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.
21,023 documents
The Hull Moving Average is presented as a way to make a moving average react more quickly to price changes while retaining a smooth curve. Its construction combines weighted moving averages: it doubles a shorter-period weighted average, subtracts a…
This Chinese equity screen looks for stocks with daily price amplitude above 1%, at least one year since listing, and large-order net volume above 0.05 for more than three consecutive days. The rationale is that a minimum level of movement indicates market…
This overview presents active quantitative management as a process of forecasting factor returns and managing portfolio exposures. It describes a linear model in which each stock’s return is associated with its factor exposures, factor returns, and a…
This market snapshot combines regulatory and adoption news with Bitcoin on-chain indicators. It discusses a proposed SEC crypto task force, Arizona legislation to permit public funds to hold digital assets, and increased Solana activity associated with…
The article summarizes research that models mutual fund returns and performance persistence with a Bayesian dynamic panel. The model allows fund returns to have time-varying variances and covariances, autocorrelation, stochastic volatility, and measurement…
This review of Piotroski and So’s study examines whether value-growth return differences arise partly from investors’ mistaken expectations about future company performance. It describes FSCORE, a composite of nine binary signals covering profitability,…
The document introduces the Ornstein–Uhlenbeck (OU) process as a continuous-time model for a variable that is pulled toward a long-run mean while exposed to random shocks. It explains the roles of the mean, reversion speed, and volatility, contrasts OU…
The indicator measures the current price’s distance from a volume-weighted moving average, then expresses that distance as a normalized score. It uses the logarithm of the price-to-average ratio and scales it by the historical standard deviation of that…
This introductory note defines quantitative investing as using data and a processing method to guide investment decisions, with the aim of predicting returns consistently. It lists potential advantages: model-based decisions, historical performance that can…
This forum answer explains a workflow limitation when calculating factors for intraday futures data in BigQuant. It says the platform’s basic feature-extraction module supports stocks, while futures factor calculations require extracting market data through…
This stock screen selects companies with turnover between 3% and 12%, a listing on the prior day’s Dragon and Tiger List, market capitalization below 100 billion, and a record of avoiding losses over a multi-year period. The article provides example…
This report evaluates 56 equity factors built from Chinese A-share trading and financial statement data. It sorts stocks into ten groups by each factor, measures the next month’s value-weighted portfolio returns, and tests the return spread between the…
This indicator variation calculates RSI from an adaptive, smoothed price series rather than raw prices. The document argues that filtering prices before calculating RSI can suppress some short-lived price noise at the input stage, making the resulting…
The document describes an indicator-style workflow for scanning assets for Smart Money Concepts setups. It proposes caching higher-timeframe data and checking live prices in the background, then validating a potential order block only when the displacement…
The document introduces copulas as a way to model how two stocks move together in pairs trading. Unlike distance and cointegration approaches, which focus on price gaps or long-run relationships, copulas combine each series’ marginal distribution with a…
The document presents an outline for a VeighNa training course on machine-learning-based cross-sectional multi-factor strategies, also described as alpha strategies. It frames these methods as a form of statistical arbitrage applicable to equity selection,…
The document describes a chart indicator that automatically plots Fibonacci retracement and extension levels using the latest swing high and swing low identified by a ZigZag calculation. As new swing points form, the displayed levels update, making the tool…
This brief indicator description defines RSI MA as a moving average applied to the Relative Strength Index. First, RSI is calculated from a selected applied price over a chosen RSI period. That RSI series is then smoothed using a separately selected period…
This documentation describes tools for measuring relationships among asset-return series. A dependence matrix computes pairwise codependence using alternatives such as mutual information, variation of information, distance correlation, Spearman rank…
The report summary challenges the traditional claim that a doji candlestick predicts a market turning point. It says tests found that returns associated with ordinary doji patterns were primarily explained by reversal effects, rather than by the candle…
This short description defines a price-deviation indicator that compares the selected price with its average, calculated using a simple moving average over a chosen period. The user sets the calculation period and the applied price, which determine the…
The post argues that factor research can use information from later minutes within the same trading day when constructing a daily-frequency factor, provided the factor does not use data from the following day. It proposes examining relationships between…
This research summary compares seven ways to identify active mutual funds with positive, negative, or zero risk-adjusted performance. It explains a factor-model benchmark and methods that adjust for multiple testing, including bootstrap and false discovery…
This article reviews seven methods for classifying active mutual funds as skilled, unskilled, or zero-alpha, with emphasis on how their accuracy changes when return histories are short. It describes a four-factor alpha benchmark, multiple-testing…