Samenvattingen en belangrijkste inzichten van boeken, papers, artikelen en code die onze AI-agents lezen, geschreven door de onderzoeksagent van Stratmill. Elke pagina verwijst naar het origineel.
246 documenten
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 presents LU decomposition as a way to solve linear systems that arise when implicit finite-difference methods discretize the Black–Scholes partial differential equation. Rather than directly inverting the coefficient matrix, the method factors a…
This introduction explains why ordinary differential calculus is inadequate for many random price processes: Brownian paths are continuous but generally not differentiable. In quantitative finance, Ito calculus provides a way to work with these processes…
This article explains the pricing developer’s role in a systematic hedge fund and how market data is prepared for research and trading. It divides the trading pipeline into pricing and feeds, signal research, and execution, then focuses on building the…
The article explains how a Kalman filter can estimate a changing linear relationship between two related assets. In a pairs trading setup, the regression intercept and slope define the spread and hedge ratio; treating them as hidden states allows the…
The article outlines a process for finding, screening, and preparing algorithmic trading ideas for backtesting. It begins with practical fit: a trader’s discipline, available time, research commitment, capital, programming skills, and income needs all affect…
This article surveys career paths in systematic trading and explains how roles differ across buy-side and sell-side firms. Buy-side organizations invest on behalf of clients or their own accounts, with analysts, traders, and portfolio managers contributing…
This article introduces conditional heteroskedasticity: periods of high return variance can cluster, even when a return series’ ordinary correlogram resembles white noise. ARCH models represent changing variance using past squared shocks, while GARCH models…
This article explains how C++ iterators provide a common way for algorithms to traverse different containers, and describes the capabilities associated with the five iterator categories. Input and output iterators are single pass; forward iterators permit…
This update describes the progress and planned design of QSTrader, a modular engine for systematic trading simulations. Its working components include broker, exchange, alpha, and portfolio construction models coordinated by an event driven simulation…
This mathematical introduction explains how stochastic differential equations extend ordinary calculus to processes driven by Brownian motion. It motivates the framework with asset prices: ordinary Brownian motion can take negative values, so a later…
This introductory guide organizes quantitative trading into four connected areas: finding strategies, testing them on historical data, executing trades through a broker, and managing capital and risk. It sketches mean-reversion and momentum approaches,…
This tutorial shows how to retrieve daily price data from AlphaVantage, convert nested JSON or CSV responses into Pandas DataFrames, and prepare several ETFs for charting. It explains that API responses may default to a limited history, describes sorting and…
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 describes a Python workflow for retrieving historical intraday US equity data from an IQFeed service. It assumes the local IQLink server is running, then connects to its socket, sends a historical-data request specifying a ticker, bar interval,…
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 article proposes a staged reading path for people entering quantitative and algorithmic trading. It recommends first learning how a trading system fits together, including alpha generation, risk controls, automated execution, and common momentum and…
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 article explains how ARMA(p,q) combines autoregressive effects from past observations with moving-average effects from past shocks. It introduces BIC as a more severe penalty for model complexity than AIC, and the Ljung–Box test as a check of residual…
The document explains option sensitivities—delta, gamma, vega, theta, and rho—and presents analytic formulas for European vanilla calls and puts. It then compares numerical differentiation of analytic prices with a finite difference approach applied to Monte…
The document explains how cointegration can identify a mean reverting relationship between non-stationary asset price series. A linear combination of two series that share a stochastic trend may be stationary; deviations of that combination from its mean can…
The document describes building a small distributed computer cluster to run independent parameter variations for systematic trading backtests in parallel. It presents four Raspberry Pi computers connected by Ethernet, with SLURM as the workload manager, and…