2026-09-19T19:47:56Zhttps://dspace.mit.edu/server/oai/requestoai:dspace.mit.edu:1721.1/918842026-06-06T00:54:30Zcom_1721.1_7582com_1721.1_7581col_1721.1_131023
Devavrah Shah.
Zhang, Kang, M. Eng. Massachusetts Institute of Technology
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science.
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
2014-11-24T18:42:30Z
2014-11-24T18:42:30Z
2014
2014
http://hdl.handle.net/1721.1/91884
894501187
Thesis: M. Eng., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2014.
Cataloged from PDF version of thesis.
In this work, we developed an quantitative trading algorithm for bitcoin that is shown to be profitable. The algorithm establishes a framework that combines parametric variables and non-parametric variables in a logistical regression model, capturing information in both the static states and the evolution of states. The combination improves the performance of the strategy. In addition, we demonstrated that we can discovery curve similarity of time series using cross correlation and L2 distance. The similarity metrics can be efficiently computed using convolution and can help us learn from the past instance using an ensemble voting scheme.
by Kang Zhang.
M.Eng.
32 pages
eng
Massachusetts Institute of Technology
M.I.T. theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission. See provided URL for inquiries about permission.
http://dspace.mit.edu/handle/1721.1/7582
Electrical Engineering and Computer Science.
Learning time series data using cross correlation and its application in bitcoin price prediction
Bitcoin price prediction using non-parametric learning method
Thesis
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Publication
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