vishalbelsare / financialvision Goto Github PK

We are standing at the threshold of the information age, the financial services industry becomes the perfect playing field for machine learning technologies. We dedicate to develop applications of deep learning in finance, study the causal relationship between the data, and make progress on related computer science theory.

The project adopts deep learning models for financial data visualization. In financial trading, techniques like pattern recognition could advance the efficiency of strategic decisions. The professional traders could identify specific patterns among the candlestick charts with their long years of experience. At the same time, a convolutional neural network (CNN) in deep learning extracts spatial features from data. Compared to the model which designed to work on time series problem, the training process of the CNN model tends to be more similar to how human learns to study financial data.

• The Protection of Data Sharing for Privacy in Financial Vision
  • Y.R. Wang and Y.C. Tsai. The Protection of Data Sharing for Privacy in Financial Vision. Accepted by Appl. Sci. (2022).
• Encoding candlesticks as images for patterns classification using convolutional neural networks [Arxiv]
  • Chen, J., Tsai, Y. Encoding candlesticks as images for pattern classification using convolutional neural networks. Financ Innov 6, 26 (2020). https://doi.org/10.1186/s40854-020-00187-0
• Explainable Deep Convolutional Candlestick Learner [Arxiv]
  • Accepted by The 32nd International Conference on Software Engineering & Knowledge Engineering (SEKE 2020), KSIR Virtual Conference Cener, Pittsburgh, USA, July 9--July 19, 2020.
• Data Augmentation For Deep Candlestick Learner [Arxiv]
• Adversarial Robustness of Deep Convolutional Candlestick Learner [Arxiv]
• Deep Reinforcement Learning for Foreign Exchange Trading [IEA/AIE 2020]
  • Tsai YC., Wang CC., Szu FM., Wang KJ. (2020) Deep Reinforcement Learning for Foreign Exchange Trading. In: Fujita H., Fournier-Viger P., Ali M., Sasaki J. (eds) Trends in Artificial Intelligence Theory and Applications. Artificial Intelligence Practices. IEA/AIE 2020. Lecture Notes in Computer Science, vol 12144. Springer, Cham. https://doi.org/10.1007/978-3-030-55789-8_34

Introduction Video

• Funder & Principal Investigator (PI): Yun-Cheng Tsai Google Scholar
• Consultant: Samuel Yen-Chi Chen Google Scholar
• Researcher: Jun-Hao Chen Google Scholar
• Researcher: Chia-Hao Chiu
• Researcher: Fu-Min Szu
• Researcher: Kuan-Jen Wang
• Researcher: Chia-Ying Tsao
• Researcher: Chih-Shiang Shur
• Researcher: Cheng-Han Wu