few-shot-learning-finance
Use when implementing models that learn from minimal data or need to adapt to new market regimes rapidly. Covers episodic learning, context sets, support and query sequences, zero-shot vs few-shot learning, meta-learning for finance, transfer learning across assets and regimes, and quick adaptation to market changes.
npx skills add majiayu000/claude-skill-registry --skill few-shot-learning-finance --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
# Few-Shot Learning for Finance ## Purpose Guide for implementing few-shot learning techniques in financial trading strategies, enabling models to quickly adapt to new market regimes or trade previously unseen assets with minimal data. ## When to Use Activate this skill when: - Implementing models that adapt to regime changes quickly - Trading new or low-liquidity assets with limited history - Building strategies that transfer knowledge across assets - Dealing with non-stationary markets or structural breaks - Implementing meta-learning for trading strategies - Creating context-based prediction systems ## Core Concepts ### 1. Few-Shot vs Zero-Shot Learning **Few-Shot Learning:** - Model has seen the target asset during training - Can use historical data from same asset (in context set) - Training set and test set overlap: `I_train ∩ I_test = I` - Example: Adapting to new regime of S&P 500 after COVID-19 **Zero-Shot Learning:** - Model has NEVER seen the target asset during training - Must transfer knowledge from different assets entirely - Training set and test set disjoint: `I_train ∩ I_test = ∅` - Example: Trading a new cryptocurrency using patterns learned from equities ```pytho
- Purpose
- When to Use
- Core Concepts
- 1. Few-Shot vs Zero-Shot Learning
- 2. Episodic Learning
- 3. Context Set Construction
- 4. Meta-Learning Architecture
- 5. Transfer Learning Scenarios
- 6. Training Objectives
- Evaluation Protocols
- Expanding Window Backtest
- Zero-Shot Evaluation
- Performance Insights from X-Trend Paper
- Few-Shot Results (2018-2023)
What does the few-shot-learning-finance skill do?
Use when implementing models that learn from minimal data or need to adapt to new market regimes rapidly. Covers episodic learning, context sets, support and query sequences, zero-shot vs few-shot learning, meta-learning for finance, transfer learning across assets and regimes, and quick adaptation to market changes.
How do I install it?
Run `npx skills add majiayu000/claude-skill-registry --skill few-shot-learning-finance --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From majiayu000/claude-skill-registry, a repository with 534 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.