1.2 Match Engine

Matching engines match buy and sell orders that are submitted to electronic trading networks, like NASDAQ or NYSQ as examples of traditional stock exchanges; or Bitfinex, Bittrex, Coinbase as examples of crypto currency exchanges.

High frequency trading and sophisticated algorithmic trading makes up a substantial part of the participants of the electronic markets. Their servers are oftentimes co-located with the exchanges and specialized computer networks have been constructed to provide millisecond advantage for arbitraging trades between the exchanges. Large institutional traders feel that they are at a disadvantage in such an environment. [1]

This section, however, describes the mechanics of a match engine, which acts as a key component of this project in order to be able to evaluate order placement. Without a local match engine, one would be forced to consult an exchange and either trade on the live market or get access to a test environment. The behaviour of the mentioned participants are taken into account in a later step, by feeding historical data where this behaviour is already embedded.

Order Types

Explained in Section order book.

Rules

Implementation

def matchOrder(self, order, seconds=None)
    """
    Matches an Order according to its type.

    This function serves as the main interface for Order matching.
    Orders are being matched differently according to their OrderType.
    In addition, an optional time interval can be defines from how long the matching process should run and therefore simulates what is generally known as *Good Till Time (GTT)*.
    After the time is consumed, the order is either removed (e.g. neglected) in case of a standard OrderType.LIMIT or a matching on market follows in case OrderType.LIMIT_T_MARKET was defined.

    Parameters
    ----------
    order : Order
        Order defines the will to buy or sell under certain conditions.
    seconds : int
        Good Till Time (GTT)

    Returns
    -------
    [Trades]
        A list of the resulted trades resulted during the matching process.
    float
        Quantity of unexecuted assets.
    int
        Index of order book where matching stopped.
    """

def matchLimitOrder(self, order, orderbookState)
    """
    Attempts to match a limit Order in an order book state.

    Parameters
    ----------
    order : Order
        Order defines the will to buy or sell under certain conditions.
    orderbookState : OrderbookState
        The state of the order book to attempt matching the provided order

    Returns
    -------
    [Trades]
        A list of the resulted trades resulted during the matching process.
    """

def matchMarketOrder(self, order, orderbookState)
    """
    Matches an within an order book state.

    Parameters
    ----------
    order : Order
        Order defines the will to buy or sell under certain conditions.
    orderbookState : OrderbookState
        The state of the order book to attempt matching the provided order

    Returns
    -------
    [Trades]
        A list of the resulted trades resulted during the matching process.
    """

Usage

from orderbook import Orderbook
book = Orderbook()
book.loadFrom...(...)

from order import Order
from order_type import OrderType
from order_side import OrderSide
o = Order(
    orderType = OrderType.MARKET, 
    orderSide = OrderSide.BUY, 
    cty = 1.0, 
    price = 9500.0
)

from match_engine import MatchEngine
engine = MatchEngine(
    orderbook = book,
    index = 0, # Optional: defines index of order book from which to start matching from
    maxRuntime = 100 # Optional: maximum runtime for future executions required for efficiency reasons
)

trades, remaining, index = engine.matchOrder(order=o, seconds=100)

---

Inspiration taken from https://github.com/IanLKaplan/matchingEngine/wiki/Market-Order-Matching-Engines

• 1. Dark Pools: The Rise of the Machine Traders and the Rigging of the U.S. Stock Market, by Scott Patterson, Crown Business, 2012