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Dennis Thiessen
2017-11-20 13:31:41 +01:00
parent 95bdeee9e2
commit 1c330ae177
14 changed files with 552 additions and 450 deletions
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12
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</component> </component>
</project> </project>

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
--------------- ---------------

144
bot/core.py
View File

@@ -1,71 +1,113 @@
#!/usr/bin/python #!/usr/bin/python
from . import market_data_crawler, market_data_analyzer, shared_config import time
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
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': "None"} dump(yellow("Retrieving market data from API"))
request = json.loads(self.request.body.decode('utf-8'))
if "token" not in request or request["token"] != "den": 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"] == "start_bot": dump(green("Retrieved API in {0:.3f}ms sec".format((time.time() - start_time)*100)))
shared_config.run_bot = True dump(yellow("Initialize Tensorflow"))
elif request["command"] == "stop_bot":
shared_config.run_bot = False
else:
response["msg"] = "Unknown command"
self.write(json.dumps(response)) 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
@gen.coroutine TS = np.array(pricevol)
def delete(self): TSo = np.array(prices)
print("Stopping server...")
response_json = json.dumps({'error': False, 'msg': "Server stopped"}) x_data = TS[:(len(TS) - (len(TS) % num_periods))]
self.write(response_json) x_batches = x_data.reshape(-1, 20, 2)
IOLoop.instance().stop() 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
class Application(tornado.web.Application): X_test, Y_test = test_data(f_horizon, num_periods)
def __init__(self):
handlers = [
(r"/?", MainHandler)
]
tornado.web.Application.__init__(self, handlers)
tf.reset_default_graph() # We didn't have any previous graph objects running, but this would reset the graphs
@gen.coroutine X = tf.placeholder(tf.float32, [None, num_periods, inputs]) # create variable objects
def run_bot(): y = tf.placeholder(tf.float32, [None, num_periods, output])
while True:
yield gen.sleep(30)
if shared_config.run_bot:
market_data_analyzer.calculate_arbitrage_opportunities(['kraken', 'bitfinex', 'binance', 'hitbtc', 'gdax', 'bittrex', 'poloniex'])
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
def main(port): learning_rate = 0.001 # small learning rate so we don't overshoot the minimum
app = Application()
app.listen(port) stacked_rnn_output = tf.reshape(rnn_output, [-1, hidden]) # change the form into a tensor
run_bot() stacked_outputs = tf.layers.dense(stacked_rnn_output, output) # specify the type of layer (dense)
IOLoop.instance().start() 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__': if __name__ == '__main__':
port = int(sys.argv[1]) print("Starting prediction ...")
print("Starting arbitrage bot on port {0}...".format(port)) main()
main(port)

70
bot/market_data_analyzer.py
View File

@@ -1,70 +0,0 @@
#!/usr/bin/python
from . import market_data_crawler
from bot.shared_config import *
import time
import sys
import operator
import pprint
def calculate_arbitrage_opportunities(exchanges):
start_time = time.time()
market_data = market_data_crawler.update_market_data_for_symbol_and_exchange(exchanges)
sorted_market_data = {}
for exchange_name, order_books in market_data.items():
for order_book in order_books:
symbol = order_book['symbol']
new_dictionary = {symbol:
{exchange_name:
{"bids": order_book['bids'][:5],
"asks": order_book['asks'][:5],
"timestamp": order_book['timestamp']}}}
if symbol not in sorted_market_data.keys():
sorted_market_data.update(new_dictionary)
else:
sorted_market_data[symbol].update(new_dictionary[symbol])
dump(green(str(len(sorted_market_data))), "possible symbols found in total:", ' '.join(sorted_market_data.keys()))
market_opport = {}
for symbol, exchanges in sorted_market_data.items():
lowest_ask = None
highest_bid = None
market_opport.update({symbol: {}})
for exchange_name, order_book in exchanges.items():
if lowest_ask is None or lowest_ask['value'] < order_book['asks'][0]:
lowest_ask = {"exchange_name":exchange_name,
"value":order_book['asks'][0],
"order_book": order_book['asks'][:3]}
if highest_bid is None or highest_bid['value'] > order_book['bids'][0]:
highest_bid = {"exchange_name": exchange_name,
"value": order_book['bids'][0],
"order_book": order_book['bids'][:3]}
spread = float(highest_bid['value'][0]) - float(lowest_ask['value'][0])
market_opport[symbol].update({"highest_bid": highest_bid,
"lowest_ask": lowest_ask,
"spread": spread,
"spread_perc": round((spread / float(highest_bid['value'][0])) * 100, 2),
"symbol": symbol})
if spread > 0:
with open("market_opportunity_found.txt", "a") as file:
file.write("\n+n--- Arbitrage oppportunity found! ---\n\n")
pprint.pprint(market_opport[symbol], stream=file)
sorted_list = sorted(market_opport.values(), key=operator.itemgetter("spread_perc"), reverse=True)
with open("market_analyzation.txt", "w") as file:
pprint.pprint(sorted_list, stream=file)
print("--- Arbitrage oportunities calculated in {0:.3f}ms ---".format((time.time() - start_time)*100))
return market_opport
if __name__ == '__main__':
calculate_arbitrage_opportunities(sys.argv[1:])

159
bot/market_data_crawler.py
View File

@@ -1,159 +0,0 @@
#!/usr/bin/python
from collections import defaultdict
from bot.shared_config import *
import sys
import time
import asyncio
import ccxt.async as ccxt
market_data = defaultdict(list)
def update_market_data_for_symbol_and_exchange(exchanges):
if len(exchanges) > 1:
start_time = time.time()
ids = list(exchanges)
exchanges = {}
for id in ids:
# instantiate the exchange by id
exchange = getattr(ccxt, id)()
# save it in a dictionary under its id for future use
exchanges[id] = exchange
dump(yellow('Loading'), 'market data for following exchanges:', ' '.join(ids))
exchanges = fetch_all_markets(exchanges)
allSymbols = [symbol for id in ids for symbol in exchanges[id].symbols]
# get all unique symbols
uniqueSymbols = list(set(allSymbols))
# filter out symbols that are not present on at least two exchanges
arbitrableSymbols = sorted([symbol for symbol in uniqueSymbols if allSymbols.count(symbol) > 1])
# filter out symbols which have a different basecoin
arbitrableSymbols = sorted([symbol for symbol in arbitrableSymbols if '/'+basecoin in symbol])
dump(yellow('Loading'), 'order books for following exchanges:', ' '.join(ids))
exchanges = fetch_all_order_books(exchanges, arbitrableSymbols)
dump(green('Finished!'), 'Responsetime:', red("{:.2f}ms".format((time.time() - start_time) * 100)))
with open("market_data.txt", "w") as file:
for exchange_name, order_books in market_data.items():
file.write("\nMarket: {}".format(exchange_name))
for order_book in order_books:
file.write("\n Order Book: {0}".format(order_book))
return market_data
else:
dump(red("Invalid number of arguments given"))
return None
def fetch_all_order_books(exchanges, arbitrableSymbols):
ob_start_time = time.time()
async def fetch_single_order_books(exchange, arbitrableSymbols):
order_books = []
available_symbols = (symbol for symbol in arbitrableSymbols if symbol in exchange.symbols)
for symbol in available_symbols:
# basic round-robin proxy scheduler
currentProxy = -1
maxRetries = len(proxies)
for numRetries in range(0, maxRetries):
# try proxies in round-robin fashion
currentProxy = (currentProxy + 1) % len(proxies)
try: # try to load exchange markets using current proxy
tmp_order_book = await exchange.fetch_order_book(symbol)
tmp_order_book['symbol'] = symbol
order_books.append(tmp_order_book)
break
except ccxt.DDoSProtection as e:
dump(yellow(type(e).__name__), e.args)
await asyncio.sleep(exchange.rateLimit / 500)
except ccxt.RequestTimeout as e:
dump(yellow(type(e).__name__), e.args)
except ccxt.AuthenticationError as e:
dump(yellow(type(e).__name__), e.args)
except ccxt.ExchangeNotAvailable as e:
dump(yellow(type(e).__name__), e.args)
except ccxt.ExchangeError as e:
dump(yellow(type(e).__name__), e.args)
except ccxt.NetworkError as e:
dump(yellow(type(e).__name__), e.args)
except Exception as e: # reraise all other exceptions
raise
dump(' ', green(exchange.id), 'loaded', green(str(len(order_books))), 'order books in', red("{:.2f}ms".format((time.time() - ob_start_time) * 100)))
market_data[exchange.id] = order_books
async_executor = []
for exchange_name, exchange in exchanges.items():
# add future to list
async_executor.append(asyncio.ensure_future(fetch_single_order_books(exchange, arbitrableSymbols)))
# wait till all futures in list completed
asyncio.get_event_loop().run_until_complete(asyncio.gather(*async_executor))
return exchanges
def fetch_all_markets(exchanges):
start_time_markets = time.time()
async def fetch_single_market(exchange):
# basic round-robin proxy scheduler
currentProxy = -1
maxRetries = len(proxies)
for numRetries in range(0, maxRetries):
# try proxies in round-robin fashion
currentProxy = (currentProxy + 1) % len(proxies)
try: # try to load exchange markets using current proxy
exchange.proxy = proxies[currentProxy]
await exchange.load_markets()
break
except ccxt.DDoSProtection as e:
dump(yellow(type(e).__name__), e.args)
except ccxt.RequestTimeout as e:
dump(yellow(type(e).__name__), e.args)
except ccxt.AuthenticationError as e:
dump(yellow(type(e).__name__), e.args)
except ccxt.ExchangeNotAvailable as e:
dump(yellow(type(e).__name__), e.args)
except ccxt.ExchangeError as e:
dump(yellow(type(e).__name__), e.args)
except ccxt.NetworkError as e:
dump(yellow(type(e).__name__), e.args)
except Exception as e: # reraise all other exceptions
raise
dump(' ', green(exchange.id), 'loaded', green(str(len(exchange.symbols))), 'markets')
async_executor = []
for exchange_name, exchange in exchanges.items():
# add future to list
async_executor.append(asyncio.ensure_future(fetch_single_market(exchange)))
# wait till all futures in list completed
asyncio.get_event_loop().run_until_complete(asyncio.gather(*async_executor))
dump(green('Loaded all markets!'), 'Responsetime:', red("{:.2f}ms".format((time.time() - start_time_markets) * 100)))
return exchanges
if __name__ == '__main__':
update_market_data_for_symbol_and_exchange(sys.argv[1:])

9
bot/shared_config.py
View File

@@ -33,12 +33,3 @@ def underline(s):
def dump(*args): def dump(*args):
print(' '.join([str(arg) for arg in args])) print(' '.join([str(arg) for arg in args]))
proxies = [
'', # no proxy by default
'https://cors-anywhere.herokuapp.com/',
]
basecoin = "ETH"
run_bot = False

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'),
] ]

8
requirements.txt
View File

@@ -1,4 +1,4 @@
nose numpy
sphinx matplotlib
tornado tensorflow
ccxt 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'))
) )