Avraam Tsantekidis

Orcid: 0000-0002-9528-9702

According to our database1, Avraam Tsantekidis authored at least 20 papers between 2017 and 2023.

Collaborative distances:
  • Dijkstra number2 of four.
  • Erdős number3 of four.

Timeline

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Links

On csauthors.net:

Bibliography

2023
Modeling limit order trading with a continuous action policy for deep reinforcement learning.
Neural Networks, August, 2023

Leveraging Deep Learning and Online Source Sentiment for Financial Portfolio Management.
CoRR, 2023

2022
Multisource financial sentiment analysis for detecting Bitcoin price change indications using deep learning.
Neural Comput. Appl., 2022

Online Knowledge Distillation for Financial Timeseries Forecasting.
Proceedings of the International Conference on INnovations in Intelligent SysTems and Applications, 2022

Sentiment-Aware Distillation for Bitcoin Trend Forecasting Under Partial Observability.
Proceedings of the IEEE International Conference on Acoustics, 2022

2021
Deep learning techniques for financial data
PhD thesis, 2021

Price Trailing for Financial Trading Using Deep Reinforcement Learning.
IEEE Trans. Neural Networks Learn. Syst., 2021

Deep adaptive group-based input normalization for financial trading.
Pattern Recognit. Lett., 2021

Diversity-driven knowledge distillation for financial trading using Deep Reinforcement Learning.
Neural Networks, 2021

Transferring trading strategy knowledge to deep learning models.
Knowl. Inf. Syst., 2021

Improving Deep Reinforcement Learning for Financial Trading Using Deep Adaptive Group-Based Normalization.
Proceedings of the 2021 IEEE 31st International Workshop on Machine Learning for Signal Processing (MLSP), 2021

Learning Sentiment-Aware Trading Strategies for Bitcoin Leveraging Deep Learning-Based Financial News Analysis.
Proceedings of the Artificial Intelligence Applications and Innovations, 2021

2020
Using Deep Learning for price prediction by exploiting stationary limit order book features.
Appl. Soft Comput., 2020

Improving Deep Reinforcement Learning for Financial Trading Using Neural Network Distillation.
Proceedings of the 30th IEEE International Workshop on Machine Learning for Signal Processing, 2020

2019
Machine Learning for Forecasting Mid-Price Movements Using Limit Order Book Data.
IEEE Access, 2019

Deep Reinforcement Learning for Financial Trading Using Price Trailing.
Proceedings of the IEEE International Conference on Acoustics, 2019

2018
Machine Learning for Forecasting Mid Price Movement using Limit Order Book Data.
CoRR, 2018

2017
Forecasting Stock Prices from the Limit Order Book Using Convolutional Neural Networks.
Proceedings of the 19th IEEE Conference on Business Informatics, 2017

Using deep learning to detect price change indications in financial markets.
Proceedings of the 25th European Signal Processing Conference, 2017

Time-series classification using neural Bag-of-Features.
Proceedings of the 25th European Signal Processing Conference, 2017


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