Python talib.MAMA Examples

The following are 5 code examples of talib.MAMA(). 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 talib , or try the search function .
Example #1
Source File: talib_series.py    From QUANTAXIS with MIT License 6 votes vote down vote up 
def MAMA(Series, fastlimit=0.5, slowlimit=0.05):
    mama, fama = talib.MAMA(Series.values, fastlimit, slowlimit)
    return pd.Series(mama, index=Series.index), pd.Series(fama, index=Series.index)


# # MAVP - Moving average with variable period
# real = talib.MAVP(close, periods, minperiod=2, maxperiod=30, matype=0)

# # MIDPOINT - MidPoint over period
# real = talib.MIDPOINT(close, timeperiod=14)

# # MIDPRICE - Midpoint Price over period
# real = talib.MIDPRICE(high, low, timeperiod=14)


# # SAREXT - Parabolic SAR - Extended
# real = SAREXT(high, low, startvalue=0, offsetonreverse=0, accelerationinitlong=0,
#               accelerationlong=0, accelerationmaxlong=0, accelerationinitshort=0, accelerationshort=0, accelerationmaxshort=0)


# # T3 - Triple Exponential Moving Average (T3)
# real = T3(close, timeperiod=5, vfactor=0)

# # TEMA - Triple Exponential Moving Average
# real = TEMA(close, timeperiod=30)

# # TRIMA - Triangular Moving Average
# real = TRIMA(close, timeperiod=30)

# # WMA - Weighted Moving Average
# real = WMA(close, timeperiod=30)
Example #2
Source File: mama.py    From jesse with MIT License 6 votes vote down vote up 
def mama(candles: np.ndarray, fastlimit=0.5, slowlimit=0.05, source_type="close", sequential=False) -> MAMA:
    """
    MAMA - MESA Adaptive Moving Average

    :param candles: np.ndarray
    :param fastlimit: float - default: 0.5
    :param slowlimit: float - default: 0.05
    :param source_type: str - default: "close"
    :param sequential: bool - default=False

    :return: MAMA(mama, fama)
    """
    if not sequential and len(candles) > 240:
        candles = candles[-240:]

    source = get_candle_source(candles, source_type=source_type)
    mama, fama = talib.MAMA(source, fastlimit=fastlimit, slowlimit=slowlimit)

    if sequential:
        return MAMA(mama, fama)
    else:
        return MAMA(mama[-1], fama[-1])
Example #3
Source File: ta.py    From dash-technical-charting with MIT License 5 votes vote down vote up 
def add_MAMA(self, fastlimit=0.5, slowlimit=0.05,
             types=['line', 'line'], colors=['secondary', 'tertiary'],
             **kwargs):
    """MESA Adaptive Moving Average.

    Note that the first argument of types and colors refers to MAMA while the
    second argument refers to FAMA.

    """
    if not self.has_close:
        raise Exception()

    utils.kwargs_check(kwargs, VALID_TA_KWARGS)
    if 'kind' in kwargs:
        kwargs['type'] = kwargs['kind']
    if 'kinds' in kwargs:
        types = kwargs['type']

    if 'type' in kwargs:
        types = [kwargs['type']] * 2
    if 'color' in kwargs:
        colors = [kwargs['color']] * 2

    mama = 'MAMA({},{})'.format(str(fastlimit), str(slowlimit))
    fama = 'FAMA({},{})'.format(str(fastlimit), str(slowlimit))
    self.pri[mama] = dict(type=types[0], color=colors[0])
    self.pri[fama] = dict(type=types[1], color=colors[1])
    self.ind[mama], self.ind[fama] = talib.MAMA(self.df[self.cl].values,
                                                fastlimit, slowlimit)
Example #4
Source File: talib_wrapper.py    From tia with BSD 3-Clause "New" or "Revised" License 5 votes vote down vote up 
def MAMA(series, fast=.5, slow=.05):
    """MESA Adaptive Moving Average"""
    return _series_to_frame(series, ['MAMA', 'FAMA'], talib.MAMA, fast, slow)
Example #5
Source File: talib_indicators.py    From qtpylib with Apache License 2.0 5 votes vote down vote up 
def MAMA(data, **kwargs):
    _check_talib_presence()
    prices = _extract_series(data)
    return talib.MAMA(prices, **kwargs)