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 document derives the Black-Scholes partial differential equation for a European contingent claim whose underlying asset follows geometric Brownian motion. It applies Ito’s lemma to express the option price change in terms of time, asset price, and…
The document introduces a research backtesting system built around three object-oriented components. A Strategy consumes single-instrument OHLCV bars and emits long, hold, or short signals. A Portfolio turns signals into positions, tracks cash and holdings,…
The tutorial explains how to create a reproducible Python workspace in Jupyter Notebook that uses packages installed in a separate virtual environment. It describes registering that environment with ipykernel, then loading equity price data into a notebook…
The article defines algorithmic backtesting as applying a strategy to historical market data, producing signals and calculating trade and cumulative profit or loss. It describes its uses in screening ideas, modeling market conditions, optimizing parameters,…
This diary entry describes a Python architecture for automated forex trading through a broker API, with practice and live endpoints. A streaming price handler places bid and ask ticks on a shared event queue; a strategy turns tick events into orders; and an…
This article proposes a staged learning path for people preparing for quantitative trading research. It begins with expectations about the work: systematic trading relies on scientific hypothesis testing, statistical analysis, programming, and…
This article outlines what mathematical finance doctoral study involves and how it differs from a financial engineering master’s route. It describes graduate training as a shift toward independent research, with program structures varying between the United…
This tutorial introduces GPU computing through vector addition, assigning each elementwise sum to a separate CUDA thread. It explains the distinction between data and task parallelism, and contrasts GPU throughput with CPU latency using a simplified example…
This article outlines second-year undergraduate mathematics topics for self-study, with links between foundational theory and quantitative finance. Its roadmap includes the Riemann integral, metric spaces, vector calculus, nonlinear ordinary differential…
The article derives risk-neutral pricing for a call option in a one-period, two-state stock model. The stock starts at 100 and can finish at 110 or 90, while the call has a strike of 100. Using the option value established by a preceding no-arbitrage hedge,…
This article explains the PortfolioHandler component in an event-driven order management system. It separates the Portfolio, which tracks cash and positions, from the handler, which connects portfolio state to strategy signals, position sizing, risk checks,…
The article introduces European call and put options and defines their payoffs at maturity. It models a market containing a risk-free asset, an underlying stock following geometric Brownian motion, and an option whose value is assumed to be a smooth function…
The article explains how the Cointegrated Augmented Dickey-Fuller approach can assess whether a linear combination of two price series is stationary, a property that may support a mean-reversion pairs strategy. It contrasts this with applying an ordinary…
The article compares fast research backtests with event-driven systems designed to model market interaction more realistically and potentially reuse strategy logic in live trading. Research environments such as MATLAB, R, Python, and spreadsheets support…
This article discusses whether to build a trading backtester, outlines common sources of bias, and contrasts simple for-loop simulations with event-driven systems. A basic loop applies calculations and trading rules to successive bars; it is straightforward…
This article explains why backtests are models of past trading rather than forecasts of live performance, and compares simple bar-by-bar loops with event-driven systems. A loop-based tester is quick and easy to build for initial strategy screening, while an…
This article surveys five books aimed at people preparing for quantitative finance interviews and early quant careers. The first four focus on interview questions across probability, statistics, calculus, derivatives, options, numerical methods, rates,…
This article explains how to estimate European cash-or-nothing digital call and put prices with Monte Carlo simulation. At expiry, each option pays one unit if its strike condition is met and zero otherwise. The method simulates terminal underlying prices…
Unsupervised learning finds structure in observations without labeled outcomes. The article contrasts this with supervised prediction and explains that unlabeled methods are useful when labels are costly or data is high-dimensional. In finance, possible uses…
This tutorial describes a workflow for retrieving intraday foreign exchange aggregates from a market data API, formatting timestamps, plotting closing prices, and calculating percentage returns. It illustrates the process with a major pair and a less…
A securities master organizes instrument reference data, historical prices, corporate actions, and related information so that research, risk, and trading systems can use consistent inputs. The article focuses on a practical equities database for a retail…
This installment explains how to connect a portfolio’s order events to simulated fill events through an execution-handler interface. The abstract handler defines an order execution method, while the simulated implementation checks for order events and places…
The article extends constant-volatility option pricing with the Heston model, in which asset returns and variance evolve stochastically and their driving processes are correlated. It explains the variance process’s long-run level, mean-reversion rate,…
The article presents an object-oriented design for statistical distributions in quantitative finance. It separates uniform random number generation from distribution-specific behavior, allowing a generator to be replaced without changing distribution…