# Fincept API ## Docs - [Advanced Workflows](https://docs.fincept.in/advanced/advanced-workflows.md): Multi-step calculations and complex workflows - [Batch Operations](https://docs.fincept.in/advanced/batch-operations.md): Efficient batch processing patterns - [Integration Patterns](https://docs.fincept.in/advanced/integration-patterns.md): Best practices for integrating Fincept API - [Performance Optimization](https://docs.fincept.in/advanced/performance-optimization.md): Reduce costs and improve speed - [API Keys](https://docs.fincept.in/api-keys.md): Complete guide to API key types, management, and lifecycle - [Altman Z-Score bankruptcy prediction](https://docs.fincept.in/api-reference/quantlib-analysis/altman-z-score-bankruptcy-prediction.md): Altman Z-Score bankruptcy prediction [Tier: STANDARD, Credits: 2] - [Beneish M-Score (earnings manipulation detection)](https://docs.fincept.in/api-reference/quantlib-analysis/beneish-m-score-earnings-manipulation-detection.md): Beneish M-Score (earnings manipulation detection) [Tier: STANDARD, Credits: 2] - [Berkus Method startup valuation](https://docs.fincept.in/api-reference/quantlib-analysis/berkus-method-startup-valuation.md): Berkus Method startup valuation [Tier: STANDARD, Credits: 2] - [Calculate cost of equity (CAPM or build-up method)](https://docs.fincept.in/api-reference/quantlib-analysis/calculate-cost-of-equity-capm-or-build-up-method.md): Calculate cost of equity (CAPM or build-up method) [Tier: STANDARD, Credits: 2] - [Calculate credit spread from probability of default](https://docs.fincept.in/api-reference/quantlib-analysis/calculate-credit-spread-from-probability-of-default.md): Calculate credit spread from probability of default [Tier: STANDARD, Credits: 2] - [Calculate distance to default](https://docs.fincept.in/api-reference/quantlib-analysis/calculate-distance-to-default.md): Calculate distance to default [Tier: STANDARD, Credits: 2] - [Calculate Economic Value Added](https://docs.fincept.in/api-reference/quantlib-analysis/calculate-economic-value-added.md): Calculate Economic Value Added [Tier: STANDARD, Credits: 2] - [Calculate Residual Income](https://docs.fincept.in/api-reference/quantlib-analysis/calculate-residual-income.md): Calculate Residual Income [Tier: STANDARD, Credits: 2] - [Calculate terminal value (perpetuity or exit multiple)](https://docs.fincept.in/api-reference/quantlib-analysis/calculate-terminal-value-perpetuity-or-exit-multiple.md): Calculate terminal value (perpetuity or exit multiple) [Tier: STANDARD, Credits: 2] - [Calculate WACC and analyze capital structure](https://docs.fincept.in/api-reference/quantlib-analysis/calculate-wacc-and-analyze-capital-structure.md): Calculate WACC and analyze capital structure [Tier: STANDARD, Credits: 2] - [Calculate Weighted Average Cost of Capital](https://docs.fincept.in/api-reference/quantlib-analysis/calculate-weighted-average-cost-of-capital.md): Calculate Weighted Average Cost of Capital [Tier: STANDARD, Credits: 2] - [Cash flow analysis (FCF, quality, coverage, sustainability)](https://docs.fincept.in/api-reference/quantlib-analysis/cash-flow-analysis-fcf-quality-coverage-sustainability.md): Cash flow analysis (FCF, quality, coverage, sustainability) [Tier: STANDARD, Credits: 2] - [Comparable company analysis (relative valuation multiples)](https://docs.fincept.in/api-reference/quantlib-analysis/comparable-company-analysis-relative-valuation-multiples.md): Comparable company analysis (relative valuation multiples) [Tier: STANDARD, Credits: 2] - [Comprehensive banking industry analysis (NIM, asset quality, efficiency, capital)](https://docs.fincept.in/api-reference/quantlib-analysis/comprehensive-banking-industry-analysis-nim-asset-quality-efficiency-capital.md): Comprehensive banking industry analysis (NIM, asset quality, efficiency, capital) [Tier: STANDARD, Credits: 2] - [Comprehensive efficiency analysis (turnover, cycles, capex)](https://docs.fincept.in/api-reference/quantlib-analysis/comprehensive-efficiency-analysis-turnover-cycles-capex.md): Comprehensive efficiency analysis (turnover, cycles, capex) [Tier: STANDARD, Credits: 2] - [Comprehensive insurance industry analysis (loss ratio, combined ratio, solvency)](https://docs.fincept.in/api-reference/quantlib-analysis/comprehensive-insurance-industry-analysis-loss-ratio-combined-ratio-solvency.md): Comprehensive insurance industry analysis (loss ratio, combined ratio, solvency) [Tier: STANDARD, Credits: 2] - [Comprehensive liquidity analysis (ratios, working capital, cash)](https://docs.fincept.in/api-reference/quantlib-analysis/comprehensive-liquidity-analysis-ratios-working-capital-cash.md): Comprehensive liquidity analysis (ratios, working capital, cash) [Tier: STANDARD, Credits: 2] - [Comprehensive profitability analysis (margins, returns, efficiency)](https://docs.fincept.in/api-reference/quantlib-analysis/comprehensive-profitability-analysis-margins-returns-efficiency.md): Comprehensive profitability analysis (margins, returns, efficiency) [Tier: STANDARD, Credits: 2] - [Comprehensive REIT analysis (FFO, AFFO, NAV, cap rate, dividend metrics)](https://docs.fincept.in/api-reference/quantlib-analysis/comprehensive-reit-analysis-ffo-affo-nav-cap-rate-dividend-metrics.md): Comprehensive REIT analysis (FFO, AFFO, NAV, cap rate, dividend metrics) [Tier: STANDARD, Credits: 2] - [Comprehensive utilities industry analysis (operating ratio, rate base, efficiency)](https://docs.fincept.in/api-reference/quantlib-analysis/comprehensive-utilities-industry-analysis-operating-ratio-rate-base-efficiency.md): Comprehensive utilities industry analysis (operating ratio, rate base, efficiency) [Tier: STANDARD, Credits: 2] - [DCF valuation using FCFF](https://docs.fincept.in/api-reference/quantlib-analysis/dcf-valuation-using-fcff.md): DCF valuation using FCFF [Tier: STANDARD, Credits: 2] - [Dividend Discount Model (single or two-stage)](https://docs.fincept.in/api-reference/quantlib-analysis/dividend-discount-model-single-or-two-stage.md): Dividend Discount Model (single or two-stage) [Tier: STANDARD, Credits: 2] - [DuPont decomposition (3-factor and 5-factor)](https://docs.fincept.in/api-reference/quantlib-analysis/dupont-decomposition-3-factor-and-5-factor.md): DuPont decomposition (3-factor and 5-factor) [Tier: STANDARD, Credits: 2] - [Earnings quality analysis (accruals, persistence, red flags)](https://docs.fincept.in/api-reference/quantlib-analysis/earnings-quality-analysis-accruals-persistence-red-flags.md): Earnings quality analysis (accruals, persistence, red flags) [Tier: STANDARD, Credits: 2] - [Expected Loss](https://docs.fincept.in/api-reference/quantlib-analysis/expected-loss.md): Expected Loss [Tier: STANDARD, Credits: 2] - [Factor model analysis (value, size, quality, momentum, profitability factors)](https://docs.fincept.in/api-reference/quantlib-analysis/factor-model-analysis-value-size-quality-momentum-profitability-factors.md): Factor model analysis (value, size, quality, momentum, profitability factors) [Tier: STANDARD, Credits: 2] - [Find optimal capital structure (WACC minimization + trade-off)](https://docs.fincept.in/api-reference/quantlib-analysis/find-optimal-capital-structure-wacc-minimization-+-trade-off.md): Find optimal capital structure (WACC minimization + trade-off) [Tier: STANDARD, Credits: 2] - [First Chicago Method (scenario-based startup valuation)](https://docs.fincept.in/api-reference/quantlib-analysis/first-chicago-method-scenario-based-startup-valuation.md): First Chicago Method (scenario-based startup valuation) [Tier: STANDARD, Credits: 2] - [Full comprehensive financial analysis (all ratios + scoring)](https://docs.fincept.in/api-reference/quantlib-analysis/full-comprehensive-financial-analysis-all-ratios-+-scoring.md): Full comprehensive financial analysis (all ratios + scoring) [Tier: STANDARD, Credits: 2] - [Gordon Growth Model (dividend discount)](https://docs.fincept.in/api-reference/quantlib-analysis/gordon-growth-model-dividend-discount.md): Gordon Growth Model (dividend discount) [Tier: STANDARD, Credits: 2] - [Growth analysis (revenue, earnings, FCF, CAGR, sustainable growth)](https://docs.fincept.in/api-reference/quantlib-analysis/growth-analysis-revenue-earnings-fcf-cagr-sustainable-growth.md): Growth analysis (revenue, earnings, FCF, CAGR, sustainable growth) [Tier: STANDARD, Credits: 2] - [Merton structural credit model (distance to default, PD)](https://docs.fincept.in/api-reference/quantlib-analysis/merton-structural-credit-model-distance-to-default-pd.md): Merton structural credit model (distance to default, PD) [Tier: STANDARD, Credits: 2] - [Ohlson O-Score bankruptcy prediction](https://docs.fincept.in/api-reference/quantlib-analysis/ohlson-o-score-bankruptcy-prediction.md): Ohlson O-Score bankruptcy prediction [Tier: STANDARD, Credits: 2] - [Ownership Dilution](https://docs.fincept.in/api-reference/quantlib-analysis/ownership-dilution.md): Ownership Dilution [Tier: STANDARD, Credits: 2] - [Piotroski F-Score (financial strength, 0-9)](https://docs.fincept.in/api-reference/quantlib-analysis/piotroski-f-score-financial-strength-0-9.md): Piotroski F-Score (financial strength, 0-9) [Tier: STANDARD, Credits: 2] - [Pro-forma adjustments (R&D capitalization, lease capitalization, goodwill, pension)](https://docs.fincept.in/api-reference/quantlib-analysis/pro-forma-adjustments-r&d-capitalization-lease-capitalization-goodwill-pension.md): Pro-forma adjustments (R&D capitalization, lease capitalization, goodwill, pension) [Tier: STANDARD, Credits: 2] - [Rating Pd](https://docs.fincept.in/api-reference/quantlib-analysis/rating-pd.md): Rating Pd [Tier: STANDARD, Credits: 2] - [Residual Income valuation model](https://docs.fincept.in/api-reference/quantlib-analysis/residual-income-valuation-model.md): Residual Income valuation model [Tier: STANDARD, Credits: 2] - [Solvency analysis (leverage, coverage, debt ratios)](https://docs.fincept.in/api-reference/quantlib-analysis/solvency-analysis-leverage-coverage-debt-ratios.md): Solvency analysis (leverage, coverage, debt ratios) [Tier: STANDARD, Credits: 2] - [Springate S-Score bankruptcy prediction](https://docs.fincept.in/api-reference/quantlib-analysis/springate-s-score-bankruptcy-prediction.md): Springate S-Score bankruptcy prediction [Tier: STANDARD, Credits: 2] - [Stock screening (value, quality, growth criteria)](https://docs.fincept.in/api-reference/quantlib-analysis/stock-screening-value-quality-growth-criteria.md): Stock screening (value, quality, growth criteria) [Tier: STANDARD, Credits: 2] - [Sum-of-the-Parts valuation (segment-level EV/EBITDA and EV/Revenue)](https://docs.fincept.in/api-reference/quantlib-analysis/sum-of-the-parts-valuation-segment-level-evebitda-and-evrevenue.md): Sum-of-the-Parts valuation (segment-level EV/EBITDA and EV/Revenue) [Tier: STANDARD, Credits: 2] - [Two-stage DCF valuation](https://docs.fincept.in/api-reference/quantlib-analysis/two-stage-dcf-valuation.md): Two-stage DCF valuation [Tier: STANDARD, Credits: 2] - [Venture Capital method valuation](https://docs.fincept.in/api-reference/quantlib-analysis/venture-capital-method-valuation.md): Venture Capital method valuation [Tier: STANDARD, Credits: 2] - [Zmijewski Z-Score (probit bankruptcy model)](https://docs.fincept.in/api-reference/quantlib-analysis/zmijewski-z-score-probit-bankruptcy-model.md): Zmijewski Z-Score (probit bankruptcy model) [Tier: STANDARD, Credits: 2] - [Array Statistics](https://docs.fincept.in/api-reference/quantlib-core/array-statistics.md): Compute mean and standard deviation of an array [Tier: BASIC, Credits: 1] - [Bivariate Normal](https://docs.fincept.in/api-reference/quantlib-core/bivariate-normal.md): Bivariate normal CDF [Tier: BASIC, Credits: 1] - [Black Scholes](https://docs.fincept.in/api-reference/quantlib-core/black-scholes.md): Black-Scholes option price (call or put) [Tier: PRO, Credits: 5] - [Black76](https://docs.fincept.in/api-reference/quantlib-core/black76.md): Black76 option price (call or put) [Tier: PRO, Credits: 5] - [Chi2 Cdf](https://docs.fincept.in/api-reference/quantlib-core/chi2-cdf.md): Chi-squared CDF [Tier: BASIC, Credits: 1] - [Chi2 Pdf](https://docs.fincept.in/api-reference/quantlib-core/chi2-pdf.md): Chi-squared PDF [Tier: BASIC, Credits: 1] - [Cholesky Decomp](https://docs.fincept.in/api-reference/quantlib-core/cholesky-decomp.md): Cholesky decomposition of a positive definite matrix [Tier: BASIC, Credits: 1] - [Compute Gradient](https://docs.fincept.in/api-reference/quantlib-core/compute-gradient.md): Compute gradient of a built-in function at given point(s) [Tier: STANDARD, Credits: 2] - [Covariance Matrix](https://docs.fincept.in/api-reference/quantlib-core/covariance-matrix.md): Compute covariance matrix from returns [Tier: BASIC, Credits: 1] - [Day Count Fraction](https://docs.fincept.in/api-reference/quantlib-core/day-count-fraction.md): Calculate day count fraction between two dates [Tier: PRO, Credits: 5] - [Days To Years](https://docs.fincept.in/api-reference/quantlib-core/days-to-years.md): Convert days to year fraction [Tier: FREE, Credits: 0] - [Discount Cashflows](https://docs.fincept.in/api-reference/quantlib-core/discount-cashflows.md): Discount a series of cashflows using discount factors [Tier: STANDARD, Credits: 2] - [Dual Eval](https://docs.fincept.in/api-reference/quantlib-core/dual-eval.md): Evaluate a function and its derivative using dual numbers (automatic differentiation) [Tier: STANDARD, Credits: 2] - [Exp Dist Cdf](https://docs.fincept.in/api-reference/quantlib-core/exp-dist-cdf.md): Exponential distribution CDF [Tier: BASIC, Credits: 1] - [Exp Dist Pdf](https://docs.fincept.in/api-reference/quantlib-core/exp-dist-pdf.md): Exponential distribution PDF [Tier: BASIC, Credits: 1] - [Exp Dist Ppf](https://docs.fincept.in/api-reference/quantlib-core/exp-dist-ppf.md): Exponential distribution inverse CDF [Tier: BASIC, Credits: 1] - [Fixed Coupon Period](https://docs.fincept.in/api-reference/quantlib-core/fixed-coupon-period.md): Create a fixed coupon period and return accrual and cashflow [Tier: PRO, Credits: 5] - [Fixed Leg](https://docs.fincept.in/api-reference/quantlib-core/fixed-leg.md): Create a fixed-rate leg and return its cashflows [Tier: PRO, Credits: 5] - [Float Coupon Period](https://docs.fincept.in/api-reference/quantlib-core/float-coupon-period.md): Create a floating coupon period and return accrual and cashflow [Tier: PRO, Credits: 5] - [Float Leg](https://docs.fincept.in/api-reference/quantlib-core/float-leg.md): Create a floating-rate leg and return its cashflows [Tier: PRO, Credits: 5] - [Format Date Endpoint](https://docs.fincept.in/api-reference/quantlib-core/format-date-endpoint.md): Format a date string into different formats [Tier: FREE, Credits: 0] - [Forward Rate](https://docs.fincept.in/api-reference/quantlib-core/forward-rate.md): Calculate forward rate from two discount factors [Tier: STANDARD, Credits: 2] - [Gamma Dist Cdf](https://docs.fincept.in/api-reference/quantlib-core/gamma-dist-cdf.md): Gamma distribution CDF [Tier: BASIC, Credits: 1] - [Gamma Dist Pdf](https://docs.fincept.in/api-reference/quantlib-core/gamma-dist-pdf.md): Gamma distribution PDF [Tier: BASIC, Credits: 1] - [Gbm Paths](https://docs.fincept.in/api-reference/quantlib-core/gbm-paths.md): Generate Geometric Brownian Motion simulation paths [Tier: PRO, Credits: 5] - [Interpolate](https://docs.fincept.in/api-reference/quantlib-core/interpolate.md): Interpolate a value using linear, log-linear, or cubic spline methods [Tier: BASIC, Credits: 1] - [List Currencies](https://docs.fincept.in/api-reference/quantlib-core/list-currencies.md): List all supported currencies [Tier: FREE, Credits: 0] - [List Frequencies](https://docs.fincept.in/api-reference/quantlib-core/list-frequencies.md): List all supported frequencies [Tier: FREE, Credits: 0] - [Math Eval](https://docs.fincept.in/api-reference/quantlib-core/math-eval.md): Evaluate a math function (sin, cos, tan, exp, log, sqrt, etc.) [Tier: FREE, Credits: 0] - [Math Two Arg](https://docs.fincept.in/api-reference/quantlib-core/math-two-arg.md): Evaluate two-argument math functions (maximum, minimum, power) [Tier: FREE, Credits: 0] - [Money Convert](https://docs.fincept.in/api-reference/quantlib-core/money-convert.md): Convert money between currencies using a given rate [Tier: FREE, Credits: 0] - [Money Create](https://docs.fincept.in/api-reference/quantlib-core/money-create.md): Create a Money object and return formatted value [Tier: FREE, Credits: 0] - [Normal Cdf](https://docs.fincept.in/api-reference/quantlib-core/normal-cdf.md): Standard normal cumulative distribution function [Tier: BASIC, Credits: 1] - [Normal Pdf](https://docs.fincept.in/api-reference/quantlib-core/normal-pdf.md): Standard normal probability density function [Tier: BASIC, Credits: 1] - [Normal Ppf](https://docs.fincept.in/api-reference/quantlib-core/normal-ppf.md): Standard normal percent point function (inverse CDF) [Tier: BASIC, Credits: 1] - [Normalize Rate](https://docs.fincept.in/api-reference/quantlib-core/normalize-rate.md): Normalize a rate value to decimal form [Tier: FREE, Credits: 0] - [Normalize Vol](https://docs.fincept.in/api-reference/quantlib-core/normalize-vol.md): Normalize a volatility value [Tier: FREE, Credits: 0] - [Notional Schedule](https://docs.fincept.in/api-reference/quantlib-core/notional-schedule.md): Generate a notional schedule (constant, linear, mortgage, bullet) [Tier: FREE, Credits: 0] - [Parse Date Endpoint](https://docs.fincept.in/api-reference/quantlib-core/parse-date-endpoint.md): Parse a date string into ISO format [Tier: FREE, Credits: 0] - [Percentile](https://docs.fincept.in/api-reference/quantlib-core/percentile.md): Compute percentile of an array [Tier: BASIC, Credits: 1] - [Rate Convert](https://docs.fincept.in/api-reference/quantlib-core/rate-convert.md): Create a Rate object and return in all units (decimal, percentage, bps) [Tier: FREE, Credits: 0] - [Spread From Bps](https://docs.fincept.in/api-reference/quantlib-core/spread-from-bps.md): Create a Spread from basis points [Tier: FREE, Credits: 0] - [T Cdf](https://docs.fincept.in/api-reference/quantlib-core/t-cdf.md): Student's t cumulative distribution function [Tier: BASIC, Credits: 1] - [T Pdf](https://docs.fincept.in/api-reference/quantlib-core/t-pdf.md): Student's t probability density function [Tier: BASIC, Credits: 1] - [T Ppf](https://docs.fincept.in/api-reference/quantlib-core/t-ppf.md): Student's t inverse CDF [Tier: BASIC, Credits: 1] - [Taylor Expansion](https://docs.fincept.in/api-reference/quantlib-core/taylor-expansion.md): Compute Taylor series coefficients of a function around x0 [Tier: STANDARD, Credits: 2] - [Tenor Add To Date](https://docs.fincept.in/api-reference/quantlib-core/tenor-add-to-date.md): Add a tenor (e.g. '3M', '1Y') to a date and return the result date [Tier: FREE, Credits: 0] - [Value At Risk](https://docs.fincept.in/api-reference/quantlib-core/value-at-risk.md): Calculate Value at Risk (historical or parametric) [Tier: PRO, Credits: 5] - [Years To Days](https://docs.fincept.in/api-reference/quantlib-core/years-to-days.md): Convert year fraction to days [Tier: FREE, Credits: 0] - [Zero Coupon Leg](https://docs.fincept.in/api-reference/quantlib-core/zero-coupon-leg.md): Create a zero-coupon leg and return its cashflow [Tier: PRO, Credits: 5] - [Zero Rate Convert](https://docs.fincept.in/api-reference/quantlib-core/zero-rate-convert.md): Convert between discount factor and zero rate [Tier: STANDARD, Credits: 2] - [Build Curve](https://docs.fincept.in/api-reference/quantlib-curves/build-curve.md): Build Curve [Tier: STANDARD, Credits: 2] - [Butterfly](https://docs.fincept.in/api-reference/quantlib-curves/butterfly.md): Butterfly [Tier: STANDARD, Credits: 2] - [Composite Curve](https://docs.fincept.in/api-reference/quantlib-curves/composite-curve.md): Composite Curve [Tier: STANDARD, Credits: 2] - [Constrained Fit](https://docs.fincept.in/api-reference/quantlib-curves/constrained-fit.md): Constrained Fit [Tier: STANDARD, Credits: 2] - [Curve Points](https://docs.fincept.in/api-reference/quantlib-curves/curve-points.md): Curve Points [Tier: STANDARD, Credits: 2] - [Discount Factor](https://docs.fincept.in/api-reference/quantlib-curves/discount-factor.md): Discount Factor [Tier: STANDARD, Credits: 2] - [Forward Rate](https://docs.fincept.in/api-reference/quantlib-curves/forward-rate.md): Forward Rate [Tier: STANDARD, Credits: 2] - [Inflation Bootstrap](https://docs.fincept.in/api-reference/quantlib-curves/inflation-bootstrap.md): Inflation Bootstrap [Tier: STANDARD, Credits: 2] - [Inflation Curve Build](https://docs.fincept.in/api-reference/quantlib-curves/inflation-curve-build.md): Inflation Curve Build [Tier: STANDARD, Credits: 2] - [Inflation Seasonality](https://docs.fincept.in/api-reference/quantlib-curves/inflation-seasonality.md): Inflation Seasonality [Tier: STANDARD, Credits: 2] - [Instantaneous Forward](https://docs.fincept.in/api-reference/quantlib-curves/instantaneous-forward.md): Instantaneous Forward [Tier: STANDARD, Credits: 2] - [Interpolate](https://docs.fincept.in/api-reference/quantlib-curves/interpolate.md): Interpolate [Tier: STANDARD, Credits: 2] - [Interpolate Derivative](https://docs.fincept.in/api-reference/quantlib-curves/interpolate-derivative.md): Interpolate Derivative [Tier: STANDARD, Credits: 2] - [Key Rate Shift](https://docs.fincept.in/api-reference/quantlib-curves/key-rate-shift.md): Key Rate Shift [Tier: STANDARD, Credits: 2] - [Monotonicity Check](https://docs.fincept.in/api-reference/quantlib-curves/monotonicity-check.md): Monotonicity Check [Tier: STANDARD, Credits: 2] - [Multicurve Basis](https://docs.fincept.in/api-reference/quantlib-curves/multicurve-basis.md): Multicurve Basis [Tier: STANDARD, Credits: 2] - [Multicurve Setup](https://docs.fincept.in/api-reference/quantlib-curves/multicurve-setup.md): Multicurve Setup [Tier: STANDARD, Credits: 2] - [Nelson Siegel Eval](https://docs.fincept.in/api-reference/quantlib-curves/nelson-siegel-eval.md): Nelson Siegel Eval [Tier: STANDARD, Credits: 2] - [Nelson Siegel Fit](https://docs.fincept.in/api-reference/quantlib-curves/nelson-siegel-fit.md): Nelson Siegel Fit [Tier: STANDARD, Credits: 2] - [Nss Eval](https://docs.fincept.in/api-reference/quantlib-curves/nss-eval.md): Nss Eval [Tier: STANDARD, Credits: 2] - [Nss Fit](https://docs.fincept.in/api-reference/quantlib-curves/nss-fit.md): Nss Fit [Tier: STANDARD, Credits: 2] - [Parallel Shift](https://docs.fincept.in/api-reference/quantlib-curves/parallel-shift.md): Parallel Shift [Tier: STANDARD, Credits: 2] - [Proxy Curve](https://docs.fincept.in/api-reference/quantlib-curves/proxy-curve.md): Proxy Curve [Tier: STANDARD, Credits: 2] - [Roll Curve](https://docs.fincept.in/api-reference/quantlib-curves/roll-curve.md): Roll Curve [Tier: STANDARD, Credits: 2] - [Scaled Curve](https://docs.fincept.in/api-reference/quantlib-curves/scaled-curve.md): Scaled Curve [Tier: STANDARD, Credits: 2] - [Smoothness Penalty](https://docs.fincept.in/api-reference/quantlib-curves/smoothness-penalty.md): Smoothness Penalty [Tier: STANDARD, Credits: 2] - [Twist](https://docs.fincept.in/api-reference/quantlib-curves/twist.md): Twist [Tier: STANDARD, Credits: 2] - [Zero Rate](https://docs.fincept.in/api-reference/quantlib-curves/zero-rate.md): Zero Rate [Tier: STANDARD, Credits: 2] - [Arrow-Pratt CE Approximation](https://docs.fincept.in/api-reference/quantlib-economics/arrow-pratt-ce-approximation.md): Calculate Arrow-Pratt second-order approximation of certainty equivalent: CE ≈ μ - (A/2)*σ² where μ is mean, σ² is variance, and A is absolute risk aversion. Valid for small risks and provides closed-form approximation without numerical inversion. Returns approximate certainty equivalent. Use for qu… - [Calculate Equilibrium Bids](https://docs.fincept.in/api-reference/quantlib-economics/calculate-equilibrium-bids.md): Calculate Bayes-Nash equilibrium bid functions for different auction formats. In first-price auctions, bidders shade their bids below valuation. In second-price auctions, truthful bidding is dominant. Returns equilibrium bid for each provided valuation. Use for bidding strategy analysis, mechanism d… - [CARA Utility Function](https://docs.fincept.in/api-reference/quantlib-economics/cara-utility-function.md): Evaluate Constant Absolute Risk Aversion (CARA) utility function: U(w) = -exp(-A*w)/A where A is the risk aversion coefficient. Returns utility value, marginal utility, absolute risk aversion (constant = A), and relative risk aversion (A*w). CARA exhibits constant absolute risk aversion regardless o… - [Certainty Equivalent](https://docs.fincept.in/api-reference/quantlib-economics/certainty-equivalent.md): Calculate the certainty equivalent (CE) of a risky lottery: the guaranteed amount that yields the same utility as the risky prospect. CE solves U(CE) = E[U(X)] where X is the random outcome. For risk-averse individuals, CE < E[X] (expected value). The difference E[X] - CE is the risk premium. Return… - [CES Consumer Demand](https://docs.fincept.in/api-reference/quantlib-economics/ces-consumer-demand.md): Compute optimal consumption bundle and utility for a consumer with Constant Elasticity of Substitution (CES) preferences. CES utility allows for varying degrees of substitutability between goods, from perfect substitutes to Leontief (perfect complements). Used in production theory, international tra… - [Cobb-Douglas Consumer Demand](https://docs.fincept.in/api-reference/quantlib-economics/cobb-douglas-consumer-demand.md): Compute optimal consumption bundle, utility level, expenditure, and indirect utility for a consumer with Cobb-Douglas preferences U(x1, x2) = x1^alpha * x2^(1-alpha). Used in microeconomic analysis to determine consumer choice under budget constraints, analyze income and substitution effects, and de… - [Compute Best Responses](https://docs.fincept.in/api-reference/quantlib-economics/compute-best-responses.md): Find the best response strategy for each player given the opponent's (possibly mixed) strategy. A best response maximizes a player's expected payoff given the opponent's strategy. Returns optimal strategies for both players. Essential for Nash equilibrium analysis, as mutual best responses character… - [Create Normal-Form Game](https://docs.fincept.in/api-reference/quantlib-economics/create-normal-form-game.md): Create a normal-form (strategic-form) game from payoff matrices and find all pure strategy Nash equilibria. A Nash equilibrium is a strategy profile where no player can improve their payoff by unilaterally changing strategy. Use for analyzing strategic interactions, competitive scenarios, and predic… - [CRRA Utility Function](https://docs.fincept.in/api-reference/quantlib-economics/crra-utility-function.md): Evaluate Constant Relative Risk Aversion (CRRA) utility function: U(w) = w^(1-gamma)/(1-gamma) for gamma ≠ 1, or U(w) = ln(w) for gamma = 1. Returns utility value, marginal utility, absolute risk aversion (gamma/w), and relative risk aversion (constant = gamma). CRRA exhibits constant relative risk… - [Eliminate Dominated Strategies](https://docs.fincept.in/api-reference/quantlib-economics/eliminate-dominated-strategies.md): Iteratively eliminate strictly dominated strategies to simplify the game. A strategy is strictly dominated if another strategy always yields a higher payoff regardless of opponent's action. Rational players never play strictly dominated strategies. Returns the reduced game size and Nash equilibria i… - [Exchange Economy Analysis](https://docs.fincept.in/api-reference/quantlib-economics/exchange-economy-analysis.md): Analyze a 2-consumer, 2-good pure exchange economy. Computes aggregate endowments, excess demand at given prices, and verifies Walras's Law (that the value of excess demand is zero). Essential for understanding general equilibrium theory, Pareto efficiency, and the core of an economy. Use before fin… - [Expected Auction Revenue](https://docs.fincept.in/api-reference/quantlib-economics/expected-auction-revenue.md): Calculate theoretical expected revenue for an auction format assuming bidders' valuations are uniformly distributed on [0,1]. By the Revenue Equivalence Theorem, all standard auction formats yield the same expected revenue under certain conditions. Returns analytical expected revenue formula result.… - [Expected Utility Calculation](https://docs.fincept.in/api-reference/quantlib-economics/expected-utility-calculation.md): Calculate expected utility of a risky prospect (lottery) under Von Neumann-Morgenstern expected utility theory: EU = Σ p_i * U(x_i). Supports CARA, CRRA, and log utility functions. If probabilities not provided, assumes uniform distribution. Returns single expected utility value. Use for portfolio c… - [Fictitious Play Algorithm](https://docs.fincept.in/api-reference/quantlib-economics/fictitious-play-algorithm.md): Find approximate Nash equilibrium using fictitious play learning dynamics. Each player best-responds to the empirical frequency of opponent's past play. Converges to Nash equilibrium for certain game classes (e.g., zero-sum games, potential games). Returns the limiting mixed strategy profile after s… - [Find Mixed Nash Equilibria](https://docs.fincept.in/api-reference/quantlib-economics/find-mixed-nash-equilibria.md): Find all Nash equilibria (both pure and mixed strategy) using support enumeration algorithm. By Nash's theorem, every finite game has at least one Nash equilibrium (possibly in mixed strategies). Returns all equilibria with their strategy profiles. Computationally intensive for large games. Use when… - [Load Classic Game](https://docs.fincept.in/api-reference/quantlib-economics/load-classic-game.md): Load a well-known game from game theory and find its Nash equilibria. Available games: Prisoner's Dilemma (cooperation vs defection), Battle of the Sexes (coordination with conflicting preferences), Chicken (brinkmanship), Stag Hunt (cooperation risk), and Matching Pennies (zero-sum). Useful for tea… - [Logarithmic Utility Function](https://docs.fincept.in/api-reference/quantlib-economics/logarithmic-utility-function.md): Evaluate logarithmic utility function: U(w) = ln(w). This is a special case of CRRA with gamma = 1. Returns utility value, marginal utility (1/w), absolute risk aversion (1/w), and relative risk aversion (constant = 1). Log utility exhibits decreasing absolute risk aversion and constant relative ris… - [Monte Carlo Auction Simulation](https://docs.fincept.in/api-reference/quantlib-economics/monte-carlo-auction-simulation.md): Run large-scale Monte Carlo simulation of auctions to estimate average revenue and variance. Randomly draws valuations from specified distribution (default: uniform), runs auctions with equilibrium bidding, and aggregates results. Returns mean and standard deviation of revenue across simulations. Us… - [Prospect Theory Value Function](https://docs.fincept.in/api-reference/quantlib-economics/prospect-theory-value-function.md): Evaluate Kahneman-Tversky Prospect Theory value function with loss aversion and diminishing sensitivity. Value function: v(x) = x^alpha for gains (x ≥ 0), v(x) = -lambda*|x|^beta for losses (x < 0). Captures behavioral phenomena: loss aversion (losses hurt more than gains feel good), reference depen… - [Quadratic Utility Function](https://docs.fincept.in/api-reference/quantlib-economics/quadratic-utility-function.md): Evaluate quadratic utility function: U(w) = a*w - b*w^2 where a, b > 0. Returns utility value, marginal utility (a - 2*b*w), absolute risk aversion (2b/(a-2bw)), and satiation point (a/2b where marginal utility = 0). Quadratic utility exhibits increasing absolute risk aversion (IARA). Has a bliss po… - [Risk Premium Calculation](https://docs.fincept.in/api-reference/quantlib-economics/risk-premium-calculation.md): Calculate the risk premium of a risky lottery: the amount an individual would pay to avoid risk. Risk premium π = E[X] - CE where E[X] is expected value and CE is certainty equivalent. For risk-averse agents, π > 0 (willing to pay to eliminate risk). Returns the maximum payment to avoid the lottery.… - [Run Auction](https://docs.fincept.in/api-reference/quantlib-economics/run-auction.md): Simulate an auction with given bidder valuations. Supports first-price sealed-bid (highest bidder wins, pays their bid), second-price/Vickrey (highest bidder wins, pays second-highest bid), and all-pay (all bidders pay their bid, highest wins). Returns winner, price paid, revenue, and all bids. Use… - [Stochastic Dominance Test](https://docs.fincept.in/api-reference/quantlib-economics/stochastic-dominance-test.md): Check if lottery A stochastically dominates lottery B. First-order stochastic dominance (FSD): A dominates B if F_A(x) ≤ F_B(x) for all x (preferred by all monotone utility functions). Second-order stochastic dominance (SSD): ∫F_A(t)dt ≤ ∫F_B(t)dt (preferred by all risk-averse utility functions). Re… - [Verify Nash Equilibrium](https://docs.fincept.in/api-reference/quantlib-economics/verify-nash-equilibrium.md): Check whether a given strategy profile (possibly mixed) is a Nash equilibrium. A strategy profile is a Nash equilibrium if no player can improve their expected payoff by unilaterally deviating. Also returns the expected payoffs for both players. Use to verify candidate equilibria or validate algorit… - [Walrasian Equilibrium Solver](https://docs.fincept.in/api-reference/quantlib-economics/walrasian-equilibrium-solver.md): Find competitive equilibrium prices and allocations in a 2-consumer, 2-good exchange economy with Cobb-Douglas preferences. Uses iterative tâtonnement process to find prices where markets clear (excess demand = 0). Returns equilibrium price vector and optimal allocations for each consumer. Essential… - [Bond Fixed Analytics](https://docs.fincept.in/api-reference/quantlib-instruments/bond-fixed-analytics.md): Bond Fixed Analytics [Tier: PRO, Credits: 5] - [Bond Fixed Cashflows](https://docs.fincept.in/api-reference/quantlib-instruments/bond-fixed-cashflows.md): Bond Fixed Cashflows [Tier: PRO, Credits: 5] - [Bond Fixed Price](https://docs.fincept.in/api-reference/quantlib-instruments/bond-fixed-price.md): Bond Fixed Price [Tier: PRO, Credits: 5] - [Bond Fixed Yield](https://docs.fincept.in/api-reference/quantlib-instruments/bond-fixed-yield.md): Bond Fixed Yield [Tier: PRO, Credits: 5] - [Bond Future Ctd](https://docs.fincept.in/api-reference/quantlib-instruments/bond-future-ctd.md): Bond Future Ctd [Tier: PRO, Credits: 5] - [Cds Hazard Rate](https://docs.fincept.in/api-reference/quantlib-instruments/cds-hazard-rate.md): Cds Hazard Rate [Tier: PRO, Credits: 5] - [Cds Value](https://docs.fincept.in/api-reference/quantlib-instruments/cds-value.md): Cds Value [Tier: PRO, Credits: 5] - [Commodity Future](https://docs.fincept.in/api-reference/quantlib-instruments/commodity-future.md): Commodity Future [Tier: PRO, Credits: 5] - [Deposit Value](https://docs.fincept.in/api-reference/quantlib-instruments/deposit-value.md): Deposit Value [Tier: PRO, Credits: 5] - [Fra Break Even](https://docs.fincept.in/api-reference/quantlib-instruments/fra-break-even.md): Fra Break Even [Tier: PRO, Credits: 5] - [Fra Value](https://docs.fincept.in/api-reference/quantlib-instruments/fra-value.md): Fra Value [Tier: PRO, Credits: 5] - [Fx Forward](https://docs.fincept.in/api-reference/quantlib-instruments/fx-forward.md): Fx Forward [Tier: PRO, Credits: 5] - [Fx Garman Kohlhagen](https://docs.fincept.in/api-reference/quantlib-instruments/fx-garman-kohlhagen.md): Fx Garman Kohlhagen [Tier: PRO, Credits: 5] - [Inflation Linked Bond](https://docs.fincept.in/api-reference/quantlib-instruments/inflation-linked-bond.md): Inflation Linked Bond [Tier: PRO, Credits: 5] - [Irs Dv01](https://docs.fincept.in/api-reference/quantlib-instruments/irs-dv01.md): Irs Dv01 [Tier: PRO, Credits: 5] - [Irs Par Rate](https://docs.fincept.in/api-reference/quantlib-instruments/irs-par-rate.md): Irs Par Rate [Tier: PRO, Credits: 5] - [Irs Value](https://docs.fincept.in/api-reference/quantlib-instruments/irs-value.md): Irs Value [Tier: PRO, Credits: 5] - [Ois Build Curve](https://docs.fincept.in/api-reference/quantlib-instruments/ois-build-curve.md): Ois Build Curve [Tier: PRO, Credits: 5] - [Ois Value](https://docs.fincept.in/api-reference/quantlib-instruments/ois-value.md): Ois Value [Tier: PRO, Credits: 5] - [Repo Value](https://docs.fincept.in/api-reference/quantlib-instruments/repo-value.md): Repo Value [Tier: PRO, Credits: 5] - [Stir Future](https://docs.fincept.in/api-reference/quantlib-instruments/stir-future.md): Stir Future [Tier: PRO, Credits: 5] - [Survival Probability](https://docs.fincept.in/api-reference/quantlib-instruments/survival-probability.md): Survival Probability [Tier: PRO, Credits: 5] - [Tbill Value](https://docs.fincept.in/api-reference/quantlib-instruments/tbill-value.md): Tbill Value [Tier: PRO, Credits: 5] - [Variance Swap](https://docs.fincept.in/api-reference/quantlib-instruments/variance-swap.md): Variance Swap [Tier: PRO, Credits: 5] - [Volatility Swap](https://docs.fincept.in/api-reference/quantlib-instruments/volatility-swap.md): Volatility Swap [Tier: PRO, Credits: 5] - [Zero Coupon Price](https://docs.fincept.in/api-reference/quantlib-instruments/zero-coupon-price.md): Zero Coupon Price [Tier: PRO, Credits: 5] - [Build Credit Scorecard](https://docs.fincept.in/api-reference/quantlib-ml/build-credit-scorecard.md): Develops a complete credit scorecard from features using WoE binning and logistic regression. Returns scorecard bins, points, and scaling factors for production deployment. [Tier: ENTERPRISE, Credits: 10] - [Calendar Features](https://docs.fincept.in/api-reference/quantlib-ml/calendar-features.md): Extracts calendar-based features from dates: day of week, day of month, month, quarter, and end-of-period flags. Useful for seasonality modeling and time-based patterns in credit risk. [Tier: ENTERPRISE, Credits: 10] - [Classification Evaluation Metrics](https://docs.fincept.in/api-reference/quantlib-ml/classification-evaluation-metrics.md): Calculates classification metrics: accuracy, precision, recall, and F1-score. Essential for evaluating binary classification models like credit default prediction. [Tier: ENTERPRISE, Credits: 10] - [Credit Model Performance Evaluation](https://docs.fincept.in/api-reference/quantlib-ml/credit-model-performance-evaluation.md): Comprehensive credit model performance metrics including AUC, KS statistic, Gini coefficient, and accuracy. Use this for model validation and regulatory reporting. [Tier: ENTERPRISE, Credits: 10] - [Credit Rating Migration Analysis](https://docs.fincept.in/api-reference/quantlib-ml/credit-rating-migration-analysis.md): Estimates credit rating transition probabilities and projects multi-period migration matrices. Used for credit portfolio risk management and expected loss calculations under IFRS 9 and Basel frameworks. [Tier: ENTERPRISE, Credits: 10] - [Cross-Sectional Transformations](https://docs.fincept.in/api-reference/quantlib-ml/cross-sectional-transformations.md): Applies cross-sectional transformations: z-score normalization, ranking, percentile scoring, demeaning, and winsorized z-score. Essential for portfolio construction and factor modeling. [Tier: ENTERPRISE, Credits: 10] - [Data Scaling (Z-Score, MinMax, Robust)](https://docs.fincept.in/api-reference/quantlib-ml/data-scaling-z-score-minmax-robust.md): Scales features to improve model performance. Z-score for normal distributions, MinMax for bounded ranges (0-1), Robust for data with outliers. Essential preprocessing for distance-based algorithms. [Tier: ENTERPRISE, Credits: 10] - [DBSCAN Clustering](https://docs.fincept.in/api-reference/quantlib-ml/dbscan-clustering.md): Density-based clustering that finds arbitrarily shaped clusters and identifies outliers. Doesn't require pre-specifying cluster count. Ideal for anomaly detection and finding natural groupings in data. [Tier: ENTERPRISE, Credits: 10] - [Discrimination Metrics for Credit Models](https://docs.fincept.in/api-reference/quantlib-ml/discrimination-metrics-for-credit-models.md): Calculates comprehensive discrimination metrics including Gini coefficient, KS statistic, AUC-ROC, and accuracy ratio. Essential for validating credit risk models and assessing their ability to distinguish between defaulters and non-defaulters. [Tier: ENTERPRISE, Credits: 10] - [Ensemble Regression (Random Forest, Gradient Boosting)](https://docs.fincept.in/api-reference/quantlib-ml/ensemble-regression-random-forest-gradient-boosting.md): Fits ensemble regression models for maximum predictive accuracy. Random Forest for variance reduction and robustness, Gradient Boosting for highest performance. Returns predictions and feature importances. [Tier: ENTERPRISE, Credits: 10] - [Exposure at Default (EAD) Model](https://docs.fincept.in/api-reference/quantlib-ml/exposure-at-default-ead-model.md): Specialized regression for modeling Exposure at Default in credit risk. Estimates the expected exposure amount when a borrower defaults, critical for Basel capital calculations and expected loss estimation. [Tier: ENTERPRISE, Credits: 10] - [Financial Ratio Features](https://docs.fincept.in/api-reference/quantlib-ml/financial-ratio-features.md): Computes financial ratio features from raw financial statement data. Automatically calculates profitability, liquidity, leverage, and efficiency ratios for credit scoring and financial analysis. [Tier: ENTERPRISE, Credits: 10] - [Full Calibration Report](https://docs.fincept.in/api-reference/quantlib-ml/full-calibration-report.md): Comprehensive calibration metrics including Brier score, Hosmer-Lemeshow test, and Spiegelhalter Z-statistic. Essential for validating that predicted probabilities match observed frequencies. [Tier: ENTERPRISE, Credits: 10] - [Full Discrimination Report](https://docs.fincept.in/api-reference/quantlib-ml/full-discrimination-report.md): Complete discrimination analysis including AUC-ROC, Gini, KS statistic, accuracy ratio, and lift charts. Comprehensive report for credit model validation and regulatory compliance. [Tier: ENTERPRISE, Credits: 10] - [Hierarchical Clustering](https://docs.fincept.in/api-reference/quantlib-ml/hierarchical-clustering.md): Agglomerative hierarchical clustering that builds a tree of clusters. Supports multiple linkage methods (ward, single, complete, average). Useful for taxonomy creation and dendrogram visualization. [Tier: ENTERPRISE, Credits: 10] - [Isolation Forest Anomaly Detection](https://docs.fincept.in/api-reference/quantlib-ml/isolation-forest-anomaly-detection.md): Detects anomalies using isolation forest algorithm. Identifies outliers as points that are easy to isolate from the rest. Excellent for fraud detection, unusual transaction identification, and data quality checks. [Tier: ENTERPRISE, Credits: 10] - [K-Means Clustering](https://docs.fincept.in/api-reference/quantlib-ml/k-means-clustering.md): Partitions data into K clusters using centroids. Useful for customer segmentation, portfolio grouping, and risk bucketing. Returns cluster labels and inertia (within-cluster sum of squares). [Tier: ENTERPRISE, Credits: 10] - [Lag and Lead Features](https://docs.fincept.in/api-reference/quantlib-ml/lag-and-lead-features.md): Generates lagged features, lead features, returns, and log returns for time series modeling. Essential for autoregressive models and capturing temporal dependencies. [Tier: ENTERPRISE, Credits: 10] - [Linear Regression (OLS, Lasso, ElasticNet)](https://docs.fincept.in/api-reference/quantlib-ml/linear-regression-ols-lasso-elasticnet.md): Fits linear regression models with optional regularization. Supports OLS for interpretability, Lasso for feature selection, and ElasticNet for balanced regularization. Returns coefficients, R-squared, and optional predictions. [Tier: ENTERPRISE, Credits: 10] - [Logistic Regression for Credit Scoring](https://docs.fincept.in/api-reference/quantlib-ml/logistic-regression-for-credit-scoring.md): Fits a logistic regression model for binary classification, commonly used for credit default prediction and PD modeling. Returns model coefficients, AIC, BIC, and optional predictions. Use this for developing credit scorecards and probability of default (PD) models. [Tier: ENTERPRISE, Credits: 10] - [Loss Given Default (LGD) Model](https://docs.fincept.in/api-reference/quantlib-ml/loss-given-default-lgd-model.md): Beta regression model for Loss Given Default, bounded between 0 and 1. Essential for Basel IRB capital calculations and IFRS 9 expected credit loss estimation. Models the proportion of exposure lost when default occurs. [Tier: ENTERPRISE, Credits: 10] - [Model Interpretability Analysis](https://docs.fincept.in/api-reference/quantlib-ml/model-interpretability-analysis.md): Analyzes model interpretability using permutation importance and partial dependence. Helps explain which features drive model predictions for regulatory and business understanding. [Tier: ENTERPRISE, Credits: 10] - [Model Probability Calibration](https://docs.fincept.in/api-reference/quantlib-ml/model-probability-calibration.md): Calibrates model probabilities using Platt scaling or isotonic regression to ensure predicted probabilities match observed frequencies. Critical for regulatory models where accurate PD estimates are required. [Tier: ENTERPRISE, Credits: 10] - [Outlier Detection](https://docs.fincept.in/api-reference/quantlib-ml/outlier-detection.md): Detects outliers using statistical methods: Z-score (distance from mean), IQR (interquartile range), or MAD (median absolute deviation). Returns outlier flags and indices for data cleaning. [Tier: ENTERPRISE, Credits: 10] - [Population Stability Index (PSI)](https://docs.fincept.in/api-reference/quantlib-ml/population-stability-index-psi.md): Calculates Population Stability Index (PSI) and Characteristic Stability Index (CSI) to monitor model degradation over time. PSI < 0.1 indicates stable population, 0.1-0.25 moderate shift, >0.25 significant shift requiring model review. [Tier: ENTERPRISE, Credits: 10] - [Power Transformations (Box-Cox, Yeo-Johnson)](https://docs.fincept.in/api-reference/quantlib-ml/power-transformations-box-cox-yeo-johnson.md): Applies power transformations to make data more Gaussian-like. Box-Cox for positive data, Yeo-Johnson for any data. Improves linear model assumptions and reduces skewness. [Tier: ENTERPRISE, Credits: 10] - [Principal Component Analysis (PCA)](https://docs.fincept.in/api-reference/quantlib-ml/principal-component-analysis-pca.md): Reduces dimensionality while preserving variance. Transforms data into orthogonal components ordered by explained variance. Essential for feature reduction, visualization, and multicollinearity removal. [Tier: ENTERPRISE, Credits: 10] - [Regression Evaluation Metrics](https://docs.fincept.in/api-reference/quantlib-ml/regression-evaluation-metrics.md): Calculates comprehensive regression metrics: R-squared, MSE (Mean Squared Error), MAE (Mean Absolute Error), and RMSE (Root Mean Squared Error). Essential for model evaluation and comparison. [Tier: ENTERPRISE, Credits: 10] - [Rolling Window Statistics](https://docs.fincept.in/api-reference/quantlib-ml/rolling-window-statistics.md): Calculates rolling window statistics: mean, standard deviation, skewness, kurtosis, correlation, and beta. Essential for time series feature engineering and momentum/volatility indicators. [Tier: ENTERPRISE, Credits: 10] - [Stationarity Transformations](https://docs.fincept.in/api-reference/quantlib-ml/stationarity-transformations.md): Transforms time series to achieve stationarity using differencing, log returns, or simple returns. Includes ADF (Augmented Dickey-Fuller) test statistic. Critical for time series modeling and forecasting. [Tier: ENTERPRISE, Credits: 10] - [Technical Indicators](https://docs.fincept.in/api-reference/quantlib-ml/technical-indicators.md): Calculates technical indicators for financial time series: RSI, EMA, MACD, Bollinger Bands, ATR, ADX, CCI, OBV. Essential for trading strategies, feature engineering, and market analysis. [Tier: ENTERPRISE, Credits: 10] - [Time Series Feature Importance](https://docs.fincept.in/api-reference/quantlib-ml/time-series-feature-importance.md): Analyzes feature importance for time series using sequential forward selection. Identifies which features contribute most to predictive power while respecting temporal ordering. [Tier: ENTERPRISE, Credits: 10] - [Tree-Based Regression (Decision Tree, Gradient Boosting)](https://docs.fincept.in/api-reference/quantlib-ml/tree-based-regression-decision-tree-gradient-boosting.md): Fits tree-based regression models. Decision trees for interpretability and non-linear relationships, gradient boosting for maximum predictive power. Returns predictions and optional feature importances. [Tier: ENTERPRISE, Credits: 10] - [Weight of Evidence (WoE) Binning](https://docs.fincept.in/api-reference/quantlib-ml/weight-of-evidence-woe-binning.md): Transforms continuous features into Weight of Evidence values for credit scorecard development. WoE encoding creates monotonic relationships and handles missing values, essential for building interpretable credit scorecards. [Tier: ENTERPRISE, Credits: 10] - [Winsorization](https://docs.fincept.in/api-reference/quantlib-ml/winsorization.md): Clips extreme values to specified percentiles to reduce outlier impact while preserving data points. Less aggressive than outlier removal. Common in financial data preprocessing. [Tier: ENTERPRISE, Credits: 10] - [Dupire Local Volatility Model](https://docs.fincept.in/api-reference/quantlib-models/dupire-local-volatility-model.md): Price European options using the Dupire local volatility model. Local volatility models assume volatility is a deterministic function of spot price and time, perfectly fitting observed market option prices. This endpoint uses a flat local volatility (constant across strikes and time) as a baseline,… - [Heston Implied Volatility](https://docs.fincept.in/api-reference/quantlib-models/heston-implied-volatility.md): Calculate Black-Scholes implied volatility from Heston model prices. This endpoint first computes the option price using the Heston model, then inverts the Black-Scholes formula to find the equivalent constant volatility. Essential for comparing Heston model outputs with market-quoted implied volati… - [Heston Model Option Pricing (Analytical)](https://docs.fincept.in/api-reference/quantlib-models/heston-model-option-pricing-analytical.md): Price European options using the Heston stochastic volatility model with analytical formulas. The Heston model captures volatility smile/skew effects better than Black-Scholes by allowing volatility to be stochastic. Perfect for pricing equity options, volatility derivatives, and understanding impli… - [Heston Monte Carlo Option Pricing](https://docs.fincept.in/api-reference/quantlib-models/heston-monte-carlo-option-pricing.md): Price options using Monte Carlo simulation with the Heston stochastic volatility model. Simulates correlated paths for both asset price and variance, providing option prices with confidence intervals. Use this for path-dependent options, American options, or when comparing with analytical Heston pri… - [Hull-White Model Calibration](https://docs.fincept.in/api-reference/quantlib-models/hull-white-model-calibration.md): Calibrate a Hull-White short rate model to match market yield curve data. The Hull-White model extends Vasicek by fitting an initial term structure, making it ideal for pricing derivatives consistently with observed market rates. Use this endpoint to calibrate the model parameters and generate fitte… - [Kou Double Exponential Jump Model](https://docs.fincept.in/api-reference/quantlib-models/kou-double-exponential-jump-model.md): Price European call options using the Kou double exponential jump-diffusion model. Unlike Merton's model with normal jumps, Kou uses asymmetric exponential distributions for jumps, better capturing the empirical fat tails and skewness in equity returns. Upward and downward jumps have different decay… - [Merton Jump-Diffusion Option Pricing](https://docs.fincept.in/api-reference/quantlib-models/merton-jump-diffusion-option-pricing.md): Price European call options using the Merton jump-diffusion model. This model extends Black-Scholes by adding random jumps to account for sudden price movements (earnings announcements, market crashes, etc.). Useful for pricing options on stocks with discontinuous price behavior and understanding ju… - [Merton Model FFT Pricing](https://docs.fincept.in/api-reference/quantlib-models/merton-model-fft-pricing.md): Price options using Fast Fourier Transform (FFT) methods for the Merton jump-diffusion model. FFT pricing is highly efficient for computing option prices across multiple strikes simultaneously, making it ideal for constructing option chains, implied volatility surfaces, and risk analysis. Returns pr… - [Short Rate Bond Option Pricing](https://docs.fincept.in/api-reference/quantlib-models/short-rate-bond-option-pricing.md): Price European call and put options on zero-coupon bonds using short rate models. Bond options are fundamental building blocks for swaptions, callable bonds, and other interest rate derivatives. The model provides analytical pricing for both calls and puts, useful for hedging bond portfolios or spec… - [Short Rate Bond Pricing](https://docs.fincept.in/api-reference/quantlib-models/short-rate-bond-pricing.md): Calculate zero-coupon bond prices using short rate models (Vasicek, CIR, or Hull-White). These models describe the evolution of interest rates and are fundamental for pricing interest rate derivatives and fixed income securities. Use this endpoint to price bonds at multiple maturities simultaneously… - [Short Rate Monte Carlo Simulation](https://docs.fincept.in/api-reference/quantlib-models/short-rate-monte-carlo-simulation.md): Simulate short rate paths using Monte Carlo methods with Vasicek, CIR, or Hull-White models. Generate multiple stochastic paths for the evolution of interest rates over time, essential for pricing path-dependent derivatives, CVA/DVA calculations, and scenario analysis. Use this for complex rate deri… - [Short Rate Yield Curve](https://docs.fincept.in/api-reference/quantlib-models/short-rate-yield-curve.md): Generate a yield curve from a short rate model (Vasicek or CIR). The yield curve represents the relationship between bond yields and their time to maturity, fundamental for bond valuation and risk management. Use this endpoint to extract continuously compounded yields from the short rate model for c… - [SVI Volatility Surface Calibration](https://docs.fincept.in/api-reference/quantlib-models/svi-volatility-surface-calibration.md): Calibrate the Stochastic Volatility Inspired (SVI) parametric model to market implied volatility data. SVI provides a smooth, arbitrage-free volatility smile that can be efficiently calibrated to market quotes. Essential for volatility surface construction, option pricing across strikes, and risk ma… - [Variance Gamma Option Pricing](https://docs.fincept.in/api-reference/quantlib-models/variance-gamma-option-pricing.md): Price European call options using the Variance Gamma (VG) model. The VG model is a pure jump process (no continuous diffusion) that effectively captures the leptokurtic (fat-tailed) and skewed nature of asset returns. It's computationally efficient and provides excellent fits to market option prices… - [Compute Matrix Inverse](https://docs.fincept.in/api-reference/quantlib-numerical/compute-matrix-inverse.md): Compute the inverse of a square non-singular matrix A such that A × A⁻¹ = I (identity matrix). Matrix must be square and non-singular (determinant ≠ 0). Uses LU decomposition for numerical stability. Used in portfolio optimization, covariance matrix inversion, solving multiple systems with the same… - [Compute Numerical Derivative](https://docs.fincept.in/api-reference/quantlib-numerical/compute-numerical-derivative.md): Calculate the numerical derivative of a built-in function at a given point using finite difference methods. Supports forward, backward, and central difference schemes. Central differences provide the most accurate approximation for smooth functions. Use this for estimating derivatives when analytica… - [Compute Numerical Gradient](https://docs.fincept.in/api-reference/quantlib-numerical/compute-numerical-gradient.md): Calculate the gradient (vector of partial derivatives) of a multi-variable function at a given point. The gradient points in the direction of steepest ascent and is essential for optimization algorithms, sensitivity analysis, and machine learning applications. Uses finite differences to approximate… - [Compute Numerical Hessian Matrix](https://docs.fincept.in/api-reference/quantlib-numerical/compute-numerical-hessian-matrix.md): Calculate the Hessian matrix (matrix of second-order partial derivatives) of a multi-variable function. The Hessian provides information about local curvature and is used in second-order optimization methods (Newton's method), analyzing critical points, and measuring convexity. Essential for advance… - [Evaluate Cubic Spline at Multiple Points](https://docs.fincept.in/api-reference/quantlib-numerical/evaluate-cubic-spline-at-multiple-points.md): Construct a cubic spline from data points and evaluate it at multiple query points. Cubic splines provide C2 continuity (smooth first and second derivatives) and are widely used in computer graphics, curve fitting, and financial modeling. Supports different boundary conditions: natural (second deriv… - [Evaluate Cubic Spline Derivative](https://docs.fincept.in/api-reference/quantlib-numerical/evaluate-cubic-spline-derivative.md): Construct a cubic spline and evaluate its derivative at a given point. The derivative of a cubic spline is a quadratic spline that is continuous (C1). Useful for computing forward rates from zero rates, implied volatility slopes, and sensitivity analysis. [Tier: BASIC, Credits: 1] - [Fast Fourier Transform (Forward)](https://docs.fincept.in/api-reference/quantlib-numerical/fast-fourier-transform-forward.md): Compute the Fast Fourier Transform of real-valued data, converting from time/spatial domain to frequency domain. FFT decomposes a signal into its constituent frequencies and is fundamental for signal processing, spectral analysis, convolution operations, and filtering. Returns complex-valued frequen… - [FFT-based Convolution](https://docs.fincept.in/api-reference/quantlib-numerical/fft-based-convolution.md): Convolve two sequences using FFT, which is more efficient than direct convolution for long sequences (O(n log n) vs O(n^2)). Convolution is fundamental in signal processing, filtering, polynomial multiplication, and probability distribution calculations. Returns the convolution of sequences a and b.… - [Find Root of Scalar Function](https://docs.fincept.in/api-reference/quantlib-numerical/find-root-of-scalar-function.md): Find a root (zero) of a scalar function f(x) in the interval [a, b] where f(a) and f(b) have opposite signs. Methods: bisect (reliable, slow convergence), brent (optimal hybrid method, recommended), ridder (exponential convergence), secant (fast but requires good initial bracket). Essential for impl… - [Find Root of System of Equations](https://docs.fincept.in/api-reference/quantlib-numerical/find-root-of-system-of-equations.md): Find a solution to a system of nonlinear equations F(x) = 0 where F and x are vectors. Supports built-in systems: linear_2x2 (system of two linear equations), nonlinear_2x2 (nonlinear system like x^2 + y^2 = 4, xy = 1). Methods: newton (multidimensional Newton-Raphson with Jacobian), broyden (quasi-… - [Find Root Using Newton's Method](https://docs.fincept.in/api-reference/quantlib-numerical/find-root-using-newtons-method.md): Find a root of f(x) = 0 using Newton's method (also called Newton-Raphson), which uses both function values and derivatives: x_{n+1} = x_n - f(x_n)/f'(x_n). Provides quadratic convergence when near the root but requires a good initial guess. Uses numerical differentiation to approximate the derivati… - [Interpolate Value](https://docs.fincept.in/api-reference/quantlib-numerical/interpolate-value.md): Interpolate a value at a given point using various methods. Supports linear (fast, simple), cubic (smooth, continuous first derivative), Lagrange (exact polynomial through all points), PCHIP (preserves monotonicity and shape), monotone convex (finance-grade, preserves monotonicity and convexity), an… - [Inverse Fast Fourier Transform](https://docs.fincept.in/api-reference/quantlib-numerical/inverse-fast-fourier-transform.md): Compute the inverse FFT to transform frequency domain data back to time/spatial domain. Used to reconstruct signals after frequency domain processing, filtering, or convolution operations. Input is treated as real values representing complex numbers with zero imaginary parts. [Tier: BASIC, Credits:… - [Matrix Decomposition](https://docs.fincept.in/api-reference/quantlib-numerical/matrix-decomposition.md): Decompose a matrix using various methods: LU decomposition (A = LU, for solving linear systems), Cholesky decomposition (A = LL^T, for positive definite matrices in risk models), QR decomposition (A = QR, for least squares and eigenvalue problems), or Singular Value Decomposition (A = UΣV^T, for dim… - [Matrix Multiplication](https://docs.fincept.in/api-reference/quantlib-numerical/matrix-multiplication.md): Multiply two matrices A and B to compute C = A × B. Matrix dimensions must be compatible (columns of A = rows of B). Fundamental operation in linear algebra, used in portfolio optimization, factor models, coordinate transformations, and solving systems of equations. [Tier: BASIC, Credits: 1] - [Matrix Transpose](https://docs.fincept.in/api-reference/quantlib-numerical/matrix-transpose.md): Transpose a matrix A to get A^T where A^T[i,j] = A[j,i]. Converts m×n matrix to n×m matrix by swapping rows and columns. Essential for matrix operations, converting between row and column vectors, and computing symmetric matrix products. [Tier: BASIC, Credits: 1] - [Matrix-Vector Multiplication](https://docs.fincept.in/api-reference/quantlib-numerical/matrix-vector-multiplication.md): Multiply a matrix A by a vector x to compute y = A × x. Essential for linear transformations, computing portfolio returns from weights, applying rotations, and evaluating linear systems. [Tier: BASIC, Credits: 1] - [Minimize Multi-Variable Function](https://docs.fincept.in/api-reference/quantlib-numerical/minimize-multi-variable-function.md): Find the minimum of a multi-variable function f(x) where x is a vector. Supports built-in test functions: rosenbrock (banana-shaped valley, tests optimization robustness), sphere (simple convex function), rastrigin (many local minima), quadratic (ellipsoidal contours). Methods: bfgs (Quasi-Newton me… - [Monte Carlo Integration](https://docs.fincept.in/api-reference/quantlib-numerical/monte-carlo-integration.md): Estimate the integral of a function over a hyper-rectangle using Monte Carlo sampling. Particularly effective for high-dimensional integrals where traditional methods suffer from the curse of dimensionality. Returns both the integral estimate and standard error, allowing confidence interval construc… - [Nonlinear Least Squares Fitting](https://docs.fincept.in/api-reference/quantlib-numerical/nonlinear-least-squares-fitting.md): Fit a nonlinear parametric model to data by minimizing the sum of squared residuals. Supports exponential_fit (y = a·e^(bx) + c, useful for decay processes and yield curves) and polynomial_fit (y = p0 + p1·x + p2·x^2 + ..., general curve fitting). Uses iterative optimization to find parameters that… - [Numerical Integration (Quadrature)](https://docs.fincept.in/api-reference/quantlib-numerical/numerical-integration-quadrature.md): Compute the definite integral of a built-in function over [a, b] using various quadrature methods. Supports trapezoid rule, Simpson's rule (more accurate for smooth functions), Gauss-Legendre quadrature (optimal for polynomials), Romberg integration (adaptive Richardson extrapolation), and adaptive… - [Solve Least Squares Problem](https://docs.fincept.in/api-reference/quantlib-numerical/solve-least-squares-problem.md): Solve the least squares problem: minimize ||Ax - b||² to find x that best fits the overdetermined system Ax ≈ b. When A has more rows than columns (more equations than unknowns), no exact solution exists, but least squares finds the best approximate solution. Uses QR decomposition or normal equation… - [Solve Linear System Ax = b](https://docs.fincept.in/api-reference/quantlib-numerical/solve-linear-system-ax-=-b.md): Solve the linear system Ax = b for x, where A is a square matrix and b is a vector. Uses LU decomposition with partial pivoting for numerical stability. Essential for portfolio optimization, computing implied parameters, solving equilibrium models, and inverting relationships. Returns the solution v… - [Solve Ordinary Differential Equation (ODE)](https://docs.fincept.in/api-reference/quantlib-numerical/solve-ordinary-differential-equation-ode.md): Solve an initial value problem (IVP) for a system of ordinary differential equations: dy/dt = f(t, y) with y(t0) = y0. Supports built-in systems: exponential_decay (population decay, radioactive decay), harmonic (spring oscillations), lotka_volterra (predator-prey dynamics), and van_der_pol (nonline… - [Stratified Sampling Integration](https://docs.fincept.in/api-reference/quantlib-numerical/stratified-sampling-integration.md): Perform stratified sampling integration, which divides the integration domain into strata and samples from each stratum independently. This variance reduction technique provides more accurate estimates than simple Monte Carlo with the same number of samples. Particularly effective when the integrand… - [Vector Dot Product](https://docs.fincept.in/api-reference/quantlib-numerical/vector-dot-product.md): Compute the dot product (inner product) of two vectors: a · b = Σ(a_i × b_i). Returns a scalar representing the projection of one vector onto another. Used in correlation calculations, computing portfolio variance, and measuring similarity. [Tier: BASIC, Credits: 1] - [Vector Norm (p-Norm)](https://docs.fincept.in/api-reference/quantlib-numerical/vector-norm-p-norm.md): Compute the p-norm of a vector: ||v||_p = (Σ|v_i|^p)^(1/p). Common cases: p=1 (Manhattan/L1 norm), p=2 (Euclidean/L2 norm, vector length), p=∞ (maximum absolute value). Used for measuring vector magnitude, distance, portfolio risk, and convergence criteria. [Tier: BASIC, Credits: 1] - [Vector Outer Product](https://docs.fincept.in/api-reference/quantlib-numerical/vector-outer-product.md): Compute the outer product of two vectors a and b to produce matrix C where C[i,j] = a[i] × b[j]. The result is an m×n matrix if a has m elements and b has n elements. Used in rank-1 matrix updates, covariance matrix construction, and tensor products. [Tier: BASIC, Credits: 1] - [Calculate 2D Ising Model Critical Temperature](https://docs.fincept.in/api-reference/quantlib-physics/calculate-2d-ising-model-critical-temperature.md): Calculates the exact critical temperature for the 2D Ising model on a square lattice. At the critical temperature T_c, the system undergoes a second-order phase transition from ordered (ferromagnetic) to disordered (paramagnetic) phase. The critical temperature is given by Onsager's exact solution. - [Calculate Boltzmann Distribution Properties](https://docs.fincept.in/api-reference/quantlib-physics/calculate-boltzmann-distribution-properties.md): Calculates properties of a Boltzmann distribution given energy levels and temperature. The Boltzmann distribution describes the probability of states in thermal equilibrium and can be applied to portfolio optimization, maximum entropy models, and market microstructure. Returns comprehensive thermody… - [Calculate Carnot Cycle Properties](https://docs.fincept.in/api-reference/quantlib-physics/calculate-carnot-cycle-properties.md): Calculates properties of an ideal Carnot heat engine cycle operating between hot and cold reservoirs. Returns maximum theoretical efficiency, work output per cycle, heat rejected, and coefficients of performance for refrigerator and heat pump modes. The Carnot efficiency sets fundamental limits on e… - [Calculate Clausius-Clapeyron Equation](https://docs.fincept.in/api-reference/quantlib-physics/calculate-clausius-clapeyron-equation.md): Calculates dP/dT along a phase transition curve using the Clausius-Clapeyron equation. This equation relates the slope of the coexistence curve to latent heat and volume change during a first-order phase transition. Applicable to modeling regime transitions and critical phenomena in markets. - [Calculate Conditional Entropy](https://docs.fincept.in/api-reference/quantlib-physics/calculate-conditional-entropy.md): Calculates the conditional entropy H(Y|X) from a joint probability distribution, measuring the remaining uncertainty in Y given knowledge of X. Conditional entropy quantifies how much information Y provides beyond what's known from X, crucial for understanding predictive relationships. - [Calculate Cross Entropy](https://docs.fincept.in/api-reference/quantlib-physics/calculate-cross-entropy.md): Calculates the cross entropy between two probability distributions P and Q, measuring the average number of bits needed to identify an event from P when using a coding scheme optimized for Q. Cross entropy is fundamental in machine learning loss functions and information theory. - [Calculate Differential Entropy](https://docs.fincept.in/api-reference/quantlib-physics/calculate-differential-entropy.md): Calculates the differential entropy for continuous probability distributions (Gaussian or exponential). Differential entropy extends the concept of entropy to continuous random variables and can be negative (unlike discrete entropy). Essential for continuous financial models, option pricing, and Bay… - [Calculate Fisher Information](https://docs.fincept.in/api-reference/quantlib-physics/calculate-fisher-information.md): Calculates the Fisher information, which measures the amount of information that an observable random variable carries about an unknown parameter. Fisher information is fundamental in parameter estimation, providing the Cramér-Rao lower bound on estimator variance. Higher Fisher information means mo… - [Calculate Ideal Gas Thermodynamic Properties](https://docs.fincept.in/api-reference/quantlib-physics/calculate-ideal-gas-thermodynamic-properties.md): Calculates thermodynamic properties of an ideal gas: entropy and internal energy. The ideal gas model is the simplest thermodynamic system and serves as a baseline for understanding more complex behaviors. Useful for theoretical benchmarks and educational purposes. - [Calculate Jensen-Shannon Divergence](https://docs.fincept.in/api-reference/quantlib-physics/calculate-jensen-shannon-divergence.md): Calculates the Jensen-Shannon (JS) divergence between two probability distributions. JS divergence is symmetric, bounded (0 to 1), and based on the KL divergence. It's particularly useful when you need a symmetric distance measure between distributions and is more numerically stable than KL divergen… - [Calculate Joint Entropy](https://docs.fincept.in/api-reference/quantlib-physics/calculate-joint-entropy.md): Calculates the joint entropy of two random variables from their joint probability distribution, measuring the total uncertainty in the combined system. Joint entropy is fundamental for understanding multivariate dependencies in financial systems. - [Calculate Joule-Thomson Coefficient](https://docs.fincept.in/api-reference/quantlib-physics/calculate-joule-thomson-coefficient.md): Calculates the Joule-Thomson coefficient μ_JT = (∂T/∂P)_H, which describes the temperature change of a gas during isenthalpic (constant enthalpy) expansion. For real gases, this coefficient can be positive (cooling) or negative (heating) depending on temperature and pressure. The inversion temperatu… - [Calculate Kullback-Leibler Divergence](https://docs.fincept.in/api-reference/quantlib-physics/calculate-kullback-leibler-divergence.md): Calculates the Kullback-Leibler (KL) divergence from distribution Q to P, measuring how one probability distribution differs from a reference distribution. KL divergence is asymmetric and always non-negative. Essential for model comparison, portfolio rebalancing decisions, and detecting distribution… - [Calculate Markov Chain Entropy Rate](https://docs.fincept.in/api-reference/quantlib-physics/calculate-markov-chain-entropy-rate.md): Calculates the entropy rate of a Markov chain, measuring the long-run average uncertainty per time step. The entropy rate characterizes the complexity and predictability of a stochastic process governed by Markov transitions, useful for modeling regime-switching markets and state-dependent strategie… - [Calculate Maximum Entropy Distribution](https://docs.fincept.in/api-reference/quantlib-physics/calculate-maximum-entropy-distribution.md): Computes the maximum entropy probability distribution subject to given moment constraints. The maximum entropy principle selects the least informative distribution consistent with constraints, avoiding unwarranted assumptions. Widely used in portfolio optimization, risk-neutral pricing, and statisti… - [Calculate Mutual Information](https://docs.fincept.in/api-reference/quantlib-physics/calculate-mutual-information.md): Calculates the mutual information I(X;Y) between two random variables, measuring the reduction in uncertainty of one variable due to knowledge of the other. Mutual information is symmetric, always non-negative, and captures both linear and non-linear dependencies, making it superior to correlation f… - [Calculate Renyi Entropy](https://docs.fincept.in/api-reference/quantlib-physics/calculate-renyi-entropy.md): Calculates the Renyi entropy, a generalization of Shannon entropy parameterized by α. Different α values emphasize different aspects of the distribution: α→0 emphasizes rare events, α=1 recovers Shannon entropy, α→∞ emphasizes the most probable event. Useful for risk-sensitive information measures i… - [Calculate Shannon Entropy](https://docs.fincept.in/api-reference/quantlib-physics/calculate-shannon-entropy.md): Calculates the Shannon entropy of a discrete probability distribution, measuring the average information content or uncertainty. Shannon entropy is fundamental in information theory and is used to quantify the uncertainty in portfolio returns, market regimes, or trading signals. Higher entropy indic… - [Calculate Thermodynamic Free Energy](https://docs.fincept.in/api-reference/quantlib-physics/calculate-thermodynamic-free-energy.md): Calculates various forms of thermodynamic free energy: Gibbs free energy (G), Helmholtz free energy (F), or enthalpy (H). Free energy represents the maximum useful work extractable from a system and is fundamental in thermodynamics. In finance, free energy concepts apply to portfolio efficiency, mar… - [Calculate Transfer Entropy](https://docs.fincept.in/api-reference/quantlib-physics/calculate-transfer-entropy.md): Calculates the transfer entropy from time series X to Y, measuring the reduction in uncertainty about Y's future when conditioning on X's past, beyond what Y's own history provides. Transfer entropy is asymmetric and detects directional information flow, making it ideal for causality detection in fi… - [Calculate Tsallis Entropy](https://docs.fincept.in/api-reference/quantlib-physics/calculate-tsallis-entropy.md): Calculates the Tsallis entropy, a non-extensive generalization of Shannon entropy parameterized by q. Particularly useful for systems with long-range correlations, power-law distributions, and non-equilibrium states common in financial markets. q=1 recovers Shannon entropy. - [Calculate Van der Waals Gas Properties](https://docs.fincept.in/api-reference/quantlib-physics/calculate-van-der-waals-gas-properties.md): Calculates properties of a Van der Waals gas, a real gas model that includes molecular interactions and finite molecular size. Returns pressure, compressibility factor, and critical point parameters. The Van der Waals equation exhibits phase transitions and is used to model non-ideal behavior in phy… - [Get Maxwell Thermodynamic Relations](https://docs.fincept.in/api-reference/quantlib-physics/get-maxwell-thermodynamic-relations.md): Returns the four Maxwell relations, which are equalities between partial derivatives of thermodynamic potentials. These relations follow from the equality of mixed partial derivatives and connect different thermodynamic properties. Maxwell relations are fundamental in thermodynamics and have analogs… - [Simulate Ising Model](https://docs.fincept.in/api-reference/quantlib-physics/simulate-ising-model.md): Simulates a 2D Ising model using Metropolis-Hastings Monte Carlo sampling. The Ising model describes interacting binary spins and exhibits phase transitions, making it useful for modeling collective behavior in financial systems, agent-based models, correlation structure, and systemic risk. Returns… - [Black-Litterman Equilibrium Returns](https://docs.fincept.in/api-reference/quantlib-portfolio/black-litterman-equilibrium-returns.md): Computes market equilibrium (implied) returns using reverse optimization from market capitalizations. These returns represent the market's consensus expectations implied by current market weights. - [Black-Litterman Posterior Returns & Weights](https://docs.fincept.in/api-reference/quantlib-portfolio/black-litterman-posterior-returns-&-weights.md): Computes posterior (updated) returns by combining market equilibrium with investor views using Bayesian inference. Returns optimal portfolio weights based on the posterior distribution. - [Comprehensive Portfolio Risk Analysis](https://docs.fincept.in/api-reference/quantlib-portfolio/comprehensive-portfolio-risk-analysis.md): Performs comprehensive risk analysis of a portfolio including volatility, downside risk, tracking error, information ratio, Sortino ratio, maximum drawdown, and other risk metrics. - [Comprehensive Portfolio Risk Assessment](https://docs.fincept.in/api-reference/quantlib-portfolio/comprehensive-portfolio-risk-assessment.md): Performs an extensive portfolio risk analysis using historical returns data. Calculates a wide range of risk metrics including VaR, CVaR, volatility, Sharpe ratio, Sortino ratio, maximum drawdown, and more. - [Efficient Frontier](https://docs.fincept.in/api-reference/quantlib-portfolio/efficient-frontier.md): Traces the complete efficient frontier by computing portfolios at different return levels. The frontier represents the set of portfolios offering the maximum expected return for each level of risk. - [Incremental Value at Risk](https://docs.fincept.in/api-reference/quantlib-portfolio/incremental-value-at-risk.md): Calculates the incremental VaR, which measures the change in portfolio VaR from adding or increasing a position in a specific asset. Useful for assessing the marginal risk impact of new positions. - [Inverse Volatility Weights](https://docs.fincept.in/api-reference/quantlib-portfolio/inverse-volatility-weights.md): Computes portfolio weights inversely proportional to asset volatilities. This simple yet effective strategy allocates more to lower-volatility assets, providing natural diversification. - [Maximum Sharpe Ratio Portfolio](https://docs.fincept.in/api-reference/quantlib-portfolio/maximum-sharpe-ratio-portfolio.md): Constructs the maximum Sharpe ratio portfolio, also known as the tangency portfolio. This portfolio maximizes risk-adjusted returns and represents the optimal risky portfolio in mean-variance theory. - [Minimum Variance Portfolio](https://docs.fincept.in/api-reference/quantlib-portfolio/minimum-variance-portfolio.md): Constructs the minimum variance portfolio (MVP) that minimizes portfolio volatility without regard to expected returns. This is the leftmost point on the efficient frontier and represents the portfolio with the lowest possible risk. - [Portfolio Conditional Value at Risk (CVaR)](https://docs.fincept.in/api-reference/quantlib-portfolio/portfolio-conditional-value-at-risk-cvar.md): Calculates portfolio Conditional Value at Risk (CVaR), also known as Expected Shortfall (ES). CVaR measures the expected loss given that the loss exceeds the VaR threshold. - [Portfolio Risk-Adjusted Performance Ratios](https://docs.fincept.in/api-reference/quantlib-portfolio/portfolio-risk-adjusted-performance-ratios.md): Calculates key risk-adjusted performance ratios including Sharpe ratio, Sortino ratio, maximum drawdown, information ratio, and tracking error. - [Portfolio Value at Risk (VaR)](https://docs.fincept.in/api-reference/quantlib-portfolio/portfolio-value-at-risk-var.md): Calculates portfolio Value at Risk (VaR) - the maximum expected loss over a given time horizon at a specified confidence level. Supports parametric (variance-covariance) method. - [Risk Contribution Analysis](https://docs.fincept.in/api-reference/quantlib-portfolio/risk-contribution-analysis.md): Decomposes portfolio risk into individual asset contributions. Calculates absolute and percentage risk contributions, marginal risk contributions, diversification ratio, and effective number of bets. - [Risk Parity Portfolio](https://docs.fincept.in/api-reference/quantlib-portfolio/risk-parity-portfolio.md): Constructs portfolios where each asset contributes equally to total portfolio risk. Includes multiple risk parity methods: equal risk contribution (ERC), hierarchical risk parity (HRP), and inverse volatility weighting. - [Target Return Portfolio](https://docs.fincept.in/api-reference/quantlib-portfolio/target-return-portfolio.md): Constructs a portfolio that achieves a specified target return with minimum variance. This optimization finds the point on the efficient frontier corresponding to your desired return level. - [Bachelier Full Greeks Suite](https://docs.fincept.in/api-reference/quantlib-pricing/bachelier-full-greeks-suite.md): Calculate complete suite of Bachelier Greeks including price and all major risk sensitivities under the normal model. - [Bachelier Greeks (Basic)](https://docs.fincept.in/api-reference/quantlib-pricing/bachelier-greeks-basic.md): Calculate basic Bachelier Greeks structure, returning all input parameters including normal volatility. For complete Greeks calculations, use the `/bachelier/greeks-full` endpoint. - [Bachelier Implied Volatility](https://docs.fincept.in/api-reference/quantlib-pricing/bachelier-implied-volatility.md): Calculate implied normal (absolute) volatility from market option price using the Bachelier model. Returns volatility in absolute price units rather than percentage. - [Bachelier Option Price](https://docs.fincept.in/api-reference/quantlib-pricing/bachelier-option-price.md): Calculate option price using the Bachelier (normal) model. This model assumes absolute price changes are normally distributed, rather than log-returns. Particularly useful for low or negative interest rate environments. - [Basket Option Price (Levy Approximation)](https://docs.fincept.in/api-reference/quantlib-pricing/basket-option-price-levy-approximation.md): Price a basket call option using Levy's moment-matching approximation. A basket option has payoff based on a weighted sum of multiple assets. - [Binomial Tree American Option](https://docs.fincept.in/api-reference/quantlib-pricing/binomial-tree-american-option.md): Price American options using binomial tree methods. American options can be exercised at any time before expiration, requiring numerical methods like binomial trees. - [Binomial Tree Barrier Option](https://docs.fincept.in/api-reference/quantlib-pricing/binomial-tree-barrier-option.md): Price barrier options using binomial trees. Barrier options activate (knock-in) or terminate (knock-out) when the underlying crosses a barrier level. - [Binomial Tree Bermudan Option](https://docs.fincept.in/api-reference/quantlib-pricing/binomial-tree-bermudan-option.md): Price Bermudan options using binomial trees. Bermudan options can be exercised only on specific dates (a middle ground between European and American). - [Binomial Tree European Option](https://docs.fincept.in/api-reference/quantlib-pricing/binomial-tree-european-option.md): Price European options using binomial tree methods. Binomial trees discretize time and price movements, providing an intuitive numerical approach. Converges to Black-Scholes in the limit. - [Black-Scholes Asset-or-Nothing Call](https://docs.fincept.in/api-reference/quantlib-pricing/black-scholes-asset-or-nothing-call.md): Price an asset-or-nothing call option that delivers one unit of the underlying asset if the spot price is above the strike at expiration, otherwise pays nothing. - [Black-Scholes Asset-or-Nothing Put](https://docs.fincept.in/api-reference/quantlib-pricing/black-scholes-asset-or-nothing-put.md): Price an asset-or-nothing put option that delivers one unit of the underlying asset if the spot price is below the strike at expiration, otherwise pays nothing. - [Black-Scholes Digital Call (Cash-or-Nothing)](https://docs.fincept.in/api-reference/quantlib-pricing/black-scholes-digital-call-cash-or-nothing.md): Price a digital (binary) call option that pays a fixed amount (typically $1) if the spot price is above the strike at expiration, otherwise pays nothing. Also known as a cash-or-nothing call. - [Black-Scholes Digital Put (Cash-or-Nothing)](https://docs.fincept.in/api-reference/quantlib-pricing/black-scholes-digital-put-cash-or-nothing.md): Price a digital (binary) put option that pays a fixed amount (typically $1) if the spot price is below the strike at expiration, otherwise pays nothing. Also known as a cash-or-nothing put. - [Black-Scholes Full Greeks Suite](https://docs.fincept.in/api-reference/quantlib-pricing/black-scholes-full-greeks-suite.md): Calculate complete suite of Black-Scholes Greeks including price and all first-order and second-order risk sensitivities. This is the most comprehensive Greeks calculation endpoint. - [Black-Scholes Greeks (Basic)](https://docs.fincept.in/api-reference/quantlib-pricing/black-scholes-greeks-basic.md): Calculate basic Black-Scholes Greeks including higher-order sensitivities (vanna and volga). This endpoint returns the Greeks structure with all input parameters and calculated second-order Greeks. - [Black-Scholes Implied Volatility](https://docs.fincept.in/api-reference/quantlib-pricing/black-scholes-implied-volatility.md): Calculate implied volatility from a market option price using the Black-Scholes model. Implied volatility is the market's expectation of future volatility, backed out from the option price. - [Black-Scholes Option Price](https://docs.fincept.in/api-reference/quantlib-pricing/black-scholes-option-price.md): Calculate European option price using the Black-Scholes model. This is the fundamental option pricing model for non-dividend paying stocks, extended here to handle continuous dividend yields. Use this for pricing vanilla European call and put options on stocks, indices, or currencies. - [Black76 Caplet Price](https://docs.fincept.in/api-reference/quantlib-pricing/black76-caplet-price.md): Price an interest rate caplet using the Black76 model. A caplet is a call option on an interest rate that pays off when the reference rate exceeds the strike rate. - [Black76 Floorlet Price](https://docs.fincept.in/api-reference/quantlib-pricing/black76-floorlet-price.md): Price an interest rate floorlet using the Black76 model. A floorlet is a put option on an interest rate that pays off when the reference rate falls below the strike rate. - [Black76 Full Greeks Suite](https://docs.fincept.in/api-reference/quantlib-pricing/black76-full-greeks-suite.md): Calculate complete suite of Black76 Greeks for options on futures and forwards. Returns price and all major risk sensitivities. - [Black76 Greeks (Basic)](https://docs.fincept.in/api-reference/quantlib-pricing/black76-greeks-basic.md): Calculate basic Black76 Greeks structure, returning all input parameters. For complete Greeks calculations including delta, gamma, vega, and theta, use the `/black76/greeks-full` endpoint. - [Black76 Implied Volatility](https://docs.fincept.in/api-reference/quantlib-pricing/black76-implied-volatility.md): Calculate implied volatility from market price of futures options using the Black76 model. Extract the market's volatility expectation for futures/forwards. - [Black76 Option Price](https://docs.fincept.in/api-reference/quantlib-pricing/black76-option-price.md): Calculate option price using the Black76 model, which is used for pricing options on futures contracts and forward contracts. This model is the futures-adapted version of Black-Scholes. - [Black76 Swaption Price](https://docs.fincept.in/api-reference/quantlib-pricing/black76-swaption-price.md): Price a European swaption using the Black76 model. A swaption is an option to enter into an interest rate swap. - [Kirk Spread Option Greeks](https://docs.fincept.in/api-reference/quantlib-pricing/kirk-spread-option-greeks.md): Calculate Greeks for spread options using Kirk's approximation. Returns all available risk sensitivities for managing spread option positions. - [Kirk Spread Option Price](https://docs.fincept.in/api-reference/quantlib-pricing/kirk-spread-option-price.md): Price a spread option using Kirk's approximation. A spread option has payoff based on the difference between two asset prices: max(F1 - F2 - K, 0) for a call. - [Margrabe Exchange Option Price](https://docs.fincept.in/api-reference/quantlib-pricing/margrabe-exchange-option-price.md): Price an exchange option using Margrabe's formula. An exchange option gives the right to exchange one asset for another: payoff = max(S1 - S2, 0) for a call. - [Shifted Lognormal Option Price](https://docs.fincept.in/api-reference/quantlib-pricing/shifted-lognormal-option-price.md): Price options using the shifted lognormal model, which applies a shift parameter to handle negative or very low rates while maintaining lognormal distribution properties. - [Volatility Conversion (Lognormal ↔ Normal)](https://docs.fincept.in/api-reference/quantlib-pricing/volatility-conversion-lognormal-↔-normal.md): Convert between lognormal (Black-Scholes) volatility and normal (Bachelier) volatility. Essential for working across different market conventions. - [Assess IFRS 9 Impairment Stage](https://docs.fincept.in/api-reference/quantlib-regulatory/assess-ifrs-9-impairment-stage.md): Determines the IFRS 9 impairment stage (Stage 1, 2, or 3) for a financial asset based on credit deterioration indicators. Stage 1: 12-month ECL for performing loans with no significant increase in credit risk (SICR). Stage 2: Lifetime ECL for loans with SICR but not credit-impaired (typically 30+ da… - [Calculate 12-Month Expected Credit Loss](https://docs.fincept.in/api-reference/quantlib-regulatory/calculate-12-month-expected-credit-loss.md): Calculates 12-month Expected Credit Loss (ECL) for Stage 1 financial assets under IFRS 9. Formula: ECL = PD_12m * LGD * EAD * discount_factor. The 12-month ECL represents expected credit losses from default events in the next 12 months, discounted to present value using the effective interest rate.… - [Calculate Basel III Capital Ratios](https://docs.fincept.in/api-reference/quantlib-regulatory/calculate-basel-iii-capital-ratios.md): Calculates all Basel III regulatory capital ratios including CET1 (Common Equity Tier 1), Tier 1, Total Capital, and optionally the Leverage Ratio. Compares results against minimum regulatory requirements (CET1 >= 4.5%, Tier 1 >= 6%, Total >= 8%) and returns compliance flags. Use this for regulatory… - [Calculate Credit Risk-Weighted Assets](https://docs.fincept.in/api-reference/quantlib-regulatory/calculate-credit-risk-weighted-assets.md): Calculates credit risk-weighted assets (RWA) using either the Standardized Approach or Internal Ratings-Based (IRB) approach under Basel III. The Standardized Approach uses external credit ratings and fixed risk weights per asset class. The IRB approach uses bank's internal estimates of PD (Probabil… - [Calculate Lifetime Expected Credit Loss](https://docs.fincept.in/api-reference/quantlib-regulatory/calculate-lifetime-expected-credit-loss.md): Calculates Lifetime Expected Credit Loss (ECL) for Stage 2 and Stage 3 financial assets under IFRS 9. Uses time-series curves of PD, EAD, and discount rates over the expected life of the instrument. Formula: sum over all periods of (PD_t * LGD * EAD_t * discount_factor_t). Lifetime ECL captures all… - [Calculate Liquidity Coverage Ratio](https://docs.fincept.in/api-reference/quantlib-regulatory/calculate-liquidity-coverage-ratio.md): Calculates the Basel III Liquidity Coverage Ratio (LCR), which measures a bank's ability to withstand a 30-day liquidity stress scenario. Formula: LCR = HQLA / Net Cash Outflows. High-Quality Liquid Assets (HQLA) are weighted by level: Level 1 at 100% (cash, central bank reserves, sovereigns), Level… - [Calculate Net Stable Funding Ratio](https://docs.fincept.in/api-reference/quantlib-regulatory/calculate-net-stable-funding-ratio.md): Calculates the Basel III Net Stable Funding Ratio (NSFR), which measures the stability of a bank's funding structure over a one-year horizon. Formula: NSFR = Available Stable Funding (ASF) / Required Stable Funding (RSF). ASF comes from stable funding sources (capital 100%, stable retail deposits 95… - [Calculate Operational Risk RWA](https://docs.fincept.in/api-reference/quantlib-regulatory/calculate-operational-risk-rwa.md): Calculates operational risk-weighted assets (RWA) using the Basic Indicator Approach (BIA) under Basel III. This method uses the average of the previous 3 years' positive annual gross income multiplied by a fixed alpha factor (15%). Use this for banks that do not qualify for advanced measurement app… - [Calculate SA-CCR Exposure at Default](https://docs.fincept.in/api-reference/quantlib-regulatory/calculate-sa-ccr-exposure-at-default.md): Calculates Exposure at Default (EAD) for derivative contracts using the Standardized Approach for Counterparty Credit Risk (SA-CCR). Returns the Replacement Cost (RC), Potential Future Exposure (PFE), and final EAD. RC represents the current market value net of collateral. PFE captures potential fut… - [Project Capital Ratios Under Stress](https://docs.fincept.in/api-reference/quantlib-regulatory/project-capital-ratios-under-stress.md): Projects capital ratios over multiple quarters under stress scenarios (e.g., CCAR, DFAST, or custom adverse scenarios). Simulates the evolution of capital and RWA by applying quarterly earnings, credit/market losses, and RWA changes. Formula for each quarter: Capital_t = Capital_(t-1) + Earnings_t -… - [Test for Significant Increase in Credit Risk](https://docs.fincept.in/api-reference/quantlib-regulatory/test-for-significant-increase-in-credit-risk.md): Determines whether a financial asset has experienced a Significant Increase in Credit Risk (SICR) under IFRS 9, which triggers the transition from Stage 1 (12-month ECL) to Stage 2 (lifetime ECL). Tests both absolute and relative thresholds: absolute threshold checks if PD increase exceeds a fixed a… - [Backtest VaR Model](https://docs.fincept.in/api-reference/quantlib-risk/backtest-var-model.md): Validates VaR model accuracy by comparing VaR estimates against actual realized losses. Performs Kupiec POF test, Christoffersen independence test, and Basel traffic light test. Essential for model validation, regulatory compliance, and ensuring risk models remain accurate over time. [Tier: PRO, Cre… - [Calculate Bucket Delta (DV01 by Tenor)](https://docs.fincept.in/api-reference/quantlib-risk/calculate-bucket-delta-dv01-by-tenor.md): Computes sensitivity to interest rate changes at specific maturity buckets (e.g., 1Y, 5Y, 10Y). Shows where along the yield curve the portfolio has risk exposure. Critical for interest rate risk management, immunization strategies, and regulatory reporting under FRTB. [Tier: PRO, Credits: 5] - [Calculate Component VaR](https://docs.fincept.in/api-reference/quantlib-risk/calculate-component-var.md): Calculates the contribution of each portfolio component (asset or risk factor) to total portfolio VaR. This decomposition helps identify which positions drive portfolio risk and enables better risk budgeting decisions. Essential for risk attribution and portfolio optimization. [Tier: PRO, Credits: 5… - [Calculate Cross-Gamma](https://docs.fincept.in/api-reference/quantlib-risk/calculate-cross-gamma.md): Computes mixed second-order sensitivities showing how delta changes with respect to multiple risk factors simultaneously. Important for managing complex derivatives with exposure to multiple underlyings or risk factors. Used in correlation trading and multi-asset option books. [Tier: PRO, Credits: 5… - [Calculate Exposure Profile (EPE and PFE)](https://docs.fincept.in/api-reference/quantlib-risk/calculate-exposure-profile-epe-and-pfe.md): Generates both Expected Positive Exposure (EPE) and Potential Future Exposure (PFE) profiles from simulated exposure paths. EPE is the average exposure at each time point, while PFE is the high quantile. Both are fundamental for counterparty credit risk management, capital allocation, and XVA calcul… - [Calculate Full Greeks Suite](https://docs.fincept.in/api-reference/quantlib-risk/calculate-full-greeks-suite.md): Computes complete set of option Greeks including first-order (delta, vega, theta, rho), second-order (gamma), and cross-sensitivities (vanna, volga). Essential for options trading, dynamic hedging, and risk management. Shows how option value changes with respect to underlying price, volatility, time… - [Calculate Historical VaR](https://docs.fincept.in/api-reference/quantlib-risk/calculate-historical-var.md): Calculates Value at Risk using the historical simulation method. This approach uses actual historical return data to estimate VaR without assuming any distribution. More accurate for non-normal distributions and captures tail risk better than parametric methods. Requires historical return time serie… - [Calculate Incremental VaR](https://docs.fincept.in/api-reference/quantlib-risk/calculate-incremental-var.md): Calculates how much portfolio VaR increases when adding a new position. This measures the absolute change in VaR from including the new asset, helping traders and portfolio managers assess the risk impact of new trades before execution. [Tier: PRO, Credits: 5] - [Calculate Key Rate Durations](https://docs.fincept.in/api-reference/quantlib-risk/calculate-key-rate-durations.md): Measures sensitivity to changes in specific points (key rates) along the yield curve while holding other rates constant. More precise than overall duration for understanding where yield curve exposure lies. Essential for bullet vs barbell strategies and curve positioning trades. [Tier: PRO, Credits:… - [Calculate Marginal VaR](https://docs.fincept.in/api-reference/quantlib-risk/calculate-marginal-var.md): Calculates the rate of change in portfolio VaR for a small increase in position size. This is the derivative of VaR with respect to position weight, useful for understanding how sensitive portfolio risk is to changes in a specific position. Critical for dynamic hedging and position sizing decisions.… - [Calculate Optimal Hedge Ratio](https://docs.fincept.in/api-reference/quantlib-risk/calculate-optimal-hedge-ratio.md): Computes the minimum variance hedge ratio - the optimal proportion of hedging instrument to hold per unit of exposure. Based on the ratio of covariance between asset and hedge to variance of hedge. Essential for futures hedging, currency hedging, and portfolio insurance strategies. [Tier: PRO, Credi… - [Calculate Parallel Shift Sensitivity (DV01)](https://docs.fincept.in/api-reference/quantlib-risk/calculate-parallel-shift-sensitivity-dv01.md): Measures portfolio sensitivity to a parallel shift in the entire yield curve (all rates move by same amount). This is the classic DV01 metric showing dollar value change for 1 basis point parallel move. Fundamental for interest rate risk reporting and hedging with interest rate futures. [Tier: PRO,… - [Calculate Parametric VaR](https://docs.fincept.in/api-reference/quantlib-risk/calculate-parametric-var.md): Calculates Value at Risk using the parametric (variance-covariance) method. This assumes normal distribution of returns and estimates VaR using portfolio volatility and confidence level. Best suited for portfolios with normally distributed returns and linear positions. Use when you have volatility e… - [Calculate Tail Dependence Coefficient](https://docs.fincept.in/api-reference/quantlib-risk/calculate-tail-dependence-coefficient.md): Measures the probability that two assets experience extreme losses simultaneously. Tail dependence is critical for portfolio diversification as assets may appear uncorrelated in normal times but become highly correlated during market crashes. Essential for risk parity strategies and crisis risk mana… - [Calculate Twist Sensitivity (Steepening/Flattening)](https://docs.fincept.in/api-reference/quantlib-risk/calculate-twist-sensitivity-steepeningflattening.md): Measures sensitivity to yield curve steepening or flattening - short rates decrease while long rates increase (steepening) or vice versa (flattening). Critical for curve trades, butterfly strategies, and understanding non-parallel curve movements that often occur during policy transitions. [Tier: PR… - [Comprehensive Tail Risk Analysis](https://docs.fincept.in/api-reference/quantlib-risk/comprehensive-tail-risk-analysis.md): Performs a complete suite of tail risk metrics including CVaR (Expected Shortfall), Tail VaR, maximum drawdown, omega ratio, and Sortino ratio. Provides a holistic view of downside risk beyond standard VaR. Essential for hedge funds, pension funds, and institutions focused on tail risk management. [… - [Correlation Stress Testing](https://docs.fincept.in/api-reference/quantlib-risk/correlation-stress-testing.md): Stress tests correlation matrices under extreme scenarios. Models correlation breakdown (crisis), decorrelation (relationships weaken), or correlation sign flips. Critical for understanding portfolio behavior during market dislocations when historical correlations fail. Used in FRTB, CCAR stress tes… - [Create Custom Stress Scenario](https://docs.fincept.in/api-reference/quantlib-risk/create-custom-stress-scenario.md): Builds a custom stress testing scenario by defining shocks to risk factors. Used to assess portfolio performance under extreme market conditions like crashes, rate spikes, or commodity shocks. Essential for regulatory stress testing (CCAR, FRTB) and internal risk management. [Tier: PRO, Credits: 5] - [Credit Valuation Adjustment (CVA)](https://docs.fincept.in/api-reference/quantlib-risk/credit-valuation-adjustment-cva.md): Calculates the Credit Valuation Adjustment - the market value of counterparty credit risk. CVA represents the expected loss from counterparty default on OTC derivatives. Includes Expected Positive Exposure (EPE) profile calculation. Required under Basel III and IFRS 13 for marking derivative books t… - [Expected Shortfall Portfolio Optimization](https://docs.fincept.in/api-reference/quantlib-risk/expected-shortfall-portfolio-optimization.md): Calculates Conditional Value at Risk (CVaR / Expected Shortfall) for each asset in a portfolio. CVaR measures the expected loss given that losses exceed VaR - it captures tail risk better than VaR alone. Used for portfolio optimization under tail risk constraints and risk budgeting. [Tier: PRO, Cred… - [Generalized Extreme Value (GEV) Distribution](https://docs.fincept.in/api-reference/quantlib-risk/generalized-extreme-value-gev-distribution.md): Fits a Generalized Extreme Value distribution to block maxima (e.g., maximum annual losses). GEV combines three types of extreme value distributions (Gumbel, Frechet, Weibull) and is used to model rare events like maximum flood levels, worst portfolio losses, or largest operational losses. Essential… - [Generalized Pareto Distribution (GPD) - Peaks Over Threshold](https://docs.fincept.in/api-reference/quantlib-risk/generalized-pareto-distribution-gpd--peaks-over-threshold.md): Fits a Generalized Pareto Distribution to extreme tail events using the Peaks Over Threshold (POT) method. GPD is specifically designed to model the tail of a distribution and estimate extreme quantiles beyond observed data. Critical for estimating tail VaR, stress losses, and operational risk capit… - [Generate Copula Samples](https://docs.fincept.in/api-reference/quantlib-risk/generate-copula-samples.md): Generates correlated random samples using various copula models (Gaussian, Student-t, Clayton, Frank, Gumbel, Joe). Copulas model dependency structures between variables while preserving marginal distributions. Essential for multivariate risk modeling, credit portfolio modeling, and Monte Carlo simu… - [Hill Estimator for Tail Index](https://docs.fincept.in/api-reference/quantlib-risk/hill-estimator-for-tail-index.md): Estimates the tail index (shape parameter) using the Hill estimator, a simple non-parametric method for heavy-tailed distributions. The tail index determines how fat the tails are - higher values indicate heavier tails and more extreme events. Used for quick tail risk assessment and for calibrating… - [Potential Future Exposure (PFE)](https://docs.fincept.in/api-reference/quantlib-risk/potential-future-exposure-pfe.md): Calculates Potential Future Exposure profile - a high quantile (typically 95th percentile) of the exposure distribution at future time points. PFE is used for counterparty credit limits, collateral requirements, and regulatory capital calculations under SA-CCR. Shows worst-case exposure evolution ov… - [Add Business Days](https://docs.fincept.in/api-reference/quantlib-scheduling/add-business-days.md): Add a specified number of business days to a date, automatically skipping weekends and holidays. Supports both positive (forward) and negative (backward) day counts. Critical for calculating T+2 settlements, option expiries, and forward value dates. [Tier: FREE, Credits: 0] - [Adjust Date](https://docs.fincept.in/api-reference/quantlib-scheduling/adjust-date.md): Adjust a date to a valid business day according to a specified business day convention and calendar. If the date falls on a weekend or holiday, it will be moved to the nearest valid business day based on the convention. Essential for ensuring payment dates, settlement dates, and trade dates fall on… - [Batch Adjust](https://docs.fincept.in/api-reference/quantlib-scheduling/batch-adjust.md): Adjust multiple dates to valid business days in a single request using the same adjustment convention and calendar. Efficient for processing multiple payment dates, a schedule of dates, or validating an entire series of dates. Returns results in the same order as input. [Tier: FREE, Credits: 0] - [Batch Year Fraction](https://docs.fincept.in/api-reference/quantlib-scheduling/batch-year-fraction.md): Calculate year fractions for multiple date pairs in a single request using the same day count convention. Efficient for processing multiple coupon periods, calculating accruals for a portfolio of bonds, or batch interest calculations. Returns results in the same order as input. [Tier: FREE, Credits:… - [Business Days Between](https://docs.fincept.in/api-reference/quantlib-scheduling/business-days-between.md): Count the number of business days between two dates, excluding weekends and holidays according to the specified calendar. Returns the count excluding the start date but including the end date. Essential for accrual calculations, SLA tracking, and interest period determination. [Tier: FREE, Credits:… - [Day Count](https://docs.fincept.in/api-reference/quantlib-scheduling/day-count.md): Calculate the raw day count (numerator) between two dates according to a specified convention. Unlike year-fraction, this returns the integer day count before division by the year basis. Useful when you need the actual day count for custom calculations or reporting. [Tier: FREE, Credits: 0] - [Generate Schedule](https://docs.fincept.in/api-reference/quantlib-scheduling/generate-schedule.md): Generate a complete payment or accrual schedule for financial instruments such as bonds, swaps, and loans. Creates a series of periods with start dates, end dates, and payment dates, automatically adjusting for business days and holidays. Supports various frequencies, stub periods, and rolling conve… - [Is Business Day](https://docs.fincept.in/api-reference/quantlib-scheduling/is-business-day.md): Check if a date is a business day according to a specific calendar. Returns whether the date is a business day, weekend, or holiday. Useful for validating payment dates, settlement dates, and trade dates in financial calculations. [Tier: FREE, Credits: 0] - [List Adjustment Methods](https://docs.fincept.in/api-reference/quantlib-scheduling/list-adjustment-methods.md): Retrieve a list of all supported business day adjustment methods. These methods define how dates that fall on non-business days should be moved to valid business days. Each method has different rules for handling month-end dates and crossing month boundaries. [Tier: FREE, Credits: 0] - [List Calendars](https://docs.fincept.in/api-reference/quantlib-scheduling/list-calendars.md): Retrieve a list of all supported business day calendars. Each calendar includes country-specific holidays and weekends. Use this endpoint to discover available calendars for your business day calculations. [Tier: FREE, Credits: 0] - [List Conventions](https://docs.fincept.in/api-reference/quantlib-scheduling/list-conventions.md): Retrieve a list of all supported day count conventions. Day count conventions define how to calculate the fraction of a year between two dates, which is critical for interest accrual calculations. Different markets and instruments use different conventions. [Tier: FREE, Credits: 0] - [Next Business Day](https://docs.fincept.in/api-reference/quantlib-scheduling/next-business-day.md): Get the next business day following a given date. If the input date is already a business day, returns the next valid business day. Essential for calculating settlement dates, payment dates, and forward-starting transactions. [Tier: FREE, Credits: 0] - [Previous Business Day](https://docs.fincept.in/api-reference/quantlib-scheduling/previous-business-day.md): Get the previous business day before a given date. If the input date is already a business day, returns the prior valid business day. Useful for backdating calculations, historical analysis, and determining prior settlement dates. [Tier: FREE, Credits: 0] - [Year Fraction](https://docs.fincept.in/api-reference/quantlib-scheduling/year-fraction.md): Calculate the year fraction between two dates using a specified day count convention. The year fraction is used to calculate accrued interest, coupon payments, and discount factors. Different conventions are used in different markets (money markets use ACT/360, bonds use 30/360, etc.). [Tier: FREE,… - [Bootstrap Discount Curve from Market Instruments](https://docs.fincept.in/api-reference/quantlib-solver/bootstrap-discount-curve-from-market-instruments.md): Bootstrap a complete discount curve from market instruments (deposits, FRAs, futures, swaps). Bootstrapping is the process of constructing a zero-coupon yield curve from the prices of coupon-bearing instruments. The algorithm solves iteratively for discount factors at each pillar point that reprice… - [Calculate Asset Swap Spread](https://docs.fincept.in/api-reference/quantlib-solver/calculate-asset-swap-spread.md): Calculate the asset swap spread - the spread over LIBOR/SOFR that makes an asset swap structure (buying a bond and entering a fixed-for-floating swap) break-even. The ASW spread isolates the credit risk component by removing interest rate risk through the swap. It represents the pure credit spread a… - [Calculate Basis](https://docs.fincept.in/api-reference/quantlib-solver/calculate-basis.md): Calculate the basis - the difference between the spot price and the futures price (Basis = Spot - Futures). Basis represents the net cost of carry and can be positive (contango) or negative (backwardation). Understanding basis is crucial for hedging strategies, arbitrage opportunities, and futures c… - [Calculate Black-Scholes Implied Volatility](https://docs.fincept.in/api-reference/quantlib-solver/calculate-black-scholes-implied-volatility.md): Solve for the implied volatility from a given option price using the Black-Scholes model. Implied volatility is the market's forecast of future volatility embedded in option prices. It is the volatility parameter that, when input into the Black-Scholes formula, reproduces the observed market price.… - [Calculate Black76 Implied Volatility](https://docs.fincept.in/api-reference/quantlib-solver/calculate-black76-implied-volatility.md): Solve for the implied volatility from a given option price using the Black76 model. Black76 is used for pricing options on futures, forwards, and in interest rate markets (caps, floors, swaptions). Unlike Black-Scholes which uses spot price, Black76 uses the forward price directly. This is the stand… - [Calculate Bond Convexity](https://docs.fincept.in/api-reference/quantlib-solver/calculate-bond-convexity.md): Calculate the convexity of a bond - the second derivative of bond price with respect to yield. Convexity measures the curvature of the price-yield relationship and captures the error in duration-based price estimates. Positive convexity is desirable as it means price increases more when yields fall… - [Calculate Bond Yield to Maturity](https://docs.fincept.in/api-reference/quantlib-solver/calculate-bond-yield-to-maturity.md): Solve for the yield to maturity (YTM) of a bond given its market price, coupon rate, and maturity. The YTM is the internal rate of return assuming the bond is held to maturity and all payments are made as scheduled. Uses iterative root-finding methods to solve the bond pricing equation. Essential fo… - [Calculate Convexity Adjustment](https://docs.fincept.in/api-reference/quantlib-solver/calculate-convexity-adjustment.md): Calculate the convexity adjustment for converting between forward rates and futures rates. Due to daily settlement of futures contracts (marking to market), futures rates differ from forward rates by a convexity adjustment. This is particularly important for Eurodollar futures and other interest rat… - [Calculate Cost of Carry](https://docs.fincept.in/api-reference/quantlib-solver/calculate-cost-of-carry.md): Calculate the annualized cost of carry - the net cost of holding the underlying asset until futures expiry. Cost of carry includes financing costs, storage costs, and income (dividends, coupons). The relationship is: Futures = Spot × e^(carry × time). Understanding carry is essential for futures pri… - [Calculate Discount Factor from Rate](https://docs.fincept.in/api-reference/quantlib-solver/calculate-discount-factor-from-rate.md): Convert an interest rate to a discount factor for a given time period. Discount factors are fundamental to all present value calculations. Supports multiple compounding conventions: continuous (e^(-rt)), simple (1/(1+rt)), and annual (1/(1+r)^t). Essential for discounting cash flows, bond pricing, a… - [Calculate Extended IRR (XIRR)](https://docs.fincept.in/api-reference/quantlib-solver/calculate-extended-irr-xirr.md): Calculate the Extended Internal Rate of Return for cash flows occurring at irregular intervals. Unlike standard IRR which assumes periodic cash flows, XIRR handles any date spacing. The dates array represents year fractions from the first cash flow date. Essential for evaluating real-world investmen… - [Calculate Forward Rate from Discount Factors](https://docs.fincept.in/api-reference/quantlib-solver/calculate-forward-rate-from-discount-factors.md): Calculate the forward interest rate between two future time periods using their discount factors. Forward rates represent the implied future interest rate consistent with the current term structure. Used for FRA pricing, forward curve construction, and understanding market expectations of future rat… - [Calculate G-Spread](https://docs.fincept.in/api-reference/quantlib-solver/calculate-g-spread.md): Calculate the G-spread (Government spread) - the difference between a bond's yield to maturity and the yield of a comparable-maturity government bond. This is the simplest spread measure and represents the additional yield investors demand for credit risk and liquidity risk relative to risk-free gov… - [Calculate I-Spread](https://docs.fincept.in/api-reference/quantlib-solver/calculate-i-spread.md): Calculate the I-spread (Interpolated spread) - the difference between a bond's yield to maturity and the swap rate of equivalent maturity. The I-spread uses the swap curve as the benchmark rather than government bonds, making it more appropriate for credit analysis since swap rates better reflect in… - [Calculate Implied Repo Rate](https://docs.fincept.in/api-reference/quantlib-solver/calculate-implied-repo-rate.md): Calculate the implied repo rate from spot and futures prices. The implied repo rate is the break-even financing rate that makes cash-and-carry arbitrage unprofitable. It accounts for the cost of borrowing cash to buy the underlying, income received (dividends/coupons), and storage costs. Used extens… - [Calculate Internal Rate of Return (IRR)](https://docs.fincept.in/api-reference/quantlib-solver/calculate-internal-rate-of-return-irr.md): Calculate the Internal Rate of Return - the discount rate that makes the net present value of all cash flows equal to zero. IRR is widely used in capital budgeting, project evaluation, and investment analysis to assess the profitability of potential investments. It represents the effective annualize… - [Calculate Macaulay Duration](https://docs.fincept.in/api-reference/quantlib-solver/calculate-macaulay-duration.md): Calculate the Macaulay duration of a bond - the weighted average time to receive all cash flows. Duration measures a bond's sensitivity to interest rate changes and is expressed in years. Higher duration means greater price sensitivity to rate changes. Essential for immunization strategies, duration… - [Calculate Modified Duration](https://docs.fincept.in/api-reference/quantlib-solver/calculate-modified-duration.md): Calculate the modified duration of a bond - an approximation of the percentage price change for a 1% change in yield. Modified duration is derived from Macaulay duration and directly measures interest rate risk. Used extensively for hedging, DV01 calculations, and measuring portfolio sensitivity to… - [Calculate Option-Adjusted Spread (OAS)](https://docs.fincept.in/api-reference/quantlib-solver/calculate-option-adjusted-spread-oas.md): Calculate the Option-Adjusted Spread - the constant spread over the risk-free curve that accounts for embedded options in a bond. OAS removes the value of embedded options (calls, puts, prepayment options) to isolate the pure credit spread. Calculated using Monte Carlo simulation or binomial trees t… - [Calculate Par Swap Rate](https://docs.fincept.in/api-reference/quantlib-solver/calculate-par-swap-rate.md): Calculate the par swap rate (also known as the swap rate) from a set of discount factors. The par rate is the fixed rate that makes the present value of fixed-leg payments equal to the present value of floating-leg payments at inception. This is the market convention for quoting interest rate swaps… - [Calculate PV01 (DV01)](https://docs.fincept.in/api-reference/quantlib-solver/calculate-pv01-dv01.md): Calculate the PV01 (Present Value of a Basis Point), also known as DV01 (Dollar Value of 01). This measures the change in present value for a one basis point (0.01%) parallel shift in the yield curve. PV01 is a fundamental risk metric used for hedging interest rate exposure, calculating hedge ratios… - [Calculate Z-Spread](https://docs.fincept.in/api-reference/quantlib-solver/calculate-z-spread.md): Calculate the Z-spread (Zero-volatility spread) - the constant spread that, when added to the spot rate curve, makes the present value of a bond's cash flows equal its market price. Unlike G-spread or I-spread which compare yields, Z-spread properly accounts for the term structure of interest rates… - [Calculate Zero Rate from Discount Factor](https://docs.fincept.in/api-reference/quantlib-solver/calculate-zero-rate-from-discount-factor.md): Convert a discount factor back to a zero (spot) rate for a given time period. This is the inverse operation of calculating a discount factor. Zero rates are the foundation of the yield curve and represent the pure rate of return for a specific maturity with no intermediate cash flows. Used extensive… - [Calibrate Vasicek Short Rate Model](https://docs.fincept.in/api-reference/quantlib-solver/calibrate-vasicek-short-rate-model.md): Calibrate the Vasicek short rate model parameters (a, b, sigma) to match an observed zero rate curve. The Vasicek model is dr = a(b - r)dt + sigma dW, where a is mean reversion speed, b is long-term mean rate, and sigma is volatility. This is a fundamental interest rate model used for derivatives pr… - [Convert Between Forward and Futures Rates](https://docs.fincept.in/api-reference/quantlib-solver/convert-between-forward-and-futures-rates.md): Convert between forward rates and futures rates accounting for the convexity adjustment due to daily marking-to-market of futures. Futures contracts are marked to market daily, creating a funding advantage/disadvantage compared to forwards depending on interest rate movements. This leads to a system… - [Ar Fit](https://docs.fincept.in/api-reference/quantlib-statistics/ar-fit.md): Fit an AR(p) model to data [Tier: STANDARD, Credits: 2] - [Ar Forecast](https://docs.fincept.in/api-reference/quantlib-statistics/ar-forecast.md): Fit AR(p) and forecast N steps ahead [Tier: STANDARD, Credits: 2] - [Arima Fit](https://docs.fincept.in/api-reference/quantlib-statistics/arima-fit.md): Fit an ARIMA(p,d,q) model to data [Tier: STANDARD, Credits: 2] - [Arima Forecast](https://docs.fincept.in/api-reference/quantlib-statistics/arima-forecast.md): Fit ARIMA(p,d,q) and forecast N steps ahead [Tier: STANDARD, Credits: 2] - [Beta Cdf](https://docs.fincept.in/api-reference/quantlib-statistics/beta-cdf.md): Evaluate Beta CDF at x [Tier: BASIC, Credits: 1] - [Beta Pdf](https://docs.fincept.in/api-reference/quantlib-statistics/beta-pdf.md): Evaluate Beta PDF at x [Tier: BASIC, Credits: 1] - [Beta Properties](https://docs.fincept.in/api-reference/quantlib-statistics/beta-properties.md): Get properties of a Beta distribution [Tier: BASIC, Credits: 1] - [Binomial Cdf](https://docs.fincept.in/api-reference/quantlib-statistics/binomial-cdf.md): Evaluate Binomial CDF at k [Tier: BASIC, Credits: 1] - [Binomial Pmf](https://docs.fincept.in/api-reference/quantlib-statistics/binomial-pmf.md): Evaluate Binomial PMF at k [Tier: BASIC, Credits: 1] - [Binomial Properties](https://docs.fincept.in/api-reference/quantlib-statistics/binomial-properties.md): Get properties of a Binomial distribution [Tier: BASIC, Credits: 1] - [Chi2 Cdf](https://docs.fincept.in/api-reference/quantlib-statistics/chi2-cdf.md): Evaluate Chi-Squared CDF at x [Tier: BASIC, Credits: 1] - [Chi2 Pdf](https://docs.fincept.in/api-reference/quantlib-statistics/chi2-pdf.md): Evaluate Chi-Squared PDF at x [Tier: BASIC, Credits: 1] - [Chi2 Properties](https://docs.fincept.in/api-reference/quantlib-statistics/chi2-properties.md): Get properties of a Chi-Squared distribution [Tier: BASIC, Credits: 1] - [Egarch Fit](https://docs.fincept.in/api-reference/quantlib-statistics/egarch-fit.md): Fit an EGARCH(p,q) model to returns data [Tier: STANDARD, Credits: 2] - [Exp Cdf](https://docs.fincept.in/api-reference/quantlib-statistics/exp-cdf.md): Evaluate Exponential CDF at x [Tier: BASIC, Credits: 1] - [Exp Pdf](https://docs.fincept.in/api-reference/quantlib-statistics/exp-pdf.md): Evaluate Exponential PDF at x [Tier: BASIC, Credits: 1] - [Exp Ppf](https://docs.fincept.in/api-reference/quantlib-statistics/exp-ppf.md): Evaluate Exponential inverse CDF [Tier: BASIC, Credits: 1] - [Exp Properties](https://docs.fincept.in/api-reference/quantlib-statistics/exp-properties.md): Get properties of an Exponential distribution [Tier: BASIC, Credits: 1] - [F Pdf](https://docs.fincept.in/api-reference/quantlib-statistics/f-pdf.md): Evaluate F distribution PDF at x [Tier: BASIC, Credits: 1] - [F Properties](https://docs.fincept.in/api-reference/quantlib-statistics/f-properties.md): Get properties of an F distribution [Tier: BASIC, Credits: 1] - [Gamma Cdf](https://docs.fincept.in/api-reference/quantlib-statistics/gamma-cdf.md): Evaluate Gamma CDF at x [Tier: BASIC, Credits: 1] - [Gamma Pdf](https://docs.fincept.in/api-reference/quantlib-statistics/gamma-pdf.md): Evaluate Gamma PDF at x [Tier: BASIC, Credits: 1] - [Gamma Properties](https://docs.fincept.in/api-reference/quantlib-statistics/gamma-properties.md): Get properties of a Gamma distribution [Tier: BASIC, Credits: 1] - [Garch Fit](https://docs.fincept.in/api-reference/quantlib-statistics/garch-fit.md): Fit a GARCH(p,q) model to returns data [Tier: STANDARD, Credits: 2] - [Garch Forecast](https://docs.fincept.in/api-reference/quantlib-statistics/garch-forecast.md): Fit GARCH(p,q) and forecast volatility N steps ahead [Tier: STANDARD, Credits: 2] - [Geometric Cdf](https://docs.fincept.in/api-reference/quantlib-statistics/geometric-cdf.md): Evaluate Geometric CDF at k [Tier: BASIC, Credits: 1] - [Geometric Pmf](https://docs.fincept.in/api-reference/quantlib-statistics/geometric-pmf.md): Evaluate Geometric PMF at k [Tier: BASIC, Credits: 1] - [Geometric Ppf](https://docs.fincept.in/api-reference/quantlib-statistics/geometric-ppf.md): Evaluate Geometric inverse CDF [Tier: BASIC, Credits: 1] - [Geometric Properties](https://docs.fincept.in/api-reference/quantlib-statistics/geometric-properties.md): Get properties of a Geometric distribution [Tier: BASIC, Credits: 1] - [Gjr Garch Fit](https://docs.fincept.in/api-reference/quantlib-statistics/gjr-garch-fit.md): Fit a GJR-GARCH(p,q) model (asymmetric GARCH) to returns data [Tier: STANDARD, Credits: 2] - [Hypergeometric Cdf](https://docs.fincept.in/api-reference/quantlib-statistics/hypergeometric-cdf.md): Evaluate Hypergeometric CDF at k [Tier: BASIC, Credits: 1] - [Hypergeometric Pmf](https://docs.fincept.in/api-reference/quantlib-statistics/hypergeometric-pmf.md): Evaluate Hypergeometric PMF at k [Tier: BASIC, Credits: 1] - [Hypergeometric Properties](https://docs.fincept.in/api-reference/quantlib-statistics/hypergeometric-properties.md): Get properties of a Hypergeometric distribution [Tier: BASIC, Credits: 1] - [Lognormal Cdf](https://docs.fincept.in/api-reference/quantlib-statistics/lognormal-cdf.md): Evaluate Lognormal CDF at x [Tier: BASIC, Credits: 1] - [Lognormal Pdf](https://docs.fincept.in/api-reference/quantlib-statistics/lognormal-pdf.md): Evaluate Lognormal PDF at x [Tier: BASIC, Credits: 1] - [Lognormal Ppf](https://docs.fincept.in/api-reference/quantlib-statistics/lognormal-ppf.md): Evaluate Lognormal inverse CDF [Tier: BASIC, Credits: 1] - [Lognormal Properties](https://docs.fincept.in/api-reference/quantlib-statistics/lognormal-properties.md): Get properties of a Lognormal distribution [Tier: BASIC, Credits: 1] - [Ma Fit](https://docs.fincept.in/api-reference/quantlib-statistics/ma-fit.md): Fit an MA(q) model to data [Tier: STANDARD, Credits: 2] - [Negbinomial Cdf](https://docs.fincept.in/api-reference/quantlib-statistics/negbinomial-cdf.md): Evaluate Negative Binomial CDF at k [Tier: BASIC, Credits: 1] - [Negbinomial Pmf](https://docs.fincept.in/api-reference/quantlib-statistics/negbinomial-pmf.md): Evaluate Negative Binomial PMF at k [Tier: BASIC, Credits: 1] - [Negbinomial Properties](https://docs.fincept.in/api-reference/quantlib-statistics/negbinomial-properties.md): Get properties of a Negative Binomial distribution [Tier: BASIC, Credits: 1] - [Normal Cdf](https://docs.fincept.in/api-reference/quantlib-statistics/normal-cdf.md): Evaluate Normal CDF at x [Tier: BASIC, Credits: 1] - [Normal Pdf](https://docs.fincept.in/api-reference/quantlib-statistics/normal-pdf.md): Evaluate Normal PDF at x [Tier: BASIC, Credits: 1] - [Normal Ppf](https://docs.fincept.in/api-reference/quantlib-statistics/normal-ppf.md): Evaluate Normal inverse CDF (percent point function) [Tier: BASIC, Credits: 1] - [Normal Properties](https://docs.fincept.in/api-reference/quantlib-statistics/normal-properties.md): Get properties of a Normal distribution (mean, variance, std, entropy, skewness, kurtosis) [Tier: BASIC, Credits: 1] - [Poisson Cdf](https://docs.fincept.in/api-reference/quantlib-statistics/poisson-cdf.md): Evaluate Poisson CDF at k [Tier: BASIC, Credits: 1] - [Poisson Pmf](https://docs.fincept.in/api-reference/quantlib-statistics/poisson-pmf.md): Evaluate Poisson PMF at k [Tier: BASIC, Credits: 1] - [Poisson Properties](https://docs.fincept.in/api-reference/quantlib-statistics/poisson-properties.md): Get properties of a Poisson distribution [Tier: BASIC, Credits: 1] - [Probability Generating Function](https://docs.fincept.in/api-reference/quantlib-statistics/probability-generating-function.md): Evaluate probability generating function for binomial, poisson, or geometric [Tier: BASIC, Credits: 1] - [Studentt Cdf](https://docs.fincept.in/api-reference/quantlib-statistics/studentt-cdf.md): Evaluate Student's t CDF at x [Tier: BASIC, Credits: 1] - [Studentt Pdf](https://docs.fincept.in/api-reference/quantlib-statistics/studentt-pdf.md): Evaluate Student's t PDF at x [Tier: BASIC, Credits: 1] - [Studentt Properties](https://docs.fincept.in/api-reference/quantlib-statistics/studentt-properties.md): Get properties of a Student's t distribution [Tier: BASIC, Credits: 1] - [Antithetic Samples](https://docs.fincept.in/api-reference/quantlib-stochastic/antithetic-samples.md): Antithetic Samples [Tier: PRO, Credits: 5] - [Apply Ito's lemma to a stochastic path](https://docs.fincept.in/api-reference/quantlib-stochastic/apply-itos-lemma-to-a-stochastic-path.md): Apply Ito's lemma to a stochastic path [Tier: PRO, Credits: 5] - [Brownian Bridge Simulate](https://docs.fincept.in/api-reference/quantlib-stochastic/brownian-bridge-simulate.md): Brownian Bridge Simulate [Tier: PRO, Credits: 5] - [Cir Bond Price](https://docs.fincept.in/api-reference/quantlib-stochastic/cir-bond-price.md): Cir Bond Price [Tier: PRO, Credits: 5] - [Cir Simulate](https://docs.fincept.in/api-reference/quantlib-stochastic/cir-simulate.md): Cir Simulate [Tier: PRO, Credits: 5] - [Correlated Bm Simulate](https://docs.fincept.in/api-reference/quantlib-stochastic/correlated-bm-simulate.md): Correlated Bm Simulate [Tier: PRO, Credits: 5] - [Correlated Normals](https://docs.fincept.in/api-reference/quantlib-stochastic/correlated-normals.md): Correlated Normals [Tier: PRO, Credits: 5] - [Covariation](https://docs.fincept.in/api-reference/quantlib-stochastic/covariation.md): Covariation [Tier: PRO, Credits: 5] - [Euler-Maruyama SDE simulation (general drift-diffusion)](https://docs.fincept.in/api-reference/quantlib-stochastic/euler-maruyama-sde-simulation-general-drift-diffusion.md): Euler-Maruyama SDE simulation (general drift-diffusion) [Tier: PRO, Credits: 5] - [Exact Cir](https://docs.fincept.in/api-reference/quantlib-stochastic/exact-cir.md): Exact Cir [Tier: PRO, Credits: 5] - [Exact Gbm](https://docs.fincept.in/api-reference/quantlib-stochastic/exact-gbm.md): Exact Gbm [Tier: PRO, Credits: 5] - [Exact Ou](https://docs.fincept.in/api-reference/quantlib-stochastic/exact-ou.md): Exact Ou [Tier: PRO, Credits: 5] - [Exact simulation of Heston stochastic volatility model](https://docs.fincept.in/api-reference/quantlib-stochastic/exact-simulation-of-heston-stochastic-volatility-model.md): Exact simulation of Heston stochastic volatility model [Tier: PRO, Credits: 5] - [Gbm Properties](https://docs.fincept.in/api-reference/quantlib-stochastic/gbm-properties.md): Gbm Properties [Tier: PRO, Credits: 5] - [Gbm Simulate](https://docs.fincept.in/api-reference/quantlib-stochastic/gbm-simulate.md): Gbm Simulate [Tier: PRO, Credits: 5] - [Heston Simulate](https://docs.fincept.in/api-reference/quantlib-stochastic/heston-simulate.md): Heston Simulate [Tier: PRO, Credits: 5] - [Ito product rule d(XY)](https://docs.fincept.in/api-reference/quantlib-stochastic/ito-product-rule-dxy.md): Ito product rule d(XY) [Tier: PRO, Credits: 5] - [Martingale Test](https://docs.fincept.in/api-reference/quantlib-stochastic/martingale-test.md): Martingale Test [Tier: PRO, Credits: 5] - [Merton Simulate](https://docs.fincept.in/api-reference/quantlib-stochastic/merton-simulate.md): Merton Simulate [Tier: PRO, Credits: 5] - [Milstein SDE simulation (higher-order correction)](https://docs.fincept.in/api-reference/quantlib-stochastic/milstein-sde-simulation-higher-order-correction.md): Milstein SDE simulation (higher-order correction) [Tier: PRO, Credits: 5] - [Multi-dimensional Euler-Maruyama SDE simulation](https://docs.fincept.in/api-reference/quantlib-stochastic/multi-dimensional-euler-maruyama-sde-simulation.md): Multi-dimensional Euler-Maruyama SDE simulation [Tier: PRO, Credits: 5] - [Multi-dimensional Milstein simulation](https://docs.fincept.in/api-reference/quantlib-stochastic/multi-dimensional-milstein-simulation.md): Multi-dimensional Milstein simulation [Tier: PRO, Credits: 5] - [Multilevel Monte Carlo estimation](https://docs.fincept.in/api-reference/quantlib-stochastic/multilevel-monte-carlo-estimation.md): Multilevel Monte Carlo estimation [Tier: PRO, Credits: 5] - [Multivariate Normal](https://docs.fincept.in/api-reference/quantlib-stochastic/multivariate-normal.md): Multivariate Normal [Tier: PRO, Credits: 5] - [Ou Simulate](https://docs.fincept.in/api-reference/quantlib-stochastic/ou-simulate.md): Ou Simulate [Tier: PRO, Credits: 5] - [Poisson Simulate](https://docs.fincept.in/api-reference/quantlib-stochastic/poisson-simulate.md): Poisson Simulate [Tier: PRO, Credits: 5] - [Quadratic Variation](https://docs.fincept.in/api-reference/quantlib-stochastic/quadratic-variation.md): Quadratic Variation [Tier: PRO, Credits: 5] - [Risk Neutral Drift](https://docs.fincept.in/api-reference/quantlib-stochastic/risk-neutral-drift.md): Risk Neutral Drift [Tier: PRO, Credits: 5] - [Sample from jump process distributions](https://docs.fincept.in/api-reference/quantlib-stochastic/sample-from-jump-process-distributions.md): Sample from jump process distributions [Tier: PRO, Credits: 5] - [Sample from various distributions](https://docs.fincept.in/api-reference/quantlib-stochastic/sample-from-various-distributions.md): Sample from various distributions [Tier: PRO, Credits: 5] - [Simulate under risk-neutral measure (Girsanov transform)](https://docs.fincept.in/api-reference/quantlib-stochastic/simulate-under-risk-neutral-measure-girsanov-transform.md): Simulate under risk-neutral measure (Girsanov transform) [Tier: PRO, Credits: 5] - [Sobol Sequence](https://docs.fincept.in/api-reference/quantlib-stochastic/sobol-sequence.md): Sobol Sequence [Tier: PRO, Credits: 5] - [Vasicek Bond Price](https://docs.fincept.in/api-reference/quantlib-stochastic/vasicek-bond-price.md): Vasicek Bond Price [Tier: PRO, Credits: 5] - [Vasicek Simulate](https://docs.fincept.in/api-reference/quantlib-stochastic/vasicek-simulate.md): Vasicek Simulate [Tier: PRO, Credits: 5] - [Vg Simulate](https://docs.fincept.in/api-reference/quantlib-stochastic/vg-simulate.md): Vg Simulate [Tier: PRO, Credits: 5] - [Wiener Simulate](https://docs.fincept.in/api-reference/quantlib-stochastic/wiener-simulate.md): Wiener Simulate [Tier: PRO, Credits: 5] - [Build Surface From Points](https://docs.fincept.in/api-reference/quantlib-volatility/build-surface-from-points.md): Construct a volatility surface from scattered market quote points (tenor, strike, volatility). Useful for building surfaces from real market data where quotes are not on a regular grid. Returns confirmation of successful surface construction. Supports optional forward prices and custom interpolation… - [Calibrate SABR Model](https://docs.fincept.in/api-reference/quantlib-volatility/calibrate-sabr-model.md): Calibrate SABR model parameters (alpha, beta, rho, nu) to match market volatilities across strikes. Uses optimization to find the best-fit SABR parameters that reproduce market smile. Beta can be fixed or calibrated. Supports multiple optimization methods and custom weighting. Essential for building… - [Constant Local Volatility](https://docs.fincept.in/api-reference/quantlib-volatility/constant-local-volatility.md): Query a constant local volatility model that returns the same local volatility for any spot and time. Local volatility represents the instantaneous volatility at a specific spot price and time, used in local volatility models for path-dependent exotic option pricing. Simplest case for testing and mo… - [Flat Volatility Surface](https://docs.fincept.in/api-reference/quantlib-volatility/flat-volatility-surface.md): Query a flat (constant) volatility surface that returns the same volatility for any expiry and strike. Useful for testing, basic Black-Scholes pricing, or when assuming constant volatility across all options. Returns both the volatility and total variance for a given query point. [Tier: PRO, Credits… - [Implied to Local Volatility Conversion](https://docs.fincept.in/api-reference/quantlib-volatility/implied-to-local-volatility-conversion.md): Convert implied volatility to local volatility using the Dupire formula. Calculates the instantaneous local volatility at a specific (strike, expiry) point from an implied volatility surface. Uses finite differences to approximate derivatives. Essential for building local volatility surfaces for exo… - [SABR Implied Volatility](https://docs.fincept.in/api-reference/quantlib-volatility/sabr-implied-volatility.md): Calculate implied volatility using the SABR (Stochastic Alpha Beta Rho) model. SABR is the industry standard for modeling volatility smiles in interest rate and FX markets. Returns the Black-equivalent implied volatility for a given strike, forward, and SABR parameters (alpha, beta, rho, nu). [Tier:… - [SABR Normal Volatility](https://docs.fincept.in/api-reference/quantlib-volatility/sabr-normal-volatility.md): Calculate normal (Bachelier) implied volatility using the SABR model. Normal volatility is used in normal (absolute) option pricing models, common in negative interest rate environments and for spread options. Converts SABR parameters to normal volatility for Bachelier option pricing. [Tier: PRO, Cr… - [SABR Probability Density](https://docs.fincept.in/api-reference/quantlib-volatility/sabr-probability-density.md): Calculate the risk-neutral probability density function implied by SABR parameters across multiple strike levels. Derived from the second derivative of call prices with respect to strike. Used for understanding market-implied distributions, calculating probabilities, and ensuring arbitrage-free surf… - [SABR Smile Dynamics](https://docs.fincept.in/api-reference/quantlib-volatility/sabr-smile-dynamics.md): Analyze SABR smile dynamics under forward price movements. Calculates sticky-strike volatility (vol remains constant at each strike) and sticky-delta volatility (vol moves with the option's delta) for a shifted forward. Essential for hedging volatility exposure and understanding how the smile evolve… - [SABR Volatility Smile](https://docs.fincept.in/api-reference/quantlib-volatility/sabr-volatility-smile.md): Generate a complete SABR volatility smile across multiple strikes for a given expiry. Returns the volatility smile curve along with key smile characteristics: ATM volatility, skew (first derivative), and curvature (second derivative). Essential for understanding market expectations and pricing optio… - [Term Structure Volatility](https://docs.fincept.in/api-reference/quantlib-volatility/term-structure-volatility.md): Query a term structure (expiry-dependent) volatility surface where volatility varies by expiry but is constant across strikes. Interpolates between provided expiry points. Useful for modeling volatility term structure for ATM options or when strike dependency is negligible. [Tier: PRO, Credits: 5] - [Total Variance from Surface](https://docs.fincept.in/api-reference/quantlib-volatility/total-variance-from-surface.md): Query total variance (volatility^2 * time) from a volatility surface at a specific expiry and strike. Total variance is fundamental for option pricing models and ensures proper scaling of volatility across different time horizons. Used in variance swaps, volatility derivatives, and model calibration… - [Volatility Smile](https://docs.fincept.in/api-reference/quantlib-volatility/volatility-smile.md): Extract the volatility smile (volatility vs strike) for a specific expiry from a volatility surface. Returns interpolated volatilities for multiple strike points at a single expiry. Essential for analyzing skew, pricing options across strikes, and understanding market expectations of future price di… - [Volatility Surface Grid](https://docs.fincept.in/api-reference/quantlib-volatility/volatility-surface-grid.md): Query a full volatility surface defined on a 2D grid of expiries and strikes. Uses bilinear interpolation between grid points. Essential for pricing exotic options, managing volatility risk, and calibrating models to market data. Supports arbitrary volatility smile and term structure shapes. [Tier:… - [Authentication](https://docs.fincept.in/authentication.md): Secure API access with API keys, MFA, and session management - [Billing FAQ](https://docs.fincept.in/billing-faq.md): Frequently asked questions about billing, payments, and refunds - [Credit System](https://docs.fincept.in/credit-system.md): How credits work, deduction logic, and balance management - [Error Handling](https://docs.fincept.in/error-handling.md): Complete guide to error codes, handling strategies, and troubleshooting - [Fixed Income Examples](https://docs.fincept.in/examples/fixed-income-examples.md): Bond pricing and yield curve examples - [Options Pricing Examples](https://docs.fincept.in/examples/options-pricing-examples.md): Real-world options pricing workflows - [Portfolio Optimization Examples](https://docs.fincept.in/examples/portfolio-optimization-examples.md): Portfolio construction workflows - [Regulatory Compliance Examples](https://docs.fincept.in/examples/regulatory-compliance-examples.md): Basel III, IFRS 9 workflows - [Risk Management Examples](https://docs.fincept.in/examples/risk-management-examples.md): VaR, stress testing workflows - [Introduction](https://docs.fincept.in/introduction.md): Professional quantitative finance library accessible via REST API - [Multi-Factor Authentication](https://docs.fincept.in/mfa-setup.md): Set up and manage MFA for enhanced account security - [Analysis Module](https://docs.fincept.in/modules/analysis-module.md): Equity/credit analysis - Standard tier - [Core Module](https://docs.fincept.in/modules/core-module.md): Dates, calendars, financial types - FREE tier - [Curves Module](https://docs.fincept.in/modules/curves-module.md): Yield curves - Standard tier - [Economics Module](https://docs.fincept.in/modules/economics-module.md): Economic models - Basic tier - [Instruments Module](https://docs.fincept.in/modules/instruments-module.md): Bonds, swaps, derivatives - Standard tier - [ML Module](https://docs.fincept.in/modules/ml-module.md): Machine learning - Pro tier - [Models Module](https://docs.fincept.in/modules/models-module.md): Advanced pricing models - Pro tier - [Numerical Module](https://docs.fincept.in/modules/numerical-module.md): Numerical methods - Basic tier - [Physics Module](https://docs.fincept.in/modules/physics-module.md): Information theory - Pro tier - [Portfolio Module](https://docs.fincept.in/modules/portfolio-module.md): Portfolio optimization - Pro tier - [Pricing Module](https://docs.fincept.in/modules/pricing-module.md): Options and derivatives pricing - Standard tier - [Regulatory Module](https://docs.fincept.in/modules/regulatory-module.md): Basel III, IFRS 9 - Pro tier - [Risk Module](https://docs.fincept.in/modules/risk-module.md): VaR, stress testing, CVA - Pro tier - [Scheduling Module](https://docs.fincept.in/modules/scheduling-module.md): Cash flow schedules and payment dates - FREE tier - [Solver Module](https://docs.fincept.in/modules/solver-module.md): Bond yield, implied vol solvers - Basic tier - [Statistics Module](https://docs.fincept.in/modules/statistics-module.md): Distributions and time series - Basic tier - [Stochastic Module](https://docs.fincept.in/modules/stochastic-module.md): Stochastic processes - Standard tier - [Volatility Module](https://docs.fincept.in/modules/volatility-module.md): Vol surfaces, SABR - Standard tier - [Pricing](https://docs.fincept.in/pricing.md): Credit-based pricing with transparent costs and flexible subscription plans - [QuantLib API Overview](https://docs.fincept.in/quantlib-overview.md): Comprehensive guide to 18 modules, 497 endpoints, and 4 subscription tiers - [Quickstart](https://docs.fincept.in/quickstart.md): Get started with FinceptQuantLib API in 5 minutes - [Rate Limits](https://docs.fincept.in/rate-limits.md): Understanding and working with API rate limits - [Response Format](https://docs.fincept.in/response-format.md): Standard JSON response structure and data types - [Security Best Practices](https://docs.fincept.in/security-best-practices.md): Comprehensive security guidelines for protecting your API keys and account - [Subscription Plans](https://docs.fincept.in/subscription-plans.md): Monthly subscription tiers, features, and one-time credit packages - [Tier System](https://docs.fincept.in/tier-system.md): Understanding tier access control and module organization - [What is Fincept?](https://docs.fincept.in/what-is-fincept.md): About Fincept Corporation and the FinceptQuantLib API ## OpenAPI Specs - [openapi](https://docs.fincept.in/openapi.json) - [volatility](https://docs.fincept.in/api-specs/volatility.json) - [stochastic](https://docs.fincept.in/api-specs/stochastic.json) - [statistics](https://docs.fincept.in/api-specs/statistics.json) - [solver](https://docs.fincept.in/api-specs/solver.json) - [scheduling](https://docs.fincept.in/api-specs/scheduling.json) - [risk](https://docs.fincept.in/api-specs/risk.json) - [regulatory](https://docs.fincept.in/api-specs/regulatory.json) - [pricing](https://docs.fincept.in/api-specs/pricing.json) - [portfolio](https://docs.fincept.in/api-specs/portfolio.json) - [physics](https://docs.fincept.in/api-specs/physics.json) - [numerical](https://docs.fincept.in/api-specs/numerical.json) - [models](https://docs.fincept.in/api-specs/models.json) - [ml](https://docs.fincept.in/api-specs/ml.json) - [instruments](https://docs.fincept.in/api-specs/instruments.json) - [economics](https://docs.fincept.in/api-specs/economics.json) - [curves](https://docs.fincept.in/api-specs/curves.json) - [core](https://docs.fincept.in/api-specs/core.json) - [base](https://docs.fincept.in/api-specs/base.json) - [analysis](https://docs.fincept.in/api-specs/analysis.json) - [full-openapi](https://docs.fincept.in/full-openapi.json)