cnnr.fit(B, Y)
#prediction
PTS = [] #Predicted time sequences
P = B[[-1]] #Most recent time sequence
for i in range(HP // NFS + 1): #Repeat prediction
YH = cnnr.predict(P)
P = np.concatenate([P[:, NFS:], YH], axis=1)
PTS.append(YH)
PTS = np.hstack(PTS).transpose((1, 0, 2))
A = np.vstack([A, PTS]) #Combine predictions with original data
A = np.squeeze(A) * SV #Remove unittime dimension and rescale
C = np.squeeze(C) * SV
nt = 4
PF = cnnr.PredictFull(B[:nt])
for i in range(nt):
fig, ax = mpl.subplots(1, 4, figsize=(16 / 1.24, 10 / 1.25))
ax[0].plot(PF[0][i])
ax[0].set_title('Input')
ax[1].plot(PF[2][i])
ax[1].set_title('Layer 1')
ax[2].plot(PF[4][i])
ax[2].set_title('Layer 2')
ax[3].plot(PF[5][i])
ax[3].set_title('Output')
fig.text(0.5, 0.06, 'Time', ha='center')
fig.text(0.06, 0.5, 'Activation', va='center', rotation='vertical')
mpl.show()
CI = list(range(C.shape[0]))
def tfann_type_1():
#%%Path to store cached currency data
datPath = "CurDat/"
if not os.path.exists(datPath):
os.mkdir(datPath)
# Different cryptocurrency types
cl = ["BTC", "LTC", "ETH", "XMR"]
# Columns of price data to use
CN = ["close", "high", "low", "open", "volume"]
# Store data frames for each of above types
D = []
for ci in cl:
dfp = os.path.join(datPath, ci + ".csv")
try:
df = pd.read_csv(dfp, sep=",")
except FileNotFoundError:
df = GetCurDF(ci, dfp)
D.append(df)
#%%Only keep range of data that is common to all currency types
cr = min(Di.shape[0] for Di in D)
for i in range(len(cl)):
D[i] = D[i][(D[i].shape[0] - cr):]
#%%Features are channels
C = np.hstack((Di[CN] for Di in D))[:, None, :]
HP = 16 # Holdout period
A = C[0:-HP]
SV = A.mean(axis=0) # Scale vector
C /= SV # Basic scaling of data
#%%Make samples of temporal sequences of pricing data (channel)
NPS, NFS = 256, 16 # Number of past and future samples
ps = PastSampler(NPS, NFS)
B, Y = ps.transform(A)
#%%Architecture of the neural network
NC = B.shape[2]
# 2 1-D conv layers with relu followed by 1-d conv output layer
ns = [
("C1d", [8, NC, NC * 2], 4),
("AF", "relu"),
("C1d", [8, NC * 2, NC * 2], 2),
("AF", "relu"),
("C1d", [8, NC * 2, NC], 2),
]
# Create the neural network in TensorFlow
cnnr = ANNR(
B[0].shape,
ns,
batchSize=32,
learnRate=2e-5,
maxIter=64,
reg=1e-5,
tol=1e-2,
verbose=True,
)
cnnr.fit(B, Y)
PTS = [] # Predicted time sequences
P, YH = B[[-1]], Y[[-1]] # Most recent time sequence
for i in range(HP // NFS): # Repeat prediction
P = np.concatenate([P[:, NFS:], YH], axis=1)
YH = cnnr.predict(P)
PTS.append(YH)
PTS = np.hstack(PTS).transpose((1, 0, 2))
A = np.vstack([A, PTS]) # Combine predictions with original data
A = np.squeeze(A) * SV # Remove unittime dimension and rescale
C = np.squeeze(C) * SV
nt = 4
PF = cnnr.PredictFull(B[:nt])
for i in range(nt):
fig, ax = mpl.subplots(1, 4, figsize=(16 / 1.24, 10 / 1.25))
ax[0].plot(PF[0][i])
ax[0].set_title("Input")
ax[1].plot(PF[2][i])
ax[1].set_title("Layer 1")
ax[2].plot(PF[4][i])
ax[2].set_title("Layer 2")
ax[3].plot(PF[5][i])
ax[3].set_title("Output")
fig.text(0.5, 0.06, "Time", ha="center")
fig.text(0.06, 0.5, "Activation", va="center", rotation="vertical")
mpl.show()
CI = list(range(C.shape[0]))
AI = list(range(C.shape[0] + PTS.shape[0] - HP))
NDP = PTS.shape[0] # Number of days predicted
for i, cli in enumerate(cl):
fig, ax = mpl.subplots(figsize=(16 / 1.5, 10 / 1.5))
hind = i * len(CN) + CN.index("high")
ax.plot(CI[-4 * HP:], C[-4 * HP:, hind], label="Actual")
ax.plot(AI[-(NDP + 1):],
A[-(NDP + 1):, hind],
"--",
label="Prediction")
ax.legend(loc="upper left")
ax.set_title(cli + " (High)")
ax.set_ylabel("USD")
ax.set_xlabel("Time")
ax.axes.xaxis.set_ticklabels([])
mpl.show()
