awesome-deep-trading
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