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 Chinese A-share example builds a daily stock-ranking strategy using LightGBM regression. Its features combine market capitalization, recent price and turnover averages, dividend yield and price-to-earnings ranks, plus two custom factors. The target is a…
This configuration describes a Qlib machine-learning workflow that trains a CatBoost regression model on Alpha158 features for CSI 300 instruments. It defines separate training, validation, and test periods, then records signal analysis and portfolio…
The article explains how the Kelly criterion can set leverage and capital allocation to maximize long-run compounded growth. Under its simplifying assumptions of normally distributed strategy returns, stable estimated means and standard deviations,…
This research overview examines risk parity within the broader development of portfolio allocation methods. It describes several risk measures and risk-allocation principles, emphasizing Euler allocation to define each asset’s contribution to portfolio risk.…
This April 2020 industry-allocation update describes a framework that combines earnings and valuation context with macroeconomic state signals, historical pattern matching, trend measures, and public-fund positioning. Its macro component responds to weaker…
The report describes a stock-selection strategy that predicts the future usefulness of seven style factors and adjusts their portfolio weights over time. It uses historical factor information coefficients (ICs), macroeconomic variables, and market variables…
The article describes a framework that links macroeconomic and style factors with traditional asset-class allocation. It proceeds from selecting factors and estimating asset exposures to building factor-mimicking portfolios, forecasting their returns,…
This career guide outlines a self-study path for aspiring quantitative developers. It emphasizes strong programming and numerical implementation skills, with language choices shaped by likely workplaces: C++ and Python for broad applicability, while Java or…
The document introduces fund-of-funds structures and distinguishes four types according to whether the parent and underlying funds are actively or passively managed. It focuses on an actively managed parent investing in passive sector ETFs, and describes an…
This method estimates portfolio weights for a spread using the Box–Tiao canonical decomposition. It first reorders the price columns so the selected dependent asset comes first, demeans the data, and fits a first-order vector autoregression. It combines the…
This Chinese-language conference excerpt introduces how artificial intelligence is being adopted by global asset managers. It frames technology as one response to falling margins per unit of managed assets, alongside efforts to grow assets under management.…
The document discusses how to judge excess returns in an index enhancement strategy. It argues that alpha should be evaluated by its persistence and consistency against a benchmark, rather than by the size of gains in a brief period. It describes monthly…
This document outlines a Turtle style trend following strategy for stocks. It buys when the close crosses above the prior 20 trading days’ high and exits when the close crosses below the prior 10 trading days’ low. The examples define signals using current…
The document describes a Chinese equity strategy that first screens for companies with high dividend yields, PEG below one, and share prices between 3 and 15, then selects stocks with market capitalizations below 10 billion yuan. It frames these rules as a…
This Chinese equity research note develops industry-rotation signals from several types of institutional money flow. It treats a flow source as useful when its derived signals show a reasonably orderly relationship across ranked groups and a tolerable…
The report presents a framework for dynamically adjusting multi-factor portfolio weights by forecasting factor returns and their covariance. It replaces unconditional expectations with conditional expectations based on external market variables, allowing…
This retrospective contrasts rule-based stock selection with machine-learning ranking and describes backtesting as a way to evaluate a strategy on historical market data. Its central caution is that a strong fit on a small sample can reflect an irrelevant…
This configuration defines a Qlib experiment that trains an IGMTF model on Alpha360 features for CSI 300 stocks. It uses historical data from 2008 through 2020, with training through 2014, validation in 2015–2016, and a held-out test period beginning in…
The document describes a framework for evaluating equity factors and combining selected factors into a portfolio. It estimates factor returns with periodic cross-sectional robust regressions, measures the relationship between factor exposures and subsequent…
This article explains industry rotation in China’s A-share market, describing how economic cycles, policy, fundamentals, and herding can cause leadership to shift across sectors. It outlines a momentum approach that ranks industry indexes by weighted returns…
The document explains Value at Risk (VaR) as a loss threshold for a portfolio over a specified period at a chosen confidence level. It outlines common uses, including setting risk limits for individual strategies and portfolios, comparing risk across…
This summary describes an analysis of actively managed equity funds and funds with substantial equity exposure. Its stated selection process combines historical return data with portfolio holdings, sector exposure, and risk considerations to identify funds…
This example presents a monthly Chinese equity strategy using constituents of the CSI 300 as its starting universe. It excludes suspended and specially treated stocks, then calculates several cumulative return measures over different lookback windows. For…
This forum question concerns modifying a portfolio sell routine so that, when the stock allocation exceeds 60% of total portfolio value, the excess exposure is reduced by selling holdings from the bottom of a ranking. The supplied code builds a set of…