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.
246 documents
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…
The document explains how to estimate the price of a double digital option using Monte Carlo simulation. The option pays one unit when the underlying asset’s value at expiry lies between a lower and an upper strike, inclusive, and pays nothing otherwise. The…
The document introduces sigma algebras and probability spaces as foundations for measure theoretic probability, with the eventual aim of preparing readers for Brownian motion, Ito calculus, and options pricing. It motivates the framework through continuously…
The document introduces linear state space models, where an underlying state evolves over time and observations provide noisy, indirect information about it. It defines the state and observation equations, their transition and measurement noise, and the…
This explanation introduces two properties used in stochastic models of asset prices. The Markov property says that, conditional on the present state, a process’s future distribution does not depend on its earlier states. The article illustrates the idea…
This update describes a planned redesign of QSTrader from an equities-focused event-driven backtester into a system spanning research, simulation, paper trading, and live trading. Its proposed architecture separates alpha forecasts from portfolio…
This article explains linear congruential generators (LCGs), deterministic algorithms that produce pseudo-random sequences for uses such as Monte Carlo simulation and risk modeling. Each value is generated from the previous one using a multiplier, increment,…
This example builds a basic Python backtest for a single equity using a moving average crossover. It calculates short and long simple moving averages, sets the position to invested when the short average is above the long average, and uses changes in that…
This tutorial explains how to estimate the value of a down-and-out call using Monte Carlo simulation on a GPU. A simulated price path is invalidated if it crosses the lower barrier before expiry; absent a rebate, the payoff depends on the terminal price…
This tutorial introduces the Interactive Brokers native Python API and explains how to establish a basic connection through Trader Workstation or IB Gateway. It describes the API’s asynchronous request and response design, with EClient sending requests and…
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 diary entry describes updates to a forex backtester that enable trading multiple currency pairs and accounts denominated in currencies other than the traded pair. It explains how positions convert profit and loss from the quote currency into the account…
This reading guide lays out a staged path for learning mathematical finance and derivative pricing. It starts with a broad introduction to instruments and markets, then recommends a mathematically lighter bridge into calculus, arbitrage, the Black–Scholes…
The article presents the Cointegrated Augmented Dickey–Fuller procedure as a way to estimate a regression hedge ratio for two assets and then test whether the resulting spread is stationary. It fits a linear regression, treats its residuals as the candidate…
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 article introduces Lévy processes as alternatives to geometric Brownian motion for modelling asset prices in derivative-pricing frameworks. Under the standard Black–Scholes assumption, log returns are normally distributed; the article argues that…
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…
This tutorial introduces paper trading as a way to test automated trading systems without placing real orders. It explains that a demo brokerage connection can help expose software bugs, exercise order handling, and develop API-based execution workflows. The…
This article describes how to simulate statically allocated, periodically rebalanced portfolios with QSTrader. It uses an All Weather style allocation across US equities, long and intermediate government bonds, gold, and commodities as an example, and…