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 trix(source, periods):
price = source['close']
for p in periods:
trix_name = 'TRIX_' + str(p)
rsi = talib.TRIX(price, p)
source = source.join(pd.Series(rsi, name=trix_name))
# Trends
for p in periods:
trix_name = 'TRIX_' + str(p)
source = do_uptrend(source, trix_name)
# Over
for i in range(len(periods)):
for j in range(i + 1, len(periods)):
trix_name1 = 'TRIX_' + str(periods[i])
trix_name2 = 'TRIX_' + str(periods[j])
source = do_over(source, trix_name1, trix_name2)
# Above
for p in periods:
trix_name = 'TRIX_' + str(p)
source = do_above(source, trix_name, 0)
# Drop
for p in periods:
trix_name = 'TRIX_' + str(p)
source = source.drop(columns=[trix_name])
return source
def test_trix():
'''test TA.TRIX'''
ma = TA.TRIX(ohlc, 20)
talib_ma = talib.TRIX(ohlc['close'], timeperiod=20)
assert round(talib_ma[-1], 2) == round(ma.values[-1], 2)
def compTRIX(self):
tx = talib.TRIX(self.close,timeperiod=self.lookback)
self.removeNullID(tx)
self.rawFeatures['TRIX'] = tx
FEATURE_SIZE_DICT['TRIX'] = 1
return
def trix(close_ts, timeperiod=30):
import talib
close_np = close_ts.cpu().detach().numpy()
close_df = pd.DataFrame(close_np)
trix = close_df.apply(lambda x: talib.TRIX(x, timeperiod=30))
trix_ts = torch.tensor(trix.values, dtype=close_ts.dtype, device=close_ts.device)
return trix_ts
def trix(close, n):
try:
trix_data = talib.TRIX(close, timeperiod=n)
return trix_data
except Exception as e:
raise (e)
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 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 trix(self, n, array=False):
"""
TRIX.
"""
result = talib.TRIX(self.close, n)
if array:
return result
return result[-1]
def trix(source, p):
price = source['Close']
trix_name = 'TRIX_' + str(p)
rsi = talib.TRIX(price, p)
source = source.join(pd.Series(rsi, name=trix_name))
return source
def trix(self, n: int, array: bool = False) -> Union[float, np.ndarray]:
"""
TRIX.
"""
result = talib.TRIX(self.close, n)
if array:
return result
return result[-1]
def TRIX(self, timeperiod=30):
real_data = np.array([self.df.close], dtype='f8')
trix = talib.TRIX(real_data[0], timeperiod=timeperiod)
# return go.Scatter(
# x=self.df.index,
# y=trix,
# name='TRIX'
# )
return trix
def TRIX(close, timeperiod=30):
''' 1-day Rate-Of-Change (ROC) of a Triple Smooth EMA
分组: Momentum Indicator 动量指标
简介:
real = TRIX(close, timeperiod=30)
'''
return talib.TRIX(close, timeperiod)
def TRIX(self, stockhist):
trix = stockhist.copy()
trix['trix'] = tal.TRIX(trix['close'], timeperiod=15) # 计算TRIX
trix['matrix'] = tal.MA(trix['trix'], 45, 0)
trix['signal'] = 0
trix.loc[trix['trix'] > trix['matrix'], 'signal'] = 1
trix.loc[trix['trix'] <= trix['matrix'], 'signal'] = 0
trix['signal'] = trix['signal'].fillna(method='ffill')
trix = trix[['date', 'dailyreturn', 'signal']].copy()
return (trix)
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 add_tech_ind(self, price_map):
# Visit for technical indicator documentation: http://mrjbq7.github.io/ta-lib/funcs.html
new_price_map = {}
for symbol in price_map:
symbol_data = price_map[symbol]
symbol_data['ts_ema_30'] = ta.TRIX(symbol_data['open'], timeperiod=30)
new_price_map[symbol] = symbol_data
return new_price_map
def TRIX(self, length):
records = self.platform.get_kline(self.time_frame)
records.reverse()
kline_length = len(records)
close_array = np.zeros(kline_length)
t = 0
for item in records:
close_array[t] = item[4]
t += 1
result = (talib.TRIX(close_array, timeperiod=length))
return result
def TRIX(self, window=14):
temp = self.origin
# .sort_index(ascending=False)
simple_trix = ta.TRIX(temp.close.values, timeperiod=window)
# Reverse before placing in
trix_name = "TRIX_{}".format(window)
aroon_down_name = "TRIX_{}".format(window)
self.info['main'].loc[:, trix_name] = list(simple_trix)
# self.info['main'].loc[:, aroon_down_name] = list(aroondown)
return self
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_TRIX(self, timeperiod=15, type='area', color='secondary', **kwargs):
"""1-day Rate of Change of Triple Smooth EMA."""
if not self.has_close:
raise Exception()
utils.kwargs_check(kwargs, VALID_TA_KWARGS)
if 'kind' in kwargs:
type = kwargs['kind']
name = 'TRIX({})'.format(str(timeperiod))
self.sec[name] = dict(type=type, color=color)
self.ind[name] = talib.TRIX(self.df[self.cl].values, timeperiod)
def test_trix(self):
name = "trix"
print_test_title(name)
period = 30
kls =self.klines
ti_key = ti.TRIX(kls, self.closekey, period)
tas = talib.TRIX(self.klines_df[self.closekey], timeperiod=period)
print(" ti %ss: %s" % (name, [round(kl[ti_key], self.digits) for kl in kls[-self.display_count:]]))
print("TA-Lib %ss: %s" % (name, [round(a, self.digits) for a in tas][-self.display_count:]))
for i in range(-self.display_count, 0):
self.assertTrue(abs(kls[i][ti_key] - tas.values[i]) < 0.01)
def OnBar(self, bar):
if self.bar_count >= 201:
close = np.array(self.array.close)
signal = ta.TRIX(close, timeperiod=30)
if signal[-1] > 0 and self.pos == 0:
self.buy(99999,5000)
elif signal[-1] < 0 and self.pos >0:
self.sell(1,self.pos)
elif signal[-1] > 0 and self.pos<0:
self.buy_to_cover(99999,-self.pos)
elif signal[-1] < 0 and self.pos == 0:
self.sell_short(1,5000)
self.strategy_output(self.pos)
def TRIX(security_list, timeperiod=30):
# 修复传入为单只股票的情况
if isinstance(security_list, str):
security_list = [security_list]
# 计算 TRIX
security_data = history(timeperiod * 2,
'1d',
'close',
security_list,
df=False,
skip_paused=True)
trix = {}
for stock in security_list:
trix[stock] = talib.TRIX(security_data[stock], timeperiod)
return trix
def add_ta_features(self):
obv = talib.OBV(self.close, self.volume)
obv_mv_avg = talib.MA(obv, timeperiod=10)
obv_mv_avg[np.isnan(obv_mv_avg)] = obv[np.isnan(obv_mv_avg)]
difference = obv - obv_mv_avg
self.df['obv'] = obv
self.df['obv_signal'] = difference
self.df['obv_cheat'] = np.gradient(difference)
upper, middle, lower = talib.BBANDS(self.close, timeperiod=20, nbdevup=2, nbdevdn=2, matype=0)
self.df['dn'] = lower
self.df['mavg'] = middle
self.df['up'] = upper
self.df['pctB'] = (self.close - self.df.dn) / (self.df.up - self.df.dn)
rsi14 = talib.RSI(self.close, 14)
self.df['rsi14'] = rsi14
macd, macdsignal, macdhist = talib.MACD(self.close, 12, 26, 9)
self.df['macd'] = macd
self.df['signal'] = macdsignal
## addtional info
self.df['adx'] = talib.ADX(self.high, self.low, self.close, timeperiod=14)
self.df['cci'] = talib.CCI(self.high, self.low, self.close, timeperiod=14)
## maximum profit
self.df['plus_di'] = talib.PLUS_DI(self.high, self.low, self.close, timeperiod=14)
## lower_bound
self.df['lower_bound'] = self.df['open'] - self.df['low'] + 1
## ATR
self.df['atr'] = talib.ATR(self.high, self.low, self.close, timeperiod=14)
## STOCH momentum
self.df = ta.stochastic_oscillator_k(self.df)
self.df = ta.stochastic_oscillator_d(self.df, n=10)
## TRIX
self.df['trix'] = talib.TRIX(self.close, timeperiod=5)
self.df['trix_signal'] = ta.moving_average(self.df['trix'], n=3)
self.df['trix_hist'] = self.df['trix'] - self.df['trix_signal']
## MFI
self.df['mfi14'] = money_flow_index(self.df, 14)
def TRIX(length, kline):
"""
三重指数平滑平均线
:param length:长度参数
:param kline:传入指定k线数据
:return:返回一个一维数组
"""
records = kline
kline_length = len(records)
close_array = np.zeros(kline_length)
t = 0
for item in records:
close_array[t] = item[4]
t += 1
result = (talib.TRIX(close_array, timeperiod=length))
return result
def signal_TRIX(close):
trix = tl.TRIX(close,timeperiod=12)
trima = tl.MA(trix,timeperiod=20)
# 画出上一时间DIFF和DEA曲线
#record(TRIX=trix[-1], TRIMA=trima[-1])
record(TRIX_DELTA=trix[-1]-trima[-1])
# 经过3天确认的金叉
if trix[-1] > trima[-1] and trix[-2] > trima[-2] \
and trix[-3] > trima[-3] and trix[-4] < trima[-4]:
return 1
# 经过3天确认的死叉
if trix[-1] < trima[-1] and trix[-2] < trima[-2] \
and trix[-3] < trima[-3] and trix[-4] > trima[-4]:
return -1
# 中间状态
return 0
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 trix(client, symbol, timeframe="6m", col="close", period=14):
"""This will return a dataframe of
1-day Rate-Of-Change(ROC) of a Triple Smooth EMA
for the given symbol across the given timeframe
Args:
client (pyEX.Client): Client
symbol (string): Ticker
timeframe (string): timeframe to use, for pyEX.chart
col (string): column to use to calculate
period (int): period to calculate across
Returns:
DataFrame: result
"""
df = client.chartDF(symbol, timeframe)
return pd.DataFrame({col: df[col].values, "trix": t.TRIX(df[col].values, period)})
def trix(candles: np.ndarray,
period=18,
sequential=False) -> Union[float, np.ndarray]:
"""
TRIX - 1-day Rate-Of-Change (ROC) of a Triple Smooth EMA
:param candles: np.ndarray
:param period: int - default: 18
:param sequential: bool - default=False
:return: float | np.ndarray
"""
if not sequential and len(candles) > 240:
candles = candles[-240:]
r = talib.TRIX(candles[:, 2], timeperiod=period) * 100
return r if sequential else r[-1]
def TRIX(self, length, kline=None):
"""
三重指数平滑平均线
:param length:长度参数
:param kline:回测时传入指定k线数据
:return:返回一个一维数组
"""
if kline:
records = kline
else:
records = self.__platform.get_kline(self.__time_frame)
kline_length = len(records)
close_array = np.zeros(kline_length)
t = 0
for item in records:
close_array[t] = item[4]
t += 1
result = (talib.TRIX(close_array, timeperiod=length))
return result
