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
This document describes a Chinese stock selection screen combining beverage and alcohol industry membership, turnover, and a Bollinger Band condition. The stated setup seeks stocks with turnover between 3% and 12% and closing prices between the middle and…
This Chinese stock-screening example combines a daily turnover-rate band with reversal-candle and arc-shaped price indicators. Its stated selection formula also filters for market capitalization and an arc-trend reading, while the accompanying explanation…
This document describes an adaptive Laguerre filter based on a method attributed to John Ehlers. It says the implementation supports Laguerre polynomials of different orders, with order four as the default. Adaptation can be controlled through a smoothing…
This document describes a normalized version of the Acceleration/Deceleration oscillator. It shifts and rescales the standard indicator, with the stated aim of making its configuration more adaptable alongside other oscillators. The description identifies…
This podcast recap discusses Rated’s work on Ethereum proof-of-stake infrastructure data. Validator assessment uses real-time and historical measures such as uptime, efficiency, and protocol adherence to give operators context for improving performance. The…
The document describes a modified Aroon oscillator intended to react more quickly to trend changes while smoothing noise. It starts from the difference between Aroon Up and Aroon Down, applies an exponential moving average, and tests a range of gain values…
The document describes an indicator that compares recent volatility with a longer-term volatility baseline. It calculates standard deviations of close-to-close momentum over short and long lookback periods, then divides the short-period value by the…
The essay cautions against treating a single factor as a reliable cause of an investment outcome. It uses stock reactions to restructuring announcements to show that the same news can be welcomed in a bull market and ignored or sold in a weak market. It also…
This article outlines a process for developing equity selection rules, from finding market examples and forming a hypothesis to implementing factors, running an initial backtest, reviewing losses, and deciding whether to adjust the logic. It proposes several…
This tutorial outlines a Fisher Transform indicator calculated from the midpoint of each price bar and the highest high and lowest low over a rolling period. A ratio between zero and one controls how much the newly normalized price affects the recursively…
The document notes that MACD values may take time to settle at the beginning of a price series. Its explanation is that MACD depends on exponential moving averages, which are updated recursively from the previous estimate and the new price. As a result,…
This indicator uses a smoothed Z-score of closing prices to identify statistically stretched conditions, then measures the price range formed during the extreme. A move down through the oversold threshold arms a bullish box, while a move up through the…
This Chinese-language forum post asks whether factor analysis should pair a factor observed on a given day with that same day's prices or with prices from the following day. The author cites the Alphalens introduction as a reason to use next-day data and…
This essay uses hypothetical investment examples to explain how returns compound asymmetrically: a loss requires a larger percentage gain to recover, and alternating gains and losses can produce a modest long-run result despite large individual moves. It…
This FAQ explains practical design and troubleshooting points for FMZ Quant Workflow strategies. It covers host-version requirements, JavaScript-only code nodes, sequential execution, trigger behavior, reading data from connected parent nodes, and sharing…
This document describes an Ergodic CCI Trigger indicator built from price momentum and repeated moving-average smoothing. It first calculates the close-to-close change and its absolute value, then smooths both through adjustable stages. Their resulting…
This brief troubleshooting post presents a ValueError raised while a BigQuant workflow runs an automatic labeling module. The traceback ends in pandas' binning routine and reports that the generated bin edges are not unique: after negative and positive…
This tutorial develops bivariate copulas as a way to describe dependence separately from the marginal distributions of two variables. It defines tail dependence and the Fréchet–Hoeffding bounds, then explains how an empirical copula can be estimated from…
This stock-selection rule screens for amplitude above 1, large-order net volume above 0.05 for at least three consecutive days, and a stochastic K value below 20. The article frames the range and net-flow measures as signals related to market sentiment and…
This catalog describes built-in quantitative data fields for Chinese A-share equities. Its factor families include historical prices, returns, trading volume and value, turnover, active money flows, valuation ratios, profitability and balance-sheet measures,…
This document proposes screening A-shares for turnover between 3% and 12%, total market value below 10 billion yuan, no reported losses, and free-float market value above 1 billion yuan. It frames the combination as a way to narrow the universe by trading…
The document describes an indicator that plots a cumulative statistical distribution of bid and ask prices. It also says the analysis can be applied to high and low prices or to open and close prices, allowing users to inspect different pairs of price series…
This tutorial walks through a small Python implementation of the skip-gram model, a method for learning word vectors by predicting surrounding tokens from a center token. It represents input and output embeddings as separate matrices and uses a toy sentence…
This beginner’s reflection defines quantitative investing as identifying relationships between inputs, or factors, and future returns, checking those relationships against historical data, then using current observations to estimate future opportunities. It…