tsbootstrap: generate bootstrapped time series samples in Python
Open-source investment analytics platform bridging academic research and retail finance. Features include portfolio risk decomposition [Fama-French Five Factor Model], retirement sustainability modeling [Block Bootstrap Monte Carlo], max drawdown/CVaR dashboards, and risk-return optimisation [Markowitz, Ledoit-Wolf] via an intuitive user interface.
A statistics package with a variety of bootstrap and other resampling tools
R package with a set of functions to select the optimal block-length for a dependent bootstrap (block-bootstrap). Includes the Hall, Horowitz, and Jing (1995) cross-validation method and the Politis and White (2004) Spectral Density Plug-in method.
A statistics package with a variety of bootstrap and other resampling tools. This repository is synced to the same-named repository owned by GNU-Octave. It exists to facilitate publication of the developmental version of the statistics-resampling toolbox at MathWorks FileExchange.
Estimate confidence intervals in means of correlated time series with a small number of effective samples (like molecular dynamics simulations).
This package implements a non-parametric method that makes use of the wavelet cross-covariance at different scales to combine the measurements coming from an array of sensors in order to deliver an optimal measurement signal with weak assumptions on the processes underlying the individual error signals.
Non-parametric portfolio risk simulator using circular block bootstrap (Politis-Romano). Simulates outcome distributions, VaR/CVaR, drawdown, DCA/SIP -- with walk-forward calibration and 52 + 58 QA invariant checks.
Probabilistic forecasting and walk-forward validation for Brazilian markets.
15. Višestruko / panel sečenje sliding window stability, expanding window, block bootstrap, stratified by era, cohort split, concept-drift detectors (ADWIN, DDM, Page-Hinkley)
How often a nominal 95% bootstrap confidence interval actually covers, when the data are autocorrelated. One file, numpy only, reproducible.
Monte Carlo study of dollar-cost-averaging strategies, 1980–2026. Ranks 56 portfolios (static, age-glide, momentum/signal) by median final wealth vs. terminal drawdown pain via block-bootstrap simulation in taxable and tax-free accounts. Full write-up in wealth_report.pdf.
Analysis of spatial distribution of income in Italy
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