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.
22,592 documents
This note describes a Chinese equity screen that starts with stocks in the metaverse theme, filters for prices above the 250-day moving average, then ranks candidates by the day’s auction amount. It proposes selecting the top five and also mentions ordering…
This stock screen combines three conditions: daily amplitude above 1%, observations dated in 2021, and a ten-day price gain greater than zero but below 35%. The document interprets the amplitude condition as selecting more volatile stocks and the gain band…
This Chinese equity screening rule combines a turnover range of 3% to 12%, at least one limit-up event in the prior 25 days, and seven consecutive sessions in which the close is no higher than the open. It is intended to find stocks with recent signs of…
The study defines an earnings surprise by comparing reported results from forecasts, flash reports, or periodic filings with analyst consensus net profit estimates from the day before the announcement. For nonannual reports, it estimates full-period…
This stock-selection note proposes screening mainland Chinese equities in the metaverse theme by current-day auction amount, taking the top five, then excluding stocks that closed at the daily limit the previous day. It provides platform formula expressions…
This research review examines whether mutual-fund outflows provide a clean measure of nonfundamental pressure on stock prices. It decomposes the MFFlow measure into components tied to the inverse of returns, the inverse of turnover, and relative fund selling…
This short strategy note describes a daily pre-open screen for mainland Chinese equities. It selects stocks with MACD above its zero line, a share price below 12 yuan, and excludes STAR Market stocks. The article explains the conditions and gives example…
The selection rule ranks stocks by volume ratio from highest to lowest, after filtering for yesterday’s turnover above 8% and a 10-day gain greater than 0% but less than 35%. The stated rationale is to find shares with strong trading activity, high turnover,…
This stock-selection rule filters for shares whose codes begin with 60, whose turnover is between 3% and 12%, and whose auction-period main-force net buying is positive. The document frames positive net buying as a sign of incoming funds and suggests that…
The document explains that exchange-traded funds cannot be processed with the platform’s basic feature extraction module. The reported run shows the extractor failing to find a close-price field for the selected instruments, producing no feature rows and…
This Chinese-language forum exchange discusses excluding special-treatment, suspended, and delisted shares from a stock universe. A reply provides two approaches based on historical name-change records: retrieve securities available around the relevant date,…
This stock-selection concept combines recent limit-up activity with moving-average behavior. It proposes finding shares where at least five moving averages converge, where there was a limit-up event within the preceding 25 days, and where limit-up events…
This screening concept combines three conditions: intraday range greater than 1, at least one daily gain of 10% or more during the preceding 25 trading days, and a current gain between -5% and 2.6%. It aims to find shares that have shown a sharp prior move…
This stock-screening idea targets mainboard equities with turnover between 3% and 12%, a daily gain above 1%, and large-order net volume above 0.05 for at least three consecutive days. It combines trading activity, recent price strength, and a measure of…
This proposed Chinese equity screen selects stocks with amplitude above 1, a weekly five-period moving average crossing above the ten-period average, and a stated increase in position holding above 5% for the day. The accompanying rationale combines a…
This stock screen requires turnover between 3% and 12%, year-over-year growth in net profit attributable to parent-company shareholders between 20% and 100%, and a share price above its 250-day moving average. It combines a trading-activity range and a…
This stock-selection proposal combines three filters: membership in the metaverse sector, positive institutional activity, and a positive share-price return during 2021. It frames institutional buying as a potentially useful signal and past gains as evidence…
This post proposes a rules-based screen for Chinese A-share stocks in a designated metaverse sector. It combines a recent trading-activity condition with a profitability filter requiring return on equity above a stated threshold across five consecutive…
This study develops two equity factors from the relationship between intraday volatility and the return-to-volatility ratio. The “rebuilding” factor uses the daily covariance between that ratio and volatility to represent cases where volatility rises without…
This weekly report compares machine-learning stock-selection portfolios across several Chinese equity universes: broad-market stocks without industry neutralization, broad-market selections neutralized to the CSI 300 or CSI 500 industries, and selections…
This stock screen combines a daily turnover range of 3% to 12%, a reversal or engulfing-style candle condition, and a stochastic K reading at or below 20. The article presents screening logic and sample indicator calculations: it derives ratios from the…
This note discusses market style and public mutual fund positioning in Chinese equities. It compares the share of CSI 300 constituents within the broader CSI 800 universe, reporting that the share was 81% at the time of the note. It also cites two recent…
This proposed stock screen selects shares with an amplitude above 1, three moving averages in a bullish ordering, and a ratio of active buying volume to active selling volume above a threshold. The article names amplitude and moving-average indicators and…
This proposed A-share stock screen filters for companies with turnover between 3% and 12%, market capitalization below 10 billion yuan, and no losses, then selects stocks with daily returns from −5% to 2.6%. The accompanying formula and Python example…