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
A multi-factor analysis reports factor coverage below the required threshold, even though basic descriptive statistics and a null check do not reveal an obvious problem. The suggested diagnosis is that a feature extraction step removes rows containing…
This Chinese equity research excerpt analyzes the beer industry’s shift from pursuing output scale toward improving profitability through premium products. It separates the outlook into volume and price per ton: production depends on the number of operating…
This Chinese equity screening idea combines three filters: a daily increase in positions above 5%, previous-day actual turnover between 3% and 28%, and a dividend ratio above 25% for 2019. The accompanying explanation treats position growth as a sign of…
This stock-screening example combines a technical condition with reported earnings growth and trading activity. It selects A-share stocks with a 14-period RSI below 65, year-over-year growth in parent-company net profit from above 20% through 100%, and a…
This article examines how training-window length affects an AI stock-selection model. It compares long windows, expanded year by year from 2005 to 2021, with shorter windows ranging from a month to a few years. The author recommends checking each window…
This discussion addresses how to use factors from BigQuant's factor library as model features. The question asks whether a factor identifier can be used directly or whether users must copy its expression, and whether factors associated with a brokerage are…
This post describes a Chinese stock-selection screen for robot-related companies. Its initial criteria are strong fund flow intensity, circulating market capitalization below 10 billion yuan, and a rising 30-day moving average. The final proposed screen adds…
This Chinese-language note describes a stock prediction exercise using four cross-sectional inputs: total market capitalization, turnover, trailing price-to-earnings, and quarterly net-profit year-over-year growth. It compares linear regression, XGBoost, and…
This A-share screening rule filters for turnover between 3% and 12%, excludes Beijing-listed shares, and requires 2021 revenue to exceed 2018 revenue by a factor greater than 1.1. The stated aim is to find companies with revenue growth while retaining a…
This Chinese-language post outlines an equity screening idea that combines three signals: rank stocks by relative volume, require the 5-, 10-, and 20-day moving averages to be aligned upward, and look for a 15-minute MACD histogram that is shrinking while…
This stock-selection proposal combines a price-range filter, a historical dividend-yield condition, and a turnover-related condition. The stated screen looks for stocks with amplitude above 1%, a 2019 dividend ratio above 25%, and a turnover measure between…
This document describes a simple screen for Chinese metaverse-related equities. It selects stocks with circulating market capitalization above the stated cutoff and a ten-day price gain that is positive but below a specified upper bound. The accompanying…
The article introduces a custom coded stock selection strategy built from five rules, mainly intended to identify stocks that have fallen sharply and begun to stabilize, with some rules meant to capture potential strong advances. It contrasts AI generated…
The document asks whether minute or second level price and volume data can be aggregated into daily net flow measures for very large, large, medium, and small trades. It points readers to two implementations: a general set of high frequency factors and a…
This A-share stock screen combines a technical condition with company earnings growth and a basic price check. It selects stocks with RSI below 65, year-over-year growth in net profit attributable to parent-company shareholders above 20% and no greater than…
This research review examines momentum in Chinese equities and compares several ways to measure it. It reports that simple one-year returns, with a recent period excluded, did not predict returns reliably across the full A-share market in the tested sample.…
This research summary examines the difference between monthly stock and industry behavior in Chinese equities: individual stocks are described as showing reversal, while industry indices show momentum. It proposes an industry-momentum factor based on the…
The article argues that trading researchers should judge machine learning papers by simplicity, reproducibility, and generality, alongside reported empirical results. A method that performs well on a benchmark may not transfer to financial prediction, and a…
The report proposes improving traditional liquidity factors for Chinese A-share stock selection by incorporating information about intraday trading structure. It frames the approach as a reusable factor research method: identify market information omitted by…
This report develops a framework for rotating between the CSI 300 and ChiNext indices, whose valuations, market capitalizations, and industry exposures differ. It organizes potential drivers of their relative performance into factors covering real economic…
This beginner’s post describes an attempted reproduction of a quantitative research report’s “Water Drops Wear Away Stone” factor and a single-factor stock-selection backtest. The author says the implementation was prepared as a learning exercise with help…
This stock-selection method looks within China’s metaverse sector for shares ranking among the top five by the day’s auction value, then filters for a 2019 stock-dividend ratio above 25%. The document gives screening logic and example implementations for two…
The described screen combines technical, valuation, and recent-return conditions for Shenzhen mainboard stocks. It seeks shares with at least five moving averages aligned or clustered, a price-to-earnings ratio from 0 to about 29, a price-to-book ratio from…
The document describes a Chinese equity screen for companies in the metaverse theme, with a minimum circulating market capitalization and positive price-to-earnings ratio. It presents the positive PE condition as a basic profitability filter and the size…