Crypto has attracted younger retail investors and newer trading institutions that are more likely to embrace systematic trading strategies that leverage pattern analysis. Our goal was to implement a model pipeline that uses deep learning model to help investors to incorporate alternative datasets into the pattern recognition process in order to more accurately detect the positive patterns that yields better future returns.
Team Project Plan and Agreements
Project Ideation
Presentation
- Jaquart, P., Dann, D., & Weinhardt, C. (2021). Short-term bitcoin market prediction via machine learning. The Journal of Finance and Data Science, 7, 45-66. doi:10.1016/j.jfds.2021.03.001
- Chen, J., & Tsai, Y. (2020). Encoding candlesticks as images for pattern classification using convolutional neural networks. Financial Innovation, 6(1). doi:10.1186/s40854-020-00187-0
- Velay, M., & Daniel, F. (2018). Stock Chart Pattern recognition with Deep Learning. Retrieved from https://arxiv.org/pdf/1808.00418.pdf
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