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10 Commits
ae8d75da25 ... master

Author SHA1 Message Date
Dennis Thiessen
5c08182aad Added SMA and improved plotting 2017-12-15 15:16:22 +01:00
Dennis Thiessen
055ada2766 Minor update 2017-11-20 15:47:05 +01:00
Dennis Thiessen
1c330ae177 Initial commit 2017-11-20 13:31:41 +01:00
Dennis Thiessen
95bdeee9e2 Analyzer now finds spread, perc and prints a sorted list; core is now able to start and stop the bot 2017-10-19 13:03:27 +02:00
Dennis Thiessen
ccd1d53715 Modified data analyzer and implemented shared_config file to avoid duplication 2017-10-18 16:27:16 +02:00
Dennis Thiessen
be7eaff10a Improved logging output 2017-10-18 14:26:48 +02:00
Dennis Thiessen
e6b677ee0e Modified data crawler to use ccxt library 2017-10-18 14:18:06 +02:00
Dennis Thiessen
8adbe96dca Implemented calculation of arbitrage opportunities 2017-10-11 15:25:09 +02:00
Dennis Thiessen
942e6081b2 Now with time performance output 2017-10-11 13:33:49 +02:00
Dennis Thiessen
a226033d21 Added Kraken support 2017-10-11 11:57:47 +02:00
13 changed files with 689 additions and 289 deletions
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@@ -359,25 +643,25 @@
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<entry file="file://$PROJECT_DIR$/Makefile"> <entry file="file://$PROJECT_DIR$/tests/__init__.py">
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@@ -385,12 +669,60 @@
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4
README.rst
View File

@@ -1,7 +1,7 @@
Crypto Arbitrage Bot Crypto Tensor Bot
======================== ========================
This bot does arbitrage with crypto This bot predicts a stock shart trend
--------------- ---------------

152
bot/core.py
View File

@@ -1,69 +1,127 @@
#!/usr/bin/python #!/usr/bin/python
from . import market_data_crawler import time
import pandas as pd
import numpy as np
import datetime as dt
import matplotlib.pyplot as plt
import matplotlib.dates as md
import tensorflow as tf
import urllib.request, json
from tornado import gen from bot.shared_config import *
from tornado.ioloop import IOLoop
import tornado.web
import json
import sys
class MainHandler(tornado.web.RequestHandler): def main():
@gen.coroutine start_time = time.time()
def post(self):
print("POST received from IP {0}".format(self.request.remote_ip))
response = {'error': False, 'msg': ""} dump(yellow("Retrieving market data from API"))
request = json.loads(self.request.body.decode('utf-8'))
if "token" not in request or request["token"] != "abc": with urllib.request.urlopen("https://api.kraken.com/0/public/OHLC?pair=ETHUSD&interval=15") as url:
response["msg"] = "Wrong token - no access granted" data = json.loads(url.read().decode())
self.write(json.dumps(response)) timestamps = []
return prices = []
volumes = []
pricevol = []
if "command" in request: for set in data["result"]['XETHZUSD'][-601:]:
print("Command received: {0}".format(request["command"])) timestamps.append(int(set[0]))
prices.append(float(set[4]))
volumes.append(float(set[6]))
pricevol.append([float(set[4]), float(set[6])])
if request["command"] == "init_market_data": dump(green("Retrieved API in {0:.3f}ms sec".format((time.time() - start_time)*100)))
yield self.update_market_data(request, response) dump(yellow("Initialize Tensorflow"))
self.write(json.dumps(response)) f_horizon = 10 # 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
@gen.coroutine TS = np.array(pricevol)
def update_market_data(self, request, response): TSo = np.array(prices)
yield market_data_crawler.update_market_data_for_basecoin(request["basecoin"])
response["msg"] = "Market Data initialized"
response["data"] = market_data_crawler.market_data
@gen.coroutine x_data = TS[:(len(TS) - (len(TS) % num_periods))]
def delete(self): x_batches = x_data.reshape(-1, 20, 2)
print("Stopping server...")
response_json = json.dumps({'error': False, 'msg': "Server stopped"}) y_data = TSo[1:(len(TSo) - (len(TSo) % num_periods)) + f_horizon]
self.write(response_json) y_batches = y_data.reshape(-1, 20, 1)
IOLoop.instance().stop() 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)
class Application(tornado.web.Application): tf.reset_default_graph() # We didn't have any previous graph objects running, but this would reset the graphs
def __init__(self):
handlers = [
(r"/?", MainHandler)
]
tornado.web.Application.__init__(self, handlers)
X = tf.placeholder(tf.float32, [None, num_periods, inputs]) # create variable objects
y = tf.placeholder(tf.float32, [None, num_periods, output])
def main(port): basic_cell = tf.contrib.rnn.BasicRNNCell(num_units=hidden, activation=tf.nn.relu) # create our RNN object
app = Application() rnn_output, states = tf.nn.dynamic_rnn(basic_cell, X, dtype=tf.float32) # choose dynamic over static
app.listen(port)
IOLoop.instance().start()
learning_rate = 0.001 # small learning rate so we don't overshoot the minimum
def exists(): stacked_rnn_output = tf.reshape(rnn_output, [-1, hidden]) # change the form into a tensor
return True 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)
xfmt = md.DateFormatter('%d.%m %H:%M:%S')
plt.subplots_adjust(bottom=0.2)
plt.xticks(rotation=25)
plt.gca().xaxis.set_major_formatter(xfmt)
dates = [dt.datetime.fromtimestamp(int(ts)) for ts in timestamps]
datenums = md.date2num(dates)
plt.plot(datenums[-40:], actual_series, "b--", linewidth=1.0, label="Actual")
plt.plot(datenums[-40:], actual_prediction, "r--", linewidth=1.0, label="Forecast")
plt.plot(datenums[-40:], actual_series.rolling(window=3, center=False).mean(), "y-", linewidth=2.0, label="Actual MA")
plt.plot(datenums[-40:], actual_prediction.rolling(window=3, center=False).mean(), "g-", linewidth=2.0, label="Predicted MA")
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__': if __name__ == '__main__':
port = int(sys.argv[1]) print("Starting prediction ...")
print("Starting arbitrage bot on port {0}...".format(port)) main()
main(port)

74
bot/market_data_crawler.py
View File

@@ -1,74 +0,0 @@
#!/usr/bin/python
import tornado.escape, tornado.httpclient
import tornado.httpclient
from tornado import gen
import time
from collections import defaultdict
@gen.coroutine
def update_market_data_for_basecoin(basecoin):
global market_data
market_data = defaultdict(list)
market_requests = []
@gen.coroutine
def call_market_data(url, response_handler):
http_client = tornado.httpclient.AsyncHTTPClient()
response = yield http_client.fetch(url)
return response_handler(response)
"""
Bittrex
"""
def handle_response_bittrex(response):
if response.error:
print("Error: %s" % response.error)
return False
else:
print("Response received from Bittrex - handling now!")
response_data = tornado.escape.json_decode(response.body)
for market in response_data["result"]:
base, target = market["MarketName"].split("-")
if base == basecoin:
market_data[target].append({"Bittrex": market["Last"]})
return True
market_requests.append(
call_market_data(
"https://bittrex.com/api/v1.1/public/getmarketsummaries",
handle_response_bittrex))
"""
Poloniex
"""
def handle_response_poloniex(response):
if response.error:
print("Error: %s" % response.error)
return False
else:
print("Response received from Poloniex - handling now!")
response_data = tornado.escape.json_decode(response.body)
for market in response_data:
base, target = market.split("_")
if base == basecoin:
market_data[target].append({"Poloniex": response_data[market]["last"]})
return True
market_requests.append(
call_market_data(
"https://poloniex.com/public?command=returnTicker",
handle_response_poloniex))
print("--- Retrieve market data now ---")
start_time = time.time()
response_dict = yield market_requests
print("--- Marked data updated in {0} seconds. Responses: {1} ---".format(time.time() - start_time, response_dict))

35
bot/shared_config.py Normal file
View File

@@ -0,0 +1,35 @@
#!/usr/bin/python
def style(s, style):
return style + s + '\033[0m'
def green(s):
return style(s, '\033[92m')
def blue(s):
return style(s, '\033[94m')
def yellow(s):
return style(s, '\033[93m')
def red(s):
return style(s, '\033[91m')
def pink(s):
return style(s, '\033[95m')
def bold(s):
return style(s, '\033[1m')
def underline(s):
return style(s, '\033[4m')
def dump(*args):
print(' '.join([str(arg) for arg in args]))

16
docs/conf.py
View File

@@ -40,8 +40,8 @@ source_suffix = '.rst'
master_doc = 'index' master_doc = 'index'
# General information about the project. # General information about the project.
project = u'sample' project = u'Crypto-Tensor Documentation'
copyright = u'2012, Kenneth Reitz' copyright = u'2017, Dennis Thiessen'
# The version info for the project you're documenting, acts as replacement for # The version info for the project you're documenting, acts as replacement for
# |version| and |release|, also used in various other places throughout the # |version| and |release|, also used in various other places throughout the
@@ -183,8 +183,8 @@ latex_elements = {
# Grouping the document tree into LaTeX files. List of tuples # Grouping the document tree into LaTeX files. List of tuples
# (source start file, target name, title, author, documentclass [howto/manual]). # (source start file, target name, title, author, documentclass [howto/manual]).
latex_documents = [ latex_documents = [
('index', 'sample.tex', u'sample Documentation', ('index', 'sample.tex', u'Crypto-Tensor Documentation',
u'Kenneth Reitz', 'manual'), u'Dennis Thiessen', 'manual'),
] ]
# The name of an image file (relative to this directory) to place at the top of # The name of an image file (relative to this directory) to place at the top of
@@ -213,8 +213,8 @@ latex_documents = [
# One entry per manual page. List of tuples # One entry per manual page. List of tuples
# (source start file, name, description, authors, manual section). # (source start file, name, description, authors, manual section).
man_pages = [ man_pages = [
('index', 'sample', u'sample Documentation', ('index', 'sample', u'Crypto-Tensor Documentation',
[u'Kenneth Reitz'], 1) [u'Dennis Thiessen'], 1)
] ]
# If true, show URL addresses after external links. # If true, show URL addresses after external links.
@@ -227,8 +227,8 @@ man_pages = [
# (source start file, target name, title, author, # (source start file, target name, title, author,
# dir menu entry, description, category) # dir menu entry, description, category)
texinfo_documents = [ texinfo_documents = [
('index', 'sample', u'sample Documentation', ('index', 'sample', u'Crypto-Tensor Documentation',
u'Kenneth Reitz', 'sample', 'One line description of project.', u'Dennis Thiessen', 'crypto-tensor', 'Predicts the future.',
'Miscellaneous'), 'Miscellaneous'),
] ]

7
requirements.txt
View File

@@ -1,3 +1,4 @@
nose numpy
sphinx matplotlib
tornado tensorflow
pandas

10
setup.py
View File

@@ -12,13 +12,13 @@ with open('LICENSE') as f:
license = f.read() license = f.read()
setup( setup(
name='sample', name='crypto-tensor',
version='0.1.0', version='0.1.0',
description='Sample package for Python-Guide.org', description='Package for Crypto-Tensor bot',
long_description=readme, long_description=readme,
author='Kenneth Reitz', author='Dennis Thiessen',
author_email='me@kennethreitz.com', author_email='dennis.thiessen@riskahead.de',
url='https://github.com/kennethreitz/samplemod', url='https://gitlab.riskahead.de/root/crypto-prediction',
license=license, license=license,
packages=find_packages(exclude=('tests', 'docs')) packages=find_packages(exclude=('tests', 'docs'))
) )