def setUpClass(cls):
cls._df = pd.read_csv(cls._filename, sep=',')
cls._params = dict(close=cls._df['Close'],
n=10,
pow1=2,
pow2=30,
fillna=False)
cls._indicator = KAMAIndicator(**cls._params)
def setUpClass(cls):
cls._df = pd.read_csv(cls._filename, sep=",")
cls._params = dict(close=cls._df["Close"],
window=10,
pow1=2,
pow2=30,
fillna=False)
cls._indicator = KAMAIndicator(**cls._params)
class TestKAMAIndicator(unittest.TestCase):
"""
https://school.stockcharts.com/doku.php?id=technical_indicators:kaufman_s_adaptive_moving_average
"""
_filename = 'ta/tests/data/cs-kama.csv'
def setUp(self):
self._df = pd.read_csv(self._filename, sep=',')
self._indicator = KAMAIndicator(close=self._df['Close'], n=10, pow1=2, pow2=30, fillna=False)
def tearDown(self):
del(self._df)
def test_kama(self):
target = 'KAMA'
result = self._indicator.kama()
pd.testing.assert_series_equal(self._df[target].tail(), result.tail(), check_names=False)
from ta.volume import OnBalanceVolumeIndicator, AccDistIndexIndicator
from google.cloud import storage
import shutil
if len(sys.argv) > 1:
batch_size = 31
symbol = sys.argv[1]
end = datetime.today()
start = datetime(2000, 9, 1)
ETH = pdr.DataReader(symbol,'yahoo',start,end)
df = pd.DataFrame(data=ETH)
kama_indicator = KAMAIndicator(close = df["Close"], window = 10, pow1 = 2, pow2 = 30, fillna = False)
df['kama'] = kama_indicator.kama()
ppo_indicator = PercentagePriceOscillator(close = df["Close"], window_slow = 20, window_fast = 10, window_sign = 9, fillna = False)
df['ppo'] = ppo_indicator.ppo()
roc_indicator = ROCIndicator(close = df["Close"], window = 12, fillna = False)
df['roc'] = roc_indicator.roc()
macd_indicator = MACD(close = df["Close"], window_slow = 20, window_fast = 12, window_sign = 9, fillna = False)
df['macd'] = macd_indicator.macd()
rsi_indicator = RSIIndicator(close = df["Close"], window = 14, fillna = False)
df['rsi'] = rsi_indicator.rsi()
aroon_indicator = AroonIndicator(close = df["Close"], window = 20, fillna = False)
def add_momentum_ta(
df: pd.DataFrame,
high: str,
low: str,
close: str,
volume: str,
fillna: bool = False,
colprefix: str = "",
vectorized: bool = False,
) -> pd.DataFrame:
"""Add trend technical analysis features to dataframe.
Args:
df (pandas.core.frame.DataFrame): Dataframe base.
high (str): Name of 'high' column.
low (str): Name of 'low' column.
close (str): Name of 'close' column.
volume (str): Name of 'volume' column.
fillna(bool): if True, fill nan values.
colprefix(str): Prefix column names inserted
vectorized(bool): if True, use only vectorized functions indicators
Returns:
pandas.core.frame.DataFrame: Dataframe with new features.
"""
# Relative Strength Index (RSI)
df[f"{colprefix}momentum_rsi"] = RSIIndicator(close=df[close],
window=14,
fillna=fillna).rsi()
# Stoch RSI (StochRSI)
indicator_srsi = StochRSIIndicator(close=df[close],
window=14,
smooth1=3,
smooth2=3,
fillna=fillna)
df[f"{colprefix}momentum_stoch_rsi"] = indicator_srsi.stochrsi()
df[f"{colprefix}momentum_stoch_rsi_k"] = indicator_srsi.stochrsi_k()
df[f"{colprefix}momentum_stoch_rsi_d"] = indicator_srsi.stochrsi_d()
# TSI Indicator
df[f"{colprefix}momentum_tsi"] = TSIIndicator(close=df[close],
window_slow=25,
window_fast=13,
fillna=fillna).tsi()
# Ultimate Oscillator
df[f"{colprefix}momentum_uo"] = UltimateOscillator(
high=df[high],
low=df[low],
close=df[close],
window1=7,
window2=14,
window3=28,
weight1=4.0,
weight2=2.0,
weight3=1.0,
fillna=fillna,
).ultimate_oscillator()
# Stoch Indicator
indicator_so = StochasticOscillator(
high=df[high],
low=df[low],
close=df[close],
window=14,
smooth_window=3,
fillna=fillna,
)
df[f"{colprefix}momentum_stoch"] = indicator_so.stoch()
df[f"{colprefix}momentum_stoch_signal"] = indicator_so.stoch_signal()
# Williams R Indicator
df[f"{colprefix}momentum_wr"] = WilliamsRIndicator(
high=df[high], low=df[low], close=df[close], lbp=14,
fillna=fillna).williams_r()
# Awesome Oscillator
df[f"{colprefix}momentum_ao"] = AwesomeOscillatorIndicator(
high=df[high], low=df[low], window1=5, window2=34,
fillna=fillna).awesome_oscillator()
# Rate Of Change
df[f"{colprefix}momentum_roc"] = ROCIndicator(close=df[close],
window=12,
fillna=fillna).roc()
# Percentage Price Oscillator
indicator_ppo = PercentagePriceOscillator(close=df[close],
window_slow=26,
window_fast=12,
window_sign=9,
fillna=fillna)
df[f"{colprefix}momentum_ppo"] = indicator_ppo.ppo()
df[f"{colprefix}momentum_ppo_signal"] = indicator_ppo.ppo_signal()
df[f"{colprefix}momentum_ppo_hist"] = indicator_ppo.ppo_hist()
# Percentage Volume Oscillator
indicator_pvo = PercentageVolumeOscillator(volume=df[volume],
window_slow=26,
window_fast=12,
window_sign=9,
fillna=fillna)
df[f"{colprefix}momentum_pvo"] = indicator_pvo.pvo()
df[f"{colprefix}momentum_pvo_signal"] = indicator_pvo.pvo_signal()
df[f"{colprefix}momentum_pvo_hist"] = indicator_pvo.pvo_hist()
if not vectorized:
# KAMA
df[f"{colprefix}momentum_kama"] = KAMAIndicator(close=df[close],
window=10,
pow1=2,
pow2=30,
fillna=fillna).kama()
return df
def add_momentum_ta(df: pd.DataFrame,
high: str,
low: str,
close: str,
volume: str,
fillna: bool = False,
colprefix: str = ""):
"""Add trend technical analysis features to dataframe.
Args:
df (pandas.core.frame.DataFrame): Dataframe base.
high (str): Name of 'high' column.
low (str): Name of 'low' column.
close (str): Name of 'close' column.
fillna(bool): if True, fill nan values.
colprefix(str): Prefix column names inserted
Returns:
pandas.core.frame.DataFrame: Dataframe with new features.
"""
# Relative Strength Index (RSI)
df[f'{colprefix}momentum_rsi'] = RSIIndicator(close=df[close],
n=14,
fillna=fillna).rsi()
# Money Flow Indicator
df[f'{colprefix}momentum_mfi'] = MFIIndicator(
high=df[high],
low=df[low],
close=df[close],
volume=df[volume],
n=14,
fillna=fillna).money_flow_index()
# TSI Indicator
df[f'{colprefix}momentum_tsi'] = TSIIndicator(close=df[close],
r=25,
s=13,
fillna=fillna).tsi()
# Ultimate Oscillator
df[f'{colprefix}momentum_uo'] = UltimateOscillatorIndicator(
high=df[high],
low=df[low],
close=df[close],
s=7,
m=14,
len=28,
ws=4.0,
wm=2.0,
wl=1.0,
fillna=fillna).uo()
# Stoch Indicator
indicator = StochIndicator(high=df[high],
low=df[low],
close=df[close],
n=14,
d_n=3,
fillna=fillna)
df[f'{colprefix}momentum_stoch'] = indicator.stoch()
df[f'{colprefix}momentum_stoch_signal'] = indicator.stoch_signal()
# Williams R Indicator
df[f'{colprefix}momentum_wr'] = WilliamsRIndicator(high=df[high],
low=df[low],
close=df[close],
lbp=14,
fillna=fillna).wr()
# Awesome Oscillator
df[f'{colprefix}momentum_ao'] = AwesomeOscillatorIndicator(
high=df[high], low=df[low], s=5, len=34, fillna=fillna).ao()
# KAMA
df[f'{colprefix}momentum_kama'] = KAMAIndicator(close=df[close],
n=10,
pow1=2,
pow2=30,
fillna=fillna).kama()
# Rate Of Change
df[f'{colprefix}momentum_roc'] = ROCIndicator(close=df[close],
n=12,
fillna=fillna).roc()
return df
def setUp(self):
self._df = pd.read_csv(self._filename, sep=',')
self._indicator = KAMAIndicator(close=self._df['Close'], n=10, pow1=2, pow2=30, fillna=False)
