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, met...
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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, met...
AUTOMATICALLY run Codex review after writing security-sensitive code (auth, crypto, SQL) - get a second opinion from OpenAI Codex
Use when implementing X-Trend or attention-based trading models. Covers LSTM encoders, cross-attention, self-attention, sequence representations, entity embeddings, Variable Selection Networks, encoder-decoder...
Use when segmenting time-series into regimes, detecting structural breaks, or constructing context sets for few-shot learning. Covers GP-CPD algorithms, Matérn kernels, likelihood ratio tests, regime identifica...
Use when working with price data, trading signals, or implementing momentum strategies. Covers returns calculation, volatility targeting, momentum factors, MACD indicators, time-series momentum (TSMOM), portfol...
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