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
The post describes a stock screen requiring price amplitude above 1, current volume above 10,000 lots, an opening above the prior close, and a specified daily decline between 4% and 5%. Its rationale is to find active, liquid stocks that open positively…
This post outlines a Chinese A-share stock screen combining three conditions: rank stocks among the top 100 by volume ratio, look for a Bollinger Band pattern described as contraction or expansion, and require at least two limit-up sessions within ten days.…
This stock-selection rule combines a 14-period RSI below 65, year-over-year growth in net profit attributable to shareholders between 20% and 100%, positive net profit, and a share price below 18.5 yuan. The post presents the screen as a way to find Chinese…
This assignment describes building a stock strategy from four factors, including small market capitalization and turnover. The author used a provided template and AI assistance to implement a linear regression strategy, then packaged three models as options…
The document describes a stock screen combining daily price amplitude above 1%, the year 2021, and three consecutive declines in closing prices. It frames the amplitude condition as a way to find more volatile stocks and the consecutive declines as a…
This daily stock screen selects shares whose MACD histogram is above zero and whose 30-day moving average is rising, then ranks qualifying shares by a measure of market attention. The document explains MACD using standard exponential averages and includes a…
This Chinese A-share screening method combines turnover, circulating market capitalization, rising moving averages, and price above several moving-average levels. Its stated final criteria cap turnover at 12%, restrict circulating market value to a specified…
This proposed A-share screen selects stocks whose intraday range exceeds 1, whose price is above its five-day moving average, whose current volume exceeds 10,000 lots, and whose opening price is above the prior close. The article presents the range and…
This stock screen combines a capital-inflow measure with two trend filters. It selects A-shares when the day’s increase in holdings exceeds 5%, weekly MACD is above zero, and the 20-day moving average is above the 120-day moving average. The article…
This Chinese-language post describes an equity screening rule based on daily amplitude above 1%, a closing price of 18.5 yuan, and a KDJ K value below 20. It interprets the KDJ threshold as an oversold condition and suggests ranking qualifying stocks by…
The article presents a Chinese equity screening idea that combines three conditions: MACD above its zero line, a price-change and large-order-flow measure, and the 20-day moving average above the 120-day moving average. It frames the combination as a way to…
This stock-selection note combines three technical filters: daily amplitude above a threshold, a positive weekly MACD histogram, and a close between the middle and upper Bollinger Bands. It characterizes the amplitude condition as a volatility screen, the…
This Chinese stock-screening note proposes selecting equities with a 14-period RSI below 65, three consecutive declining sessions, and no limit-up on the previous day. It presents the combination as a way to find technically weaker or less overheated stocks,…
This Chinese stock-selection idea filters for stocks with turnover between 3% and 12%, displayed buy-one volume greater than sell-one volume, and a rounded price pattern in which a decline is followed by a rebound. The stated rationale is that turnover and…
This Chinese stock-selection post describes a screen for companies associated with the metaverse theme and a rounded price pattern. Its initial rule combines the prior day’s turnover rate with the ratio of current auction volume to the prior day’s volume,…
This stock screen selects companies in the metaverse industry whose share price is above its five-day moving average and that have outstanding convertible bonds. The moving-average condition is intended to retain stocks showing short-term price strength; the…
This Chinese equity screening strategy ranks stocks using cross-sectional measures of circulating market capitalization, closing price, and earnings per share, then combines the ranked factors into a total score. Its stated filters exclude special-treatment…
This report examines two style-allocation ideas for Chinese equities: rotating between value and growth using value spread, and reducing market exposure to systematic risk through low-beta investing. It argues that value spread tends to revert toward its…
This stock-selection rule screens for turnover between 3% and 12%, a ratio of outside volume to inside volume above 1.3, and exclusion of China’s Science and Technology Innovation Board. The accompanying implementation also includes a recent volume surge…
This stock screen looks for turnover between 3% and 12%, at least one limit-up event in the prior 25 days, and no limit-up on the previous day. The stated rationale is to find liquid, actively followed stocks with a recent strong move, followed by a possible…
This note outlines a workflow for creating an AI-assisted stock selection strategy. It recommends combining signals about the broad market, industry groups, and individual stocks rather than relying on a single factor. Example inputs include index returns…
The response explains how to structure a multi-stock, multi-date dataset for a multiple regression of stock returns on factors. Each stock on each date forms one observation: the return is the target variable, and that stock-date’s factor values are the…
This stock-selection example combines three filters: daily amplitude above 1%, circulating shares no greater than 5.5 billion, and simultaneous bullish crossovers among 5-, 10-, and 20-day moving averages. The article describes calculating amplitude from the…
The discussion addresses missing broad-market risk-control data in a rolling-training workflow. Its suggested remedy is to load benchmark index history directly in the backtest data-preparation function instead of relying on an external data merge. The…