def connect():
logger = logging.getLogger('client')
logger.setLevel('ERROR')
print("Setup was successful.")
e = Exchange()
if not e.is_connected():
e.connect()
print('connected')
else:
print('already connected')
return e
import time
from optibook.synchronous_client import Exchange
import logging
logger = logging.getLogger('client')
logger.setLevel('ERROR')
print("Setup was successful.")
# compare the bid and ask prices of the two instruments
e = Exchange()
a = e.connect()
instrument_limit = 190
instrument_limit1 = 100
orderBatch = 0
def get_info():
#print("in get_info")
print(e.get_positions())
positions = e.get_positions()
for p in positions:
print(p, positions[p])
pnl = e.get_pnl()
print("current pnl is", pnl)
class AutoTrader:
"""
This is the "main" class which houses our algorithm. You will see there are a few helper functions already here,
as well as a main "trade" function which runs the algorithm. We've done some work for you already there, but you
will need to write the bulk of the strategy yourself.
"""
def __init__(self):
self.exchange_client = Exchange()
def connect(self):
"""
Connect to the optibook exchange
"""
self.exchange_client.connect()
def get_order_book_for_instrument(self, instrument):
return self.exchange_client.get_last_price_book(instrument)
def get_position_for_instrument(self, instrument):
positions = self.exchange_client.get_positions()
return positions[instrument]
def get_top_of_book(self, order_book):
"""
Get the best bid and best ask of the order book you pass in as a parameter.
"""
best_bid_price = None
best_bid_volume = None
if len(order_book.bids) > 0:
best_bid_price = round(order_book.bids[0].price, 2)
best_bid_volume = round(order_book.bids[0].volume, 2)
best_ask_price = None
best_ask_volume = None
if len(order_book.asks) > 0:
best_ask_price = round(order_book.asks[0].price, 2)
best_ask_volume = round(order_book.asks[0].volume, 2)
return TopOfBook(best_bid_price, best_bid_volume, best_ask_price,
best_ask_volume)
def print_top_of_book(self, instrument, top_of_book):
print(
f'[{instrument}] bid({top_of_book.best_bid_volume}@{top_of_book.best_bid_price})-ask({top_of_book.best_ask_volume}@{top_of_book.best_ask_price})'
)
def insert_buy_order(self, instrument, price, volume, order_type):
"""
Insert an order to buy. Note that volume must be positive. Also note that you have no guarantee that your
order turns into a trade.
instrument: str
The name of the instrument to buy.
price: float
The price level at which to insert the order into the order book on the bid side.
volume: int
The volume to buy.
order_type: int
You can set this to 'limit' or 'ioc'. 'limit' orders stay in the book while any remaining volume of an
'ioc' that is not immediately matched is cancelled.
return:
an InsertOrderReply containing a request_id as well as an order_id, the order_id can be
used to e.g. delete or amend the limit order later.
"""
return self.exchange_client.insert_order(instrument,
price=price,
volume=volume,
side='bid',
order_type=order_type)
def insert_sell_order(self, instrument, price, volume, order_type):
"""
Insert an order to sell. Note that volume must be positive. Also note that you have no guarantee that your
order turns into a trade.
instrument: str
The name of the instrument to sell.
price: float
The price level at which to insert the order into the order book on the ask side.
volume: int
The volume to sell.
order_type: int
You can set this to 'limit' or 'ioc'. 'limit' orders stay in the book while any remaining volume of an
'ioc' that is not immediately matched is cancelled.
return:
an InsertOrderReply containing a request_id as well as an order_id, the order_id can be
used to e.g. delete or amend the limit order later.
"""
return self.exchange_client.insert_order(instrument,
price=price,
volume=volume,
side='ask',
order_type=order_type)
def increase(self, array):
last = array[0]
for element in array:
if (element < last):
return False
last = element
return True
def decrease(self, array):
last = array[0]
for element in array:
if (element > last):
return False
last = element
return True
def trade(self):
"""
This function is the main trading algorithm. It is called in a loop, and in every iteration of the loop
we do the exact same thing.
We start by getting the order books, formatting them a little bit and then you will have to make a trading
decision based on the prices in the order books.
"""
# First we get the current order books of both instruments
full_book_liquid = self.get_order_book_for_instrument(
LIQUID_INSTRUMENT)
full_book_illiquid = self.get_order_book_for_instrument(
ILLIQUID_INSTRUMENT)
# Then we extract the best bid and best ask from those order books
top_book_liquid = self.get_top_of_book(full_book_liquid)
top_book_illiquid = self.get_top_of_book(full_book_illiquid)
# If either the bid side or ask side is missing, in the order books, then we stop right here and wait for the
# next cycle, in the hopes that then the order books will have both the bid and ask sides present
if not top_book_liquid.has_bid_and_ask(
) or not top_book_illiquid.has_bid_and_ask():
print(
'There are either no bids or no asks, skipping this trade cycle.'
)
return
# Print the top of each book, this will be very helpful to you when you want to understand what your
# algorithm is doing. Feel free to add more logging as you see fit.
self.print_top_of_book(LIQUID_INSTRUMENT, top_book_liquid)
self.print_top_of_book(ILLIQUID_INSTRUMENT, top_book_illiquid)
print('')
# Trade!
# Take if from here, and implement your actual strategy with the help of the pre-processing we have done for you
# above. Note that this is very rudimentary, and there are things we have left out (e.g. position management is
# missing, hedging is missing, and how much credit you ask for is also missing).
#
# Maybe a first step is to run this code as is, and see what it prints out to get some inspiration if you are
# stuck. Otherwise, come to us, we are always happy to help. Check the client documentation for all the
# functions that are at your disposal.
#
# -----------------------------------------
# TODO: Implement trade logic here
'''
instruments = ['PHILIPS_A', 'PHILIPS_B']
SIZE = 5
for index, instrument in enumerate(instruments):
asks[index].append(self.get_top_of_book(self.get_order_book_for_instrument(instrument)).best_ask_price)
if len(asks[index]) > SIZE:
asks[index].pop(0)
bids[index].append(self.get_top_of_book(self.get_order_book_for_instrument(instrument)).best_bid_price)
if len(bids[index]) > SIZE:
bids[index].pop(0)
print(asks)
print(bids)
positions = self.exchange_client.get_positions()
stocks = positions[instrument]
if (len(bids[index]) == SIZE and self.increase(bids[index])):
print("stocks"+str(stocks))
doTrade = self.insert_sell_order(instrument, self.get_top_of_book(self.get_order_book_for_instrument(instrument)).best_bid_price, max(1, int(stocks * 1/5)), 'ioc') # come back and change volume
print("sell")
elif len(asks[index]) == SIZE and self.decrease(asks[index]) and (positions[instruments[1]] + positions[instruments[0]]) <200:
print("positions: " + str(positions[instrument]))
doTrade = self.insert_buy_order(instrument, self.get_top_of_book(self.get_order_book_for_instrument(instrument)).best_ask_price, 1, 'ioc') #change volvume
print("buy")
'''
'''
instruments = ['PHILIPS_A', 'PHILIPS_B']
SIZE = 100
for index, instrument in enumerate(instruments):
aux = self.get_top_of_book(self.get_order_book_for_instrument(instrument)).best_ask_price
if aux:
asks[index].append(aux)
if len(asks[index]) > SIZE:
asks[index].pop(0)
aux = self.get_top_of_book(self.get_order_book_for_instrument(instrument)).best_bid_price
bids[index].append(aux)
if len(bids[index]) > SIZE:
bids[index].pop(0)
if SIZE == len(asks[index]):
averageAsk = sum(asks[index]) / len(asks)
averageBid = sum(bids[index]) / len(bids)
positions = self.exchange_client.get_positions()
stocks = positions[instrument]
if averageBid > self.get_top_of_book(self.get_order_book_for_instrument(instrument)).best_bid_price and abs(stocks) < 100:
print(abs(stocks))
doTrade = self.insert_buy_order(instrument, self.get_top_of_book(self.get_order_book_for_instrument(instrument)).best_bid_price - 0.5, , 'ioc')
print("buy")
if averageAsk < self.get_top_of_book(self.get_order_book_for_instrument(instrument)).best_ask_price :
doTrade = self.insert_sell_order(instrument, self.get_top_of_book(self.get_order_book_for_instrument(instrument)).best_ask_price + 0.5, max(1, int(stocks * 1/2)), 'ioc') # come back and change volume
print("sell")
'''
bidA = None
askB = None
instruments = ['PHILIPS_A', 'PHILIPS_B']
SIZE = 1
while (not askB) or (not bidA):
bidA = self.get_top_of_book(
self.get_order_book_for_instrument(
instruments[0])).best_bid_price
askB = self.get_top_of_book(
self.get_order_book_for_instrument(
instruments[1])).best_ask_price
if bidA - askB > 0 and askB < 1000:
doTrade = self.insert_sell_order(instruments[0], bidA, SIZE, 'ioc')
doTrade = self.insert_buy_order(instruments[1], askB, SIZE, 'ioc')
print("bidA and askB")
bidB = None
askA = None
while (not askA) or (not bidB):
bidB = self.get_top_of_book(
self.get_order_book_for_instrument(
instruments[1])).best_bid_price
askA = self.get_top_of_book(
self.get_order_book_for_instrument(
instruments[0])).best_ask_price
if bidB - askA > 0 and askA < 1000:
doTrade = self.insert_sell_order(instruments[1], bidB, SIZE, 'ioc')
doTrade = self.insert_buy_order(instruments[0], askA, SIZE, 'ioc')
print("bidB and askA")
class AutoTrader:
"""
This is the "main" class which houses our algorithm. You will see there are a few helper functions already here,
as well as a main "trade" function which runs the algorithm. We've done some work for you already there, but you
will need to write the bulk of the strategy yourself.
"""
def __init__(self):
self.exchange_client = Exchange()
def connect(self):
"""
Connect to the optibook exchange
"""
self.exchange_client.connect()
def get_order_book_for_instrument(self, instrument):
return self.exchange_client.get_last_price_book(instrument)
def get_position_for_instrument(self, instrument):
positions = self.exchange_client.get_positions()
return positions[instrument]
def get_top_of_book(self, order_book):
"""
Get the best bid and best ask of the order book you pass in as a parameter.
"""
best_bid_price = None
best_bid_volume = None
if len(order_book.bids) > 0:
best_bid_price = round(order_book.bids[0].price, 2)
best_bid_volume = round(order_book.bids[0].volume, 2)
best_ask_price = None
best_ask_volume = None
if len(order_book.asks) > 0:
best_ask_price = round(order_book.asks[0].price, 2)
best_ask_volume = round(order_book.asks[0].volume, 2)
return TopOfBook(best_bid_price, best_bid_volume, best_ask_price,
best_ask_volume)
def print_top_of_book(self, instrument, top_of_book):
print(
f'[{instrument}] bid({top_of_book.best_bid_volume}@{top_of_book.best_bid_price})-ask({top_of_book.best_ask_volume}@{top_of_book.best_ask_price})'
)
def insert_buy_order(self, instrument, price, volume, order_type):
"""
Insert an order to buy. Note that volume must be positive. Also note that you have no guarantee that your
order turns into a trade.
instrument: str
The name of the instrument to buy.
price: float
The price level at which to insert the order into the order book on the bid side.
volume: int
The volume to buy.
order_type: int
You can set this to 'limit' or 'ioc'. 'limit' orders stay in the book while any remaining volume of an
'ioc' that is not immediately matched is cancelled.
return:
an InsertOrderReply containing a request_id as well as an order_id, the order_id can be
used to e.g. delete or amend the limit order later.
"""
return self.exchange_client.insert_order(instrument,
price=price,
volume=volume,
side='bid',
order_type=order_type)
def insert_sell_order(self, instrument, price, volume, order_type):
"""
Insert an order to sell. Note that volume must be positive. Also note that you have no guarantee that your
order turns into a trade.
instrument: str
The name of the instrument to sell.
price: float
The price level at which to insert the order into the order book on the ask side.
volume: int
The volume to sell.
order_type: int
You can set this to 'limit' or 'ioc'. 'limit' orders stay in the book while any remaining volume of an
'ioc' that is not immediately matched is cancelled.
return:
an InsertOrderReply containing a request_id as well as an order_id, the order_id can be
used to e.g. delete or amend the limit order later.
"""
return self.exchange_client.insert_order(instrument,
price=price,
volume=volume,
side='ask',
order_type=order_type)
def trade(self):
"""
This function is the main trading algorithm. It is called in a loop, and in every iteration of the loop
we do the exact same thing.
We start by getting the order books, formatting them a little bit and then you will have to make a trading
decision based on the prices in the order books.
"""
# First we get the current order books of both instruments
full_book_liquid = self.get_order_book_for_instrument(
LIQUID_INSTRUMENT)
full_book_illiquid = self.get_order_book_for_instrument(
ILLIQUID_INSTRUMENT)
# Then we extract the best bid and best ask from those order books
top_book_liquid = self.get_top_of_book(full_book_liquid)
top_book_illiquid = self.get_top_of_book(full_book_illiquid)
# If either the bid side or ask side is missing, in the order books, then we stop right here and wait for the
# next cycle, in the hopes that then the order books will have both the bid and ask sides present
if not top_book_liquid.has_bid_and_ask(
) or not top_book_illiquid.has_bid_and_ask():
print(
'There are either no bids or no asks, skipping this trade cycle.'
)
return
# Print the top of each book, this will be very helpful to you when you want to understand what your
# algorithm is doing. Feel free to add more logging as you see fit.
self.print_top_of_book(LIQUID_INSTRUMENT, top_book_liquid)
self.print_top_of_book(ILLIQUID_INSTRUMENT, top_book_illiquid)
print('')
class Bot:
instruments = ["PHILIPS_A", "PHILIPS_B"]
def __init__(self):
self.e = Exchange()
logging.info(self.e.connect())
logging.info("Setup was successful.")
def get_out_of_positions(self):
# Get out of all positions you are currently holding, regardless of the loss involved. That means selling whatever
# you are long, and buying-back whatever you are short. Be sure you know what you are doing when you use this logic.
print(self.e.get_positions())
for s, p in self.e.get_positions().items():
if p > 0:
self.e.insert_order(s,
price=1,
volume=p,
side='ask',
order_type='ioc')
elif p < 0:
self.e.insert_order(s,
price=100000,
volume=-p,
side='bid',
order_type='ioc')
print(self.e.get_positions())
# Logging functions
def log_new_trade_ticks(self):
logger.info("Polling new trade ticks")
for i in self.instruments:
tradeticks = self.e.poll_new_trade_ticks(i)
for t in tradeticks:
logger.info(
f"[{t.instrument_id}] price({t.price}), volume({t.volume}), aggressor_side({t.aggressor_side}), buyer({t.buyer}), seller({t.seller})"
)
def log_positions_cash(self):
logger.info(self.e.get_positions_and_cash())
def log_all_outstanding_orders(self):
for i in self.instruments:
logger.info(self.e.get_outstanding_orders(i))
def wait_until_orders_complete(self):
orders_outstanding = True
while orders_outstanding:
orders_outstanding = False
for i in self.instruments:
if len(self.e.get_outstanding_orders(i)) > 0:
orders_outstanding = True
self.log_all_outstanding_orders()
#time.sleep(0.1)
def mainloop(self):
while True:
# check for trade differences
# m1 ask < m2 bid
#logger.info("Checking for discrepancies:")
books = [self.e.get_last_price_book(x) for x in self.instruments]
for m1, m2 in [(0, 1), (1, 0)]:
m1_id = self.instruments[m1]
m2_id = self.instruments[m2]
try:
m1_ask = books[m1].asks[0]
m2_bid = books[m2].bids[0]
if m1_ask.price < m2_bid.price:
logger.info(
f"Can profit: buy {m1_id} at {m1_ask} and sell {m2_id} at {m2_bid}"
)
self.e.insert_order(m1_id,
price=m1_ask.price,
volume=1,
side='bid',
order_type='limit')
self.e.insert_order(m2_id,
price=m2_bid.price,
volume=1,
side='ask',
order_type='limit')
self.log_all_outstanding_orders()
self.wait_until_orders_complete()
self.log_positions_cash()
except Exception as e:
print(logger.error(e))
continue
time.sleep(1.0 / 25)
import time
import logging
from optibook.synchronous_client import Exchange
from strategy import should_kill_attempt, arbitrage, stoikov_mm
from utils import balance_positions
from moving_average import MovingAverage
logging.getLogger('client').setLevel('ERROR')
exchange = Exchange()
exchange.connect()
START_PNL = exchange.get_pnl()
ma_A = MovingAverage(exchange, "PHILIPS_A")
tick = 1
while not should_kill_attempt(exchange, START_PNL):
time.sleep(0.11)
print(f"tick {tick}")
tick += 1
ma_A.update()
# Don't want to balance our trades from our MM positions
exchange.delete_orders("PHILIPS_A")
def connect():
global exchange
exchange = Exchange()
return exchange.connect()
import safe_dual_strat
import difference_strat
import extremes_strat
import dual_paper_strat
import dual_hedge_strat
import mimic_strat
import sys
logger = logging.getLogger('client')
logger.setLevel('INFO')
# Main loop: run a number of different strategies
if __name__ == '__main__':
exchange = Exchange()
logging.info(exchange.connect())
logging.info("Setup was successful.")
try:
# we can run multiple strategies at once, but really having separate bots to do this would be simpler
# since currently we can't ignore our own orders in aggregate data such as the pricebook
# strats = [dual_market_strat.DualMarketStrat(exchange, ["PHILIPS_A", "PHILIPS_B"])]
# strats = [safe_dual_strat.SafeDualStrat(exchange, ["PHILIPS_A", "PHILIPS_B"])]
# strats = [difference_strat.DifferenceStrat(exchange, ["PHILIPS_A", "PHILIPS_B"])]
#strats = [dual_paper_strat.DualPaperStrat(exchange, ["PHILIPS_A", "PHILIPS_B"])]
strats = [dual_hedge_strat.DualHedgeStrat(exchange, "PHILIPS_A", "PHILIPS_B")]
while True:
time.sleep(0.2)
for s in strats:
try:
s.update();
except AssertionError as e: # if get disconnected than do something else