def test_STOCHSTOCHRSI(self):
class MySTOCHRSI(OperatorSTOCHRSI):
def __init__(self, name, **kwargs):
super(MySTOCHRSI, self).__init__(100, name, **kwargs)
self.env.add_operator('stochrsi', {
'operator': MySTOCHRSI,
})
string = 'stochrsi(0, 14, 5, 3, open)'
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]
o = self.env.get_data_value('open').values
res0, res1, res = [], [], []
for i, val in ser0.iteritems():
res0.append(
talib.STOCHRSI(
o[:100, i], timeperiod=14, fastk_period=5, fastd_period=3)
[0][-1] == val)
for i, val in ser1.iteritems():
res1.append(
talib.STOCHRSI(o[1:100 + 1, i],
timeperiod=14,
fastk_period=5,
fastd_period=3)[0][-1] == val)
res.append(
talib.STOCHRSI(o[:100 + 1, i],
timeperiod=14,
fastk_period=5,
fastd_period=3)[0][-1] != val)
self.assertTrue(all(res0) and all(res1) and any(res))
def stochrsi(close_ts, timeperiod=14, fastk_period=5, fastd_period=3, fastd_matype=0):
import talib
close_np = close_ts.cpu().detach().numpy()
close_df = pd.DataFrame(close_np)
fastk = close_df.apply(lambda x: talib.STOCHRSI(x, timeperiod=14, fastk_period=5, fastd_period=3, fastd_matype=0)[0])
fastd = close_df.apply(lambda x: talib.STOCHRSI(x, timeperiod=14, fastk_period=5, fastd_period=3, fastd_matype=0)[1])
fastk_ts = torch.tensor(fastk.values, dtype=close_ts.dtype, device=close_ts.device)
fastd_ts = torch.tensor(fastd.values, dtype=close_ts.dtype, device=close_ts.device)
return fastk_ts, fastd_ts
def stoch_lb_test(self):
fastk, fastd = talib.STOCHRSI(self.close, timeperiod=ind_duration)
li = []
for i in range(ind_duration, len(fastd)):
if (fastd[i] < lowerbound):
li.append(i)
return li
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 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 preprocess_data(self):
"""calculate returns and percentiles, then removes missing values"""
self.data['returns'] = self.data.close.pct_change()
self.data['ret_2'] = self.data.close.pct_change(2)
self.data['ret_5'] = self.data.close.pct_change(5)
self.data['ret_10'] = self.data.close.pct_change(10)
self.data['ret_21'] = self.data.close.pct_change(21)
self.data['rsi'] = talib.STOCHRSI(self.data.close)[1]
self.data['macd'] = talib.MACD(self.data.close)[1]
self.data['atr'] = talib.ATR(self.data.high, self.data.low,
self.data.close)
slowk, slowd = talib.STOCH(self.data.high, self.data.low,
self.data.close)
self.data['stoch'] = slowd - slowk
self.data['atr'] = talib.ATR(self.data.high, self.data.low,
self.data.close)
self.data['ultosc'] = talib.ULTOSC(self.data.high, self.data.low,
self.data.close)
self.data = (self.data.replace(
(np.inf, -np.inf), np.nan).drop(['high', 'low', 'close', 'volume'],
axis=1).dropna())
r = self.data.returns.copy()
if self.normalize:
self.data = pd.DataFrame(scale(self.data),
columns=self.data.columns,
index=self.data.index)
features = self.data.columns.drop('returns')
self.data['returns'] = r # don't scale returns
self.data = self.data.loc[:, ['returns'] + list(features)]
log.info(self.data.info())
def calculate_stochrsi(self):
self._fastk_list, self._fastd_list = talib.STOCHRSI(np.array(
self._close_list),
timeperiod=14,
fastk_period=3,
fastd_period=3,
fastd_matype=0)
def STOCHRSI(self, timeperiod, fastk_period, fastd_period, kline=None):
"""
计算STOCHRSI
:param timeperiod: 参数1
:param fastk_period:参数2
:param fastd_period:参数3
:param kline:回测时传入指定k线数据
:return: 返回一个字典 {'STOCHRSI': STOCHRSI数组, 'fastk': fastk数组}
"""
if kline:
records = kline
else:
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.STOCHRSI(close_array,
timeperiod=timeperiod,
fastk_period=fastk_period,
fastd_period=fastd_period,
fastd_matype=0))
STOCHRSI = result[1]
fastk = talib.MA(STOCHRSI, 3)
dict = {'stochrsi': STOCHRSI, 'fastk': fastk}
return dict
def technical_index(context):
#EMA,RSI指标
context.var.ts['EMA_5min'] = talib.EMA(np.array(context.var.ts.close),
timeperiod=5)
context.var.ts['EMA_10min'] = talib.EMA(np.array(context.var.ts.close),
timeperiod=10)
context.var.ts['EMA_15min'] = talib.EMA(np.array(context.var.ts.close),
timeperiod=15)
context.var.ts['EMA_20min'] = talib.EMA(np.array(context.var.ts.close),
timeperiod=20)
context.var.ts['RSI'] = talib.RSI(np.array(context.var.ts.close))
STOCHRSI_usual = talib.STOCHRSI(np.array(context.var.ts.close))
# BOLL-BAND指标
BBANDS_usual = talib.BBANDS(np.array(context.var.ts.close))
upperband, middleband, lowerband = BBANDS_usual
context.var.ts['upperband'] = upperband
context.var.ts['middleband'] = middleband
context.var.ts['lowerband'] = lowerband
arrClose = np.array(context.var.ts.close)
arrHigh = np.array(context.var.ts.high)
arrLow = np.array(context.var.ts.low)
arrVolume = np.array(context.var.ts.volume, dtype=np.float)
# MACD指标
MACD_usual = talib.MACD(arrClose)
macd, macdsignal, macdhist = MACD_usual
context.var.ts['macd'] = macd
context.var.ts['macdsignal'] = macdsignal
context.var.ts['macdhist'] = macdhist
# KDJ指标
KDJ_usual = talib.STOCH(arrHigh, arrLow, arrClose)
slowk, slowd = KDJ_usual
context.var.ts['slowk'] = slowk
context.var.ts['slowd'] = slowd
# ATR指标
ATR_usual = talib.ATR(arrHigh, arrLow, arrClose)
context.var.ts['ATR'] = ATR_usual
# WILLR指标
WILLR_usual = talib.WILLR(arrHigh, arrLow, arrClose)
context.var.ts['WILLR'] = WILLR_usual
# BOV指标
OBV_usual = talib.OBV(arrClose, arrVolume)
context.var.ts['OBV'] = OBV_usual
# SAR指标
SAR_usual = talib.SAR(arrHigh, arrLow)
context.var.ts['SAR'] = SAR_usual
# DEMA指标
DEMA_usual = talib.DEMA(arrClose)
context.var.ts['DEMA'] = DEMA_usual
#MOM指标
MOM_usual = talib.MOM(arrClose)
context.var.ts['MOM'] = MOM_usual
def StochRSI(data):
fastk, fastd = ta.STOCHRSI(data,
timeperiod=14,
fastk_period=5,
fastd_period=3,
fastd_matype=0)
return fastk, fastd
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 stochrsi(close, graph=False, **kwargs):
'''
STOCHRSI - Stochastic Relative Strength Index
'''
result = talib.STOCHRSI(close, **kwargs)
df = pd.concat([pd.DataFrame(close), pd.DataFrame(result).T], axis=1)
df.columns = ['close', 'fastk', 'fastd']
if graph:
title = 'STOCHRSI - Stochastic Relative Strength Index'
fname = '20_stochrsi.png'
fig, axes = plt.subplots(nrows=2,
sharex=True,
gridspec_kw={'height_ratios': [2, 1]})
fig.suptitle(title)
axes[0].plot(df['close'], label='close')
axes[1].plot(df['fastk'], label='fastk')
axes[1].plot(df['fastd'], label='fastd')
for ax in axes:
ax.legend(loc='upper left')
plt.savefig(fname)
if SHOW_GRAPH:
plt.show()
plt.close()
return df
def STOCHRSI(self, timeperiod, fastk_period, fastd_period):
"""
计算STOCHRSI
:param self.platform: 交易所
:param self.instrument_id: 合约ID
:param self.time_frame: k线周期
:return: 返回一个字典 {'STOCHRSI': STOCHRSI数组, 'fastk': fastk数组}
"""
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.STOCHRSI(close_array,
timeperiod=timeperiod,
fastk_period=fastk_period,
fastd_period=fastd_period,
fastd_matype=0))
STOCHRSI = result[1]
fastk = talib.MA(STOCHRSI, 3)
dict = {'stochrsi': STOCHRSI, 'fastk': fastk}
return dict
def add_features(df):
# add MACD, CCI, ATR, BOLL, EMA20, MA5, MA10, MOM6, MOM12, ROC, RSI, WR, KDJ
open_ = df['open'].values
high = df['high'].values
low = df['low'].values
close = df['close'].values
macd, macdsignal, macdhist = tb.MACD(close,
fastperiod=12,
slowperiod=26,
signalperiod=9)
cci = tb.CCI(high, low, close, timeperiod=14)
atr = tb.ATR(high, low, close, timeperiod=14)
boll_up, boll_mid, boll_low = tb.BBANDS(close,
timeperiod=5,
nbdevup=2,
nbdevdn=2,
matype=0)
ema20 = tb.EMA(close, timeperiod=20)
ma5 = tb.SMA(close, timeperiod=5)
ma10 = tb.SMA(close, timeperiod=10)
mom6 = tb.MOM(close, timeperiod=6)
mom12 = tb.MOM(close, timeperiod=12)
roc = tb.ROC(close, timeperiod=14)
rsi_k, rsi_d = tb.STOCHRSI(close,
timeperiod=14,
fastk_period=5,
fastd_period=3,
fastd_matype=0)
wr = tb.WILLR(high, low, close, timeperiod=14)
kdj_j = 3 * rsi_k - 2 * rsi_d
feat_list = [
macd, macdsignal, macdhist, cci, atr, boll_up, boll_mid, boll_low,
ema20, ma5, ma10, mom6, mom12, roc, rsi_k, rsi_d, wr, kdj_j
]
handle_nan(feat_list)
df.insert(len(df.columns), 'macd', macd)
df.insert(len(df.columns), 'macdsignal', macdsignal)
df.insert(len(df.columns), 'macdhist', macdhist)
df.insert(len(df.columns), 'cci', cci)
df.insert(len(df.columns), 'atr', atr)
df.insert(len(df.columns), 'boll_up', boll_up)
df.insert(len(df.columns), 'boll_mid', boll_mid)
df.insert(len(df.columns), 'boll_low', boll_low)
df.insert(len(df.columns), 'ema20', ema20)
df.insert(len(df.columns), 'ma5', ma5)
df.insert(len(df.columns), 'ma10', ma10)
df.insert(len(df.columns), 'mom6', mom6)
df.insert(len(df.columns), 'mom12', mom12)
df.insert(len(df.columns), 'roc', roc)
df.insert(len(df.columns), 'rsi_k', rsi_k)
df.insert(len(df.columns), 'rsi_d', rsi_d)
df.insert(len(df.columns), 'wr', wr)
df.insert(len(df.columns), 'kdj_j', kdj_j)
return df
def TALIB_STOCHRSI(close,
timeperiod=14,
fastk_period=5,
fastd_period=3,
fastd_matype=talib.MA_Type.SMA):
'''00384,5,2'''
return talib.STOCHRSI(close, timeperiod, fastk_period, fastd_period,
fastd_matype)
def calculate_stochrsi(self, period_name, closing_prices):
fastk, fastd = talib.STOCHRSI(closing_prices,
timeperiod=14,
fastk_period=3,
fastd_period=3,
fastd_matype=0)
self.current_indicators[period_name]['stochrsi_fastk'] = fastk[-1]
self.current_indicators[period_name]['stochrsi_fastd'] = fastd[-1]
def handle_data(self, kl):
self.kl = kl #.copy()
self.data['t'] = kl['t']
self.data['fastk'], self.data['fastd'] = ta.STOCHRSI(
kl['c'], **self.params)
self.data['up'] = pd.Series([80] * kl['c'].size)
self.data['low'] = pd.Series([20] * kl['c'].size)
self.data['mid'] = pd.Series([50] * kl['c'].size)
def STOCHRSI(df, n=14, fastk_period=5, fastd_period=3):
fastk, fastd = talib.STOCHRSI(df['close'].valkues,
timeperiod=n,
fastk_period=fastk_period,
fastd_period=fastd_period)
fk = pd.Series(fastk, index=df.index, name="STOCRSI_FK_%s" % (str(n)))
fd = pd.Series(fastd, index=df.index, name="STOCRSI_FD_%s" % (str(n)))
return pd.concat([fk, fd], join='outer', axis=1)
def get_stochrsi(ohlc):
stochrsi_fastk, stochrsi_fastd = ta.STOCHRSI(ohlc['4_close'], timeperiod=14, fastk_period=5, fastd_period=3,
fastd_matype=0)
ohlc = ohlc.assign(
stochrsi_fastk=stochrsi_fastk
, stochrsi_fastd=stochrsi_fastd
)
return ohlc
def STOCHRSI(close,
timeperiod=14,
fastk_period=5,
fastd_period=3,
fastd_matype=0):
# STOCHASTIC RSI
fastk, fastd = ta.STOCHRSI(close, timeperiod, fastk_period, fastd_period,
fastd_matype)
return fastk, fastd
def STOCHRSI(Series,
timeperiod=14,
fastk_period=5,
fastd_period=3,
fastd_matype=0):
fastk, fastd = talib.STOCHRSI(Series.values, fastk_period, fastd_period,
fastd_matype)
return pd.Series(fastk, index=Series.index), pd.Series(fastd,
index=Series.index)
def AddStochRSI(df, indicator_list, askbidmid, timeperiod=14):
df['rsi_fastk'], df['rsi_fastd'] = talib.STOCHRSI(
df['{}_c'.format(askbidmid)].values,
timeperiod=14,
fastk_period=3,
fastd_period=3,
fastd_matype=1)
indicator_list.extend(['rsi_fastk', 'rsi_fastd'])
return df, indicator_list
def stochastic_rsi(close, n, fast_period1=5, fast_period2=3, fastd_matype=0):
try:
fastk_data, fastd_data = talib.STOCHRSI(close, n, fast_period1,
fast_period2, fastd_matype)
df = pd.DataFrame()
df['fast_period1'] = fastk_data
df['fast_period2'] = fastd_data
return df
except Exception as e:
raise (e)
def trade(self):
rsi,k,d = talib.STOCHRSI(self.bar.close, self.rsi_time, self.rsi_fastk, self.rsi_fastd, self.rsi_fastd_matype);
if self.trend():
if crossover(k,d) and k <50:
price=self.bar.close[-1]
self.broker.buy(self.symbol,self.qty,price)
else:
if crossunder(k, d) and k > 50:
price = self.bar.close[-1]
self.broker.sell(self.symbol,self.qty,price)
def add_indicators(df):
df['rsi'] = talib.RSI(np.array(df['close']))
df['adx'] = talib.ADX(np.array(df['high']), np.array(df['low']),
np.array(df['close']))
df['stochrsi_k'], df['stochrsi_d'] = talib.STOCHRSI(
np.array(df['close']))
df['macd'], df['macd_sig'], _ = talib.MACD(np.array(df['close']))
df['boll_up'], df['boll_mid'], df['boll_low'] = talib.BBANDS(
df['close'])
df['datetime'] = df['datetime'].apply(datetime.datetime.fromtimestamp)
return df
def STOCHRSI(Close,
timeperiod=14,
fastk_period=5,
fastd_period=3,
fastd_matype=0):
fastk, fastd = pd.DataFrame(), pd.DataFrame()
for i in Close.columns:
fastk[i], fastd[i] = ta.STOCHRSI(Close[i], timeperiod,
fastk_period, fastd_period,
fastd_matype)
return fastk, fastd
def STOCHRSI(self, timeperiod=14, fastk_period=5, fastd_period=3, fastd_matype=0):
real_data = np.array([self.df.close], dtype='f8')
# fastk, fastd = talib.STOCHRSI(real_data[0], timeperiod=timeperiod, fastk_period=fastk_period,
# fastd_period=fastd_period, fastd_matype=fastd_matype)
# return go.Scatter(
# x=self.df.index,
# y=fastk,
# name='STOCHRSI'
# )
return talib.STOCHRSI(real_data[0], timeperiod=timeperiod, fastk_period=fastk_period,
fastd_period=fastd_period, fastd_matype=fastd_matype)
def STOCHRSI(raw_df,
timeperiod=14,
fastk_period=5,
fastd_period=3,
fastd_matype=0):
# extract necessary data from raw dataframe (close)
# returns 2 columns
fastk, fastd = ta.STOCHRSI(raw_df.Close.values, timeperiod, fastk_period,
fastd_period, fastd_matype)
singleMerged = np.stack((fastk, fastd), axis=-1)
return singleMerged.tolist()
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 measure_stochrsi(values_n: int, period: int, k_period: int, d_period: int):
close = numpy.random.random(values_n)
start = time.time_ns()
output = StochRSI(period, period, k_period, d_period, close)
output[-1]
t1 = (time.time_ns() - start) / 1000000
start = time.time_ns()
output = talib.STOCHRSI(close, timeperiod = period, fastk_period=k_period, fastd_period=d_period)
output[-1]
t2 = (time.time_ns() - start) / 1000000
t3 = 0.0
output = None
for i in range(period, values_n):
close = numpy.random.random(i)
start = time.time_ns()
output = talib.STOCHRSI(close, timeperiod = period, fastk_period=k_period, fastd_period=d_period)
t3 += (time.time_ns() - start) / 1000000
print(f"StochRSI({period},{k_period},{d_period});{values_n:,};{round(t1, 3)};{round(t2, 3)};{round(t3, 3)}")
