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 catalog describes built-in quantitative data fields for Chinese A-share equities. Its factor families include historical prices, returns, trading volume and value, turnover, active money flows, valuation ratios, profitability and balance-sheet measures,…
This stock screen combines three filters: daily amplitude above a threshold, a shrinking negative MACD histogram on a 15-minute interval, and positive indicators associated with beverage and alcohol imports and exports. The stated rationale is to seek…
This document proposes screening A-shares for turnover between 3% and 12%, total market value below 10 billion yuan, no reported losses, and free-float market value above 1 billion yuan. It frames the combination as a way to narrow the universe by trading…
This document describes a stock-selection rule for A-shares using three conditions: price amplitude above 1%, a 2021 date filter, and best bid volume greater than best ask volume. It presents the rule as a way to identify more volatile stocks with potential…
This Chinese-language Q&A clarifies two optional inputs for BigQuant strategy backtests or simulations. Backtest historical data is the replayed market bar data and corresponds to the data argument of the trade function. Other data input supplies external…
This article describes a daily pre-market stock screen for Chinese equities. It selects stocks whose MACD value is above zero, whose trailing price-to-earnings ratio is positive, and whose observation falls in 2021. The article interprets positive MACD as an…
The document outlines a way to weight stock positions using a momentum signal and an estimate of volatility. It describes two alternatives: an exponentially weighted moving average based on squared returns, and a Yang–Zhang variation of Garman–Klass…
This post proposes an equity screen combining daily range above a threshold, rising lows, and price above its five-day moving average. Its expanded version adds a longer-term trend check using the 20-day and 250-day averages, valuation limits, market-cap…
This beginner’s reflection defines quantitative investing as identifying relationships between inputs, or factors, and future returns, checking those relationships against historical data, then using current observations to estimate future opportunities. It…
This post describes a short-term stock screen combining three prior-day conditions: price amplitude above 1%, appearance on the market’s named trading list, and turnover above 8%. The rationale is to find shares with notable movement and active trading,…
This article introduces convolutional neural networks and explains how one-dimensional convolutions can extract local patterns from financial time series. It describes convolution as applying learned weights across sequence windows, while pooling summarizes…
This Chinese-language course description presents limit-up stock counts as a way to identify active areas of the China equity market. It proposes comparing counts across market capitalization, sectors, and industries, using recent industry counts to locate…
This document describes a short-term stock screen using three conditions: daily amplitude above a threshold, a high daily control measure, and a positive product between price change and net flow from very large orders. Its refined version also restricts…
This note describes a screen for A-shares whose codes begin with 60, whose turnover is between 3% and 12%, and whose top 20% of shareholders collectively hold less than 70% of shares. It presents these conditions as filters for liquidity and ownership…
This note describes a stock screen for A-shares whose codes begin with 60, whose turnover is between 3% and 12%, and whose price-to-earnings ratio is positive. The code example obtains a current trading date and filters listed stocks using those criteria.…
This note describes an A-share selection rule that combines a turnover range of 3%–12%, market capitalization below 10 billion yuan, and a screen for companies without losses. It then selects stocks when the five-period moving average crosses above the…
This report reviews Chinese public quantitative funds and private quantitative market-neutral strategies using observations available through March 2022. It groups public funds into market-neutral products, CSI 300 enhanced funds, and CSI 500 enhanced funds,…
This proposed equity screen selects stocks with amplitude above 1, an RSI below 65, and prior-day turnover above 60 million. It combines a price-range measure with a momentum oscillator and a liquidity threshold, aiming to find stocks with activity while…
This proposed equity screen combines membership in the metaverse sector, a rounded price pattern, and a circulating share count capped at 5.5 billion. The article frames the float limit as a way to focus on relatively smaller stocks and suggests…
This stock-selection screen combines price amplitude above 1, a dividend-related payout ratio above 25% for 2019, and a rising-bottom condition. The explanation treats larger amplitude as a sign of greater trading opportunity, the dividend filter as a way to…
This configuration describes a Qlib workflow for training a binary LightGBM model on one-minute CSI 300 data from China. It uses the Alpha158 feature handler, robust feature normalization, missing-value filling, and cross-sectional label ranking. The label…
This Chinese equity screening rule ranks stocks by capital strength and requires more than two limit-up days in the past ten days. It also requires a nonempty name for an outstanding convertible bond. The article explains these conditions as signs of…
The article describes a Chinese stock-selection screen using three conditions: turnover between 3% and 12%, external trading volume more than 1.3 times internal trading volume, and market capitalization of at least 200 million. It provides an SQL-style…
This research summary reviews six studies on industry rotation and index enhancement in China. Its multi-factor framework considers expected and historical fundamentals, price and volume information, market leadership, and system-risk concentration. It…