# Fincept MCP

> Connect Claude Code, Codex, Cursor and any MCP client to Fincept Terminal's markets, research and analytics, and to 15 Fincept engines for statistics, forecasting, volatility, econometrics, technicals, regimes, dependence, pricing and portfolio construction.

- [Fincept MCP](https://docs.fincept.in/index.md): Fincept Terminal's markets, research, analytics and statistics as tools for your coding agent.
- [Quickstart](https://docs.fincept.in/quickstart.md): Connect your coding agent to Fincept MCP and make a first call.
- [Claude Code](https://docs.fincept.in/clients/claude-code.md): Add Fincept MCP to Claude Code and sign in.
- [Codex](https://docs.fincept.in/clients/codex.md): Add Fincept MCP to the Codex CLI and sign in.
- [Cursor](https://docs.fincept.in/clients/cursor.md): Add Fincept MCP to Cursor and sign in.
- [VS Code](https://docs.fincept.in/clients/vs-code.md): Add Fincept MCP to VS Code (GitHub Copilot agent mode) and sign in.
- [Other clients](https://docs.fincept.in/clients/other-clients.md): What an MCP client needs to connect to Fincept MCP.
- [Finding tools](https://docs.fincept.in/guides/finding-tools.md): How your agent discovers and calls every Fincept tool without loading the whole catalog.
- [Results](https://docs.fincept.in/guides/results.md): The one result format every Fincept tool returns, and how large results are delivered.
- [Data references](https://docs.fincept.in/guides/data-references.md): Feed Fincept data straight into any engine tool with a $fincept reference.
- [Fincept Stats](https://docs.fincept.in/guides/fincept-stats.md): Statistical and econometric models as MCP tools: regression, time series, tests, forecasting and more.
- [Fincept Forecast](https://docs.fincept.in/guides/fincept-forecast.md): Machine-learning forecasting as MCP tools: one series or a panel, conformal intervals, backtests and automatic model search.
- [Fincept Allocation](https://docs.fincept.in/guides/fincept-allocation.md): Portfolio optimisation and risk as MCP tools: 26 risk measures, risk parity, hierarchical portfolios, Black-Litterman and risk analytics.
- [Fincept Portfolio Optimizer](https://docs.fincept.in/guides/fincept-portfolio-optimizer.md): Portfolio construction as MCP tools: mean-variance, downside risk, critical line, hierarchical risk parity, Black-Litterman and share allocation.
- [Fincept Quant](https://docs.fincept.in/guides/fincept-quant.md): Time-series analytics as MCP tools: returns, volatility, rolling statistics, technical indicators, basket backtests, event studies and date tools.
- [Fincept Volatility](https://docs.fincept.in/guides/fincept-volatility.md): Volatility modelling as MCP tools: GARCH-family models, VaR and expected-shortfall forecasts and backtests, unit roots, cointegration and bootstrap inference.
- [Fincept Panel](https://docs.fincept.in/guides/fincept-panel.md): Panel and cross-section econometrics as MCP tools: fixed and random effects, Fama-MacBeth, instrumental variables, systems of equations and factor asset pricing.
- [Fincept TS Forecast](https://docs.fincept.in/guides/fincept-ts-forecast.md): Statistical time-series forecasting as MCP tools: automatic ARIMA, ETS, Theta, TBATS and MSTL on one series or a panel, with intervals, backtests and simulated paths.
- [Fincept Technicals](https://docs.fincept.in/guides/fincept-technicals.md): Technical indicators as MCP tools: 201 indicators from moving averages and bands to momentum, volume, volatility and cycle, and all 61 candlestick patterns, on OHLCV bars.
- [Fincept Change Points](https://docs.fincept.in/guides/fincept-change-points.md): Change-point detection as MCP tools: regime breaks in returns, volatility, trends, betas and mean reversion, with penalty selection and segmentation metrics.
- [Fincept Regimes](https://docs.fincept.in/guides/fincept-regimes.md): Hidden Markov regime models as MCP tools: calm and turbulent market regimes, transition probabilities, today's regime, scenarios and the number of regimes.
- [Fincept Copula](https://docs.fincept.in/guides/fincept-copula.md): Dependence modelling as MCP tools: rank and tail dependence, bivariate and vine copulas, joint distributions, copula VaR and stress scenarios.
- [Fincept Multi-Period](https://docs.fincept.in/guides/fincept-multi-period.md): Trading over time as MCP tools: optimisation and rule-based policies back-tested with costs, today's optimal trades, pre-trade checks and aversion tuning.
- [Fincept Portfolio Lab](https://docs.fincept.in/guides/fincept-portfolio-lab.md): Portfolio research as MCP tools: optimisation over 15 risk measures on any prior, walk-forward and combinatorial cross-validation, stress tests and cross-sectional factor models.
- [Fincept Pricing](https://docs.fincept.in/guides/fincept-pricing.md): Derivatives and fixed-income pricing as MCP tools: yield curves, bonds, swaps, credit, inflation, rate options, equity and FX options, volatility surfaces and calibration.
- [Example prompts](https://docs.fincept.in/guides/prompts.md): Workflows that combine Fincept data, analytics and statistics.
- [Authentication](https://docs.fincept.in/guides/authentication.md): How sign-in works, how long it lasts, and how to revoke an agent's access.
- [Credits and limits](https://docs.fincept.in/guides/credits-and-limits.md): What Fincept MCP requires, what it costs, and the limits that apply to every call.
- [Security](https://docs.fincept.in/guides/security.md): What an agent connected to Fincept MCP can and cannot do with your account.
- [Troubleshooting](https://docs.fincept.in/guides/troubleshooting.md): Fixes for common connection, sign-in and tool errors.
- [Endpoint](https://docs.fincept.in/reference/endpoint.md): The MCP endpoint, its transport and the OAuth 2.1 endpoints behind it.
- [Discovery tools](https://docs.fincept.in/reference/gateway-tools.md): The tools that search the catalog, describe a tool, call any tool and read stored results.
- [Result envelope](https://docs.fincept.in/reference/result-envelope.md): Every field of the Fincept Result Protocol (result/v1) and every block type.
- [Errors](https://docs.fincept.in/reference/errors.md): HTTP errors from the endpoint and error codes inside result envelopes.
- [Toolsets](https://docs.fincept.in/reference/toolsets/overview.md): Every platform toolset and what it covers.
- [Credits by tool](https://docs.fincept.in/reference/toolsets/credits.md): Every tool that can spend credits, and when it charges.
- [Markets](https://docs.fincept.in/reference/toolsets/market.md): Quotes, candles, fundamentals, profiles, peers, option chains, futures curves, corporate actions, holidays and the economic calendar.
- [Backtests](https://docs.fincept.in/reference/toolsets/backtest.md): Strategy backtests and parameter sweeps, templates, validation, cost profiles, saved strategies and run history.
- [Algo strategies](https://docs.fincept.in/reference/toolsets/algo.md): Algorithm library, versions, baskets and paper deployments with their metrics and trades.
- [Crypto](https://docs.fincept.in/reference/toolsets/crypto.md): Crypto venues, markets, derivatives data, your crypto accounts and orders, risk, compliance, custody and DeFi. Read-only.
- [Data](https://docs.fincept.in/reference/toolsets/data.md): Global economic and financial series from every provider, datasets, vendor fundamentals, adjusted prices, options data and scheduled refreshes.
- [Filings](https://docs.fincept.in/reference/toolsets/filings.md): SEC EDGAR companies and filings, insiders, 13F holders, funds and SEDAR+.
- [Deals](https://docs.fincept.in/reference/toolsets/deals.md): M&A deals, comparable-company builds, private placements, private raises and private credit.
- [Portfolios](https://docs.fincept.in/reference/toolsets/portfolio.md): Portfolios, valuation, performance, attribution, risk, scenarios, ledger, tax lots, limits, marks and benchmarks.
- [Paper trading](https://docs.fincept.in/reference/toolsets/paper.md): The Fincept Paper account: books, orders, positions, trades and equity curves. No real money.
- [Workspace](https://docs.fincept.in/reference/toolsets/workspace.md): Watchlists, alerts, notes, notebooks, reports, chat sessions, custom indices, news monitors and scan watches.
- [Research](https://docs.fincept.in/reference/toolsets/research.md): Ask Fincept's research model and list the available models.
- [Agents](https://docs.fincept.in/reference/toolsets/agents.md): Server-side agent runs, schedules and memories: inspect, cancel, pause and delete.
- [Account](https://docs.fincept.in/reference/toolsets/account.md): Credit balance, usage, the credit ledger and account notices.
- [Analytics](https://docs.fincept.in/reference/toolsets/analytics.md): Portfolio analytics, attribution, technicals, macro factors, market regimes, option strategies, volatility surfaces and rule backtests.
- [Screener](https://docs.fincept.in/reference/toolsets/screener.md): Stock screens and their past runs.
- [Quant lab](https://docs.fincept.in/reference/toolsets/quantlab.md): Research experiments, trials, deflated Sharpe ratios, signals, correlation checks and lineage.
- [News](https://docs.fincept.in/reference/toolsets/news.md): News feed and search, sentiment, AI analysis and summaries, keyword monitors and automations.
- [Alternative data](https://docs.fincept.in/reference/toolsets/altdata.md): Geopolitical events, sovereign bonds and yield curves, maritime and AIS data, shipping flows, ports and trade statistics.
- [Fincept Stats](https://docs.fincept.in/reference/stats/overview.md): Every Fincept Stats module and its toolset.
- [Descriptive statistics](https://docs.fincept.in/reference/stats/descriptive.md): Summaries of every column, weighted statistics, correlation matrices, robust location and scale, empirical distributions and Q-Q comparisons.
- [Regression](https://docs.fincept.in/reference/stats/regression.md): Linear models: ordinary, weighted and generalized least squares, AR errors, quantile, robust, recursive, rolling and beta regression, with robust covariances and predictions.
- [Generalized linear and mixed models](https://docs.fincept.in/reference/stats/glm.md): Generalized linear models for every family and link (logistic, Poisson, gamma, inverse Gaussian, negative binomial, Tweedie), GEE for clustered and panel data (including ordinal and nominal outcomes), linear mixed models with random intercepts, slopes and variance components, Bayesian mixed GLMs and…
- [Discrete choice and count models](https://docs.fincept.in/reference/stats/discrete.md): Binary logit and probit, multinomial and conditional (fixed-effects) logit, ordered logit and probit, Poisson, negative binomial and generalized Poisson counts, zero-inflated, hurdle and truncated counts, with marginal effects, predicted probabilities and classification tables.
- [Time-series models](https://docs.fincept.in/reference/stats/tsa-models.md): Forecasting models for one series: ARIMA/SARIMAX with seasonality and regressors, autoregression and ARDL/error correction, exponential smoothing (Holt-Winters, ETS), Theta, unobserved components, Markov regime switching, STL forecasting, ARMA order selection and theoretical ARMA processes.
- [Time-series analysis](https://docs.fincept.in/reference/stats/tsa-analysis.md): Autocorrelation, partial autocorrelation and cross-correlation with Ljung-Box tests; unit-root and stationarity tests (ADF, KPSS, Zivot-Andrews, range); Engle-Granger cointegration; BDS independence and variance-break tests; classical, STL and MSTL seasonal decomposition; Hodrick-Prescott, Baxter-Ki…
- [Multivariate time series](https://docs.fincept.in/reference/stats/tsa-multivariate.md): Vector autoregression with lag selection, forecasts, impulse responses, variance decompositions, Granger and instantaneous causality, whiteness, normality and stability checks; structural VAR; VARMAX; vector error correction with Johansen cointegration rank; dynamic factor models (including mixed mo…
- [Hypothesis tests](https://docs.fincept.in/reference/stats/tests.md): t, z and Welch tests, equivalence (TOST), one-way, factorial and repeated-measures ANOVA, Tukey HSD and pairwise comparisons, multiple-testing corrections, proportion, binomial and Poisson-rate tests, contingency tables, runs, normality and goodness-of-fit tests, effect sizes, rater agreement, media…
- [Model diagnostics](https://docs.fincept.in/reference/stats/diagnostics.md): Tests on a fitted linear regression: heteroskedasticity (Breusch-Pagan, White, Goldfeld-Quandt, ARCH), residual autocorrelation (Ljung-Box, Breusch-Godfrey, Durbin-Watson), specification (RESET, rainbow, Harvey-Collier, White), structural breaks (CUSUM, Hansen), non-nested model comparison (Cox, J,…
- [Power and sample size](https://docs.fincept.in/reference/stats/power.md): Statistical power and required sample size for t, z, ANOVA, regression F and chi-square tests (solve for effect size, sample size, alpha or power), power curves, proportion sample sizes and power, and the power of equivalence tests.
- [Multivariate analysis](https://docs.fincept.in/reference/stats/multivariate.md): Principal components (yield-curve and return PCA), factor analysis with rotations (factor models of returns, risk factors), MANOVA, canonical correlation, and tests of multivariate means and of the structure of a covariance matrix.
- [Nonparametric estimation](https://docs.fincept.in/reference/stats/nonparametric.md): Kernel density estimates of return distributions (univariate, multivariate and conditional), LOWESS smoothing, and kernel regression (local constant or local linear, optionally censored) with marginal effects.
- [Survival and duration analysis](https://docs.fincept.in/reference/stats/survival.md): Time-to-event analysis with censoring: Cox proportional hazards regression with hazard ratios and baseline hazards, Kaplan-Meier survival curves with confidence bands and median survival, log-rank and weighted tests between groups, and cumulative incidence under competing risks.
- [Missing-data imputation](https://docs.fincept.in/reference/stats/imputation.md): Multiple imputation of missing values: chained equations (MICE) with an analysis model pooled by Rubin's rules and the fraction of missing information, and Bayesian multivariate-normal imputation of gaps in a panel of series with the posterior mean and covariance.
- [Fincept Forecast](https://docs.fincept.in/reference/forecast/overview.md): Every Fincept Forecast module and its toolset.
- [Machine-learning forecasting](https://docs.fincept.in/reference/forecast/forecasting.md): Forecast one series or a panel with LightGBM, XGBoost, CatBoost, random forests, ridge, lasso and 40 other regressors on lags, rolling/seasonal/expanding/EWM statistics, calendar and exogenous features; recursive or direct multi-step; conformal prediction intervals; in-sample fitted values; feature…
- [Backtesting](https://docs.fincept.in/reference/forecast/backtest.md): Rolling-origin cross-validation of machine-learning forecasters: several windows, refit schedule, expanding or fixed-size training, interval coverage, accuracy metrics by model, series and window; LightGBM boosting-round search with early stopping.
- [Panel preparation](https://docs.fincept.in/reference/forecast/data.md): Validate a long table of series (duplicates, missing periods, missing values), insert missing periods on each series' calendar and fill them, and generate synthetic panels to try the forecasting tools.
- [Forecast evaluation](https://docs.fincept.in/reference/forecast/evaluation.md): Score forecasts against actuals: MAE, RMSE, MAPE, sMAPE, WAPE, MASE, RMSSE, bias, quantile and multi-quantile losses, CRPS, interval coverage, Winkler score and calibration, per series, window or aggregated (weighted); Pareto-optimal models; errors by step ahead; residual rectification.
- [Feature engineering](https://docs.fincept.in/reference/forecast/features.md): The feature matrix the models train on (lags, rolling/seasonal/expanding/EWM statistics, calendar, static and dynamic exogenous columns, per-step targets), lags and rolling statistics of exogenous series, Fourier seasonality, trend and calendar terms with their future values, and the future rows a f…
- [Model update and transfer](https://docs.fincept.in/reference/forecast/transfer.md): Reuse a trained forecaster: append new observations without retraining and forecast from the new end; forecast series the models never saw (a new ticker, store or market) with intervals recalibrated, rescaled or density-ratio reweighted for them; covariate-shift weights between two feature sets.
- [Automatic model search](https://docs.fincept.in/reference/forecast/tuning.md): Search each model's hyperparameters jointly with lags, rolling/EWM statistics, calendar features and target transforms by backtest, with a bounded number of trials; retrain the best and forecast.
- [Fincept Allocation](https://docs.fincept.in/reference/allocation/overview.md): Every Fincept Allocation module and its toolset.
- [Estimation of returns, risk and views](https://docs.fincept.in/reference/allocation/estimation.md): Expected returns and covariance by 6 mean and 15 covariance estimators, coskewness and cokurtosis, Black-Litterman (plain, augmented, Bayesian) posteriors, factor models with stepwise or principal components loadings, entropy pooling, bootstrap uncertainty sets, random-matrix denoising and covarianc…
- [Hierarchical clustering and networks](https://docs.fincept.in/reference/allocation/hierarchical.md): Hierarchical portfolios (HRP, HERC, HERC2, NCO) over 12 codependence measures, 8 linkages (DBHT included) and 38 risk measures; the assets' cluster tree, codependence and distance matrices, and their minimum spanning tree or planar filtered graph with centralities.
- [Portfolio optimisation](https://docs.fincept.in/reference/allocation/optimize.md): Mean-risk optimisation over 26 risk measures and four objectives on historical, Black-Litterman, factor-model and entropy-pooling inputs, with bounds, budgets, cardinality, risk limits, linear, integer, turnover, tracking-error and network constraints; efficient frontiers; worst-case robust, OWA and…
- [Risk parity and risk budgeting](https://docs.fincept.in/reference/allocation/parity.md): Portfolios whose assets (or classes, or factors) contribute risk in chosen proportions: equal risk contribution and risk budgeting for 22 risk measures, relaxed risk parity, factor risk parity and optimisation under factor risk-contribution limits; each answer shows the achieved risk contributions.
- [Portfolio risk analytics](https://docs.fincept.in/reference/allocation/risk.md): Risk of any portfolio or asset: 40 risk measures with return/risk ratios, the full risk report table, risk contributions per asset, class and factor, drawdown paths, the return distribution with its tail and range measures, portfolio comparisons, Brinson attribution and conversion of weights into sh…
- [Fincept Portfolio Optimizer](https://docs.fincept.in/reference/portfolio-optimizer/overview.md): Every Fincept Portfolio Optimizer module and its toolset.
- [Discrete allocation](https://docs.fincept.in/reference/portfolio-optimizer/allocation.md): Whole-share orders from target weights: latest prices from a price history or given, a portfolio value, greedy rounding or integer programming, long/short with a short ratio and optional reinvestment of short proceeds; the leftover cash and the error against the targets.
- [Portfolio analytics](https://docs.fincept.in/reference/portfolio-optimizer/analytics.md): Performance of given weights: expected return, volatility, Sharpe, semideviation and Sortino, each asset's risk contribution, and the value of every optimisation objective (variance, return, Sharpe, L2, quadratic utility, transaction cost, ex-ante and ex-post tracking error); cleaned weights.
- [Black-Litterman](https://docs.fincept.in/reference/portfolio-optimizer/black-litterman.md): Black-Litterman allocation: a market-implied (or equal, estimated, given) prior of returns blended with absolute and relative views, view confidences (Idzorek), view uncertainty and tau into posterior returns and covariance; implied, max-Sharpe, min-volatility or max-utility weights; the market-impl…
- [Critical line algorithm](https://docs.fincept.in/reference/portfolio-optimizer/cla.md): Markowitz's critical line algorithm: the exact mean-variance frontier under per-asset bounds through its turning points, the max-Sharpe and min-volatility portfolios and the frontier curve.
- [Downside-risk optimisation](https://docs.fincept.in/reference/portfolio-optimizer/downside.md): Portfolios that control losses instead of variance, from historical return scenarios: mean-semivariance (downside deviation below a threshold, Sortino ratio), mean-CVaR (expected shortfall at a confidence level) and mean-CDaR (conditional drawdown at risk, with the drawdown curve); minimum risk, tar…
- [Return and risk estimates](https://docs.fincept.in/reference/portfolio-optimizer/estimates.md): Annual expected returns (mean historical, exponentially weighted, CAPM) and covariance matrices (sample, semicovariance, exponential, minimum covariance determinant, manual, Ledoit-Wolf and oracle shrinkage), correlation, conversions between prices and returns, and positive-semidefinite repair.
- [Hierarchical risk parity](https://docs.fincept.in/reference/portfolio-optimizer/hrp.md): Hierarchical risk parity: cluster the assets by correlation distance (single, complete, average, weighted, centroid, median or Ward linkage), order them along the tree and split risk by recursive bisection; returns the weights, performance, linkage, dendrogram and clustered correlation heatmap.
- [Mean-variance optimisation](https://docs.fincept.in/reference/portfolio-optimizer/mean-variance.md): Efficient-frontier portfolios: max Sharpe (tangency), min volatility, max quadratic utility, max return for a target volatility and min volatility for a target return, long-only or long/short, market neutral, with per-asset, sector and linear constraints and L2, transaction-cost and tracking-error t…
- [Fincept Quant](https://docs.fincept.in/reference/quant/overview.md): Every Fincept Quant module and its toolset.
- [Basket and rule backtests](https://docs.fincept.in/reference/quant/backtest.md): Backtests on your own prices: a weighted basket rebalanced daily, weekly or monthly with transaction costs (index level and drifting weights), the basket's weighted average realized volatility and pairwise correlation, and rule-triggered trading (dates, signal levels, mean reversion) with an equity…
- [Business-day and date tools](https://docs.fincept.in/reference/quant/dates.md): Business-day offsets, checks, counts, previous/next business dates and business-date ranges on your own holiday calendar and week mask; relative date rules ('1m', '-1b', month ends, third Friday) and schedules; day count fractions; tenor lengths and market-point ordering.
- [Returns, volatility and performance](https://docs.fincept.in/reference/quant/econometrics.md): Returns (simple, log, absolute) over observations or tenors, prices from returns, rebasing, excess returns over a cash rate or index, realized, swap-convention and exponentially weighted volatility, rolling correlation, correlation-swap correlation, beta and covariance, rolling maximum drawdown and…
- [Epidemic models](https://docs.fincept.in/reference/quant/epidemic.md): SIR and SEIR compartmental models fitted to observed daily infected and removed counts (transmission and recovery rates, reproduction number, projected path), and simulations of SIR, SEIR, SEIRCM with quarantine, cases and deaths, and its age-stratified form.
- [Event studies](https://docs.fincept.in/reference/quant/events.md): Frame a series around event dates (earnings, central-bank meetings, data releases you supply), or detect events from another series' threshold moves and measure the response over chosen horizons.
- [Calendar operations on dated series](https://docs.fincept.in/reference/quant/calendar.md): Bucket a series by week, month, quarter or year; cut a date range; keep only business days of a holiday calendar; day, month, year, quarter and weekday of each date; accrual fractions between dates; interpolation onto chosen dates; and splicing several series end to end.
- [Rolling statistics and regression](https://docs.fincept.in/reference/quant/stats.md): Rolling or expanding statistics of a dated series by observation count or tenor: min, max, range, arithmetic and quadratic mean, median, mode, sum, product, standard deviation, variance, z-scores, winsorizing, percentiles and percentile ranks, exponentially weighted deviation; static and rolling lin…
- [Technical indicators](https://docs.fincept.in/reference/quant/technicals.md): Simple, smoothed (Wilder) and exponential moving averages, Bollinger bands, relative strength index, MACD, and the trend or seasonally adjusted series from a classical additive or multiplicative decomposition.
- [Series algebra and transforms](https://docs.fincept.in/reference/quant/algebra.md): Element-wise maths on dated series (exp, log, powers, floors, caps), value and date filters, lags, differences, spike and outlier smoothing, run counts, arithmetic between two series with date alignment, weighted sums and logical combinations of many series, and conditional selection.
- [Fincept Volatility](https://docs.fincept.in/reference/volatility/overview.md): Every Fincept Volatility module and its toolset.
- [Bootstrap inference](https://docs.fincept.in/reference/volatility/bootstrap.md): Bootstrap confidence intervals (percentile, basic, studentized, normal, bias-corrected, BCa) and replicate distributions for return statistics (mean, volatility, Sharpe, Sortino, quantiles, expected shortfall, correlation, beta, differences) under IID, stationary, circular-block, moving-block and in…
- [Forecast comparison](https://docs.fincept.in/reference/volatility/comparison.md): Compare forecasting models on their losses: superior predictive ability (SPA) and reality check p-values and the StepM set of models that beat a benchmark, and the model confidence set of the best models, all with block-bootstrap inference.
- [Long-run covariance](https://docs.fincept.in/reference/volatility/covariance.md): Kernel (HAC) long-run covariance estimators: Bartlett/Newey-West, Parzen/Gallant, quadratic spectral/Andrews, Parzen-Riesz, Parzen geometric, Parzen-Cauchy, Tukey-Hamming, Tukey-Hanning and Tukey-Parzen, with data-driven bandwidths, kernel weights and HAC standard errors of the mean.
- [Volatility models](https://docs.fincept.in/reference/volatility/models.md): GARCH-family conditional volatility models (GARCH, GJR, TARCH, EGARCH, APARCH, FIGARCH, HARCH, MIDAS hyperbolic, EWMA, RiskMetrics 2006, fixed variance) with constant, zero, AR, HAR, regression or volatility-in-mean means and normal, Student t, skewed t or GED errors: estimation, diagnostics, foreca…
- [Volatility forecasting and VaR backtests](https://docs.fincept.in/reference/volatility/forecast.md): Out-of-sample volatility, Value-at-Risk and expected-shortfall forecasts with rolling or expanding re-estimation and hit-rate backtests; forecasts from many origins (hedgehog view, origin or target alignment); scenario forecasts from crisis-window or scaled shocks.
- [Unit roots and cointegration](https://docs.fincept.in/reference/volatility/unitroot.md): Unit-root and stationarity tests (ADF, DF-GLS, Phillips-Perron, KPSS, Zivot-Andrews with a break), variance-ratio random-walk tests, Engle-Granger and Phillips-Ouliaris cointegration tests, cointegrating vectors by dynamic OLS, fully modified OLS and canonical cointegrating regression, and p-values…
- [Fincept Panel](https://docs.fincept.in/reference/panel/overview.md): Every Fincept Panel module and its toolset.
- [Factor asset pricing](https://docs.fincept.in/reference/panel/asset-pricing.md): Linear factor models of test-asset returns: time-series regressions on traded factors, two-pass cross-sectional regressions (traded or non-traded factors, optional zero-beta rate) and GMM; risk premia with standard errors, betas, pricing errors and the J test of zero alphas.
- [Panel data and datasets](https://docs.fincept.in/reference/panel/data.md): Classic econometrics example datasets (wage panel, job training, Mroz, Card, fertility, birth weight, Fama-French portfolios, Munnell public capital, fringe benefits, MEPS) and simulated panels; panel structure, balance and within/between variation, demeaning, first differences and group means; HAC…
- [Instrumental variables](https://docs.fincept.in/reference/panel/iv.md): Instrumental-variables regression for endogenous regressors: two-stage least squares, LIML and k-class (Fuller), GMM and continuously updated GMM with robust, HAC or clustered weighting, first-stage strength, over-identification (Sargan, Basmann, J) and endogeneity (Durbin, Wu-Hausman, C) tests, and…
- [Panel regression](https://docs.fincept.in/reference/panel/panel.md): Regressions on a long panel (entity x period): fixed effects with entity, time and other effects, random effects, between, first difference, pooled and Fama-MacBeth, with robust, one- or two-way clustered, Driscoll-Kraay and autocorrelation-robust errors; estimated effects; side-by-side comparison w…
- [System regression](https://docs.fincept.in/reference/panel/system.md): Several linked regression equations estimated together: seemingly unrelated regressions (SUR/FGLS) with cross-equation constraints, three-stage least squares and system GMM for equations with endogenous regressors; residual covariance, system R² and tests of a diagonal covariance.
- [Fincept TS Forecast](https://docs.fincept.in/reference/ts-forecast/overview.md): Every Fincept TS Forecast module and its toolset.
- [ARIMA models](https://docs.fincept.in/reference/ts-forecast/arima.md): Automatic and fixed-order (seasonal) ARIMA and autoregression with exogenous regressors, fitted series by series: orders, coefficients with standard errors, AIC/AICc/BIC, forecasts with intervals; unit-root and seasonal-strength tests that choose the differencing.
- [Statistical forecasting](https://docs.fincept.in/reference/ts-forecast/forecasting.md): Forecast one series or a panel with any mix of statistical models (automatic ARIMA, ETS, CES, Theta, TBATS, MSTL, boosted decomposition, structural, intermittent, GARCH, benchmarks, regression), native or conformal intervals, exogenous regressors and a fallback model; the model and parameter referen…
- [Backtesting and evaluation](https://docs.fincept.in/reference/ts-forecast/backtest.md): Rolling-origin cross-validation of statistical models (expanding or fixed window, refit schedule, conformal or native intervals) scored with MAE, RMSE, MAPE, sMAPE, MASE, RMSSE, bias, quantile, coverage and Winkler metrics per model, window and series; scoring of any forecasts against actuals.
- [Benchmarks and intermittent demand](https://docs.fincept.in/reference/ts-forecast/benchmarks.md): Benchmark forecasters every model should beat (naive, seasonal naive, random walk with drift, historic and window averages, conformal seasonal pool, constant) and intermittent-demand methods for series with many zero periods (Croston classic, optimized and SBA, ADIDA, IMAPA, TSB).
- [Sample data](https://docs.fincept.in/reference/ts-forecast/data.md): The monthly international airline passengers series (1949-1960) and synthetic panels of seasonal series of chosen length, frequency and static features, in the long id/date/value layout every tool accepts.
- [Regression on exogenous columns](https://docs.fincept.in/reference/ts-forecast/regression.md): Per-series regression of the target on its exogenous columns (linear, regularized, robust, quantile, tree ensembles, gradient boosting, nearest neighbours, support vector and kernel ridge) forecast from future exogenous values, with conformal intervals and coefficients or importances.
- [Scenario simulation](https://docs.fincept.in/reference/ts-forecast/scenarios.md): Sample future paths of fitted statistical models (ARIMA, ETS, CES, Theta, exponential smoothing, benchmarks) with normal, Student t, Laplace, skew-normal, generalized-error or bootstrapped errors: quantile fans, path statistics and raw sample paths for risk and scenario analysis.
- [Multiple seasonality](https://docs.fincept.in/reference/ts-forecast/seasonal.md): Models for series with several seasonal cycles (daily data with weekly and yearly patterns, hourly data with daily and weekly ones): TBATS, MSTL decomposition with a trend model, and gradient-boosted decomposition; components, harmonics, Box-Cox and forecasts with intervals.
- [Exponential smoothing and Theta](https://docs.fincept.in/reference/ts-forecast/smoothing.md): Automatic ETS state-space models, Holt and Holt-Winters, simple and seasonal exponential smoothing, complex exponential smoothing, and the standard, optimized and dynamic Theta methods: smoothing weights, damping, initial states, AIC/AICc/BIC and forecasts with intervals.
- [Unobserved components](https://docs.fincept.in/reference/ts-forecast/structural.md): Structural time-series models estimated by Kalman filter: local level, local linear trend, smooth trend, seasonal, cycle and autoregressive components with exogenous regressors; component variances, smoothed components and forecasts with intervals.
- [Volatility (GARCH)](https://docs.fincept.in/reference/ts-forecast/volatility.md): GARCH(p,q) and ARCH(p) conditional-variance models of return series: coefficients, persistence, unconditional variance, in-sample conditional volatility and the volatility forecast.
- [Fincept Technicals](https://docs.fincept.in/reference/technicals/overview.md): Every Fincept Technicals module and its toolset.
- [Cycle indicators (Hilbert transform)](https://docs.fincept.in/reference/technicals/cycle.md): Hilbert-transform dominant cycle period and phase, in-phase and quadrature components, sine and lead-sine waves, and the trend-versus-cycle mode.
- [Any indicator](https://docs.fincept.in/reference/technicals/indicators.md): Every indicator by name: many at once over the same bars (one aligned table and chart), and each indicator's documentation: inputs, parameters with defaults and meaning, outputs, warm-up and unstable period, moving-average types.
- [Math transforms and operators](https://docs.fincept.in/reference/technicals/math.md): Element-wise arccosine, arcsine, arctangent, ceiling, cosine, hyperbolic cosine, exponential, floor, natural and base-10 logarithm, sine, hyperbolic sine, square root, tangent and hyperbolic tangent; addition, subtraction, multiplication and division of two series; cumulative sum; rolling sum, highe…
- [Momentum indicators](https://docs.fincept.in/reference/technicals/momentum.md): RSI, ADX/ADXR/DX with the directional indicators and movement, Aroon, CCI, CMO, MFI, momentum and rate of change, TRIX, Williams %R, ultimate oscillator, balance of power, Coppock, DPO, efficiency ratio, Elder ray, forecast oscillator, Williams fractals, IMI, Qstick, TSI, VHF, vortex, Williams A/D;…
- [Overlap studies](https://docs.fincept.in/reference/technicals/overlap.md): Indicators on the price's own scale: simple, exponential, weighted, double and triple exponential, triangular, Kaufman adaptive, MESA adaptive, T3, Hull, zero-lag, Wilder and volume-weighted moving averages, variable-period MA, Hilbert trendline, midpoint and midprice; Bollinger, Keltner, Donchian a…
- [Candlestick patterns](https://docs.fincept.in/reference/technicals/patterns.md): All 61 Japanese candlestick patterns (doji family, hammer and hanging man, engulfing, harami, stars, three white soldiers and black crows, kicking, marubozu, hikkake, ...) on OHLC bars: one pattern's signal at every bar, or a scan of many patterns listing which fired on which dates.
- [Price transforms](https://docs.fincept.in/reference/technicals/price-transform.md): Average price, median price, typical price, weighted close, Heikin-Ashi candles and the average (mean absolute) deviation.
- [Rolling statistics](https://docs.fincept.in/reference/technicals/statistics.md): Rolling beta and Pearson correlation of two series, rolling linear regression (end value, slope, intercept, angle, one-bar forecast), standard deviation, variance, nearest-rank percentile and percent rank.
- [Volatility indicators](https://docs.fincept.in/reference/technicals/volatility.md): True range, average true range (ATR) and normalized ATR, average day range, Chaikin's volatility, the mass index and the relative volatility index.
- [Volume indicators](https://docs.fincept.in/reference/technicals/volume.md): On-balance volume, Chaikin accumulation/distribution line and oscillator, Chaikin money flow, Elder's force index, market facilitation index, negative and positive volume indexes, percentage volume oscillator, price-volume trend, relative volume and the cumulative VWAP.
- [Fincept Change Points](https://docs.fincept.in/reference/change-points/overview.md): Every Fincept Change Points module and its toolset.
- [Change-point search](https://docs.fincept.in/reference/change-points/search.md): Offline change-point detection on one series or several jointly: PELT, binary segmentation, bottom-up, window sliding, dynamic programming and kernel search over ten segment costs (mean, median, mean-and-covariance, kernel distribution, cosine, regression, continuous trend, rank, Mahalanobis, autore…
- [Segmentation metrics](https://docs.fincept.in/reference/change-points/metrics.md): Compare two sets of change points (Hausdorff distance, Rand index, Hamming distance, precision, recall and F1 within a margin, mean time error) and score given break dates against the data under a segment cost (each break's gain against the BIC/AIC reference penalty).
- [Financial regimes and structural breaks](https://docs.fincept.in/reference/change-points/regimes.md): Regime shifts in returns and volatility (one asset, or covariance and correlation regimes of several), trend changes of prices, rates and macro series, breaks in a regression (beta, hedge ratio) and in autoregressive dynamics (persistence, mean reversion of a spread), each with statistics per regime…
- [Penalty and model selection](https://docs.fincept.in/reference/change-points/selection.md): How many breaks: the number of change points found at each penalty of a grid (with the widest stable plateau and the BIC/AIC reference penalty), and the total cost of the best segmentation with 0..K breaks scored by BIC, AIC or the elbow of the cost curve.
- [Synthetic teaching signals](https://docs.fincept.in/reference/change-points/synthetic.md): Synthetic piecewise signals with known change points (constant means, Gaussian correlation switches, linear-model coefficients, sine frequencies) from a caller-chosen seed, for demonstrations and for checking how well a method and cost recover the true breaks.
- [Fincept Regimes](https://docs.fincept.in/reference/regimes/overview.md): Every Fincept Regimes module and its toolset.
- [Regime analytics](https://docs.fincept.in/reference/regimes/analysis.md): Statistics of any returns series by regime (return, volatility, ratio, spells and their compounded returns, empirical transitions) and transition-matrix analytics (stationary distribution, expected durations, half-lives, mixing, h-step forecast, simulated regime paths).
- [Discrete regime models](https://docs.fincept.in/reference/regimes/discrete.md): Hidden Markov models on symbols and counts: categorical (coded series, return buckets, down/flat/up days; maximum likelihood or variational), Poisson (counts such as large moves, trades or defaults per period) and multinomial (counts over categories per period).
- [Gaussian regime models](https://docs.fincept.in/reference/regimes/gaussian.md): Hidden Markov models with Gaussian, Gaussian-mixture or variational Gaussian emissions on returns or feature tables: regimes labelled by volatility or mean, transition matrix, per-regime means and volatilities, state path and probabilities, AIC/BIC, convergence, forecast and the fitted model.
- [Apply a fitted regime model](https://docs.fincept.in/reference/regimes/inference.md): Use data.model of a regime fit without refitting: decode new or recent data into regimes with smoothed or real-time filtered probabilities, score it (log-likelihood, AIC/BIC, emission densities), and simulate regime scenarios.
- [Number of regimes](https://docs.fincept.in/reference/regimes/selection.md): Choose how many regimes the data supports: a model family fitted across a range of state counts with random restarts, compared by BIC, AIC, log-likelihood, variational lower bound or held-out log-likelihood; the winning model is returned ready for regime_decode and regime_simulate.
- [Fincept Copula](https://docs.fincept.in/reference/copula/overview.md): Every Fincept Copula module and its toolset.
- [Dependence measures](https://docs.fincept.in/reference/copula/dependence.md): Kendall's tau, Spearman's rho, Pearson, Blomqvist's beta, Hoeffding's D and Chatterjee's xi matrices between assets, implied copula parameters, tail dependence from pairwise copulas, pseudo-observations, quasi-random uniform samples and latent samples for discrete pairs.
- [Margins and joint distributions](https://docs.fincept.in/reference/copula/distribution.md): Univariate margins (boundary-corrected kernel densities for continuous, discrete and zero-inflated data, or the best parametric family by AIC/BIC/AICc, or a density given on a grid) and joint distributions of many assets combining margins with a vine copula: density, joint probabilities, Rosenblatt…
- [Bivariate copulas](https://docs.fincept.in/reference/copula/pair.md): Copulas for two assets: fit a chosen family and rotation or select the best of every family (Gaussian, Student, Clayton, Gumbel, Frank, Joe, BB1, BB6, BB7, BB8, Tawn, nonparametric TLL) by AIC/BIC/mBIC, compare families, parameter standard errors; densities, distribution and h-functions with their i…
- [Vine regression](https://docs.fincept.in/reference/copula/regression.md): Conditional mean and quantiles of one asset given others from a vine copula with kernel margins: nonlinear, tail-aware regression such as a stock's 5% quantile given the market (CoVaR-style).
- [Copula portfolio risk](https://docs.fincept.in/reference/copula/risk.md): Portfolio value at risk and expected shortfall from vine-copula scenarios mapped through each asset's empirical, kernel or parametric margin, with Gaussian-copula and historical benchmarks and expected shortfall contributions; stress scenarios that hold chosen assets at a shock and draw the rest fro…
- [Vine copulas](https://docs.fincept.in/reference/copula/vine.md): Vine copulas for many assets: R-vine structure selection (maximum or random spanning trees on Kendall's tau, Spearman, Hoeffding, Chatterjee or mutual information), C- and D-vines, given matrices, truncation, thresholding, family sets and criteria; structures built by hand; density, distribution, Ro…
- [Fincept Multi-Period](https://docs.fincept.in/reference/multi-period/overview.md): Every Fincept Multi-Period module and its toolset.
- [Back-tests](https://docs.fincept.in/reference/multi-period/backtest.md): Back-tests of trading policies on your own history with a market simulator that charges spreads, market impact, per-share fees and holding costs: single- and multi-period optimisation policies with constraints, and rule-based policies (hold, equal weight, fixed weights, rebalancing, rank long-short,…
- [Forecasts and data](https://docs.fincept.in/reference/multi-period/forecast.md): The estimates trading policies use, computed at any date from earlier data only: mean returns, variances, volatilities, standard errors, covariance matrices, PCA and SVD factor models and mean traded volumes, with rolling windows and exponential half-lives; and how your history is prepared (trading…
- [Trades now](https://docs.fincept.in/reference/multi-period/trade.md): What to trade today: the optimal trades of a single- or multi-period optimisation policy (with the planned weights of every step ahead), the trades of a rule-based policy, and a pre-trade evaluation of any proposed trade (expected return, risk, transaction and holding costs, constraint checks).
- [Aversion tuning](https://docs.fincept.in/reference/multi-period/tuning.md): Tune an optimisation policy's risk, trade and holding aversions by back-testing: a grid of values side by side (risk-return frontier of realized results) or a greedy search that keeps moving the values while Sharpe ratio, return or growth improves.
- [Fincept Portfolio Lab](https://docs.fincept.in/reference/portfolio-lab/overview.md): Every Fincept Portfolio Lab module and its toolset.
- [Portfolio analytics](https://docs.fincept.in/reference/portfolio-lab/analytics.md): 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 fro…
- [Distributions, copulas and stress tests](https://docs.fincept.in/reference/portfolio-lab/distributions.md): 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 generat…
- [Cross-sectional factor lab](https://docs.fincept.in/reference/portfolio-lab/factorlab.md): 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 characterist…
- [Portfolio optimisation](https://docs.fincept.in/reference/portfolio-lab/optimize.md): 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 an…
- [Hierarchical portfolios](https://docs.fincept.in/reference/portfolio-lab/hierarchical.md): 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…
- [Moment estimators](https://docs.fincept.in/reference/portfolio-lab/moments.md): 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…
- [Priors and views](https://docs.fincept.in/reference/portfolio-lab/priors.md): 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 fact…
- [Validation and tuning](https://docs.fincept.in/reference/portfolio-lab/validation.md): 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…
- [Fincept Pricing](https://docs.fincept.in/reference/pricing/overview.md): Every Fincept Pricing module and its toolset.
- [Bonds](https://docs.fincept.in/reference/pricing/bonds.md): Fixed, zero-coupon, amortizing and floating-rate bonds (term, overnight and CMS indexes, caps and floors), callable and puttable bonds on short-rate trees, convertible bonds, bond forwards and asset swaps: clean/dirty price, yield, z-spread, option-adjusted spread, durations, convexity, BPS and cash…
- [Credit](https://docs.fincept.in/reference/pricing/credit.md): Default-probability curves bootstrapped from CDS spreads or upfronts (or given as hazard rates, survival probabilities or densities), credit default swaps (fair spread, upfront, RPV01, implied hazard, midpoint/ISDA/integral engines), CDS options and bonds priced with default risk.
- [Yield curves](https://docs.fincept.in/reference/pricing/curves.md): Bootstrapped yield curves from deposits, FRAs, futures, swaps, OIS, bonds and basis swaps with every interpolation; curves from zero rates, discount factors, forwards or par yields; evaluation of zero, discount and forward rates; spreaded, implied, composite and ultimate-forward curves; Nelson-Siege…
- [Dates, calendars and schedules](https://docs.fincept.in/reference/pricing/dates.md): Business-day calendars of every market (joint and custom ones), holiday lists, date adjustment and advancing, business-day counts, day count conventions and year fractions, coupon schedules with every date-generation rule, IMM/ASX/ECB dates, CDS maturities and tenor arithmetic.
- [Inflation](https://docs.fincept.in/reference/pricing/inflation.md): Zero-coupon and year-on-year inflation curves from swap quotes or breakevens over published CPI/RPI/HICP fixings with seasonality, projected index levels, zero-coupon / year-on-year / CPI swaps, inflation-linked bonds (TIPS, linkers: real yield, index ratio) and year-on-year caps and floors.
- [Numerical tools](https://docs.fincept.in/reference/pricing/math.md): Interpolation (linear to monotone cubic, Kruger, Fritsch-Butland, parabolic, convex-monotone, Lagrange, Chebyshev, bilinear, bicubic), integration and Gaussian quadrature, probability distributions, statistics and risk measures, matrix decompositions and solvers, pseudo-random and low-discrepancy se…
- [Models and calibration](https://docs.fincept.in/reference/pricing/models.md): Heston, Bates, piecewise time-dependent Heston and GJR-GARCH calibrated to option volatilities; Hull-White, Black-Karasinski, G2++, Vasicek, CIR, extended CIR and GSR calibrated to swaption and cap volatilities; zero-coupon bond prices, bond options and short-rate statistics under a short-rate model…
- [Equity and FX options](https://docs.fincept.in/reference/pricing/options.md): Vanilla and exotic options on one or several underlyings: European, American, Bermudan; barrier, double barrier, Asian, lookback, digital, compound, chooser, extensible, forward-start, cliquet, basket, spread, exchange, quanto and swing options; Black-Scholes, Heston, Bates, SABR, CEV, variance gamm…
- [Swaps, FRAs and futures](https://docs.fincept.in/reference/pricing/swaps.md): Fixed-float, overnight-indexed (SOFR, ESTR, SONIA ...), zero-coupon, multiple-reset, amortizing, basis (float-float, CMS) and cross-currency swaps, FX forwards, FRAs, overnight-index and perpetual futures, and equity total-return swaps: NPV, par rates, spreads, leg values, DV01 and cash flows.
- [Interest-rate options](https://docs.fincept.in/reference/pricing/rate-options.md): Caps, floors and collars; European and Bermudan swaptions (physical or cash settled); Black, shifted Black, Bachelier and short-rate model engines (Hull-White, Black-Karasinski, G2, Vasicek, CIR, GSR, Markov functional); caplet stripping, swaption volatility matrices and SABR cubes; CMS coupons and…
- [Interest-rate maths and currencies](https://docs.fincept.in/reference/pricing/rates.md): Converting interest rates between simple, compounded and continuous conventions, frequencies and day counts; compound and discount factors; implied rates; currency facts, roundings and conversion through your exchange rates.
- [Volatility surfaces and smiles](https://docs.fincept.in/reference/pricing/volatility.md): Black volatility surfaces, Dupire and fixed local volatility, Andreasen-Huge arbitrage-free interpolation, FX delta-quoted surfaces, Heston-implied surfaces, SABR / SVI / ZABR / Kahale smiles (evaluated or fitted), and risk-neutral densities under Black-Scholes, Heston, local volatility, CEV, square…


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