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)