> ## Documentation Index
> Fetch the complete documentation index at: https://docs.fincept.in/llms.txt
> Use this file to discover all available pages before exploring further.

# Fincept Portfolio Lab

> Every Fincept Portfolio Lab module and its toolset.

48 tools in 8 modules, plus `pflab_catalog`, which lists them from inside your agent. All are free.

| Module | Toolset | Covers | Tools |
| - | - | - | - |
| [Portfolio analytics](/reference/portfolio-lab/analytics) | `pflab_analytics` | Every return, risk and ratio measure of a portfolio (46 measures incl. CVaR, EVaR, CDaR, EDaR, ulcer index, Gini, Sortino, Calmar), costs, fees and turnover, cumulative return and drawdown paths, return distribution, risk contributions, rolling measures, comparison of many portfolios with Pareto fronts, rebalancing schedules with weight drift, raw return-series measures, asset pre-selection screens, prices to returns, and factor risk and return attribution. | 9 |
| [Distributions, copulas and stress tests](/reference/portfolio-lab/distributions) | `pflab_distributions` | Fitted return distributions per asset (normal, Student t, Johnson SU, normal inverse Gaussian, best by AIC/BIC) with quantiles and Q-Q numbers, bivariate copulas (Gaussian, Student t, Clayton, Gumbel, Joe, independent, rotations) with tail dependence, regular vine copulas, synthetic scenario generation and conditional stress tests of a portfolio. | 4 |
| [Cross-sectional factor lab](/reference/portfolio-lab/factorlab) | `pflab_factorlab` | Characteristics panels (date x asset fields: returns, market cap, fundamentals, industry ...), 46 descriptors (value, momentum, volatility, beta, size, liquidity, quality, growth, leverage, yield), cross-sectional winsorizing, z-scoring and rank transforms, factor exposures, Barra-style characteristics factor models with factor returns, risk forecasts, diagnostics, attribution and optimisation, cross-sectional regressions, and alpha models with forecast evaluation. | 8 |
| [Portfolio optimisation](/reference/portfolio-lab/optimize) | `pflab_optimize` | Mean-risk optimisation over 15 risk measures and four objectives with any prior (sample moments, Black-Litterman, entropy pooling, factor model, vine-copula scenarios), efficient frontiers, risk budgeting, maximum diversification, distributionally robust CVaR, benchmark tracking, naive portfolios and stacking ensembles, under bounds, budgets, cardinality, group, linear, turnover, tracking-error and risk-limit constraints, costs, fees and worst-case uncertainty sets. | 8 |
| [Hierarchical portfolios](/reference/portfolio-lab/hierarchical) | `pflab_hierarchical` | Hierarchical risk parity, hierarchical equal risk contribution, Schur complementary allocation and nested clusters optimisation over any risk measure, codependence distance (Pearson, Kendall, Spearman, covariance, distance correlation, mutual information) and linkage, with the clustering tree, leaf order, clusters and seriated codependence matrices behind them. | 3 |
| [Moment estimators](/reference/portfolio-lab/moments) | `pflab_moments` | Expected returns (sample, exponentially weighted, market equilibrium, James-Stein, Bayes-Stein, Bodnar-Okhrin shrinkage), covariance (sample, EW, Ledoit-Wolf, OAS, shrunk, Gerber, denoised, detoned, graphical lasso, geodesic shrinkage, regime-adjusted EW, implied-volatility), per-asset variance and out-of-sample covariance forecast evaluation (calibration, exceedances, QLIKE). | 4 |
| [Priors and views](/reference/portfolio-lab/priors) | `pflab_priors` | The return distribution behind an optimisation: sample or shrunk moments with log-normal projection, Black-Litterman views, entropy pooling views on means, variances, correlations, skews, kurtosis, VaR and CVaR (on history or synthetic scenarios), opinion pooling of several experts, time-series factor models, vine-copula synthetic scenarios, and the uncertainty sets of robust optimisation. | 6 |
| [Validation and tuning](/reference/portfolio-lab/validation) | `pflab_validation` | Out-of-sample testing of whole portfolio pipelines: walk-forward backtests with calendar rebalancing, purging and sequential costs and turnover, strategy comparison on the same periods, combinatorial purged cross-validation (distribution of out-of-sample Sharpe over many paths), multiple randomized CV, CV planning (optimal number of folds), grid and randomized hyper-parameter search, and online learning backtests and searches. | 6 |


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