AI savdosi, bozor tahlili va DeFi kelajagi haqida chuqur tahlillar.
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How we built researcher.marketmaker.cc — a unified, full-text-searchable archive of quant research (arXiv papers, GitHub repos, quant blogs, TradingView Pine scripts) that humans browse and AI agents query over MCP.
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.