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.
5,922 documents
This report describes a process for choosing equity factors and turning them into portfolios. It first narrows a larger set of candidate factors by weighing their correlations, volatility, cumulative returns, Sharpe ratios, and economic rationale. It then…
This tutorial presents pyfolio as a Python toolkit for evaluating trading strategies through return, position, transaction, stress-period, and Bayesian analyses. Its central output is a set of tear sheets combining tables and plots, with lower-level plotting…
This overview surveys practical considerations for AI and quantitative trading, from data quality and model choice to backtesting, risk controls, and portfolio allocation. It highlights that historical results can mislead when models are overfit or…
This report studies whether changes in leading companies’ expected earnings can help select industries for rotation. Its premise is that revisions to a sector leader’s expected fundamentals may influence subsequent returns across related stocks. The analysis…
This report examines whether selecting stocks within industries can improve factor comparisons by reducing structural differences between sectors. It groups stocks using a detailed industry classification, removes recent listings and very small firms whose…
This report examines stocks that mutual funds hold at a larger portfolio weight than their benchmark index. It describes these holdings as tending toward larger, higher-valued companies and certain sectors, and reports that many outperformed during fund…
This configuration defines a Qlib workflow for predicting short-horizon CSI 300 stock returns with a temporal convolutional network (TCN). It uses Alpha158 features, filters a specified set of feature columns, applies robust cross-sectional normalization,…
This Chinese-language research summary examines fund performance, subscriptions and redemptions, manager concentration, and sector returns around the first half of 2020. It reports gains for three categories of active equity funds and compares the scale of…
The document examines how factor exposure limits in a multi-factor portfolio optimization model affect active returns and risk. Raising the caps can increase excess return, drawdown, and tracking error, so the change in return relative to the change in risk…
The post reports a discrepancy between a backtesting platform’s monthly return for the CSI 300 benchmark and an external reference calculation. It attributes the difference to the platform using the first day of the month’s closing price as the starting…
This forum post raises a portfolio-selection question about factor neutralization when the stock universe changes over time. The author describes selecting the five highest-scoring stocks from an initial group, then adding more stocks the next day and…
This document presents the case for an ETF tracking a Chinese innovative-drug industry index, which covers related companies listed in Shanghai and Shenzhen. It describes the index as spanning the drug-development value chain and frames the fund as a way to…
The document presents Follow-the-Leader, a factor-based method for constructing a smaller portfolio that tracks a broad equity index. Instead of selecting stocks only by index weight or correlation with index returns, the method estimates the number of…
This research overview outlines a framework for constructing high-frequency equity factors from Level 2 market data. It recommends reducing tick or intraday observations into daily measures, then transforming those measures over time with operations such as…
Qlib is introduced as a modular platform for researching quantitative investment strategies with AI and machine learning. Its components are loosely coupled, so parts of the platform can be used independently. The architecture is organized into…
The document describes a proposed dashboard indicator for monitoring active symbols and pairs in spread or equity trading. Its interface is meant to track changes in the selected symbols, retain settings during terminal changes, and let users set each pair's…
This documentation explains how Lumibot represents cash flows separately from trading activity in strategy backtests and live broker data. It distinguishes deposits and withdrawals from performance while accounting for financing, dividends, fees, interest,…
This research overview introduces six broad model families used in investment work: neural, graphical, clustering and encoding, linear, tree-based, and ensemble methods. It describes their general strengths, such as nonlinear function fitting, relationship…
The post introduces machine learning as an increasingly used approach in quantitative investing. It describes two applications: forecasting market movements and selecting portfolios. It also points to potential benefits, including handling nonlinear…
This bot aims to mirror a named member of Congress’s reported stock holdings. A research agent checks House disclosure filings, using annual reports as the starting portfolio and applying later trade reports to update it. It excludes options, real estate,…
This report reviews factor performance across the broad market, sectors, industries, and indices during the first half of 2018, then examines factor selection in a market with sharply different outcomes across stocks and sectors. It reports that most factors…
The article describes an ability parity model for allocating among actively managed funds. It extends portfolio optimization by estimating each fund manager’s stock-selection skill and market-timing skill, with timing measured using the Treynor–Mazuy…
This article reviews global equity factor performance in the third quarter of 2021 and compares defensive factor positioning. It reports that momentum and low residual volatility led the pure factor results, while liquidity lagged. Regional index outcomes…
This article reviews global equity factor performance in the third quarter of 2021 and compares defensive factor positioning. It reports that momentum and low residual volatility led the pure factor results, while liquidity lagged. Regional index outcomes…