Fuzzy modeling of stock trading with fuzzy candlesticks

Naranjo, Rodrigo; Arroyo, Javier; Santos, Matilde

doi:10.1016/j.eswa.2017.10.002

Cited by 38 publications

(13 citation statements)

References 35 publications

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“…Stock trading is an important challenge for investors because trading decision and stock prices can be affected by the variety and complexity of information including economic conditions, local politics, international politics, and social factors (Hadavandi et al, 2010;Chourmouziadis and Chatzoglou, 2016;Naranjo et al, 2018). Stock trading involves buying and selling shares in companies.…”

Section: Stock Tradingmentioning

confidence: 99%

A Survey on Machine Learning for Stock Price Prediction: Algorithms and Techniques

Obthong

Tantisantiwong

Jeamwatthanachai

et al. 2020

Proceedings of the 2nd International Conference on Finance, Economics, Management and IT Business

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Stock market trading is an activity in which investors need fast and accurate information to make effective decisions. Since many stocks are traded on a stock exchange, numerous factors influence the decision-making process. Moreover, the behaviour of stock prices is uncertain and hard to predict. For these reasons, stock price prediction is an important process and a challenging one. This leads to the research of finding the most effective prediction model that generates the most accurate prediction with the lowest error percentage. This paper reviews studies on machine learning techniques and algorithm employed to improve the accuracy of stock price prediction.

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Section: Stock Tradingmentioning

confidence: 99%

A Survey on Machine Learning for Stock Price Prediction: Algorithms and Techniques

Obthong

Tantisantiwong

Jeamwatthanachai

et al. 2020

Proceedings of the 2nd International Conference on Finance, Economics, Management and IT Business

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show abstract

“…Naranjo et al [ 37 ] presented a model that used the K-nearest neighbors (KNN) algorithm to forecast the candlestick one day ahead using the fuzzy candlestick representation. Naranjo et al [ 38 ] fuzzified the gap between candles and added it as an extended element in candlesticks patterns. However, Japanese candlestick has contradictory information due to the market’s noise [ 38 ].…”

Section: Preliminaries and Literature Reviewmentioning

confidence: 99%

“…Naranjo et al [ 38 ] fuzzified the gap between candles and added it as an extended element in candlesticks patterns. However, Japanese candlestick has contradictory information due to the market’s noise [ 38 ]. Recently, the Heikin–Ashi technique modifies the traditional candlestick chart and makes it easier to reduce the noise, eliminate the gaps between candles, and smoothen the movement of the market and let the traders focus on the main trend.…”

Section: Preliminaries and Literature Reviewmentioning

confidence: 99%

“…In general, most existing fuzzy time series forecasting models follow fuzzy rules according to the relationships between neighboring states without considering the inconsistency of fluctuations for a related period [ 38 , 39 , 40 ]. This paper proposes a new perspective to study the problem of prediction, in which inconsistency is quantified and regarded as a key characteristic of prediction rules by utilizing a combination of the cloud model, Heikin–Ashi candlesticks, and fuzzy time series (FTS) in a unified model that can represent both fluctuation trend and fluctuation consistency information.…”

Section: Preliminaries and Literature Reviewmentioning

confidence: 99%

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Application of Cloud Model in Qualitative Forecasting for Stock Market Trends

Hassen

Darwish

Abu

et al. 2020

Entropy

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Forecasting stock prices plays an important role in setting a trading strategy or determining the appropriate timing for buying or selling a stock. The use of technical analysis for financial forecasting has been successfully employed by many researchers. The existing qualitative based methods developed based on fuzzy reasoning techniques cannot describe the data comprehensively, which has greatly limited the objectivity of fuzzy time series in uncertain data forecasting. Extended fuzzy sets (e.g., fuzzy probabilistic set) study the fuzziness of the membership grade to a concept. The cloud model, based on probability measure space, automatically produces random membership grades of a concept through a cloud generator. In this paper, a cloud model-based approach was proposed to confirm accurate stock based on Japanese candlestick. By incorporating probability statistics and fuzzy set theories, the cloud model can aid the required transformation between the qualitative concepts and quantitative data. The degree of certainty associated with candlestick patterns can be calculated through repeated assessments by employing the normal cloud model. The hybrid weighting method comprising the fuzzy time series, and Heikin–Ashi candlestick was employed for determining the weights of the indicators in the multi-criteria decision-making process. Fuzzy membership functions are constructed by the cloud model to deal effectively with uncertainty and vagueness of the stock historical data with the aim to predict the next open, high, low, and close prices for the stock. The experimental results prove the feasibility and high forecasting accuracy of the proposed model.

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“…To the best of the authors' knowledge, fuzzy logic has not been studied yet for energy trading in a fog-enabled smart grid setup. Different research groups have recently proposed fuzzy logic-based models to deal with the issue of stock trading [11,12]; these applications have inspired us to study fuzzy logic for energy trading in a smart grid.…”

Section: Introductionmentioning

confidence: 99%

Optimal Fuzzy Energy Trading System in a Fog-Enabled Smart Grid

2021

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With the recent technological advancements, it has become possible to conceive numerous valuable applications for efficient utilization of energy resources in a smart grid. As distributed energy generation and distributed storage systems become cost-effective, trading energy becomes a lucrative alternative for both prosumers and manufacturers. In this paper, we make use of fuzzy logic to propose a system for optimal energy trading in a fog-enabled smart grid set-up. The existing systems in this realm have inherited issues of network latency, computational expensiveness, information availability, scalability, and performance. Some systems require a specialized transmission line for energy trading and plenty of them based on the dedicated producer-consumer model, putting limits to their practical effectiveness. Our framework makes use of fog-computing infrastructure to address scalability, information availability, and network latency issues. We exploit the fuzzy logic paradigm to handle the issues with crisp values and to improve the computational efficiency of the system. Our model of energy-trading system incorporates various input parameters to decide on the excess energy, including real-time price, time of day, outdoor temperature, buyers’ interest, and storage capacity. Simulation results show that our proposed system possesses promising potential to maximize the profit of energy trading and to minimize electricity usage from the main grid.

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Fuzzy modeling of stock trading with fuzzy candlesticks

Cited by 38 publications

References 35 publications

A Survey on Machine Learning for Stock Price Prediction: Algorithms and Techniques

A Survey on Machine Learning for Stock Price Prediction: Algorithms and Techniques

Application of Cloud Model in Qualitative Forecasting for Stock Market Trends

Optimal Fuzzy Energy Trading System in a Fog-Enabled Smart Grid

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