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 proposed equity screen combines several filters: a reported increase in position holdings above 5%, a price change below 2.6% and above -5%, market capitalization no greater than 10 billion yuan, and a history without losses. The post interprets the…
This overview explains the main stages of a machine-learning workflow for quantitative investing, using a fruit-selection analogy to introduce training data, labels, features, prediction, and validation. It recommends defining the market and stock universe,…
This Chinese equity screening proposal selects stocks with amplitude above 1, a specified ratio between yesterday’s turnover rate and today’s auction volume relative to yesterday’s volume between 0.5 and 2, and a circulating share count no greater than 5.5…
This Chinese stock screen combines three conditions: amplitude above 1, a shrinking negative MACD histogram on a 15-minute chart, and prior-day trading value above 60 million. The document interprets amplitude as a way to find more volatile stocks, the MACD…
The document describes a simple A-share stock screen using a minimum daily high-low range, a specified closing price, and a bounded daily return. It frames the filters as a way to select for price movement while restricting the current price and recent…
This research summary examines stock selection factors derived from operating financial statement items, especially changes in operating current liabilities. It reports that these factors showed selection ability, with the strongest cited result for a…
This stock selection approach filters for shares with amplitude above 1, excludes those that were limit-up the previous day, and removes stocks from China's STAR Market. The article frames the board exclusion as a way to avoid mixing securities with distinct…
This Chinese equity screen selects stocks with RSI below 65, a positive return over the prior ten days that remains below 35%, and no limit-up move on the previous day. The article presents these filters as a way to identify stocks with recent gains while…
This stock selection method combines three conditions: daily price amplitude above 1, positive institutional fund flow, and a newly formed bullish KDJ crossover. The accompanying examples calculate amplitude from the high, low, and opening price; sum…
This Chinese equity screening idea combines a daily price range above 1%, a rising 30-day average, and a morning-star-like short-term reversal pattern. The article describes these filters as a way to find active stocks where a short-term change in price…
This short-term Chinese stock screen looks within the metaverse sector for shares that had a limit-up day during the prior 25 days and then form what the article describes as a reversal or engulfing setup. It adds a condition that the opening price after the…
This Chinese equity screening idea selects stocks classified in the metaverse industry, with circulating shares no greater than 5.5 billion and prior-day trading value above 60 million. The criteria combine a thematic industry classification, a float-size…
This proposed Chinese equity screen combines three filters: daily high-low range above 1, return on equity above 15% in each of five years, and a turnover-weighted ratio of current auction volume to prior-day volume between 0.5 and 2. It is intended to pair…
This article describes a stock selection rule for Chinese equities. It looks for stocks with turnover between 3% and 12%, an external-to-internal trade volume ratio above 1.3, market capitalization no greater than 10 billion yuan, and nonnegative profits.…
This Chinese-language article describes an A-share stock screen combining three technical conditions: RSI below 65, MACD above its zero line, and a shortening green MACD histogram on a 15-minute chart. It presents the combination as a way to find stocks with…
This April 2020 industry-allocation update describes a framework that combines earnings and valuation context with macroeconomic state signals, historical pattern matching, trend measures, and public-fund positioning. Its macro component responds to weaker…
This A-share stock selection example combines three signals: a 14-period RSI below 65, estimated external trading volume exceeding internal volume by a stated ratio threshold, and a shortening MACD histogram on a 15-minute chart. The post presents the rules…
This short forum post gives a data access pattern for retrieving historical benchmark or stock data from a trade module. The example requests closing prices and volume for a benchmark symbol over a specified lookback, using daily frequency, and assigns the…
This post describes a technical stock screen combining three conditions: daily price amplitude above a stated threshold, the opening price near the ten-day moving average, and a pattern described as seven consecutive down days. The accompanying examples…
This Chinese-language post outlines an equity screen for stocks in the metaverse sector that had a positive return and appeared on the previous day’s “Dragon and Tiger” list, a market list associated with notable price moves and trading activity. It presents…
This brief indicator guide explains the colors used in a candle-monitoring display. Red marks a previous candle with a negative direction, while lime marks a positive one. In a separate table column labeled “now,” red indicates that candle movement has…
This Chinese-market stock screen combines three conditions: turnover between 3% and 12%, a positive product of the current day's price change and net large-order volume, and a prior-day 9:15 matching price at the limit-down level. The accompanying…
This post proposes a short-term stock screen combining three conditions: membership in the metaverse theme, a positive institutional-flow indicator, and a threshold for the share of positions increased that day. It frames the criteria as a way to focus on a…
The report describes a stock-selection strategy that predicts the future usefulness of seven style factors and adjusts their portfolio weights over time. It uses historical factor information coefficients (ICs), macroeconomic variables, and market variables…