pyalloq-simulation
Institutional Monte Carlo scenario generation, missing-history imputation, and Meucci Entropy Pooling for the PyAlloq SDK.
Capabilities
- Missing Data Imputation (
FactorBackcastImputer): Back-casts missing history for recent IPOs and fills interior gaps in return space via PCA factor regression and idiosyncratic volatility matching. - Scenario Generators (
BaseScenarioGenerator):GaussianGenerator: Multivariate Normal baseline with Ledoit-Wolf shrinkage and Cholesky factorization.PCAFactorGenerator: Latent PCA factor simulation for high-dimensional universes.FHSGenerator: Filtered Historical Simulation (GARCH(1,1) de-volatization + synchronous residual bootstrapping).GARCHEVTCopulaGenerator: AR(1)-GARCH(1,1) filtration + Extreme Value Theory (Generalized Pareto Distribution tails) + Student-$t$ Copula.GMMRegimeGenerator: $K$-regime Gaussian Mixture Model with empirical Markov transition matrix.
- Memory & Batching: Default
float32tensor precision andgenerate_batches(n_paths, horizon, batch_size)streaming. - Meucci Entropy Pooling (
ViewBuilder,EntropyPooler): First-moment (AbsoluteView,RelativeView) and second-moment (VolatilityView,CovarianceView) market views solved via Lagrange dual optimization. - Path-Dependent Risk Evaluation (
ScenarioEvaluator): Posterior probability-weighted Expected Return, Volatility, Sharpe Ratio, 95% CVaR, View-Adjusted Maximum Drawdown (view_adjusted_mdd), and Probability of Ruin (probability_of_ruin).
1 maintainer
Metadata
1 maintainer
Release files for pyalloq-simulation 0.1.22
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Release files / pyalloq_simulation-0.1.22.tar.gz
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