Paper: arXiv 2212.03443

Authors: Jiashu Lou, Leyi Cui, Ye Li

Abstract

With the increasing enrichment and development of the financial derivatives market, the frequency of transactions is also faster and faster. Due to human limitations, algorithms and automatic trading have recently become the focus of discussion. In this paper, we propose a bidirectional LSTM neural network based on an attention mechanism, which is based on two popular assets, gold and bitcoin. In terms of Feature Engineering, on the one hand, we add traditional technical factors, and at the same time, we combine time series models to develop factors. In the selection of model parameters, we finally chose a two-layer deep learning network. According to AUC measurement, the accuracy of bitcoin and gold is 71.94% and 73.03% respectively. Using the forecast results, we achieved a return of 1089.34% in two years. At the same time, we also compare the attention Bi-LSTM model proposed in this paper with the traditional model, and the results show that our model has the best performance in this data set. Finally, we discuss the significance of the model and the experimental results, as well as the possible improvement direction in the future.

Complexity vs Empirical Score

  • Math Complexity: 4.5/10
  • Empirical Rigor: 5.0/10
  • Quadrant: Philosophers — conceptual discussion, limited math and data

Why this score: The paper applies standard deep learning architectures (Bi-LSTM with Attention) to cryptocurrency and commodity price forecasting with feature engineering. While the reported trading returns are exceptionally high, the methodology lacks statistical rigor, robust out-of-sample backtesting standards, and deep novel contributions to machine learning or financial economics.

Research Flowchart

  flowchart TD
    A[Research Goal: Predict Gold & Bitcoin Prices for Algorithmic Trading] --> B[Data Inputs: Multi-Asset Historical Data]
    B --> C[Feature Engineering: Technical Factors + Time Series Factors]
    C --> D[Model Training: 2-Layer Bi-LSTM with Attention Mechanism]
    D --> E[Evaluation: Model Comparison & AUC Performance]
    E --> F[Outcomes: High Accuracy BTC 71.94%, Gold 73.03% & 1089.34% 2-Yr Return]