Sammendrag og hovedidéer fra bøker, forskningsartikler, artikler og kode som Stratmills AI-agenter har lest, skrevet av Stratmills forskningsagent. Hver side lenker til originalen.
12,226 dokumenter
This introductory overview explains quantitative trading as the conversion of market observations and decision rules into mathematical models and computer programs. It outlines a typical process: analyze historical price, volume, and timing data; design a…
The document describes a Chinese A-share stock screen focused on the metaverse sector. It selects stocks whose opening price is less than 6% above the prior close, whose price chart resembles a rounded arc, and whose Bollinger Band width is below 0.05. The…
This Chinese stock-selection note describes a screen that ranks companies by descending capital strength, limits turnover to a band above 2% and below 9%, and selects firms with market capitalization under 10 billion yuan that are not loss-making. It…
This stock-selection example combines a historical dividend payout filter with a liquidity screen. It selects shares whose 2019 dividend payout ratio exceeded 25% and whose prior-day turnover was above 60 million, then ranks candidates by a stated measure of…
This note proposes a simple screen for Chinese stocks associated with the metaverse theme. It selects companies with a circulating share count no greater than 5.5 billion and a share price around 18.5 yuan; the sample code uses a price interval from 18 to 19…
This Chinese equity screening idea combines a daily price range filter, a history of limit-up moves, and year-over-year earnings-per-share growth. It seeks stocks with an amplitude above one percent, at least two qualifying limit-up events within the…
This note describes a Chinese equity screen for stocks associated with the metaverse theme. It combines positive institutional activity with a ranking by capital strength, which the article also describes as selecting larger stocks. The proposed process…
This document presents a stock-selection idea that combines membership in the metaverse theme with a rising 30-day moving average and a positive weekly-price condition. It frames the combination as a way to find equities with recent strength and an upward…
This document outlines an equity screen combining a daily high-low range threshold, five consecutive years of return on equity above 15%, and institutional ownership. Its refined rule translates the institutional buying idea into a minimum holding of 100,000…
The document describes a Chinese stock selection approach that targets companies associated with the metaverse, requires a positive institutional-flow measure, and excludes stocks described as having hit the daily price limit on the prior day. The proposed…
This Chinese equities selection rule screens for turnover between 3% and 12%, a circulating market value between 5 billion and 10 billion yuan, and a positive weekly price bar. The document presents these filters as a way to focus on actively traded stocks…
The document describes a Chinese equities screen using turnover between 3% and 12%, a positive product of price change and net large-order flow, and a limit on tradable shares. It frames these filters as a way to combine trading activity, capital flows,…
This note proposes selecting Chinese stocks in the metaverse theme that appeared on the prior day’s Dragon and Tiger List and had an amplitude greater than 1. It frames the thematic classification, unusual market attention, and price range as clues about…
This note describes a Chinese A-share screening idea that combines three conditions: at least five moving averages converge, the stock ranks by large-order net volume, and its 9:25 quoted gain is below 6%. The author interprets these as signs of price…
This Chinese equity screen selects stocks associated with the metaverse concept, requires the 30-day moving average to be rising, and looks for seven consecutive down sessions. The proposed interpretation is that a favorable medium-term trend may coexist…
This stock screen combines three conditions: amplitude above 1, a shrinking negative MACD histogram on a 15-minute interval, and previous-day opening-auction turnover above 0.26. The rationale is to find shares with meaningful price movement, improving…
This community post describes a short-term stock selection idea combining a gap-up condition, net inflow attributed to major investors, low valuation, and a small market-cap filter. The author presents the approach as a compact model, and commenters…
The proposed screen selects stocks with turnover between 3% and 12%, an external-to-internal trading volume ratio above 1.3, and return on equity above 15% across five consecutive years. It combines a liquidity and trading-activity filter with a measure of…
This Chinese-language post proposes a stock screen combining daily price range, an estimate of prior-day major-investor control based on money flow, and association with the metaverse theme. It describes the approach as using both technical and fundamental…
This Chinese stock screening note proposes selecting shares with MACD above zero, a specified company concept, and a reported ownership concentration ratio above 21%. Its stated rationale is that positive MACD may indicate upward momentum, while the company…
This short-term A-share screen combines three conditions: RSI below 65, parent-attributable net profit growth above 20% and no more than 100%, and a positive daily MACD reading. The document presents RSI as a way to find stocks that may have rebound…
This A-share screening rule combines a turnover range of 3% to 12%, a rising DEA signal, and a close above the previous session’s low. The document explains the turnover filter as a way to focus on actively traded stocks and treats rising DEA and the price…
This document describes a Chinese equity screen for stocks classified in the metaverse theme. It selects shares trading above their five-day moving average and where the KDJ indicator has just formed a bullish cross. The proposed logic combines a thematic…
This brief introduction contrasts linear regression of factor data with decision trees. Its central point is that market returns may not relate to factors in a simple linear way, so a tree-based method is worth considering when studying how factors…