Lab
Open implementations
Reference Python, Rust, and R code for Daru Finance’s public M-models and supporting analyses. These are the implementations behind articles published on this site, intentionally minimal, intentionally readable, intentionally reproducible.
Everything, in the open
PCA + UMAP geometry of the strategy population
PCA + UMAP embedding of large strategy populations from a 90-feature metric vector, with connectivity-based separation of robust vs fragile strategies.
Read method Most recentSharpe density and (μ, σ, t) population scatter
Population-level Sharpe density and (mean, std, t) scatter over a strategy universe. Supporting library for empirical analyses.
Read methodEigenspectrum of the strategy correlation matrix
Marchenko–Pastur and parallel-analysis eigenspectrum of strategy correlation matrices. Reference implementation of the firm's M/01 model.
Sparse signal detection with FDR control
Higher Criticism plus Model-X knockoffs for FDR-controlled strategy selection. Reference implementation of the firm's M/02 model.
Persistence barcodes on strategy structure
H0 persistence barcode under correlation-distance Vietoris–Rips on strategy populations. Reference implementation of the firm's M/03 model.
Peaks-over-threshold and pairwise tail-coupling
Peaks-over-threshold GPD fits and pairwise tail-coupling χ on cross-asset returns. Reference implementation of the firm's M/04 model.
Decomposing the IS-OOS Sharpe gap
Variance decomposition of the in-sample / out-of-sample Sharpe gap into selection bias, parameter-choice noise, and residual skill across 10 deep-WFO crypto assets.
Does in-sample smoothness predict out-of-sample skill?
Pre-registered empirical test of whether in-sample Sharpe-surface smoothness under a fixed five-perturbation suite predicts out-of-sample skill across SOL / DOGE / BTC walk-forward partitions.
PCA + UMAP geometry of the strategy population
PCA + UMAP embedding of large strategy populations from a 90-feature metric vector, with connectivity-based separation of robust vs fragile strategies.
Hidden-Markov regime segmentation
Gaussian HMM regime segmentation on (logret, volatility, trend) features with K selected by BIC; cross-asset 4-state preference across crypto majors.
Universe-saturation of minimum-variance portfolios
Universe-saturation analysis for minimum-variance portfolios drawn from large strategy pools, comparing Ledoit–Wolf shrinkage, Huber-style robust, and sample covariance estimators.
Sharpe density and (μ, σ, t) population scatter
Population-level Sharpe density and (mean, std, t) scatter over a strategy universe. Supporting library for empirical analyses.
Cross-asset rolling correlation cube
Cross-asset sample correlation cube over a 9-asset universe. Supporting library for population-level analyses.
The signal is collective
Reproducible synthetic demos behind the article 'The signal is collective'.
Deflated Sharpe Ratio calculator
Deflated and Probabilistic Sharpe Ratio, minimum track record and backtest length in dependency-free Python, checked against every example in the source papers.
Frameworks (full backtester implementations) and the Monte-Carlo paper reproducibility package are intentionally excluded from this list, they live in their own dedicated places. See github.com/DaruFinance for the full repository index.