fintech-algorithms
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Financial Risk and Performance Statistics

10 algorithms in Financial Mathematics, Statistics, and Data Foundations · 10 with asserted arithmetic.

In this family#

  1. Volatility and Annualized Volatility verified

    volatilityAndAnnualizedVolatility(input)
  2. Downside Deviation and Target Shortfall verified

    downsideDeviationAndTargetShortfall(input)
  3. Drawdown and Maximum Drawdown verified

    drawdownAndMaximumDrawdown(input)
  4. Loss Distributions and Loss Quantiles verified

    lossDistributionsAndLossQuantiles(input)
  5. Value at Risk Intuition verified

    valueAtRiskIntuition(input)
  6. Expected Shortfall Intuition verified

    expectedShortfallIntuition(input)
  7. Beta and Market-Relative Risk verified

    betaAndMarketRelativeRisk(input)
  8. Sharpe, Sortino, and Information Ratio Intuition verified

    sharpeSortinoAndInformationRatioIntuition(input)
  9. Active Return and Tracking Error verified

    activeReturnAndTrackingError(input)
  10. Covariance Matrices, Portfolio Variance, and Diversification verified

    covarianceMatricesPortfolioVarianceAndDiversification(input)

What they share#

Every topic here is a record-transform, so once you have called one the rest follow the same shape. Import paths differ only in the final segment:

ts
import { volatilityAndAnnualizedVolatility } from "fintech-algorithms/financial-mathematics-statistics-and-data-foundations/financial-risk-and-performance-statistics/volatility-and-annualized-volatility";
import { downsideDeviationAndTargetShortfall } from "fintech-algorithms/financial-mathematics-statistics-and-data-foundations/financial-risk-and-performance-statistics/downside-deviation-and-target-shortfall";

Read them in the order above — the sequence is pedagogical, not alphabetical.

Where this sits#

Financial Mathematics, Statistics, and Data Foundations collects 120 algorithms across 12 families. For the concept behind this family rather than the call signatures, see the concept guides.