Few-Shot Learning Patterns in Financial Time-Series for Trend-Following Strategies

ArXiv ID: 2310.10500 “View on arXiv”

Authors: Unknown

Abstract

Forecasting models for systematic trading strategies do not adapt quickly when financial market conditions rapidly change, as was seen in the advent of the COVID-19 pandemic in 2020, causing many forecasting models to take loss-making positions. To deal with such situations, we propose a novel time-series trend-following forecaster that can quickly adapt to new market conditions, referred to as regimes. We leverage recent developments from the deep learning community and use few-shot learning. We propose the Cross Attentive Time-Series Trend Network – X-Trend – which takes positions attending over a context set of financial time-series regimes. X-Trend transfers trends from similar patterns in the context set to make forecasts, then subsequently takes positions for a new distinct target regime. By quickly adapting to new financial regimes, X-Trend increases Sharpe ratio by 18.9% over a neural forecaster and 10-fold over a conventional Time-series Momentum strategy during the turbulent market period from 2018 to 2023. Our strategy recovers twice as quickly from the COVID-19 drawdown compared to the neural-forecaster. X-Trend can also take zero-shot positions on novel unseen financial assets obtaining a 5-fold Sharpe ratio increase versus a neural time-series trend forecaster over the same period. Furthermore, the cross-attention mechanism allows us to interpret the relationship between forecasts and patterns in the context set.

Keywords: trend-following, few-shot learning, deep learning, cross-attention, market regimes

Complexity vs Empirical Score

• Math Complexity: 8.5/10
• Empirical Rigor: 9.0/10
• Quadrant: Holy Grail
• Why: The paper employs advanced deep learning architectures (cross-attention) and few-shot learning frameworks with formal loss functions, indicating high mathematical complexity. It also provides extensive empirical validation with backtests across multiple asset classes, specific performance metrics (Sharpe ratios), and a reference to public code, demonstrating high empirical rigor.

  flowchart TD
    A["Research Goal: Develop a fast-adapting financial forecaster for changing market regimes."] --> B["Methodology: Few-Shot Learning with Cross-Attention.<br>Proposed Model: X-Trend Network"]
    B --> C{"Data & Inputs: Financial Time-Series"}
    C --> D["Training: Context Set<br>(Source Regimes)"]
    C --> E["Inference: Target Regime<br>(New Market Conditions)"]
    D --> F["Computation: X-Trend Network<br>1. Attend to Context Patterns<br>2. Transfer Trends<br>3. Generate Forecast"]
    E --> F
    F --> G["Outcome: Trend-Following Trading Strategy"]
    G --> H["Key Findings:<br>• 18.9% ↑ Sharpe Ratio vs. Neural Forecaster<br>• 10x ↑ Sharpe vs. Conventional Momentum<br>• 2x Faster Recovery from COVID Drawdown<br>• Zero-Shot capability on unseen assets<br>• Interpretability via Cross-Attention"]