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.
5,018 documents
The document asks how to give one variable more influence when calculating multivariate normal probability densities for observations relative to a specified mean. It describes a four-variable example using an identity covariance matrix, reports density…
The document asks how the size and frequency of bond trades affect subsequent bid–ask spreads. It frames the problem as a market microstructure question: trading can change liquidity, supply and demand, and sentiment, while the size of the effect is…
The author describes an hourly forex classification pipeline using fractional differentiation, CUSUM event sampling, technical features, and XGBoost. Reported test accuracy rises substantially in some configurations, especially when labels are based on…
The document introduces statistical methods for investigating whether one time series tends to lead another. Lagged correlation, also called cross-correlation in this context, measures association after shifting one series relative to the other. Examining…
The document considers prediction of a continuous variable when correctly ordering observations matters more than matching their values in a way that maximizes Pearson correlation. One simple approach is to transform the target into ranks and fit an ordinary…
The document concerns estimating parameters for a stochastic volatility model in which variance follows a Cox–Ingersoll–Ross (CIR) square-root process. The question asks for estimation from time-series data rather than option prices, with quasi-maximum…
The question concerns fitting a GARCH model to daily log returns to estimate stock market volatility. One answer points users to Stata’s built-in GARCH command and its help documentation, but does not provide a detailed step-by-step procedure or discuss…
The discussion argues that the right graduate education for quantitative finance depends on the work a person wants to do. Financial engineering programs may be especially relevant for valuation and pricing model development, while quantitative trading has…
The document proposes estimating common return factors and asset loadings with a rolling singular value decomposition of volatility-normalized close-to-close returns. It then applies the estimated loadings to other return intervals, such as overnight or…
The document outlines a proposed search process for automated intraday strategies in FX and equity index futures. The plan uses short forecast horizons, many candidate features, genetic feature selection, and SVM models ranked by cross-validation error. The…
The document surveys data sources that systematic traders can use beyond historical prices, including traded volume, company fundamentals, and alternative data such as satellite imagery, news text, supply-chain information, and card transactions. Its main…
The document discusses how to choose the number of hidden states in a hidden Markov model, using financial return data as an example. It compares selecting states by out-of-sample log likelihood with choosing the best in-sample fit using an information…
The document addresses how to normalize real-time observations for a support vector machine trained with automatic scaling. Its central guidance is to apply the mean and standard deviation estimated from the training data to out-of-sample observations.…
The document discusses choosing the ARCH and GARCH lag orders for a volatility model. One proposed approach estimates a range of candidate orders, checks whether coefficients are statistically significant, inspects residual autocorrelation, and uses…
The document asks about geometric Brownian motion models whose drift or volatility depends on the stock’s past path, rather than only its current value and time. It gives examples of drift built from an accumulated function of earlier prices or from the…
The document asks what changed in Monte Carlo methods for pricing American options between 2005 and 2016, following earlier machine-learning work on optimal stopping. One answer argues that the period brought few groundbreaking Monte Carlo advances and that…
The document asks for a route from beginner-level C++ to quantitative trading, from a learner who already programs professionally in Visual Basic and has trading experience. The response suggests starting with a general C++ text, then studying resources…
The document addresses fitting a skewed Student t distribution to financial limit order book returns and then simulating from the fitted model. It points to an R distribution-fitting library for maximum likelihood estimation and to a package that implements…
The discussion considers how to scale a large set of technical indicators when training a model across many stocks with different price levels and feature ranges. Suggested approaches include range scaling, mean-zero standardization, and converting skewed…
The document describes the Quote Instability Factor used by IEX’s Crumbling Quote Indicator, which informs the D-Limit order type. It presents the factor as a logistic transformation of a weighted combination of hard-coded variables. Those inputs describe…
The document challenges the idea that PCA and machine learning are rarely used in trading. It notes that machine learning covers many methods, from linear regression to random forests, and that firms have had varying degrees of success with them. It then…
The document discusses the purpose of meta-labeling in machine-learning trading systems. It responds to the concern that a secondary model merely filters trades and might overfit historical examples. The proposed framing is to start with a primary strategy…
The document asks why fitting an autoregressive model of order one to stock prices can yield a high test-set R-squared while fitting the same form to returns can yield a negative score. An AR(1) regression predicts the current observation from its lag, but…
The note gives an econometric rationale for adding lagged versions of predictors to a recession classification model. Its example is the yield curve: an inversion may precede a downturn by many months, so the curve observed earlier can be relevant to whether…