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 study describes active risk parity as a portfolio built around a risk-parity allocation, with controlled deviations based on investment views. In its two-asset example, Chinese equities and corporate bonds are rebalanced using rolling covariance…
This opinion piece argues that, in a sideways market, capital may rotate from large companies whose prior gains have reduced their perceived upside toward lower-priced, smaller companies. It distinguishes catalyst-driven sector rotation from buying weak…
This April 2020 market-monitoring report combines valuation, trading activity, sentiment, sector rotation, capital concentration, and style-factor observations for Chinese equities. It describes sentiment as having recovered to an optimistic range after a…
This research summary describes stock-selection factors built from tick-level aggressive buy and sell amounts. It applies an informed-trading probability model to filter noisy order flow, then forms informed sell share, informed buy share, and net informed…
This overview contrasts efficient markets, where prices are assumed to incorporate available information, with inefficient markets, where prices may diverge from estimated value. It outlines the weak, semi-strong, and strong forms of the efficient market…
This research summary examines factor weighting, orthogonalization, and timing through the relationship between composite information-coefficient weighting and Fama–MacBeth regressions. Under the stated condition that returns and factors are z-scored, it…
The article describes adding a trailing stop to a stock selection and rotation workflow. Its example sells a holding when the latest price falls more than 10% from the highest price observed since purchase. It recommends tracking stop-triggered stocks,…
This personal account discusses trading strong Chinese stocks during a broad market decline. The author describes using stock-ranking models to pursue strong stocks, next-day rebounds after pullbacks, and limit-up leaders, while emphasizing that these…
This meetup compilation surveys practical topics in quantitative investing, including factor design, machine learning, strategy validation, portfolio sizing, live execution, and sector rotation. It recommends building a factor library by collecting and…
The document describes a library that packages deep learning models for quantitative research, including feedforward networks, LSTMs, Transformers, TabNet, and TRA. Its quick-start workflow covers selecting a model, configuring an optimizer and loss…
This A-share strategy narrows a stock universe in three stages: rank industry or concept indices by short- and medium-term momentum, select stocks with a current limit-up and a recent prior limit-up, then confirm strength after the open before buying.…
This advice article presents five principles for surviving in stock trading: use technical analysis as context, follow established trends, keep short-term trades distinct from long-term investments, assess broad market direction, and protect capital by…
This overview explains machine learning as a way to model patterns in data and describes how that capability may be applied to investment decisions. It outlines a workflow spanning data collection, feature creation, preprocessing, model training, model…
This note describes a reproduction of a sell-side factor based on overnight stock returns, intended to capture information advantages held by informed traders. It says the research defines the factor in stages, from a traditional overnight-return measure to…
The article describes a research workflow that pairs a language model’s reading of crypto news with live exchange data. The agent identifies token mentions, retrieves current prices and four-hour candles, and can consult order books when warranted. It then…
This 2020 market note reviews Chinese equity style performance and outlines a short-term outlook. It reports that small-cap reversal strengthened, value began to recover, and earnings-growth exposure showed a brief rebound. Its recommendations draw on…
This discussion distinguishes two approaches to Chinese A-share limit-up trading: a daily model that forecasts which stocks may hit the limit the next day, and an intraday model that uses current market information to identify candidates for the remainder of…
A BigQuant user reports an apparent issue when a strategy sells before buying back to close a position. In the user's tests, buybacks above half of the initial capital fail, while smaller buybacks work. The report describes varying the amount used for the…
This article summarizes a study of U.S. mutual funds that links SEC N-SAR options disclosures with fund returns and characteristics. The sample covers 1998 through November 2013 and includes 106,357 filings. Cross-sectional and panel analyses compare funds…
The article explains why standard stochastic gradient descent can remain inefficient when training Word2Vec on a large vocabulary. Even when an update concerns only a few words, calculating gradients across the full parameter matrices wastes work, and the…
The document summarizes research into whether changes in Google search activity for finance-related terms can help explain stock-market fluctuations. It frames search volume as behavioral data: shifts in what people seek online may offer insight into…
This brief support exchange addresses whether a strategy’s data is standardized anew each day during simulated trading. The response says that when the strategy uses parameters bound for live trading, simulated trading standardizes that day’s data. A…
This overview explains XGBoost regression as an ensemble of decision trees built sequentially to reduce a loss. Each new tree uses gradient information from prior predictions, while complexity controls and regularization are intended to limit overfitting.…
This document explains a reverse-doubling strategy designed for an older crypto-margined futures venue. It focuses on the strategy’s control flow: open a position, place take-profit orders, monitor both profit-taking and reverse stop triggers, cancel…