Evaluation Method of Forex Trading Analysis Tool

FOREX (Foreign Currency Exchange) is concerned with the exchange rates of foreign currencies compared to one another. These rates provide significant data necessary for currency trading in the international monetary markets. FOREX rates are impacted by a variety of factors including economic and political events, and even the psychological state of individual traders and investors. These factors are correlated highly and interact with one another in a highly complex manner. Those interactions are very unstable, dynamic, and volatile. This complexity makes predicting FOREX changes exceedingly difficult. The people involved in the field of international monetary exchange have searched for explanations of rate changes; thereby, hoping to improve prediction capabilities. It is this ability to correctly predict FOREX rate changes that allows for the maximization of profits. Trading at the right time with the relatively correct strategies can bring large profit, but a trade based on wrong movement can risk big losses. Using the right analytical tool and good methods can reduce the effect of mistakes and also can increase profitability. Data mining involves the use of sophisticated data analysis tools to discover previously unknown, valid patterns and relationships in large data sets. These tools can include statistical models, mathematical algorithms, and machine learning methods. Consequently, data mining consists of more than collecting and managing data, it also includes analysis and prediction. Keywords— FOREX, Data mining, Option mining. I. LITERATURE REVIEW Piche [18] uses a trend visualization plot on a moving average oscillator. For example, he uses an exponential moving average oscillator method to compute the fractional returns and then uses the trend visualization algorithm to plot the trend visualization matrix. By setting different parameters on the currency exchange rates of various national currencies, the results show this method is useful in gaining insight into other aspects of the market. Staley and Kim [23] have suggested that interest rates are the most important variable determining the currency exchange rates; “self-fulfilling” behaviour may also contribute to the movements in the rates. Therefore, they use two inputs: One relates to the changes in interest rates, and the other is the short-term trend in the exchange rate to search for patterns in the data. They indicate the model could be improved if more variables were added and the results were tested. Additionally, confidence regions (or error bars) could be added to the predictions so more appropriate validation sets could be chosen. This information could suggest whether on not the prediction should be applied on any given day. Demster, Payne, Romahi and Thompson [7] have shown two learning strategies based on a genetic (programming) algorithm (GA) and reinforcement learning, and on two simple methods based on a Markov decision problem and a simple heuristic technique. All methods generate significant in-sample and out-of-sample profit when transaction costs are zero. The GA approach is superior for nonzero transaction costs. They also state that when in-sample learning is not constrained, then there is the risk of over-fitting. Chen and Teong [4] use a simple neural network to improve regular technical analyses. The result of using a neural network not only enhances profitability but also turns losing systems into profitable ones. This provides one with the opportunity to enter and exit trades before a majority of other traders do. A neural network is also able to adapt itself to new patterns emerging in the market place. This is important because currency market conditions change very rapidly. Refenes, Azema-Barac and Karoussos [22] demonstrate that by using a neural network system and an error backpropagation algorithm with hourly feedback and careful network configurations, short term training can be improved. Feedback propagation is a more effective method of forecasting time series than forecasting without a feedback neural network. They also considered the impact of varying learning times and learning rates on the convergence and generalization performance. They discovered that multi-step predictions are better than single-step predictions as well as the fact that appropriate training set selection is important. Laddad, Desai and Poonacha [16] use a multi-layer perceptron (MLP) network for predicting problems. The raw data contained a considerable volume of noise so they decomposed the data into many less complex time series data and used a separate MLP to learn each of them. Another method is to use two new weight initialization schemes. Both methods provide faster learning and improved prediction accuracy. The authors use the Random Optimization Algorithm rather than back-propagation because it gives faster learning and a smaller mean squared error. Ip and Wong [14] apply the Dempster-Shafer theory of evidence to the foreign exchange forecasting domain based on evidential reasoning. This theory provides a means for interpreting the partial truth or falsity of a hypothesis and for reasoning under uncertainties. Within the mathematical framework of the theory, evidence can be brought to bear upon a hypothesis in one of three ways: to confirm, to refuse International Journal of Computer Trends and Technologyvolume3Issue12012 ISSN: 2231-2803 http://www.internationaljournalssrg.org Page 35 or to ignore. Different factors affect the exchange rate at different degrees at different times. Various competing hypotheses are assigned to the factors under consideration. Some factors reflect the economy of a country. The economy in turn provides evidence for the movement of its currency. Based on historical data that implicitly record trends and other external factors, the system is able to evolve. The accumulation of more data regarding time-varying parameters and past performance hypotheses is reflected in the accuracy of future hypotheses. White and Racine [24] use ANN to provide inferences regarding whether a particular input or group of inputs “belong” in a particular model. The test of these inferences is based on estimated feed-forward neural network models and statistical re sampling techniques. The results suggest foreign exchange rates are predictable, but the nature of the predictive relation changes through time. Ghoshray [10] used a fuzzy inferencing method on the fuzzy time series data to predict movement of foreign exchange rates. A fuzzy inference method uses one of the ingredients of chaos theory, which are the results of the previous iterations fed back repeatedly into the next one. He used fuzzy functions to express the dynamics of deterministic chaos. After certain steps any specific predicted value of the data vector could be obtained. He also found that fundamental analysis is useful in predicting long-term trends but of little use in predicting short-term movements of exchange rates. Even though technical analysis can be useful in predicting short-term period changes, there is a lack of consistent accuracy. The author has examined several forecasting techniques, considered the behaviour of time series data and advanced a fuzzy inference technique to predict future exchange rate fluctuations. Iokibe, Murata and Koyama [13] use Takens’ embedding theorem and local fuzzy reconstruction technology to predict short-term foreign exchange rates. The Takens’ theory is that the vector X(t) = (y(t), y(t-τ), y(t-2τ), ....... y(t-(n-1)τ)) is generated from the observed time series y(t), where “τ” is a time delay. The embedded latest data vector is replaced with Z(T) = (y(T), y(T-τ), y(T-2τ), ....... y(T-(n-1)τ)). After one step is fetched, the data vector including this data is replaced with Z(T+1). The value of the time series data is predicted by the local fuzzy reconstruction method after “s” steps. This sequence is iterated up to the last data by setting dimensions of embedding (n=5) and a delay time of (τ=2) and the number of neighbouring data vectors (N=5) on the currency exchange rate data of different countries. The satisfactory results have proven this method suffers less on irregular phenomenon governed by contingencies of the financial market. . Muhammad and King [17] indicate fuzzy networks provide better general logic for modelling non-linear, multivariate and stochastic problems by using four layers; i.e., using fuzzy input, fuzzy rules, normalizing and defuzzifying sequences. This method not only improves the root mean square error (RMSE) but also gives a good track of the actual change in the foreign exchange market. II. METHODOLOGY One of the current challenges in time series forecasting approaches is in the area of financial time series. It is a function approximation problem. Pattern recognitions are performed on the monthly foreign currency exchange rate data to predict the future exchange rate. The future value is predicted by using time series analysis or regression techniques in this thesis. Regression involves the learning of the function that does this mapping. Regression assumes that the target data fits into some known type of function (e.g., linear or logistics) then discerns the best function that models the given data. An appropriate type of error analysis (MSE, for example) is used to determine which function is the most efficient. We use option mining to recognize patterns within the data by adjusting the weights and biases so that the set of inputs generates the desired set of outputs.