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 atr(hlc, window=14):
''' ATR '''
high, low, close = utils.safe_hlc(hlc)
close_tm1 = close.shift(1)
true_high = (high>=close_tm1)*high + (high<close_tm1)*close_tm1
true_low = (low<=close_tm1)*low + (low>close_tm1)*close_tm1
true_range = true_high - true_low
atr_ = ma.ema(true_range, window=window)
return pd.DataFrame(dict(tr=true_range, atr=atr_,
trueHigh=true_high,
trueLow=true_low),
index=hlc.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 mfi(hlc, volume, window=14):
'''MFI'''
high, low, close = utils.safe_hlc(hlc)
volume = utils.safe_series(volume) / 1000
price = (high+low+close) * 1.0 / 3
mf = price * volume
pmf = (mf > mf.shift(1)).astype(int) * mf
nmf = (mf < mf.shift(1)).astype(int) * mf
mr = pd.rolling_sum(pmf, window) / pd.rolling_sum(nmf, window)
rval = 100 - (100/(1 + mr))
utils.safe_name(rval, name='MFI')
rval.index = hlc.index
return rval
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 adx(hlc, window=14, atr_=None):
''' ADX '''
if atr_ is None:
atr_ = atr(hlc, window)
high, low, _= utils.safe_hlc(hlc)
up_move = high-high.shift(1)
down_move = low.shift(1)-low
pos_dm = ((up_move>down_move)*(up_move>0)*(up_move)).astype(np.float)
neg_dm = ((down_move>up_move)*(down_move>0)*(down_move)).astype(np.float)
tr_sum = fast.wilder_sum(atr_.tr, window)
DIp = 100 * fast.wilder_sum(pos_dm, window) / tr_sum
DIn = 100 * fast.wilder_sum(neg_dm, window) / tr_sum
dx = 100 * np.abs((DIp-DIn)/(DIp+DIn))
adx = ma.ema(dx, window=window)
return pd.DataFrame(dict(DIp=DIp, DIn=DIn, DX=dx, ADX=adx), index=hlc.index)
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)
