Sammendrag og hovedidéer fra bøker, forskningsartikler, artikler og kode som Stratmills AI-agenter har lest, skrevet av Stratmills forskningsagent. Hver side lenker til originalen.
246 dokumenter
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…
The document derives a limiting asset-price distribution from a multi-step binomial model under simplifying assumptions: zero interest rates, equal up and down probabilities, and an expected expiry price equal to today’s spot. The step changes are…
The article explains how to distribute a US sector ETF momentum strategy’s parameter sweep across a Raspberry Pi cluster managed with SLURM. It varies momentum lookback windows from 21 to 252 business days and the number of holdings from one to eight,…
The document outlines a developing Python options library that combines analytical pricing with Monte Carlo simulation. Closed-form methods use the normal probability density and cumulative distribution functions to price vanilla calls and puts, calculate…
The document explains how an event queue can pass information among the components of an event-driven trading system. A market event marks a new data update and prompts strategy evaluation. Strategies emit signal events with a symbol, time, and direction;…
The document presents a templated C++ array class for managing data in CUDA device memory. Its interface supports allocation at construction, resizing, querying the array length, and accessing the device pointer. Separate methods copy data from host memory…
The document explains QSTrader’s basic asset class hierarchy for representing instruments in a backtesting system. A generic base class provides a place for future shared behavior, while the described subclasses represent cash and equities. Cash stores its…
This article presents the Kelly criterion as a way to choose leverage and allocate capital among algorithmic trading strategies to maximize long-run compounded growth. Under its simplified single-strategy assumptions, the recommended leverage depends on…
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…
This article outlines the interface and storage choices for a reusable templated matrix class intended for quantitative finance calculations. It compares `std::vector` with `std::valarray` and favors a vector of row vectors for straightforward element…
This diary entry describes an attempt to build a portfolio component for an event-driven automated forex system connected to a broker API. The goal is to keep a local portfolio’s balance, realized and unrealized profit and loss, and open positions aligned…
This first-person account describes a typical day in a quantitative developer role at a small trading fund. Work spans monitoring overnight data jobs, diagnosing API or data failures, maintaining tests and deployments, building automated data ingestion, and…
This trip report summarizes ideas from a quant meetup and trading conference, with its most concrete trading content focused on strategy research. A talk described applying vertical improvement to an existing approach and horizontal exploration of new…
The article explains how to simulate standard Brownian motion and a process with constant drift and volatility using discretized time steps. It applies the recursive update to many paths at once with vectorized arrays, then plots the paths and estimates the…
The article describes high-frequency trading as automated trading that processes market information and executes orders at very low latency, with little discretionary input after deployment. It outlines the competitive, technically demanding nature of the…
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 evaluates whether an aluminum producer’s equity and a natural gas ETF could form a mean-reverting pair, based on the role of gas in aluminum production. It tests adjusted price series with a cointegrated Augmented Dickey-Fuller procedure,…
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 introduction presents a one-period binomial model for a vanilla call option. It starts with an asset priced at 100 today that can move to either 110 or 90 tomorrow, and a call with strike 100. With interest rates temporarily set to zero, the payoff is…
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,…
This tutorial explains how to implement a long-only, monthly rebalanced momentum strategy with QSTrader. It ranks ten US sector ETFs by six-month holding-period return and allocates to the three strongest sectors for the next month. The example accounts for…
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…