114 lines
4.6 KiB
Python
114 lines
4.6 KiB
Python
#!/usr/bin/python
|
|
import time
|
|
import pandas as pd
|
|
import numpy as np
|
|
import matplotlib.pyplot as plt
|
|
import tensorflow as tf
|
|
import urllib.request, json
|
|
|
|
from bot.shared_config import *
|
|
|
|
def main():
|
|
start_time = time.time()
|
|
|
|
dump(yellow("Retrieving market data from API"))
|
|
|
|
with urllib.request.urlopen("https://api.kraken.com/0/public/OHLC?pair=ETHUSD&interval=15") as url:
|
|
data = json.loads(url.read().decode())
|
|
timestamps = []
|
|
prices = []
|
|
volumes = []
|
|
pricevol = []
|
|
|
|
for set in data["result"]['XETHZUSD'][-601:]:
|
|
timestamps.append(int(set[0]))
|
|
prices.append(float(set[4]))
|
|
volumes.append(float(set[6]))
|
|
pricevol.append([float(set[4]), float(set[6])])
|
|
|
|
dump(green("Retrieved API in {0:.3f}ms sec".format((time.time() - start_time)*100)))
|
|
dump(yellow("Initialize Tensorflow"))
|
|
|
|
f_horizon = 1 # forecast horizon, one period into the future
|
|
num_periods = 20 # number of periods per vector we are using to predict one period ahead
|
|
inputs = 2 # number of vectors submitted
|
|
hidden = 100 # number of neurons we will recursively work through, can be changed to improve accuracy
|
|
output = 1 # number of output vectors
|
|
|
|
TS = np.array(pricevol)
|
|
TSo = np.array(prices)
|
|
|
|
x_data = TS[:(len(TS) - (len(TS) % num_periods))]
|
|
x_batches = x_data.reshape(-1, 20, 2)
|
|
|
|
y_data = TSo[1:(len(TSo) - (len(TSo) % num_periods)) + f_horizon]
|
|
y_batches = y_data.reshape(-1, 20, 1)
|
|
|
|
def test_data(forecast, num_periods):
|
|
test_x_setup = TS[-(num_periods + forecast):]
|
|
testX = test_x_setup[:num_periods].reshape(-1, 20, 2)
|
|
testY = TSo[-(num_periods):].reshape(-1, 20, 1)
|
|
return testX, testY
|
|
|
|
X_test, Y_test = test_data(f_horizon, num_periods)
|
|
|
|
tf.reset_default_graph() # We didn't have any previous graph objects running, but this would reset the graphs
|
|
|
|
X = tf.placeholder(tf.float32, [None, num_periods, inputs]) # create variable objects
|
|
y = tf.placeholder(tf.float32, [None, num_periods, output])
|
|
|
|
basic_cell = tf.contrib.rnn.BasicRNNCell(num_units=hidden, activation=tf.nn.relu) # create our RNN object
|
|
rnn_output, states = tf.nn.dynamic_rnn(basic_cell, X, dtype=tf.float32) # choose dynamic over static
|
|
|
|
learning_rate = 0.001 # small learning rate so we don't overshoot the minimum
|
|
|
|
stacked_rnn_output = tf.reshape(rnn_output, [-1, hidden]) # change the form into a tensor
|
|
stacked_outputs = tf.layers.dense(stacked_rnn_output, output) # specify the type of layer (dense)
|
|
outputs = tf.reshape(stacked_outputs, [-1, num_periods, output]) # shape of results
|
|
|
|
loss = tf.reduce_sum(tf.square(outputs - y)) # define the cost function which evaluates the quality of our model
|
|
optimizer = tf.train.AdamOptimizer(learning_rate=learning_rate) # gradient descent method
|
|
training_op = optimizer.minimize(
|
|
loss) # train the result of the application of the cost_function
|
|
|
|
init = tf.global_variables_initializer() # initialize all the variables
|
|
|
|
epochs = 1000 # number of iterations or training cycles, includes both the FeedFoward and Backpropogation
|
|
|
|
with tf.Session() as sess:
|
|
init.run()
|
|
dump(green("Initialized Tensorflow in {0:.3f}ms sec".format((time.time() - start_time) * 100)))
|
|
dump(yellow("Start Training"))
|
|
|
|
for ep in range(epochs):
|
|
sess.run(training_op, feed_dict={X: x_batches, y: y_batches})
|
|
if ep % 100 == 0:
|
|
mse = loss.eval(feed_dict={X: x_batches, y: y_batches})
|
|
print(ep, "\tMSE:", mse)
|
|
|
|
dump(green("Finished training in {0:.3f}ms sec".format((time.time() - start_time) * 100)))
|
|
|
|
dump(yellow("Start Predicting"))
|
|
y_pred = sess.run(outputs, feed_dict={X: X_test})
|
|
dump(green("Prediction finished in {0:.3f}ms sec".format((time.time() - start_time) * 100)))
|
|
|
|
dump(yellow("Start Plotting and output"))
|
|
|
|
actual_series = pd.Series(np.concatenate([np.ravel(X_test)[::2],np.ravel(Y_test)]))
|
|
actual_prediction = pd.Series(np.concatenate([np.ravel(X_test)[::2],np.ravel(y_pred)]))
|
|
|
|
plt.title("Forecast vs Actual", fontsize=14)
|
|
plt.plot(actual_series, "b-", markersize=10, label="Actual")
|
|
# plt.plot(pd.Series(np.ravel(Y_test)), "w*", markersize=10)
|
|
plt.plot(actual_prediction, "r-", markersize=7, label="Forecast")
|
|
plt.legend(loc="upper left")
|
|
plt.xlabel("Time Periods")
|
|
|
|
dump(green("Finished complete program in {0:.3f}ms sec".format((time.time() - start_time) * 100)))
|
|
plt.show()
|
|
|
|
|
|
if __name__ == '__main__':
|
|
print("Starting prediction ...")
|
|
main()
|