Deep Learning for Markets
Neural forecasting for crypto - transformers, diffusion models, and foundation models, and how conformal prediction keeps their uncertainty honest.
Temporal Fusion Transformers for Multi-Horizon Portfolio Forecasting
How Google's Temporal Fusion Transformer brings interpretable multi-horizon forecasting to quantitative portfolio management, with attention-based variable selection, quantile outputs, and a worked pytorch-forecasting pipeline.
Diffusion Models vs Cryptocurrency Anarchy: Why DDPM Can Predict Bitcoin Crashes Better Than Your Astrologist
How diffusion models are revolutionizing cryptocurrency forecasting, overcoming volatility chaos and creating new opportunities for algorithmic trading.
Kronos: A Foundation Model That Teaches Candlestick Charts to Speak Transformer Language
Review of Kronos - a foundation model for OHLCV candle forecasting. BSQ tokenizer, hierarchical decoder, two-stage sampling, Qlib training pipeline. How a model learns the 'language' of the exchange.
Conformal Prediction for Risk-Aware Position Sizing
Distribution-free prediction intervals with guaranteed coverage. We use split conformal, jackknife+, and adaptive conformal inference to calibrate trading risk and size positions without parametric assumptions.
