DeepLOB: Deep Convolutional Neural Networks for Limit Order Books

Zhang, Zi-Hao; Zohren, Stefan; Roberts, Stephen

doi:10.1109/tsp.2019.2907260

Cited by 204 publications

(206 citation statements)

References 59 publications

Supporting

Mentioning

180

Contrasting

Unclassified

Order By: Relevance

“…While a variety of CNN and RNN models have been proposed, they typically frame the forecasting task as a classification problem, demonstrating the improved accuracy of their method in predicting the direction of the next price movement. Trading rules are then manually defined in relation to class probabilities -either by using thresholds on classification probabilities to determine when to initiate positions [26], or incorporating these thresholds into the classification problem itself by dividing price movements into buy, hold and sell classes depending on magnitude [12,38]. In addition to restricting the universe of strategies to those which rely on high accuracy, further gains might be made by learning trading rules directly from the data and removing the need for manual specification -both of which are addressed in our proposed method.…”

Section: B Deep Learning In Financementioning

confidence: 99%

“…While numerous papers have investigated the use of machine learning for financial time series prediction, they typically focus on casting the underlying prediction problem as a standard regression or classification task [23,24,25,12,26,19,27]with regression models forecasting expected returns, and classification models predicting the direction of future price movements. This approach, however, could lead to suboptimal performance in the context time-series momentum for several reasons.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Enhancing Time Series Momentum Strategies Using Deep Neural Networks

2019

Self Cite

View full text Add to dashboard Cite

While time series momentum [1] is a wellstudied phenomenon in finance, common strategies require the explicit definition of both a trend estimator and a position sizing rule. In this paper, we introduce Deep Momentum Networks -a hybrid approach which injects deep learning based trading rules into the volatility scaling framework of time series momentum. The model also simultaneously learns both trend estimation and position sizing in a data-driven manner, with networks directly trained by optimising the Sharpe ratio of the signal. Backtesting on a portfolio of 88 continuous futures contracts, we demonstrate that the Sharpe-optimised LSTM improved traditional methods by more than two times in the absence of transactions costs, and continue outperforming when considering transaction costs up to 2-3 basis points. To account for more illiquid assets, we also propose a turnover regularisation term which trains the network to factor in costs at run-time.

show abstract

Section: B Deep Learning In Financementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Enhancing Time Series Momentum Strategies Using Deep Neural Networks

2019

Self Cite

View full text Add to dashboard Cite

show abstract

“…Machine learning (ML) methods adapted from among deep learning algorithms have been recently applied to financial time series prediction with a number of publications in computer science journals (Greff et al 2017;Fe-Fei et al 2003;Zhang et al 2018), as well as in economics and finance journals (Koutmos 2018;Kristoufek 2018). There is a gap in the existing literature, however, which is pronounced in the uncovered field of the applications of machine learning methods for time series to cryptocurrency trading data.…”

Section: Introductionmentioning

confidence: 99%

Trend Prediction Classification for High Frequency Bitcoin Time Series with Deep Learning

Shintate

Pichl

2019

JRFM

View full text Add to dashboard Cite

We provide a trend prediction classification framework named the random sampling method (RSM) for cryptocurrency time series that are non-stationary. This framework is based on deep learning (DL). We compare the performance of our approach to two classical baseline methods in the case of the prediction of unstable Bitcoin prices in the OkCoin market and show that the baseline approaches are easily biased by class imbalance, whereas our model mitigates this problem. We also show that the classification performance of our method expressed as the F-measure substantially exceeds the odds of a uniform random process with three outcomes, proving that extraction of deterministic patterns for trend classification, and hence market prediction, is possible to some degree. The profit rates based on RSM outperformed those based on LSTM, although they did not exceed those of the buy-and-hold strategy within the testing data period, and thus do not provide a basis for algorithmic trading.

show abstract

“… Translation invariance. Security comovement is translation invariance within different horizontal and vertical ranges [32]. Such comovements relationships among neighboring options are also time-series patterns.…”

Section: Cnn-based Predictive Modelmentioning

confidence: 99%

A CNN Based System for Predicting the Implied Volatility and Option Prices

Wei

Xie

Cheng

et al. 2020

Proceedings of the Annual Hawaii International Conference on System Sciences

View full text Add to dashboard Cite

The evaluations of option prices and implied volatility are critical for option risk management and trading. Common strategies in existing studies relied on the parametric models. However, these models are based on several idealistic assumptions. In addition, previous research on option pricing mainly depends on the historical transaction records without considering the performance of other concurrent options. To address these challenges, we proposed a convolutional neural network (CNN) based system for predicting the implied volatility and the option prices. Specifically, the customized non-parametric learning approach is first used to estimate the implied volatility. Second, several traditional parametric models are also implemented to estimate these prices as well. The convolutional neural network is utilized to obtain the predictions based on the estimation of the implied volatility. Our experiments based on Chinese SSE 50 ETF options demonstrate that the proposed framework outperforms the traditional methods with at least 40.11% performance enhancement in terms of RMSE.

show abstract

DeepLOB: Deep Convolutional Neural Networks for Limit Order Books

Cited by 204 publications

References 59 publications

Enhancing Time Series Momentum Strategies Using Deep Neural Networks

Enhancing Time Series Momentum Strategies Using Deep Neural Networks

Trend Prediction Classification for High Frequency Bitcoin Time Series with Deep Learning

A CNN Based System for Predicting the Implied Volatility and Option Prices

Contact Info

Product

Resources

About