def BASPN(data, period=40):
"""
Buy and Sell Pressure Normalized
:param pd.DataFrame data: pandas DataFrame with open, high, low, close data
:param int period: period used for indicator calculation
:return pd.Series: with indicator data calculation results
"""
return TA.BASPN(data, period)
def test_baspn():
"""test TA.BASPN"""
basp = TA.BASPN(ohlc)
assert isinstance(basp["Buy."], series.Series)
assert isinstance(basp["Sell."], series.Series)
assert basp["Buy."].values[-1] == 0.56374213100174275
assert basp["Sell."].values[-1] == 0.74103021131003344
def test_baspn():
"""test TA.BASPN"""
basp = TA.BASPN(ohlc).round(decimals=8)
assert isinstance(basp["Buy."], series.Series)
assert isinstance(basp["Sell."], series.Series)
assert basp["Buy."].values[-1] == 0.56374213
assert basp["Sell."].values[-1] == 0.74103021
def test_baspn():
'''test TA.BASPN'''
basp = TA.BASPN(ohlc)
assert isinstance(basp['Buy.'], series.Series)
assert isinstance(basp['Sell.'], series.Series)
assert basp['Buy.'].values[-1] == 0.56374213100174275
assert basp['Sell.'].values[-1] == 0.74103021131003344
def get_baspn(data):
"""Calculate the normalized buying and selling pressure of given dataframe.
:param data: a dataframe in OHLC format
:return: a concatenated Pandas series
"""
if data is None:
raise EmptyDataError("[!] Invalid data value")
result = TA.BASPN(data)
if result is None:
raise IndicatorException
return result
# Bull and bear power
prices["BullPower"], prices["BearPower"] = TA.EBBP(ohlc)
# Ease of movement oscillator - distance from zero indicates ease (neg is down, positive is up)
prices["EMI"] = TA.EMV(ohlcv)
# Commodity Channel Index - above +100 is overbought, below -100 is oversold
prices["CCI"] = TA.CCI(ohlc)
# Coppock curve - momentum indicator (buy when it crosses from negative to positive
prices["COPP"] = TA.COPP(ohlc)
# Buying and selling pressure indicator
prices["BASP_buy"], prices["BASP_sell"] = TA.BASP(ohlc)
prices["BASPN_buy"], prices["BASPN_sell"] = TA.BASPN(ohlc)
# Double exponential moving average - attempts to remove the moving average lag
prices["DEMA"] = TA.DEMA(ohlc)
# Triple exponential moving average - attempts to remove the moving average lag
prices["TEMA"] = TA.TEMA(ohlc)
# rate of change of TEMA - BUY/SELL triggered by zero crossings
prices["TRIX"] = TA.TRIX(ohlc)
prices["TRIX-signal"] = prices["TRIX"] / prices['TRIX'].ewm(span=9).mean()
# Triangular moving average
prices["TRIMA"] = TA.TRIMA(ohlc)
# Volume adjusted moving average
prices["VAMA"] = TA.VAMA(ohlcv)
