def __init__(self, id, name, type, mkt_open, mkt_close, symbols, book_freq='S', pipeline_delay = 40000,
computation_delay = 1, stream_history = 0, log_orders = False, random_state = None):
super().__init__(id, name, type, random_state)
# Do not request repeated wakeup calls.
self.reschedule = False
# Store this exchange's open and close times.
self.mkt_open = mkt_open
self.mkt_close = mkt_close
# Right now, only the exchange agent has a parallel processing pipeline delay. This is an additional
# delay added only to order activity (placing orders, etc) and not simple inquiries (market operating
# hours, etc).
self.pipeline_delay = pipeline_delay
# Computation delay is applied on every wakeup call or message received.
self.computation_delay = computation_delay
# The exchange maintains an order stream of all orders leading to the last L trades
# to support certain agents from the auction literature (GD, HBL, etc).
self.stream_history = stream_history
# Log all order activity?
self.log_orders = log_orders
# Create an order book for each symbol.
self.order_books = {}
for symbol in symbols:
self.order_books[symbol] = OrderBook(self, symbol)
# At what frequency will we archive the order books for visualization and analysis?
self.book_freq = book_freq
def __init__(self,
id,
name,
type,
mkt_open,
mkt_close,
symbols,
book_freq='S',
wide_book=False,
pipeline_delay=40000,
computation_delay=1,
stream_history=0,
log_orders=False,
random_state=None):
super().__init__(id, name, type, random_state)
# dummy for printing purposes
self.counter = 0
# Do not request repeated wakeup calls.
self.reschedule = False
# Store this exchange's open and close times.
self.mkt_open = mkt_open
self.mkt_close = mkt_close
# Right now, only the exchange agent has a parallel processing pipeline delay. This is an additional
# delay added only to order activity (placing orders, etc) and not simple inquiries (market operating
# hours, etc).
self.pipeline_delay = pipeline_delay
# Computation delay is applied on every wakeup call or message received.
self.computation_delay = computation_delay
# The exchange maintains an order stream of all orders leading to the last L trades
# to support certain agents from the auction literature (GD, HBL, etc).
self.stream_history = stream_history
# Log all order activity?
self.log_orders = log_orders
# Create an order book for each symbol.
self.order_books = {}
for symbol in symbols:
self.order_books[symbol] = OrderBook(self, symbol)
# At what frequency will we archive the order books for visualization and analysis?
self.book_freq = book_freq
# Store orderbook in wide format? ONLY WORKS with book_freq == 0
self.wide_book = wide_book
# The subscription dict is a dictionary with the key = agent ID,
# value = dict (key = symbol, value = list [levels (no of levels to recieve updates for),
# frequency (min number of ns between messages), last agent update timestamp]
# e.g. {101 : {'AAPL' : [1, 10, pd.Timestamp(10:00:00)}}
self.subscription_dict = {}
def __init__(self,
id,
name,
mkt_open,
mkt_close,
symbols,
book_freq='S',
pipeline_delay=40000,
computation_delay=1,
stream_history=0):
super().__init__(id, name)
# Do not request repeated wakeup calls.
self.reschedule = False
# Store this exchange's open and close times.
self.mkt_open = mkt_open
self.mkt_close = mkt_close
# Right now, only the exchange agent has a parallel processing pipeline delay.
self.pipeline_delay = pipeline_delay
self.computation_delay = computation_delay
# The exchange maintains an order stream of all orders leading to the last L trades
# to support certain agents from the auction literature (GD, HBL, etc).
self.stream_history = stream_history
# Create an order book for each symbol.
self.order_books = {}
for symbol in symbols:
self.order_books[symbol] = OrderBook(self, symbol)
# At what frequency will we archive the order books for visualization and analysis?
self.book_freq = book_freq