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.
4,510 documents
This Chinese-language post describes a stock screen that combines metaverse-sector membership, a positive institutional-flow measure, and circulating market value between 5 and 10 billion yuan. It presents the criteria as a way to find moderately sized…
This Chinese-language seminar summary frames AI adoption as a response to falling asset-management margins despite growing assets under management. It sketches a progression from adjusting active portfolios using factor analysis, to building long-short…
This screening idea selects Chinese A-share stocks associated with the metaverse, limits their circulating share count to no more than 5.5 billion, and ranks the remaining names by recent fund inflows. The suggested flow measure is net capital inflow over…
The post presents a Chinese stock selection idea combining daily price range, a three-session limit-up streak, and an external-to-internal trading volume ratio above 1.3. It interprets a large range as elevated activity, repeated limit-ups as strong recent…
This post outlines a Chinese stock selection idea combining a daily price-range threshold, afternoon large-order net inflow, and a ranking by stock popularity. It frames popularity as a proxy for market attention and order flow as a possible indication of…
This Chinese A-share screening rule selects stocks with turnover in a specified range, at least one limit-up event during the prior 25 days, and a share price below a stated cap. The post frames the filters as a way to find lower-priced shares while taking…
This Chinese-language post describes a stock screen combining three conditions: membership in the metaverse theme, positive net buying attributed to major participants during the opening auction, and exclusion of Beijing-listed A-shares. It frames the theme…
This stock-selection rule combines three filters: turnover between 3% and 12%, a listing year of 2021, and a prior-day price condition described as a limit-down match price at 9:15. The accompanying Python example outlines how to identify candidate stocks,…
This Chinese-language article outlines an equity screening idea combining three filters: membership in the metaverse concept group, positive net buying by major participants during the opening auction, and a turnover rate between 3% and 12%. It frames the…
The document explains how to prepare Commitments of Traders report files for an indicator. It directs users to choose either futures-only or combined futures-and-options text reports, download several annual files, rename each file by year, and place them in…
This note describes a Chinese equity screen combining daily price amplitude above 1, a controlling-shareholder measure above 21%, and a high ranking for large-order net money flow. It explains that amplitude and control are intended to identify active,…
The document describes an equity screen combining three conditions: a 14-period RSI below 65, greater displayed buy-side than sell-side volume at the best quotes, and prior-day turnover above 8%. It presents the RSI as a price-state measure, the quote-volume…
This article proposes an A-share screening recipe for companies associated with the robotics theme. It combines a minimum daily price range, a circulating market-cap ceiling, and a ranking by individual-stock market attention. The accompanying examples…
This document describes a Chinese mainboard stock screen combining daily price movement, large-order net volume, and a run of weak sessions. It selects stocks with an amplitude above 1, large-order net volume above 0.05 for at least three consecutive days,…
This note describes a Chinese stock screen combining metaverse concept membership with a close above the previous day’s low and at least one recent limit-up event. Its rationale is to focus on stocks associated with a market theme that also show a basic sign…
The newsletter argues that Bitcoin remained in a short volatility and price consolidation regime, while Solana offered more relative volatility interest. It points to Bitcoin’s modest weekly price change, options-implied weekly movement, and limited…
This proposed stock screen combines a price-range condition, a ranking based on large-order net volume, and a seven-session declining-price condition. The explanation frames range and order-flow ranking as market-activity filters, while consecutive declines…
This stock-selection rule combines three conditions: amplitude greater than 1, institutional participation above 25% over 15 days as the article defines institutional buying, and a recent high that is the highest across a two-day window. The post frames the…
This stock selection idea combines a turnover range of 3% to 12%, three consecutive declining sessions, and a ratio of external to internal trading volume above 1.3. It aims to find Chinese equities that have recently weakened but may still show buying…
This post outlines a stock-selection screen using turnover between 3% and 12%, a positive product of price change and net volume from very large orders, and prior-day appearance on a market leaderboard. Its accompanying discussion frames turnover as an…
This document proposes screening for metaverse-related stocks with positive institutional-flow readings and a concentration measure below 20%. Its rationale is to combine an industry theme, a signal associated with institutional activity, and a concentration…
This document proposes a stock screen combining price movement, a 15-minute MACD histogram condition, and buying activity around the opening auction. The stated criteria include an amplitude greater than 1, a shrinking negative MACD histogram, large…
This Chinese stock-screening example combines three conditions: a 14-period RSI below 65, first-level bid volume greater than ask volume, and an appearance on the previous day’s trading leaderboard. The explanation treats RSI as a price-condition filter,…
The document reports survey responses from U.S. crypto holders about emotion and investing. It states that 63% believe emotional decisions harmed their portfolios, 81% made decisions based on fear, uncertainty, and doubt, and 84% acted on fear of missing out…