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
This article proposes a compact framework for reviewing the equity market by sector rather than inspecting every stock individually. To gauge sector persistence, it counts stocks moving from an initial limit-up session to a second consecutive limit-up…
This research summary describes a study of retail investor attention and asymmetric volatility in China’s A-share market. The authors use the balance of positive, negative, and neutral posts on online stock message boards to construct a proxy for asymmetry…
This user post describes rolling model training for stock forecasts using two approaches. The StockRanker example updates the model annually, fitting on one year of data and predicting over the following year. The XGBoost example instead constructs monthly…
This Chinese equity research excerpt analyzes the beer industry’s shift from pursuing output scale toward improving profitability through premium products. It separates the outlook into volume and price per ton: production depends on the number of operating…
This configuration sets up a Qlib experiment that uses a gated recurrent unit model with Alpha360 features to rank CSI 300 constituents. Feature values are robustly normalized with outlier clipping and missing-value filling; labels use cross-sectional rank…
This note proposes screening Chinese mainboard stocks using an RSI below 65, a daily gain above 1%, and prior-day trading value above 60 million yuan. The stated intent is to combine a moderate RSI reading, recent upward price movement, and a liquidity…
This note describes a Chinese equity screen combining daily price range, free-float market capitalization, and the direction of the 30-day moving average. It selects stocks with an intraday range of at least 1%, free-float capitalization above 10 billion…
This short-term A-share screening idea focuses on stocks classified in the metaverse theme that appeared on the prior day’s exchange trading-activity list, while excluding stocks that reached the stated near-limit-up threshold. The article expresses these…
This A-share stock selection idea combines a turnover filter with a short-term price decline and a longer-term trend condition. It screens for stocks with turnover between 3% and 12%, seven consecutive down days, and weekly MACD above zero. The article…
This note proposes a Chinese equity screen requiring price amplitude above 1%, a reversal or engulfing-style pattern, and positive net buying attributed to major participants during the opening auction. It explains the signals as indicators of short-term…
This note describes a simple Chinese equity screen combining daily price amplitude above 1%, a closing price of 18.5 yuan, and a positive return below 35% over ten days. It frames the filters as a way to find volatile stocks with moderate prices and recent…
This Chinese equity screening idea combines three filters: a daily increase in positions above 5%, previous-day actual turnover between 3% and 28%, and a dividend ratio above 25% for 2019. The accompanying explanation treats position growth as a sign of…
This document outlines a Chinese equity screening rule that combines daily price movement with a short moving average and a lower low. The stated final criteria are amplitude above 1, the close crossing above a five-day moving average, and today’s low…
This stock-screening example combines a technical condition with reported earnings growth and trading activity. It selects A-share stocks with a 14-period RSI below 65, year-over-year growth in parent-company net profit from above 20% through 100%, and a…
This screening concept selects stocks tagged with the metaverse theme, whose close is above the prior day's low, and whose weekly five-period moving average has crossed above the weekly ten-period average. The article interprets the industry label as a…
This stock-screening idea combines three conditions: MACD above zero, upward-moving averages, and price above the five-day moving average. The accompanying Python example calculates MACD from exponential moving averages and compares the five-day average with…
This stock-selection screen combines three conditions: daily range as a percentage of the prior close above a threshold, the current high equal to the highest high over the current and previous day, and the 20-day moving average above the 120-day moving…
This post outlines a short-term Chinese stock screen that requires amplitude above 1, ranks stocks by the day’s auction amount, keeps the top five, and requires the closing price to exceed the previous day’s low. The suggested interpretation is that elevated…
This document describes a Chinese stock screen using daily price movement and convertible bond information. It selects stocks with an amplitude above 1, a nonempty name for outstanding convertible debt, and a daily return between negative 5% and positive…
This document proposes a daily pre-open stock screen centered on MACD being above the zero line, then ranks qualifying names by a measure of individual-stock attention. It also includes a listing-age filter, described as favoring companies that have been…
This document outlines a Chinese equity screen for stocks classified in the metaverse theme. Its stated conditions include a positive return, a threshold on the product of price change and very large order net volume relative to a volume benchmark, and a…
This document describes a Chinese equity screen that combines a prior-session price range above 1%, a circulating share count no greater than 5.5 billion, and best-level buy volume greater than sell volume. It presents the conjunction of these filters as a…
This document describes a Chinese stock screen combining intraday amplitude above 1, a named morning-star candlestick condition, and a 9:25 price gain below 6%. It presents the combination as a way to find active stocks with potential upside while limiting…
This discussion addresses how to use factors from BigQuant's factor library as model features. The question asks whether a factor identifier can be used directly or whether users must copy its expression, and whether factors associated with a brokerage are…