def rsi(close, n=14):
"""
Cutler's relative strength index
"""
diff = close[1:] - close[:-1]
# up trend close
uclose = diff.copy()
uclose[uclose <= 0] = 0
# down trend close
dclose = diff
dclose[dclose >= 0] = 0
dclose = np.abs(dclose)
rsi = 100 * sma(uclose, n) / (sma(uclose, n) + sma(dclose, n))
# replace NaN
rsi[np.isnan(rsi)] = 100
return rsi
def rsi(close, n=14):
"""
Cutler's relative strength index
"""
diff = close[1:] - close[:-1]
# up trend close
uclose = diff.copy()
uclose[uclose <= 0] = 0
# down trend close
dclose = diff
dclose[dclose >= 0] = 0
dclose = np.abs(dclose)
rsi = 100 * sma(uclose, n) / (sma(uclose, n) + sma(dclose, n))
# replace NaN
rsi[np.isnan(rsi)] = 100
return rsi
def stochastic(high, low, close, x=14, y=3):
size = high.size - x + 1
# stride arrays
high_strided = np.zeros(shape=(size, x))
low_strided = np.zeros(shape=(size, x))
close_strided = np.zeros(shape=(size, x))
for i in range(x):
high_strided[:, i] = high[i:size + i]
low_strided[:, i] = low[i:size + i]
close_strided[:, i] = close[i:size + i]
lmin = low_strided.min(axis=1)
hmax = high_strided.max(axis=1)
kfast = 100 * (close_strided[:, -1] - lmin) / (hmax - lmin)
dfast = sma(kfast, y)
kslow = dfast
dslow = sma(kslow, y)
return kfast, dfast, kslow, dslow
def stochastic(high, low, close, x=14, y=3):
size = high.size - x + 1
# stride arrays
high_strided = np.zeros(shape=(size, x))
low_strided = np.zeros(shape=(size, x))
close_strided = np.zeros(shape=(size, x))
for i in range(x):
high_strided[:, i] = high[i:size + i]
low_strided[:, i] = low[i:size + i]
close_strided[:, i] = close[i:size + i]
lmin = low_strided.min(axis=1)
hmax = high_strided.max(axis=1)
kfast = 100 * (close_strided[:, -1] - lmin) / (hmax - lmin)
dfast = sma(kfast, y);
kslow = dfast;
dslow = sma(kslow, y)
return kfast, dfast, kslow, dslow
def cci(high, low, close, n=20):
tp = (high + low + close) / 3.
sma_tp = sma(tp, n)
size = sma_tp.size
# stride array
tp_strided = np.zeros(shape=(size, n))
for i in range(n):
tp_strided[:, i] = tp[i:size + i]
md = np.abs(tp_strided - sma_tp[:, None]).sum(axis=1) / n
return (tp[-sma_tp.size:] - sma_tp) / (.015 * md)
def cci(high, low, close, n=20):
tp = (high + low + close) / 3.
sma_tp = sma(tp, n)
size = sma_tp.size
# stride array
tp_strided = np.zeros(shape=(size, n))
for i in range(n):
tp_strided[:, i] = tp[i:size + i]
md = np.abs(tp_strided - sma_tp[:, None]).sum(axis=1) / n
return (tp[-sma_tp.size:] - sma_tp) / (.015 * md)
