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
This introduction explains XGBoost as a gradient boosting method that combines sequential models, typically decision trees, with later learners aimed at correcting earlier errors. It outlines classification and regression, describes model components such as…
The article surveys how traders identify and control exposure to market and operational shocks. It discusses diversification, portfolio optimization, position sizing, hedging, stop-loss orders, and take-profit rules, with an opening recommendation for…
This guide outlines portfolio analysis as a way to assess absolute and benchmark-relative performance alongside risk. It introduces holding-period return, arithmetic mean, Sharpe ratio, alpha, tracking error, information ratio, and Sortino ratio, then…
The document teaches how heatmaps can display financial data as colored cells, focusing on two uses: comparing single-day percentage price changes across stocks and viewing correlations among stock price changes. Its Seaborn workflow organizes stock symbols…
The document presents the Sharpe ratio as excess investment return divided by return volatility, and explains how the measure supports comparisons of strategies and portfolios. It describes annualizing a ratio from periodic returns and gives a worked…
This article compiles research papers, books, and other learning resources on reinforcement learning in finance, with expert recommendations attributed to Paul Bilokon. The cited applications include option hedging and dynamic replication, wealth management,…
The guide explains hierarchical clustering as an unsupervised method that builds nested groups and displays their relationships in a dendrogram. It contrasts clustering with supervised classification and compares hierarchical clustering with K-means: K-means…
This project describes a long-only portfolio that reallocates capital among a small basket of sector or asset ETFs and cash on a fixed weekly schedule. It compares classifier-based allocations with a rule-based momentum baseline. The baseline averages recent…
This article introduces a MATLAB toolbox for obtaining historical stock prices and developing algorithmic strategies. Its most substantive research concept is walk-forward analysis: repeat optimization on an in-sample window, then evaluate the selected setup…
The document explains how quantitative value investing uses financial data and predefined rules to identify potentially undervalued stocks. It describes common measures including price-to-earnings, price-to-book, price-to-sales, price-to-cash-flow, return on…
The document introduces risk parity as a portfolio allocation method that sets weights according to asset risk rather than equal capital or expected returns. Its basic procedure estimates each asset’s volatility, assigns larger weights to lower-volatility…
This article explains Hierarchical Risk Parity (HRP) as an alternative to covariance-inversion methods such as the Critical Line Algorithm. It identifies estimation errors, unstable matrix inversion, computational burden, and the loss of meaningful asset…
The article introduces Black-Litterman as a Bayesian approach that combines CAPM equilibrium returns with investor views to produce portfolio allocations. It motivates the method by describing common mean-variance optimization problems: sensitivity to…
This article explains how minimum spanning trees (MSTs) represent relationships among assets using a connected graph with minimal total edge weight. It describes visualizing trees with industry colors and market-cap node sizes, and reviews measures such as…
This document reviews research practices for applying machine learning and quantitative methods to investing. It outlines common barriers to financial machine learning, including the interdisciplinary nature of the work, limited data, and markets shaped by…
The article explains Theory-Implied Correlation (TIC), a method for estimating portfolio correlations by combining observed correlations with an externally specified hierarchy of assets. It describes three stages: fit a hierarchical tree to empirical…
The document explains online portfolio strategies that find historical market windows resembling current conditions. CORN measures similarity with Pearson correlation rather than Euclidean distance and uses the resulting matches to guide portfolio weights.…
This overview compares online portfolio momentum approaches across six equity and market-index datasets. Exponential Gradient updates portfolio weights using recent relative performance, with a learning rate and regularization intended to limit abrupt…
This introduction compares four portfolio selection benchmarks using a collection of 23 ETFs with closing prices from 2008 to 2016. Buy and Hold starts with fixed allocations and lets weights drift with asset prices; Best Stock selects the strongest asset…
This article explains how a Planar Maximally Filtered Graph (PMFG) represents similarities among assets while preserving more network structure than a Minimum Spanning Tree. It ranks nodes by a combination of graph centrality measures, then compares…
This article explains how to build vectorized equity curves while distinguishing long-only return calculations from long-short pair-trading P&L. For a single asset or a long-only portfolio with positive value, it recommends calculating portfolio returns and…
The article surveys hedge ratio estimation methods, separating single-period approaches that assume independent, identically distributed returns from multi-period approaches that model changes over time. The static methods covered are ordinary least squares…
This document outlines three futures trading rules built from skew and kurtosis: a standalone skew signal, skew conditioned on kurtosis, and kurtosis conditioned on skew. Signals are normalized by a robust volatility estimate and smoothed; conditioned…
The article extends an MQL5 portfolio dashboard with strategy-level drawdown tracking, threshold alerts, protective actions, trade exports, and an asynchronous AI review. Its heatmap calculation processes trades chronologically for each strategy, tracks…