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.
12,140 documents
This document describes a China A-share screening rule that combines a turnover range of 3% to 12%, appearance on the previous day’s 龙虎榜 (a public list highlighting unusual trading activity), and an increase in the KDJ indicator’s K value. It also gives…
This article presents six proposed bullish chart setups: a pullback that holds above a limit-up level and a moving average, rising volume after a contraction, a two-stage expansion from a price low, a large down day on reduced volume, a sequence of pullbacks…
This stock-selection rule filters for turnover rates between 3% and 12%, a rising K line from the KDJ oscillator, and a rising DEA line. The post’s sample formula also excludes stocks in industry type 1, which it identifies as the STAR Market. Its Python…
This post describes a stock screen using three conditions: daily amplitude above 1, a positive weekly MACD histogram, and a rising-bottom pattern. It offers example formulas and Python code that scan listed stocks, evaluate daily amplitude and weekly MACD,…
This Chinese stock-screening post combines three filters: RSI below 65, an industry concentration ratio above 70% or below 20%, and a shortening MACD histogram on a 15-minute chart. It presents the approach as a way to find stocks with improving momentum…
This article argues that divergence and MACD crosses are best interpreted as signs of changing momentum, rather than as automatic buy or sell commands. It describes divergence as evidence of countertrend activity, with the strength of that activity helping…
The post outlines a Chinese-equity screening rule that combines daily price range, a stock-code prefix beginning with 60, and a recent large upward move. It also suggests sorting qualifying stocks by market capitalization and provides example implementations…
This post describes a short-term China A-share screening rule that combines three signals: positive net buying by large investors during the opening auction, a daily position-increase ratio above 5%, and a 10-day price gain greater than zero but below 35%.…
This community post outlines a China A-share stock screen that excludes Beijing-listed companies and selects stocks with turnover between 3% and 12% and a positive but limited recent price gain. It then proposes refining the candidates using net profit…
This Chinese-language post describes an A-share screening idea that combines daily turnover between 3% and 12%, a newly formed KDJ bullish crossover, and a company-type filter. It frames turnover as a liquidity condition and the KDJ signal as a short-term…
This proposed Chinese equity selection rule screens for stocks with turnover between 3% and 12%, then requires the product of the day’s percentage price change and net buying flow attributed to very large orders to be positive. Eligible stocks are ranked by…
This stock-selection screen filters for turnover between 3% and 12%, circulating market capitalization between 1 and 55 in the stated units, and at least one daily gain of 10% or more during the previous 25 trading days. The post gives both a formula-style…
This indicator combines Stochastic RSI with Heikin Ashi style candles calculated from smoothed oscillator values. It first computes RSI, scales it against its recent high and low, smooths the resulting %K and %D series, then centers them around zero.…
The document presents a Chinese stock selection rule combining daily amplitude above 1, a morning-star pattern, and weekly MACD above zero. Its indicator examples add moving-average ordering and other price, volume, and size conditions. The intended…
The document describes a Chinese equity screen requiring a position increase above 5%, evidence of large-investor control on the prior day, and a gain below 6% at 9:25. Its rationale is that rising holdings may indicate inflows, institutional ownership may…
This stock-selection idea filters for companies in the metaverse industry, then looks for a rising five-day moving average and a potential start of a strong advance. The proposed rules combine an industry classification with price and volume conditions,…
This Chinese equity screening idea combines three signals: today’s position increase share above 5%, the product of daily return and net large-order volume above 0.5, and a shortening MACD histogram green bar on a 15-minute chart. The document interprets the…
This A-share stock-selection approach combines an earnings-surprise proxy with the price response to earnings news. Instead of comparing reported profit with a numerical consensus estimate, it analyzes analyst research-report titles for wording that signals…
This live-robot page shows a screening and observation workflow for newly active tokens across several chains. The displayed table ranks candidates by one-hour volume expansion and reports liquidity, fully diluted valuation relative to liquidity, token age,…
This A-share screening strategy combines daily price and trading activity conditions with a weekly trend signal. It selects stocks whose amplitude exceeds the stated threshold, whose prior-day turnover rate multiplied by the ratio of today’s opening-auction…
This note proposes screening Chinese shares for turnover between 3% and 12%, a positive but less than 35% return over ten days, current volume above 10,000 lots, and a higher open. The sample code also applies exclusions for certain listing boards and…
This note describes a Chinese equity screen requiring turnover between 3% and 12%, circulating share count no greater than 5.5 billion, and no limit-up close on the prior day. Its accompanying Python example uses turnover, circulating market capitalization,…
This post outlines a simple daily stock screen for Chinese equities. It selects stocks whose MACD indicator is above zero and whose prior-day opening-auction turnover rate exceeds a stated threshold, then ranks candidates by stock popularity. The screen is…
The article describes a long-only Chinese equity strategy that uses a ResNet101 image classifier to identify strong stocks during pullbacks in an established uptrend. Its rationale combines buying a retracement in a strong trend, favoring stocks that…