Blog

A deep look at algo-investor-skills — a set of Claude Code skills that take an algotrading strategy from raw measured facts to an audited, honesty-forward investor proposal. Six composable skills, a financial-models engine, an independent-verification proof pack, and a mandatory skeptical-investor audit gate that never fabricates a number.

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.