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 screen combines a price-range filter, a five-day moving-average condition, and a metaverse-related classification. It selects shares whose high-to-low range, measured against the previous close, exceeds a threshold; whose closing price is above…
This overview compares machine learning and deep learning methods for predicting stock prices and trends. It describes LSTM and GRU recurrent networks for sequential data, CNNs for extracting patterns, bidirectional LSTMs, and deep neural networks. It also…
This example outlines a disclosure-following workflow based on public House periodic transaction reports. It distinguishes the transaction date from the date a filing becomes public, and says a strategy should only make a record available from publication…
This stock screen combines a turnover range of 3% to 12%, a K value below 20, and year-over-year net profit growth above 20% and no more than 100%. The article frames the turnover condition as a liquidity filter, the K threshold as a technical selection…
This Chinese stock-screening example combines a technical threshold with price movement, large-order net volume, and a popularity ranking. It proposes selecting shares with RSI below 65, using the product of percentage change and large-order net volume as a…
This stock-selection rule combines three short-term conditions: daily amplitude above 1%, an opening price within about 5% of the 10-day moving average, and buying-side volume more than 1.3 times selling-side volume. The accompanying explanation interprets…
This document outlines a Chinese stock selection rule using turnover of 3% to 12%, circulating market capitalization from 1 to 55 billion yuan, and a control activity indicator above 21. The page gives the criteria in prose and in formula-like and Python…
The document describes a Chinese equity screen that selects beverage manufacturing stocks with daily turnover between 3% and 12% and a reversal or engulfing-style pattern. Its rationale is that turnover may indicate trading activity while the pattern may…
This post proposes screening Chinese stocks by daily price amplitude, a measure described as current-day shareholder control, and prior-day major-investor net buying. It presents the combination as a way to capture market sentiment and capital flows. The…
This post describes a Chinese equity screening idea that combines three criteria: a dividend payout ratio above 25% in 2019, strong capital activity ranked from high to low, and evidence of major-investor control on the prior day. It suggests turnover and…
This product description outlines an automated trading system that uses an “Accelerator” indicator and trades from the open price of a candle. It says the EA is intended for major forex pairs and NASDAQ stocks, across timeframes, and recommends trying it on…
This Chinese equity screen combines three conditions: daily price amplitude above 1%, a stated 2019 dividend ratio above 25%, and circulating market capitalization above 10 billion yuan. The post explains amplitude as a measure of price fluctuation, dividend…
This Chinese equity screening rule combines a turnover filter with a recent price pattern and listing-year condition. It selects stocks with turnover between 3% and 12%, listed in 2021, whose closing prices have fallen for three consecutive days. The post…
This Chinese-market stock screen combines three historical activity filters: daily amplitude above 1%, turnover between 2% and 9%, and at least two limit-up events within a 500-day lookback. The document frames amplitude as a volatility measure, turnover as…
This stock screen targets companies classified in the metaverse sector whose opening price is near the 10-day moving average and whose prior-day turnover exceeds 8%. It combines a sector filter with a short-term price condition and a measure of recent…
This document describes an MQL5 class for calculating the Average Directional Movement Index (ADX) with a ring buffer. The class accepts high, low, and close price data and supports calculation across full arrays or one value at a time. Its initialization…
This stock selection method combines three conditions: turnover between 3% and 12%, an opening price near the ten-day moving average, and positive institutional activity. Its example implementation also requires the close to exceed the previous close, making…
This Chinese stock-selection note proposes screening for shares with an amplitude above one, ranking among the top five by the day’s auction amount, current volume above ten thousand lots, and a positive opening gap. The accompanying explanation treats a…
This intraday A-share strategy combines strong thematic sectors with stocks that have recently reached their daily price limits. It ranks concept indexes by short- and medium-term returns, retaining the top two percent on both measures, then screens stocks…
This Chinese-language post proposes a stock screen combining three conditions: daily price range above 1%, a positive ten-day return below 35%, and a measure labeled today’s position increase above 5%. It interprets the range as a sign of active price…
This Chinese A-share screening idea filters for stocks classified in the metaverse industry, limits circulating shares to at most 5.5 billion, and selects the five with the highest amount traded in the current day’s opening auction. The article provides a…
This stock selection idea combines three conditions: prior-day price amplitude above 1%, appearance on the previous day’s broker trading list, and a price pattern intended to indicate an upward-turning arc. The article gives sample indicator and Python…
This literature review explains time-series and cross-sectional momentum strategies applied to equity anomaly factors. The time-series approach buys factors with positive trailing returns and shorts those with negative returns; the cross-sectional approach…
This literature review explains time-series and cross-sectional momentum strategies applied to equity anomaly factors. The time-series approach buys factors with positive trailing returns and shorts those with negative returns; the cross-sectional approach…