The profitability of Ichimoku Kinkohyo based trading rules in stock markets and <scp>FX</scp> markets

Yu, Haoran; Wei, Chenyang; Yang, Tianxiang; Shimada, Takehiko

doi:10.1002/ijfe.2067

Cited by 20 publications

(9 citation statements)

References 13 publications

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“…Note that, in almost all of the trading rules based on candlestick charts another parameter denoting the current market trend is used. In this work, the market trend is detected using equation (2) in which the µ w denotes the w day moving average which is computed by equation (3), and v is a window size for the number of candles before time t to be considered in the calculation of the trend.…”

Section: Trading Rules Based On Candlestick Chart Patternsmentioning

confidence: 99%

“…In (A.1) ψ t (P t ) shows the trading signal at time step t. This function is used in SignalingF unction of (A.8). In (A.1), MT is defined in (2).…”

Section: Appendix a Rules By The Traditional Rule-based Agentmentioning

confidence: 99%

“…Many researchers tried to verify that technical trading rules are appropriate for making investment decisions on financial assets. Different technical trading rules such as moving-average [1], trading rage break [1], and more complex indicators such as Japanese Ichimoku Kinkohyo [2] were investigated and proved that the technical strategies are able to make profit. Further investigations were accomplished to verify the performance of technical strategies in different markets [3] [4].…”

Section: Introductionmentioning

confidence: 99%

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Learning Financial Asset-Specific Trading Rules via Deep Reinforcement Learning

Taghian¹,

Asadi²,

Safabakhsh³

2020

Preprint

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Generating asset-specific trading signals based on the financial conditions of the assets is one of the challenging problems in automated trading. Various asset trading rules are proposed experimentally based on different technical analysis techniques. However, these kind of trading strategies are profitable, extracting new asset-specific trading rules from vast historical data to increase total return and decrease the risk of portfolios is difficult for human experts. Recently, various deep reinforcement learning (DRL) methods are employed to learn the new trading rules for each asset. In this paper, a novel DRL model with various feature extraction modules is proposed. The effect of different input representations on the performance of the models is investigated and the performance of DRL-based models in different markets and asset situations is studied. The proposed model in this work outperformed the other state-of-theart models in learning single asset-specific trading rules and obtained a total return of almost 262% in two years on a specific asset while the best state-ofthe-art model get 78% on the same asset in the same time period.

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Section: Trading Rules Based On Candlestick Chart Patternsmentioning

confidence: 99%

“…In (A.1) ψ t (P t ) shows the trading signal at time step t. This function is used in SignalingF unction of (A.8). In (A.1), MT is defined in (2).…”

Section: Appendix a Rules By The Traditional Rule-based Agentmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Learning Financial Asset-Specific Trading Rules via Deep Reinforcement Learning

Taghian¹,

Asadi²,

Safabakhsh³

2020

Preprint

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“…During the past two decades, the effect of the Ichimokubased strategies on different market environments has been studied in many works. Deng et al [9] investigated the profitability of Ichimoku-based trading rules. They indicated that some Ichimoku-based trading strategies could prove profitable when trading stock indices, but not when trading currencies.…”

Section: Introductionmentioning

confidence: 99%

Profitability of Ichimoku-based trading rule in Vietnam stock market in the context of the COVID-19 outbreak

Che‐Ngoc

Do-Thi

Nguyen‐Trang

2022

Preprint

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Ichimoku Kinkohyo or Ichimoku Cloud Chart is one of the most popular technical indicators used by traders all over the world. However, its profitability is heavily influenced by the market environment, to which it is applied. Furthermore, the COVID-19 outbreak may have an impact on the market environment as well as the performance of all technical indicators. This study is the first to look into the profitability of Ichimoku-based trading rules in the Vietnamese stock market in the context of the COVID-19 outbreak. More particularly, the COVID-19 outbreak has a positive influence on the performance of this strategy when applied to the four industries consisting of real estate industry, food and beverage industry, resource industry, and automotive and electronic components industry. The obtained results demonstrate that the Ichimoku-based strategy can be considered a suitable method for applying Vietnam stock market, indeed no matter if there would be adverse effects of the pandemic on the industries or not.

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“…The technical analysis method generally assumes that history will repeat [8]- [10], and the market can be predicted by judging the change of price movement through one or more technical indicators [11]. For stock prices or futures prices' fluctuation analysis, time series analysis method is one kind of the most commonly used approaches to predict their future prices.…”

Section: Introductionmentioning

confidence: 99%