Doctoral School, Cracow University of Economics, ul. Rakowicka 27, 31-510 Cracow, Poland.
Quantitative Finance Research Group, Department of Quantitative Finance, Faculty of Economic Sciences, University of Warsaw, ul. Długa 44/50, 00-241 Warsaw, Poland.
Sensors (Basel). 2022 Jan 25;22(3):917. doi: 10.3390/s22030917.
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.
我们使用 LSTM 网络来预测 BTC 和标普 500 指数的价值，使用 2013 年至 2020 年底的数据，频率分别为每日、1 小时和 15 分钟数据。我们引入了创新的损失函数，该函数提高了 LSTM 模型在算法投资策略中的预测能力的有用性。基于 LSTM 模型的预测，我们生成买卖投资信号，并将其应用于算法投资策略中，为我们的投资创建股票线。为此，我们使用各种组合的 LSTM 模型，在样本内周期进行优化，并在样本外周期进行测试，使用滚动窗口方法。我们特别注意输入层的数据预处理，以避免在估计和优化过程中出现过拟合，并在测试开始时正确选择超参数。下一阶段是将来自不同频率的信号结合到一个集成模型中，并通过类似投资组合分析中的方式优化给定标准来选择样本外周期的最佳组合。最后，我们对模型的主要参数和超参数进行敏感性分析。
