A Forex trading expert system based on a new approach to the rule-base evidential reasoning

Dymova, Ludmila; Sevastjanov, Pavel; Kaczmarek, Krzysztof

doi:10.1016/j.eswa.2015.12.028

Cited by 68 publications

(35 citation statements)

References 40 publications

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“…Typically, trading systems are classification algorithms, which deal with labeling the predictor variables into classes [8]. There are many different trading systems: daily trading system [21], fuzzy logic rules trading system [10], and others [14,22,30,51].…”

Section: Introductionmentioning

confidence: 99%

Deep Learning for Stock Market Trading: A Superior Trading Strategy?

Fister¹,

Mun²,

Jagrić³

et al. 2019

NNW

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Deep-learning initiatives have vastly changed the analysis of data. Complex networks became accessible to anyone in any research area. In this paper we are proposing a deep-learning long short-term memory network (LSTM) for automated stock trading. A mechanical trading system is used to evaluate its performance. The proposed solution is compared to traditional trading strategies, i.e., passive and rule-based trading strategies, as well as machine learning classifiers. We have discovered that the deep-learning long short-term memory network has outperformed other trading strategies for the German blue-chip stock, BMW, during the 2010-2018 period. system (MTS) to sequentially (day-by-day) execute trading signals (decisions), and thus trade with real stocks [66] in virtual time. Consequently, the quality and effectiveness of a trading system over a period of a few years can be evaluated in a matter of seconds.The MTS works by giving three common trading signals, i.e., buy, hold, and sell, for each of the stocks. It is given an initial amount of cash to buy stocks that can be held in the portfolio or be sold at a later date. Buying and selling stocks can generate profits if trading signals are given rationally or generate loss if they are not. Profits and losses can be monitored and reviewed at the end of the trading period to realize strengths and weaknesses of the trading decisions [47]. Trading decisions are typically given by trading strategies, i.e., automated algorithms that constantly monitor market behaviour and react accordingly. Multiple trading strategies are usually incorporated within the trading system, by each giving unique trading decisions. The latter can be evaluated quickly and inexpensively either in realworld time or using the MTS.In this paper we are proposing a concept of an automated, single stock, trading system using the MTS, examining its potential by five different trading strategies, realizing their strengths and weaknesses, and proving the correctness of an optimal strategy. Based on this, we encourage further analysis and the design of an automated portfolio trading system for many similarly treated stocks in parallel. Here we employ three different trading strategies: (1) passive, (2) rule-based -relative strength index (RSI) and moving average convergence/divergence (MACD) technical indicators, and (3) surrogate model trading strategies using machine learning classifiers (MLC) and long short-term memory network (LSTM). Each of them can provide three trading signals: buy, hold, or sell.Since deep learning (DL) [37] is primarily intended for engineering applications, such as image and sound processing, we have not found many examples of DL in the fields of finance, banking, or insurance. In line with this, we would like to apply the DL to the area of finance, particularly mechanical trading systems as an alternative, and thus test whether the DL can be successfully deployed into this area.The structure of the paper is as follows: Section 2 presents the literature review, MLC application...

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Section: Introductionmentioning

confidence: 99%

Deep Learning for Stock Market Trading: A Superior Trading Strategy?

Fister¹,

Mun²,

Jagrić³

et al. 2019

NNW

View full text Add to dashboard Cite

show abstract

“…For this reason, we point out to all readers an annotated bibliography [5] that very briefly discusses the main results obtained in the field of HFT. More contemporary discussion of the results of research on HFT can be found in another study [6]. There works mostly show congruent results on the characteristics and impact of HFT on trading in currency and other asset markets on macroeconomic news releases, building market depth, liquidity, and reduction of short-term volatility.…”

Section: Introductionmentioning

confidence: 87%

The Forex Trading System for Speculation with Constant Magnitude of Unit Return

Piasecki

Stasiak

2019

Mathematics

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The main purpose of this article is to investigate a speculative trading system with a constant magnitude of return rate. We consider speculative operations related to the exchange rate given as the quotient of the base exchange medium by the quoted currency. An exchange medium is understood as any currency or any precious metal. The unit return is defined as the return expressed in the quoted currency by the amount of base exchange medium. All possible states of the exchange market form a finite elemental space. All knowledge about the dynamics of this market is presented as a prediction table describing the conditional probability distributions of incoming exchange rate changes. On the other hand, in the proposed trading system each speculative operation is concluded in such a way that the gross payment is determined by the given magnitude of unit return. The paper contains an analysis of the following evaluation criteria: annual number of transaction, success probability, expected unit payment, expected unit profit, risk index, unit risk premium, return rate, interest rate, and interest risk premium. Both of these indices can be used to select the effective trading systems. Effectiveness is considered in the local sense and in the global sense.

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“…As there are many factors involved in forecasting, Hidden Markov Model is not a good choice for the time series forecasting [16]. Multi layer Perceptron (MLP) models are time consuming and failed to restore the past data though it is used widely for forecasting [17]. Optimized Elman Recurrent Neural Network (Elman NN) was proposed for prediction of Taiwan stock price trends which have tried to overcome the limitations of past models [18] .…”

Section: Introductionmentioning

confidence: 99%

Optimal Prediction of Foreign Exchange Market Exploring the Effect of Water Cycle Algorithm Strategy on Data Segregation

Mohanty¹,

Mishra²

2019

IJRTE

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The market of foreign exchange is very huge, complex, and volatile in nature. Prediction task for this market is highly challenging as the data is highly chaotic, volatile and noisy. In this work Artificial Neural Networks (ANN), Functional Link Artificial Neural Network (FLANN), Extreme Learning Machine (ELM) are the models used to predict the price. Simple Moving Average (SMA), Stochastic Oscillator, Exponential Moving Average (EMA), Momentum, Moving Average Convergence Divergence(MACD), Average True Range (ATR), Relative Strength Index (RSI), are different technical indicators used by economists to gain an insight into the market and predict the exchange rate of currency. Generally technical indicators are calculated from price, open price, low price, high price, change percentage. The proposed network is optimized by Genetic algorithm (GA), Differential Evolution (DE), Particle Swarm Optimization (PSO), Water Cycle Algorithm (WCA). The dataset collected for this experiment comprises of 4000 days of past currency exchange rates of the two currency pairs that is USA Dollar (USD) to Indian Rupees (INR) (USD\INR) and Soudi Arabia Riyal (SAR) to INR (SAR\INR). The proposed datasets are segregated into many parts and each part is trained individually. Optimization techniques such as GA, DE, PSO and WCA deployed in segregated datasets as well as the whole dataset. The experimental result shows that the segregated WCA is giving the better result when WCA applied on the whole dataset. The ELM and WCA with segregated dataset produces better result than other models what experimented in this work.He is an assistant professor with the department of CSIT, Siksha 'O' Anusandhan Deemed to be University, Bhubaneswar, India. His research area includes soft computing, computational finance, evolutionary computing and data mining.Debahuti Mishra received the B.

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A Forex trading expert system based on a new approach to the rule-base evidential reasoning

Cited by 68 publications

References 40 publications

Deep Learning for Stock Market Trading: A Superior Trading Strategy?

Deep Learning for Stock Market Trading: A Superior Trading Strategy?

The Forex Trading System for Speculation with Constant Magnitude of Unit Return

Optimal Prediction of Foreign Exchange Market Exploring the Effect of Water Cycle Algorithm Strategy on Data Segregation

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