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#!/usr/bin/python3
# _*_ coding=utf-8 _*_
# original source-https://nicholastsmith.wordpress.com/2017/11/13/cryptocurrency-price-prediction-using-deep-learning-in-tensorflow/
import argparse
import code
import readline
import signal
import sys
from TFANN import ANNR
import numpy as np
import os
import pandas as pd
import urllib.request
import matplotlib.pyplot as mpl
def SigHandler_SIGINT(signum, frame):
print()
sys.exit(0)
class Argparser(object):
def __init__(self):
parser = argparse.ArgumentParser()
parser.add_argument("--string", type=str, help="string")
parser.add_argument("--bool", action="store_true", help="bool", default=False)
parser.add_argument("--dbg", action="store_true", help="debug", default=False)
self.args = parser.parse_args()
def GetAPIUrl(cur, sts = 1420070400):
return 'https://poloniex.com/public?command=returnChartData¤cyPair=USDT_{:s}&start={:d}&end=9999999999&period=7200'.format(cur, sts)
def GetCurDF(cur, fp):
openUrl = urllib.request.urlopen(GetAPIUrl(cur))
r = openUrl.read()
openUrl.close()
df = pd.read_json(r.decode())
df['date'] = df['date'].astype(np.int64) // 1000000000
return df
class PastSampler:
def __init__(self, N, K):
self.K = K
self.N = N
def transform(self, A, Y = None):
M = self.N + self.K #Number of samples per row (sample + target)
#Matrix of sample indices like: {{1, 2..., M}, {2, 3, ..., M + 1}}
I = np.arange(M) + np.arange(A.shape[0] - M + 1).reshape(-1, 1)
B = A[I].reshape(-1, M * A.shape[1], *A.shape[2:])
ci = self.N * A.shape[1] #Number of features per sample
return B[:, :ci], B[:, ci:] #Sample matrix, Target matrix
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()
# write code here
def premain(argparser):
signal.signal(signal.SIGINT, SigHandler_SIGINT)
#here
tfann_type_1()
def main():
argparser = Argparser()
if argparser.args.dbg:
try:
premain(argparser)
except Exception as e:
print(e.__doc__)
if e.message: print(e.message)
variables = globals().copy()
variables.update(locals())
shell = code.InteractiveConsole(variables)
shell.interact(banner="DEBUG REPL")
else:
premain(argparser)
if __name__ == "__main__":
main()
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