Abstract
Algorithmic trading is one important financial area of interest to both academic and industrial researchers. With the development of machine learning and deep learning, all kinds of models and techniques are utilized in algorithmic trading. This paper proposes a novel framework for enhancing stock technical analysis strategies by survival analysis. The main idea is to integrate an existing trading strategy with a survival model and make them complementary to each other. By means of survival analysis, the original trading strategy can be extended to introduce an investment target, which is treated as the event of interest. On the other hand, the original trading signal provides survival analysis with a simple and clear starting time point of observation. The trained survival models are used to filter out false trading signals to improve the strategy performance. Under the framework, we propose different filtering methods, utilize different deep survival models, and compare their performance from both trading and model perspectives. We perform extensive and strict backtesting on the daily trading data of 380 plus stocks. The experimental results show that the framework can well improve the performance of technical analysis strategies in different market situations.
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Funding
This work was supported by [Zhejiang Provincial Philosophy and Social Sciences Project] (Grant Numbers [20NDJC225YB]).
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All authors contributed to the study conception and design. Material preparation, data collection and analysis were performed by Wenbin Hu and Junzi Zhou. The first draft of the manuscript was written by Wenbin Hu and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript.
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Appendix
Appendix
1.1 Detailed Experimental Results
This section provides the detailed experimental results on both training dataset and testing dataset for the DEMA, MACD and RSI strategies. The enhanced strategies are all built with the Cox PH model and the MST filtering method.
Table 9 reports the backtesting results of the OTSs. There is no hyper-parameter for the OTSs, so that all the periods are out of sample. Tables 10, 11 and 12 list the results of hyper-parameter tuning for the three enhanced strategies. For each strategy, there are three \(\alpha _M\) values, the best parameter is the one that has the best performance on the training dataset, which is in sample. E.g., in Table 10, \(\alpha _M=0.45\) have the best mean Shape and Calmar values on the four periods of training dataset. Each of the other two values only has one best metrics. As a result, \(\alpha _M=0.45\) is the selected hyper-parameter. However, the strategy with this selected parameter does not necessarily have the best performance on the testing dataset, which is out of sample. E.g., in Table 11, \(\alpha _M=0.44\) is selected, but \(\alpha _M=0.4\) has the best performance on the testing dataset.
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Hu, W., Zhou, J. Trading Signal Survival Analysis: A Framework for Enhancing Technical Analysis Strategies in Stock Markets. Comput Econ (2024). https://doi.org/10.1007/s10614-024-10567-8
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DOI: https://doi.org/10.1007/s10614-024-10567-8