Last Updated on 23 July, 2024 by Trading System
A limit order book is a critical component of any exchange platform that enables the trading of financial assets. It is essentially a database that tracks the orders made by buyers and sellers, and matches them based on price and time priority. In this article, we will discuss the implementation of a limit order book in Python and its importance in the world of algorithmic trading.
What is a Limit Order Book?
A limit order book is a real-time database that keeps a record of all the outstanding limit orders for a particular financial asset. Limit orders are instructions given by traders to buy or sell an asset at a specific price, or better. The limit order book tracks the orders and matches them based on the price and time priority rule. This means that the orders with the highest price are executed first, followed by the orders with the next highest price, and so on.
Why is a Limit Order Book Important?
A limit order book is an important tool for algorithmic traders as it provides them with an accurate representation of the supply and demand for a particular asset. This information is critical in the development of trading algorithms, as it allows traders to make informed decisions based on real-time market data. In addition, a limit order book also provides traders with a level playing field, as all orders are executed based on the price and time priority rule, without any intervention from the exchange.
The Components of a Limit Order Book
A limit order book is made up of two main components: the bid side and the ask side. The bid side represents the orders from buyers who are willing to pay a specific price or better for a particular asset, while the ask side represents the orders from sellers who are willing to sell an asset at a specific price or better. The bid and ask sides are updated in real-time as new orders are added and existing orders are executed.
Implementing a Limit Order Book in Python
To implement a limit order book in Python, we will use the popular open-source library, pandas. This library provides us with a powerful and flexible data structure, known as a DataFrame, that will allow us to easily store and manipulate our data.
The first step in implementing a limit order book is to define our data structure. For this, we will create a DataFrame with columns for the price, quantity, and side of each order. The side column will be used to distinguish between bid and ask orders.
import pandas as pd
data = {
‘price’: [100, 90, 95, 105, 110],
‘quantity’: [100, 200, 50, 75, 125],
‘side’: [‘bid’, ‘bid’, ‘bid’, ‘ask’, ‘ask’]
}
df = pd.DataFrame(data)
Next, we will implement a function to add new orders to our limit order book. This function will take in the price, quantity, and side of the order, and update the DataFrame accordingly.
def add_order(df, price, quantity, side):
new_order = pd.DataFrame({
'price': [price],
'quantity': [quantity],
'side': [side]
})
df = df.append(new_order, ignore_index=True)
return df
Finally, we will implement a function to match orders in our limit order book. This function will take in the DataFrame and the side of the order to be executed, and return a DataFrame containing the matched orders. The matching process will be done by iterating over the orders in the DataFrame and executing the orders with the highest price first, until the desired quantity has been reached.
def match_orders(df, side):
matched_orders = pd.DataFrame(columns=['price', 'quantity', 'side'])
if side == 'bid':
df = df.sort_values(by='price', ascending=False)
else:
df = df.sort_values(by='price', ascending=True)
for index, row in df.iterrows():
if row['side'] == side:
matched_orders = matched_orders.append(row)
if matched_orders['quantity'].sum() >= row['quantity']:
break
df = df[~df.isin(matched_orders)].dropna()
return matched_orders, df
Conclusion
In this article, we have discussed the implementation of a limit order book in Python and its importance in the world of algorithmic trading. We have seen how to use the pandas library to create a DataFrame to store our data, and how to implement functions to add new orders and match orders in the limit order book. With this basic implementation, traders can develop more sophisticated algorithms and take advantage of real-time market data to make informed trading decisions.