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Diffstat (limited to '')
28 files changed, 177 insertions, 8 deletions
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b/lstm-models/ethereum_model_randseed_796.h5 diff --git a/lstm-models/ethereum_model_randseed_797.h5 b/lstm-models/ethereum_model_randseed_797.h5 Binary files differnew file mode 100644 index 0000000..3c0114d --- /dev/null +++ b/lstm-models/ethereum_model_randseed_797.h5 diff --git a/lstm-models/ethereum_model_randseed_798.h5 b/lstm-models/ethereum_model_randseed_798.h5 Binary files differnew file mode 100644 index 0000000..59d9a13 --- /dev/null +++ b/lstm-models/ethereum_model_randseed_798.h5 diff --git a/lstm-models/ethereum_model_randseed_799.h5 b/lstm-models/ethereum_model_randseed_799.h5 Binary files differnew file mode 100644 index 0000000..bdc74fa --- /dev/null +++ b/lstm-models/ethereum_model_randseed_799.h5 @@ -17,6 +17,7 @@ from keras.models import Sequential from keras.layers import Activation, Dense from keras.layers import LSTM from keras.layers import Dropout +from keras.models import load_model window_len = 10 split_date = "2017-06-01" @@ -75,7 +76,7 @@ def get_sets(crypto, model_data): LSTM_test_inputs = [np.array(LSTM_test_inputs) for LSTM_test_inputs in LSTM_test_inputs] LSTM_test_inputs = np.array(LSTM_test_inputs) - return LSTM_training_inputs, LSTM_test_inputs, training_set + return LSTM_training_inputs, LSTM_test_inputs, training_set, test_set def build_model(inputs, output_size, neurons, activ_func="linear", dropout=0.25, loss="mae", optimizer="adam"): model = Sequential() @@ -89,7 +90,7 @@ def build_model(inputs, output_size, neurons, activ_func="linear", dropout=0.25, def lstm_type_1(crypto, crypto_short): model_data = getData_CMC(crypto, crypto_short) np.random.seed(202) - training_inputs, test_inputs, training_set = get_sets(crypto, model_data) + training_inputs, test_inputs, training_set, test_set = get_sets(crypto, model_data) model = build_model(training_inputs, output_size=1, neurons=20) training_outputs = (training_set['Close'][window_len:].values/training_set['Close'][:-window_len].values)-1 history = model.fit(training_inputs, training_outputs, epochs=50, batch_size=1, verbose=2, shuffle=True) @@ -97,7 +98,7 @@ def lstm_type_1(crypto, crypto_short): def lstm_type_2(crypto, crypto_short, pred_range, neuron_count): model_data = getData_CMC(crypto, crypto_short) np.random.seed(202) - training_inputs, test_inputs, training_set = get_sets(crypto, model_data) + training_inputs, test_inputs, training_set, test_set = get_sets(crypto, model_data) model = build_model(training_inputs, output_size=pred_range, neurons=neuron_count) training_outputs = (training_set['Close'][window_len:].values/training_set['Close'][:-window_len].values)-1 training_outputs = [] @@ -109,7 +110,7 @@ def lstm_type_2(crypto, crypto_short, pred_range, neuron_count): def lstm_type_3(crypto, crypto_short, pred_range, neuron_count): model_data = getData_CMC(crypto, crypto_short) np.random.seed(202) - training_inputs, test_inputs, training_set = get_sets(crypto, model_data) + training_inputs, test_inputs, training_set, test_set = get_sets(crypto, model_data) model = build_model(training_inputs, output_size=1, neurons=neuron_count) training_outputs = (training_set['Close'][window_len:].values/training_set['Close'][:-window_len].values)-1 training_outputs = [] @@ -118,7 +119,16 @@ def lstm_type_3(crypto, crypto_short, pred_range, neuron_count): np.random.seed(rand_seed) temp_model = build_model(training_inputs, output_size=1, neurons=neuron_count) temp_model.fit(training_inputs, (training_set['Close'][window_len:].values/training_set['Close'][:-window_len].values)-1, epochs=50, batch_size=1, verbose=0, shuffle=True) - temp_model.save(crypto + '_model_randseed_%d.h5'%rand_seed) + temp_model.save("./lstm-models/" + crypto + '_model_randseed_%d.h5'%rand_seed) + +def load_models(crypto, crypto_short): + preds = [] + model_data = getData_CMC(crypto, crypto_short) + np.random.seed(202) + training_inputs, test_inputs, training_set, test_set = get_sets(crypto, model_data) + for rand_seed in range(775,800): + temp_model = load_model("./lstm-models/" + crypto + '_model_randseed_%d.h5'%rand_seed) + preds.append(np.mean(abs(np.transpose(temp_model.predict(test_inputs))-(test_set['Close'].values[window_len:]/test_set['Close'].values[:-window_len]-1)))) # write code here def premain(argparser): @@ -126,7 +136,8 @@ def premain(argparser): #here #lstm_type_1("ethereum", "ether") #lstm_type_2("ethereum", "ether", 5, 20) - lstm_type_3("ethereum", "ether", 5, 20) + #lstm_type_3("ethereum", "ether", 5, 20) + load_models("ethereum", "eth") def main(): argparser = Argparser() @@ -6,8 +6,9 @@ import code import readline import signal import sys -from ltsm import lstm_type_1, lstm_type_2, lstm_type_3 -from cnn import marrionette_type_1 +from lstm import lstm_type_1, lstm_type_2, lstm_type_3 +from marionette import marrionette_type_1 +from tfann import tfann_type_1 def SigHandler_SIGINT(signum, frame): print() diff --git a/tfann.py b/tfann.py new file mode 100755 index 0000000..326182c --- /dev/null +++ b/tfann.py @@ -0,0 +1,157 @@ +#!/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() |