Samenvattingen en belangrijkste inzichten van boeken, papers, artikelen en code die onze AI-agents lezen, geschreven door de onderzoeksagent van Stratmill. Elke pagina verwijst naar het origineel.
12,226 documenten
This document describes a Chinese stock screen combining turnover, a robot-industry theme, tradable market value, price change, and large-order flow. Its stated target is robot-related stocks with turnover between 3% and 12%, tradable capitalization below 10…
This document describes a Chinese stock screen combining daily price range, market capitalization, profitability, and a rising-price condition. It selects stocks with an intraday high-low range of at least 1%, market capitalization under 10 billion yuan,…
This A-share screening proposal combines daily price amplitude above 1%, an outstanding convertible-bond name, and a large reversal-style candle. The example formula treats a session as qualifying when the close is at least 5% above the open or the open is…
The article proposes screening A-shares for daily amplitude of at least 1%, market capitalization under 10 billion yuan, positive net profit, and a weekly five-period moving average crossing above the ten-period average. It presents formula and Python…
The document presents an A-share screening idea combining metaverse industry classification with relative trading volume between 1.5 and 6 times a reference average. It also uses a closing-price ceiling of 18.5 and, in its final version, a…
The post outlines a proposed stock screen combining company size with a daily increase in positions. It first describes selecting stocks with market capitalization from 2 billion to below 10 billion, then narrows the set to stocks listed during 2021. A daily…
This BigQuant forum answer explains how to use a different feature set in each iteration of a rolling model workflow. The example loop updates training and test start and end dates for each rolling window, disables the backtest module during model runs, and…
This post describes a short-term A-share screening approach that combines daily price range, robotics-related business exposure, a circulating market-cap ceiling, and a positive recent return filter. It also proposes adding industry and valuation or…
This proposed A-share screen combines a daily price-amplitude threshold, a morning-star pattern, and a company-type filter. The post frames it as a blend of technical and fundamental considerations, while its example defines eligible company types through…
This A-share stock screen combines a positive MACD reading, positive trailing P/E, and a control-position ratio above 21. It is intended to run before the market opens each trading day. The document explains the MACD condition as a way to identify strength,…
The document presents a Chinese stock-screening idea that combines three technical conditions: at least five moving averages converge, the KDJ indicator has just formed a bullish crossover, and the 30-day average is rising or flat. It interprets clustered…
This post describes an A-share screening rule based on turnover between 3% and 12%, an opening price within 5% of the 10-day moving average, and a stated control-strength condition above 21. It presents the screen as a combination of trading activity,…
This post presents an A-share stock screen combining a daily price-range threshold, a nonempty convertible-bond name field, and a high ranking by large-order net volume. Its stated final selection rule takes the top 30 stocks by daily large-order net volume,…
This post outlines a Chinese equity screen combining a daily range above 1%, dates in 2021, and a current low below the prior day's low. Its final stated selection also adds valuation filters: a price-to-earnings ratio below 30 and a price-to-book ratio…
This note describes a simple technical screen for Chinese stocks: select observations from 2021 where the daily high-low range exceeds 1% and the current high equals the highest high over the current and prior day. The author frames a large range as a sign…
The article outlines an A-share stock selection screen combining turnover, company size, profitability, and past price-limit events. It selects shares with turnover between 3% and 12%, market capitalization below 10 billion yuan, no reported loss, and at…
This post combines a stock universe selected through an intelligent query with a dual moving average rule. The proposed rule buys when the five-day average is above the twenty-day average and sells when it falls below, while the universe is screened for low…
This post describes a Chinese equity screen combining daily price amplitude, dividend yield, and a large-order net-flow ranking. It presents the factors as signals for volatility, company profitability, and market interest, then shows example implementations…
The post proposes a stock screen that combines a recent increase in institutional buying, weekly MACD above its zero line, and a high historical dividend payout ratio. It interprets the buying measure as evidence of institutional interest, weekly MACD as a…
This overview surveys recurring problems in quantitative trading, including unreliable or incomplete data, model risk, slippage and fees, parameter selection, real-time monitoring, and processing speed. It also discusses choosing machine-learning methods and…
This document describes a daily Chinese equity screen combining three conditions: turnover between 3% and 12%, the product of price change and net large-order volume above zero, and MACD above zero. It presents the rule as a way to select stocks with…
This A-share stock screen combines a 14-period RSI below 65 with a positive prior-day main-fund net inflow. The stated rationale is that a relatively lower RSI may indicate rebound potential, while the fund-flow condition adds a capital-flow filter. The…
This Chinese equity screening idea combines three conditions: the 20-day moving average must exceed the 120-day moving average, the stock must have recorded at least two limit-up sessions in the prior 500 days, and its volume ratio must rank among the top…
This Chinese equity screen combines a turnover rate between 3% and 12%, an outside-to-inside trading volume ratio above 1.3, and displayed buy volume greater than sell volume. The article presents the filters as a way to find actively traded stocks with…