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.
246 documents
This article introduces event-driven backtesting as an alternative to vectorized simulation. A loop processes market data and queued events in sequence: the data handler produces market events, a strategy turns them into signals, the portfolio generates…
The document introduces daily equity data fields: open, high, low, close, volume, and adjusted close. It explains that opening prices can differ from the prior close after after-hours trading, whose lower liquidity and wider spreads can amplify price moves.…
This tutorial describes an Ubuntu-based setup for Python research using the Anaconda distribution, Conda package management, and isolated virtual environments. It explains that Anaconda bundles common data-analysis libraries and works with Jupyter, while…
The document extends one-step binomial option pricing to a two-step stock tree, where the initial price of 100 can move through intermediate values of 105 or 95 and finish at 110, 100, or 90. It works through a call with strike 100, determining values at…
This article introduces statistical tools for assessing whether a price series is mean reverting, random-walking, or trending. It explains the Augmented Dickey-Fuller test as a unit-root test: the null hypothesis represents a random walk, and a sufficiently…
This article compares simple systematic strategies, such as indicator signals on liquid markets, with more advanced approaches that may use niche instruments, statistical analysis, and explicit portfolio construction. It frames the choice around an…