Correlation estimation using components of Japanese candlesticks

Popov, Valentin

doi:10.1080/14697688.2016.1157625

Cited by 2 publications

(3 citation statements)

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“…The K-line [30], also is called candlestick, originated from the trading of rice market in feudal Japan, which was used to calculate the daily fluctuation of rice price. After 300 years of development, K-line has been widely applied to the securities markets, such as stock, futures and foreign exchange.…”

Section: Model Description a K-line Presentation Of Financial Datamentioning

confidence: 99%

“…Through the fuzziness of price, the noise can be reduced to a certain extent, but the fuzzy processing will increase the state space, that made it harder to generalize about current market trends. Gabrielsson and Johansson [29] combined features based on Japanese candlesticks [30] with recurrent reinforcement learning to produce a high-frequency algorithmic trading system, and empirical study showed a statistically significant increase in both return and Sharpe ratio compared to relevant benchmarks. Li et al [2] utilized the stacked denoising autoencoders and LSTM (SDAEs-LSTM) to propose a novel trading agent, which can autonomously make trading decisions and gain profits in the dynamic financial markets.…”

Section: Introductionmentioning

confidence: 99%

“…In order to solve the difficulties mentioned above, this paper proposes a deep reinforcement learning model based on the decomposition of K-line [30] and clustering for online trading. The K-line uses candlesticks as a generalization of price movements over a specified period, where the process can have the initial effect of denoising.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

An Adaptive Financial Trading System Using Deep Reinforcement Learning With Candlestick Decomposing Features

Ding

Luo

2020

IEEE Access

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When applying artificial intelligence technology to quantitative trading, high noise and unpredictability of market environment are the first practical problems to be considered. Therefore, how to select the learning features of the market based on rapidly changing financial data is particularly important. In this paper, the real time financial data are first processed by K-line theory, which uses candlesticks as a generalization of price movements over a period of time, so this process can play the role of de-noising. Then, the candlesticks are decomposed into different subparts by mean of a specified spatio-temporal relationship, based on which cluster analysis of the subparts to get the learning features. Further, the learning features that are clustered by the above K-lines are put into the model, and the online adaptive control of the parameters in the unknown environment is realized by the deep reinforcement learning method, so as to realize the high frequency transaction strategy. In order to verify the performance of the model, the data on different financial derivatives transactions such as stocks, financial futures and commodity futures are used. The proposal approach is compared with other methods which are based on price, fuzzified price and K-lines for features learning. In order to verify the accuracy of the proposal approach, prediction-based methods such as recurrent neural network and fuzzy neural network are used for comparison. Experimental results show that the proposed method has higher robustness and prediction accuracy. INDEX TERMS Deep reinforcement learning, K-line decomposing, clustering, trading system.

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Section: Model Description a K-line Presentation Of Financial Datamentioning

confidence: 99%