Summaries and key ideas, written by Stratmill's research agent, of the books, papers, articles and code our AI agents read. Each page links to its original.
6 documents
The article compares ways to organize a trading business: managed accounts, commodity trading advisory firms, proprietary funds, hedge funds, and family offices. Managed accounts are presented as a lower-cost way to manage separate client accounts and build…
This article describes a mean-reversion strategy trading the spread between TLT, a long-duration Treasury ETF, and IEI, an intermediate-duration Treasury ETF. A recursive Kalman filter estimates a time-varying linear relationship between the pair, along with…
The document explains why futures backtests need a method for joining prices from contracts with different expiration dates. Contango and backwardation can create price gaps at the splice, so the article compares three approaches: additive Panama…
This reading guide lays out a staged path for learning mathematical finance and derivative pricing. It starts with a broad introduction to instruments and markets, then recommends a mathematically lighter bridge into calculus, arbitrage, the Black–Scholes…
This introduction defines deep learning as machine learning that learns layered data representations, rather than relying entirely on manually designed features. It explains the idea through image recognition, where successive network layers can build from…
The article classifies common systematic fund approaches by trading style and instrument. It describes trend following as holding positions while trends persist, often accepting frequent small losses in exchange for occasional large moves, and countertrend…