LSTM in Algorithmic Investment Strategies on BTC and S&P500 Index
| Autor: | Robert Ślepaczuk, Paweł Sakowski, Jakub Michańków |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2022 |
| Předmět: |
neural network
Chemical technology TP1-1185 systematic trading systems Biochemistry long short-term memory model Atomic and Molecular Physics and Optics Analytical Chemistry machine learning recurrent neural networks algorithmic investment strategies loss function walk-forward optimization Neural Networks Computer Electrical and Electronic Engineering Instrumentation Forecasting |
| Zdroj: | Sensors, Vol 22, Iss 917, p 917 (2022) Sensors; Volume 22; Issue 3; Pages: 917 |
| ISSN: | 1424-8220 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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