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.
12 documents
This article describes an introductory online course on momentum trading offered through B3’s education platform in partnership with QuantInsti. It presents the course as suitable for learners with basic Python knowledge and says the material covers…
The document explains how to stitch successive futures contracts into a longer time series for analysis when each individual contract has limited history. Simply joining contract prices can create artificial jumps because adjacent expiries may trade at…
The article introduces spread trading as a hedged position that buys and sells related contracts, such as options on the same security with different strikes or expiries, or futures with different delivery months, commodities, or locations. It recommends…
This article introduces Nasdaq Data Link as a source of traditional financial, ESG, and alternative datasets, then explains how to retrieve data through the Quandl API in Python. It describes dataset categories and subscription access, and outlines the…
This interview follows Xavier, an Australian IT architect with engineering and computer science training, as he moves from market research and investing to day trading and an interest in building an algorithmic trading desk. He describes exploring company…
This overview explains how standardized futures contracts differ from private forward agreements, and describes contract expiry, delivery months, tickers, margin, and profit and loss. It also introduces futures continuation series, which join successive…
This project describes a mean-reversion pairs strategy implemented and backtested with quantstrat. It uses a stock pair from the same sector as its example and also introduces a separate example involving commodity futures on different exchanges. The…
The article outlines a supervised learning pipeline for forecasting the next day’s closing price of the gold ETF GLD. It uses three-day and nine-day moving averages as predictors, shifts the closing-price series to define the next-day target, and fits a…
This project uses computer vision to classify the next trading day’s direction for selected precious and industrial metals. It turns fixed windows of daily candlesticks into 224-by-224 images and trains ResNet CNNs to predict whether the following day’s…
This interview traces a trader’s progression from executing commodity orders to coding trading systems and researching algorithmic strategies. The subject describes developing trend detection and momentum systems with position sizing, then building a…
This overview explains how different crises can affect markets and industries in different ways. It distinguishes natural disasters, technological failures, rumors, and man-made events, and reviews historical episodes in which markets fell sharply before…
This tutorial describes a basic workflow for retrieving continuous futures data with a Python finance library, preparing date-indexed price data, and plotting closing prices. It demonstrates fetching one contract and then multiple contracts, grouping the…