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 document describes a stock-selection screen for Chinese A-shares. It combines daily turnover between 3% and 12%, an opening price within 5% of the ten-day moving average, market capitalization no greater than 10 billion yuan, and positive earnings…
This document describes a Chinese A-share selection rule that combines turnover between 3% and 12%, market capitalization below 10 billion yuan, positive earnings, and institutional activity above zero. Its refined version focuses on companies whose…
This proposed A-share screen combines market capitalization below 10 billion yuan, a non-loss-making business, a current turnover range of 3% to 12%, and prior-day actual turnover between 3% and 28%. It treats moderate turnover as a way to favor liquid,…
This Chinese-language forum post asks whether factor analysis should pair a factor observed on a given day with that same day's prices or with prices from the following day. The author cites the Alphalens introduction as a reason to use next-day data and…
This article proposes screening Chinese equities using three kinds of signals: RSI below 65, the product of price change and the net ratio of very large order flow above 1, and year-over-year growth in net profit attributable to parent-company shareholders…
This document describes a Chinese equity screen that combines daily price range, sustained profitability, and dividend payout history. The initial rule requires amplitude above 1, return on equity above 15% for five consecutive years, and a dividend payout…
This Chinese-language post outlines an equity screening strategy combining a positive MACD condition, descending money-flow strength, a market-cap ceiling of 10 billion yuan, positive earnings measures, and ROE above 10. Its accompanying Python example…
This catalog describes built-in quantitative data fields for Chinese A-share equities. Its factor families include historical prices, returns, trading volume and value, turnover, active money flows, valuation ratios, profitability and balance-sheet measures,…
This document proposes screening A-shares for turnover between 3% and 12%, total market value below 10 billion yuan, no reported losses, and free-float market value above 1 billion yuan. It frames the combination as a way to narrow the universe by trading…
This beginner’s reflection defines quantitative investing as identifying relationships between inputs, or factors, and future returns, checking those relationships against historical data, then using current observations to estimate future opportunities. It…
This article introduces convolutional neural networks and explains how one-dimensional convolutions can extract local patterns from financial time series. It describes convolution as applying learned weights across sequence windows, while pooling summarizes…
This note describes a stock screen for A-shares whose codes begin with 60, whose turnover is between 3% and 12%, and whose price-to-earnings ratio is positive. The code example obtains a current trading date and filters listed stocks using those criteria.…
This report reviews Chinese public quantitative funds and private quantitative market-neutral strategies using observations available through March 2022. It groups public funds into market-neutral products, CSI 300 enhanced funds, and CSI 500 enhanced funds,…
This research summary reviews six studies on industry rotation and index enhancement in China. Its multi-factor framework considers expected and historical fundamentals, price and volume information, market leadership, and system-risk concentration. It…
The document describes a Chinese equity screening rule combining three filters: turnover between 3% and 12%, a positive ten-day return below 35%, and year-over-year growth in net profit attributable to parent-company shareholders above 20% and at most 100%.…
This proposed Chinese stock screen combines a daily price-range threshold with a market capitalization cap, positive net profit, and a close above the prior day's low. The accompanying selection logic also includes minimum tradable market value and a…
The document defines AI-based stock selection as using machine learning and deep learning to analyze financial and market information, identify patterns, forecast possible price moves, and generate investment suggestions. It describes inputs such as…
The document presents Benford’s law as a quantitative screening method for assessing whether company financial figures may have been manipulated. It explains that in many naturally occurring datasets, the first nonzero digit appears with a nonuniform…
The document describes a Chinese stock screen that combines a 2019 dividend ratio above 25%, daily price amplitude greater than 1%, and ranking by a stock heat measure. Its example code approximates the heat ranking using traded value and also applies…
This page identifies itself as a reproduction of a Chinese multi-factor stock-selection research report about information contained in moments of unusually high trading volume. It also references related research on converting intraday minute-level spike…
This report adapts Peter Lynch’s six company categories to China’s A-share market: slow-growing, stable-growing, fast-growing, cyclical, turnaround, and asset-rich companies. Its selection framework first screens for a positive price-to-earnings ratio and a…
This study examines whether Chinese fund management companies’ visits to listed firms are associated with the performance of the funds they manage. It proposes that visits can improve investment decisions by giving institutions access to operational details…
This forum post describes an attempt to compare a platform’s single-factor analysis module with the open-source Alphalens package. The author uses the CSI 300 universe and the PE_TTM_0 factor, with a 22-day rebalancing interval. They aim to prepare…
The article examines why sector-themed equity funds attract investors and which industries may suit them, then develops two portfolio design approaches. It links investor demand to the visibility of past winners on financial technology fund platforms and…