Papers of deep learning for trading.
| Paper Title | Publish Date | Venue | Author | Cited By | Objective | Loss | Models | Data |
|---|---|---|---|---|---|---|---|---|
| Financial Trading Model with Stock Bar Chart Image Time Series with Deep Convolutional Neural Networks | ||||||||
| 20190311 | arXiv | Omer Berat Sezer | 4 | Trend | TODO | CNN | OHLCV | |
| Forecasting stock prices from the limit order book using convolutional neural networks | ||||||||
| 20170821 | IEEE | Avraam Tsantekidis | 81 | Trend | TODO | CNN | LOB | |
| Using Deep Learning for price prediction by exploiting stationary limit order book features | ||||||||
| 20181023 | arXiv | Avraam Tsantekidis | 12 | Trend | TODO | CNN+LSTM | LOB | |
| DeepLOB: Deep Convolutional Neural Networks for Limit Order Books | ||||||||
| 20190601 | IEEE | Zihao Zhang | 24 | Trend | TODO | CNN+LSTM | LOB | |
| Financial series prediction using Attention LSTM | ||||||||
| 20190228 | arXiv | Sangyeon Kim | 7 | Trend | TODO | Attention LSTM | OHLCV | |
| Financial Time Series Forecasting with Deep Learning : A Systematic Literature Review: 2005-2019 | ||||||||
| 20191029 | arXiv | Omer Berat Sezer | 5 | N/A | TODO | N/A | N/A | |
| Deep learning with long short-term memory networks for financial market predictions | ||||||||
| 20181016 | EJOR | Thomas Fischer | 323 | Trend | TODO | LSTM | S&P 500 | |
| An ensemble of LSTM neural networks for high‐frequency stock market classification | ||||||||
| 20190321 | Wiley | Svetlana Borovkova | 16 | Trend | TODO | LSTM | OHLCV | |
| Cryptocurrency Price Analysis with Artificial Intelligence | ||||||||
| 20190516 | IEEE | Wang Yiying | 3 | Price | TODO | MLP, LSTM | OHLC | |
| Predicting the Price of Bitcoin Using Machine Learning | ||||||||
| 20161203 | NCIRL | Sean McNally | 49 | Price | TODO | RNN, LSTM, ARIMA | OHLC | |
| Artificial Neural Networks for Realized Volatility Prediction in Cryptocurrency Time Series | ||||||||
| 20190626 | Springer | Ryotaro Miura | 2 | RV | TODO | Ridge Regression | OHLCV | |
| Fundamental research questions and proposals on predicting cryptocurrency prices using DNNs | ||||||||
| 202002 | N/A | Emmanuel Pintelas | 1 | Price, Trend | RMSE, ACC | CNN-LSTM, CNN-BiLSTM | OHLCV | |
| Ensemble Deep Learning Models for Forecasting Cryptocurrency Time-Series | ||||||||
| 20200510 | MDPI | Ioannis E. Livieris | 0 | Price, Trend | RMSE, ACC | CNN-LSTM, CNN-BiLSTM | OHLCV | |
| Statistical Arbitrage in Cryptocurrency Markets | ||||||||
| 20190213 | JRFM | Thomas Günter Fischer | 11 | Classification | ACC | RF, LR | OHLCV | |
| Deep Learning for Limit Order Books | ||||||||
| 20160705 | JQF | Justin A. Sirignano | 50 | Classification | TODO | DNN | LOB | |
| Feature engineering for mid-price prediction with deep learning | ||||||||
| 20190621 | IEEE | Adamantios Ntakaris | 5 | Classification | ACC | MLP, CNN, LSTM | LOB | |
| Deep Learning for Spatio-Temporal Modeling: Dynamic Traffic Flows and High Frequency Trading | ||||||||
| 20180507 | arXiv | Adamantios Ntakaris | 27 | Classification | ACC | DNN | LOB | |
| Machine Learning for Forecasting Mid-Price Movements Using Limit Order Book Data | ||||||||
| 20190514 | IEEE | Paraskevi Nousi | 10 | Classification | ACC | SVN, ANN | LOB | |
| Extending Deep Learning Models for Limit Order Books to Quantile Regression | ||||||||
| 20190611 | arXiv | Zihao Zhang | 3 | QL | QL | DeepLOB-QR | LOB | |
| Deep Reinforcement Learning for Trading | ||||||||
| 20191022 | JFDS | Zihao Zhang | 2 | TODO | TODO | DRL | LOB | |
| Benchmark dataset for mid‐price forecasting of limit order book data with machine learning methods | ||||||||
| 20200311 | arXiv | Adamantios Ntakaris | 25 | N/A | N/A | N/A | LOB | |
LOB: Limit Order Book OHLCV: Open, High, Low, Close, Volume
RF: Random Forest LR: Logistic Regression
QL: Quantile Regression, Quantile Loss
RMSE: Root Mean Squared Error ACC: Accuracy
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