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diff --git a/tfann.py b/tfann.py
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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&currencyPair=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()