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 Chinese-language research summary examines how differences between reported macroeconomic data and consensus forecasts may relate to returns in Chinese equities, bonds, and commodities. It compares forecast data sources, noting that Bloomberg records…
This report reviews U.S. mutual fund and ETF expense ratios through 2017, comparing equity, balanced, bond, money market, target date, active, and index products. It describes broad fee declines over time and relates them to investor demand for low cost…
This indicator concept applies min-max normalization to Average True Range, a commonly used measure of market volatility. ATR itself is unbounded, so its raw value does not provide a fixed scale for deciding whether volatility is near a relative high or low.…
The document explains quantiles as cutpoints that divide a probability distribution or sample into groups with equal, or nearly equal, probability or observation counts. For a division into q groups, there are q − 1 cutpoints. It names familiar cases such as…
This short introductory note describes quantitative investing as the use of mathematical models to turn investment ideas into measurable criteria. Those criteria can be used to identify opportunities, place trades, and manage risk. It highlights potential…
The indicator builds upper and lower price channels around a moving average, using standard deviations measured over three lookback periods inspired by Ichimoku. It averages the three estimates to form the main bands and also plots secondary bands using…
The document describes an indicator that calculates percentage changes in both price and margin. This offers a way to view changes in relative terms, which can help users compare movements across different price or margin levels. The description does not…
This platform update describes additions to an expression engine for quantitative analysis. The new rolling functions calculate sums, means, standard deviations, variances, skewness, and kurtosis over a specified lookback while handling missing values. It…
This reference describes a Python toolkit for calculating technical indicators and analyzing trading returns on pandas time series. Its functions cover lagged values, rolling standard deviation and simple averages, exponentially or linearly weighted…
The document explains Bayesian optimization as a way to search expensive black-box objectives, such as portfolio return or risk-adjusted performance. It describes using a Gaussian process as a probabilistic model: observations update the model’s posterior,…
This document describes an MQL5 indicator that estimates downside tail risk using the Peaks-Over-Threshold method. It converts price changes into loss magnitudes, selects a high quantile as a threshold, and fits a Generalized Pareto Distribution to losses…
This community post reports a failure in a VeighNa CTA backtest on Windows with a Tushare data service. Historical one-minute futures data downloads successfully and the strategy loads, but the run fails when the backtesting engine calculates performance…
The document describes a Chinese stock screen combining turnover between 3% and 12%, an appearance on the previous day's trading leaderboard, positive trailing price-to-earnings ratio, and a listing-age condition of more than one year. The stated intent is…
This article collects practical advice for building and debugging deep learning models, using a generative adversarial network for manga coloring as its motivating example. It recommends beginning with a small working baseline, visualizing predictions and…
This document describes John Ehlers’ Adaptive Relative Strength Index, an oscillator whose lookback changes with an estimated market cycle. It first smooths the midpoint of each bar, then applies digital filters to derive in-phase and quadrature components.…
The document introduces the correlation coefficient as a measure of the direction and strength of a linear relationship between two variables. It defines the coefficient using covariance and the variables’ standard deviations, and explains that its values…
This stock-selection rule screens for turnover between 3% and 12%, a rising K value from the KDJ indicator, and a daily price change bounded around the previous close. The article describes these as technical and market-activity filters intended to identify…
This research note studies whether stocks featured on China’s daily price-change leaderboards attract investor attention and how that attention relates to later returns. Using Sogou search index data as an attention proxy, it reports a U-shaped relationship…
The document answers a brief question about counting stocks within each industry group in a quantitative data expression. Its example groups records by the first-level Shenwan industry field and sums a conditional value: one for rows whose amount is…
This account describes applying the Campisi model to attribute pure bond fund returns using portfolio holdings and a benchmark. It separates total and excess returns into four effects: coupon income, government-bond yield changes, credit-spread changes, and…
Gaus_MA is a moving-average indicator that modifies a linearly weighted moving-average approach. Instead of using ordinary linear smoothing coefficients, it calculates the weights with a radial basis function, specifically a Gaussian function. The document…
This reference explains Stochastic RSI as a stochastic calculation applied to RSI values rather than directly to price. It identifies six adjustable settings: the lookback periods for the stochastic %K and %D lines, a slowing period, the RSI period, and…
This Qlib documentation explains why historical strategies need the versions of financial data that were available at each past decision time. Financial statements can be revised after publication; using only the latest value in a historical simulation can…
This report outlines ways to classify equities by market capitalization, growth, and value. It uses the median market capitalization to separate large and small companies, and describes three approaches to growth and value classification: composite ranking,…