Deep Inception Networks: A General End-to-End Framework for Multi-asset Quantitative Strategies

ArXiv ID: 2307.05522 “View on arXiv”

Authors: Unknown

Abstract

We introduce Deep Inception Networks (DINs), a family of Deep Learning models that provide a general framework for end-to-end systematic trading strategies. DINs extract time series (TS) and cross sectional (CS) features directly from daily price returns. This removes the need for handcrafted features, and allows the model to learn from TS and CS information simultaneously. DINs benefit from a fully data-driven approach to feature extraction, whilst avoiding overfitting. Extending prior work on Deep Momentum Networks, DIN models directly output position sizes that optimise Sharpe ratio, but for the entire portfolio instead of individual assets. We propose a novel loss term to balance turnover regularisation against increased systemic risk from high correlation to the overall market. Using futures data, we show that DIN models outperform traditional TS and CS benchmarks, are robust to a range of transaction costs and perform consistently across random seeds. To balance the general nature of DIN models, we provide examples of how attention and Variable Selection Networks can aid the interpretability of investment decisions. These model-specific methods are particularly useful when the dimensionality of the input is high and variable importance fluctuates dynamically over time. Finally, we compare the performance of DIN models on other asset classes, and show how the space of potential features can be customised.

Keywords: Deep Learning, Systematic Trading, Time Series Feature Extraction, Cross Sectional Momentum, Futures

Complexity vs Empirical Score

• Math Complexity: 7.5/10
• Empirical Rigor: 8.5/10
• Quadrant: Holy Grail
• Why: The paper introduces complex deep learning architectures (Inception modules, LSTMs, TFTs) with custom loss functions, requiring advanced math, while providing extensive empirical validation using real-world futures data, transaction cost analysis, multiple asset classes, and robustness checks across random seeds.

  flowchart TD
    A["Research Goal:<br>General End-to-End Framework<br>for Multi-Asset Quant"] --> B["Core Methodology:<br>Deep Inception Networks<br>TS + CS Features"]
    B --> C["Data Inputs:<br>Futures Daily Price Returns"]
    C --> D["Computational Process:<br>Optimize Portfolio Sharpe<br>+ Turnover Regularization"]
    D --> E["Key Findings:<br>Outperform Benchmarks<br>Robust to Transaction Costs<br>Interpretable via Attention/VS"]