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
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 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…
This article describes using a Gaussian Hidden Markov Model (HMM) as a risk filter for a simple S&P 500 trend-following strategy. The model is trained on historical SPY adjusted returns to identify latent volatility regimes. A QSTrader risk manager then…
The article describes a daily directional forecasting strategy for the S&P 500, with trades placed in SPY. A quadratic discriminant analysis model uses the prior two daily index returns to predict whether the market will rise or fall. The strategy takes a…
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…
The article surveys skills it expects employers to seek across quant finance and data-focused roles. It links cheaper market data, open-source analysis tools, alternative data, and heavier post-crisis regulation to changing hiring needs. It describes…
The article introduces bootstrap resampling and three decision tree ensemble methods. Bagging fits trees to separate samples drawn with replacement and averages their predictions, aiming to reduce the high variance of individual trees. Random forests add…
The article explains how PhD graduates can assess their fit for quantitative finance jobs. It describes competition for research roles, notes that sought-after candidates may be recruited for specialized expertise, and points out that smaller funds can offer…
The article surveys common quantitative finance roles and ways to prepare for them. It distinguishes work in systematic trading, research, risk, derivatives pricing, and quantitative programming, and advises candidates to match their strengths to the role.…
This tutorial outlines a supervised text-classification pipeline that could support sentiment analysis or trading filters. It explains how labeled documents become feature vectors, and how a support vector machine separates classes using decision boundaries,…
The document explains how quantitative methods have expanded across finance as electronic trading, data driven asset management, and stronger risk oversight have changed the industry. It describes three career areas: portfolio management, where statistics…
This article proposes advanced undergraduate and early postgraduate topics for learners preparing for quantitative finance study or work. Its suggested curriculum emphasizes Brownian motion, stochastic analysis, stochastic calculus for finance and stochastic…
This article explains QR decomposition, which factors a matrix into an orthogonal matrix and an upper triangular matrix. It connects the method to least-squares problems used in regression and quantitative analysis, emphasizing that QR is more numerically…
The article explains how a perceptron learns a linear classification boundary. Its learning rule adjusts each weight according to the difference between the true and predicted label, the corresponding input feature, and a learning-rate parameter. It relates…
The article contrasts ordinary least squares with Bayesian linear regression. In the classical model, coefficients are point estimates chosen to minimize residual error; in the Bayesian model, the response is described probabilistically and inference yields…
This reading guide surveys ways for quantitative analysts to learn Python, from beginner programming fundamentals to data analysis, finance applications, and more advanced software development. It recommends introductory texts for syntax, control flow,…
The document explains how model flexibility affects prediction error in supervised regression and why the lowest training error does not necessarily identify the best model. It distinguishes training mean squared error from test error, which measures…
The article introduces supervised binary classification for predicting whether the S&P 500 will rise or fall. It uses the first two lagged daily returns as predictors and compares logistic regression, linear discriminant analysis, and quadratic discriminant…
The article demonstrates a machine-learning use of path signatures: classify handwritten digits by treating pen coordinates recorded over time as a path. It describes training a signature-based model on the UCI Pen-Based Recognition dataset, using ordered…
The article compares the typical financial engineering master’s curriculum with the capabilities quantitative funds seek when hiring. It lists common coursework such as derivatives pricing, numerical methods, portfolio optimization, risk, programming, and…
This career guide compares quantitative researcher and quantitative developer roles for engineers considering a move into quantitative finance. Researchers need evidence of rigorous analysis and stronger statistical skills, including time series methods and…
The document explains decision trees for regression and classification, describing them as models that divide feature space into rectangular regions using axis-aligned splits. For regression, each region predicts the mean response of its training…
The article introduces Hidden Markov Models (HMMs) as a way to represent market regimes that cannot be observed directly but affect visible asset returns. Regimes may correspond to changing return behavior, volatility, serial dependence, or correlations. In…
This guide compares five books for learning machine learning through Python, with an emphasis on practical programming. It distinguishes books that teach algorithms through pure Python implementations from those focused on using scikit-learn and related…