Problem-
An inefficient order matching engine can lead to delays in trade execution, causing slippage and liquidity issues.
Solution-
Use a priority queue (MinHeap/MaxHeap) to match buy and sell orders efficiently.
Example Implementation in Python-
python
import heapq
class OrderBook:
def __init__(self):
self.buy_orders = []
self.sell_orders = []
def place_order(self, price, amount, order_type):
if order_type == 'buy':
heapq.heappush(self.buy_orders, (-price, amount)) # MaxHeap for highest buy price
else:
heapq.heappush(self.sell_orders, (price, amount)) # MinHeap for lowest sell price
self.match_orders()
def match_orders(self):
while self.buy_orders and self.sell_orders:
buy_price, buy_amount = self.buy_orders[0]
sell_price, sell_amount = self.sell_orders[0]
if -buy_price >= sell_price: # Match found
match_amount = min(buy_amount, sell_amount)
print(f"Trade executed: {match_amount} @ {sell_price}")
if buy_amount > match_amount:
self.buy_orders[0] = (buy_price, buy_amount - match_amount)
else:
heapq.heappop(self.buy_orders)
if sell_amount > match_amount:
self.sell_orders[0] = (sell_price, sell_amount - match_amount)
else:
heapq.heappop(self.sell_orders)
else:
break # No match found
book = OrderBook()
book.place_order(50000, 1, 'buy')
book.place_order(49000, 1, 'sell')
Using a priority queue (heapq) optimizes trade execution.
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