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
