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 stock-screening idea combines three filters: at least five moving averages converging, high individual-stock popularity, and a market capitalization below 10 billion yuan with no reported losses. The post interprets moving-average convergence as a…
The article proposes a stock screen focused on the metaverse industry. Its initial conditions require a volume ratio above 1.5 and below 6, alongside a positive return over the referenced lookback period. The article explains that recent gains and trading…
The document raises a question about reproducing a 60-day moving-average comparison in a Chinese quantitative research platform. The user’s feature flags observations when a close-price series is below a 60-day mean calculated from prices divided by an…
This note presents a technical screen for stocks combining three conditions: a daily high-to-low range above 1% of the opening price, prior-day actual turnover between 3% and 28%, and a 14-period RSI below 65. It frames the range as a measure of price…
This note describes a stock screen for Chinese shares whose codes begin with 60. It selects stocks with a daily high-to-low range greater than 1% of the opening price and a 10-day return above 0% but below 35%. The accompanying examples show how to express…
This post proposes a Chinese stock screen for observations in 2021, combining daily amplitude above one percent with a reported daily decline between four and five percent. It frames the filters as a way to find volatile stocks that have fallen substantially…
This note presents a simple crossover interpretation of Kaufman’s Adaptive Moving Average. It describes a possible entry when price has been below the indicator and a bullish candle moves above it. A downward cross, especially when the indicator changes…
This post describes a Chinese equity screen combining daily price range, recent performance, and company size. It seeks stocks with an amplitude above one percent, a positive ten-day return below 35 percent, and market capitalization of at least two hundred…
This brief BigQuant discussion answers how to retrieve the MACD histogram value for the day on which the highest close occurred within a prior 30-day window. The suggested workflow is to use a time-series argmax function to locate the offset of the maximum…
This A-share screening method combines three technical conditions: daily price amplitude above 1, upward dispersion in the day’s moving averages, and a weekly crossover in which the five-period moving average rises above the ten-period average. The intended…
This A-share stock screen combines turnover of 3% to 12%, market capitalization below 10 billion yuan, positive profitability, and positive net buying attributed to large traders during the opening auction. The article frames the filters as a way to find…
This article proposes screening stocks for a daily price range above one percent, a weekly MACD reading above zero with a bullish signal relationship, and a large absolute product of daily percentage change and a measure of net large-order flow relative to…
This article describes a stock-selection screen for companies associated with the metaverse theme. It combines bullish MACD and moving-average crossovers with a recent limit-up condition, a volume-ranking filter, and price and high-range checks. The stated…
The document explains MACD as a trend and momentum indicator made up of a MACD line, a signal line, and a histogram. It describes bullish signals when the MACD line crosses above the signal line or rises while below zero, and bearish signals when it crosses…
This Chinese-language post proposes screening A-share stocks using three conditions: RSI below 65, seven consecutive sessions in which the close is below the open, and a turnover-adjusted ratio involving the current opening-auction volume and the prior day’s…
This research summary describes adapting daily price-volume alpha research to intraday high-frequency data for stock selection. It examines relationships between price and volume, including ordinary and unusual activity, and reports constructing three…
This proposed screen targets main-board Chinese stocks with turnover between 3% and 12%, a daily gain greater than 1%, and at least one limit-up event during the preceding 25 days. The post frames turnover as a way to avoid both weakly traded and excessively…
This Chinese stock-selection proposal combines three technical filters: daily amplitude above 1%, weekly MACD above its zero line, and the K value from KDJ below 20. The weekly MACD condition is intended to keep stocks with a positive broader trend, while a…
This stock selection rule screens for shares with amplitude above 1, circulating market value above 10 billion yuan, and turnover between 2% and 9%. It combines a price-movement condition with company size and trading activity, aiming to select larger,…
The article describes a stock screen that looks for a positive MACD value and a short moving average above a longer one, while excluding stocks that hit the daily limit-up on the previous day. It frames these conditions as signs of upward momentum while…
The document describes a Chinese equity screen for metaverse companies that have recorded a limit-up day within the prior 25 trading days and maintained return on equity above 15% for five consecutive years. It presents ROE as a measure of sustained…
This Chinese equity screen combines three conditions: daily price range above a stated threshold, afternoon large-order net inflow, and company scale above a stated floor. The article presents these as signals of short-term demand and sentiment, with size…
This indicator reference explains the Blau Stochastic Index oscillator, which measures a close relative to the recent high-low range and centers the result around zero. It defines the price difference as the close minus the lowest price over the selected…
This technical note introduces the Money Flow Index (MFI) and outlines its calculation from high, low, close, and volume data over a chosen lookback period. It first computes typical price as the average of the day's high, low, and close. Volume-weighted…