Binary options trading: candlestick prediction using Support Vector Machine (SVM) on M5 time period

Rumpa, Lantana Dioren; Limbongan, Mey Enggane; Biringkanae, Astriwati; Tammu, Rahma Gusmawati

doi:10.1088/1757-899x/1088/1/012107

Cited by 4 publications

(3 citation statements)

References 5 publications

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“…The Nadex platform and many other trading platforms, provide the trader with technical analysis tools to help in this regard. Some studies have been published on the possible success obtainable in this way; see e.g., Rumpa et al (2021) or Kolkova and Lenertova (2016). The results of such back-testing studies can at best be described as mixed.…”

Section: Discussionmentioning

confidence: 99%

Trading Binary Options Using Expected Profit and Loss Metrics

Venter

Jongh

2022

Risks

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Trading in binary options is discussed using an approach based on expected profit (EP) and expected loss (EL) as metrics of reward and risk of trades. These metrics are reviewed and the role of the EL/EP ratio as an indicator of quality of trades, taking risk tolerance into account, is discussed. Formulas are derived for the EP and EL of call and put binaries assuming that the price of the underlying asset follows a geometric Brownian motion. The results are illustrated with practical data from the Nadex trading platform. The Black–Scholes notion of implied volatility is extended to wider notions of implied drift and volatility of the price process of the underlying asset. Illustrations show how these notions can be used to identify attractive binary trades, taking anticipated price movement into account. The problem of selecting portfolios of call and put binary options which maximize portfolio EP while constraining the portfolio EL to satisfy risk tolerance and diversification requirements, is formulated and solved by linear programming. This is also illustrated with the Nadex data under various scenarios.

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Section: Discussionmentioning

confidence: 99%

Trading Binary Options Using Expected Profit and Loss Metrics

Venter

Jongh

2022

Risks

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“…Ref. [16] presents a candlestick prediction methodology for binary options traders by using a support vector machine (SVM) classifier. Ref.…”

Section: Trading Algorithmsmentioning

confidence: 99%

Analytical Modeling and Empirical Analysis of Binary Options Strategies

2022

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This study analyzes binary option investment strategies by developing mathematical formalism and formulating analytical models. The binary outcome of binary options represents either an increase or a decrease in a parameter, typically an asset or derivative. The investor receives only partial returns if the prediction is correct but loses all the investment otherwise. Mainstream research on binary options aims to develop the best dynamic trading strategies. This study focuses on static tactical easy-to-implement strategies and investigates the performance of such strategies in relation to prediction accuracy, payout percentage, and investment strategy decisions.

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“…Additionally, Rumpa et al (2021) investigated the M5 model tree's capability to forecast flow, comparing its results with those obtained from the support vector machine (SVM) technique.…”

Section: Introductionmentioning

confidence: 99%

Multi-Step-Ahead Rainfall-Runoff Modeling: Decision Tree-Based Clustering for Hybrid Wavelet Neural- Networks Modeling

Molajou,

Nourani,

Tajbakhsh

et al. 2024

Preprint

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This paper introduces a novel hybrid approach for predicting the rainfall-runoff (r-r) phenomenon across different data division scenarios (50%-50%, 60%-40%, and 75%-25%) within two distinct watersheds, encompassing both monthly and daily scales. Additionally, the effectiveness of this newly proposed hybrid method is evaluated in multi-step ahead prediction (MSAP) scenarios. The proposed method comprises three primary steps. Initially, to address the non-stationarity of the runoff and rainfall time series, these series are decomposed into multiple sub-time series using the wavelet (WT) decomposition method. Subsequently, in the second step, the decomposed sub-series are utilized as input data for the M5 model tree, a decision tree-based model. The M5 model tree classifies the samples of decomposed runoff and rainfall time series into distinct classes. Finally, each class is modeled using an artificial neural network (ANN). The results demonstrate the superior efficiency of the proposed WT-M5-ANN method compared to other available hybrid methods. Specifically, the calculated R2 was 0.93 for the proposed WT-M5-ANN method, whereas it was 0.89 and 0.81 for the WT-ANN and WT-M5 methods, respectively, for the Lobbs Hole Creek watershed at the daily scale.

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Binary options trading: candlestick prediction using Support Vector Machine (SVM) on M5 time period

Cited by 4 publications

References 5 publications

Trading Binary Options Using Expected Profit and Loss Metrics

Trading Binary Options Using Expected Profit and Loss Metrics

Analytical Modeling and Empirical Analysis of Binary Options Strategies

Multi-Step-Ahead Rainfall-Runoff Modeling: Decision Tree-Based Clustering for Hybrid Wavelet Neural- Networks Modeling

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