def priceChange_random(stock, time, volume, proportion=1000):
"""
Updates the stockPool and hurstPool as price change
Random +/- increment to Hurst
"""
global stockPool, hurstPool
increment = float(
np.random.choice(np.linspace(-volume / proportion, volume / proportion,
7),
size=1))
if increment != 0:
# print(volume)
# print(increment)
h0 = hurstPool[stock][time]
numberNewPrices = len(stockPool[stock][time + 1:])
p0 = (stockPool[stock][time])
h1 = h0 + increment
if h1 < 0.45:
h1 = 0.45
elif h1 > 0.75:
h1 = 0.75
hurstPool[stock, time + 1:] = (h1)
fbmNew = fbm((h1), 2**14, 2**14)
fbmNew = abs(fbmNew[:numberNewPrices] + float(p0))
print('stock ', stock, ' original H', h0, ' to ', (h1))
stockPool[stock][time + 1:] = np.asarray(fbmNew)
def generate_1d_fbms(N=10000,n=256,reCalc=False):
#reCalc = True
#reCalc = False
if os.path.exists('data1d.bin') and not reCalc:
print 'loading data from file'
print 'disabling gc'
gc.disable()
Xtrain, Xtest, Ytrain, Ytest = pickle.load(open('data1d.bin','rb'),pickle.HIGHEST_PROTOCOL)
gc.enable()
else:
print 'generating data and saving'
np.random.seed(0)
X = []
H = np.linspace(0.05,0.8,N)# [0.5]*N
for i in H:
fbmr,fgnr,times= fbm(n-1,i,L=1)
fbmr = fbmr-np.min(fbmr)
fbmr = fbmr/np.max(fbmr)
X.append(fbmr)
X0=np.array(X)
H = np.array(H)
Y = H
X=np.expand_dims(X0,2) # to make input of size (n,1) instead of just (n)
Xtrain, Xtest, Ytrain, Ytest = train_test_split(X0,Y,test_size=0.2,random_state=0)
pickle.dump([Xtrain,Xtest,Ytrain,Ytest],open('data1d.bin','w'))
def priceChange_updown(stock, time, volume, proportion=10000):
"""
Updates the stockPool and hurstPool as price change
Sell => decrease Hurst (more volatile)
Buy => increase Hurst (less volatile)
"""
global stockPool, hurstPool
increment = volume / proportion
h0 = hurstPool[stock][time]
numberNewPrices = len(stockPool[stock][time:])
p0 = stockPool[stock][time]
if volume < 0:
h1 = h0 - increment
if h1 < 0.4:
h1 = 0.4
hurstPool[stock][time:] = h1
else:
h1 = h0 + increment
if h1 > 0.8:
h1 = 0.8
hurstPool[stock][time:] = h1
fbmNew = fbm(h1, 2**14, 2**14)
fbmNew = abs(fbmNew[:numberNewPrices] + p0)
print('stock ', stock, ' original H', h0, ' to ', h1)
stockPool[stock][time:] = fbmNew
## ################################################
## generate fbms
reCalc = True
#reCalc = False
n = 256
N = 10000
if os.path.exists('data.bin') and not reCalc:
print 'loading data from file'
Xtrain, Xtest, Ytrain, Ytest = pickle.load(open('data.bin', 'r'))
else:
print 'generating data and saving'
np.random.seed(0)
X = []
H = np.linspace(0.05, 0.8, N) # [0.5]*N
for i in H:
fbmr, fgnr, times = fbm(n - 1, i, L=1)
fbmr = fbmr - np.min(fbmr)
fbmr = fbmr / np.max(fbmr)
X.append(fbmr)
X0 = np.array(X)
H = np.array(H)
Y = H
X = np.expand_dims(X0,
2) # to make input of size (n,1) instead of just (n)
Xtrain, Xtest, Ytrain, Ytest = train_test_split(X0,
Y,
test_size=0.2,
random_state=0)
pickle.dump([Xtrain, Xtest, Ytrain, Ytest], open('data.bin', 'w'))