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
The document examines how trend following could address the equity risk concentration in conventional target-date funds. Their glide paths reduce stock allocations as retirement approaches, yet the portfolio may remain dominated by equity risk, leaving…
This BigQuant support exchange explains a possible source of confusion in backtests: the platform’s `hold_days` setting defines a build-up period during which the strategy can buy, and does not itself require each purchased stock to remain in the portfolio…
This strategy forms a long-short equity portfolio by ranking stocks on trailing returns, buying the strongest group and shorting the weakest. The ranking uses a twelve-month lookback while skipping the most recent month, a common way to reduce the influence…
The document presents an individual-investor approach inspired by Peter Lynch: use familiar products and services to generate company ideas, then assess business quality, growth, debt, and valuation. Its suggested checks include sustained profit growth,…
The document explains how to define and run a Qlib research workflow through a YAML configuration and the `qrun` command. It lays out the main stages: loading and preprocessing market data, training and applying a model, then analyzing forecast signals and…
This algorithm describes a weekly long-short strategy among large U.S. equities. It first filters for liquid stocks, then selects the largest companies by market capitalization. From that group, it buys the ten stocks with the weakest returns over the prior…
The study summary examines whether active stock selection that increases a mutual fund portfolio’s technological similarity predicts subsequent performance. Company patent distributions are compared using cosine similarity, and a fund’s active technological…
This summary of an equity research report explains portfolio optimization as a framework for managing risk exposures, holdings, weights, turnover, and market impact while incorporating investor views. It argues that simple portfolio construction may perform…
This Python module prepares portfolio optimization inputs for a greedy allocation routine. It takes target and prior weights, covariance estimates, instrument values, trading costs, and optional constraints, then aligns the data to instruments with valid…
This forum post presents a Chinese stock strategy that reportedly produced backtest trades but no live signals. Its example queries daily stock factors for non-ST, non-suspended companies outside specified listing sectors, then applies filters for minimum…
This Chinese-language listing introduces a talk by the CEO of quantitative investment firm High-Flyer. The described presentation surveys what artificial intelligence is, how it has developed and been applied, examples of AI in investment, the firm’s work in…
The article presents a framework for managing crypto portfolio risk through sector diversification, active hedging, quantitative monitoring, and rebalancing. It suggests diversifying across asset categories such as Layer 1 tokens, DeFi governance tokens, and…
This press release summarizes a survey of asset managers and hedge funds in the United States and Europe about digital-asset activity and infrastructure needs. It reports that 48% of respondents had digital assets under management, while 25% had a dedicated…
This document summarizes a method for evaluating mutual fund managers while accounting for both factor exposures and the characteristics of the stocks held. Its premise is that standard factor models, including the Carhart four-factor model, may leave return…
This research summary describes a Chinese equity approach that first screens stocks within broad industry groups using selected fundamental indicators, then combines the resulting preferred-stock universe with a multi-factor framework. It discusses a…
This tutorial explains how to assess strategy performance by examining completed round-trip trades: positions opened and later wholly or partly closed. It argues that trade-level frequency, duration, and profitability can reveal whether results came from…
The document explains how small-cap and value premiums in China’s A-share market may vary over time. It frames stock valuation as depending on expected future cash flows and the discount rates applied to them. A two-beta model separates a stock’s sensitivity…
This article explains how current and historical crypto market data can support risk decisions. It describes volatility measures such as standard deviation and beta, and liquidity indicators including trading volume, order books, market depth, bid-ask…
The document presents a brief summary of a research report on identifying the investment style of equity funds. It argues that fund style is closely related to fund returns and should be assessed before selecting a fund. The proposed approach is to identify…
This document explains a portfolio management interface for monitoring strategy-level positions, trades, and profit and loss during the trading day. It treats each order source, such as manual trading or a strategy module, as a separate portfolio and…
This implementation describes a bivariate Student-t copula for modeling dependence between two variables represented by uniform pseudo-observations. It explains sampling from a correlated Student-t distribution, evaluating copula density and cumulative…
The page introduces FinRL as a framework for applying deep reinforcement learning to automated stock trading. It explains that reinforcement learning agents learn through interaction and trial and error, while deep neural networks approximate the functions…
This document explains copula-based measures for comparing financial return series by separating marginal distributions from dependence. It presents Spearman’s rho as a rank-based dependence measure and contrasts it with Pearson correlation, which captures…
This A-share stock selection strategy buys the ten smallest companies by market capitalization each day and rebalances daily. Its universe is drawn from the CSI 1000 and excludes ST and delisted stocks. Additional filters require a positive trailing…