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  • License MIT

financial math library

Package Exports

  • cfs.js

This package does not declare an exports field, so the exports above have been automatically detected and optimized by JSPM instead. If any package subpath is missing, it is recommended to post an issue to the original package (cfs.js) to support the "exports" field. If that is not possible, create a JSPM override to customize the exports field for this package.

Readme

cfa.js

still in beta - ironing some bugs... readme is due to chcnges

  1. Time value of money functions
  2. Statistics
  3. Linear Regression
  4. Probability
  5. Helper Functions

Example

Calculate rolling return in an array

rolling return day by day (you can input any number of days - lower than array length of course)

let array = [1, 1.02, 1.03, 1, 1.03, 1.05]
cfa.yield_array(array2, 1)

returns:

[
  0.020000000000000018,
  0.009803921568627416,
  -0.029126213592232997,
  0.030000000000000027,
  0.01941747572815533
]

TIME VALUE OF MONEY FUNCTIONS

Future value

cfa.fv(pv, r, n, m)

Present value

cfa.pv(fv,r,n,m)

Continous compounding

cfa.fv_continous(pv, r, n)

Effective Annual Rate

cfa.ear(r,m)

Continous Effective Annual Rate

cfa.ear_continous(r)

Future Value of Ordinary Annuity

cfa.fv_annuity_ordinary(a,r,n)

Present Value of Ordinary Annuity

cfa.pv_annuity_ordinary(a,r,n)

Future Value of Unequal Cash Flows

cfa.fv_unequal_cf(array, r)

Present Value of Unequal Cash Flows

cfa.pv_unequal_cf(array, r)

Present Value of Perpetuity

cfa.pv_perpetuity(a, r)

Net Present Value

cfa.npv(array, r)

Internal Rate of Return

cfa.npv(array)

returns object: 

{rate, NPV}

for instance:

let array = [-50, 20, 10, 5, 30, 50]

cfa.irr(array)

returns:

{ rate: 27.860000000001556, NPV: 0.007680729691662336 }

Statistics

Median

cfa.median(array)

Mode - Single modality so far

cfa.mode(array)

Weighted Mean

cfa.weighted_mean(array)

Geometric Mean

cfa.geometric_mean(array)

Harmonic Mean

cfa.harmonic_mean(array)

Covariance

cfa.covariance(array_X, array_Y)

Sample variance

cfa.sample_variance(array)

Sample standard deviation

cfa.standard_deviation(array)

Sample correlation

cfa.sample_correlation(array_X, array_Y)

Significance of correlation coefficient

cfa.corr_significance(array_X, array_Y)

Linear regression

Mean Absolute Deviation

cfa.mad(array)

Semivariance

cfa.semivariance(array)

Semideviation

cfa.semideviation(array)

Target semivariance

cfa.target_semivariance(array, target)

Target Semideviation

cfa.target_semideviation(array, target)

Coefficient of variation

cfa.cv(array)

Sharpe Ratio

cfa.sharpe(array_portfolio, array_rf)

Sample Skewness

cfa.sample_skewness(array)

Sample Kurtosis

cfa.sample_kurtosis(array)

Probabililty

Covariance Matrix - takes array of arrays

cfa.covariance_matrix(array)

Correlation Matrix - takes array of arrays

cfa.correlation_matrix(array)

HELPER FUNCTIONS

Yield

cfa.yield(a,b)

Average

cfa.average(array)

Array parse to float

cfa.float_array(array)

Sum of array

cfa.sum(array)

Array - sort descending

cfa.sort_desc(array)

Array - Extent

cfa.extent(array)

MAX

cfa.max(array)

MIN

cfa.min(array)

Range

cfa.range(array)

k days yield array

cfa.yield_array(array, days)

Rolling function with callback

cfa.rolling(array, days, callback)