1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
|
#!/usr/bin/python3
# _*_ coding=utf-8 _*_
#original source:https://github.com/dashee87/blogScripts/blob/master/Jupyter/2017-11-20-predicting-cryptocurrency-prices-with-deep-learning.ipynb
#@#!pip install lxml
#@#!mkdir lstm-models
import argparse
import code
import readline
import signal
import sys
import pandas as pd
import json
import os
import numpy as np
import urllib3
import time
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"
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 getData_CMC(crypto, crypto_short):
market_info = pd.read_html("https://coinmarketcap.com/currencies/"+crypto+"/historical-data/?start=20130428&end="+time.strftime("%Y%m%d"))[0]
market_info = market_info.assign(Date=pd.to_datetime(market_info['Date']))
if crypto == "ethereum": market_info.loc[market_info["Market Cap"]=="-","Market Cap"]=0
if crypto == "dogecoin": market_info.loc[market_info["Volume"]=="-","Volume"]=0
market_info["Volume"] = market_info["Volume"].astype("int64")
market_info.columns = market_info.columns.str.replace("*", "")
print(type(market_info))
print(crypto + " head: ")
print(market_info.head())
kwargs = {'close_off_high': lambda x: 2*(x['High']- x['Close'])/(x['High']-x['Low'])-1, 'volatility': lambda x: (x['High']- x['Low'])/(x['Open'])}
market_info = market_info.assign(**kwargs)
model_data = market_info[['Date']+[coin+metric for coin in [""] for metric in ['Close','Volume','close_off_high','volatility']]]
model_data = model_data.sort_values(by='Date')
print(model_data.head())
return model_data
def get_sets(crypto, model_data):
training_set, test_set = model_data[model_data['Date']<split_date], model_data[model_data['Date']>=split_date]
training_set = training_set.drop('Date', 1)
test_set = test_set.drop('Date', 1)
norm_cols = [coin+metric for coin in [] for metric in ['Close', 'Volume']]
LSTM_training_inputs = []
for i in range(len(training_set) - window_len):
temp_set = training_set[i:(i+window_len)].copy()
for col in norm_cols:
temp_set.loc[:, col] = temp_set[col]/temp_set[col].iloc[0] -1
LSTM_training_inputs.append(temp_set)
LSTM_training_outputs = (training_set["Close"][window_len:].values/training_set["Close"][:-window_len].values) - 1
LSTM_test_inputs = []
for i in range(len(test_set)-window_len):
temp_set = test_set[i:(i+window_len)].copy()
for col in norm_cols:
temp_set.loc[:, col] = temp_set[col]/temp_set[col].iloc[0] - 1
LSTM_test_inputs.append(temp_set)
LSTM_test_outputs = (test_set['Close'][window_len:].values/test_set['Close'][:-window_len].values)-1
print(LSTM_training_inputs[0])
LSTM_training_inputs = [np.array(LSTM_training_input) for LSTM_training_input in LSTM_training_inputs]
LSTM_training_inputs = np.array(LSTM_training_inputs)
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, test_set
def build_model(inputs, output_size, neurons, activ_func="linear", dropout=0.25, loss="mae", optimizer="adam"):
model = Sequential()
model.add(LSTM(neurons, input_shape=(inputs.shape[1], inputs.shape[2])))
model.add(Dropout(dropout))
model.add(Dense(units=output_size))
model.add(Activation(activ_func))
model.compile(loss=loss, optimizer=optimizer)
return model
def lstm_type_1(crypto, crypto_short):
model_data = getData_CMC(crypto, crypto_short)
np.random.seed(202)
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)
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, 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 = []
for i in range(window_len, len(training_set['Close'])-pred_range):
training_outputs.append((training_set['Close'][i:i+pred_range].values/training_set['Close'].values[i-window_len])-1)
training_outputs = np.array(training_outputs)
history = model.fit(training_inputs[:-pred_range], training_outputs, epochs=50, batch_size=1, verbose=2, shuffle=True)
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, 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 = []
for rand_seed in range(775, 800):
print(rand_seed)
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("./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):
signal.signal(signal.SIGINT, SigHandler_SIGINT)
#here
lstm_type_1("ethereum", "ether")
#lstm_type_2("ethereum", "ether", 5, 20)
#lstm_type_3("ethereum", "ether", 5, 20)
#load_models("ethereum", "eth")
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()
|
