def gen_pf_bollinger(dl, ol, hl, ll, cl, vl, need_y=True):
span = 25
# vspan = 5
period = 52
futspan = 20
pointsnum = 7
# div_num = 9
calc_past = max(span, period)
invalid_past = max(span * 2, span + period)
invalid_fut = futspan
if len(dl) < invalid_past + invalid_fut:
return []
###########################
# Get history #
###########################
# om = vct.add_history(ol, calc_past)
hm = vct.add_history(hl, calc_past)
lm = vct.add_history(ll, calc_past)
cm = vct.add_history(cl, calc_past)
vm = vct.add_history(vl, calc_past)
###########################
# Modify data #
###########################
pmfi = idc.mfi(hm, lm, cm, vl, span)
me = idc.moving_average(cm, span)
# vme = idc.moving_average(vm, vspan)
std = idc.moving_std(cm, span)
sz = idc.get_size(std, me)
uboll = me + std * 2.0
dboll = me - std * 2.0
# ds = idc.moving_average(((hm-lm)/lm)[:,-span:], span)
(joints, subjoints, indexes) = vct.get_pnf_plus(cm, hm, lm, pointsnum, [uboll, dboll, pmfi])
# (joints, subjoints, indexes) = vct.get_pnf(cm, hm, lm, pointsnum, [uboll, dboll, pmfi, vme])
# (vjoints, vindexes) = vct.get_vpnf(vm, pointsnum)
uboll = idc.add_history(uboll, calc_past)
dboll = idc.add_history(dboll, calc_past)
ma = np.max(np.c_[uboll, hm], axis=1)
mi = np.min(np.c_[dboll, lm], axis=1)
# vma = np.max(vm, axis=1)
# vmi = np.min(vm, axis=1)
doplot = False
if doplot:
tmp = pmfi[invalid_past:]
pl.plot_kabuka(range(0, len(tmp)), [tmp], ["mfi"])
###########################
# Cut invalid past #
###########################
ma = ma[invalid_past:]
mi = mi[invalid_past:]
# vma = vma[invalid_past:]
# vmi = vmi[invalid_past:]
joints = joints[invalid_past:, :]
dl = dl[invalid_past:]
indexes = indexes[invalid_past:]
for i in range(0, len(subjoints)):
subjoints[i] = subjoints[i][invalid_past:, :]
sz = sz[invalid_past:]
# vjoints = vjoints[invalid_past:,:]
# vindexes = vindexes[invalid_past:]
###########################
# Normalization #
###########################
# subjoints = [uboll, dboll, pmfi, vme]
joints = vct.rate_norm(joints, ma, mi)
subjoints[0] = vct.rate_norm(subjoints[0], ma, mi)
subjoints[1] = vct.rate_norm(subjoints[1], ma, mi)
# subjoints[3] = vct.rate_norm(subjoints[3], vma, vmi)
indexes = indexes / period
# vjoints = vct.rate_norm(vjoints, vma, vmi)
###########################
# Plot #
###########################
doplot = False
if doplot:
y1 = []
x1 = indexes[-1]
y1.append(joints[-1, :])
for i in range(0, len(subjoints)):
tmp = subjoints[i][-1, :]
y1.append(subjoints[i][-1, :])
labels1 = ["p&f", "plus theta", "minus theta", "mfi", "volume"]
pl.plot_kabuka([x1], [y1], [labels1])
# X = np.c_[indexes, joints]
# sz = sz
X = np.array(joints)
for s in subjoints:
X = np.c_[X, s]
###########################
# Emphasize main points #
###########################
joints[:, -1] = 4 * joints[:, -1]
joints[:, -2] = 3 * joints[:, -2]
for i in range(0, len(subjoints)):
subjoints[i][:, -1] = 2 * subjoints[i][:, -1]
# i = 0
# for i in range(len(X)):
# X[i] = X[i].tolist()
if need_y == False:
return (dl, sz, X)
###########################
# Get max/min prices #
###########################
fut = add_history(cl, futspan, False)
# (m, n) = fut.shape
max_prices = (np.max(fut, axis=1) - fut[:, 0]) * 1.0 / fut[:, 0]
min_prices = (fut[:, 0] - np.min(fut, axis=1)) * 1.0 / fut[:, 0]
max_prices = _cut_invalid(max_prices, invalid_past, invalid_fut)
min_prices = _cut_invalid(min_prices, invalid_past, invalid_fut)
# for i in range(len(Y)):
# Y[i] = Y[i].tolist()
return (dl, sz, X, max_prices * 100, min_prices * 100)
def gen_pf_pnf(dl, ol, hl, ll, cl, vl, need_y=True):
span = 25
#vspan = 5
period = 52
futspan = 20
pointsnum = 7
#div_num = 9
calc_past = max(span, period)
invalid_past = max(span*2, span+period)
invalid_fut = futspan
if len(dl) < invalid_past + invalid_fut:
return []
###########################
# Get history #
###########################
#om = vct.add_history(ol, calc_past)
hm = vct.add_history(hl, calc_past)
lm = vct.add_history(ll, calc_past)
cm = vct.add_history(cl, calc_past)
vm = vct.add_history(vl, calc_past)
###########################
# Modify data #
###########################
me = idc.moving_average(cm, span)
std = idc.moving_std(cm, span)
sz = idc.get_size(std, me)
uboll = me + std*2.0
dboll = me - std*2.0
(joints, subjoints, indexes) = vct.get_pnf(cm, hm, lm, pointsnum, [uboll, dboll])
uboll = idc.add_history(uboll, calc_past)
dboll = idc.add_history(dboll, calc_past)
ma = np.max(np.c_[uboll,hm], axis=1)
mi = np.min(np.c_[dboll,lm], axis=1)
###########################
# Cut invalid past #
###########################
ma = ma[invalid_past:]
mi = mi[invalid_past:]
joints = joints[invalid_past:,:]
dl = dl[invalid_past:]
indexes = indexes[invalid_past:]
for i in range(0, len(subjoints)):
subjoints[i] = subjoints[i][invalid_past:,:]
sz = sz[invalid_past:]
###########################
# Normalization #
###########################
joints = vct.rate_norm(joints, ma, mi)
subjoints[0] = vct.rate_norm(subjoints[0], ma, mi)
subjoints[1] = vct.rate_norm(subjoints[1], ma, mi)
indexes = indexes/period
###########################
# Plot #
###########################
doplot = False
if doplot:
y1 = []
x1 = indexes[-1]
y1.append(joints[-1,:])
for i in range(0, len(subjoints)):
tmp = subjoints[i][-1,:]
y1.append(subjoints[i][-1,:])
labels1 = ["p&f", "plus theta", "minus theta", "mfi", "volume"]
pl.plot_kabuka([x1], [y1], [labels1])
X = np.array(joints)*2
X = np.c_[X, indexes]
if need_y == False:
return (dl, sz, X)
###########################
# Get max/min prices #
###########################
fut = add_history(cl, futspan, False)
#(m, n) = fut.shape
max_prices = (np.max(fut, axis=1)-fut[:,0])*1.0/fut[:,0]
min_prices = (fut[:,0] - np.min(fut, axis=1))*1.0/fut[:,0]
max_prices = _cut_invalid(max_prices, invalid_past, invalid_fut)
min_prices = _cut_invalid(min_prices, invalid_past, invalid_fut)
return (dl, sz, X, max_prices*100, min_prices*100)