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.
3,481 documents
This article argues that China’s A-share T+1 settlement rule can constrain retail traders who buy shares during a session, while some institutions may approximate intraday trading through previously held inventory or short selling. It describes two proposed…
This weekly competition report presents diagnostic summaries for the ten highest-ranked submitted factors and tracks a China 1000 index-enhancement portfolio. Factor diagnostics include average exposures to BARRA style factors and sector-level average Rank…
This introductory note describes quantitative investing as turning research, asset selection, timing, and position decisions into explicit rules that can be tested and executed by a computer. It distinguishes cross-sectional stock selection, which compares…
This tutorial describes assembling a matrix of daily closing prices for Hong Kong stocks using BigQuant data sources and pandas. It first retrieves unique instrument identifiers, then loops through them to collect date and close fields over a specified…
This report reviews the relative performance of Chinese quantitative equity funds and considers what rising assets under management mean for strategy design. It attributes the period’s advantage for index-enhanced and quantitative long products largely to…
The document introduces reinforcement learning through the interaction of an agent and an environment, where actions produce feedback and the objective is to maximize discounted future rewards. It frames the problem as a Markov decision process, explaining…
This research summary presents asset allocation as a continuing process: define investor objectives, set an investment strategy, implement it, and review performance. Objectives specify the desired return, acceptable risk, and other investor needs. The…
This overview describes several ways quantitative trading attempts to identify and capture market opportunities. It presents historical data analysis and backtesting as tools for assessing a strategy, then discusses automated execution, momentum examples,…
A BigQuant forum question asks how to select the ten stocks with the strongest return over the previous calendar year on the first trading day of a new year. The user’s current approximation compares prices about 250 trading days apart, but they want a…
A BigQuant forum exchange discusses why feature extraction can return multiple records for a stock on a given day, even when one record is expected. The suggested cause is that the feature input stage reads across tables; joining data with multiple matching…
This report presents an A-share stock-selection framework that applies StockRanker, a gradient-boosted learning-to-rank model, to fundamental, financial, and trading factors. It tests 282 individual factors across short, medium, and long holding horizons,…
The document explains how to adapt a StockRanker multi-factor stock-selection workflow to Hong Kong and US equities on BigQuant. The backtest uses BigTrader’s custom historical data input, which must include date, instrument, and daily open, high, low,…
This report reviews Chinese market and exchange-traded product developments for the week of March 30 to April 3, 2020. It summarizes moves in domestic equity indexes, bond yields, precious metals, currencies, and traded ETP categories. Equity and…
The document explains how BigQuant’s factor research platform handles user-created factors and downstream dependencies. A researcher can combine platform factors A and B into a derived factor C, submit C to the platform, and let other users with access clone…
This research summary compares common stock-selection factors in the U.S. and Chinese markets, with a test of seven factor groups and 24 signals in the Russell 3000 universe. It reports that profitability, valuation, and growth measures were among the…
This entry introduces two overseas research topics: persistent bias in analyst expectations and holiday effects in stock markets. The first paper is described as examining habitual distortions in conventional analyst expectation factors and developing a…
This weekly report summarizes factor returns across broad Chinese equity universes and several index or liquidity-based stock pools. It describes positive performance for profitability and book-to-price factors in the full market, while size, beta, and…
This literature summary describes a stock-market return forecasting method that decomposes returns into dividend yield, profit growth, and price-to-earnings multiple growth. It says the source study uses the different time-series properties of these…
A stock screen for a moving average crossover can return no matches if its query loads only the target date. The note explains that time-series indicators need earlier observations to calculate their rolling values, so the data query should begin before the…
This Chinese-language research summary describes equity industry-rotation models for cyclical sectors, non-cyclical sectors, and the full market. It ranks industries using composite scores built from different factor groups: historical and expected…
This post describes a scheduling problem in a daily China-stock simulation: a strategy launched at 5 a.m. after a weekend or holiday may use the previous calendar day as both its start and end date. Because that date has no market session, the run fails. The…
This note presents a behavioral-finance explanation for a possible link between World Cup final outcomes and investor activity. A national team’s victory may lift local sentiment and trading, while defeat may discourage participation. The analysis compares…
The document explains how to decide whether to remove sector exposures from equity factor portfolios. It splits a stock characteristic into a within-sector component, which ranks companies against peers, and an across-sector component, which captures…
This article presents five discretionary rules for interpreting stock turnover, support and resistance, trend direction, and price-volume behavior. It treats unusually high turnover near price lows as possible accumulation and high turnover after a sharp…