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LSTM in Algorithmic Investment Strategies on BTC and S&P500 Index

Sensors (Basel, Switzerland), 2022-01, Vol.22 (3), p.917 [Peer Reviewed Journal]

COPYRIGHT 2022 MDPI AG ;2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. ;2022 by the authors. 2022 ;ISSN: 1424-8220 ;EISSN: 1424-8220 ;DOI: 10.3390/s22030917 ;PMID: 35161663

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  • Title:
    LSTM in Algorithmic Investment Strategies on BTC and S&P500 Index
  • Author: Michańków, Jakub ; Sakowski, Paweł ; Ślepaczuk, Robert
  • Subjects: Algorithms ; Bias ; Deep learning ; Financial analysis ; Forecasting ; Forecasts and trends ; Hypotheses ; Investment analysis ; Investment strategy ; Investments ; Literature reviews ; Mathematical models ; Neural networks ; Neural Networks, Computer ; Optimization ; Parameter sensitivity ; Prices ; Sensitivity analysis ; Stock exchanges ; Time series
  • Is Part Of: Sensors (Basel, Switzerland), 2022-01, Vol.22 (3), p.917
  • Description: We use LSTM networks to forecast the value of the BTC and S&P500 index, using data from 2013 to the end of 2020, with the following frequencies: daily, 1 h, and 15 min data. We introduce our innovative loss function, which improves the usefulness of the forecasting ability of the LSTM model in algorithmic investment strategies. Based on the forecasts from the LSTM model we generate buy and sell investment signals, employ them in algorithmic investment strategies and create equity lines for our investment. For this purpose we use various combinations of LSTM models, optimized on in-sample period and tested on out-of-sample period, using rolling window approach. We pay special attention to data preprocessing in the input layer, to avoid overfitting in the estimation and optimization process, and assure correct selection of hyperparameters at the beginning of our tests. The next stage is devoted to the conjunction of signals from various frequencies into one ensemble model, and the selection of best combinations for the out-of-sample period, through optimization of the given criterion in a similar way as in the portfolio analysis. Finally, we perform a sensitivity analysis of the main parameters and hyperparameters of the model.
  • Publisher: Switzerland: MDPI AG
  • Language: English
  • Identifier: ISSN: 1424-8220
    EISSN: 1424-8220
    DOI: 10.3390/s22030917
    PMID: 35161663
  • Source: DOAJ Directory of Open Access Journals
    GFMER Free Medical Journals
    MEDLINE
    PubMed Central
    ROAD: Directory of Open Access Scholarly Resources
    ProQuest Central
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