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.
2,013 documents
The article describes a daily, long-only ETF rotation system that applies a five-day and a 42-day closing-price moving average to each asset. Crossovers generate buy and sell decisions, while available capital is allocated equally among positions with buy…
This document introduces an MQL5 example for presenting market data with color scales, gradients, and heatmaps. It uses the RGB color model, which makes it possible to perform mathematical operations on color values, and mentions a standard conversion…
The document describes an indicator that calculates the Pearson correlation between a chart symbol and a user-selected second symbol. The user can configure the comparison period and applied price, along with minimum and maximum correlation settings that…
This overview classifies quantitative funds by strategy, market, instrument, and time horizon. It describes trend following, which seeks sustained price moves and can have a low win rate while relying on occasional large trends, and countertrend trading,…
This document describes a chart indicator that displays three Slow Volume Strength Index (SVSI) series from different timeframes on one chart. Its purpose is to let a trader compare volume-strength readings across time horizons without changing the chart…
This indicator displays Stochastic Oscillator readings for a user-selected period across timeframes in a text block. Users can adjust the block's size and font, and hide timeframes that are not in use. The display supports up to ten indicators at once. Its…
This research summary presents asset allocation as a continuing process: define investor objectives, set an investment strategy, implement it, and review performance. Objectives specify the desired return, acceptable risk, and other investor needs. The…
This report reviews Chinese market and exchange-traded product developments for the week of March 30 to April 3, 2020. It summarizes moves in domestic equity indexes, bond yields, precious metals, currencies, and traded ETP categories. Equity and…
This guide explains how advanced users can run Lumibot backtests with their own historical data. It supports intraday and daily testing and describes assets including stocks, futures, cryptocurrency, and foreign exchange. Input data must be converted into a…
This indicator displays trend information by combining seven JFatlSpeed readings from different timeframes. Each reading is represented by its own line: the line changes to dark lime when that oscillator rises and to pink when it falls. Colored dots mark…
The document describes a chart dashboard for viewing trading hours across major financial centers. It brings together broker server time, a user's local time, and UTC, alongside the current time and open or closed status for Sydney, Tokyo, London, and New…
This strategy maintains a portfolio split between a broad US stock ETF and a long-term Treasury ETF. It targets a 60% stock allocation and 40% bond allocation, checks prices daily, and submits trades to move holdings toward those weights. The code schedules…
Risk parity allocates portfolio weights so that assets contribute more evenly to total portfolio risk, rather than assigning capital according to expected returns or equal dollar amounts. The document outlines a workflow: estimate asset volatilities and…
This review surveys three directions in factor investing: refining established factor methods, incorporating new data and models, and using factors as a basis for asset allocation. It describes improving value signals with multiple valuation measures,…
This review surveys three directions in factor investing: refining established factor methods, incorporating new data and models, and using factors as a basis for asset allocation. It describes improving value signals with multiple valuation measures,…
The article explains how tokenization can represent fractional claims on real-world assets such as real estate, commodities, collectibles, private credit, equities, and intellectual property. A custodian or other trusted entity holds the underlying asset…
This article presents a multi-asset allocation framework that combines traditional asset selection with allocation to systematic factors. It maps macroeconomic variables and style factors to asset classes, estimates exposures with time-series regressions,…
This overview introduces risk parity as an asset-allocation approach and places it alongside the classical mean-variance framework. Mean-variance optimization seeks portfolios that maximize expected return for a given risk level or minimize risk for a target…
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…
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 meetup Q&A surveys quantitative research topics, including collecting and cleaning market data, creating features, training models, and evaluating strategies through backtests. It lists possible information sources beyond price and volume, such as…
The document summarizes a macro-factor framework for strategic asset allocation, illustrated with portfolios for endowments, life insurers, and public pension plans. It lays out four stages: derive macro factors from principal components of monthly returns…
This document explains an indicator that displays trend direction across selected chart timeframes in one panel. For each timeframe, it independently compares a fast moving average with a slow moving average: the fast average above the slow one is bullish,…