statistics Essay Did you like this example? Stock Trading using Computational Intelligence t Computational Intelligence has been widely used in recent years in many areas, such as speech recognition, image analysis, adaptive control and time series prediction. This research attempts to explore the usefulness of neural network and support vector machine in financial market. Two popular stock market indexes have been studied: Hong Kong Hang Seng Stock Index and Dow Jones Transportation Index. The performance of neural network and support vector machine are evaluated in two dimensions: error in forecasting and trading profits. Popular technical indicator, percentage price oscillator (PPO), has been selected as training input and output. Predictive models use previous 8 days PPO to forecast future 5 days PPO. Empirical results on Hong Kong Hang Seng Index show that multilayer perceptron optimized with GA (MLP-GA) trading system obtain 6.71 times of original capital from 1997-1-29 to 2007-3-8, totally 2500 trading days. While support vector regression optimized by genetic algorithms (SVR-GA) trading system generates 5.705 times of original capital during the same time horizon. In contrast, conventional non-predictive trading system only produces 2.064 times of starting equity. Buy and Hold strategy gives 1.605 times return to investors. A recent published fuzzy trading system provides 5.781 dollars as final equity for 1 dollar initial investment. Don’t waste time! Our writers will create an original "Stock trading using computational intelligence" essay for you Create order Further evaluations of two intelligent trading systems have been made. A back test using the same parameters and same assumptions on Dow Jones Transportation Index have further proved the robustness of the proposed trading systems. MLP-GA trading system provides 4.87 times of initial capital and SVR-GA trading system obtains 5.168 as final equity. These two intelligent trading systems again outperform conventional trading system, which generate 2.805 dollars for 1 dollar investment. Acknowledgements I am very grateful to my final year project supervisor, Associate professor Wang Lipo, and would like to take this opportunity to thank him for his patient and insightful guidance throughout the project. Professor Wang always offers me detailed and valuable explanations and suggestions in our discussion, and provides me useful knowledge about doing research. Not only professor Wang enlightens me in academic area, he also arranges meeting with industrial professionals for me to discuss this project. Again, I would like to express my sincere appreciation to professor Wang. Zhu Ming April, 2010. Stock Trading using Computational Intelligence List of Figures Fig 21 A multi layer neural network with L layers 13 Fig 22 Maximum-margin hyperplane and margins for a SVM trained with samples from two classes. 16 Fig 23 Genetic Algorithm flowchart, with maximum 100 generation 18 Fig 24 One point crossover 19 Fig 25 roulette-wheel selection 20 Fig 31 Dow Jones Industrial Average price, with EMA plotted. 23 Fig 32 Using single EMA 23 Fig 33 Using two EMA to make decision 24 Fig 34 A predictive trading system. 26 Fig 35 Structure of GA optimized MLP 28 Fig 41 Training performance of MLP 33 Fig 42 MSE for out of sample data 34 Fig 43 Linear regression for trained neural network 35 Fig 44 Linear regression for out of sample data 36 Fig 45 Equity curve for intelligent and conventional trading systems 37 Fig 46 Trading signal of NN+GA trading system 38 Fig 47 Trading signal of conventional trading system 39 Fig 48 MSE for GA+SVR model 41 Fig 49 Equity curve for GA+SVR trading system and conventional trading system 42 Fig 410 Comparison of 4 trading systems 43 Fig 411 Equity curves of different trading system on DJT 44 List of Tables Table 31 Settings for GA and NN 26 Table 32 Settings for GA and SVR 29 Table 41 Data distributions for training and testing neural network 32 Table 42 Total return for different prediction time horizon 34 Table 43 Trading performance comparison 42 Stock Trading using Computational Intelligence Chapter 1 Introduction 1.1 Background Analyzing stock market is one of the most important and fascinating issue as it is highly related with the profitability of investment. There are two main types of analysis in financial market: technical analysis and fundamental analysis. Fundamental analysis is based on the premise that a stock, bond, fund, commodity, or a market as a whole has an underlying intrinsic value. By analyzing the fundamental characteristics, such as assets, liabilities, income, supply or demand, values can be determined [11]. Normally fundamental analysts use a trading strategy called Buy and Hold, since they tend to buy the stocks of undervalued companies or the companies with great growth potentials. They believe that the share price would rise eventually since the company they buy is growing. Hence, they would like to keep the stocks for a relative long time. On the other hand, technical analysis believes that the markets price reflects all the relevant information, such as news and events. Thus, pric e is the only information they need to analyze. In their perspective, history will repeat itself such that we could trade for profits. Therefore, technical analysis only employs historical data to build the model for future investment. Over the past decade, Computational Intelligence has been widely used in stock trading, such as using neural networks (NN) [10]). Using computational intelligence could provide opportunities for investors to combine the information gathered from fundamental analysis and technical analysis to make trading decision. Mainly, two types of input data have been used in computational intelligence. One type, price or technical indicators, is considered as technical analysis. The other type includes macroeconomic indices and information related to a specific company, such as the interest rate and P/E ratio. Many pioneer scholars have focused on minimizing the mean square error (MSE) in price direction prediction as well as providing paper profits in trading financial market. Patel et al [10] uses hierarchical coevolutionary fuzzy system (HiCEFS) to predict a technical indicator and hence build a prudent trading strategy. Furthermore, by testing this model with real world data of Hong Kong Hang Seng Index and NOL stock in Singapore Exchange, they achieved a final return of 14.251 times of original capital on NOL stock in 2329 trading days and 5.781 times of original capital on Hang Seng Index in 2461 trading days. 1.2 Objectives and Scope The objective of this project is to explore and examine the usefulness of computational intelligence in stock trading on Hong Kong Hang Seng Index and Dow Jones Transportation Index. The intelligent trading system built on matlab could analyze the historical data and generate buy or sell signals for any given time series. The main objectives are as follows: 1. Apply intelligent trading system on Hong Kong Hang Seng index to generate buy and sell signals. The intelligent trading system could be constructed with neural networks optimized by genetic algorithm or support vector machine optimized by genetic algorithm. 2. Examine entry and exit signals generated by intelligent trading system and non-intelligent trading system. Compare the empirical trading profits between them. 3. Compare the trading performance of intelligent trading system with other researchers work, using the same data and trading rules. 4. Further validate the trading systems performance by applying the proposed system on Dow Jones Transportation Average Index, and compare the trading profits with non-intelligent trading system. 1.3 Organisations This report is organized into 5 chapters: Chapter 1 provides some background knowledge of financial market and other researchers accomplishment on using computational intelligence in financial market. It also gives a detailed project objectives and scope. Chapter 2 introduces the background knowledge for this project, such as neural network, support vector machine and genetic algorithm. Chapter 3 describes the proposed methodology of this project. It introduces the technical indicators and inputs to the intelligent trading system, the architectures of the trading system. In addition, it also provides the settings for each intelligent prediction model, as well as the data preparation for these prediction models. Chapter 4 presents the empirical results of trading Hong Kong Hang Seng Index and Dow Jones Transportation Average Index. Furthermore, it compares the results with non-intelligent trading system as well as buy and hold strategy. Chapter 5 summarizes the project and provides the future work for the project. Chapter 2 Literature Review 2.1 Artificial Neural Networks An artificial neural network (ANN) is inspired by the structure and functions of biological neural networks, and expressed using mathematical models. It consists of an interconnected group of artificial neurons and processes information using a connectionist approach to computation. In most cases an ANN is an adaptive system that changes its structure based on external or internal information that flows through the network during the learning phase. Modern neural networks are non-linear statistical data modeling tools. They are usually used to model complex relationships between inputs and outputs or to find patterns in data. Neural networks are considered as highly parallel system which could learn from the past data and would be able to apply the knowledge learned to new data. 2.1.1 Multilayer Perceptron Neural Networks There are varies of ANN structures, multilayer perceptron neural networks (MLP) is one of them. It is a feed-forward network has a layered structure. Each layer consists of units which receive their input from units from a layer directly below and send their output to units in a layer directly above the unit. There are no connections within a layer Fig 21. The inputs are fed into the first layer and each input is associated with a weight. The first layer outputs are considered as second layers input and eventually calculated the final output. The activation function for each layer is described as: in which Information in MLP networks only move in the forward direction, from the input nodes through the hidden layers and to the output layer. There are also no loops in a MLP network. Fig 21 A multi layer neural network with L layers 2.1.2 Back Propagation Back propagation is a common method of teaching artificial neural networks how to perform a given task. It was first described by Arthur E. Bryson and Yu-Chi Ho in 1969,[14]. Back propagation is a supervised learning method, and is an implementation of the Delta rule. It requires a teacher that knows, or can calculate, the desired output for any given input. In another word, it has to be provided with desired output in order to calculate the errors. The errors propagate backwards from the output nodes to the inner nodes and from the inner nodes to input nodes. Hence back propagation is a method to calculate the gradient of the error for the network with respect to the networks modifiable weights, either in input layer or in hidden layer. In short, back propagation algorithm could be describe as below. Summary of the backpropagation technique: 1. Present a training sample to the neural network. 2. Compare the networks output to the desired output from that sample. Calculate the error in each output neuron. 3. For each neuron, calculate what the output should have been, and a scaling factor, how much lower or higher the output must be adjusted to match the desired output. This is the local error. 4. Adjust the weights of each neuron to lower the local error. 5. Assign blame for the local error to neurons at the previous level, giving greater responsibility to neurons connected by stronger weights. 6. Repeat from step 3 on the neurons at the previous level, using each ones blame as its error. 2.1.3 Levenberg-Marquardt Algorithm Levenberg-Marquardt Algorithm is used for training the neural network. It could be used to modify the ANNs weights of each layer. The Levenberg-Marquardt Algorithm interpolates between the Gauss-Newton algorithm and the method of gradient descent. It is more robust than the Gauss-Newton algorithm, which means that in many cases it finds a solution even if it starts very far off the final minimum. On the other hand, for well-behaved functions and reasonable starting parameters, the Levenberg-Marquardt Algorithm tends to be a bit slower than the Gauss-Newton algorithm. Levenberg-Marquardt Algorithm could be expressed as [15] 2.2 Support Vector Machine Support Vector Machine (SVM) is a relatively new learning method developed from statistical learning theory. Compared with traditional statistics, statistical learning theory does not assume infinite samples, but rather focused on estimations utilizing small samples. The basic idea of support vector machine is to find a hyperplane which separates the d-dimensional data perfectly into its two classes. Support Vector Machine is a supervised learning method which could map the input space to output space Fig 22. Given that a training set (), i = 1, the support vector machine requires the minimum value of following formula [17]. Fig 22 Maximum-margin hyperplane and margins for a SVM trained with samples from two classes. 2.2.1 Support Vector Regression Support Vector Machine used in regression was proposed in 1996 by Vladimir Vapnik, Harris Drucker, Chris Burges, Linda Kaufman and Alex Smola [18], which is called support vector regression (SVR). The model produced by support vector machine used in solving classification problems depends only on a subset of the training data or called support vectors, because the cost function for building the model does not care about training points that lie beyond the margin. Similarly, the model produced by SVR depends only on a subset of the training data, because the cost function for building the model ignores any training data close to the model prediction. Given a training set (), i = 1, the target of SVR is to find a linear function that could minimize the discrepancy between the desired output and predicted output. The optimal regression function is the same with SVM. There are several kernel functions commonly used in SVR, which includes liner, polynomial, radial basis function and sigmoid kernel function. Their respective formula is as below [23]: n Linear: n Polynomial: n Radial Basis Function (RBF): n Sigmoid: Here, are kernel parameters Support Vector Machine or SVR has some advantages when comparing to Neural Networks. For instance, it does not over fit the training data since it uses only several training data as support vectors. However, parameters in SVR would affect the final results in spite that SVR has much fewer parameters compared to NN. The main parameters in SVR are error insensitive tube around the regression function [19] and the balance of training errors with model complexity. 2.3 Genetic Algorithm Genetic algorithm (GA) is a searching technique to look for exact or approximate solutions for optimization and searching problems. It is considered as global search heuristics.GA uses techniques inspired by evolutionary biology such as inheritance, mutation, selection, and crossover. A typical genetic algorithm requires: 1. a genetic representation of the solution domain 2. fitness function to evaluate the solution domain In GA, an abstract representation of candidate solutions is called chromosomes, and it could be used in an optimization problem evolves toward better solutions. Solutions are represented in some encoding method, such as binary encoding. A fitness function is a particular type of objective function that prescribes the optimality of a solution so that a particular chromosome may be ranked against all the other chromosomes. The evolution usually starts from a population of randomly generated individuals. In each generation, the fitness of every individual in the population is evaluated. Based on their fitness, the fittest group of individuals are selected and through reproduction, crossover or mutation to form a new population. The new population is then used in the next iteration of the algorithm. Commonly, the algorithm terminates when either a maximum number of generations has been produced, or a satisfactory fitness level has been reached for the population. A common genetic algorit hm is shown Fig 23. Fig 23 Genetic Algorithm flowchart, with maximum 100 generation 2.3.1 Operators of Genetic Algorithm When generating the next generation population of solutions, GA would use genetic operators: crossover, and/or mutation. For each new solution to be produced, a pair of parent solutions is selected for breeding from the pool selected previously. By producing a child solution using the above methods of crossover and mutation, a new solution is created which typically shares many of the characteristics of its parents. Crossover selects genes from parent chromosomes and creates a new offspring. One common way is using single crossover point on both parents organism strings. All data beyond that point in either organism string is swapped between the two parent organisms. An illustration on one point crossover is shown in Fig 24 Fig 24 One point crossover There are other ways for crossover, for example two crossover points could be chosen. Crossover can be rather complicated and very depends on encoding of chromosome. In some cases, GA performance could be enhanced by trying out other crossover techniques. After a crossover is performed, mutation takes place. The purpose of mutation in GA is to preserve and introduce diversity. Local minima could be prevented because of mutation, and the population of chromosomes would not be too similar to each other so that the evolution could continue. Mutation changes the new offspring randomly. For binary encoding, a common way is switching a few randomly chosen bits from 1 to 0 or from 0 to 1. 2.3.2 Selection in Genetic Algorithm Selection would choose individual genomes from a population for breeding next generation. There are varies of selection algorithms, such as roulette-wheel selection, rank selection or Tournament selection. Roulette-wheel selection chooses parents according to their fitness. The chromosome has high fitness possesses the higher chances to be selected. The fitness level is used to associate a probability of selection with each individual chromosome. This algorithm could be imagined as roulette wheel in casino, where the larger piece has higher probability to be chosen, as shown in Fig 25. If is the fitness of individual i in the population, its probability of being selected is, where N is the number of individuals in the population. Fig 25 roulette-wheel selection Tournament selection involves running several tournaments among a few individuals chosen at random from the population. The winner of each tournament (the one with the best fitness) is selected. Selection pressure is easily adjusted by changing the tournament size. If the tournament size is larger, weak individuals have a smaller chance to be selected. Chapter 3 Intelligent Trading System Design 3.1 Technical Analysis Technical analysts seek to identify price patterns and trends in financial markets and attempt to exploit those patterns.[20] People who are using technical analysis would search for archetypal patterns, such as the well-known head and shoulders or double top reversal patterns, study indicators such as moving averages, and look for forms such as lines of support, resistance, channels, and more obscure formations such as flags, pennants or balance days. In this project, only indicators have been studied since they are quantitative and do not require ambiguous identifications. Among all the technical indicators, moving average is considered as the simplest and most useful one. It is popular because moving average could discover the trends by smoothing the prices. Most importantly, moving average could be a useful tool since investors can make profits through trends. Exponential moving average (EMA), being one of the moving average indicators, is considered as more adaptive since it puts more weights on recent prices, e.g., todays close price, while putting less weights on earlier days. Equation below shows the calculation of EMA: The plot of long term EMA of 45 days and short term EMA of 15 days are plotted with close price for Dow Jones Industrial Average Index in Fig 31, all data and figures are provided by yahoo finance. Dow Jones Industrial Average price, with EMA plotted. There are many ways of using EMA, and two common uses are introduced here. First, investors could take a long position, or buy the stock index when close price is above the EMA, and take a short position when close price is under EMA. An example is shown in Fig 32, using 30 days of EMA on Dow Jones Industrial Average. Although there are some whipsaw in the middle, using single EMA is helpful to investor when making buy or sell decisions. Fig 32 Using single EMA Another way of using EMA is taking a long position (buy) when short term EMA is above long term EMA, and taking a short position when short term EMA is under long term EMA. An example of how to buy or sell is illustrated in Fig 33, using 15 days EMA and 45 days of EMA. As we could see on the chart, this method is effective by taking large profits and suffering small losses. Fig 33 Using two EMA to make decision It is clear that EMA could help investors to identify the trend. However, being able to discover the trend is not good enough, the trading rule should be established to take profits through the trend. However, using chart and technical indicators are not sufficient since there are some serious disadvantages. For example, we do not know whether this technical indicator could bring investors consistent long term profits. Also, we do not know how many shares should we buy or sell. Without providing more information on these topics, investors may not dare to trade with real money. However, a quantitative trading system based on these indicators could concur the shortcomings. A well established trading system would be able to tell when to buy and when to sell, as well as how many shares to buy and sell. In addition, a trading system could provide back testing results, which could present the trading performance to investors, such as the equity curve or maximum drawdown. Therefore, in this project, a quantitative trading system is built and tested. This trading system uses a technical indicator named Percentage Price Oscillator (PPO), PPO is calculated as formula below: A buy signal is triggered if PPO is greater than 0, in other words, when short term EMA crosses over with long term EMA. A sell signal is triggered if PPO is less than 0, which means long term EMA is above short term EMA. This trading system is a typical trend following system which could catch every major trend to make promising profit, while suffering minuscule losses when significant trends are absent in the market. 3.2 Computational Intelligence in Trading When using PPO trading system, there would be a lag between the time when the trend starts and the time when the trading system detects it. Failing to compensate the lag has been a dominant disadvantage of traditional trading systems (without prediction). An intelligent trading system attempts to predict PPO in the near future, so as to enter the market before the trend while closing the position before the market falls. The input for our intelligence trading system studied in this paper is PPO of the last 8 days and the output is PPO in the future 5 days. The intelligent model is either an MLP optimized by GA or an SVM optimized by GA. 0.2% of transaction cost and slippage are counted in the process of calculating profits, as indicated in Fig 34. Fig 34 A predictive trading system. 3.3 Experimental Settings 3.3.1 GA optimized neural network In this project, a feed-forward MLP with one hidden layer is used. The number of hidden neurons is determined to be 30 by the trial and error. The Levenberg-Marquardt algorithm is used to train the MLP. Initial weights of the neural network are determined by GA. The settings for the NN+GA model are selected as Table 31. GA settings the population size of GA 300 Maximum Generation 800 Stop criteria maximum generation reached the probability of mutation 0.02 Neural Network Settings layers Single hidden layer with 30 neurons Transfer function Transig, purelin Training Levenberg-Marquardt performance Mse (mean square error) Table 31 Settings for GA and NN Using genetic algorithm to determine the initial weight and bias is essential since they have great impact on the generalization ability of the neural network. If the weights and bias are initialized with some random number and they happen to be far way from a good solution, or near local optimum, the neural network may not be trained to achieve good performance. Being trapped in local extremes is normally happened. On the other hand, appropriate initialization would put the weights and bias near a good solution, and hence provide a high chance for neural network to reach better outcome. In this project, genetic algorithm is chosen to provide the initial weights and bias for neural network. The structure of using GA to optimize MLP is shown in Fig 35. The fitness in GA is based on the error of predicted output and desired output, shown as below Where is the desired output and is predicted output. 3.3.2 GA optimized SVR Main parameters in SVR are error insensitive tube around the regression function [14] and the balance of training errors with model complexity. In this paper, GA is used to determine the best SVR parameters. The structure of GA optimized SVR is the same as using GA to optimize MLP, where GA is trying to minimize the difference between desired output and predicted output. The settings for GA optimized SVR model are listed in Table 32 Fig 35 Structure of GA optimized MLP GA settings the population size of GA 30 Maximum Generation 200 Stop criteria maximum generation reached the probability of mutation 0.05 the probability of crossover 0.4 SVR Settings Kernel function radial basis function Table 32 Settings for GA and SVR 3.4 Preprocessing Input Data Once the appropriate raw input data has been selected (in this case, they are previous 8 days PPO) , it must be preprocessed; otherwise, the neural network will not produce accurate forecasts. The decisions made in this phase of development are critical to the performance of a network. Normalization is commonly used to distribute the input data evenly and scale it into an acceptable range for the network. Knowledge of the domain is important in choosing preprocessing methods to highlight underlying features in the data, which can increase the networks ability to learn the association between inputs and outputs. In normalizing data, the goal is to ensure that the statistical distribution of values for each net input and output is roughly uniform. In addition, the values should be scaled to match the range of the input neurons. This means that along with any other transformations performed on network inputs, each input should be normalized as well. In this project, mapping the training input minimum and maximum values between -1 and 1 is adopted as normalizing method. In this method, it is assumed that the input has only finite real values, and that the elements are not all equal, as indicated below. Where in this case is 1, is -1. is the largest number of training input, while is the smallest number of training input. stands for each individual training data, and is the normalized training data. For the testing set, data should also be scaled to a certain range, as training set does. However, the largest number and smallest number of testing set are not available since we assume these data are unknown for trading simulation. Therefore, the testing data set are scaled using the parameters in training input data. In specific, and are still the largest number and smallest number in training data set. Chapter 4 Results and Evaluation This chapter illustrates the experiment results for 2 intelligent trading models, which are using GA optimized MLP and using GA optimized SVR. In addition, it introduces some evaluation criteria, and evaluates the prediction models according to these criteria. Furthermore, it analyzes and compares the return of capital and maximum drawdown with other publication as well as conventional trading method. 4.1 Experimental Data This intelligent trading system uses Hong Kong Hang Seng Stock Index (HSI) from 1986-12-31 to 1997-1-28, total 2500 daily close price as in sample training session, and uses HSI from 1997-1-29 to 2007-3-8, total 2500 daily price as out of sample testing data. All the HSI index data was obtained from Yahoo Finance (https://finance.yahoo.com/q/hp?s=^HSI). In sample data used to train the neural network have been separated into three sets: training, validation, and testing. In this project, we divide the input data randomly such that the first 60% of the samples are assigned to the training set, the next 20% to the validation set, and the last 20% to the test set. Table 41 is to summarize the distribution of experimental data. Data Set Distribution (%) Distribution(data) Training Data Training set 60% 1500 Validation set 20% 500 Test set 20% 500 Total 100% 2500 Testing Data 100% 2500 Table 41 Data distributions for training and testing neural network 4.2 GA Optimized MLP Trading System 4.2.1 Forecasting Performance The GA optimized MLP model is used to predict the future 5 days PPO. The performance of this predicative model could be evaluated by mean square error (MSE). MSE could be expressed as below Where is the target output and is the predicted output. The performance of forecasting in terms of MSE is 0.0087 for out of sample data, while 0.00213 for in sample data. In either case, we could see that the MSE is relatively small, which means the prediction is acceptable. In Fig 42, the difference between desired output and predicted output is plotted, as we could see, although there are some large errors in prediction, most of the forecasting is acceptable. Fig 41 Training performance of MLP The training results of neural networks could be further evaluated by linear regression. The best network is indicated by the correlation coefficient, r closed to unity (r à ¢Ã¢â‚¬ °Ã‹â€  1) Fig 44 shows the linear regression for out of sample data, which is 0.91227. Although it is nearly 8% lower compare with performance of in sample data, this model could still be considered as well trained neural network. Fig 42 MSE for out of sample data 4.2.2 Empirical Trading Results and Evaluation PPO of future 5 days is selected to be desired output after prudent consideration. As a matter of fact, forecasting larger time horizon would definitely produce more profit, which is made by early entry and early exit. On the other hand, the larger the time horizon, the harder it is to predict. This would increase the chance of wrong prediction, which decreases the profit. Table 42 is total return of investing 1 dollar, with different prediction time horizon. Prediction Time Horizon Total return No prediction 2.064 Predict future 3 days PPO 4.357 Predict future 5 days PPO 6.910 Predict future 7 days PPO 5.464 Table 42 Total return for different prediction time horizon In this experiment, reinvesting all capital is selected as the money management strategy, in which the trading system would re-invest all the profit and initial capital for next buy or sell decision. Fig 43 Linear regression for trained neural network Fig 44 Linear regression for out of sample data The proposed trading system assumes that it is possible to enter the market using the close price on the same day which triggers the trading signal. In addition, it assumes that the initial capital is 1 dollar and it is valid to buy or sell fraction number of the HSI. The PPO is calculated using parameters that short term of 15 days EMA and long term of 45 days EMA. The equity curves of proposed intelligent trading systems are shown in Fig 45 with equity curve of conventional trading system and equity curve for buy and hold trading strategy in contrast. The predictive MLP+GA model achieves 6.71 times of original capital from 1997-1-28 to 2007-3-8 while in the mean time, a non-predictive trading system only achieves 2.064 for 1 dollar investment, and buy and hold trading strategy generates 1.605 as final capital. In comparison, Huang and Quek et al. [10] use hierarchical coevolutionary fuzzy system (HiCEFS) to achieve 5.781 times of original capital on Hang Seng Index on the same trading days. Fig 45 Equity curve for intelligent and conventional trading systems Sample testing data is shown in Fig 47, it is obvious that prediction trading system would enter the market and exit the market earlier compared with trading system without prediction. However, using prediction has certain disadvantage. During non-trendy time, the proposed trading system may make wrong prediction and hence suffer some losses. For example, NN+GA trading system enters the market at day 61 at price 13030 and exit on the day 138 at price 15600, takes profit of 2570 points. On the other hand, for trading system without prediction, it enters the market at day 65 at price 13630 and exit at day 144 at price 13710, takes a profit of 80 points. That is the reason why the predictive model performs better than trading system without prediction. But during non-trendy market, such as around day 400, the trading system without prediction holds the position while the intelligent model made a wrong prediction. In this case, the investment incurred some losses. Fig 46 Trading signal of NN+GA trading system Fig 47 Trading signal of conventional trading system Moreover, another important criterion to evaluate the trading system is the maximum drawdown (MDD). MDD is defined as the maximum cumulative loss from a market peak to the following trough [22] The trading system using NN+GA suffers a MDD from 3.079 dollars to 2.443 dollars, which is 20.65% of the highest capital. In contrast, the trading system without prediction would have a MDD from 1.705 dollars to 1 dollar, which is 41.34% of the highest capital. Buy and Hold strategy suffers a MDD from 1 dollar to 0.466 dollar, which is 53.4% drop from the peak capital. Thus the NN+GA trading system reduced the risk involved. As it is shown in Fig 45 regarding the conventional trading system without prediction, the capital is back to original 1 dollar after 1276 trading days. This may shake peoples will to follow this system. On the other hand, the MDD happened in NN+GA trading system is from day 903 to day 930, which is easier for investors to follow the trading system. All the trading records are listed in appendix A. 4.3 GA Optimized SVR Trading System 4.3.1 Forecasting Performance The performance of forecasting future 5 days PPO using GA optimized SVR is evaluated in terms of MSE. MSE is 0.0058 for out of sample data, in contrast, MSE is 0.0087 in using GA optimized NN model for the same data. In another word, GA optimized SVR has smaller MSE, or better forecasting. In Fig 48, the difference between desired output and predicted output is plotted. However, better forecasting does not guarantee better profitability. Some wrong prediction at the top or at the bottom would bring larger losses comparing with wrong prediction at other situations. 4.3.2 Empirical Trading Results and Evaluation The same assumptions are made as using GA+NN trading system. In addition, 15 days EMA and 45 days EMA are used to form PPO. The equity curve of GA+SVR trading system is shown in Fig 49 with equity curve of conventional trading system in contrast. This GA+SVR trading system achieves 5.705 times of original capital. Fig 48 MSE for GA+SVR model Although this predictive model does not achieve profit as much as GA+NN model, it has its own advantage. First, this SVR model would provide consistent performance after each training session. Second, in term of prediction accuracy, GA+SVR model offers smaller prediction errors while GA+NN mode has larger errors. Last, it trades less frequently compared with GA+NN model, this would give investors different options to choose which type of trading systems are fitting to them. For active traders, GA+NN model could be more suitable for them, while for less active investors, GA+SVR model could be adopted since it trades less frequently. The comparison of GA+NN trading system, GA+SVR trading system, conventional trading system and buy and hold strategy is shown in Fig 410, the equity curves for 4 trading system mentioned above are plotted together for comparison. Fig 49 Equity curve for GA+SVR trading system and conventional trading system Trading System Final Equity MDD Win ratio Trading times Long position times Short position times GA+NN trading system 6.71 20.65% 49.6% 127 63 64 GA+SVR trading system 5.705 28.5% 44.8% 67 30 37 Conventional trading system 2.064 41.34% 48.7% 41 20 21 Buy and hold strategy 1.605 53.4% 100% 1 1 0 Table 43 Trading performance comparison Fig 410 Comparison of 4 trading systems 4.4 Further Evaluation In designing trading system, one of the most important issues is to avoid over curve fitting the system to back testing data. The more you bend your system around to improve performance on past data, the less likely it is your system will trade profitably in the future. Past performance will only approximate future performance to the extent the system is not over curve fitted. There are many ways to examine the over curve fitting trap. One way is to do back testing long enough. The longer the historical time period a system can trade profitably, the more robust it is. Another way to guard effectively against over-curve-fitting is to make sure your system works in many markets using the same parameters. Hence, the trading system is further evaluated by applying to Dow Jones Transportation Index (DJT). The data used as in sample training data is from 1968-9-20 to 1978-9-5, totally 2500 trading days, and data used as out of sample testing data is from 1978-9-6 to 1988-7-26, which is 2500 trading days. All data is from yahoo finance (https://finance.yahoo.com/q?s=^DJT). All the same assumptions are the same as trading HSI using intelligent trading systems. The equity curves of GA+NN trading system and GA+SVR trading system are shown in Fig 411 with equity curve of conventional trading system in contrast. This GA+SVR trading system achieves 5.168 times of original capital, while the predictive GA+NN model achieves 4.87 times of original capital while in the mean time, a non-predictive trading system only achieves 2.805 for 1 dollar investment. Fig 411 Equity curves of different trading system on DJT GA+NN trading system and GA+SVR trading system outperform the conventional trading system again on DJT. This further proves that using computational intelligence would enhance the performance of conventional trading system. In addition, the proposed intelligent trading systems, using GA+NN or using GA+SVR, would survive in different market, such as DJT and HSI, and be able to generate profits consistently. Chapter 5 Conclusion In this project, a predictive trading system is proposed to trade on real market data of Hong Kong Hang Seng Index, and trade on Dow Jones Transportation Index as cross market validation. Neural network optimized by GA and support vector regression optimized by GA are implemented as predictive model in the trading system. The trading system mainly uses technical indicator price percentage oscillator (PPO) as trading rules. Hence the predictive model uses last 8 days PPO as input to predict future 5 days PPO, and based on predicted PPO to make trading decisions. The testing period is 10 years, which is long enough to reduce the possibility of curve fitting. The proposed predictive trading system produces around 3 times more profits on HSI compared with conventional trading system without prediction, and around 2 times more profits on DJT compared with non predictive trading system. Despite promising profits generated by the trading system, further improvements such as applying the system to other new immerging markets, such as China Stock market, or applying a better money management strategy can be considered as future research area. Furthermore, due to the randomness introduced by GA, neural network may not always be trained well enough every time. We shall study effective ways to assure reasonable performance for each training session. References [1] E. F. Fama, The Behavior of Stock Market Prices, Business, vol. 38, pp. 34-105, 1965. [2] A. P. N. Refenes, A. N. Burgess, and Y. Bentz, Neural networks in financial engineering: A study in methodology, IEEE Transactions on Neural Networks, vol. 8, no. 6, pp. 1222 1267, 1997. [3] CHEN, Kuan-Yu and Chia-Hui HO, An Improved Support Vector Regression Modeling for Taiwan Stock Exchange Market Weighted Index Forecasting, ICNNB 05: International Conference on Neural Networks and Brain, Volume 3, , 2005 [4] L. Cao and F. Tay, Support Vector Machine with adaptive parameters in financial time series forecasting, IEEE Transactions on Neural Networks, vol. 14, no. 6, pp. 1506-1518, 2003. [5] P.B. Patel and T. Marwala, forecasting closing price indices using neural networks. In International Conference on Systems, Man and Cybernetics, pp. 2351-2356, Oct 8-11, 2006, Taipei, Taiwan. [6] S.H. Lee, H.J. Kim and J.S. Lim, forecasting short term KOSPI time series based on NEWFM, in Advance Language Processing and Web Information Technology (ALPIT), pp. 303-307, July, 2007. [7] B. Doeksen, A. Abraham, J. Thomas, and M. Paprzycki, Real stock trading using soft computing models, in Information Technology: Coding and Computing (ITCC), 2005, vol. 2, pp. 162-167. [8] A.S. Chen, M. T. Leung, and H. Daouk, Application of neural networks to an emerging financial market: Forecasting and trading the Taiwan Stock Index, Computers and Operations Research, vol. 30, no. 6, pp. 901-923, May 2003. [9] K.K. Ang and C. Quek, Stock Trading Using RSPOP: A Novel Rough Set-Based Neuro-Fuzzy Approach, IEEE Transactions on Neural Networks, vol. 17, no.5, pp. 1301 1315, 2006. [10] H.M. Huang, M. Pasquier, and C. Quek, Financial Market Trading System With a Hierarchical Coevolutionary Fuzzy Predictive Model, IEEE Transactions on Evolutionary Computation, vol. 13, no.1, pp. 56 70, 2009. [11] H. Bandy, Quantitative Trading Systems, Blue Owl Press, 2007. [12] B. Krose and P.V.D Smagt, Introduction to Neural Network. The University of Amsterdam, 1996. [13] S. Russell and P. Norvig. Artificial Intelligence A Modern Approach. p. 578. [14] A.E.Bryson and Yu-Chi Ho. Applied optimal control: optimization, estimation, and control. Xerox College Publishing. pp. 481. [15] P.N. Bahrun and M.N. Taib, Selected Malaysia Stock Predictions using Artificial Neural Network, in International Colloquium on Signal Processing Its Applications (CSPA), 2009, pp. 428 431. [16] Lipo Wang (ed.), Support Vector Machines: Theory and Applications. Berlin, Springer, 2005. [17] C.W. Hsu, C.C. Chang, and C.J. Lin, A practical guide to support vector classification, Department of Computer Science and Information Engineering, National Taiwan University, Taipei, Taiwan, 2003. [Online]. Available: https://www.csie.ntu.edu.tw/~cjlin/papers/guide/guide.pdf [18] H. Drucker, C. J.C. Burges, L. Kaufman, A. Smola and V. Vapnik. Support Vector Regression Machines. Advances in Neural Information Processing Systems 9, NIPS 1996, 155-161, MIT Press. [19] A. J. Smola and B. Scholkopf, A tutorial on support vector regression, NeuroCOLT2 Technical Report NC2-TR-1998-030, 2003. [20] John J. Murphy, Technical Analysis of the Financial Markets ,New York Institute of Finance, 1999, pages 1-5,24-31. [21] M. Magdon-Ismail, A. Atiya, Maximum Drawdown, Risk Magazine, Volume 17, Number 10, pp. 99-102, October, 2004. [22] M. Magdon-Ismail, A. Atiya, A. Pratap, Y. Abu-Mostafa, On the Maximum Drawdown of a Brownian Motion, Journal of Applied Probability, Vol. 41, no. 1, PP. 147-161, March, 2004. [23] Appendix The trading details of NN+GA trading system on HSI are listed below. There would be price difference between exit and enter on the same day. This is due to consideration of slippage and commissions. enter short position at price 12414.3 at trading day 45 exit short position at price 13020.8 at trading day 61 enter long position at price 13033.8 at trading day 61 exit long position at price 15598.9 at trading day 138 enter short position at price 15583.3 at trading day 138 exit short position at price 15547.2 at trading day 139 enter long position at price 15562.7 at trading day 139 exit long position at price 15534 at trading day 140 enter short position at price 15518.5 at trading day 140 exit short position at price 14776.8 at trading day 165 enter long position at price 14791.6 at trading day 165 exit long position at price 14810.8 at trading day 166 enter short position at price 14796 at trading day 166 exit short position at price 10525.5 at trading day 244 enter long position at price 10536 at trading day 244 exit long position at price 10232 at trading day 254 enter short position at price 10221.8 at trading day 254 exit short position at price 10671 at trading day 255 enter long position at price 10681.7 at trading day 255 exit long position at price 11151.6 at trading day 295 enter short position at price 11140.4 at trading day 295 exit short position at price 10968.3 at trading day 296 enter long position at price 10979.3 at trading day 296 exit long position at price 10977.5 at trading day 297 enter short position at price 10966.5 at trading day 297 exit short position at price 8189.25 at trading day 395 enter long position at price 8197.44 at trading day 395 exit long position at price 7849.96 at trading day 397 enter short position at price 7842.11 at trading day 397 exit short position at price 7701.61 at trading day 408 enter long position at price 7709.31 at trading day 408 exit long position at price 7946.04 at trading day 409 enter short position at price 7938.09 at trading day 409 exit short position at price 7837.61 at trading day 410 enter long position at price 7845.45 at trading day 410 exit long position at price 7883.46 at trading day 411 enter short position at price 7875.58 at trading day 411 exit short position at price 7564.54 at trading day 412 enter long position at price 7572.1 at trading day 412 exit long position at price 7744.72 at trading day 413 enter short position at price 7736.98 at trading day 413 exit short position at price 8506.79 at trading day 415 enter long position at price 8515.3 at trading day 415 exit long position at price 9499.5 at trading day 488 enter short position at price 9490 at trading day 488 exit short position at price 9913.58 at trading day 511 enter long position at price 9923.49 at trading day 511 exit long position at price 12436.9 at trading day 567 enter short position at price 12424.4 at trading day 567 exit short position at price 12346.9 at trading day 568 enter long position at price 12359.3 at trading day 568 exit long position at price 12409.2 at trading day 569 enter short position at price 12396.8 at trading day 569 exit short position at price 12308.5 at trading day 570 enter long position at price 12320.8 at trading day 570 exit long position at price 12059.3 at trading day 571 enter short position at price 12047.2 at trading day 571 exit short position at price 12471.6 at trading day 575 enter long position at price 12484.1 at trading day 575 exit long position at price 13093.7 at trading day 609 enter short position at price 13080.6 at trading day 609 exit short position at price 13473.8 at trading day 616 enter long position at price 13487.3 at trading day 616 exit long position at price 13591 at trading day 617 enter short position at price 13577.4 at trading day 617 exit short position at price 13254.3 at trading day 618 enter long position at price 13267.6 at trading day 618 exit long position at price 13167.1 at trading day 619 enter short position at price 13153.9 at trading day 619 exit short position at price 13566.7 at trading day 629 enter long position at price 13580.3 at trading day 629 exit long position at price 13214.4 at trading day 652 enter short position at price 13201.2 at trading day 652 exit short position at price 13322.1 at trading day 677 enter long position at price 13335.4 at trading day 677 exit long position at price 15574.6 at trading day 730 enter short position at price 15559 at trading day 730 exit short position at price 15275.3 at trading day 732 enter long position at price 15290.6 at trading day 732 exit long position at price 15167.5 at trading day 735 enter short position at price 15152.4 at trading day 735 exit short position at price 15917.8 at trading day 738 enter long position at price 15933.7 at trading day 738 exit long position at price 15653.9 at trading day 741 enter short position at price 15638.2 at trading day 741 exit short position at price 15789.8 at trading day 742 enter long position at price 15805.6 at trading day 742 exit long position at price 16491.4 at trading day 785 enter short position at price 16474.9 at trading day 785 exit short position at price 16850.7 at trading day 787 enter long position at price 16867.6 at trading day 787 exit long position at price 16487.7 at trading day 788 enter short position at price 16471.2 at trading day 788 exit short position at price 15278.3 at trading day 793 enter long position at price 15293.6 at trading day 793 exit long position at price 15367.1 at trading day 795 enter short position at price 15351.8 at trading day 795 exit short position at price 15900.1 at trading day 824 enter long position at price 15916 at trading day 824 exit long position at price 16629.8 at trading day 893 enter short position at price 16613.2 at trading day 893 exit short position at price 15820.8 at trading day 930 enter long position at price 15836.6 at trading day 930 exit long position at price 15504.8 at trading day 932 enter short position at price 15489.3 at trading day 932 exit short position at price 15329.6 at trading day 955 enter long position at price 15344.9 at trading day 955 exit long position at price 15024.5 at trading day 959 enter short position at price 15009.5 at trading day 959 exit short position at price 15188 at trading day 960 enter long position at price 15203.2 at trading day 960 exit long position at price 14659.3 at trading day 962 enter short position at price 14644.7 at trading day 962 exit short position at price 15436.5 at trading day 971 enter long position at price 15452 at trading day 971 exit long position at price 15527.4 at trading day 999 enter short position at price 15511.8 at trading day 999 exit short position at price 13718.1 at trading day 1046 enter long position at price 13731.9 at trading day 1046 exit long position at price 13600.8 at trading day 1048 enter short position at price 13587.2 at trading day 1048 exit short position at price 13585.1 at trading day 1050 enter long position at price 13598.7 at trading day 1050 exit long position at price 13636.6 at trading day 1052 enter short position at price 13623 at trading day 1052 exit short position at price 13459.2 at trading day 1057 enter long position at price 13472.6 at trading day 1057 exit long position at price 13721.3 at trading day 1058 enter short position at price 13707.5 at trading day 1058 exit short position at price 13878 at trading day 1059 enter long position at price 13891.8 at trading day 1059 exit long position at price 13174.4 at trading day 1066 enter short position at price 13161.2 at trading day 1066 exit short position at price 13703.4 at trading day 1071 enter long position at price 13717.1 at trading day 1071 exit long position at price 13523.3 at trading day 1075 enter short position at price 13509.8 at trading day 1075 exit short position at price 10609.3 at trading day 1176 enter long position at price 10619.9 at trading day 1176 exit long position at price 11209.4 at trading day 1219 enter short position at price 11198.2 at trading day 1219 exit short position at price 11013.6 at trading day 1220 enter long position at price 11024.6 at trading day 1220 exit long position at price 10964.1 at trading day 1221 enter short position at price 10953.1 at trading day 1221 exit short position at price 11003 at trading day 1253 enter long position at price 11014 at trading day 1253 exit long position at price 10863.1 at trading day 1265 enter short position at price 10852.2 at trading day 1265 exit short position at price 11032.9 at trading day 1269 enter long position at price 11044 at trading day 1269 exit long position at price 10878 at trading day 1270 enter short position at price 10867.2 at trading day 1270 exit short position at price 11217.2 at trading day 1281 enter long position at price 11228.4 at trading day 1281 exit long position at price 11359.8 at trading day 1311 enter short position at price 11348.4 at trading day 1311 exit short position at price 11312.5 at trading day 1312 enter long position at price 11323.9 at trading day 1312 exit long position at price 11402.4 at trading day 1313 enter short position at price 11391 at trading day 1313 exit short position at price 9787.49 at trading day 1411 enter long position at price 9797.28 at trading day 1411 exit long position at price 9560.46 at trading day 1415 enter short position at price 9550.9 at trading day 1415 exit short position at price 9655.36 at trading day 1419 enter long position at price 9665.02 at trading day 1419 exit long position at price 9613.84 at trading day 1424 enter short position at price 9604.23 at trading day 1424 exit short position at price 9865.65 at trading day 1427 enter long position at price 9875.52 at trading day 1427 exit long position at price 9656.46 at trading day 1448 enter short position at price 9646.8 at trading day 1448 exit short position at price 9834.08 at trading day 1465 enter long position at price 9843.91 at trading day 1465 exit long position at price 9552.02 at trading day 1470 enter short position at price 9542.47 at trading day 1470 exit short position at price 9155.57 at trading day 1544 enter long position at price 9164.73 at trading day 1544 exit long position at price 13024.1 at trading day 1753 enter short position at price 13011 at trading day 1753 exit short position at price 12326.9 at trading day 1811 enter long position at price 12339.2 at trading day 1811 exit long position at price 12050.7 at trading day 1817 enter short position at price 12038.6 at trading day 1817 exit short position at price 12185.5 at trading day 1824 enter long position at price 12197.7 at trading day 1824 exit long position at price 12285.8 at trading day 1827 enter short position at price 12273.5 at trading day 1827 exit short position at price 12220.1 at trading day 1828 enter long position at price 12232.4 at trading day 1828 exit long position at price 11939.4 at trading day 1837 enter short position at price 11927.5 at trading day 1837 exit short position at price 12123.6 at trading day 1840 enter long position at price 12135.8 at trading day 1840 exit long position at price 12395.1 at trading day 1841 enter short position at price 12382.7 at trading day 1841 exit short position at price 12320.2 at trading day 1842 enter long position at price 12332.5 at trading day 1842 exit long position at price 12852.4 at trading day 1907 enter short position at price 12839.5 at trading day 1907 exit short position at price 13054.7 at trading day 1910 enter long position at price 13067.7 at trading day 1910 exit long position at price 13712 at trading day 1958 enter short position at price 13698.3 at trading day 1958 exit short position at price 13578.3 at trading day 1976 enter long position at price 13591.8 at trading day 1976 exit long position at price 13555.8 at trading day 1977 enter short position at price 13542.2 at trading day 1977 exit short position at price 13845.6 at trading day 1981 enter long position at price 13859.5 at trading day 1981 exit long position at price 13772 at trading day 1997 enter short position at price 13758.2 at trading day 1997 exit short position at price 13941.5 at trading day 1999 enter long position at price 13955.4 at trading day 1999 exit long position at price 13890.9 at trading day 2001 enter short position at price 13877 at trading day 2001 exit short position at price 13906.9 at trading day 2002 enter long position at price 13920.8 at trading day 2002 exit long position at price 13832.5 at trading day 2004 enter short position at price 13818.7 at trading day 2004 exit short position at price 13776.5 at trading day 2008 enter long position at price 13790.2 at trading day 2008 exit long position at price 13603.6 at trading day 2009 enter short position at price 13590 at trading day 2009 exit short position at price 13750.2 at trading day 2029 enter long position at price 13764 at trading day 2029 exit long position at price 13627 at trading day 2044 enter short position at price 13613.4 at trading day 2044 exit short position at price 13867.1 at trading day 2053 enter long position at price 13880.9 at trading day 2053 exit long position at price 14847.8 at trading day 2144 enter short position at price 14832.9 at trading day 2144 exit short position at price 14629.5 at trading day 2169 enter long position at price 14644.1 at trading day 2169 exit long position at price 14627.4 at trading day 2170 enter short position at price 14612.8 at trading day 2170 exit short position at price 14788 at trading day 2172 enter long position at price 14802.8 at trading day 2172 exit long position at price 15542.1 at trading day 2249 enter short position at price 15526.5 at trading day 2249 exit short position at price 15519.8 at trading day 2250 enter long position at price 15535.3 at trading day 2250 exit long position at price 15720.4 at trading day 2251 enter short position at price 15704.6 at trading day 2251 exit short position at price 15729 at trading day 2252 enter long position at price 15744.8 at trading day 2252 exit long position at price 16313.4 at trading day 2294 enter short position at price 16297 at trading day 2294 exit short position at price 15805.5 at trading day 2295 enter long position at price 15821.3 at trading day 2295 exit long position at price 15864.6 at trading day 2296 enter short position at price 15848.7 at trading day 2296 exit short position at price 16326.7 at trading day 2324 enter long position at price 16343 at trading day 2324 27

What Everybody Is Saying About Gender Roles Essay Samples Is Wrong and Why

<h1> What Everybody Is Saying About Gender Roles Essay Samples Is Wrong and Why </h1> <h2> Gender Roles Essay Samples - the Conspiracy</h2> <p>Other individuals believe that sexual orientation jobs can't ever be annulled because of the organic contrasts among ladies and men, and that customary sex practices are simply in our tendency. The paper has applied sociological creative mind in this issue and built up that it's a self related social angle. Rather, sexual orientation should be thought of as practices and individual distinguishing pieces of proof which exist along a range. Along these lines, it ought not be limited to the sex of an individual, since sex isn't really an organic event. </p> <p>So it's critical to society to show up past sex jobs and assess the individuals in question. Numerous unmistakable sociologists have created different statures of hypotheses. For various decades, our general public was represented by a few sexual orientation jobs. The writers caution the peruser in the event that we don't recognize the new sexual orientation jobs, society will in any case have correctly a similar attitude from fifty years back and every sex won't advance. </p> <p>It is imperative to interface sociological clarification of sex with various orders like sexuality so as to grasp the standard ideas of sex. By correlation, object-relations scholars focus on the results of socialization on sexual orientation advancement. Optional, the possibility of hermaphrodism shows up to the fore. As expressed by the extreme inversion of society, the thought of the sexual orientation job gets changed. </p> <p>Modern Japanese sexual orientation jobs took on an odd blend of American perspectives and customary perspectives at this time. Know the sort of article that you're composing. Sexual orientation versus Sex There has at any point been a lot of disarray about the contrasts among sex and sexual orientation. Test of an incredible individual exposition. </p> <h2> Facts, Fiction and Gender Roles Essay Samples</h2> <p>Girls in anime make a huge arrangement out of making lunch for their favored person since it's a wifely thing. The media which contain numerous particular pictures of ladies and men alongside numerous messages about ladies and men importantly affect the sentiment of character. </p> <p>As an outcome, ladies were not allowed to learn proficient capacities, for example, being a society, a questioner, a doctor or another calling. For example, in the customary society, ladies and men were relied upon to dress with a specific goal in mind because of their sexual orientation jobs. In case you're seen as female, you're attempting to tempt men. Men should work professionally time ladies are foreseen to remain at home and keep the house and take care of the children. </p> <p>Additionally, satisfies his guarantee made with the mammoth to return to his mansion significantly in the wake of realizing it would be a risk on account of his life. Some may modify the way that they present themselves. In the activity place anyway it's the ladies on the less than desirable end if there should arise an occurrence of deviation. The back bar folks are a wide range of unusual that way. </p> <h2> The Argument About Gender Roles Essay Samples</h2> <p>Gender isn't a simple discussion to get, it makes individuals awkward. Sex jobs impact ladies and men in for all intents and purposes each aspect of life. They affect how and whether people approach basic assets, for example, instruction, data, discretionary cashflow, and wellbeing administrations. Sex jobs and generalizations influence ladies and men in various manners. </p> <p>Women were required to remain at home, acquiring no proper business, to deal with the house, and complete the entirety of the family unit obligations. They need to manage making not as much as men in compensation and a troublesome time progressing to the most noteworthy situations inside an organization. They are expected to effectively work. They were well on the way to realize how to carry out residential responsibilities contrasted with men. </p>

Help on Research Paper

<h1>Help on Research Paper</h1><p>When you are a doctoral understudy, you will discover help on an examination paper can be rare. There are a couple of things you have to do to compose the best one possible.</p><p></p><p>Know who your crowd is. How would you know the individuals who will peruse your paper? You need to design your exploration venture so the crowd fits in with the subject of your paper. In the event that you have a gathering of loved ones that are keen on science and research, remember them for your examination papers. They will be bound to peruse it.</p><p></p><p>Make sure that your work is intelligible. The peruser ought to consistently feel as though you are conversing with them about a similar subject. At the point when you compose, ensure that your paper makes sense.</p><p></p><p>Make sure that your exploration paper is designed appropriately. It is significant that your pape r understands well. Having strong letters is adequate, yet italics and underlining ought to be maintained a strategic distance from. A decent practice is to simply duplicate the format from a model. It tends to be extremely useful when attempting to make sense of how to arrange your paper.</p><p></p><p>Create a structure. You ought to have an away from for each segment of your paper. Incorporate the motivation behind your paper. The request where you will introduce your thoughts is additionally significant. You ought to organize your thoughts with the goal that they show up in the request wherein they belong.</p><p></p><p>Use guides to represent your thoughts. Another accommodating stunt is to give models. Models give lucidity and course to your paper. You can likewise utilize these guides to give you how your thoughts associate with past ideas.</p><p></p><p>Helpon an exploration paper ought to be organized and sorted out. Numerous individuals disregard this and ruin their exploration papers. On the off chance that you are attempting to compose your own exploration paper, you should attempt to discover help online.</p><p></p><p>When you are doing your examination, ensure you prepare. You should know about how much time you have accessible to compose your paper. Follow the above tips, and your exploration paper will be finished faster.</p>

How to Cite Research Paper With Multiple Authors

<h1>How to Cite Research Paper With Multiple Authors</h1><p>When it goes to your examination paper, there are numerous things you have to find out about how to refer to explore papers. Because you have more than one creator on your examination paper doesn't mean you need to be confounded about how to do it. Truth be told, realizing this data will assist you with bettering comprehend the necessities of your papers and how to appropriately refer to them.</p><p></p><p>You should ensure that you comprehend what is implied by adding more than one creator to your paper. Frequently, there will be cases where you have just one creator in your exploration paper. Notwithstanding, you can likewise utilize more than one writer on the off chance that you are doing an exceptional undertaking where more than one individual plays a part recorded as a hard copy it.</p><p></p><p>One thing that you should think about with regards to how to refer to look into paper with different writers is the distinction between being a co-writer and a chosen one. Both of these terms are comparable, however they are additionally totally different. Much of the time, you should consider yourself a co-creator on your paper, since that is the thing that the diary generally does. Assuming, be that as it may, you are doling out the paper to another person, you ought to distinguish that individual as a trustee in your paper.</p><p></p><p>To outline, suppose that you are going to refer to your paper as 'A Study of Consistent Differences in Error Rates,' however your co-writer is really composing the paper. That implies that they composed the article where you are refering to as your examination paper. In the event that, toward the finish of the paper, you need to refer to your co-creator as the creator, you would not just put their name in the creator's container; rather, you would put 'the creators' before their n ame.</p><p></p><p>You ought to know that having more than one co-creator on your paper may make you run into different lawful issues on the off chance that you are being sued. Also, if the two co-creators are attempting to get acknowledgment for the work you did, you may find that you can't do so in view of the irreconcilable circumstance that exists. It is significant that you be clear about this on your paper, as you would prefer not to give the feeling that you are attempting to conceal something from the reader.</p><p></p><p>One thing that you ought to likewise think about how to refer to examine paper with numerous writers is the contrast between a reference list and a manager's note. A reference list is typically utilized when you have just one creator. For this situation, the essayist will record their name in the reference list toward the start of the paper. The supervisor's note is like a standard note in the content of a book , yet it ought not be remembered for the paper.</p><p></p><p>The motivation behind why the reference list isn't ordinarily utilized is on the grounds that it can now and again become confounding to the peruser on the off chance that you have an excessive number of individuals on the rundown. In the event that you do utilize a reference show, you ought to demonstrate that it is for the creator as it were. Subsequently, you won't need to stress over disarray about what your other co-creators think about your work.</p><p></p><p>These are only a couple of the things that you should think about how to refer to investigate paper with different creators. Whenever you are composing your own paper, you should set aside some effort to consider what is associated with utilizing the best possible phrasing for the references. This will assist you with being clear about what the activity is and how to refer to your work with relative ease.</p>

Research Paper on Heart Disease

Research Paper on Heart DiseaseIf you're a student studying biology, be prepared to write a research paper on heart disease. Heart disease is a condition where the heart becomes enlarged heart is a deadly condition. This article will explain what you should include in your paper.Some common heart problems can be studied in a research paper on heart disease. Some people assume they are the same but they are not. For example, cardiac catheterization is a procedure that uses a catheter to treat the heart and this type of treatment involves the removal of some of the blood vessels so that there will be less pressure on the heart and the patient will feel less pain.Another common problem that researchers study is atherosclerosis, which is a condition that is caused by stress on the heart because of age, genetics, and a long history of smoking. It can also be caused by having a high blood pressure. The body can form plaque and this plaque can become hard and have deposits of fat and calciu m as well as cholesterol.When writing a research paper on heart disease, it's important to note that the paper has to be honest. You don't want to make up things to make a point or to make someone look bad. If you have information that you think someone has put together that isn't true you should mention it so that the research paper on heart disease can be reviewed to see if there is any truth to the claims that you are making.In addition to writing about human anatomy you have to understand how it affects the heart. You have to know if it is at risk or has been diagnosed. You also have to know if your body has any risk factors for heart disease so that you can write about that as well.Then you have to talk about the most common treatments for heart disease. You have to talk about medications, therapy, and special diets that are good for patients with heart problems. You can write about basic guidelines for caring for patients who have heart disease so that the reader can know how they should act and what they should do for their patients.You have to write about the old saying that prevention is better than cure. You also have to write about prevention through diet. If you can help patients cut down on foods like salt, sugar, and saturated fats, then you can help them cut down on the chances of a heart attack.When writing a research paper on heart disease, you have to have a background in both human anatomy and nutrition. It is important to know if patients are at risk and to also know if patients have had heart attacks. These are important aspects of understanding that can make a big difference in your research paper on heart disease.

Rhetorical Analysis Of Martin Luther King Jr. s I Have...

Martin Luther King Jr. was the man who wrote the speech entitled â€Å"I have a dream† and presented it to nearly 250,000 people on August 23, 1963. In that speech, MLK Jr. used several different types of figurative language/rhetorical devices in order to convey his message to the people on a deeper level. These devices include personification, allusion, symbolism, hyperbole, metaphor, simile, and anaphora. Personification is a form of figurative language in which something has nonhuman human qualities. One example of this in MLK Jr.’s writing is â€Å"I have a dream that one day this nation will rise up, live out the true meaning of its creed†. Here, he is giving the U.S. the human qualities of being able to rise up from the ground as well as†¦show more content†¦One example of symbolism he used would be, â€Å"Now is the time to rise from the dark and desolate valley of segregation to the sunlit path of justice.† In that quote MLK talks about a dark and desolate valley of segregation and the sunlit path of justice. The dark and desolate valley of segregation is referring to the U.S. at that point in time. People were separated based on their skin color and there was no social or legal justice for the people of dark colored skin. It was a horrible time to live in. The sunlit path of justice MLK Jr. also talks about is how the U.S. should be now and in the future. There would be no segregation or injustice based on skin color. There would be peace between the two races. So, using that knowledge, MLK Jr. is saying, with the use of symbolism, that the U.S. needs to change from its unequal and horrid ways to ones that treat everyone equal. MLK Jr. used hyperboles in his writing not all that often, as he could elaborate on what he had to say to his audience at the time with the use of other types of figurative language and literary devices. A hyperbole is an extremely exaggerated statement that is not being used literally. The o ne big example of of MLK using this type of figurative language would be, â€Å"When we allow freedom to ring - when we let it ring from every city and every hamlet, from every state and every city, we will be able to speed up that day when all of God’s children, black men and white men,Show MoreRelatedRhetorical Analysis Of Dr. Martin Luther King Jr. s I Have A Dream 1448 Words   |  6 Pagesmore influential words have been spoken than those uttered by Dr. Martin Luther King Jr.’s, â€Å"I have a dream,† speech. Perhaps one of the most famous and paradigm shifting speeches in all of history, Dr. King’s was spoken with candor, authenticity, fervor, and an enormous amount of tact. With his incredible intelligence and eloquence as a doctorate in Theological Studies, his establishment as such a respected leader, and his fervor and charisma in delivering the speech, Dr. King effectively establishedRead MoreRhetorical Analysis of Dr. Martin Luther King, Jr .s I Have A Dream Speech915 Words   |  4 Pages Dr. Martin Luther King delivered his I Have a Dream speech to the thousands of African Americans who had marched on Washington, D.C. at the height of the Civil Rights Movement. The date of the speech was August 28, 1963, but it is one that will live for generations. Of course his purpose was to convince his audience on several fronts: he sought to persuade the black community to stand up for the rights afforded them under the Constitution, and he also sought to Read MoreThe Fight for Freedom1312 Words   |  6 Pageslate 1950’s though the 1960’s, however; Tricia Andryszewski informs her readers that Black Americans had been working for change since before the civil war, but mainly beyond. Some of the most prominent civil rights leaders include Martin Luther King Jr., Rosa Parks, Malcolm X, Philip Randolph, and Bayard Rustin. The two main goals of the civil rights activists being, equal rights and treatment for all races. As a result, the â€Å"I Have a Dream† speech was written by Martin Luther King, Jr., a man whoRead MoreAnalysis of Martin Luther King ´s Speech: I Have a Dream1309 Words   |  6 Pageslate 1950’s through the 1960’s, however; Tricia Andryszewski informs her readers that Black Americans had been working for change since before the civil war, but mainly beyond. Some of the most prominent civil rights leaders include Martin L uther King Jr., Rosa Parks, Malcolm X, Philip Randolph, and Bayard Rustin. The two main goals of the civil rights activists being, equal rights and treatment for all races. As a result, the â€Å"I Have a Dream† speech was written by Martin Luther King, Jr., a man whoRead MoreEssay on Martin Luther King Rhetorical Analysis1420 Words   |  6 PagesDreaming About Freedom Martin Luther King Jr.s I Have a Dream speech is one of the most successful and most legendary speeches in United States history. Martin Luther King Jr. was a masterful speaker, who established a strong command of rhetorical strategies. By his eloquent use of ethos, logos, and pathos, as well as his command of presentation skills and rhetorical devices, King was able to persuade his generation that the Negro is not free (King 1). His speech became the rallying cry forRead MoreRhetorical Analysis Of Martin Luther King Jr.1046 Words   |  5 PagesRhetorical Analysis Essay Civil rights activist, Martin Luther King Jr. gave his memorable â€Å"I Have a Dream† speech while standing at the feet of the Lincoln Memorial in Washington D.C. His uplifting speech is one of the most admired during the civil rights era and arguably one of the best in American history. On August 28th, 1963, Martin Luther King Jr. spoke about the true American dream: equality. Although the video of his oral spectacle is powerful, the written document portrays exactly howRead MoreEssay on Critical Analysis of Martin Luther King, Jr.s Speech1674 Words   |  7 PagesCritical Analysis of Martin Luther King, Jr.s Speech Introduction In this critical analysis I am going to look at Martin Luther King, Jr and the I have a dream speech. Martin Luther King, Jr is very distinguished due to the many outstanding achievements he accomplished throughout his life. He was an American clergyman and he accomplished the Nobel Prize for one of the principal leaders of the American civil rights movement. Kings defiance to segregation andRead MoreUse And Manipulation Of The English Language1394 Words   |  6 Pages reap a great deal of power when mastered. As hyperbolic as it sounds, being able to use and manipulate the English language properly into our writing and speaking can be very influential in advocating ideas towards a community. â€Å"As a speaker, you have some influence on the extent to which others see you as having authority† (Fontaine and Smith 13). To gain authority over an audience, one must write and speak with confidence and be skilled enough to use proper English: that is, following the standardRead MoreEssay on The Kings Dream1588 Words   |  7 PagesThe Civil Rights Movement in the 1950’s through 1960’s had many leaders, such as Martin Luther King Jr., Rosa Parks, Malcom X and many more. But King was the only one who stood out of the pack. His purpose was to have equality for all races, not just African Americans. King had addressed a speech that he had written and spoke of it at the Lincoln memorial in Washington D.C on August 28, 1963. In King’s â€Å"I Have a Dream† he motivated and touched not only African Americans but white folks as well inRead MoreThe Civil Rights Movement : Martin Luther King Jr. Essay1690 Words   |  7 PagesA civil rights leader by the name of Reverend (PBS, 2016) Martin Luther King Jr. changed the world he occupied and changed the future course of the United States of America by advocating for desegregation. Martin Luther King Junior was on a mission to end the segregation of the African American community. Segregation was the post result of slavery throughout the United States of America which enslaved Africans. He challenged the status quo of the time. Protesting peacefully and advocating for social

Project Management Data Acquisition System

Question: Discuss about theProject Managementfor Data Acquisition System. Answer: Risk Assessment of this Project The major objective of this project is to resolve the consequences associated with the operation of the existing Data Acquisition System in the test lab of the company. On the other hand, the major issue is the old connectivity layout aligned with the conventional technology (Burke 2013). Therefore a wireless system has been decided to be implemented in terms of solving the problem, which would remove the utilization of the connections as well as circuits. Thus, the operators can utilize easily the wireless connection. This innovative system would be able to attract customers who visit to the test labs and at the same time it would reduce the overall operational cost (Joslin and Mller 2015). However, this system has few risks along with its several capabilities. One of the most crucial risks associated with this system is that the Gauges break down is harder often for the operator for using and it also slows down the work process of this system (Heagney 2016). On the other side, it c an also be seen that the current devices are limited in number with the ranges of temperature. Apart from that, this wireless system can also have few other risks, which can prevent this device to work properly (Mir and Pinnington 2014). However, the most important fact is that all the identified risks associated the system can be resolved with the help of applying and adopting the recommending strategies. Therefore, in this regard, it must be stated that this project has less risks as all the risks can be resolved very easily by following the recommendation techniques in a proper manner. Cost Benefit Analysis Cost Benefit analysis in a project is referred to a systematic approach in order to estimate the weaknesses as well as the strengths of alternatives (Hwang and Ng 2013). Moreover, it is also utilized for determining the options that give the best approach for achieving the advantages during preserving savings. Therefore, according to the cost benefit analysis of this particular, it can be stated that the cost factors associated with this project is affordable. This is simply because of the fact that while a project is comprised of several risks which are very difficult to resolve, then the cost along with that project automatically increases (Pemsel and Wiewiora 2013). Thus, the project requires several investments in terms of technologies which can be costly, which would ultimately lead to rise in its prices. However, in case of this particular project, all the risks or the barriers associated with the wireless system can be resolved by applying several technologies (Marcelino-Sdaba et al. 2014). Most importantly, all the technologies are affordable so that the incorporation of the technologies for resolving the risks would come within the budget kept for the project. This particular innovation project is cost effective, and a single unit is needed to be built and tested in this year in terms of validating this project effectiveness (Dez Rodrguez et al. 2014). Therefore, from this discussion, it can be said that this project is cost effective and is doable or executable. Project Budget Budget for the Project "Wireless Communication to the Gauges" Project Resources Budget Actual $ Difference % Difference Wireless Transmitter $ 150.00 $ 175 ($ 25) -16.67 % Required rechargeable battery $ 30.00 $ 25 $ 5 16.67 % Signal Conditioners $ 75.00 $ 80 ($ 5) -6.67 % Gauges $ 80 $75 $ 5 6.25 % Labor $ 9,000.00 $ 9,750 ($ 750) -8.33 % Delivery Costs $ 600.00 $ 582 $ 18 3.00 % Telephone $ 450.00 $ 496 ($ 46) -10.22 % Advertising $ 700.00 $ 894 ($ 194) -27.71 % Postage $ 100.00 $ 85 $ 15 15.00 % TOTAL EXPENSES $ 11,185 $ 12,162 -$ 977 -8.73 % Table 1: Budget for the Project "Wireless Communication to the Gauges" (Source: Created by Author) Critical Project Barriers While conducting a project, there can be few significant risks those are faced by the project management team who has the responsibility of executing that particular project. Hence in case of this project, the wireless system can face several critical barriers (Joslin and Mller 2015). These are as follows: The Gauges break down occurred within the wireless system is harder often for the operator for using and it also slows down the work process of this system (Mir and Pinnington 2014). On the other hand, the current wireless devices have the limitation with the ranges of temperature (Hwang and Ng 2013). Apart from that, the current system associated with this project also has the limitation with the scan rate In addition, load cell within this wireless system also give out the outputs with the reading of mille volts as well as it also requires a conditioner of signal for amplifying a particular signal (Pemsel and Wiewiora 2013). Apart from that, the current wireless device in this project do not have the potential of powering up as well as receiving the signal from the switches, string pot, torque cell, LVDT as well as load cell at the same time. Moreover, the operators would not be capable of picking the wireless transmitter with the help of the current system in this particular project (Marcelino-Sdaba et al. 2014). On the other side, this current system has the limitation of the number of devices which can connect in this particular project (Dez Rodrguez et al. 2014). Furthermore, this current system in this project has the National Instruments, software as well as hardware. The lag time in the wireless signal is a very crucial issue for this wireless system in the project (Pemsel and Wiewiora 2013). Noise level as well as the signal strength of this device should be studied properly in this project. References Burke, R., 2013. Project management: planning and control techniques.New Jersey, USA. Dez Rodrguez, J.J., Oliver, C., Vicente, L. and Ahumada Cervantes, B., 2015. Addressing strategic environmental assessment of Mexico's transition towards renewable energy. InAEIPRO 2015: International Congress on Project Engineering(pp. 1121-1132). Heagney, J., 2016.Fundamentals of project management. AMACOM Div American Mgmt Assn. Hwang, B.G. and Ng, W.J., 2013. Project management knowledge and skills for green construction: Overcoming challenges.International Journal of Project Management,31(2), pp.272-284. Joslin, R. and Mller, R., 2015. Relationships between a project management methodology and project success in different project governance contexts.International Journal of Project Management,33(6), pp.1377-1392. Marcelino-Sdaba, S., Prez-Ezcurdia, A., Lazcano, A.M.E. and Villanueva, P., 2014. Project risk management methodology for small firms.International Journal of Project Management,32(2), pp.327-340. Mir, F.A. and Pinnington, A.H., 2014. Exploring the value of project management: linking project management performance and project success.International Journal of Project Management,32(2), pp.202-217. Pemsel, S. and Wiewiora, A., 2013. Project management office a knowledge broker in project-based organisations.International Journal of Project Management,31(1), pp.31-42.