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 Chinese equity screening concept combines positive reported returns, a market capitalization below 10 billion yuan, a history without losses, and capital strength ranked from high to low. The article interprets capital strength as a measure of net money…
This stock selection method combines three filters: membership in the metaverse sector, a positive institutional-flow indicator, and appearance on the previous day’s Dragon-Tiger trading list. The document describes the rationale that sector growth,…
This document explains an indicator that calculates a time-weighted average price from a selected cash-market open through the end of the broker's server day. Unlike tick-volume-weighted VWAP, each unit of session time receives equal weight. To handle…
This document presents a Chinese-stock ranking workflow built around an XGBoost RankNet model. It constructs price-return and trading-activity features, labels stocks using clipped forward returns divided into quantile bins, and separates training data from…
This document proposes screening Chinese stocks for amplitude above 1, a three-consecutive-limit-up pattern as of the prior day, and large-order net volume above 0.05 for at least three days. It presents the combination as a way to find active, widely…
This document describes a Chinese stock-selection screen with three conditions: daily turnover between 3% and 12%, the product of the day's price change and super-large-order net volume above zero, and the previous day's auction turnover above 0.26. Its…
This article argues for entering strong, rising stocks instead of trying to buy after declines. Its proposed triggers are a break above a consolidation range or a shift from a gradual rise into faster gains, marked by the first large-volume bullish candle.…
This document describes a Chinese equity screen combining daily price range, tradable share count, and MACD. It selects stocks with an amplitude above 1, a circulating share count no greater than 5.5 billion, and MACD above zero. The accompanying explanation…
This stock selection rule screens Chinese A-shares using three conditions: turnover between 3% and 12%, a newly formed KDJ bullish crossover, and first-level bid volume greater than first-level ask volume. The article presents the rule as a way to combine…
This Chinese-language article presents an A-share stock screen combining an RSI reading below 65, seven consecutive sessions in which the close is below the open, and a closing-price condition involving the previous day's low. It also discusses adding…
The screen selects stocks whose daily high-low range exceeds one percent, excludes Beijing-listed A-shares, and requires the opening price to lie above the ten-day moving average of closing prices but below 110% of that average. The article presents the…
The document outlines a Chinese A-share screening idea focused on metaverse stocks with prior-day turnover above 8% and excluding specially treated stocks. Its proposed limit-up pattern looks for activity before 10 a.m. and checks successive five-minute bars…
This stock-selection approach screens Chinese equities in the metaverse industry for a volume ratio above 1.5 and below 6, with the 20-day moving average above the 120-day moving average. A later version adds a market-cap floor of 1 billion yuan. The article…
The document presents a Chinese A-share stock screen combining market activity, a prior session’s 9:15 matching price at the limit-down level, and profitability history. It first describes conditions of amplitude above 1 and ROE above 15% for five…
This Chinese-language strategy note proposes screening stocks with turnover between 3% and 12% after seven consecutive declining sessions, while excluding selected board classifications. It frames the screen as a possible way to find established companies…
The document describes a stock screen combining three conditions: daily high-low range above one percent of the previous close, a trading date in 2021, and a MACD value below zero from two trading days earlier. It gives corresponding formula and Python…
This stock-selection method targets companies in the metaverse concept group, with relative volume above 1.5 and below 6, alongside afternoon net inflows from large orders. The document links large-order inflows to market sentiment and describes the…
This A-share screening method combines a turnover-rate band of 3% to 12%, a positive but limited 10-day gain, and a positive large-order control measure above the stated threshold of 21. The document describes control as the difference between large buy and…
This stock screen combines membership in the metaverse concept group with an upward-sloping 30-day moving average and a requirement that at least five moving averages overlap. It is presented as a trend-following filter intended to find stocks with aligned…
This Chinese stock-selection strategy screens for shares with turnover between 3% and 12%, year-over-year growth in net profit attributable to the parent company above 20% and at most 100%, and a positive trailing price-to-earnings ratio. The article frames…
The document describes a way to display the Relative Strength Index on the main price chart rather than in a separate indicator pane. It says this version uses a highest-high and lowest-low channel to map the oscillator onto the chart, allowing RSI and price…
This document describes a Chinese equity screening rule combining turnover, year-over-year net profit growth attributable to parent-company shareholders, and recent price-limit activity. It selects stocks with turnover between 3% and 12%, profit growth above…
This Chinese equity screening rule combines a daily price-range threshold, a market-cap ceiling, positive quarterly earnings, and a KDJ bullish crossover. The crossover is described as J moving above K, alongside recent indicator comparisons; the example…
This brief indicator note introduces Island Reversal, abbreviated IR, and lists its inputs as close, high, low, a lookback length, and a percentage parameter. Its pseudocode calculates prior-period high and low boundaries, then derives two price levels…