def obv(price, volume):
'''OBV'''
price = utils.safe_series(price)
volume = utils.safe_series(volume)
obv = (roc(price) > 0).astype(int) * volume - (roc(price) <= 0).astype(int) * volume
obv[0:1] = volume[0:1]
obv = obv.cumsum()
rval = obv
utils.safe_name(rval, name='OBV')
rval.index = price.index
return rval
def pd_ema(arg, ratio=0.1):
''' EMA, implemented with `pandas.stats.moments.ewma` '''
span = 2.0 / (1-ratio) - 1
arg = utils.safe_series(arg)
rval = ewma(arg, span=span)
utils.safe_name(rval, name='pdEMA')
return rval
def wilder_sum(arg, window=14):
''' An internal function of R TTR package '''
arg = utils.safe_series(arg)
rval = fast.wilder_sum(arg, window)
rval.name = arg.name
utils.safe_name(rval, name='wilderSum')
rval.index = arg.index
return rval
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 tdi(price, window=20, multiple=2):
''' Trend Detection Index '''
price = utils.safe_series(price)
mom = price - price.shift(window)
mom[np.isnan(mom)] = 0
di = pd.rolling_sum(mom, window)
di_abs = di.abs()
mom_2n_abs = pd.rolling_sum(mom.abs(), window*multiple)
mom_1n_abs = pd.rolling_sum(mom.abs(), window)
tdi_ = di_abs - (mom_2n_abs - mom_1n_abs)
return pd.DataFrame(dict(tdi=tdi_, di=di), index=price.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 roc(arg, window=1, type_='continuous'):
arg = utils.safe_series(arg)
arg = arg.astype(float)
if type_ == 'continuous':
rval = np.log(arg) - np.log(arg.shift(window))
elif type_ == 'discrete':
rval = arg / arg.shift(window) - 1
else:
raise NotImplementedError()
rval.name = arg.name
utils.safe_name(rval, name='ROC')
rval.index = arg.index
return rval
def dpo(arg, window, ma_type='sma', shift=None, percent=False):
if shift is None:
shift = window / 2 + 1
arg = utils.safe_series(arg)
ma_fn = eval('ma.%s'%ma_type)
arg_mean = ma_fn(arg, window=window)
arg_mean = arg_mean.shift(-shift)
if percent:
rval = 100 * (arg/arg_mean - 1)
else:
rval = arg - arg_mean
rval.name = arg.name
utils.safe_name(rval, name='DPO')
rval.index = arg.index
return rval
def rsi(arg, window=14, ma_type='ema'):
''' Relative strength index '''
arg = utils.safe_series(arg)
ma_fn = eval('ma.%s'%ma_type)
last = arg.shift(1).fillna(0)
diff = arg - last
up = diff * (diff > 0)
down = diff * (diff < 0) * -1
rs = ma_fn(up, window=window) / ma_fn(down, window=window)
rval = 100 - 100 / (1+rs)
rval.name = arg.name
utils.safe_name(rval, name='RSI')
rval.index = arg.index
return rval
def sma(arg, window=10):
''' Simple Mean Average '''
arg = utils.safe_series(arg)
rval = pd.rolling_mean(arg, window)
utils.safe_name(rval, name='SMA')
return rval