def stoch(hlc, n_fastK=14, n_fastD=3, n_slowD=3, ma_type='sma', bounded=True, smooth=1):
''' Stochastic Oscillator '''
high, low, close = utils.safe_hlc(hlc)
if bounded:
hmax = pd.rolling_max(high, n_fastK)
lmin = pd.rolling_min(low, n_fastK)
else:
raise NotImplementedError()
num = close - lmin
den = hmax - lmin
mafunc = ma.get_ma(ma_type)
num_ma = mafunc(num, smooth)
den_ma = mafunc(den, smooth)
fastK = num_ma / den_ma
fastK[np.isnan(fastK)] = 0.5
fastD = mafunc(fastK, n_fastD)
slowD = mafunc(fastD, n_slowD)
return pd.DataFrame(dict(fastK=fastK,
fastD=fastD,
slowD=slowD),
index=hlc.index)
def emv(hl, volume, window=9, ma_type='sma', vol_divisor=1000):
'''EMV'''
high, low = utils.safe_hl(hl)
volume = utils.safe_series(volume)
mid = .5 * (high + low)
volume /= vol_divisor
rval = (mid - mid.shift(1)) / (volume / (high - low))
rval_ma = ma.get_ma(ma_type)(rval, window)
return pd.DataFrame(dict(emv=rval, maEMV=rval_ma), index=hl.index)
def trix(price, window=20, n_sig=9, ma_type='ema', percent=True):
''' Triple Smoothed Exponential Oscillator '''
warnings.warn('The parameter of n_sig is not used in TTR. So currently it is not used as well here for unittest purpose.')
price = utils.safe_series(price)
mafunc = ma.get_ma(ma_type)
mavg0 = mafunc(price, window)
mavg1 = mafunc(mavg0, window)
mavg2 = mafunc(mavg1, window)
if percent:
trix_ = 100 * roc(mavg2, window=1, type_='discrete')
else:
trix_ = mavg2 - mavg2.shift(1)
signal = mafunc(trix_, window)
return pd.DataFrame(dict(TRIX=trix_, signal=signal), index=price.index)
def bbands(hlc, window=20, ma_type='sma', sd=2, **kwargs):
''' Bolling Bands '''
high, low, close = utils.safe_hlc(hlc)
price = (high + low + close) / 3
ma_fn = ma.get_ma(ma_type)
mean = ma_fn(price, window, **kwargs)
sdev = utils.biased_rolling_std(price, window=window)
up = mean + sd * sdev
down = mean - sd * sdev
pctB = (price - down) / (up - down)
return pd.DataFrame(dict(dn=down, mavg=mean, up=up, pctB=pctB),
index=hlc.index)
def cci(hlc, window=20, ma_type='sma', c=0.015, **kwargs):
''' Commodity Channel Index (CCI)
:returns: pandas.Series
'''
high, low, close = utils.safe_hlc(hlc)
ma_fn = ma.get_ma(ma_type)
# true price
tp = (high + low + close) / 3
tp_mean = ma_fn(tp, window)
tp_md = utils.rolling_mean_dev(tp, window)
cci_ = (tp - tp_mean) / c / tp_md
cci_.name = 'CCI'
return cci_
def smi(hlc, window=13, n_fast=2, n_slow=25, n_sig=9, ma_type='sma', bounded=True):
''' Stochastic Momentum Index '''
high, low, close = utils.safe_hlc(hlc)
if bounded:
hmax = pd.rolling_max(high, window)
lmin = pd.rolling_min(low, window)
else:
raise NotImplementedError()
hl_diff = hmax - lmin
c_diff = close - (hmax+lmin) / 2
mafunc = ma.get_ma(ma_type)
num0 = mafunc(c_diff, n_slow)
den0 = mafunc(hl_diff, n_slow)
num1 = mafunc(num0, n_fast)
den1 = mafunc(den0, n_fast)
smi_ = 100 * (num1 / den1 * 2)
signal = mafunc(smi_, n_sig)
return pd.DataFrame(dict(SMI=smi_, signal=signal), index=hlc.index)
