Alphanume Research publishes quantitative trading posts pitching its proprietary datasets, plus a 'Sketchy Trades' series dissecting market exploits such as crypto funding-rate arbitrage and instant-deposit abuse. Readers get a conversational, opinionated look at how systematic strategies and data businesses work, though the sample mixes sales pitch with practical detail.
Website: https://www.alphanume-research.com
Kris from Robot Wealth writes about building and running systematic trading strategies, drawing on live trading experience and research. Issues cover mechanism-first strategy design, statistical arbitrage details, and lessons on judgment and trading operations.
A daily pre-market brief that picks five verified signals, mostly recent quant finance and machine learning preprints plus market data, and labels each as fact, inference or uncertainty. It also draws out implications for volatility, execution, factors and MATLAB implementation.
Independent research essays on markets, macro and AI-related investing, often backed by data, charts and backtests. Readers get analysis of valuation signals, risk management rules and the power and capital constraints on the AI buildout.
Technical newsletter on quantitative trading research, covering event-driven strategies, macroeconomic signal construction, and hypothesis testing frameworks, often with code. Readers get structured, methodology-focused lessons on avoiding look-ahead bias, defining signals, and validating trading ideas.
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