def RSI_n_sf(N, data, step=1, filtering=False):
# from the RSI indicator
sub_data = down_sample(data, step) if filtering else data[::step]
t_subdata = range(0, len(data), step)
variations = np.diff(sub_data)
#variations = np.diff(sub_data) / sub_data[:-1]
# h = np.array(list(map(lambda x: max(x,0), variations)))
# b = np.array(list(map(lambda x: abs(min(x,0)), variations)))
# or that to avoid zeros
h = np.array(list(map(lambda x: max(x, 0.000001), variations)))
b = np.array(list(map(lambda x: abs(min(x, -0.000001)), variations)))
# exp or linear
# hn = np.interp(range(len(data)), t_subdata[1:], MMexp_n(N, h))
# bn = np.interp(range(len(data)), t_subdata[1:], MMexp_n(N, b))
hn = np.interp(range(len(data)), t_subdata[1:], MM_n(N, h))
bn = np.interp(range(len(data)), t_subdata[1:], MM_n(N, b))
# prefer to return in normalized 0..1
rsi = hn / (hn + bn)
return rsi
def RSI_n_sf(N, data, step=1, filtering=False):
"""
Calcule le RSI sur N periodes en prenant un echantillon tous les step avec ou sans filtrage prealable
Retourne un array de la même taille que data. Lorsque step > 1, les valeurs sont interpolees lineairement.
"""
# have a difference with others algo, it seems doubling N make the deals... but what's the problem
sub_data = down_sample(data, step) if filtering else data [::step]
t_subdata = range(0,len(data),step)
variations = np.diff(sub_data)
#variations = np.diff(sub_data) / sub_data[:-1]
# h = np.array(list(map(lambda x: max(x,0), variations)))
# b = np.array(list(map(lambda x: abs(min(x,0)), variations)))
# or that to avoid zeros
h = np.array(list(map(lambda x: max(x,0.000000001), variations)))
b = np.array(list(map(lambda x: abs(min(x,-0.000000001)), variations)))
# exp or linear
# hn = np.interp(range(len(data)), t_subdata[1:], MMexp_n(N, h))
# bn = np.interp(range(len(data)), t_subdata[1:], MMexp_n(N, b))
hn = np.interp(range(len(data)), t_subdata[1:], MM_n(N, h))
bn = np.interp(range(len(data)), t_subdata[1:], MM_n(N, b))
# prefer to return in normalized 0..1
rsi = hn/(hn+bn)
return rsi
def Stochastic_sf(N, data, N_D=3, step=1, filtering=False):
"""
Calcul des stochastiques.
N est le nombre de periodes a observer pour repérer le min et le max du cours.
N_D est le nombre d'echantillons de K a utiliser pour le calcul de D
step permet de ne selectionner qu'un echantillon sur step dans data.
filtering permet de filtrer ou non les donnees avant d'appliquer la selection.
Retourne les stochastiques K, D interpolees lineairement ; meme taille que data.
"""
sub_data = down_sample(data, step) if filtering else data[::step]
K = np.zeros(len(sub_data))
t_subdata = range(0, len(data), step)
for (j, d) in enumerate(sub_data):
i = min(j, N)
highest = max(sub_data[j - i:j + 1])
lowest = min(sub_data[j - i:j + 1])
if highest == lowest:
highest += 0.000000001
K[j] = (d - lowest) / (highest - lowest) # +epsilon to avoid 0
D = MM_n(N_D, K)
return np.interp(range(len(data)), t_subdata,
K), np.interp(range(len(data)), t_subdata, D)
def RSI_n(N, data):
# from the RSI indicator
variations = np.diff(data)
#variations = np.diff(data) / data[:-1]
# h = np.array(list(map(lambda x: max(x,0), variations)))
# b = np.array(list(map(lambda x: abs(min(x,0)), variations)))
# or that to avoid zeros
h = np.array(list(map(lambda x: max(x, 0.000001), variations)))
b = np.array(list(map(lambda x: abs(min(x, -0.000001)), variations)))
# exp or linear
hn = MM_n(N, h) # MMexp_n(N, h)
bn = MM_n(N, b) # MMexp_n(N, b
# prefer to return in normalized 0..1
rsi = hn / (hn + bn)
return rsi
def SMA_n_sf(N, data, step=1, filtering=False):
"""
Calcule une SMA sur N periodes en prenant un echantillon tous les step avec ou sans filtrage prealable
Retourne un array de la même taille que data. Lorsque step > 1, les valeurs sont interpolees lineairement.
"""
sub_data = down_sample(data, step) if filtering else data[::step]
t_subdata = range(0, len(data), step)
sma = MM_n(N, data)
return sma
def VWMA_n_sf(N, prices, volumes, step=1, filtering=False):
"""
Calcule une VWMA sur N periodes en prenant un echantillon tous les step avec ou sans filtrage prealable
Retourne un array de la meme taille que data. Lorsque step > 1, les valeurs sont interpolees lineairement.
"""
p_sub_data = down_sample(
prices, step) if filtering else np.array(prices)[::step]
v_sub_data = down_sample(
volumes, step) if filtering else np.array(volumes)[::step]
t_subdata = range(0, len(prices), step)
# cannot deal with zero volume, then set it to 1 will have no effect on the result, juste give a price
for i, v in enumerate(v_sub_data):
if v <= 0:
v_sub_data[i] = 1.0
pvs = MM_n(N, p_sub_data * v_sub_data)
vs = MM_n(N, v_sub_data)
return pvs / vs
def WMA_n_sf(N, prices, step=1, filtering=False):
"""
Calcule une WMA sur N periodes en prenant un echantillon tous les step avec ou sans filtrage prealable
Retourne un array de la même taille que data. Lorsque step > 1, les valeurs sont interpolees lineairement.
"""
sub_data = down_sample(prices, step) if filtering else np.array(prices) [::step]
t_subdata = range(0,len(prices),step)
weights = np.array([x for x in range(1, len(sub_data)+1)])
wma = MM_n(N, sub_data*weights) / weights
return wma
def RSI_n(N, data):
"""
Calcule le RSI sur N periodes en prenant un echantillon tous les step avec ou sans filtrage prealable
Retourne un array de la même taille que data. Lorsque step > 1, les valeurs sont interpolees lineairement.
"""
variations = np.diff(data)
#variations = np.diff(data) / data[:-1]
# h = np.array(list(map(lambda x: max(x,0), variations)))
# b = np.array(list(map(lambda x: abs(min(x,0)), variations)))
# or that to avoid zeros
h = np.array(list(map(lambda x: max(x,0.000000001), variations)))
b = np.array(list(map(lambda x: abs(min(x,-0.000000001)), variations)))
# exp or linear
hn = MM_n(N, h) # MMexp_n(N, h)
bn = MM_n(N, b) # MMexp_n(N, b)
# prefer to return in normalized 0..1
rsi = hn/(hn+bn)
return rsi
def BB(N, data):
mm = MM_n(N, data)
up_bol = copy.deepcopy(mm)
bottom_bol = copy.deepcopy(mm)
for (j, d) in enumerate(data):
i = min(j, N)
sample = data[j - i:j + 1]
sigma = 0 if j == 0 else np.sqrt(stat.variance(sample, up_bol[j]))
up_bol[j] = up_bol[j] + 2 * sigma
bottom_bol[j] = bottom_bol[j] - 2 * sigma
return (bottom_bol, mm, up_bol)
def StochRSI(N, rsi, N_D=3):
# from the Stochastic indicator
K = np.zeros(len(rsi))
for (j, d) in enumerate(rsi):
i = min(j, N)
highest = max(rsi[j - i:j + 1])
lowest = min(rsi[j - i:j + 1])
if highest == lowest:
highest += 0.000000001
K[j] = (d - lowest) / (highest - lowest) # +epsilon to avoid 0
D = MM_n(N_D, K)
return K, D
def Stochastic(N, data, N_D=3):
K = np.zeros(len(data))
for (j, d) in enumerate(data):
i = min(j, N)
highest = max(data[j - i:j + 1])
lowest = min(data[j - i:j + 1])
if highest == lowest:
highest += 0.000000001
K[j] = (d - lowest) / (highest - lowest) # +epsilon to avoid 0
D = MM_n(N_D, K)
return (K, D)
def BB_sf(N, data, step=1, filtering=False):
"""
Calcul des bandes de Bollinger
N est le nombre de periodes à observer pour calculer la MM et sigma
step permet de selectionner un echantillon tout les steps, avec filtrage ou non
Retourne 3 courbes : Bollinger bas ; MM_N ; Bollinger haut, interpolees linéairement
"""
sub_data = down_sample(data, step) if filtering else data[::step]
t_subdata = range(0, len(data), step)
mm = MM_n(N, sub_data)
up_bol = copy.deepcopy(mm)
bottom_bol = copy.deepcopy(mm)
for (j, d) in enumerate(sub_data):
i = min(j, N)
sample = sub_data[j - i:j + 1]
sigma = 0 if j == 0 else np.sqrt(stat.variance(sample, up_bol[j]))
up_bol[j] = up_bol[j] + 2 * sigma
bottom_bol[j] = bottom_bol[j] - 2 * sigma
return (np.interp(range(len(data)), t_subdata, bottom_bol),
np.interp(range(len(data)), t_subdata,
mm), np.interp(range(len(data)), t_subdata, up_bol))
def HMA_n_sf(N, data, step=1, filtering=False):
"""
Calcule un HMA sur N periodes en prenant un echantillon tous les step avec ou sans filtrage prealable
Retourne un array de la même taille que data. Lorsque step > 1, les valeurs sont interpolees lineairement.
"""
sub_data = down_sample(data, step) if filtering else data [::step]
t_subdata = range(0,len(data),step)
N_2 = int(N / 2)
N_sqrt = int(math.sqrt(N))
weights = np.array([x for x in range(1, len(sub_data)+1)])
# 1) calculate a WMA with period n / 2 and multiply it by 2
hma12 = 2 * MM_n(N_2, sub_data*weights) / MM_n(N_2, weights)
# 2) calculate a WMA for period n and subtract if from step 1
hma12 = hma12 - (MM_n(N, sub_data*weights) / MM_n(N, weights))
# 3) calculate a WMA with period sqrt(n) using the data from step 2
hma = (MM_n(N_sqrt, hma12*weights) / MM_n(N_sqrt, weights))
return hma
def WMA_n(N, prices):
weights = np.array([x for x in range(1, len(prices) + 1)])
wma = MM_n(N, prices * weights) / weights
return wma
