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 screening idea combines a daily high-low range threshold, consistently strong return on equity over five years, and a filter related to the previous day’s limit-up status. The stated rationale is to pair a measure of price movement with a longer-term…
This stock-selection proposal focuses on companies classified in the metaverse sector. It selects stocks that experienced a limit-up move within the prior 25 days and whose current close is above the previous day’s low. The post describes selecting…
This Chinese equity screen selects stocks with turnover between 3% and 12%, displayed first-level bid volume greater than ask volume, and a weekly five-period moving average crossing above the ten-period average. The note describes these filters as proxies…
This note proposes a Chinese equity screen for stocks with amplitude above 1, rising lows, membership in the robotics concept group, and circulating market capitalization below 10 billion yuan. It combines a price-pattern condition with a sector theme and a…
This note describes a Chinese equity screen combining three conditions: price amplitude above 1, circulating market capitalization above 10 billion yuan, and at least one limit-up event in the prior 25 days. It frames the screen as a way to find larger,…
This stock selection approach filters for shares associated with the metaverse theme, then checks for an upward-sloping 30-day moving average and sorts qualifying names by a measure of individual stock interest. The article presents this as a way to combine…
This Chinese stock-selection note proposes screening for price amplitude above 1, large-order net-volume readings above 0.05 over at least three consecutive days, and then ranking by fund strength. It presents the combination as a short- to medium-term way…
This Chinese stock-screening note combines three ideas: rank stocks by volume ratio as a proxy for fund strength, require the previous day's adjusted turnover rate to exceed 8%, and look for a shortening MACD histogram on a 15-minute chart. It presents the…
This Chinese stock-selection note combines a turnover-rate range of 3% to 12% with a reversal pattern and a signal described as the start of a major advance. Its example formula adds a close-above-moving-average condition and platform-specific filters. The…
This short-term equity screen combines a large daily range, a recent strong up day, elevated current volume, and an opening price above the prior close. The stated lookback is 25 trading days for the strong-gain condition. The article describes the…
This research outline proposes allocating among equity industries by tracking the behavior of different market participants. It motivates industry rotation with the observation that returns can diverge substantially across sectors and styles, so broad asset…
This stock screen combines an amplitude threshold with simultaneous crossovers among three moving-average pairs and at least one limit-up event during roughly the prior month. It is presented as a way to find shares showing strong recent movement and…
The post describes a stock screen requiring a ticker that begins with 60, turnover between 3% and 12%, and total market value above 200 million yuan. Its Python example retrieves listed-stock information, checks the ticker prefix, and then filters daily data…
This post outlines a stock selection screen based on three stated conditions: association with the metaverse theme, an upward-sloping 30-day moving average, and turnover between 2% and 9%. The accompanying indicator references and Python example illustrate…
This document outlines an event-driven study of how MSCI inclusion announcements affected the prices of Chinese A-shares. It describes estimating CAPM parameters from a historical period, using those parameters and subsequent market index returns to…
This document presents a short-term Chinese stock selection rule based on three market activity measures: turnover between 3% and 12%, first-level bid volume greater than ask volume, and a volume ratio between 1.5 and 6. It frames the turnover and order-book…
This document describes a daily stock screen combining price movement and a basic valuation condition. It selects stocks with amplitude above 1, at least two limit-up events within the prior 500 days, and a positive P/E ratio. The rationale is that recent…
This tutorial compares three ways to train an XGBoost model for stock selection: ranking securities by a score, classifying outcomes into categories, and predicting a numeric target through regression. It frames these choices within a broader modeling…
This tutorial compares three ways to train an XGBoost model for stock selection: ranking securities by a score, classifying outcomes into categories, and predicting a numeric target through regression. It frames these choices within a broader modeling…
This Chinese-language article proposes screening mainland-listed stocks for a turnover rate between 3% and 12%, excluding Beijing-listed shares, and requiring a rising-bottom pattern. Its accompanying Python example adds further filters, including excluding…
This indicator labels price bars using comparisons between each bar’s high and low and those of the preceding bar. It distinguishes inside bars, outside bars, bars making both a higher high and higher low, and bars making both a lower high and lower low. The…
The document describes a Chinese equity screening rule combining three conditions: daily amplitude above a threshold, evidence of main-fund control on the previous day, and a close above the previous day’s low. It frames the combination as a way to find…
This China-stock screen combines three conditions: turnover between 3% and 12%, appearance on the previous day’s Dragon-Tiger list, and a 20-day moving average above the 120-day moving average. The post frames turnover and the market activity list as…
This brief support note addresses a BigQuant workflow where a ranking strategy appears to backtest normally but produces no rebalance signals in simulated trading. It points to configuration and data-window checks: bind the code-list module’s end date to…