To calculate all these functions, CubeCalc leverages the Python libraries Numpy, functools and scipy. This article lists all functions available in CubeCalc:
| Name | Function | Parameters |
|---|---|---|
| effect | manual | effect(nominal_rate, npery) |
| fv | numpy.fv | fv(rate, nper, pmt, pv, when=0) |
| fv_schedule | functools.reduce | functools.reduce(lambda x, y: x + (x * y), values, float(principal)) |
| irr | numpy.irr | irr(values) |
| mirr | numpy.mirr | mirr(values, finance_rate, reinvest_rate) |
| mirr | numpy.nper | nper(rate, pmt, pv, fv=0, when=0) |
| nominal | manual | nominal(effect_rate, npery) |
| npv | numpy.npv | npv(rate, values) |
| _nroot | _nroot(value, n) | nominal(effect_rate, npery) |
| pmt | numpy.pmt | ppmt(rate, per, nper, pv, fv=0, when=0) |
| pv | numpy.pv | pv(rate, nper, pmt, fv, when=0) |
| rate | numpy.rate | rate(nper, pmt, pv, fv=0, when=0, guess=0.1, maxiter=100) |
| stdev_p | numpy.std | numpy.std(values) |
| stdev_p | numpy.std | numpy.std(values, ddof=1) |
| sln | manual | sln(cost, salvage, life) |
| xirr | scipy.optimise.newtown | optimize.newton(lambda r: xnpv(r, values, dates), float(guess)) |
| xnpv | manual | xnpv(rate, values, dates) |