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.
79,386 documents
This A-share selection rule combines a daily high-low range above one percent, a nonempty unsettled convertible-bond name, and a newly formed upward KDJ crossover. The provided formula further requires the K value to meet or exceed D after being at or below…
This note presents an A-share stock screen that ranks names by capital strength, keeps the five with the largest auction value for the day, and requires the 30-day moving average to be rising. It interprets fund flow and auction activity as signs of…
The document proposes screening Chinese metaverse-related stocks for a positive daily close-to-close return while the session low falls between 4% and 5% below the previous close. This combines a positive closing result with a substantial intraday decline,…
The document defines crypto trading signals as alerts, supplied manually or algorithmically, that can identify potential entries, exits, and risk levels. Their stated purpose is to help traders act on trade ideas without having to develop every idea…
This post describes a Chinese equity screening rule combining daily price range, a historical dividend measure, and a fixed share price. Its stated logic selects stocks whose high-to-low amplitude exceeds a threshold, whose 2019 dividend ratio is above a…
This opinion piece argues that quantitative trading’s advantages extend beyond execution speed. It emphasizes systematic research and iteration: models can combine many fundamental and price-based signals, test relationships across historical data, and…
This market snapshot links a sharp crypto price swing with changes in centralized exchange activity, DeFi lending flows, and miner behavior. It reports that spot trading volumes recovered after a brief decline, with Binance gaining share and Bybit trading…
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 document describes a MetaTrader 5 SuperTrend indicator that adapts its volatility bands and selected trade parameters using prior signal outcomes. It offers reversal and failed-breakout signal modes, with optional confirmation from RSI, tick-volume…
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 ProRealTime indicator estimates average forward price movement conditional on the current reading of RSI, Stochastic, Williams %R, or ADX. It divides indicator readings into 20 five-point ranges, then separates observations according to whether the…
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 indicator modification takes a standard volume measure and divides it by the spread. The author removed the original color-changing behavior because it was considered unhelpful after the transformation, then added an option to turn spread adjustment on…