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.
5,922 documents
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 compares retail algorithmic traders with institutional quantitative funds across capacity, crowding, market impact, leverage, liquidity, information access, risk oversight, investor relations, and technology. It argues that smaller accounts can…
The article outlines a proposed end-to-end system for researching, backtesting, and operating automated trades, initially focused on US equities and ETFs through a brokerage interface. Its architecture separates data ingestion and validation, price and…
The document describes the source-side implementation of a templated C++ matrix class intended for numerical linear algebra in quantitative finance. It covers construction, copying, assignment, element access, matrix and scalar arithmetic, transpose, vector…
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…
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,…
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 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…
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 article introduces the Johansen procedure for testing cointegration among multiple time series and estimating stationary linear combinations. It describes expressing a vector autoregressive model as a vector error correction model, then using the rank of…
This article explains why tactical asset allocation strategies can be difficult to evaluate over long periods: allocation signals are often monthly, leaving relatively few observations, and market regimes may persist for years. Retail investors may also lack…
The article explains how to evaluate Python frameworks for systematic strategy research and how backtesting fits between strategy development and live deployment. It distinguishes historical performance testing from trade simulation and real-time order…
The article walks through installing QSTrader on secondary Raspberry Pi nodes managed by SLURM, then checking the setup by running its 60/40 equity and bond example across the cluster. It describes installing Python build dependencies, creating a virtual…
The document reports a reader survey about which quantitative trading subjects the QuantStart community wanted to study in 2020. Machine learning and deep learning led the responses, followed by mathematical finance and coding and data science. Tactical…
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 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…
The document explains Cholesky decomposition as a way to transform uncorrelated random samples into correlated variables, a step used in quantitative finance simulations such as Monte Carlo methods. For a real symmetric positive-definite matrix, the…
The document describes a QSTrader backtest framework for comparing monthly rebalanced ETF portfolios. Each month, the strategy liquidates holdings and reallocates account equity to target weights. The examples include a 60/40 US stock and bond mix, a…
This document explains how to create synthetic equity price scenarios with a sector-based correlation structure. Assets are assigned to sectors; pairwise correlations are sampled from different ranges for same-sector, ordinary cross-sector, and selected…
The article defines a 60/40 benchmark as a portfolio holding 60% equities and 40% bonds, with periodic rebalancing to restore the target weights. It explains the rationale: stock and bond returns may diversify one another, potentially reducing volatility and…
This introduction describes tactical asset allocation as a long-horizon portfolio approach that adjusts broad asset-class exposures at relatively infrequent intervals. It contrasts the approach with fixed buy-and-hold allocations and short-term trading, and…
The document demonstrates a systematic monthly rebalancing strategy using SPY for US equities and AGG for investment-grade bonds. It invests 60% and 40% of initial capital, respectively, at the first month end after the backtest begins. At each later month…
The article surveys career paths where people with quantitative, statistical, and software skills can apply machine learning outside traditional asset management. It discusses financial technology use cases such as liquidity prediction, credit assessment,…
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,…