Deep Candlestick Predictor: A Framework toward Forecasting the Price Movement from Candlestick Charts

Guo, Siou-Jhih; Hsu, Fu-Chun; Hung, Chih‐Chieh

doi:10.1109/paap.2018.00044

Cited by 10 publications

(4 citation statements)

References 9 publications

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“…This study is an extended version of our previous work [28,29]. To our best knowledge, our previous work is the first study that uses k-day candlestick charts to predict the price movement which is defined as the difference of the close prices of k-th day and the (k + 1)-th day [28,29].…”

Section: Analysis Of Candlestick Chartsmentioning

confidence: 99%

DPP: Deep predictor for price movement from candlestick charts

Hung

Chen

2021

PLoS ONE

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Forecasting the stock market prices is complicated and challenging since the price movement is affected by many factors such as releasing market news about earnings and profits, international and domestic economic situation, political events, monetary policy, major abrupt affairs, etc. In this work, a novel framework: deep predictor for price movement (DPP) using candlestick charts in the stock historical data is proposed. This framework comprises three steps: 1. decomposing a given candlestick chart into sub-charts; 2. using CNN-autoencoder to acquire the best representation of sub-charts; 3. applying RNN to predict the price movements from a collection of sub-chart representations. An extensive study is operated to assess the performance of the DPP based models using the trading data of Taiwan Stock Exchange Capitalization Weighted Stock Index and a stock market index, Nikkei 225, for the Tokyo Stock Exchange. Three baseline models based on IEM, Prophet, and LSTM approaches are compared with the DPP based models.

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Section: Analysis Of Candlestick Chartsmentioning

confidence: 99%

DPP: Deep predictor for price movement from candlestick charts

Hung

Chen

2021

PLoS ONE

Self Cite

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“…Candlesticks and their applications mainly focus on the image processing of candlesticks, identifying and interpreting certain candlestick patterns, while also striving to enhance recognition accuracy. Birogul et al [20] and Guo et al [21] encoded candlestick data into 2D candlestick charts and learned the morphological features of the candlestick data through deep neural networks. Chen et al [22] proposed a two-step approach for the automatic recognition of eight candlestick patterns, with an average accuracy surpassing that of the LSTM model.…”

Section: Candlestick Patterns Analysismentioning

confidence: 99%

Stock Price Prediction Using Candlestick Patterns and Sparrow Search Algorithm

Chen,

Hu,

Xue

2024

Electronics

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Accurately forecasting the trajectory of stock prices holds crucial significance for investors in mitigating investment risks and making informed decisions. Candlestick charts visually depict price information and the trends in stocks, harboring valuable insights for predicting stock price movements. Therefore, the challenge lies in efficiently harnessing candlestick patterns to forecast stock prices. Furthermore, the selection of hyperparameters in network models has a profound impact on the forecasting outcomes. Building upon this foundation, we propose a stock price prediction model SSA-CPBiGRU that integrates candlestick patterns and a sparrow search algorithm (SSA). The incorporation of candlestick patterns endows the input data with structural characteristics and time series relationships. Moreover, the hyperparameters of the CPBiGRU model are optimized using an SSA. Subsequently, the optimized hyperparameters are employed within the network model to conduct predictions. We selected six stocks from different industries in the Chinese stock market for experimentation. The experimental results demonstrate that the model proposed in this paper can effectively enhance the prediction accuracy and has universal applicability. In comparison to the LSTM model, the proposed model produces an average of 31.13%, 24.92%, and 30.42% less test loss in terms of MAPE, RMSE and MAE, respectively. Moreover, it achieves an average improvement of 2.05% in R2.

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“…To compensate for the lack of sufficient information in one-dimensional input, researchers attempted to provide more sufficient financial variables for CNN to extract market features. In fact, some researchers directly used the candlestick chart as the input of CNN [23,24]. Furthermore, instead of directly taking the image as the input of CNN, Sim et al [25] employed high-frequency data of close price to construct the input image as the input [6] recently proposed an approach to build a three-dimensional input tensor for CNN to extract market features.…”

Section: Feature Extractionmentioning

confidence: 99%

Deep Learning for Price Movement Prediction Using Convolutional Neural Network and Long Short-Term Memory

Yang

Zhai

Tao

2020

Mathematical Problems in Engineering

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The prediction of stock price movement direction is significant in financial studies. In recent years, a number of deep learning models have gradually been applied for stock predictions. This paper presents a deep learning framework to predict price movement direction based on historical information in financial time series. The framework combines a convolutional neural network (CNN) for feature extraction and a long short-term memory (LSTM) network for prediction. We specifically use a three-dimensional CNN for data input in the framework, including the information on time series, technical indicators, and the correlation between stock indices. And in the three-dimensional input tensor, the technical indicators are converted into deterministic trend signals and the stock indices are ranked by Pearson product-moment correlation coefficient (PPMCC). When training, a fully connected network is used to drive the CNN to learn a feature vector, which acts as the input of concatenated LSTM. After both the CNN and the LSTM are trained well, they are finally used for prediction in the testing set. The experimental results demonstrate that the framework outperforms state-of-the-art models in predicting stock price movement direction.

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Deep Candlestick Predictor: A Framework toward Forecasting the Price Movement from Candlestick Charts

Cited by 10 publications

References 9 publications

DPP: Deep predictor for price movement from candlestick charts

DPP: Deep predictor for price movement from candlestick charts

Stock Price Prediction Using Candlestick Patterns and Sparrow Search Algorithm

Deep Learning for Price Movement Prediction Using Convolutional Neural Network and Long Short-Term Memory

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