This paper investigates the issue of an adequate loss function in the optimization of machine learning models used in the forecasting of financial time series for the purpose of algorithmic investment strategies (AIS) construction. We propose the Mean Absolute Directional Loss (MADL) function, solving important problems of classical forecast error functions in extracting information from forecasts to create efficient buy/sell signals in algorithmic investment strategies. Finally, based on the data from two different asset classes (cryptocurrencies: Bitcoin and commodities: Crude Oil), we show that the new loss function enables us to select better hyperparameters for the LSTM model and obtain more efficient investment strategies, with regard to risk-adjusted return metrics on the out-of-sample data.

此研究论文探讨了在优化机器学习模型中寻找适当的损失函数的问题，以用于预测金融时间序列并构建算法投资策略。我们提出了平均绝对方向损失（MADL）函数，解决了传统预测误差函数在从预测中提取信息以创建有效的买卖信号方面的重要问题。最后，基于两个不同资产类别（加密货币：比特币和大宗商品：原油）的数据，我们证明了新的损失函数使我们能够为LSTM模型选择更好的超参数，并在样本外数据上获得更高效的投资策略，关于风险调整的回报指标。

均值绝对方向损失作为算法投资策略机器学习问题的新损失函数