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.
149 documents
This tutorial introduces the notation and basic objects of linear algebra used in machine learning and quantitative finance. It defines scalars, vectors, matrices, and higher-order tensors, explains their dimensions and indexing, and gives examples such as…
The article introduces time series analysis as a statistical way to study sequential data modeled as outcomes of an underlying stochastic process. It highlights trends, seasonal patterns, and serial dependence, including volatility clustering, as features…
The document explains how virtual destructors support safe cleanup in C++ inheritance hierarchies. When code deletes a derived object through a pointer to its base class, a non-virtual base destructor may prevent the derived destructor from running. If the…
The article develops an object-oriented framework for generating synthetic correlation matrices as an initial component of a tool for creating correlated financial time series. An abstract base class defines a common generation interface so different models…
The article introduces serial correlation, also called autocorrelation, as dependence between observations at different times. It reviews expectation, variance, covariance, and correlation, then explains why correlation is a normalized measure of linear…
This article introduces Markov Chain Monte Carlo as a numerical way to approximate Bayesian posterior distributions when analytical calculations, including conjugate-prior shortcuts, are unavailable. It explains the Metropolis algorithm as a sequence of…
This beginner's guide explains Bayesian statistics as a framework for updating uncertainty when new evidence arrives. It contrasts Bayesian probability, interpreted as confidence in possible outcomes, with the frequentist view of probability as long-run…
This career guide describes steps for PhD graduates pursuing junior quantitative roles. It surveys several paths—quant trading, structuring, financial engineering, and quant development—and advises candidates to research how different firms use each role…
The article introduces matrix inversion through systems of simultaneous linear equations. It represents the equations as A x = b, defines the identity matrix, and explains that when an inverse exists, multiplying by it gives the solution x = A⁻¹b. This…
The article argues that entering quantitative finance in one’s thirties is feasible and frames the transition around skills and preparation rather than age. It recommends an honest assessment of mathematical background, especially linear algebra, calculus,…
The document explains how to separate random number generation from Monte Carlo pricing code through an abstract generator interface. It describes exposing seed controls, draw dimensionality, integer generation, and uniform samples so that downstream…
This article introduces statistical learning as the task of estimating a relationship between response variables and predictor features. A quantitative finance example frames index values as responses and company fundamentals as possible predictors. It…
This article describes a directional S&P 500 strategy that refits a return model on a rolling window, forecasts the next day, and takes a long or short position according to the forecast sign. For each window, it selects an ARMA specification by Akaike…
This article explains how to use an annualised rolling Sharpe ratio to monitor whether a trading strategy’s risk-adjusted performance is weakening. It calculates the ratio from excess returns over a trailing year of observations, scaling the…
This article defines Value at Risk as a loss threshold for a portfolio over a specified time horizon and confidence level. It explains that VaR can be applied to an individual strategy or a larger portfolio, with the horizon chosen to reflect the time needed…
This guide explains support vector machines as supervised binary classifiers. It builds from a separating hyperplane to the maximal margin classifier, which chooses a boundary with the greatest distance from nearby training points. Because real data often…
This article relaxes the constant volatility assumption in Black–Scholes by allowing the asset's volatility to vary over time. It models log volatility with a mean reverting Ornstein–Uhlenbeck style equation driven by a stochastic process. To represent…
This reading guide presents a staged path for learning C++ as a quantitative finance practitioner. It explains that quant work involves implementing mathematical models, so programming ability and software engineering practices matter alongside financial…
This career guide explains how candidates can approach roles at quantitative hedge funds. It argues that top tier firms often seek exceptional, specialized research or computing skills, while smaller firms may be more open to candidates who enter through…
The article explains the Sharpe ratio as a way to compare a strategy’s average excess return with the variability of those returns. It describes annualizing the measure according to the return sampling interval, using a suitable benchmark, and treating…
The article lays out a progression for learning financial econometrics, starting with probability and statistics before moving through introductory econometrics, financial data analysis, specialist time-series texts, and current research. It highlights…
The document describes the source-side implementation of a templated C++ matrix class intended for numerical linear algebra in quantitative finance. It covers construction, copying, assignment, element access, matrix and scalar arithmetic, transpose, vector…
The document introduces geometric Brownian motion as a model for an asset price whose proportional changes have a constant drift and volatility. It outlines the derivation of the process solution using Itô's lemma: transform the price to its logarithm,…
This tutorial uses minute-level foreign exchange prices to build return series and calculate rolling realized volatility. It defines realized volatility from squared returns over a chosen interval and applies a rolling standard deviation to represent recent…