Python numpy.irr() Examples

The following are 30 code examples of numpy.irr(). You can vote up the ones you like or vote down the ones you don't like, and go to the original project or source file by following the links above each example. You may also want to check out all available functions/classes of the module numpy , or try the search function .
Example #1
Source File: test_financial.py    From recruit with Apache License 2.0 7 votes vote down vote up 
def test_irr(self):
        v = [-150000, 15000, 25000, 35000, 45000, 60000]
        assert_almost_equal(np.irr(v), 0.0524, 2)
        v = [-100, 0, 0, 74]
        assert_almost_equal(np.irr(v), -0.0955, 2)
        v = [-100, 39, 59, 55, 20]
        assert_almost_equal(np.irr(v), 0.28095, 2)
        v = [-100, 100, 0, -7]
        assert_almost_equal(np.irr(v), -0.0833, 2)
        v = [-100, 100, 0, 7]
        assert_almost_equal(np.irr(v), 0.06206, 2)
        v = [-5, 10.5, 1, -8, 1]
        assert_almost_equal(np.irr(v), 0.0886, 2)

        # Test that if there is no solution then np.irr returns nan
        # Fixes gh-6744
        v = [-1, -2, -3]
        assert_equal(np.irr(v), np.nan)
Example #2
Source File: test_financial.py    From lambda-packs with MIT License 7 votes vote down vote up 
def test_irr(self):
        v = [-150000, 15000, 25000, 35000, 45000, 60000]
        assert_almost_equal(np.irr(v), 0.0524, 2)
        v = [-100, 0, 0, 74]
        assert_almost_equal(np.irr(v), -0.0955, 2)
        v = [-100, 39, 59, 55, 20]
        assert_almost_equal(np.irr(v), 0.28095, 2)
        v = [-100, 100, 0, -7]
        assert_almost_equal(np.irr(v), -0.0833, 2)
        v = [-100, 100, 0, 7]
        assert_almost_equal(np.irr(v), 0.06206, 2)
        v = [-5, 10.5, 1, -8, 1]
        assert_almost_equal(np.irr(v), 0.0886, 2)

        # Test that if there is no solution then np.irr returns nan
        # Fixes gh-6744
        v = [-1, -2, -3]
        assert_equal(np.irr(v), np.nan)
Example #3
Source File: test_financial.py    From Serverless-Deep-Learning-with-TensorFlow-and-AWS-Lambda with MIT License 6 votes vote down vote up 
def test_irr(self):
        v = [-150000, 15000, 25000, 35000, 45000, 60000]
        assert_almost_equal(np.irr(v), 0.0524, 2)
        v = [-100, 0, 0, 74]
        assert_almost_equal(np.irr(v), -0.0955, 2)
        v = [-100, 39, 59, 55, 20]
        assert_almost_equal(np.irr(v), 0.28095, 2)
        v = [-100, 100, 0, -7]
        assert_almost_equal(np.irr(v), -0.0833, 2)
        v = [-100, 100, 0, 7]
        assert_almost_equal(np.irr(v), 0.06206, 2)
        v = [-5, 10.5, 1, -8, 1]
        assert_almost_equal(np.irr(v), 0.0886, 2)

        # Test that if there is no solution then np.irr returns nan
        # Fixes gh-6744
        v = [-1, -2, -3]
        assert_equal(np.irr(v), np.nan)
Example #4
Source File: test_financial.py    From pySINDy with MIT License 6 votes vote down vote up 
def test_irr(self):
        v = [-150000, 15000, 25000, 35000, 45000, 60000]
        assert_almost_equal(np.irr(v), 0.0524, 2)
        v = [-100, 0, 0, 74]
        assert_almost_equal(np.irr(v), -0.0955, 2)
        v = [-100, 39, 59, 55, 20]
        assert_almost_equal(np.irr(v), 0.28095, 2)
        v = [-100, 100, 0, -7]
        assert_almost_equal(np.irr(v), -0.0833, 2)
        v = [-100, 100, 0, 7]
        assert_almost_equal(np.irr(v), 0.06206, 2)
        v = [-5, 10.5, 1, -8, 1]
        assert_almost_equal(np.irr(v), 0.0886, 2)

        # Test that if there is no solution then np.irr returns nan
        # Fixes gh-6744
        v = [-1, -2, -3]
        assert_equal(np.irr(v), np.nan)
Example #5
Source File: test_financial.py    From coffeegrindsize with MIT License 6 votes vote down vote up 
def test_irr(self):
        v = [-150000, 15000, 25000, 35000, 45000, 60000]
        assert_almost_equal(np.irr(v), 0.0524, 2)
        v = [-100, 0, 0, 74]
        assert_almost_equal(np.irr(v), -0.0955, 2)
        v = [-100, 39, 59, 55, 20]
        assert_almost_equal(np.irr(v), 0.28095, 2)
        v = [-100, 100, 0, -7]
        assert_almost_equal(np.irr(v), -0.0833, 2)
        v = [-100, 100, 0, 7]
        assert_almost_equal(np.irr(v), 0.06206, 2)
        v = [-5, 10.5, 1, -8, 1]
        assert_almost_equal(np.irr(v), 0.0886, 2)

        # Test that if there is no solution then np.irr returns nan
        # Fixes gh-6744
        v = [-1, -2, -3]
        assert_equal(np.irr(v), np.nan)
Example #6
Source File: test_financial.py    From predictive-maintenance-using-machine-learning with Apache License 2.0 6 votes vote down vote up 
def test_irr(self):
        v = [-150000, 15000, 25000, 35000, 45000, 60000]
        assert_almost_equal(np.irr(v), 0.0524, 2)
        v = [-100, 0, 0, 74]
        assert_almost_equal(np.irr(v), -0.0955, 2)
        v = [-100, 39, 59, 55, 20]
        assert_almost_equal(np.irr(v), 0.28095, 2)
        v = [-100, 100, 0, -7]
        assert_almost_equal(np.irr(v), -0.0833, 2)
        v = [-100, 100, 0, 7]
        assert_almost_equal(np.irr(v), 0.06206, 2)
        v = [-5, 10.5, 1, -8, 1]
        assert_almost_equal(np.irr(v), 0.0886, 2)

        # Test that if there is no solution then np.irr returns nan
        # Fixes gh-6744
        v = [-1, -2, -3]
        assert_equal(np.irr(v), np.nan)
Example #7
Source File: excellib.py    From koala with GNU General Public License v3.0 6 votes vote down vote up 
def irr(values, guess = None):
    """
    Function to calculate the internal rate of return (IRR) using payments and periodic dates. It resembles the
    excel function IRR().

    Excel reference: https://support.office.com/en-us/article/IRR-function-64925eaa-9988-495b-b290-3ad0c163c1bc

    :param values: the payments of which at least one has to be negative.
    :param guess: an initial guess which is required by Excel but isn't used by this function.
    :return: a float being the IRR.
    """
    if isinstance(values, Range):
        values = values.values

    if is_not_number_input(values):
        return numeric_error(values, 'values')

    if guess is not None and guess != 0:
        raise ValueError('guess value for excellib.irr() is %s and not 0' % guess)
    else:
        try:
            return np.irr(values)
        except Exception as e:
            return ExcelError('#NUM!', e)
Example #8
Source File: test_financial.py    From GraphicDesignPatternByPython with MIT License 6 votes vote down vote up 
def test_irr(self):
        v = [-150000, 15000, 25000, 35000, 45000, 60000]
        assert_almost_equal(np.irr(v), 0.0524, 2)
        v = [-100, 0, 0, 74]
        assert_almost_equal(np.irr(v), -0.0955, 2)
        v = [-100, 39, 59, 55, 20]
        assert_almost_equal(np.irr(v), 0.28095, 2)
        v = [-100, 100, 0, -7]
        assert_almost_equal(np.irr(v), -0.0833, 2)
        v = [-100, 100, 0, 7]
        assert_almost_equal(np.irr(v), 0.06206, 2)
        v = [-5, 10.5, 1, -8, 1]
        assert_almost_equal(np.irr(v), 0.0886, 2)

        # Test that if there is no solution then np.irr returns nan
        # Fixes gh-6744
        v = [-1, -2, -3]
        assert_equal(np.irr(v), np.nan)
Example #9
Source File: test_financial.py    From mxnet-lambda with Apache License 2.0 6 votes vote down vote up 
def test_irr(self):
        v = [-150000, 15000, 25000, 35000, 45000, 60000]
        assert_almost_equal(np.irr(v), 0.0524, 2)
        v = [-100, 0, 0, 74]
        assert_almost_equal(np.irr(v), -0.0955, 2)
        v = [-100, 39, 59, 55, 20]
        assert_almost_equal(np.irr(v), 0.28095, 2)
        v = [-100, 100, 0, -7]
        assert_almost_equal(np.irr(v), -0.0833, 2)
        v = [-100, 100, 0, 7]
        assert_almost_equal(np.irr(v), 0.06206, 2)
        v = [-5, 10.5, 1, -8, 1]
        assert_almost_equal(np.irr(v), 0.0886, 2)

        # Test that if there is no solution then np.irr returns nan
        # Fixes gh-6744
        v = [-1, -2, -3]
        assert_equal(np.irr(v), np.nan)
Example #10
Source File: test_financial.py    From Mastering-Elasticsearch-7.0 with MIT License 6 votes vote down vote up 
def test_irr(self):
        v = [-150000, 15000, 25000, 35000, 45000, 60000]
        assert_almost_equal(np.irr(v), 0.0524, 2)
        v = [-100, 0, 0, 74]
        assert_almost_equal(np.irr(v), -0.0955, 2)
        v = [-100, 39, 59, 55, 20]
        assert_almost_equal(np.irr(v), 0.28095, 2)
        v = [-100, 100, 0, -7]
        assert_almost_equal(np.irr(v), -0.0833, 2)
        v = [-100, 100, 0, 7]
        assert_almost_equal(np.irr(v), 0.06206, 2)
        v = [-5, 10.5, 1, -8, 1]
        assert_almost_equal(np.irr(v), 0.0886, 2)

        # Test that if there is no solution then np.irr returns nan
        # Fixes gh-6744
        v = [-1, -2, -3]
        assert_equal(np.irr(v), np.nan)
Example #11
Source File: test_financial.py    From elasticintel with GNU General Public License v3.0 6 votes vote down vote up 
def test_irr(self):
        v = [-150000, 15000, 25000, 35000, 45000, 60000]
        assert_almost_equal(np.irr(v), 0.0524, 2)
        v = [-100, 0, 0, 74]
        assert_almost_equal(np.irr(v), -0.0955, 2)
        v = [-100, 39, 59, 55, 20]
        assert_almost_equal(np.irr(v), 0.28095, 2)
        v = [-100, 100, 0, -7]
        assert_almost_equal(np.irr(v), -0.0833, 2)
        v = [-100, 100, 0, 7]
        assert_almost_equal(np.irr(v), 0.06206, 2)
        v = [-5, 10.5, 1, -8, 1]
        assert_almost_equal(np.irr(v), 0.0886, 2)

        # Test that if there is no solution then np.irr returns nan
        # Fixes gh-6744
        v = [-1, -2, -3]
        assert_equal(np.irr(v), np.nan)
Example #12
Source File: test_financial.py    From vnpy_crypto with MIT License 6 votes vote down vote up 
def test_irr(self):
        v = [-150000, 15000, 25000, 35000, 45000, 60000]
        assert_almost_equal(np.irr(v), 0.0524, 2)
        v = [-100, 0, 0, 74]
        assert_almost_equal(np.irr(v), -0.0955, 2)
        v = [-100, 39, 59, 55, 20]
        assert_almost_equal(np.irr(v), 0.28095, 2)
        v = [-100, 100, 0, -7]
        assert_almost_equal(np.irr(v), -0.0833, 2)
        v = [-100, 100, 0, 7]
        assert_almost_equal(np.irr(v), 0.06206, 2)
        v = [-5, 10.5, 1, -8, 1]
        assert_almost_equal(np.irr(v), 0.0886, 2)

        # Test that if there is no solution then np.irr returns nan
        # Fixes gh-6744
        v = [-1, -2, -3]
        assert_equal(np.irr(v), np.nan)
Example #13
Source File: test_financial.py    From twitter-stock-recommendation with MIT License 6 votes vote down vote up 
def test_irr(self):
        v = [-150000, 15000, 25000, 35000, 45000, 60000]
        assert_almost_equal(np.irr(v), 0.0524, 2)
        v = [-100, 0, 0, 74]
        assert_almost_equal(np.irr(v), -0.0955, 2)
        v = [-100, 39, 59, 55, 20]
        assert_almost_equal(np.irr(v), 0.28095, 2)
        v = [-100, 100, 0, -7]
        assert_almost_equal(np.irr(v), -0.0833, 2)
        v = [-100, 100, 0, 7]
        assert_almost_equal(np.irr(v), 0.06206, 2)
        v = [-5, 10.5, 1, -8, 1]
        assert_almost_equal(np.irr(v), 0.0886, 2)

        # Test that if there is no solution then np.irr returns nan
        # Fixes gh-6744
        v = [-1, -2, -3]
        assert_equal(np.irr(v), np.nan)
Example #14
Source File: test_financial.py    From ImageFusion with MIT License 6 votes vote down vote up 
def test_irr(self):
        v = [-150000, 15000, 25000, 35000, 45000, 60000]
        assert_almost_equal(np.irr(v),
                            0.0524, 2)
        v = [-100, 0, 0, 74]
        assert_almost_equal(np.irr(v),
                            -0.0955, 2)
        v = [-100, 39, 59, 55, 20]
        assert_almost_equal(np.irr(v),
                            0.28095, 2)
        v = [-100, 100, 0, -7]
        assert_almost_equal(np.irr(v),
                            -0.0833, 2)
        v = [-100, 100, 0, 7]
        assert_almost_equal(np.irr(v),
                            0.06206, 2)
        v = [-5, 10.5, 1, -8, 1]
        assert_almost_equal(np.irr(v),
                            0.0886, 2)
Example #15
Source File: test_financial.py    From Computable with MIT License 5 votes vote down vote up 
def test_irr(self):
        v = [-150000, 15000, 25000, 35000, 45000, 60000]
        assert_almost_equal(np.irr(v),
                            0.0524, 2)
Example #16
Source File: analysis.py    From cashflows with MIT License 5 votes vote down vote up 
def irr(cflo):
    """Computes the internal rate of return of a generic cashflow as a periodic
    interest rate.

    Args:
        cflo (pandas.Series): Generic cashflow.

    Returns:
        Float or list of floats.

    **Examples.**

    >>> cflo = cashflow([-200] + [100]*4, start='2000Q1', freq='Q')
    >>> irr(cflo) # doctest: +ELLIPSIS
    34.90...

    >>> irr([cflo, cflo]) # doctest: +ELLIPSIS
    0    34.90...
    1    34.90...
    dtype: float64

    """
    

    if isinstance(cflo, pd.Series):
        cflo = [cflo]
    retval = pd.Series([0] * len(cflo), dtype=np.float64)
    for index, xcflo in enumerate(cflo):
        retval[index] = (100 * np.irr(xcflo))
    if len(retval) == 1:
        return retval[0]
    return retval

## modified internal rate of return
Example #17
Source File: test_financial.py    From auto-alt-text-lambda-api with MIT License 5 votes vote down vote up 
def test_irr(self):
        v = [-150000, 15000, 25000, 35000, 45000, 60000]
        assert_almost_equal(np.irr(v), 0.0524, 2)
        v = [-100, 0, 0, 74]
        assert_almost_equal(np.irr(v), -0.0955, 2)
        v = [-100, 39, 59, 55, 20]
        assert_almost_equal(np.irr(v), 0.28095, 2)
        v = [-100, 100, 0, -7]
        assert_almost_equal(np.irr(v), -0.0833, 2)
        v = [-100, 100, 0, 7]
        assert_almost_equal(np.irr(v), 0.06206, 2)
        v = [-5, 10.5, 1, -8, 1]
        assert_almost_equal(np.irr(v), 0.0886, 2)
Example #18
Source File: excellib.py    From koala with GNU General Public License v3.0 5 votes vote down vote up 
def xirr(values, dates, guess=0):
    """
    Function to calculate the internal rate of return (IRR) using payments and non-periodic dates. It resembles the
    excel function XIRR().

    Excel reference: https://support.office.com/en-ie/article/xirr-function-de1242ec-6477-445b-b11b-a303ad9adc9d

    :param values: the payments of which at least one has to be negative.
    :param dates: the dates as excel dates (e.g. 43571 for 16/04/2019).
    :param guess: an initial guess which is required by Excel but isn't used by this function.
    :return: a float being the IRR.
    """

    if isinstance(values, Range):
        values = values.values

    if all(value < 0 for value in values):
        return 0

    if isinstance(dates, Range):
        dates = dates.values

    if is_not_number_input(values):
        return numeric_error(values, 'values')

    if is_not_number_input(dates):
        return numeric_error(dates, 'dates')

    if guess is not None and guess != 0:
        raise ValueError('guess value for excellib.irr() is %s and not 0' % guess)
    else:
        try:
            try:
                return scipy.optimize.newton(lambda r: xnpv(r, values, dates, lim_rate_low=False, lim_rate_high=True), 0.0)
            except (RuntimeError, FloatingPointError, ExcelError):  # Failed to converge?
                return scipy.optimize.brentq(lambda r: xnpv(r, values, dates, lim_rate_low=False, lim_rate_high=True), -1.0, 1e5)
        except Exception:
            return ExcelError('#NUM', 'IRR did not converge.')
Example #19
Source File: test_financial.py    From keras-lambda with MIT License 5 votes vote down vote up 
def test_irr(self):
        v = [-150000, 15000, 25000, 35000, 45000, 60000]
        assert_almost_equal(np.irr(v), 0.0524, 2)
        v = [-100, 0, 0, 74]
        assert_almost_equal(np.irr(v), -0.0955, 2)
        v = [-100, 39, 59, 55, 20]
        assert_almost_equal(np.irr(v), 0.28095, 2)
        v = [-100, 100, 0, -7]
        assert_almost_equal(np.irr(v), -0.0833, 2)
        v = [-100, 100, 0, 7]
        assert_almost_equal(np.irr(v), 0.06206, 2)
        v = [-5, 10.5, 1, -8, 1]
        assert_almost_equal(np.irr(v), 0.0886, 2)
Example #20
Source File: financial.py    From formulas with European Union Public License 1.1 5 votes vote down vote up 
def xirr(values, guess=0.1):
    with np.errstate(divide='ignore', invalid='ignore'):
        res = np.irr(tuple(flatten(text2num(replace_empty(values)).ravel())))
        res = (not np.isfinite(res)) and Error.errors['#NUM!'] or res

        def _(g):
            e = isinstance(g, str) and Error.errors['#VALUE!']
            return get_error(g, e) or res

        guess = text2num(replace_empty(guess))
        return np.vectorize(_, otypes=[object])(guess).view(Array)
Example #21
Source File: financial.py    From Splunking-Crime with GNU Affero General Public License v3.0 4 votes vote down vote up 
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 #22
Source File: financial.py    From elasticintel with GNU General Public License v3.0 4 votes vote down vote up 
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 #23
Source File: financial.py    From predictive-maintenance-using-machine-learning with Apache License 2.0 4 votes vote down vote up 
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 #24
Source File: financial.py    From coffeegrindsize with MIT License 4 votes vote down vote up 
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 #25
Source File: financial.py    From Carnets with BSD 3-Clause "New" or "Revised" License 4 votes vote down vote up 
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 #26
Source File: financial.py    From Serverless-Deep-Learning-with-TensorFlow-and-AWS-Lambda with MIT License 4 votes vote down vote up 
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 #27
Source File: financial.py    From twitter-stock-recommendation with MIT License 4 votes vote down vote up 
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 #28
Source File: financial.py    From keras-lambda with MIT License 4 votes vote down vote up 
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 #29
Source File: financial.py    From ImageFusion with MIT License 4 votes vote down vote up 
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 #30
Source File: financial.py    From mxnet-lambda with Apache License 2.0 4 votes vote down vote up 
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)