def test_ao():
"""test TA.AO"""
ao = TA.AO(ohlc)
assert isinstance(ao, series.Series)
assert ao.values[-1] == -957.63459032713035
def test_ao():
"""test TA.AO"""
ao = TA.AO(ohlc).round(decimals=8)
assert isinstance(ao, series.Series)
assert ao.values[-1] == -957.63459033
def get_ao(data):
"""Calculate the awesome oscillator of given dataframe.
:param data: a dataframe in OHLC format
:return: a Pandas series
"""
if data is None:
raise EmptyDataError("[!] Invalid data value")
result = TA.AO(data)
if result is None:
raise IndicatorException
return result
# Assesses price direction and strength - Allows the trader to differentiate between strong and weak trends
prices["DMI_plus"], prices["DMI_minus"] = TA.DMI(ohlc)
# Trend strength - below 20 is weak, above 40 is strong and above 50 is extremely strong
prices["Trend_strength"] = TA.ADX(ohlc)
# Stochastic oscillator
prices["STOCH"] = TA.STOCH(ohlc)
prices["STOCHD"] = TA.STOCHD(ohlc)
prices["STOCHRSI"] = TA.STOCHRSI(ohlc)
# Williams %R is a technical analysis oscillator
prices["WR"] = TA.WILLIAMS(ohlc)
# Awesome Oscillator - measures market momentum
prices["awesome_oscil"] = TA.AO(ohlc)
prices["ultimate_oscil"] = TA.UO(ohlc)
# Mass index - measures high-low range expansion to identify trend reversals. Essentially volatility indicator
prices["MI"] = TA.MI(ohlc)
# Balance of power indicator
prices["BOP"] = TA.BOP(ohlc)
# Vortex indicator is two oscillating lines one for positive trend movement and one for negative trend movment
prices["VIm"], prices["VIp"] = TA.VORTEX(ohlc)
# Know sure thing momentum oscillator
prices["KST_k"], prices["KST_signal"] = TA.KST(ohlc)
# True strength index momentum oscillator
