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Example 1
def calc_excel_npv(rate, values):
orig_npv = np.npv(rate, values)
excel_npv = orig_npv/(1+rate)
return excel_npv Example 2
def mirr(values, finance_rate, reinvest_rate):
"""
Modified internal rate of return.
Parameters
----------
values : array_like
Cash flows (must contain at least one positive and one negative
value) or nan is returned. The first value is considered a sunk
cost at time zero.
finance_rate : scalar
Interest rate paid on the cash flows
reinvest_rate : scalar
Interest rate received on the cash flows upon reinvestment
Returns
-------
out : float
Modified internal rate of return
"""
values = np.asarray(values, dtype=np.double)
n = values.size
pos = values > 0
neg = values < 0
if not (pos.any() and neg.any()):
return np.nan
numer = np.abs(npv(reinvest_rate, values*pos))
denom = np.abs(npv(finance_rate, values*neg))
return (numer/denom)**(1.0/(n - 1))*(1 + reinvest_rate) - 1 Example 3
def test_npv(self):
assert_almost_equal(
np.npv(0.05, [-15000, 1500, 2500, 3500, 4500, 6000]),
122.89, 2) Example 4
def mirr(values, finance_rate, reinvest_rate):
"""
Modified internal rate of return.
Parameters
----------
values : array_like
Cash flows (must contain at least one positive and one negative
value) or nan is returned. The first value is considered a sunk
cost at time zero.
finance_rate : scalar
Interest rate paid on the cash flows
reinvest_rate : scalar
Interest rate received on the cash flows upon reinvestment
Returns
-------
out : float
Modified internal rate of return
"""
values = np.asarray(values, dtype=np.double)
n = values.size
pos = values > 0
neg = values < 0
if not (pos.any() and neg.any()):
return np.nan
numer = np.abs(npv(reinvest_rate, values*pos))
denom = np.abs(npv(finance_rate, values*neg))
return (numer/denom)**(1.0/(n - 1))*(1 + reinvest_rate) - 1 Example 5
def test_npv(self):
assert_almost_equal(
np.npv(0.05, [-15000, 1500, 2500, 3500, 4500, 6000]),
122.89, 2) Example 6
def mirr(values, finance_rate, reinvest_rate):
"""
Modified internal rate of return.
Parameters
----------
values : array_like
Cash flows (must contain at least one positive and one negative
value) or nan is returned. The first value is considered a sunk
cost at time zero.
finance_rate : scalar
Interest rate paid on the cash flows
reinvest_rate : scalar
Interest rate received on the cash flows upon reinvestment
Returns
-------
out : float
Modified internal rate of return
"""
values = np.asarray(values, dtype=np.double)
n = values.size
pos = values > 0
neg = values < 0
if not (pos.any() and neg.any()):
return np.nan
numer = np.abs(npv(reinvest_rate, values*pos))
denom = np.abs(npv(finance_rate, values*neg))
return (numer/denom)**(1.0/(n - 1))*(1 + reinvest_rate) - 1 Example 7
def test_npv(self):
assert_almost_equal(
np.npv(0.05, [-15000, 1500, 2500, 3500, 4500, 6000]),
122.89, 2) Example 8
def mirr(values, finance_rate, reinvest_rate):
"""
Modified internal rate of return.
Parameters
----------
values : array_like
Cash flows (must contain at least one positive and one negative
value) or nan is returned. The first value is considered a sunk
cost at time zero.
finance_rate : scalar
Interest rate paid on the cash flows
reinvest_rate : scalar
Interest rate received on the cash flows upon reinvestment
Returns
-------
out : float
Modified internal rate of return
"""
values = np.asarray(values, dtype=np.double)
n = values.size
pos = values > 0
neg = values < 0
if not (pos.any() and neg.any()):
return np.nan
numer = np.abs(npv(reinvest_rate, values*pos))
denom = np.abs(npv(finance_rate, values*neg))
return (numer/denom)**(1.0/(n - 1))*(1 + reinvest_rate) - 1 Example 9
def test_npv(self):
assert_almost_equal(
np.npv(0.05, [-15000, 1500, 2500, 3500, 4500, 6000]),
122.89, 2) Example 10
def mirr(values, finance_rate, reinvest_rate):
"""
Modified internal rate of return.
Parameters
----------
values : array_like
Cash flows (must contain at least one positive and one negative
value) or nan is returned. The first value is considered a sunk
cost at time zero.
finance_rate : scalar
Interest rate paid on the cash flows
reinvest_rate : scalar
Interest rate received on the cash flows upon reinvestment
Returns
-------
out : float
Modified internal rate of return
"""
values = np.asarray(values, dtype=np.double)
n = values.size
pos = values > 0
neg = values < 0
if not (pos.any() and neg.any()):
return np.nan
numer = np.abs(npv(reinvest_rate, values*pos))
denom = np.abs(npv(finance_rate, values*neg))
return (numer/denom)**(1.0/(n - 1))*(1 + reinvest_rate) - 1 Example 11
def test_npv(self):
assert_almost_equal(
np.npv(0.05, [-15000, 1500, 2500, 3500, 4500, 6000]),
122.89, 2) Example 12
def calc_npv(r, ubi, inputs, n):
x = np.zeros((ubi['Years Post Transfer'], n))
y = np.zeros((ubi['Years Post Transfer'], n))
# iterate through years of benefits
for j in range(1, ubi['Years Post Transfer'] + 1):
# sum benefits during program
if(j < r):
x[j - 1] += ubi['Expected baseline per capita consumption (nominal USD)']* \
np.power((1.0 + inputs['UBI']['Expected annual consumption increase (without the UBI program)']), float(j))* \
inputs['UBI']['Work participation adjustment'] + \
ubi['Annual quantity of transfer money used for immediate consumtion (pre-discounting)']
# benefits after program
else:
x[j - 1] += ubi['Expected baseline per capita consumption (nominal USD)']* \
np.power((1.0 + inputs['UBI']['Expected annual consumption increase (without the UBI program)']), float(j))
# investments calculations
for k in range(n):
if(j < r + inputs['UBI']['Duration of investment benefits (in years) - UBI'][k]):
x[j - 1][k] += ubi['Annual return for each year of transfer investments (pre-discounting)'][k]* \
np.min([j, inputs['UBI']['Duration of investment benefits (in years) - UBI'][k], \
r, (inputs['UBI']['Duration of investment benefits (in years) - UBI'][k] + r - j)])
if(j > r):
x[j - 1][k] += ubi['Value eventually returned from one years investment (pre-discounting)'][k]
# log transform and subtact baseline
y[j - 1] = np.log(x[j - 1])
y[j - 1] -= np.log(ubi['Expected baseline per capita consumption (nominal USD)']* \
np.power((1.0 + inputs['UBI']['Expected annual consumption increase (without the UBI program)']), float(j)))
# npv on yearly data
z = np.zeros(n)
for i in range(n):
z[i] = np.npv(inputs['Shared']['Discount rate'][i], y[:, i])
return z Example 13
def mirr(values, finance_rate, reinvest_rate):
"""
Modified internal rate of return.
Parameters
----------
values : array_like
Cash flows (must contain at least one positive and one negative
value) or nan is returned. The first value is considered a sunk
cost at time zero.
finance_rate : scalar
Interest rate paid on the cash flows
reinvest_rate : scalar
Interest rate received on the cash flows upon reinvestment
Returns
-------
out : float
Modified internal rate of return
"""
values = np.asarray(values, dtype=np.double)
n = values.size
pos = values > 0
neg = values < 0
if not (pos.any() and neg.any()):
return np.nan
numer = np.abs(npv(reinvest_rate, values*pos))
denom = np.abs(npv(finance_rate, values*neg))
return (numer/denom)**(1.0/(n - 1))*(1 + reinvest_rate) - 1 Example 14
def test_npv(self):
assert_almost_equal(
np.npv(0.05, [-15000, 1500, 2500, 3500, 4500, 6000]),
122.89, 2) Example 15
def npv(rate, values):
"""
Returns the NPV (Net Present Value) of a cash flow series.
Parameters
----------
rate : scalar
The discount rate.
values : array_like, shape(M, )
The values of the time series of cash flows. The (fixed) time
interval between cash flow "events" must be the same as that for
which `rate` is given (i.e., if `rate` is per year, then precisely
a year is understood to elapse between each cash flow event). By
convention, investments or "deposits" are negative, income or
"withdrawals" are positive; `values` must begin with the initial
investment, thus `values[0]` will typically be negative.
Returns
-------
out : float
The NPV of the input cash flow series `values` at the discount
`rate`.
Notes
-----
Returns the result of: [G]_
.. math :: \\sum_{t=0}^{M-1}{\\frac{values_t}{(1+rate)^{t}}}
References
----------
.. [G] L. J. Gitman, "Principles of Managerial Finance, Brief," 3rd ed.,
Addison-Wesley, 2003, pg. 346.
Examples
--------
>>> np.npv(0.281,[-100, 39, 59, 55, 20])
-0.0084785916384548798
(Compare with the Example given for numpy.lib.financial.irr)
"""
values = np.asarray(values)
return (values / (1+rate)**np.arange(0, len(values))).sum(axis=0) Example 16
def npv(rate, values):
"""
Returns the NPV (Net Present Value) of a cash flow series.
Parameters
----------
rate : scalar
The discount rate.
values : array_like, shape(M, )
The values of the time series of cash flows. The (fixed) time
interval between cash flow "events" must be the same as that for
which `rate` is given (i.e., if `rate` is per year, then precisely
a year is understood to elapse between each cash flow event). By
convention, investments or "deposits" are negative, income or
"withdrawals" are positive; `values` must begin with the initial
investment, thus `values[0]` will typically be negative.
Returns
-------
out : float
The NPV of the input cash flow series `values` at the discount
`rate`.
Notes
-----
Returns the result of: [G]_
.. math :: \\sum_{t=0}^{M-1}{\\frac{values_t}{(1+rate)^{t}}}
References
----------
.. [G] L. J. Gitman, "Principles of Managerial Finance, Brief," 3rd ed.,
Addison-Wesley, 2003, pg. 346.
Examples
--------
>>> np.npv(0.281,[-100, 39, 59, 55, 20])
-0.0084785916384548798
(Compare with the Example given for numpy.lib.financial.irr)
"""
values = np.asarray(values)
return (values / (1+rate)**np.arange(0, len(values))).sum(axis=0) Example 17
def npv(rate, values):
"""
Returns the NPV (Net Present Value) of a cash flow series.
Parameters
----------
rate : scalar
The discount rate.
values : array_like, shape(M, )
The values of the time series of cash flows. The (fixed) time
interval between cash flow "events" must be the same as that for
which `rate` is given (i.e., if `rate` is per year, then precisely
a year is understood to elapse between each cash flow event). By
convention, investments or "deposits" are negative, income or
"withdrawals" are positive; `values` must begin with the initial
investment, thus `values[0]` will typically be negative.
Returns
-------
out : float
The NPV of the input cash flow series `values` at the discount
`rate`.
Notes
-----
Returns the result of: [G]_
.. math :: \\sum_{t=0}^{M-1}{\\frac{values_t}{(1+rate)^{t}}}
References
----------
.. [G] L. J. Gitman, "Principles of Managerial Finance, Brief," 3rd ed.,
Addison-Wesley, 2003, pg. 346.
Examples
--------
>>> np.npv(0.281,[-100, 39, 59, 55, 20])
-0.0084785916384548798
(Compare with the Example given for numpy.lib.financial.irr)
"""
values = np.asarray(values)
return (values / (1+rate)**np.arange(0, len(values))).sum(axis=0) Example 18
def npv(rate, values):
"""
Returns the NPV (Net Present Value) of a cash flow series.
Parameters
----------
rate : scalar
The discount rate.
values : array_like, shape(M, )
The values of the time series of cash flows. The (fixed) time
interval between cash flow "events" must be the same as that for
which `rate` is given (i.e., if `rate` is per year, then precisely
a year is understood to elapse between each cash flow event). By
convention, investments or "deposits" are negative, income or
"withdrawals" are positive; `values` must begin with the initial
investment, thus `values[0]` will typically be negative.
Returns
-------
out : float
The NPV of the input cash flow series `values` at the discount
`rate`.
Notes
-----
Returns the result of: [G]_
.. math :: \\sum_{t=0}^{M-1}{\\frac{values_t}{(1+rate)^{t}}}
References
----------
.. [G] L. J. Gitman, "Principles of Managerial Finance, Brief," 3rd ed.,
Addison-Wesley, 2003, pg. 346.
Examples
--------
>>> np.npv(0.281,[-100, 39, 59, 55, 20])
-0.0084785916384548798
(Compare with the Example given for numpy.lib.financial.irr)
"""
values = np.asarray(values)
return (values / (1+rate)**np.arange(0, len(values))).sum(axis=0) Example 19
def npv(rate, values):
"""
Returns the NPV (Net Present Value) of a cash flow series.
Parameters
----------
rate : scalar
The discount rate.
values : array_like, shape(M, )
The values of the time series of cash flows. The (fixed) time
interval between cash flow "events" must be the same as that for
which `rate` is given (i.e., if `rate` is per year, then precisely
a year is understood to elapse between each cash flow event). By
convention, investments or "deposits" are negative, income or
"withdrawals" are positive; `values` must begin with the initial
investment, thus `values[0]` will typically be negative.
Returns
-------
out : float
The NPV of the input cash flow series `values` at the discount
`rate`.
Notes
-----
Returns the result of: [G]_
.. math :: \\sum_{t=0}^{M-1}{\\frac{values_t}{(1+rate)^{t}}}
References
----------
.. [G] L. J. Gitman, "Principles of Managerial Finance, Brief," 3rd ed.,
Addison-Wesley, 2003, pg. 346.
Examples
--------
>>> np.npv(0.281,[-100, 39, 59, 55, 20])
-0.0084785916384548798
(Compare with the Example given for numpy.lib.financial.irr)
"""
values = np.asarray(values)
return (values / (1+rate)**np.arange(0, len(values))).sum(axis=0) Example 20
def npv(rate, values):
"""
Returns the NPV (Net Present Value) of a cash flow series.
Parameters
----------
rate : scalar
The discount rate.
values : array_like, shape(M, )
The values of the time series of cash flows. The (fixed) time
interval between cash flow "events" must be the same as that for
which `rate` is given (i.e., if `rate` is per year, then precisely
a year is understood to elapse between each cash flow event). By
convention, investments or "deposits" are negative, income or
"withdrawals" are positive; `values` must begin with the initial
investment, thus `values[0]` will typically be negative.
Returns
-------
out : float
The NPV of the input cash flow series `values` at the discount
`rate`.
Notes
-----
Returns the result of: [G]_
.. math :: \\sum_{t=0}^{M-1}{\\frac{values_t}{(1+rate)^{t}}}
References
----------
.. [G] L. J. Gitman, "Principles of Managerial Finance, Brief," 3rd ed.,
Addison-Wesley, 2003, pg. 346.
Examples
--------
>>> np.npv(0.281,[-100, 39, 59, 55, 20])
-0.0084785916384548798
(Compare with the Example given for numpy.lib.financial.irr)
"""
values = np.asarray(values)
return (values / (1+rate)**np.arange(0, len(values))).sum(axis=0) 