Blog

A strategy with positive expected value can still blow up your account if you get the bet size wrong. We walk through the Kelly criterion from deriving the formula to a portfolio of strategies: why full Kelly is dangerous, how fractional Kelly captures 75% of the growth at half the volatility, and an interactive calculator that shows how the Kelly fraction moves return and risk.

A deep dive into daily_stock_analysis by ZhuLinsen — an open-source system that fetches market data across A-shares, HK, US, and more, runs technical and news analysis through an LLM, and pushes a structured 'decision dashboard' to your messenger every trading day. Architecture, data fallback, agent strategies, limitations.

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.

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.

Decomposing and predicting bid-ask spreads with ML — from Roll's implicit estimator to gradient boosting and neural networks — with the units, leakage, and benchmarking pitfalls that bite in production.

How DeepLOB combines a CNN, an inception module, and an LSTM to predict mid-price moves from raw order book data — the architecture, the real FI-2010 numbers, and a working PyTorch reimplementation.

A deep dive into Pipeline — the composite allocation algorithm we built on top of HRP. Hierarchical Risk Parity as the base, a long/short overlay driven by agent signals and confidence, and a final risk correction via CVaR with a Hull-White volatility adjustment. The full math from our spec, plus the actual Rust implementation.