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 stock selection rule filters for turnover between 3% and 12%, companies listed in 2021, and large-order net flow above 0.05 for at least three consecutive days. The document defines net flow as large buy value minus large sell value and gives a ratio…
This document describes an equity screen that combines daily price amplitude, institutional participation, metaverse theme exposure, and a minimum company size. Its final stated criteria require amplitude above 1, institutional participation above 25% over…
This short-term stock screen combines three signals: a daily high-low range greater than one percent, a high ranking for large-order net volume, and the product of price change and very-large-order net volume. The stated rationale is to combine price…
This proposed Chinese stock screen selects shares with an amplitude above 1, ranks candidates by the day’s auction amount, and uses a market-attention ranking. The stated intent is to combine price activity, pre-market auction activity, and investor interest…
This note outlines a screen for Chinese stocks associated with the metaverse. It combines a reported rounded-bottom price pattern with positive large-order net flow above 0.05 for at least three consecutive days. The final stated selection logic also adds an…
The article reviews research on salience theory in equity pricing. Investors are proposed to overweight unusually prominent past returns relative to the market: strong standout gains can attract demand and raise prices, while conspicuous declines can depress…
This Chinese-language post presents an equity screen focused on stocks classified in the metaverse industry. It combines a volume-ratio range with a condition based on the prior day’s turnover rate multiplied by the ratio of current auction volume to…
The described stock screen ranks shares by the previous day’s combined buy and sell amounts from exchange trading disclosures, then filters for companies with market capitalization below 10 billion yuan and no past losses. The ranking is intended to reflect…
This Chinese stock screen combines a technical threshold, a money-flow condition, and a convertible-bond field. It selects shares with RSI below 65, a positive product of percentage price change and net large-order volume, and a nonempty convertible-bond…
The document describes a Chinese equity screening rule that combines a Metaverse industry classification with a price condition around the ten-day moving average and prior-day auction turnover above 0.26. Its stated rationale is to combine an industry theme…
The document summarizes research on whether seasonal shifts in investor mood help explain recurring cross-sectional patterns in stock returns. It treats January, March, and Fridays as relatively optimistic periods, and September, October, and Mondays as…
This strategy uses SPY, VIX, and the Brain Market Sentiment indicator to determine exposure to an overnight SPY trade. It checks each series against its 20-day average: SPY and sentiment must be above their averages, while VIX must be below its average. Each…
This Chinese stock screen selects shares with current turnover between 3% and 12%, an external-to-internal trading volume ratio above 1.3, and prior-day turnover above 8%. The accompanying explanation presents turnover and the balance of trades classified as…
This Chinese stock selection note describes a screen combining price range, turnover, opening auction volume, and estimated main-force buying. It selects stocks with amplitude above 1, a product of prior-day turnover and the ratio of current auction volume…
This Chinese stock-selection note describes a screen combining turnover, price movement, large-order net volume, trading volume, and a price near 18.5. Its central condition selects stocks with turnover between 3% and 12% and a positive product of price…
This article explains how crypto order book heatmaps display resting limit orders across price and time, and how traders can use them to inspect apparent supply, demand, and liquidity. It describes combining books from centralized exchanges and comparing…
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 prior-day turnover above 60 million. It frames the theme…
This weekly market commentary links macro events, including US inflation data and Federal Reserve expectations, with Bitcoin and Ether price behavior and options positioning. It describes Bitcoin and Ether volatility and term structures, including a return…
This post proposes a stock screen combining a reported increase in daily position accumulation above five percent with an external-to-internal trading-volume ratio above 1.3. It also references 2021 as a time filter, describing it as a way to exclude…
This stock selection rule filters for names with turnover between 3% and 12%, a positive product of the daily price change and net flow attributed to very large orders, and a positive price-to-earnings ratio. The document explains the intended combination as…
This post describes a Chinese stock screen for companies classified under the metaverse theme that had turnover above 8% on the prior day and appeared on the prior day’s trading leaderboard. It treats leaderboard inclusion as a signal of notable investor…
This research review examines whether a US equity mutual fund’s performance relates to other funds following its trades. It extends a herding measure to classify funds by how strongly their purchases or sales are followed in later quarters, then compares…
This Chinese-market stock screen combines three filters: metaverse-related stocks, a positive institutional-flow measure, and a restriction excluding stocks that closed at the prior day’s upper price limit. The stated rationale is to focus on a specific…
This post describes a short-term Chinese stock screen that first selects shares with an amplitude above 1 and reported main-force control activity on the prior day, then ranks qualifying names by individual stock popularity. It frames the method as a way to…