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.
5,701 documents
The document presents example ETF allocations for investors with different amounts to invest and different risk targets. For larger portfolios, it gives mixes of UK, Asian, European, and US equities alongside emerging-market, global government, global…
The article examines how geometric returns relate to compounding, diversification, and portfolio construction. It considers the claim that diversification can justify additional costs and argues that an all-equity portfolio may be inferior to one that…
The post considers how to organize portfolio fitting across a grid of instruments and trading forecasts, such as momentum and carry rules. It compares fitting all rule and instrument combinations together, clustering correlated combinations, fitting first…
This document examines how the stock allocation that maximizes expected compound annual growth changes with stock–bond correlation and relative expected returns. It assumes a fully invested two-asset portfolio of global equities and bonds, normally…
This article describes a mean-reversion strategy trading the spread between TLT, a long-duration Treasury ETF, and IEI, an intermediate-duration Treasury ETF. A recursive Kalman filter estimates a time-varying linear relationship between the pair, along with…
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…
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 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 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…
The article introduces the Vasicek model as a one-factor stochastic model for short-term interest rates. Its drift pulls rates toward a long-run mean at a speed set by the reversion parameter, while Brownian shocks create random fluctuations. It gives the…
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 guide lays out a learning path for aspiring quantitative analysts and financial engineers, whose work often centers on pricing derivatives. It starts with market and product fundamentals, then builds toward risk-neutral valuation, option pricing,…
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 classifies common systematic fund approaches by trading style and instrument. It describes trend following as holding positions while trends persist, often accepting frequent small losses in exchange for occasional large moves, and countertrend…
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,…
This article presents an MQL5 Expert Advisor that combines UK gilt and FTSE 100 market data in a linear regression model. It describes conventional gilts and inflation-linked gilts, and outlines the common tendency for investors to move between equities and…
The article introduces principal component analysis as a way to reduce the dimensions of data while retaining directions that explain much of its variance. It reviews eigenvectors and eigenvalues, compares PCA with singular value decomposition and power…
The article explores whether US and Japanese 10-year government bond data can help forecast USDJPY. It compares three predictor sets: currency OHLCV alone, bond OHLCV, and a combined set. The target is a future USDJPY close, and the workflow includes joining…
This article tests whether five-year U.S. Treasury yield data can improve forecasts of the S&P 500, motivated by the idea that stocks and government bonds may reflect shifts in risk appetite. It compares models predicting a future S&P 500 close using index…
This document describes a daily ETF candidate universe covering equities, fixed income, commodities, and currencies. It outlines a workflow for downloading market data, loading it for analysis, inspecting coverage by symbol and category, and filtering by…
This research-agent record considers whether the Federal Reserve will raise the upper bound of its target rate during 2026. It contains a market price, search traces, and agent probability estimates. The first rationale favors a hike, citing inflation risks…
This notebook applies principal component analysis to changes in Treasury yields across maturities. Standardizing changes gives each maturity equal influence, and the resulting components are interpreted as level shifts, steepening or flattening, and…
This notebook explains how to interpret Kalshi’s federal funds rate contracts and prepare their prices for quantitative research. A binary contract price represents an implied event probability, but the feed contains the highest standing YES bid rather than…
This notebook compares six long-only ETF allocation methods designed to reduce reliance on unstable estimates. It applies Ledoit-Wolf covariance shrinkage to estimators that use covariance, and contrasts mean-variance maximum Sharpe with minimum variance,…