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.
5,922 documents
This tutorial adapts a GLFT-based grid market-making backtest to multiple futures assets. It normalizes order size to a common notional amount, sets inventory limits in units of that order size, estimates trade-arrival intensity and price volatility from…
The available text outlines a research report on quantitative industry selection and allocation. It organizes the topic around several perspectives: fundamental conditions, technical market behavior, and institutional investor activity. It also points toward…
This introduction shows how to build a futures trading rule and assemble it into a larger systematic trading process. Its example EWMAC forecast subtracts a slow exponential moving average from a fast one, then normalizes the difference by a robust estimate…
This introductory tutorial presents active equity portfolio management as a connected process. An alpha model forecasts stocks’ excess returns, while estimates of return dispersion help inform position sizes. A structured risk model describes current…
This research note describes a tactical asset allocation method that converts the tone of company and macroeconomic news into a slower-moving signal. News sentiment scores are aggregated weekly, then filtered with a two-sided cumulative sum (CUSUM) procedure…
This stock-selection proposal focuses on Chinese companies classified in the metaverse sector. It requires the prior day's price to be above the 250-day moving average and at least five moving averages to converge. The indicator reference names averages over…
This research summary examines whether internet search attention can help anticipate rotations between Chinese large-cap and small-cap equity indices. It treats search-index changes related to the two market styles as a proxy for investor sentiment,…
This brief overview frames stock selection around an investor’s financial goals, risk tolerance, and strategy. It points readers toward company fundamentals such as revenue trends, profit margins, and debt, alongside longer-term demand. It also highlights…
The document describes a reporting workflow for analyzing a trading strategy from return data and, when available, holdings, transactions, benchmark returns, market data, and factor information. Its full report brings together return and event analysis, then…
The document outlines an end-to-end portfolio construction framework that embeds a differentiable convex optimization layer within a neural network. Raw asset data passes through the network for feature extraction and combination, then an optimization layer…
The document explains how to connect Databento historical market data to Lumibot backtests. It covers API-key setup, asset definitions, timeframes, date-range configuration, caching, and handling common retrieval errors. Examples include stocks, continuous…
This short forum exchange addresses how to use a custom benchmark for a strategy such as pair trading, where a portfolio of two stocks may be a more relevant comparison than a broad market index. The suggested approach is to construct an index from the two…
This report describes combining event-driven signals with a multi-factor stock selection model for Chinese equities. It first recommends estimating event-related abnormal returns with cross-sectional regressions that control for industry and market-cap…
This report proposes analyzing stocks within distinct domains, such as broad-market groups, market segments, and industries, to capture differences in their investment drivers. It argues that fitting factors within these groups can address nonlinear market…
This Qlib documentation explains how to simulate model-driven trading through a persistent account. A configuration specifies a model, a portfolio strategy, and initial cash; the system then generates orders from predictions, executes them under exchange…
This example describes a daily stock selection process in which a research agent ranks large US stocks using recent prices, trends, and news. Bull and bear agents assess the same research from opposing perspectives, then a judge and trading agent select the…
This study examines whether investors can incorporate environmental, social, and governance information into value, growth, and momentum equity strategies without materially harming financial results. It builds multidimensional style scores, uses past…
The document summarizes a research study that applies hidden Markov models to identify regimes for individual assets and adjust portfolio exposure in response to price trends. It describes a global allocation analysis spanning January 2004 through December…
The module implements a finite-horizon dynamic allocation approach for a mean-reverting arbitrage spread, drawing on a published model by Jurek and Yang. It constructs total-return indices from two price series, estimates cointegrating spread weights, and…
The strategy estimates sector return states with a Markov chain, which models each state as dependent on the preceding state. It uses the resulting conditional mean and variance estimates as inputs to a Black–Litterman portfolio process, treating the…
The document describes a tool for assessing portfolios of expert advisors (EAs) across three dimensions: correlation between daily profit-and-loss series, the hours and weekdays when strategies trade, and diversity across asset classes. It combines dimension…
This report introduces environmental, social, and governance investing and outlines six approaches, including exclusions, positive screening, ESG integration, thematic investing, and shareholder engagement. It compares MSCI and Thomson Reuters rating methods…
The document explains why a strategy’s maximum capital capacity cannot be calculated from backtest returns and volatility alone. Capacity depends on the strategy’s logic and trading frequency, the liquidity and order book depth of its instruments, the size…
The document outlines a financial forum presentation on the use of artificial intelligence in quantitative investing. Its listed examples include a random forest approach to market timing and hedging, a pattern matching method for industry rotation, and a…