def ta_williams_r_indicator(df):
"""
Williams R Indicator calculation.
:param df: pandas dataframe
:return: pandas dataframe
"""
temp_df = df.copy()
temp = WilliamsRIndicator(high=temp_df["High"],
low=temp_df["Low"],
close=temp_df["Close"],
fillna=False)
temp_df["will_r_ind"] = temp.williams_r()
return temp_df
def setUpClass(cls):
cls._df = pd.read_csv(cls._filename, sep=',')
cls._params = dict(high=cls._df['High'],
low=cls._df['Low'],
close=cls._df['Close'],
lbp=14,
fillna=False)
cls._indicator = WilliamsRIndicator(**cls._params)
def setUpClass(cls):
cls._df = pd.read_csv(cls._filename, sep=",")
cls._params = dict(
high=cls._df["High"],
low=cls._df["Low"],
close=cls._df["Close"],
lbp=14,
fillna=False,
)
cls._indicator = WilliamsRIndicator(**cls._params)
class TestWilliamsRIndicator(unittest.TestCase):
"""
https://school.stockcharts.com/doku.php?id=technical_indicators:williams_r
"""
_filename = 'ta/tests/data/cs-percentr.csv'
def setUp(self):
self._df = pd.read_csv(self._filename, sep=',')
self._indicator = WilliamsRIndicator(
high=self._df['High'], low=self._df['Low'], close=self._df['Close'], lbp=14, fillna=False)
def tearDown(self):
del(self._df)
def test_wr(self):
target = 'Williams_%R'
result = self._indicator.wr()
pd.testing.assert_series_equal(self._df[target].tail(), result.tail(), check_names=False)
def WILLIANSR(self, df):
williamsr = WilliamsRIndicator(df['High'], df['Low'], df['Close'])
df['%R'] = williamsr.williams_r()
return df
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 applyIndicator(self, full_company_price):
self.data = full_company_price
high = self.data['high']
low = self.data['low']
close = self.data['close']
volume = self.data['volume']
EMA12 = EMAIndicator(close, 12, fillna=False)
EMA30 = EMAIndicator(close, 20, fillna=False)
EMA60 = EMAIndicator(close, 60, fillna=False)
MACD1226 = MACD(close, 26, 12, 9, fillna=False)
MACD2452 = MACD(close, 52, 24, 18, fillna=False)
ROC12 = ROCIndicator(close, 12, fillna=False)
ROC30 = ROCIndicator(close, 30, fillna=False)
ROC60 = ROCIndicator(close, 60, fillna=False)
RSI14 = RSIIndicator(close, 14, fillna=False)
RSI28 = RSIIndicator(close, 28, fillna=False)
RSI60 = RSIIndicator(close, 60, fillna=False)
AROON25 = AroonIndicator(close, 25, fillna=False)
AROON50 = AroonIndicator(close, 50, fillna=False)
AROON80 = AroonIndicator(close, 80, fillna=False)
MFI14 = MFIIndicator(high, low, close, volume, 14, fillna=False)
MFI28 = MFIIndicator(high, low, close, volume, 28, fillna=False)
MFI80 = MFIIndicator(high, low, close, volume, 80, fillna=False)
CCI20 = CCIIndicator(high, low, close, 20, 0.015, fillna=False)
CCI40 = CCIIndicator(high, low, close, 40, 0.015, fillna=False)
CCI100 = CCIIndicator(high, low, close, 100, 0.015, fillna=False)
WILLR14 = WilliamsRIndicator(high, low, close, 14, fillna=False)
WILLR28 = WilliamsRIndicator(high, low, close, 28, fillna=False)
WILLR60 = WilliamsRIndicator(high, low, close, 60, fillna=False)
BBANDS20 = BollingerBands(close, 20, 2, fillna=False)
KC20 = KeltnerChannel(high, low, close, 20, 10, fillna=False)
STOCH14 = StochasticOscillator(high, low, close, 14, 3, fillna=False)
STOCH28 = StochasticOscillator(high, low, close, 28, 6, fillna=False)
STOCH60 = StochasticOscillator(high, low, close, 60, 12, fillna=False)
CMI20 = ChaikinMoneyFlowIndicator(high,
low,
close,
volume,
20,
fillna=False)
CMI40 = ChaikinMoneyFlowIndicator(high,
low,
close,
volume,
40,
fillna=False)
CMI100 = ChaikinMoneyFlowIndicator(high,
low,
close,
volume,
100,
fillna=False)
self.data['ema12'] = (close - EMA12.ema_indicator()) / close
self.data['ema30'] = (close - EMA30.ema_indicator()) / close
self.data['ema60'] = (close - EMA60.ema_indicator()) / close
self.data['macd1226'] = MACD1226.macd() - MACD1226.macd_signal()
self.data['macd2452'] = MACD2452.macd() - MACD2452.macd_signal()
self.data['roc12'] = ROC12.roc()
self.data['roc30'] = ROC30.roc()
self.data['roc60'] = ROC60.roc()
self.data['rsi14'] = RSI14.rsi()
self.data['rsi28'] = RSI28.rsi()
self.data['rsi60'] = RSI60.rsi()
self.data['aroon25'] = AROON25.aroon_indicator()
self.data['aroon50'] = AROON50.aroon_indicator()
self.data['aroon80'] = AROON80.aroon_indicator()
self.data['mfi14'] = MFI14.money_flow_index()
self.data['mfi28'] = MFI28.money_flow_index()
self.data['mfi80'] = MFI80.money_flow_index()
self.data['cci20'] = CCI20.cci()
self.data['cci40'] = CCI40.cci()
self.data['cci100'] = CCI100.cci()
self.data['willr14'] = WILLR14.wr()
self.data['willr28'] = WILLR28.wr()
self.data['willr60'] = WILLR60.wr()
self.data['bband20up'] = (BBANDS20.bollinger_hband() - close) / close
self.data['bband20down'] = (close - BBANDS20.bollinger_lband()) / close
self.data['stoch14'] = STOCH14.stoch()
self.data['stoch28'] = STOCH28.stoch()
self.data['stoch60'] = STOCH60.stoch()
self.data['cmi20'] = CMI20.chaikin_money_flow()
self.data['cmi40'] = CMI40.chaikin_money_flow()
self.data['cmi100'] = CMI100.chaikin_money_flow()
self.data['kc20up'] = (KC20.keltner_channel_hband() - close) / close
self.data['kc20down'] = (close - KC20.keltner_channel_lband()) / close
return self.data
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 = WilliamsRIndicator(
high=self._df['High'], low=self._df['Low'], close=self._df['Close'], lbp=14, fillna=False)