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.
7 documents
This conference trip report summarizes a talk about seeking trading signals in alternative data. Examples include satellite and drone imagery, purchase receipts, social media, industrial sensor data, agriculture, energy supply and demand, weather, and…
This article explains how to use an annualised rolling Sharpe ratio to monitor whether a trading strategy’s risk-adjusted performance is weakening. It calculates the ratio from excess returns over a trailing year of observations, scaling the…
This tutorial outlines a supervised text-classification pipeline that could support sentiment analysis or trading filters. It explains how labeled documents become feature vectors, and how a support vector machine separates classes using decision boundaries,…
This trip report summarizes ideas from a quant meetup and trading conference, with its most concrete trading content focused on strategy research. A talk described applying vertical improvement to an existing approach and horizontal exploration of new…
The article describes a long-only equity strategy that uses timestamped vendor sentiment scores as trading events in QSTrader. It enters a stock when its sentiment reaches the positive threshold of +6 and exits when the score falls to -1. Three versions…
This guide compares five books for learning machine learning through Python, with an emphasis on practical programming. It distinguishes books that teach algorithms through pure Python implementations from those focused on using scikit-learn and related…
This study guide explains why quantitative trading research uses statistical learning and the scientific method to assess ideas. It describes a cycle of forming hypotheses, testing them against data, scrutinizing results, and refining or replacing strategies…