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.
4,194 documents
This stock-selection note combines three filters: an average daily range above 1, a 2019 dividend payout ratio above 25%, and a same-day control measure above 21%. It presents the control measure as an indication of concentrated trading influence or…
This research reproduction describes factors based on the share of a Chinese stock’s trading volume occurring in the opening call auction and near the close. The opening-auction factor, OCVP, is smoothed with a simple moving average and an exponentially…
This Chinese-language research summary studies whether public equity fund stock exposure can inform market timing in the China A-share market. It uses a moving-average system to distinguish trending from range-bound regimes, analyzes how fund positioning…
The document introduces VeighNa, an open-source Python framework for quantitative trading, with particular attention to its vnpy.alpha module. That module organizes research into feature creation, model training, strategy development, and workflow…
This note outlines an A-share stock screen combining metaverse sector membership, a rounded or arc-shaped price pattern, and return on equity above 15% for five consecutive years. It then adds a monthly five-period moving average crossing above the…
The article proposes screening Chinese stocks using price amplitude, company quality, and performance tied to 2021. Its expanded version adds valuation and fundamental comparisons, allows technical indicators such as KDJ, RSI, or MACD, and suggests…
This summary describes a method for constructing broad stock factor exposures and checking factor usefulness in a multifactor model. It presents returns as a linear combination of factor contributions plus an unexplained residual, and emphasizes examining…
This research report proposes improving a conventional stock reversal signal by splitting each stock’s recent daily returns according to average trade size. For each lookback window, it ranks days by daily turnover divided by trade count, compounds returns…
The document outlines a Chinese A-share stock screen centered on a metaverse industry classification, elevated recent turnover, share prices above a stated threshold, larger market capitalization, and return on equity. It first presents a simpler theme,…
This post proposes screening Chinese equities for daily price amplitude above 1, return on equity above 15% for five consecutive years, and a rounded price pattern. It presents the combination as a way to pair a volatility condition with sustained…
This Chinese-language post outlines a stock selection rule using price amplitude, relative trading volume, and return on equity. It specifies amplitude above 1%, a volume ratio between 1.5 and 6, and ROE above 15% in each of the past five years. The intended…
This Chinese-language post describes an A-share stock screen combining price amplitude, a 2019 dividend yield threshold, and a range for floating market capitalization. The intended logic selects stocks with amplitude above 1, dividend yield above 25%, and…
This factor-monitoring summary compares Chinese equity signals over weekly, monthly, year-to-date, and one-year windows. It reports rankings for long-only absolute returns, long-short returns, information ratios, and relative strength. The factors discussed…
This factor note defines a volume-weighted measure of a stock’s intraday relative price range. For each instrument and date, it calculates the high-low range divided by the opening price, weights that value by volume, and divides the summed weighted values…
This article summary presents a quantitative framework for combining conventional alpha factors with ESG-related signals in equity portfolios. It distinguishes exclusion screens, ESG integration, and impact investing, then focuses on integration: investors…
This stock-screening idea combines a minimum intraday high-to-low price range with consistently high return on equity across five years and a limit on the stock’s gain at 9:25. The stated rationale is to seek profitable companies showing some price movement…
This document presents an equity selection rule that combines a positive MACD signal, a 20-day moving average above the 120-day moving average, and year-over-year parent-company net profit growth above 20% and up to 100%. Its example implementation scans…
This document describes a Chinese equity screening rule for beverage and alcohol import-export companies. It selects stocks with turnover between 3% and 12% and year-over-year parent-company net profit growth above 20% and up to 100%. The article also gives…
This article surveys six implementation choices that shape equity factor strategies: selecting proxy measures, constructing portfolios, combining factors, allocating among them, trading, and managing risk. It argues that one factor can be represented by…
This report reviews market conditions relevant to Chinese quantitative equity strategies in July 2022. It tracks the number of unstable factors as a proxy for how supportive conditions may be for strategy excess returns. The report says this count stayed…
This paper summary examines whether historical trading data can predict the next month’s cross-sectional returns of Chinese A-shares. It describes a dataset of 108 stock characteristics from 1997 to 2019 and compares traditional econometric methods with six…
This stock-selection post proposes combining three filters: an increase in holdings share above a stated threshold, weekly MACD above its zero line, and at least two limit-up sessions within a stated lookback period. The accompanying explanation treats these…
The document studies a definition of an industry leader based on analyst coverage and strong links between a stock’s fundamentals and those of its industry peers. It describes the selected stocks’ general characteristics and examines whether their price…
This article introduces XGBoost as a machine-learning method for quantitative stock selection using price and volume factors. It explains boosting as a process that adds weak learners in sequence, then contrasts AdaBoost’s reweighting of misclassified…