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.
3,481 documenten
This document summarizes a research approach that uses Google Trends search activity to inform equity portfolio weights. It treats search volume as a measure of how popular a stock is and assumes that popularity is related to risk. The portfolio therefore…
The article argues that algorithmic trading has changed the experience of retail equity investors. It describes quant systems as data-driven and fast, and claims their trading can contribute to index moves that diverge from the performance of individual…
The report outlines a framework for timing equity factors whose performance has become less stable. It first examines indicators such as valuation spreads and pairwise correlations, testing their relationship with future factor returns. It then uses a random…
This assignment response translates two discretionary stock approaches into rule-based proposals. One combines recent institutional fund inflows, positive company earnings, improving per-share profit, elevated trading volume, and a price ceiling relative to…
This forum post describes an AttributeError in a BigQuant high-frequency backtest. The copied trade-module code treats each key in the portfolio positions mapping as an object with a symbol attribute. In the HFTrade interface, the key is already a string…
The document summarizes a 2020 study on whether investor attention measured through Baidu search activity can help forecast volatility in Chinese equities. The researchers compare a baseline GARCH model with an expanded version that includes search volumes…
This presentation interprets findings from a 2021 survey of Chinese quantitative investment institutions and discusses how the sector was developing at that time. It covers strategy mixes, research organization, talent, artificial intelligence, alternative…
This meetup Q&A contrasts futures CTA strategies, often framed around trend following, with equity multi-factor strategies that combine signals such as value, momentum, quality, and size. It outlines a Bollinger Band example for futures: calculate a…
This research summary examines quantitative stock selection among Chinese technology companies. It highlights research and development spending as a candidate signal and also discusses profitability, earnings growth, valuation, company size, turnover, and…
This example builds a simple portfolio analysis workflow that generates a daily value series for several allocation weights and plots the paths together. A configuration object holds the tested weights, chart dimensions, and date range. The demonstration's…
This project explores combining strategies associated with different market styles. The author says market styles can persist over a period, so a strategy that fits a clearly expressed style may adapt better to prevailing conditions. They changed a provided…
This research summary examines analyst recoverage: the first new recommendation after an analyst or brokerage has stopped covering a stock for at least six months. It compares recoverage with initial coverage and ordinary rating changes, using U.S. analyst…
The document describes a beginner’s question about passing results from earlier BigQuant modules into a backtest. The proposed strategy uses a fixed universe of ten stocks, ranks them daily by five-day return in ascending order, buys the five lowest-ranked…
This article proposes using a dashboard of the Hurst exponent, ADX, and a linear-regression score to contextualize Smart Money Concepts and ICT price-action setups. Hurst is calculated from log returns with rescaled range analysis: readings above 0.55 are…
This Chinese-language research summary studies whether public equity fund stock exposure can inform market timing in the China A-share market. It uses a moving-average system to distinguish trending from range-bound regimes, analyzes how fund positioning…
This guide presents a relative strength index strategy using overbought and oversold thresholds. It describes calculating RSI from rolling average gains and losses, generating short signals above 70 and long signals below 30, and optionally filtering trades…
This summary describes a method for constructing broad stock factor exposures and checking factor usefulness in a multifactor model. It presents returns as a linear combination of factor contributions plus an unexplained residual, and emphasizes examining…
This research report proposes improving a conventional stock reversal signal by splitting each stock’s recent daily returns according to average trade size. For each lookback window, it ranks days by daily turnover divided by trade count, compounds returns…
This tutorial introduces Apache Arrow as a columnar format for in-memory computing and PyArrow as its Python interface, with integration for pandas, NumPy, and native Python objects. It demonstrates creating an Arrow scalar, converting a pandas DataFrame…
This BigQuant platform report investigates Beijing Stock Exchange records in the Chinese stock factors table and how they interact with a basic stock-selection query. The author queries instruments with the Beijing suffix for a single date and reports 249…
This factor-monitoring summary compares Chinese equity signals over weekly, monthly, year-to-date, and one-year windows. It reports rankings for long-only absolute returns, long-short returns, information ratios, and relative strength. The factors discussed…
This factor note defines a volume-weighted measure of a stock’s intraday relative price range. For each instrument and date, it calculates the high-low range divided by the opening price, weights that value by volume, and divides the summed weighted values…
This article summary presents a quantitative framework for combining conventional alpha factors with ESG-related signals in equity portfolios. It distinguishes exclusion screens, ESG integration, and impact investing, then focuses on integration: investors…
This tutorial explains how to combine daily stock-price observations with less frequent dividend records using an ASOF JOIN. The example pairs records by instrument and date, allowing each daily price row to be associated with a nearby dividend record even…