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 article outlines a simple screen for Chinese equities associated with the metaverse theme. It selects stocks whose actual turnover rate from two trading days earlier falls between 3% and 28%, while excluding stocks that hit the upper price limit on the…
The document describes how to build daily return data for level-two industries and use it in stock selection. It proposes joining stock industry classifications with daily returns and float market capitalizations, then grouping by industry and date. Each…
The post outlines a Chinese equity screening idea that combines at least five overlapping moving averages, company or industry characteristics, and a 15-minute MACD histogram whose negative bars are getting shorter. It interprets moving average overlap as…
This stock screen selects shares whose daily amplitude exceeds a threshold, whose price is above the five-day moving average, and whose prior session was not a limit-up day. The article frames a larger daily range as potentially useful for short-term trading…
The proposed screen seeks Chinese stocks with turnover between 3% and 12% and a positive gain below 35% over ten days, then looks for three technical indicators to cross upward at the same time. The accompanying formula describes rising 12-period and…
This brief summary of a 2018 Chinese new-share market review reports that IPO issuance slowed while subscription winning rates remained stable. It also says that new-share subscription returns differed by investor category: A- and B-class investors…
This document describes a Chinese stock selection rule combining daily price range, recent limit-up activity, and a long-term moving average. Stocks enter the candidate pool when their amplitude exceeds 1%, at least one limit-up occurred in the prior 25…
This stock-screening proposal combines three conditions: a 14-period RSI below 65, first-level buy volume greater than first-level sell volume, and a rising KDJ component. It presents these as filters for stocks with moderate momentum, buying pressure, and…
This forum post raises a factor-construction question about accessing older financial statement observations beyond a platform's precomputed factors. The example is operating revenue: the author understands the suffix-zero field to represent the latest…
This post describes a Chinese main-board stock screen requiring turnover between 3% and 12%, a daily gain above 1%, and a nonempty name for an outstanding convertible bond. It presents the bond-linked filter as a way to identify companies with…
This post presents a stock screen using three conditions: daily price range above 1%, positive price-to-earnings ratio, and a ten-day return above zero but below 35%. The intended rationale is to find profitable companies showing recent gains and active…
This post describes a Chinese A-share screening rule: select stocks in the metaverse theme with a positive institutional-flow signal and prior-session auction turnover above 0.26. It gives indicator references and a Python example intended to implement the…
This proposed stock screen combines a daily turnover rate between 3% and 12%, a positive product of daily price change and large-order net flow, and a 2019 dividend ratio above 25%. The article frames turnover as a measure of market activity and the…
This stock screen selects companies assigned to the metaverse industry using two size conditions: circulating shares at or below 5.5 billion and a stated size threshold of at least 200 million. The document presents the screen as a way to find larger…
This stock-selection recipe filters Shenzhen main-board listings using turnover between 3% and 12%, year-over-year growth in net profit attributable to parent-company shareholders above 20% and up to 100%, price-to-earnings ratios from 0 to 29.01, and…
This stock screen combines a daily range filter, a qualitative company-quality condition, and a trend rule requiring the 20-day moving average to exceed the 120-day average. The article interprets the moving-average relationship as evidence that recent price…
This stock screen combines a daily price-amplitude threshold, a MACD condition above the zero line, and year-over-year growth in net profit attributable to parent-company shareholders. The stated profit-growth range is above 20% and up to 100%. The rationale…
This Chinese research note examines the common practice of relating price-to-earnings ratios to expected earnings growth, including the assumption that a PEG ratio of one indicates fair value. Its hypothetical comparison shows that companies with PE and…
The article proposes a Chinese stock screen combining three price-based conditions: amplitude above a threshold, a rising bottom pattern, and at least one limit-up move within the prior 25 days. It frames wide swings and rising lows as technical signals and…
The Double Seven method is presented as a long-only daily strategy for ETF or futures index markets. It first requires the close to be above its 200-day moving average. An entry occurs when the day’s low reaches or falls below the lowest close over the…
This stock-screening approach ranks shares by reported buy and sell amounts in the previous day’s trading leaderboard, then filters for names with leaderboard activity and a 20-day moving average above the 120-day average. The ranking is intended to…
This Chinese equity-screening note selects stocks with MACD above zero, positive price-to-earnings ratio, and turnover between three and twelve percent. It interprets the MACD condition as an upward trend filter, positive PE as a basic valuation sanity…
This Chinese stock-screening note combines three conditions: daily trading range above one percent of the previous close, weekly MACD above zero, and a prior-day 9:15 matching price at the lower price limit. It presents the screen as a way to find stocks…
This research summary describes using machine learning to predict equity returns from alpha factors. It compares LASSO, support vector machines, boosted decision trees, and random forests, selecting random forests for their relatively simple structure,…