def add_indicators(df): high = df["HA_High"].values close = df["HA_Close"].values low = df["HA_Low"].values _open = df["HA_Open"].values volume = df["volume"].values.astype('uint32') df["APO"] = talib.APO(close, fastperiod=9, slowperiod=21, matype=0) df["APO"] = talib.APO(close, fastperiod=9, slowperiod=21, matype=0) df["aroondown"], df["aroonup"] = talib.AROON(high, low, timeperiod=14) df["BOP"] = talib.BOP(_open, high, low, close) df["CCI"] = talib.CCI(high, low, close, timeperiod=10) df["DX"] = talib.DX(high, low, close, timeperiod=10) df["MOM"] = talib.MOM(close, timeperiod=10) df["slowk"], df["slowd"] = talib.STOCH(high, low, close, fastk_period=5, slowk_period=3, slowk_matype=0, slowd_period=3, slowd_matype=0) df["OBV"] = talib.OBV(close, np.asarray(volume, dtype='float')) df["ADOSC"] = talib.ADOSC(high, low, close, np.asarray(volume, dtype='float'), fastperiod=3, slowperiod=10) df["upperband"], df["middleband"], df["lowerband"] = talib.BBANDS( close, timeperiod=5, nbdevup=2, nbdevdn=2, matype=0) return df
def trade(context, data): for stock in context.stocks1: if data.can_trade(stock) and not data.is_stale(stock): prices = data.history(stock, 'price', 50, '1d') try: apo = talib.APO(prices, fastperiod=12, slowperiod=26, matype=context.matype)[-1] except: context.stocks1.remove(stock) log.info(stock) continue position = context.portfolio.positions[stock].amount if apo > 0 and position == 0: order_target_percent(stock, percent) elif apo < 0 and position > 0: order_target_percent(stock, 0) for stock in context.stocks2: if data.can_trade(stock) and not data.is_stale(stock): prices = data.history(stock, 'price', 50, '1d') try: apo = talib.APO(prices, fastperiod=12, slowperiod=26, matype=context.matype)[-1] except: context.stocks2.remove(stock) log.info(stock) continue position = context.portfolio.positions[stock].amount if apo > 0 and position == 0: order_target_percent(stock, percent) elif apo < 0 and position > 0: order_target_percent(stock, 0) for stock in context.stocks3: if data.can_trade(stock) and not data.is_stale(stock): prices = data.history(stock, 'price', 50, '1d') try: apo = talib.APO(prices, fastperiod=12, slowperiod=26, matype=context.matype)[-1] except: context.stocks3.remove(stock) log.info(stock) continue position = context.portfolio.positions[stock].amount if apo > 0 and position == 0: order_target_percent(stock, percent) elif apo < 0 and position > 0: order_target_percent(stock, 0) record(leverage=context.account.leverage)
def getMomentumIndicators(df): high = df['High'] low = df['Low'] close = df['Close'] open = df['Open'] volume = df['Volume'] df['ADX'] = ta.ADX(high, low, close, timeperiod=14) df['SMA'] = ta.ADXR(high, low, close, timeperiod=14) df['APO'] = ta.APO(close, fastperiod=12, slowperiod=26, matype=0) df['AROONDOWN'], df['AROOONUP'] = ta.AROON(high, low, timeperiod=14) df['AROONOSC'] = ta.AROONOSC(high, low, timeperiod=14) df['BOP'] = ta.BOP(open, high, low, close) df['CCI'] = ta.CCI(high, low, close, timeperiod=14) df['CMO'] = ta.CMO(close, timeperiod=14) df['DX'] = ta.DX(high, low, close, timeperiod=14) df['MACD'], df['MACDSIGNAL'], df['MACDHIST'] = ta.MACD(close, fastperiod=12, slowperiod=26, signalperiod=9) df['MFI'] = ta.MFI(high, low, close, volume, timeperiod=14) df['MINUS_DI'] = ta.MINUS_DI(high, low, close, timeperiod=14) df['MINUS_DM']= ta.MINUS_DM(high, low, timeperiod=14) df['MOM'] = ta.MOM(close, timeperiod=10) df['PLUS_DM'] =ta.PLUS_DM(high, low, timeperiod=14) df['PPO'] = ta.PPO(close, fastperiod=12, slowperiod=26, matype=0) df['ROC'] = ta.ROC(close, timeperiod=10) df['ROCP'] = ta.ROCP(close, timeperiod=10) df['ROCR'] = ta.ROCR(close, timeperiod=10) df['ROCR100'] = ta.ROCR100(close, timeperiod=10) df['RSI'] = ta.RSI(close, timeperiod=14) df['SLOWK'], df['SLOWD'] = ta.STOCH(high, low, close, fastk_period=5, slowk_period=3, slowk_matype=0, slowd_period=3, slowd_matype=0) df['FASTK'], df['FASTD'] = ta.STOCHF(high, low, close, fastk_period=5, fastd_period=3, fastd_matype=0) df['FASTK2'], df['FASTD2'] = ta.STOCHRSI(close, timeperiod=14, fastk_period=5, fastd_period=3, fastd_matype=0) df['TRIX'] = ta.TRIX(close, timeperiod=30) df['ULTOSC'] = ta.ULTOSC(high, low, close, timeperiod1=7, timeperiod2=14, timeperiod3=28) df['WILLR'] = ta.WILLR(high, low, close, timeperiod=14)
def test_apo(self): """ Test Absolute Price Oscillator (APO). """ q = qufilab.apo(self.close, 200, 100, "sma") t = talib.APO(self.close, 200, 100, 0) np.testing.assert_allclose(q, t, rtol=self.tolerance)
def extract_features(data): high = data['High'] low = data['Low'] close = data['Close'] volume = data['Volume'] open_ = data['Open'] data['ADX'] = ta.ADX(high, low, close, timeperiod=19) data['CCI'] = ta.CCI(high, low, close, timeperiod=19) data['CMO'] = ta.CMO(close, timeperiod=14) data['MACD'], X, Y = ta.MACD(close, fastperiod=10, slowperiod=30, signalperiod=9) data['MFI'] = ta.MFI(high, low, close, volume, timeperiod=19) data['MOM'] = ta.MOM(close, timeperiod=9) data['ROCR'] = ta.ROCR(close, timeperiod=12) data['RSI'] = ta.RSI(close, timeperiod=19) data['STOCHSLOWK'], data['STOCHSLOWD'] = ta.STOCH(high, low, close, fastk_period=5, slowk_period=3, slowk_matype=0, slowd_period=3, slowd_matype=0) data['TRIX'] = ta.TRIX(close, timeperiod=30) data['WILLR'] = ta.WILLR(high, low, close, timeperiod=14) data['OBV'] = ta.OBV(close, volume) data['TSF'] = ta.TSF(close, timeperiod=14) data['NATR'] = ta.NATR(high, low, close)#, timeperiod=14) data['ULTOSC'] = ta.ULTOSC(high, low, close) data['AROONOSC'] = ta.AROONOSC(high, low, timeperiod=14) data['BOP'] = ta.BOP(open_, high, low, close) data['LINEARREG'] = ta.LINEARREG(close) data['AP0'] = ta.APO(close, fastperiod=9, slowperiod=23, matype=1) data['TEMA'] = ta.TRIMA(close, 29) return data
def momentum(self): adx = talib.ADX(self.high,self.low,self.close,self.period) adxr = talib.ADXR(self.high,self.low,self.close,self.period) apo = talib.APO(self.high,self.low,self.close,self.period) aroondown, aroonup = talib.AROON(self.high, self.low, period) aroonosc = talib.AROONOSC(self.high,self.low,self.period) bop = talib.BOP(self.opens,self.high,self.low,self.close) cci = talib.CCI(self.high,self.low,self.close,self.period) cmo = talib.CMO(self.close,self.period) dx = talib.DX(self.high,self.low,self.close,self.period) macd, macdsignal, macdhist = talib.MACD(self.close, fastperiod=period, slowperiod=period*5, signalperiod=period*2) macd1, macdsignal1, macdhist1 = talib.MACDEXT(self.close, fastperiod=12, fastmatype=0, slowperiod=26, slowmatype=0, signalperiod=9, signalmatype=0) macd2, macdsignal2, macdhist2 = talib.MACDFIX(self.close, signalperiod=9) mfi = talib.MFI(self.high, self.low, self.close, self.volume, timeperiod=14) minus_di = talib.MINUS_DI(self.high, self.low, self.close, timeperiod=14) minus_dm = talib.MINUS_DM(self.high, self.low, timeperiod=14) mom = talib.MOM(self.close, timeperiod=10) plus_di = talib.PLUS_DI(self.high, self.low, self.close, timeperiod=14) plus_dm = talib.PLUS_DM(self.high, self.low, timeperiod=14) ppo = talib.PPO(self.close, fastperiod=12, slowperiod=26, matype=0) roc = talib.ROC(self.close, timeperiod=10) rocp = talib.ROCP(self.close, timeperiod=10) rocr = talib.ROCR(self.close, timeperiod=10) rocr100 = talib.ROCR100(self.close, timeperiod=10) rsi = talib.RSI(self.close, timeperiod=14) slowk, slowd = talib.STOCH(self.high, self.low, self.close, fastk_period=5, slowk_period=3, slowk_matype=0, slowd_period=3, slowd_matype=0) fastk, fastd = talib.STOCHF(self.high, self.low, self.close, fastk_period=5, fastd_period=3, fastd_matype=0) fastk1, fastd1 = talib.STOCHRSI(self.close, timeperiod=14, fastk_period=5, fastd_period=3, fastd_matype=0) trix = talib.TRIX(self.close, timeperiod=30) ultosc = talib.ULTOSC(self.high, self.low, self.close, timeperiod1=7, timeperiod2=14, timeperiod3=28) willr = talib.WILLR(self.high, self.low, self.close, timeperiod=14)
def apo(candles: np.ndarray, fastperiod=12, slowperiod=26, matype=0, sequential=False) -> Union[float, np.ndarray]: """ APO - Absolute Price Oscillator :param candles: np.ndarray :param fastperiod: int - default: 12 :param slowperiod: int - default: 26 :param matype: int - default: 0 :param sequential: bool - default=False :return: float | np.ndarray """ if not sequential and len(candles) > 240: candles = candles[-240:] res = talib.APO(candles[:, 2], fastperiod=fastperiod, slowperiod=slowperiod, matype=matype) return res if sequential else res[-1]
def apo(candles: np.ndarray, fast_period: int = 12, slow_period: int = 26, matype: int = 0, source_type: str = "close", sequential: bool = False) -> Union[float, np.ndarray]: """ APO - Absolute Price Oscillator :param candles: np.ndarray :param fast_period: int - default: 12 :param slow_period: int - default: 26 :param matype: int - default: 0 :param source_type: str - default: "close" :param sequential: bool - default=False :return: float | np.ndarray """ candles = slice_candles(candles, sequential) source = get_candle_source(candles, source_type=source_type) res = talib.APO(source, fastperiod=fast_period, slowperiod=slow_period, matype=matype) return res if sequential else res[-1]
def calculations(self): '''calculations''' self.df['rsi'] = ta.RSI(self.df['close'], timeperiod=5) self.df['apo'] = ta.APO(self.df['close'], fastperiod=10, slowperiod=5, matype=0) self.df['upperband'], self.df['middleband'], self.df[ 'lowerband'] = ta.BBANDS(self.df['close'], timeperiod=5, nbdevup=2, nbdevdn=2, matype=0) self.df['ema'] = ta.EMA(self.df['close'], timeperiod=5) self.df['ma'] = ta.MA(self.df['close'], timeperiod=5, matype=0) self.df['sma'] = ta.MA(self.df['close'], timeperiod=5) self.df['t3'] = ta.T3(self.df['close'], timeperiod=5, vfactor=0) self.df['wma'] = ta.WMA(self.df['close'], timeperiod=5) self.df['aroonosc'] = ta.AROONOSC(self.df['high'], self.df['low'], timeperiod=5) self.df['cci'] = ta.CCI(self.df['high'], self.df['low'], self.df['close'], timeperiod=5) self.df['cmo'] = ta.CMO(self.df['close'], timeperiod=14) self.df['macd'], self.df['macdsignal'], self.df[ 'macdhist'] = ta.MACDEXT(self.df['close'], fastperiod=12, fastmatype=0, slowperiod=26, slowmatype=0, signalperiod=9, signalmatype=0) self.df['slowk'], self.df['slowd'] = ta.STOCH(self.df['high'], self.df['low'], self.df['close'], fastk_period=5, slowk_period=3, slowk_matype=0, slowd_period=3, slowd_matype=0) self.df['fastk'], self.df['fastd'] = ta.STOCHRSI(self.df['close'], timeperiod=5, fastk_period=5, fastd_period=3, fastd_matype=0) self.df['ultosc'] = ta.ULTOSC(self.df['high'], self.df['low'], self.df['close'], timeperiod1=7, timeperiod2=14, timeperiod3=28) self.df['adosc'] = ta.ADOSC(self.df['high'], self.df['low'], self.df['close'], self.df['volume'], fastperiod=3, slowperiod=10) return self.df
def generate_feature(data): high = data.High.values low = data.Low.values close = data.Close.values feature_df = pd.DataFrame(index=data.index) feature_df["ADX"] = ADX = talib.ADX(high, low, close, timeperiod=14) feature_df["ADXR"] = ADXR = talib.ADXR(high, low, close, timeperiod=14) feature_df["APO"] = APO = talib.APO(close, fastperiod=12, slowperiod=26, matype=0) feature_df["AROONOSC"] = AROONOSC = talib.AROONOSC(high, low, timeperiod=14) feature_df["CCI"] = CCI = talib.CCI(high, low, close, timeperiod=14) feature_df["CMO"] = CMO = talib.CMO(close, timeperiod=14) feature_df["DX"] = DX = talib.DX(high, low, close, timeperiod=14) feature_df["MINUS_DI"] = MINUS_DI = talib.MINUS_DI(high, low, close, timeperiod=14) feature_df["MINUS_DM"] = MINUS_DM = talib.MINUS_DM(high, low, timeperiod=14) feature_df["MOM"] = MOM = talib.MOM(close, timeperiod=10) feature_df["PLUS_DI"] = PLUS_DI = talib.PLUS_DI(high, low, close, timeperiod=14) feature_df["PLUS_DM"] = PLUS_DM = talib.PLUS_DM(high, low, timeperiod=14) feature_df["PPO"] = PPO = talib.PPO(close, fastperiod=12, slowperiod=26, matype=0) feature_df["ROC"] = ROC = talib.ROC(close, timeperiod=10) feature_df["ROCP"] = ROCP = talib.ROCP(close, timeperiod=10) feature_df["ROCR100"] = ROCR100 = talib.ROCR100(close, timeperiod=10) feature_df["RSI"] = RSI = talib.RSI(close, timeperiod=14) feature_df["ULTOSC"] = ULTOSC = talib.ULTOSC(high, low, close, timeperiod1=7, timeperiod2=14, timeperiod3=28) feature_df["WILLR"] = WILLR = talib.WILLR(high, low, close, timeperiod=14) feature_df = feature_df.fillna(0.0) matrix = np.stack(( ADX, ADXR, APO, AROONOSC, CCI, CMO, DX, MINUS_DI, ROCR100, ROC, MINUS_DM, MOM, PLUS_DI, PLUS_DM, PPO, ROCP, WILLR, ULTOSC, RSI)) matrix = np.nan_to_num(matrix) matrix = matrix.transpose() return feature_df, matrix
def apo(close_ts, fastperiod=12, slowperiod=26, matype=0): import talib close_np = close_ts.cpu().detach().numpy() close_df = pd.DataFrame(close_np) apo = close_df.apply(lambda x: talib.APO(x, fastperiod=12, slowperiod=26, matype=0)) apo_ts = torch.tensor(apo.values, dtype=close_ts.dtype, device=close_ts.device) return apo_ts
def apo(self, n, array=False): """ APO. """ result = talib.APO(self.close, n) if array: return result return result[-1]
def apo(self, n: int, array: bool = False) -> Union[float, np.ndarray]: """ APO. """ result = talib.APO(self.close, n) if array: return result return result[-1]
def APO(close, fastperiod=12, slowperiod=26, matype=0): # Absolute Price Oscillator param = { 'fastperiod': fastperiod, 'slowperiod': slowperiod, 'matype': matype, } return talib.APO(close, **param)
def apo(df, n=5, m=10, normalize=False, col='Close'): c = df[col].as_matrix() _apo = pd.Series(ta.APO(c, fastperiod=n, slowperiod=m), index=df.index, name="APO_" + str(n) + "_" + str(m)) if normalize: _apo = z_score(_apo) return _apo
def APO(self, fastPeriod=12, slowPeriod=26, matype=0): real_data = np.array([self.df.close], dtype='f8') apo = talib.APO(real_data[0], fastperiod=fastPeriod, slowperiod=slowPeriod, matype=matype) # return go.Scatter( # x=self.df.index, # y=apo, # name='APO' # ) return apo
def applyAPO(nsedailyquoteDF): apodf = pd.Series(talib.APO(nsedailyquoteDF['CLOSE'].values, fastperiod=12, slowperiod=26, matype=0), index=nsedailyquoteDF.TIMESTAMP, name='APO') return apodf
def NMA_factor(self, df, fastperiod=12, slowperiod=26, matype=0): """ the custom function that compute normalized apo the equation is (fast ma - slow ma)/slowma """ numerator = talib.APO(df.loc[:, self.map_dict['default']].values, fastperiod, slowperiod, matype) denominator = talib.MA(df.loc[:, self.map_dict['default']].values, slowperiod, matype) return numerator / denominator
def APO(close, fastperiod=12, slowperiod=26, matype=0): ''' Absolute Price Oscillator 分组: Momentum Indicator 动量指标 简介: real = APO(close, fastperiod=12, slowperiod=26, matype=0) ''' return talib.APO(close, fastperiod, slowperiod, matype)
def apotrade(context, data): for stock in context.stocks: prices = data.history(stock, 'price', 50, '1d') apo = talib.APO(prices, fastperiod=12, slowperiod=26, matype=0)[-1] position = context.portfolio.positions[stock].amount if apo > 0 and position == 0: order_target_percent(stock, pct) elif apo < 0 and position > 0: order_target_percent(stock, 0)
def generate_feature(data): high = data.High.values low = data.Low.values close = data.Close.values # feature_df = pd.DataFrame(index=data.index) feature_df = data.copy() feature_df["ADX"] = ADX = talib.ADX(high, low, close, timeperiod=14) feature_df["ADXR"] = ADXR = talib.ADXR(high, low, close, timeperiod=14) feature_df["APO"] = APO = talib.APO(close, fastperiod=12, slowperiod=26, matype=0) feature_df["AROONOSC"] = AROONOSC = talib.AROONOSC(high, low, timeperiod=14) feature_df["CCI"] = CCI = talib.CCI(high, low, close, timeperiod=14) feature_df["CMO"] = CMO = talib.CMO(close, timeperiod=14) feature_df["DX"] = DX = talib.DX(high, low, close, timeperiod=14) feature_df["MINUS_DI"] = MINUS_DI = talib.MINUS_DI(high, low, close, timeperiod=14) feature_df["MINUS_DM"] = MINUS_DM = talib.MINUS_DM(high, low, timeperiod=14) feature_df["MOM"] = MOM = talib.MOM(close, timeperiod=10) feature_df["PLUS_DI"] = PLUS_DI = talib.PLUS_DI(high, low, close, timeperiod=14) feature_df["PLUS_DM"] = PLUS_DM = talib.PLUS_DM(high, low, timeperiod=14) feature_df["PPO"] = PPO = talib.PPO(close, fastperiod=12, slowperiod=26, matype=0) feature_df["ROC"] = ROC = talib.ROC(close, timeperiod=10) feature_df["ROCP"] = ROCP = talib.ROCP(close, timeperiod=10) feature_df["ROCR100"] = ROCR100 = talib.ROCR100(close, timeperiod=10) feature_df["RSI"] = RSI = talib.RSI(close, timeperiod=14) feature_df["ULTOSC"] = ULTOSC = talib.ULTOSC(high, low, close, timeperiod1=7, timeperiod2=14, timeperiod3=28) feature_df["WILLR"] = WILLR = talib.WILLR(high, low, close, timeperiod=14) feature_df = feature_df.fillna(0.0) # Exclude columns you don't want feature_df = feature_df[feature_df.columns[ ~feature_df.columns.isin(['Open', 'High', 'Low', 'Close'])]] matrix = feature_df.values return feature_df, matrix
def _get_indicators(security, open_name, close_name, high_name, low_name, volume_name): """ expand the features of the data through technical analysis across 26 different signals :param security: data which features are going to be expanded :param open_name: open price column name :param close_name: close price column name :param high_name: high price column name :param low_name: low price column name :param volume_name: traded volumn column name :return: expanded and extracted data """ open_price = security[open_name].values close_price = security[close_name].values low_price = security[low_name].values high_price = security[high_name].values volume = security[volume_name].values if volume_name else None security['MOM'] = talib.MOM(close_price) security['HT_DCPERIOD'] = talib.HT_DCPERIOD(close_price) security['HT_DCPHASE'] = talib.HT_DCPHASE(close_price) security['SINE'], security['LEADSINE'] = talib.HT_SINE(close_price) security['INPHASE'], security['QUADRATURE'] = talib.HT_PHASOR( close_price) security['ADXR'] = talib.ADXR(high_price, low_price, close_price) security['APO'] = talib.APO(close_price) security['AROON_UP'], _ = talib.AROON(high_price, low_price) security['CCI'] = talib.CCI(high_price, low_price, close_price) security['PLUS_DI'] = talib.PLUS_DI(high_price, low_price, close_price) security['PPO'] = talib.PPO(close_price) security['MACD'], security['MACD_SIG'], security[ 'MACD_HIST'] = talib.MACD(close_price) security['CMO'] = talib.CMO(close_price) security['ROCP'] = talib.ROCP(close_price) security['FASTK'], security['FASTD'] = talib.STOCHF( high_price, low_price, close_price) security['TRIX'] = talib.TRIX(close_price) security['ULTOSC'] = talib.ULTOSC(high_price, low_price, close_price) security['WILLR'] = talib.WILLR(high_price, low_price, close_price) security['NATR'] = talib.NATR(high_price, low_price, close_price) security['RSI'] = talib.RSI(close_price) security['EMA'] = talib.EMA(close_price) security['SAREXT'] = talib.SAREXT(high_price, low_price) # security['TEMA'] = talib.EMA(close_price) security['RR'] = security[close_name] / security[close_name].shift( 1).fillna(1) security['LOG_RR'] = np.log(security['RR']) if volume_name: security['MFI'] = talib.MFI(high_price, low_price, close_price, volume) # security['AD'] = talib.AD(high_price, low_price, close_price, volume) # security['OBV'] = talib.OBV(close_price, volume) security[volume_name] = np.log(security[volume_name]) security.drop([open_name, close_name, high_name, low_name], axis=1) security = security.dropna().astype(np.float32) return security
def apo(self, fast_period: int, slow_period: int, matype: int = 0, array: bool = False) -> Union[float, np.ndarray]: """ APO. """ result = talib.APO(self.close, fast_period, slow_period, matype) if array: return result return result[-1]
def get_momentum_studies(open, low, high, close, volume, df): # Momentum studies # https://mrjbq7.github.io/ta-lib/func_groups/momentum_indicators.html df['MACD'], df['MACD_SIGN'], df['MACD_HIST'] = talib.MACD( close, fastperiod=12, slowperiod=26, signalperiod=9) df['STOCH-SLOW-K'], df['STOCH-SLOW-D'] = talib.STOCH(high, low, close, fastk_period=5, slowk_period=3, slowk_matype=0, slowd_period=3, slowd_matype=0) df['STOCH-FAST-K'], df['STOCH-FAST-D'] = talib.STOCHF(high, low, close, fastk_period=5, fastd_period=3, fastd_matype=0) df['STOCH-RSI-K'], df['STOCH-RSI-D'] = talib.STOCHRSI(close, timeperiod=14, fastk_period=5, fastd_period=3, fastd_matype=0) df['AROON-DOWN'], df['AROON-UP'] = talib.AROON(high, low, timeperiod=14) df["MINUS_DI"] = talib.MINUS_DI(high, low, close, timeperiod=14) df["MINUS_DM"] = talib.MINUS_DM(high, low, timeperiod=14) df["PLUS_DI"] = talib.PLUS_DI(high, low, close, timeperiod=14) df["PLUS_DM"] = talib.PLUS_DM(high, low, timeperiod=14) df["MOM"] = talib.MOM(close, timeperiod=10) df["MFI"] = talib.MFI(high, low, close, volume, timeperiod=14) df["ADX"] = talib.ADX(high, low, close, timeperiod=14) df["ADXR"] = talib.ADXR(high, low, close, timeperiod=14) df["APO"] = talib.APO(close, fastperiod=12, slowperiod=26, matype=0) df["AROONOSC"] = talib.AROONOSC(high, low, timeperiod=14) df["BOP"] = talib.BOP(open, high, low, close) df["CCI"] = talib.CCI(high, low, close, timeperiod=14) df["CMO"] = talib.CMO(close, timeperiod=14) df["DX"] = talib.DX(high, low, close, timeperiod=14) df["PPO"] = talib.PPO(close, fastperiod=12, slowperiod=26, matype=0) df["ROC"] = talib.ROC(close, timeperiod=10) df["RSI"] = talib.RSI(close, timeperiod=14) df["TRIX"] = talib.TRIX(close, timeperiod=30) df["ULT"] = talib.ULTOSC(high, low, close, timeperiod1=7, timeperiod2=14, timeperiod3=28) df["WILLR"] = talib.WILLR(high, low, close, timeperiod=14)
def test_apo(self): result = pandas_ta.apo(self.close) self.assertIsInstance(result, Series) self.assertEqual(result.name, 'APO_12_26') try: expected = tal.APO(self.close, 12, 26) pdt.assert_series_equal(result, expected, check_names=False) except AssertionError as ae: try: corr = pandas_ta.utils.df_error_analysis(result, expected, col=CORRELATION) self.assertGreater(corr, CORRELATION_THRESHOLD) except Exception as ex: error_analysis(result, CORRELATION, ex)
def generate_tech_data_default(stock, open_name, close_name, high_name, low_name, volume_name='vol'): open_price = stock[open_name].values close_price = stock[close_name].values low_price = stock[low_name].values high_price = stock[high_name].values volume = stock[volume_name].values data = stock.copy() data['MOM'] = talib.MOM(close_price) data['HT_DCPERIOD'] = talib.HT_DCPERIOD(close_price) data['HT_DCPHASE'] = talib.HT_DCPHASE(close_price) data['sine'], data['leadsine'] = talib.HT_SINE(close_price) data['inphase'], data['quadrature'] = talib.HT_PHASOR(close_price) data['ADXR'] = talib.ADXR(high_price, low_price, close_price) data['APO'] = talib.APO(close_price) data['AROON_UP'], _ = talib.AROON(high_price, low_price) data['CCI'] = talib.CCI(high_price, low_price, close_price) data['PLUS_DI'] = talib.PLUS_DI(high_price, low_price, close_price) data['PPO'] = talib.PPO(close_price) data['macd'], data['macd_sig'], data['macd_hist'] = talib.MACD(close_price) data['CMO'] = talib.CMO(close_price) data['ROCP'] = talib.ROCP(close_price) data['fastk'], data['fastd'] = talib.STOCHF(high_price, low_price, close_price) data['TRIX'] = talib.TRIX(close_price) data['ULTOSC'] = talib.ULTOSC(high_price, low_price, close_price) data['WILLR'] = talib.WILLR(high_price, low_price, close_price) data['NATR'] = talib.NATR(high_price, low_price, close_price) data['MFI'] = talib.MFI(high_price, low_price, close_price, volume) data['RSI'] = talib.RSI(close_price) data['AD'] = talib.AD(high_price, low_price, close_price, volume) data['OBV'] = talib.OBV(close_price, volume) data['EMA'] = talib.EMA(close_price) data['SAREXT'] = talib.SAREXT(high_price, low_price) data['TEMA'] = talib.EMA(close_price) #data = data.drop([open_name, close_name, high_name, low_name, volume_name, 'amount', 'count'], axis=1) data = drop_columns(data, [ open_name, close_name, high_name, low_name, volume_name, 'amount', 'count' ]) data = data.dropna().astype(np.float32) return data
def get_additional_factors(open, high, low, close, volume): # Overlap Studies Functions mat = get_all_factors(open, high, low, close, volume) mat = np.column_stack((mat, talib.HT_TRENDLINE(close))) ## close mat = np.column_stack((mat, talib.KAMA(close, timeperiod=30))) ##close #Momentum Indicator Functions mat = np.column_stack((mat, talib.ADX(high, low, close, timeperiod=14))) mat = np.column_stack((mat, talib.ADXR(high, low, close, timeperiod=14))) mat = np.column_stack( (mat, talib.APO(close, fastperiod=12, slowperiod=26, matype=0))) mat = np.column_stack((mat, talib.AROONOSC(high, low, timeperiod=14))) mat = np.column_stack((mat, talib.BOP(open, high, low, close))) mat = np.column_stack((mat, talib.MOM(close, timeperiod=10))) #Volume Indicator Functions mat = np.column_stack((mat, talib.AD(high, low, close, volume))) mat = np.column_stack( (mat, talib.ADOSC(high, low, close, volume, fastperiod=3, slowperiod=10))) mat = np.column_stack((mat, talib.OBV(close, volume))) #Volatility Indicator Functions mat = np.column_stack((mat, talib.NATR(high, low, close, timeperiod=14))) mat = np.column_stack((mat, talib.TRANGE(high, low, close))) #Price Transform Functions mat = np.column_stack((mat, talib.AVGPRICE(open, high, low, close))) mat = np.column_stack((mat, talib.MEDPRICE(high, low))) mat = np.column_stack((mat, talib.TYPPRICE(high, low, close))) mat = np.column_stack((mat, talib.WCLPRICE(high, low, close))) #Cycle Indicator Functions mat = np.column_stack((mat, talib.HT_DCPERIOD(close))) mat = np.column_stack((mat, talib.HT_DCPHASE(close))) mat = np.column_stack((mat, talib.HT_TRENDMODE(close))) # 20 return mat
def generateIndicatorAPO(dictDataSpec): """ https://www.investopedia.com/articles/technical/02/041002.asp """ df = dictDataSpec['df'] sReal = pd.Series(talib.APO(df.Close.values, 12, 26), index=df.index) sIndicator = sReal.apply(lambda x: np.nan) print sReal.describe() ADXThresholdUpper = 50 ADXThresholdLower = 50 NDayHist = 60 NDayHistSL = 20 sIndicator[(sReal > ADXThresholdUpper)&(sReal.shift(1) < ADXThresholdUpper)&(df['Close'] / df['Close'].rolling(NDayHist).min() > 1.1)] = 1 sIndicator[(sReal < ADXThresholdLower)&(sReal.shift(1) > ADXThresholdLower)] = 0 sIndicator[(df['Close']<df['Close'].shift(1).rolling(NDayHistSL).quantile(0.1))] = -1 return sIndicator
def init_state(indata, test=False): openn = indata['open'].values close = indata['close'].values high = indata['high'].values low = indata['low'].values volume = indata['volume'].values diff = np.diff(close) diff = np.insert(diff, 0, 0) sma30 = talib.SMA(close, 30) sma60 = talib.SMA(close, timeperiod=60) rsi = talib.RSI(close, timeperiod=14) atr = talib.ATR(high, low, close, timeperiod=14) trange = talib.TRANGE(high, low, close) macd, macdsignal, macdhist = talib.MACD(close, 12, 26, 9) upper, middle, lower = talib.BBANDS(close, 20, 2, 2) ema = talib.EMA(close, 30) ma = talib.MA(close, 30) wma = talib.WMA(close, timeperiod=30) tema = talib.TEMA(close, 30) obv = talib.OBV(close, np.asarray(volume, dtype='float')) adx = talib.ADX(high, low, close, 14) apo = talib.APO(close, 12, 2, 0) bop = talib.BOP(openn, high, low, close) mom = talib.MOM(close,10) ppo = talib.PPO(close, 12, 26, 0) slowk, slowd = talib.STOCH(high, low, close, fastk_period=5, slowk_period=3, slowk_matype=0, slowd_period=3, slowd_matype=0) ad = talib.AD(high, low, close, np.asarray(volume, dtype='float')) wcl = talib.WCLPRICE(high, low, close) #--- Preprocess data xdata = np.column_stack((close, diff, sma30, sma60, rsi, atr, macd, macdsignal, macdhist, lower, middle, upper, ema, ma, wma, adx, apo, bop, mom, ppo, slowk, slowd, trange, wcl)) xdata = np.nan_to_num(xdata) if test == False: scaler = preprocessing.StandardScaler() xdata = np.expand_dims(scaler.fit_transform(xdata), axis=1) joblib.dump(scaler, 'data/scaler.pkl') elif test == True: scaler = joblib.load('data/scaler.pkl') xdata = np.expand_dims(scaler.fit_transform(xdata), axis=1) state = xdata[0:1, 0:1, :] return state, xdata, close
def get_technicals_of_series(self, indivations): result = indivations result = np.vstack((result, ta.SMA(indivations, timeperiod=5))) result = np.vstack((result, ta.SMA(indivations, timeperiod=14))) result = np.vstack((result, ta.BBANDS(indivations))) result = np.vstack((result, ta.MAMA(indivations))) result = np.vstack((result, ta.APO(indivations))) result = np.vstack((result, ta.CMO(indivations))) result = np.vstack((result, ta.MACD(indivations))) result = np.vstack((result, ta.MOM(indivations))) result = np.vstack((result, ta.ROC(indivations))) result = np.vstack((result, ta.RSI(indivations))) result = np.vstack((result, ta.HT_TRENDMODE(indivations))) result = np.vstack((result, ta.LINEARREG(indivations))) result = result[:, ~np.isnan(result).any(axis=0)] result = result.T print(np.shape(result)) return result