def test_DX(self): class MyDX(OperatorDX): def __init__(self, name, **kwargs): super(MyDX, self).__init__(100, name, **kwargs) self.env.add_operator('dx', { 'operator': MyDX, }) string = 'dx(14, high, low, close)' gene = self.env.parse_string(string) self.assertRaises(IndexError, gene.eval, self.env, self.dates[98], self.dates[-1]) df = gene.eval(self.env, self.dates[99], self.dates[100]) ser0, ser1 = df.iloc[0], df.iloc[1] h = self.env.get_data_value('high').values l = self.env.get_data_value('low').values c = self.env.get_data_value('close').values res0, res1, res = [], [], [] for i, val in ser0.iteritems(): res0.append( talib.DX(h[:100, i], l[:100, i], c[:100, i], timeperiod=14)[-1] == val) for i, val in ser1.iteritems(): res1.append( talib.DX(h[1:100 + 1, i], l[1:100 + 1, i], c[1:100 + 1, i], timeperiod=14)[-1] == val) res.append( talib.DX(h[:100 + 1, i], l[:100 + 1, i], c[:100 + 1, i], timeperiod=14)[-1] != val) self.assertTrue(all(res0) and all(res1) and any(res))
def DMI(single_stock_df, col_h='high', col_l='low', col_c='close', timeperiod=14): """ 计算DMI指标(DI、MINUS_DI 、ADX、ADXR),timeperiod=14 重要参数:high最高价 low最低价 close收盘价,此处算出为负值需要取绝对值 """ single_stock_df['dmi_di'] = talib.DX(single_stock_df[col_h], single_stock_df[col_l],single_stock_df[col_c],timeperiod=timeperiod) single_stock_df['dmi_mdi'] = talib.MINUS_DI(single_stock_df[col_h], single_stock_df[col_l], single_stock_df[col_c], timeperiod=timeperiod) single_stock_df['dmi_adx'] = talib.ADX(single_stock_df[col_h], single_stock_df[col_l], single_stock_df[col_c], timeperiod=timeperiod) single_stock_df['dmi_adxr'] = talib.DX(single_stock_df[col_h], single_stock_df[col_l], single_stock_df[col_c], timeperiod=timeperiod) return single_stock_df
def AverageDirectionalIndex(self, timeperiod=14): ''' Average Direction Movement Index (ADX) ADX is used to determine the stength of a trend. If -DI is above the +DX, down trend If -DI is below the +DX, up trend ADX below 20, price is trendless ADX above 20, price is trending Parameters ---------- timeperiod : TYPE, optional DESCRIPTION. The default is 14. Returns ------- TYPE DESCRIPTION. ''' dmi = pd.DataFrame() # Need to figure out if it should be PLUS_DM, MINUS_DM, MINUS_DI, PLUS_DI, Directional Movement Index or Average Directional Movement Index dmi['+di'] = ta.PLUS_DM(self.data.high, self.data.low, timeperiod) dmi['-di'] = ta.MINUS_DM(self.data.high, self.data.low, timeperiod) dmi['dmi'] = ta.DX(self.data.high, self.data.low, self.data.close, timeperiod) return dmi
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 dx( client, symbol, timeframe="6m", highcol="high", lowcol="low", closecol="close", period=14, ): """This will return a dataframe of Directional Movement Index for the given symbol across the given timeframe Args: client (pyEX.Client): Client symbol (string): Ticker timeframe (string): timeframe to use, for pyEX.chart highcol (string): column to use to calculate lowcol (string): column to use to calculate closecol (string): column to use to calculate period (int): period to calculate across Returns: DataFrame: result """ df = client.chartDF(symbol, timeframe) x = t.DX(df[highcol].values, df[lowcol].values, df[closecol].values, period) return pd.DataFrame( { highcol: df[highcol].values, lowcol: df[lowcol].values, closecol: df[closecol].values, "dx": x, } )
def compDX(self): dx = talib.DX(self.high,self.low,self.close,timeperiod=self.lookback) self.removeNullID(dx) self.rawFeatures['DX'] = dx FEATURE_SIZE_DICT['DX'] = 1 return
def get_features(data): tech_data = pd.DataFrame(index=data.index); for t in periods_list: tech_data[f'SMA_{t}'] = talib.SMA(data.close,timeperiod=t) tech_data[f'MOM_{t}'] = talib.MOM(data.close, timeperiod=t) tech_data[f'RSI_{t}'] = talib.RSI(data.close, timeperiod=t) tech_data[f'MA_{t}'] = talib.MA(data.close, timeperiod=t) tech_data[f'DX_{t}'] = talib.DX(data.high, data.low, data.close, timeperiod=t) tech_data[f'volume_change_{t}'] = data.volume.pct_change(periods=t) tech_data[f'volatility_{t}'] = data.close.pct_change(periods=t).std() tech_data[f'ADX_{t}'] = talib.ADX(data.high, data.low, data.close, timeperiod=t) tech_data[f'ADXR_{t}'] = talib.ADXR(data.high, data.low, data.close, timeperiod=t) tech_data[f'AROONOSC_{t}'] = talib.AROONOSC(data.high, data.low, timeperiod=t) tech_data[f'ROC_{t}'] = talib.ROC(data.close, timeperiod=t) tech_data[f'BIAS_{t}'] = (data['close'] - data['close'].rolling(t, min_periods=1).mean())/ data['close'].rolling(t, min_periods=1).mean()*100 tech_data[f'BOLL_upper_{t}'], tech_data[f'BOLL_middle_{t}'], tech_data[f'BOLL_lower_{t}'] = talib.BBANDS( data.close, timeperiod=t, nbdevup=2, nbdevdn=2, matype=0) tech_data['SAR'] = talib.SAR(data.high, data.low) tech_data['AD'] = talib.AD(data.high, data.low, data.close, data.volume) tech_data['OBV'] = talib.OBV(data.close, data.volume) tech_data['target'] = data.close.pct_change().shift(-1).apply(lambda x: 1 if x > 0 else -1).fillna(0) tech_data['time'] = data.time tech_data = tech_data.set_index('time') reduce(lambda x, y: cross_over(x, y, tech_data), periods_list) features = list(set(tech_data.columns) - set(data.columns) - set(['target'])) return tech_data.dropna(), features
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 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 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 dx(self, n, array=False): """ DX. """ result = talib.DX(self.high, self.low, self.close, n) if array: return result return result[-1]
def dx(self, n: int, array: bool = False) -> Union[float, np.ndarray]: """ DX. """ result = talib.DX(self.high, self.low, self.close, n) if array: return result return result[-1]
def add_dx_ft(df, daily_count, count): for idx in range(1, count + 1): col_name = 'DX_' + str(idx) df[col_name] = talib.DX(df.high, df.low, df.close, timeperiod=daily_count * idx) return df
def DX(self, timeperiod=14): real_data = np.array([self.df.high, self.df.low, self.df.close], dtype='f8') dx = talib.DX(real_data[0], real_data[1], real_data[2], timeperiod=timeperiod) # return go.Scatter( # x=self.df.index, # y=dx, # name='DX' # ) return dx
def results(self, data_frame): try: directional_index = talib.DX( data_frame['%s_High' % self.symbol].values, data_frame['%s_Low' % self.symbol].values, data_frame['%s_Close' % self.symbol].values, timeperiod=self.period) data_frame[self.value] = directional_index except KeyError: data_frame[self.value] = np.nan
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 dx(self, sym, frequency, *args, **kwargs): if not self.kbars_ready(sym, frequency): return [] highs = self.high(sym, frequency) lows = self.low(sym, frequency) closes = self.close(sym, frequency) v = ta.DX(highs, lows, closes, *args, **kwargs) return v
def ADX(DF,N=14): H = DF['high'] L = DF['low'] C = DF['close'] PDI = ta.PLUS_DI(H.values,L.values,C.values,N) MDI = ta.MINUS_DI(H.values,L.values,C.values,N) DX = ta.DX(H.values,L.values,C.values,N) ADX = ta.ADX(H.values,L.values,C.values,N) VAR = pd.DataFrame({'PDI':PDI,'MDI':MDI,'DX':DX,'ADX':ADX},index=H.index.values) return VAR
def DX(high, low, close, timeperiod=14): ''' Directional Movement Index DMI指标又叫动向指标或趋向指标 分组: Momentum Indicator 动量指标 简介: 通过分析股票价格在涨跌过程中买卖双方力量均衡点的变化情况, 即多空双方的力量的变化受价格波动的影响而发生由均衡到失衡的循环过程, 从而提供对趋势判断依据的一种技术指标。 real = DX(high, low, close, timeperiod=14) ''' return talib.DX(high, low, close, timeperiod)
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 add_DX(self, timeperiod=14, type='line', color='secondary', **kwargs): """Directional Movement Index.""" if not (self.has_high and self.has_low and self.has_close): raise Exception() utils.kwargs_check(kwargs, VALID_TA_KWARGS) if 'kind' in kwargs: type = kwargs['kind'] name = 'DX({})'.format(str(timeperiod)) self.sec[name] = dict(type=type, color=color) self.ind[name] = talib.DX(self.df[self.hi].values, self.df[self.lo].values, self.df[self.cl].values, timeperiod)
def dx(candles: np.ndarray, period: int = 14, sequential: bool = False) -> Union[float, np.ndarray]: """ DX - Directional Movement Index :param candles: np.ndarray :param period: int - default: 14 :param sequential: bool - default: False :return: float | np.ndarray """ candles = slice_candles(candles, sequential) res = talib.DX(candles[:, 3], candles[:, 4], candles[:, 2], timeperiod=period) return res if sequential else res[-1]
def strategy_adx_di(self) -> list: adx = talib.DX(self.high, self.low, self.close) plus_di = talib.PLUS_DI(self.high, self.low, self.close) minus_di = talib.MINUS_DI(self.high, self.low, self.close) signal = [] for i in range(len(adx)): if adx[i] < 25: signal.append(0) else: if plus_di[i] > minus_di[i]: signal.append(100) else: signal.append(-100) return signal
def dx(candles: np.ndarray, period=14, sequential=False) -> Union[float, np.ndarray]: """ DX - Directional Movement Index :param candles: np.ndarray :param period: int - default: 14 :param sequential: bool - default=False :return: float | np.ndarray """ if not sequential and len(candles) > 240: candles = candles[-240:] res = talib.DX(candles[:, 3], candles[:, 4], candles[:, 2], timeperiod=period) return res if sequential else res[-1]
def GetTi(self): C = np.array(self.df['收盤'], dtype=float, ndmin=1) H = np.array(self.df['最高'], dtype=float, ndmin=1) L = np.array(self.df['最低'], dtype=float, ndmin=1) # -------- Average Directional Movement Index Begin . -------- self.df['PLUS_DI'] = talib.PLUS_DI(H, L, C, timeperiod=14) self.df['MINUS_DI'] = talib.MINUS_DI(H, L, C, timeperiod=14) self.df['DX'] = talib.DX(H, L, C, timeperiod=14) self.df['ADX'] = talib.ADX(H, L, C, timeperiod=14) # ------- Average Directional Movement Index End . -------- # -------- Bollinger Bands Begin. -------- # 布林 是 OK,但倒過來 self.df['tmpMA20'] = talib.SMA(C, 20) self.df['tmpMA60'] = talib.SMA(C, 60) self.df['Upperband'], self.df['Middleband'], self.df[ 'Dnperband'] = talib.BBANDS(C, timeperiod=20, nbdevup=2, nbdevdn=2, matype=0) self.df['%BB'] = (C - self.df['Dnperband']) / (self.df['Upperband'] - self.df['Dnperband']) self.df['W20'] = (self.df['Upperband'] - self.df['Dnperband']) / self.df['tmpMA20'] # -------- Bollinger Bands Begin. -------- # ---------------- 乖離 指標 Begin. ------------------------ # 乖離 OK, 但比較是倒過來 # 20 Bias=(C-SMA20)/SMA20 # 60 Bias=(C-SMA60)/SMA60 self.df['20 Bias'] = (C - self.df['tmpMA20']) / self.df['tmpMA20'] self.df['60 Bias'] = (C - self.df['tmpMA60']) / self.df['tmpMA60'] self.df.drop('tmpMA60', axis=1, level=None, inplace=True) self.df.drop('tmpMA20', axis=1, level=None, inplace=True) # ---------------- 乖離 指標 End. ------------------------ self.df = self.df.iloc[::-1]
def dx(candles: np.ndarray, period: int = 14, sequential: bool = False) -> Union[float, np.ndarray]: """ DX - Directional Movement Index :param candles: np.ndarray :param period: int - default: 14 :param sequential: bool - default=False :return: float | np.ndarray """ warmup_candles_num = get_config('env.data.warmup_candles_num', 240) if not sequential and len(candles) > warmup_candles_num: candles = candles[-warmup_candles_num:] res = talib.DX(candles[:, 3], candles[:, 4], candles[:, 2], timeperiod=period) return res if sequential else res[-1]
def columnizer(dft): dft['MA60'] = tb.MA(dft.close.values, timeperiod=60) dft['MA200'] = tb.MA(dft.close.values, timeperiod=200) dft['MA400'] = tb.MA(dft.close.values, timeperiod=400) dft['MA800'] = tb.MA(dft.close.values, timeperiod=800) dft['HTL'] = tb.HT_TRENDLINE(dft.close.values) dft['UBB'], dft['BB'], dft['LBB'] = tb.BBANDS(dft.close.values, timeperiod=60, nbdevup=2, nbdevdn=2) dft['RSI'] = tb.MA(tb.RSI(dft.close.values, timeperiod=800), basema) dft['MOM'] = tb.MA(tb.MOM(dft.close.values, timeperiod=800), basema) dft['DX'] = tb.MA( tb.DX(dft.high, dft.low, dft.close, timeperiod=800), basema) dft['ATR'] = tb.ATR(dft.high, dft.low, dft.close) dft['AD'] = tb.AD(dft.high, dft.low, dft.close, dft.volume) for i in range(5): dft['ichi' + str(i)] = ichimoku(dft.close)[i] dft['HTBB'] = dft.HTL - dft.BB dft['HTBB_v'] = dft.HTBB.diff() dft['BBv'] = dft.BB.diff() dft['dBB'] = dft.UBB - dft.LBB dft['brLBB'] = dft.close - dft.LBB dft['ichspan'] = dft.ichi2 - dft.ichi3 dft['UBBv'] = dft.UBB.diff() dft['LBBv'] = dft.LBB.diff() dft['deltma'] = dft.MA200 - dft.MA60 dft['deltma_v'] = dft.deltma.diff() dft['RSI_v'] = dft.RSI.diff() dft['volume_v'] = dft.volume.diff() dft['close_v'] = dft.close.diff() return dft
def create_Indicators(data): dfs = data df = dfs.copy() df['ADX'] = talib.ADX(df.high, df.low, df.close, timeperiod=14) df['ADXR'] = talib.ADXR(df.high, df.low, df.close, timeperiod=14) df['APO'] = talib.APO(df.close, fastperiod=12, slowperiod=26, matype=0) df['AROONOSC'] = talib.AROONOSC(df.high, df.low, timeperiod=14) df['BOP'] = talib.BOP( df.open, df.high, df.low, df.close) # (Close price – Open price) / (High price – Low price) df["CCI"] = talib.CCI(df.high, df.low, df.close, timeperiod=14) df["CMO"] = talib.CMO(df.close, timeperiod=14) df["DX"] = talib.DX(df.high, df.low, df.close, timeperiod=14) #df["MFI"] = talib.MFI(df.high, df.low, df.close, df.volume, timeperiod=14).pct_change() df["MINUS_DI"] = talib.MINUS_DI(df.high, df.low, df.close, timeperiod=14) df["MINUS_DM"] = talib.MINUS_DM(df.high, df.low, timeperiod=14) df["MOM"] = talib.MOM(df.close, timeperiod=10) df["PLUS_DI"] = talib.PLUS_DI(df.high, df.low, df.close, timeperiod=14) df["PLUS_DM"] = talib.PLUS_DM(df.high, df.low, timeperiod=14) #X_corr["PPO"] = PPO(df.close, fastperiod=12, sdf.lowperiod=26, matype=0) df["ROC"] = talib.ROC(df.close, timeperiod=10) df["ROCP"] = talib.ROCP(df.close, timeperiod=10) df["ROCR"] = talib.ROCR(df.close, timeperiod=10) df["ROCR100"] = talib.ROCR100(df.close, timeperiod=10) df["RSI"] = talib.RSI(df.close, timeperiod=14) #sdf.lowk, sdf.lowd = STOCH(df.high, df.low, df.close, fastk_period=5, sdf.lowk_period=3, sdf.lowk_matype=0, sdf.lowd_period=3, sdf.lowd_matype=0) df["TRIX"] = talib.TRIX(df.close, timeperiod=30) df["ULTOSC"] = talib.ULTOSC(df.high, df.low, df.close, timeperiod1=7, timeperiod2=14, timeperiod3=28) df["WILLR"] = talib.WILLR(df.high, df.low, df.close, timeperiod=14) return df
def DX(DataFrame, N=14): res = talib.DX(DataFrame.high.values, DataFrame.low.values, DataFrame.close.values, N) return pd.DataFrame({'DX': res}, index=DataFrame.index)
def calc_features(df): open = df['op'] high = df['hi'] low = df['lo'] close = df['cl'] volume = df['volume'] orig_columns = df.columns hilo = (df['hi'] + df['lo']) / 2 df['BBANDS_upperband'], df['BBANDS_middleband'], df[ 'BBANDS_lowerband'] = talib.BBANDS(close, timeperiod=5, nbdevup=2, nbdevdn=2, matype=0) df['BBANDS_upperband'] -= hilo df['BBANDS_middleband'] -= hilo df['BBANDS_lowerband'] -= hilo df['DEMA'] = talib.DEMA(close, timeperiod=30) - hilo df['EMA'] = talib.EMA(close, timeperiod=30) - hilo df['HT_TRENDLINE'] = talib.HT_TRENDLINE(close) - hilo df['KAMA'] = talib.KAMA(close, timeperiod=30) - hilo df['MA'] = talib.MA(close, timeperiod=30, matype=0) - hilo df['MIDPOINT'] = talib.MIDPOINT(close, timeperiod=14) - hilo df['SMA'] = talib.SMA(close, timeperiod=30) - hilo df['T3'] = talib.T3(close, timeperiod=5, vfactor=0) - hilo df['TEMA'] = talib.TEMA(close, timeperiod=30) - hilo df['TRIMA'] = talib.TRIMA(close, timeperiod=30) - hilo df['WMA'] = talib.WMA(close, timeperiod=30) - hilo 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['AROON_aroondown'], df['AROON_aroonup'] = talib.AROON(high, low, timeperiod=14) 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['DX'] = talib.DX(high, low, close, timeperiod=14) df['MACD_macd'], df['MACD_macdsignal'], df['MACD_macdhist'] = talib.MACD( close, fastperiod=12, slowperiod=26, signalperiod=9) # skip MACDEXT MACDFIX たぶん同じなので df['MFI'] = talib.MFI(high, low, close, volume, timeperiod=14) df['MINUS_DI'] = talib.MINUS_DI(high, low, close, timeperiod=14) df['MINUS_DM'] = talib.MINUS_DM(high, low, timeperiod=14) df['MOM'] = talib.MOM(close, timeperiod=10) df['PLUS_DI'] = talib.PLUS_DI(high, low, close, timeperiod=14) df['PLUS_DM'] = talib.PLUS_DM(high, low, timeperiod=14) df['RSI'] = talib.RSI(close, timeperiod=14) df['STOCH_slowk'], df['STOCH_slowd'] = talib.STOCH(high, low, close, fastk_period=5, slowk_period=3, slowk_matype=0, slowd_period=3, slowd_matype=0) df['STOCHF_fastk'], df['STOCHF_fastd'] = talib.STOCHF(high, low, close, fastk_period=5, fastd_period=3, fastd_matype=0) df['STOCHRSI_fastk'], df['STOCHRSI_fastd'] = talib.STOCHRSI(close, timeperiod=14, fastk_period=5, fastd_period=3, fastd_matype=0) df['TRIX'] = talib.TRIX(close, timeperiod=30) df['ULTOSC'] = talib.ULTOSC(high, low, close, timeperiod1=7, timeperiod2=14, timeperiod3=28) df['WILLR'] = talib.WILLR(high, low, close, timeperiod=14) df['AD'] = talib.AD(high, low, close, volume) df['ADOSC'] = talib.ADOSC(high, low, close, volume, fastperiod=3, slowperiod=10) df['OBV'] = talib.OBV(close, volume) df['ATR'] = talib.ATR(high, low, close, timeperiod=14) df['NATR'] = talib.NATR(high, low, close, timeperiod=14) df['TRANGE'] = talib.TRANGE(high, low, close) df['HT_DCPERIOD'] = talib.HT_DCPERIOD(close) df['HT_DCPHASE'] = talib.HT_DCPHASE(close) df['HT_PHASOR_inphase'], df['HT_PHASOR_quadrature'] = talib.HT_PHASOR( close) df['HT_SINE_sine'], df['HT_SINE_leadsine'] = talib.HT_SINE(close) df['HT_TRENDMODE'] = talib.HT_TRENDMODE(close) df['BETA'] = talib.BETA(high, low, timeperiod=5) df['CORREL'] = talib.CORREL(high, low, timeperiod=30) df['LINEARREG'] = talib.LINEARREG(close, timeperiod=14) - close df['LINEARREG_ANGLE'] = talib.LINEARREG_ANGLE(close, timeperiod=14) df['LINEARREG_INTERCEPT'] = talib.LINEARREG_INTERCEPT( close, timeperiod=14) - close df['LINEARREG_SLOPE'] = talib.LINEARREG_SLOPE(close, timeperiod=14) df['STDDEV'] = talib.STDDEV(close, timeperiod=5, nbdev=1) return df