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.
765 documents
This project tests a market-neutral pairs strategy on Brazilian equities, grouping stocks by sector and screening pairs with the Johansen cointegration test. It keeps pairs with a consistently signed spread and a half-life no longer than 60 days. Entry and…
This project describes a statistical arbitrage strategy for Chinese futures. It screens contract pairs with an Augmented Dickey-Fuller test for stationary spreads, estimates a dynamic hedge ratio with a Kalman filter, and uses the spread’s half-life to set a…
The guide distinguishes statistical independence, correlation, and cointegration, concepts that are often confused when assessing diversification and trading relationships. Independence means observing one variable does not change the probability…
This project describes a market-neutral pairs-trading approach using NSE-listed stocks from different sectors. It selects candidate pairs within a sector, estimates a hedge ratio with ordinary least squares, forms a price spread, and applies an Augmented…
This project outlines a mean-reversion strategy for liquid, shortable stocks organized across five sectors. It first screens candidate pairs for correlation, then tests their spread for stationarity with the Augmented Dickey-Fuller test. When a qualifying…
The document introduces pairs trading as a market-neutral approach that buys one security and shorts another when their relationship diverges. It distinguishes correlation from cointegration: correlated prices can continue trending apart, whereas a…
Presented as a dialogue with a trading expert, the article outlines a beginner’s path into algorithmic trading: learn a programming language, study markets and strategies, identify potential inefficiencies, then backtest ideas on historical data. It…
The article introduces mean reversion through time-series concepts, distinguishing trend, cycles, seasonality, and irregular movements. It explains the intuition of buying when price falls below an average and selling when it rises above one, then relates…
This project describes a mean-reversion pairs strategy implemented and backtested with quantstrat. It uses a stock pair from the same sector as its example and also introduces a separate example involving commodity futures on different exchanges. The…
The article introduces unsupervised learning as a way to find structure in data without target labels, contrasting it with supervised prediction. Its trading example uses K-means to group stocks by return on equity and market beta. The workflow standardizes…
The article explains K-means clustering as an unsupervised method for grouping stocks with similar feature values, then applies it to candidate selection for statistical arbitrage. It contrasts clustering with manually choosing pairs by sector or industry,…
This project outlines a statistical arbitrage approach to trading cryptocurrency perpetual contracts on Binance. It screens contract price series for stationarity and cointegration, then forms a spread between a selected pair and uses deviations from the…
This project describes a daily statistical arbitrage strategy applied to six INR currency pairs over the stated 2011–2013 sample. It screens pair combinations with cointegration tests, then applies a cointegrated Augmented Dickey–Fuller test to selected…
This project develops a market-neutral mean-reversion strategy for pairs drawn from 25 large-cap NSE stocks across several sectors. It estimates hedge ratios with ordinary least squares, tests spread residuals for stationarity using a lag-zero ADF procedure,…
The document presents statistical arbitrage as a family of systematic strategies that seek to trade relative mispricing, often using mean reversion in historically related instruments. It describes pairs trading as one approach: identify assets whose prices…
This project describes a stock pairs strategy that periodically selects candidate pairs based on whether their price spread appears stationary. It estimates a hedge ratio with linear regression, forms a spread, and applies an Augmented Dickey-Fuller test to…
The document introduces the Johansen cointegration test as a way to assess long-run relationships among several time series. It explains that the trace and maximum eigenvalue tests use eigenvalues to estimate the number of cointegrating relationships, and…
The document explains simple and multiple linear regression as ways to estimate a target from one or more predictors. It presents Ordinary Least Squares as the method for fitting coefficients by minimizing squared residuals, with coefficients describing the…
This project compares a Monte Carlo portfolio allocation approach with a mean-reversion pair-trading strategy on Indian equities. It uses three stocks from each of five sectors and evaluates portfolios with Sharpe, Sortino, and Calmar ratios. For allocation,…
The article introduces time series as observations arranged over time and outlines how traders can analyze and forecast such data. It distinguishes univariate from multivariate series and stationary from nonstationary series. Autocorrelation and partial…
The document explains how the Augmented Dickey-Fuller test can help assess whether the residuals between two securities are stationary, a condition used in pairs trading. It introduces stationarity, non-stationarity, unit roots, and the test’s null and…
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…
The project describes a stock pairs strategy that models one price as a time-varying linear function of another. A Kalman filter updates the intercept, hedge ratio, spread, and spread volatility; spread z-scores set entry and exit signals. It screens…
The document outlines a daily pairs-trading strategy for related equities, implemented in R and illustrated with Johannesburg Stock Exchange shares. It selects similar companies, calculates a price ratio as the spread, estimates its mean and standard…