Pendeteksi Sinyal Jual/Beli Saham dengan Fuzzy Rule-Based Evidential Reasoning dan C-means Clustering

Pendeteksi Sinyal Jual/Beli Saham dengan Fuzzy Rule-Based Evidential Reasoning dan C-means Clustering

Agung Prasetijo at Universitas Diponegoro

Stocks are securities indicating the share of ownership of a company. In stock market, most of traded stocks fluctuate in price at all times and traders take an advantage for taking profit. Traders often use technical analysis to determine the trend of stock price movements. The problem is on how traders take positions (buying/selling stocks) with minimal trading decision so they can maximize profits. Fuzzy rule-based evidential reasoning approach can map the conditions of stock movements. Clustering can help the mapping conducted with a high degree of equality with each other. One of the clustering methods is fuzzy C-means clustering. This method is used to determine the number of membership functions for each attribute. To increase profit/Return of Investment (ROI), verification of output decision is required to analyze stock trends when placing buy or sell. From the results experimented, an ROI of 83.80% profit is obtained

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. OCR dapat mengenali text dalam berbagai bahasa dengan tingkat error rata-rata 15 % seperti dalam penjelasan survey yang dilakukan oleh Singh [12] bahkan sampai konversi image to text dari huruf arab gundul [13] dan kanji [14], dan rata-rata tingkat akurasi di atas 80% hasil survey perbandingan berbagai metodologi dan classifier [15][16][17][18] OCR secara online telah berhasil disimulasikan oleh Onyejegbu berbasis cloud azure [19]. Pada penelitian sebelumnya, analisa jual beli saham dilakukan berdasarkan pola sinyal yang dihasilkan oleh Indikator Commodity Channel Index (CCI) [20] berdasarkan pendekatan Rule-Based Evidential Reasoning dan metode Fuzzy C-Means (FCM), namun hasilnya masih membutuhkan verifikasi. Maka pada penelitian ini diusulkan suatu solusi dengan pemanfaatan teknologi OCR, suatu support-system yang dapat membantu para investor pemula untuk membuat keputusan dalam jual-beli saham. .

  • Jun 2021

The investor must be able to use instinct to evaluate when to sell and buy stocks. This is, of fact, a weakness for inexperienced investors, in addition to the decision's inaccuracy and the time it takes to evaluate a slew of ineffective results. So that, a support system is needed to help the investors make decisions in buying and selling shares. This support system creates an online analysis curve display through text data in the BEI stock price application. The data processing based on pattern recognition will be carried out so that a buying and selling decision can be made to calculate the profit and loss by investors. As the first step of the whole system, this research has built an image-to-text conversion system based on OCR (Optical Character Recognition) that can convert the non-editable text (.jpg) to be editable (.text) online. After obtaining this .text data, the will used the system in further research to analyze stock buying and selling decisions. According to research on eight companies, the OCR-based image to text conversion has a 96.8% accuracy rate. Meanwhile, using Droid serif, Takao PGhotic, and Waree fonts at 12pt font sizes, it has 100 percent accuracy in Libre Office.

. Dalam hal identifikasi kesehatan pada manusia, metode fuzzy logic ini juga telah menjadi prioritas utama untuk digunakan seperti pengolahan citra untuk deteksi sel kanker payudara [9] . Metode evanescent juga telah diperkenalkan dalam mendeteksi kadar gula darah dalam urin. .

  • Sep 2019

In this research, we propose a prototype to measure glucose index in human body after applying benedict reagen into urine samples. This system divides into two main components that are identification device and android smartphone. The identification device consists of TCS3200 colour sensor and a raspberry pi. The TCS3200 colour sensor's function is to predict the alteration of urine sample and determine the colour category according to the benedict rule and to measure the glucose in the sample. The Raspberry pi function is to process the data that acquired from the colour sensor. By optimizing with Tsukamoto Fuzzy Logic Control, the research successfully identifies the glucose by achieving 100% and the result of fuzzy logic control on Raspberry Pi as decision making by urine in 90% and by conflicting minimum error in 5.6%.

  • Jun 2014

The stock market has become a popular investment channel in recent years because of the low return rates of other investment. The stock price prediction is in the interest of both private and institution investors. Accurate forecasting of stock prices is an appealing yet difficult activity in the business world. Therefore, stock prices forecasting is regarded as one of the most challenging topics in business. The forecasting techniques used in the literature can be classified into two categories: linear models and non linear models. One of forecasting techniques in nonlinear models is support vector regression (SVR). Basically, SVR adopts the structural risk minimization principle to estimate a function by minimizing an upper bound of the generalization. The optimal parameters of SVR can be use Grid Search Algorithm method. Concept of this method is using cross validation (CV). In this paper, the SVR model use linear kernel function. The accurate prediction of stock price, in telecommunication, is 92.47% for training data and 83.39% for testing data. Keywords: Stock price, SVR, Grid Search, Linear kernel function.

  • Jun 2016

Currently FOREX (foreign exchange market) is the largest financial market over the world. Usually the Forex market analysis is based on the Forex time series prediction. Nevertheless, trading expert systems based on such predictions do not usually provide satisfactory results. On the other hand, stock trading expert systems called also “mechanical trading systems”, which are based on the technical analysis, are very popular and may provide good profits. Therefore, in this paper we propose a Forex trading expert system based on some new technical analysis indicators and a new approach to the rule-base evidential reasoning (RBER) (the synthesis of fuzzy logic and the Dempster–Shafer theory of evidence). We have found that the traditional fuzzy logic rules lose an important information, when dealing with the intersecting fuzzy classes, e.g., such as Low and Medium and we have shown that this property may lead to the controversial results in practice. In the framework of the proposed in the current paper new approach, an information of the values of all membership functions representing the intersecting (competing) fuzzy classes is preserved and used in the fuzzy logic rules. The advantages of the proposed approach are demonstrated using the developed expert system optimized and tested on the real data from the Forex market for the four currency pairs and the time frames 15 m, 30 m, 1 h and 4 h.

  • Feb 2017
  • Jan 2005
  • Apr 2006

In this paper, a generic rule-base inference methodology using the evidential reasoning (RIMER) approach is proposed. Existing knowledge-base structures are first examined, and knowledge representation schemes under uncertainty are then briefly analyzed. Based on this analysis, a new knowledge representation scheme in a rule base is proposed using a belief structure. In this scheme, a rule base is designed with belief degrees embedded in all possible consequents of a rule. Such a rule base is capable of capturing vagueness, incompleteness, and nonlinear causal relationships, while traditional if-then rules can be represented as a special case. Other knowledge representation parameters such as the weights of both attributes and rules are also investigated in the scheme. In an established rule base, an input to an antecedent attribute is transformed into a belief distribution. Subsequently, inference in such a rule base is implemented using the evidential reasoning (ER) approach. The scheme is further extended to inference in hierarchical rule bases. A numerical study is provided to illustrate the potential applications of the proposed methodology.

  • Jan 2008
  • P Situmorang
  • E T Luthfi

E.T. Luthfi, "Fuzzy C-Means untuk Clustering Data (Studi Kasus: Data Performance Mengajar Dosen)," Seminar Nasional Teknologi 2007 (SNT 2007), 2007, hal. 1-7.

  • Jan 2012
  • C D Kirkpatrick Dan
  • J R Dahlquist

C.D. Kirkpatrick dan J.R. Dahlquist, Why Technical Analysis? Hoboken, NJ, USA: John Wiley & Sons, Inc., 2012.

  • Jan 2003
  • S Kusumadewi

S. Kusumadewi, Artificial Intelligence (Teknik dan Aplikasinya). Yogyakarta, Indonesia: Graha Ilmu, 2003.


High Performance Server Development

Automated trading based on biclustering mining and fuzzy modeling

Qinghua Huang

More and more records or charts of historical financial data are used for technical analysis, hoping to identify patterns that can be exploited to achieve excess profits. Technical analysis has been widely used in the real stock market to forecast stock price or stock trading points. The good association of technical indicators can obtain good prediction results in stock markets. But the . [Show full abstract] selection of technical indicators is also a tough problem. In this paper, we introduce a forecasting model incorporating biclustering algorithm with a new fuzzy inference method. Biclustering algorithm discover biclusters which are regarded as trading patterns. And a new fuzzy inference method is used for determining trading points. The proposed forecasting model (called BM-FM) was used for predicting three real-world stock data. The experiment is designed by comparing the profit ratio in TPP-based strategy, IPLR and IPLR-ANN with the profit ratio in our forecasting model. According to experimental results, it is indicated that our model obtains more earnings and higher profit ratio than other comparative methods.

Fuzzy Rule-Based Systems and f-Geometry Objects in Fast Thinking of Traders: Stochastic Oscillator C.

Konstantin Yu. Degtiarev

Evgeny Kudryashov

The preamble of the paper is related to technical analysis of stock market trading as one of mainstream methodologies applied for predicting price movements. In particular, stochastic oscillator as a momentum technical indicator for comparison of stock prices within a given time period is amongst the most popular measuring tools; it endeavors to predict the price movement breakthrough by . [Show full abstract] comparing the closing price of stock to its price range. One of the approaches to interpret stochastic oscillator values as signals 'sell' or 'buy' is connected with definition of some extreme area of special attention in order to consider those values that fall into it. It appears problematic to define sound and formally substantiated size of such extreme area. The uncertainty inherent in the oscillator has evident influence on the quality of (technical) signals, the linguistic statement "closeness of signal's value to extreme area" is fuzzy and imprecise, thus fuzzy logic approach can be used to match the size of extreme area with profit margins. The paper discusses simple rule-based model that describes the relationship between linguistic variables 'extremum deviation' and 'profit'; it is shown that such model can be treated as a light constituent "unit" utilized in the process of trader's fast thinking (following D. Kahneman). The transition from type-1 membership functions to simplified f-geometry objects (rectangles supplied with 4-color shading scheme) that can be brought into play in the model is discussed. It is shown that such objects constitute suitable geometric forms that conform with both specificity of the problem ('on the fly' analysis of instantly nascent situations) and their usability for group of traders and other stakeholders. Historical S&P500 daily stock prices (as well as AT&T, AAPL, GE, MSFT and CSCO indicators) were used to test the model and to reveal its strength and drawbacks.

A new approach to the bi-criteria multi-period fuzzy portfolio selection

Krzysztof Kaczmarek

P. Sevastjanov

The proposed approach to the bi-criteria multi-period fuzzy portfolio selection is based on the observation that the treating the variance as a measure of portfolio risk provides sometimes questionable results. Therefore, the simple criteria of portfolio risk and return are proposed. Based on them and three popular methods for local criteria aggregation, a new fuzzy portfolio selection one-period . [Show full abstract] model has been developed. It is shown that this model provides reasonable results coinciding with common sense. Based on this model, a new two-stage bi-criteria optimization approach to portfolio selection has been developed, tested and used as a main component of proposed multi-period portfolio selection model. A method for obtaining fuzzy distributions of stocks returns based on real market data is developed and used for the optimal portfolio selection. In some cases, to make the presentation of developed methods features more transparent, the problem simplification, when all market decisions (Buy, Sell and Hold) were considered as right ones was used. But finally, the real-world market decisions which are generated using the stock trading expert system based on the real market data were used for the portfolio selection. To do this, the known stock trading expert system has been applied, which was adapted for the conditions of the considered stock markets (NYSE and NASDAQ) providing best results after optimization. Using the real-world examples, it is shown that incorporating the stock signals (decisions) Buy, Sell and Hold in the multi-period portfolio selection models improves strongly the models results making them closer to reality.

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