def make_orders(exchange: Exchange, orders: List[Order]):
# Orders in form of [Order]
order_results = list(
map(
lambda order: exchange.insert_order(order.instrument_id,
price=order.price,
volume=order.volume,
side=order.side,
order_type=order.order_type),
orders))
for (order, order_id) in zip(orders, order_results):
trade_history = exchange.get_trade_history(order.instrument_id)
if len(trade_history) > 0 and trade_history[-1].order_id == order_id:
success_msg = "SUCCESS "
elif order.order_type == "ioc":
success_msg = "FAILED "
else:
success_msg = "OUTSTANDING"
order_log = f"Order: {order_id} {success_msg} | Instrument: {order.instrument_id} | Price: {order.price:.1f} | Volume: {order.volume} | Side: {order.side} | Order Type: {order.order_type}"
g_recent_orders.pop(0)
g_recent_orders.append(order_log)
print(order_log)
def clear_all_positions(exchange: Exchange) -> None:
""" Clear all positions without regard to loss
"""
for s, p in exchange.get_positions().items():
if p > 0:
exchange.insert_order(s,
price=1,
volume=p,
side='ask',
order_type='ioc')
elif p < 0:
exchange.insert_order(s,
price=100000,
volume=-p,
side='bid',
order_type='ioc')
if volume > 3:
volume = 3
#if volume > 10:
if ((price_A - mid_price) / 2 > (mid_price - price_B)):
if ((positions['PHILIPS_B'] > -instrument_limit1
or positions['PHILIPS_A'] < instrument_limit1)
and abs(positions['PHILIPS_B']) +
abs(positions['PHILIPS_A']) < 400):
print("sell B at:", price_B, " and buy A:", price_A,
" for", volume, " lots")
result = e.insert_order('PHILIPS_B',
price=price_B,
volume=volume,
side='ask',
order_type='ioc')
print(f"Order Id: {result} 1 B-ask")
#if result != None:
#result = e.insert_order('PHILIPS_A', price=price_A, volume=volume, side='bid', order_type='ioc')
print(f"Order Id: {result} 2 A-bid")
# clearing of positions using limit order from weighted mid price
mid_price = round((price_A + price_B) / 2, 3)
diff = round(abs(price_A - mid_price), 3)
print(mid_price)
print(mid_price + .10)
import time
import logging
from statistics import mean
import random
logger = logging.getLogger('client')
logger.setLevel('ERROR')
print("Setup was successful.")
e = Exchange()
a = e.connect()
print(e.get_positions())
for s, p in e.get_positions().items():
if p > 0:
e.insert_order(s, price=1, volume=p, side='ask', order_type='ioc')
elif p < 0:
e.insert_order(s,
price=100000,
volume=-p,
side='bid',
order_type='ioc')
print(e.get_positions())
instrument_id = 'PHILIPS_A'
book = e.get_last_price_book(instrument_id)
print(book.bids)
instrument_id = 'PHILIPS_A'
#result = e.insert_order(instrument_id, price=98, volume=40, side='bid', order_type='limit')
#print(f"Order Id: {result}")
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)
# bid, volume p, price 1 - 'I want to buy p units at price 1'
# ask, volume p, price 1 - 'I want to sell p units at price 1'
# print(e.get_trade_history(instrument_id))
#book = e.get_last_price_book(instrument_id)
#print(book.bids)
#print(book.asks)
# current positions
print(e.get_positions_and_cash())
processed_order_book = e.get_last_price_book(instrument_id)
print("bid | price | ask")
for level in processed_order_book:
print(f"{level.bid_volume}|{level.price_level}|{level.ask_volume}")
# e.insert_order(instrument_id, price=71.70, volume=1, side='bid', order_type='limit')
"""
# buy something
e.insert_order(instrument_id, price=70, volume=1, side='bid', order_type='limit')
print(e.get_positions_and_cash())
time.sleep(1)
# sell something
e.insert_order(instrument_id, price=71, volume=1, side='ask', order_type='limit')
print(e.get_positions_and_cash())
"""
average = average_b(2000)
#print(average)
positions = e.get_positions_and_cash()
volume_left_a = positions[philips_a]['volume']
volume_left_b = positions[philips_b]['volume']
# if current_bid_a-np.mean(weighted_prices_a)>1.5:
# SELLING A WHEN HIGH
if current_bid_a > np.mean(weighted_prices_a) and volume_left_a > -200:
price = current_bid_a
volume = max(int(10 * (current_bid_a - stats_a[1]) / max_range_a), 1)
buying_order = e.insert_order(philips_a,
price=current_bid_a,
volume=float(volume),
side='ask',
order_type='limit')
print('Selling %s A at price %s ****** Expect to buy at %s' %
(volume, current_bid_a, np.mean(weighted_prices_a)))
bought_prices.append(price)
updated_weights = [price] * volume
weighted_prices_a = updated_weights + weighted_prices_a
time.sleep(1)
timer += 1
# BUYING A WHEN LOW
if current_ask_a < np.mean(weighted_prices_a) and volume_left_a < 200:
sell_volume = min(
int(10 * (np.mean(weighted_prices_a) - current_ask_a) *
max_range_a), np.abs(volume_left_a))
instrument=INSTRUMENT,
instrumentB="PHILIPS_A",
orderVolume=ORDER_VOLUME,
weightingFactor=WEIGHTING_FACTOR,
volumeWeighting=VOLUME_WEIGHTING)
executing = True
count = 0
while executing:
firstTrader.trade()
firstTrader.hedge()
# This is a last-minute strategy to take advantage of other people's inefficient algorithms
# We set up very favourable orders in an attempt to catch those without appropriate checks on their order volume and price
e.insert_order("PHILIPS_B",
price=20.1,
volume=300,
side="bid",
order_type="limit")
e.insert_order("PHILIPS_B",
price=139.9,
volume=300,
side="ask",
order_type="limit")
if count > TRADES:
executing = False
count += 1
sleep(TIME_PERIOD)
print(diagonosticsOutput)
firstTrader.close()
from optibook.synchronous_client import Exchange
from MainFunctions import getBestAsk
import time
e = Exchange()
a = e.connect()
instr_ids = ['PHILIPS_A', 'PHILIPS_B']
index = int(input()) # 0 or 1
instr = instr_ids[index]
book = e.get_last_price_book(instr)
positions = e.get_positions().values()
totalPosition = sum(positions)
if (book.asks[0].volume >= abs(totalPosition)):
print("Estimated cash by end")
cash = e.get_cash()
bestAsk = getBestAsk(book)
loss = bestAsk * abs(totalPosition)
print(cash - loss)
print("Proceed?")
if (input() == "y"):
e.insert_order(instr,
price=bestAsk,
volume=abs(totalPosition),
side='bid',
order_type='ioc')
time.sleep(5)
time.sleep(TIME_DELAY)
data, prev_data = get_data(e, prev_data)
moving_sum.append((data[0][2] + data[1][2])/2)
if len(moving_sum) > MAX_HISTORY: moving_sum.pop(0)
momentum = sum(moving_sum[-MOMENTUM_HISTORY:])/MOMENTUM_HISTORY
moving_ave = sum(moving_sum)/len(moving_sum)
moving_ave = (momentum + moving_ave) / 2
print(moving_ave)
positions = e.get_positions()
if data[0][0] > moving_ave + THRESHOLD and positions["PHILIPS_A"] > -MAX_POSITION:
#PHILIPS_A is buying at above moving_ave + THRESHOLD
#time to sell
result = e.insert_order("PHILIPS_A", price=data[0][0], volume=VOLUME, side='ask', order_type='ioc')
print("created order to sell PHILIPS_A")
elif data[0][1] < moving_ave - THRESHOLD and positions["PHILIPS_A"] < MAX_POSITION:
#PHILIPS_A is selling at below moving_ave - THRESHOLD
#time to buy
result = e.insert_order("PHILIPS_A", price=data[0][1], volume=VOLUME, side='bid', order_type='ioc')
print("created order to buy PHILIPS_A")
elif data[1][0] > moving_ave + THRESHOLD and positions["PHILIPS_B"] > -MAX_POSITION:
#PHILIPS_B is buying at above moving_ave + THRESHOLD
#time to sell
result = e.insert_order("PHILIPS_B", price=data[1][0], volume=VOLUME, side='ask', order_type='ioc')
print("created order to sell PHILIPS_B")
elif data[1][1] < moving_ave - THRESHOLD and positions["PHILIPS_B"] < MAX_POSITION:
#PHILIPS_B is selling at below moving_ave - THRESHOLD
#time to buy
result = e.insert_order("PHILIPS_B", price=data[1][1], volume=VOLUME, side='bid', order_type='ioc')
return 5
#execute
lowLiq = "PHILIPS_B"
highLiq = "PHILIPS_A"
count_trades = 0
while (count_trades < 10):
hLBook = e.get_last_price_book(highLiq)
bestAsk = getBestAsk(hLBook)
if (bestAsk is not None):
e.insert_order(highLiq,
price=bestAsk + 0.1,
volume=5,
side='ask',
order_type='limit')
time.sleep(get_lag() - 2)
currentTradeId = e.insert_order(lowLiq,
price=bestAsk,
volume=5,
side='bid',
order_type='limit')
print("Sell to A")
count_trades = count_trades + 1
#trades = e.poll_new_trades("PHILIPS_B")
time.sleep(3)
if (e.get_outstanding_orders("PHILIPS_B").values()):
# While there are still outstanding orders
e.insert_order(highLiq,
from optibook.synchronous_client import Exchange
from MainFunctions import getBestBid
import time
e = Exchange()
a = e.connect()
instr_ids = ['PHILIPS_A', 'PHILIPS_B']
index = int(input()) # 0 or 1
instr = instr_ids[index]
book = e.get_last_price_book(instr)
positions = e.get_positions().values()
totalPosition = sum(positions)
if (book.bids[0].volume >= abs(totalPosition)):
print("Estimated cash by end")
cash = e.get_cash()
bestBid = getBestBid(book)
gain = bestBid * abs(totalPosition)
print(cash + gain)
print("Proceed?")
if (input() == "y"):
e.insert_order(instr,
price=bestBid,
volume=abs(totalPosition),
side='ask',
order_type='ioc')
time.sleep(5)
from optibook.synchronous_client import Exchange
e = Exchange()
a = e.connect()
# Simple algorithm to clear all current positions so that it is easier to test hedging algorithms
for s, p in e.get_positions().items():
if p > 0:
e.insert_order(s, price=1, volume=p, side='ask', order_type='ioc')
elif p < 0:
e.insert_order(s,
price=100000,
volume=-p,
side='bid',
order_type='ioc')
time.sleep(0.25)
else:
A_best_bid = e.get_last_price_book(instrument_id1).bids[0].price
A_best_ask = e.get_last_price_book(instrument_id1).asks[0].price
B_best_bid = e.get_last_price_book(instrument_id2).bids[0].price
B_best_ask = e.get_last_price_book(instrument_id2).asks[0].price
# print(A_best_bid, A_best_ask, B_best_bid, B_best_ask)
if A_best_bid > B_best_ask:
A_best_bid_vol = e.get_last_price_book(instrument_id1).bids[0].volume
B_best_ask_vol = e.get_last_price_book(instrument_id2).asks[0].volume
volume = min(A_best_bid_vol, B_best_ask_vol)
result = e.insert_order(instrument_id1, price = A_best_bid, volume=volume, side='bid', order_type='limit')
result = e.insert_order(instrument_id2, price = B_best_ask, volume=volume, side='ask', order_type='limit')
print(f"Order Id: {result}")
if B_best_bid > A_best_ask:
A_best_ask_vol = e.get_last_price_book(instrument_id1).asks[0].volume
B_best_bid_vol = e.get_last_price_book(instrument_id2).bids[0].volume
volume = min(A_best_ask_vol, B_best_bid_vol)
result = e.insert_order(instrument_id2, price = B_best_bid, volume=volume, side='bid', order_type='limit')
result = e.insert_order(instrument_id1, price = A_best_ask, volume=volume, side='ask', order_type='limit')
print(f"Order Id: {result}")
time.sleep(0.25)