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.
5,018 documents
The document compares Python threading and multiprocessing for improving simulation performance, with Monte Carlo pricing and strategy backtests as relevant examples. It explains that CPython’s Global Interpreter Lock limits CPU-bound Python threads to one…
The document describes a framework for generating synthetic correlated asset-price paths by combining a correlation-matrix generator with individual time-series models. Independent standard normal shocks are transformed using a matrix factorization so that…
This career guide considers how a software developer in quantitative finance might move into trading or research. It assumes strong programming and engineering skills but less depth in probability, statistics, econometrics, derivatives pricing or…
The article introduces artificial neural networks as computational models inspired by biological neurons, then focuses on the perceptron as an early supervised method for binary classification. It explains that the model combines scalar input features with…
The document reports a reader survey about which quantitative trading subjects the QuantStart community wanted to study in 2020. Machine learning and deep learning led the responses, followed by mathematical finance and coding and data science. Tactical…
The article develops a supervised learning approach that represents streams of data as paths and uses truncated path signatures as model features. A path signature is a sequence of iterated integrals; the full signature identifies a bounded-variation path up…
The article explains how cross-validation can estimate a model’s out-of-sample prediction error and help choose its flexibility, using a FTSE 100 forecasting example. Predictors are lagged daily prices or returns, and the response is the next day’s value.…
The article introduces deep learning, explains its layered approach to learning data representations, and outlines why it may help reduce hand-built feature engineering. It discusses possible quantitative finance applications, including time-series analysis,…
The document discusses how degree choices relate to four broad quantitative finance roles: quant analyst, quant developer, quant trader or researcher, and quant risk manager. It argues that mathematics is a strong general choice because it builds skills used…
This article maps out advanced subjects commonly encountered in the third year of a mathematics degree and discusses their possible relevance to quantitative careers. Topics include complex analysis, topology, ring theory, fluid dynamics, measure theory,…
This introduction explains matrix addition and multiplication as core operations in linear algebra, with an emphasis on their role in machine learning. It defines elementwise matrix addition for equal-sized matrices, scalar addition across every entry, and…
This article lays out a route for learning advanced mathematics independently, aimed at people considering quantitative finance, data science, or scientific computing. It weighs possible motivations, describes the substantial time commitment, and surveys…
This article derives a batch Bayesian method for estimating the intercept and slope of a univariate linear regression. It assumes normally distributed observation noise with known variance and assigns the regression parameters a normal prior with a specified…
This article explains Bayesian inference for the probability of success in repeated two-outcome trials, using coin flips as its example. It sets out the modelling assumptions: outcomes are binary, trials are independent and identically distributed, and the…
This study guide explains why quantitative trading research uses statistical learning and the scientific method to assess ideas. It describes a cycle of forming hypotheses, testing them against data, scrutinizing results, and refining or replacing strategies…
This introduction defines deep learning as machine learning that learns layered data representations, rather than relying entirely on manually designed features. It explains the idea through image recognition, where successive network layers can build from…
The article frames multiple linear regression as a probabilistic supervised learning model. It assumes that the response is a linear function of the features plus normally distributed error with constant variance. It also explains that transformations of the…
This document is a categorized reading list for people entering quantitative finance. It groups books on financial markets, quant interviews, systematic and high frequency trading, econometrics, mathematical finance, interest rate derivatives, and…
This document explains how to create synthetic equity price scenarios with a sector-based correlation structure. Assets are assigned to sectors; pairwise correlations are sampled from different ranges for same-sector, ordinary cross-sector, and selected…
This overview introduces linear regression as a model for a continuous response, expressed as a linear combination of input features plus random error. It explains the role of the intercept and describes the compact matrix representation of the model. The…
This curated directory surveys free or freely accessible resources for quantitative finance, grouped into market data, financial engineering, algorithmic trading, programming, and software. It highlights data-quality considerations: foreign-exchange tick…
This introduction explains path signatures as collections of iterated integrals, organized by tensor order. The first level records path increments; second-level terms encode additional ordering information and obey identities linking them to products of…
The article explains why GPU acceleration can shorten deep learning training and compares renting cloud compute with buying a local workstation for quantitative research. It frames the decision around model and parameter-search demands, electricity costs,…
This article applies rough path signatures to a supervised classification task: infer whether a company is based in the United States, United Kingdom, or Germany from its daily closing prices and trading volumes over a year. Stocks are represented as…