Rajandran R Telecom Engineer turned Full-time Derivative Trader. Mostly Trading Nifty, Banknifty, USDINR and High Liquid Stock Derivatives. Trading the Markets Since 2006 onwards. Using Market Profile and Orderflow for more than a decade. Designed and published 100+ open source trading systems on various trading tools. Strongly believe that market understanding and robust trading frameworks are the key to the trading success. Writing about Markets, Trading System Design, Market Sentiment, Trading Softwares & Trading Nuances since 2007 onwards. Author of Marketcalls.in)

How to Place Orders Concurrently using ThreadPoolExecutor – Python Tutorial

2 min read

Creating concurrent orders is essential for active traders, especially those handling large funds, as it allows for executing multiple trade orders simultaneously, thereby maximizing trading opportunities and managing risk more effectively. The key to achieving this concurrency in Python is by utilizing the ThreadPoolExecutor from the concurrent.futures module, as demonstrated in the provided sample code.

This approach enables traders to send multiple orders in parallel, significantly reducing the time it takes to execute trades and allowing for a more efficient management of trading strategies. Below, we’ll go through a brief tutorial on how to place concurrent orders using Python, leveraging the ThreadPoolExecutor to achieve efficient concurrency.

What is ThreadPoolExecuteor?

ThreadPoolExecutor is a high-level interface for asynchronously executing callables, provided by the concurrent.futures module in Python. It abstracts the management of a pool of threads, enabling you to run tasks concurrently, thereby improving the performance of I/O-bound and high-latency activities. This executor is particularly useful in scenarios where executing operations in parallel can significantly reduce the overall execution time, such as making multiple network requests, performing I/O operations, or handling computational tasks that can be distributed across multiple threads.

import requests
import time
from concurrent.futures import ThreadPoolExecutor

# Define the URL and the JSON payload
url = 'http://127.0.0.1:5000/api/v1/placeorder'
data = {
    "apikey": "<apikey>",
    "strategy": "Test Strategy",
    "symbol": "NIFTY29FEB2422100CE",
    "action": "BUY",
    "exchange": "NFO",
    "pricetype": "MARKET",
    "product": "NRML",
    "quantity": "1000"
}

# Function to send a POST request
def send_order(data):
    start_time = time.time()
    response = requests.post(url, json=data)
    end_time = time.time()
    response_time_ms = (end_time - start_time) * 1000
    return f"API Order Response Time: {response_time_ms:.2f} milli seconds"

# Start measuring total time
total_start_time = time.time()

# Use ThreadPoolExecutor to send requests in parallel
with ThreadPoolExecutor(max_workers=5) as executor:
    # Create a list to hold references to the future objects returned by submit
    futures = [executor.submit(send_order, data) for _ in range(10)]
    
    # Iterate through the future objects to get the results
    for count, future in enumerate(futures):
        response_time = future.result()
        print(f"Order No {count} {response_time}")

# Stop measuring total time
total_end_time = time.time()
total_time_ms = (total_end_time - total_start_time) * 1000

print(f"Total time taken to place all 10 orders: {total_time_ms:.2f} milli seconds")

Output

Understand the Setup

Before diving into concurrency, it’s crucial to understand the setup. The provided sample code uses the requests library to send HTTP POST requests to a trading API and the concurrent.futures module to achieve concurrency. The target API endpoint is a mock URL (http://127.0.0.1:5000/api/v1/placeorder), and the data payload includes details such as the API key, trading strategy, symbol, action, exchange, price type, product, and quantity.

Define the Order Sending Function

The send_order function takes a data dictionary as input, representing the order details. It sends a POST request to the API endpoint and measures the response time, returning a string that includes the API order response time in milliseconds. This function is essential for encapsulating the logic needed to send an individual order to the trading platform.

Implement Concurrency with ThreadPoolExecutor

The real power of the sample code lies in its use of the ThreadPoolExecutor from the concurrent.futures module to send orders concurrently. The ThreadPoolExecutor is initialized with a max_workers parameter, which specifies the number of threads to use for sending orders. In the example, max_workers=5 means that five orders can be sent in parallel.

Send Concurrent Orders

Within the ThreadPoolExecutor context manager, futures are created for sending orders concurrently. A future represents an asynchronous execution that can be queried for status or result without blocking the calling thread. By calling executor.submit(send_order, data) for each order, the code schedules the send_order function to be executed concurrently for each order. The submit method returns a future object, which is stored in a list (futures).

After submitting the orders, the code iterates over the futures list, calling future.result() on each. This call blocks until the order’s response is received, allowing the collection of response times for each order.

Measure Overall Execution Time

The total execution time for placing all orders is measured by recording the start and end times before and after the concurrent execution block. This demonstrates the efficiency gained by using concurrent orders instead of sending them sequentially.

By using ThreadPoolExecutor for concurrent order placement, active traders can significantly reduce the time taken to execute multiple orders. This approach is particularly beneficial when dealing with large funds, where the speed of order execution can impact the overall trading strategy’s success. The provided sample code showcases a straightforward method to implement concurrent orders, emphasizing the importance of concurrency in active trading environments.

Rajandran R Telecom Engineer turned Full-time Derivative Trader. Mostly Trading Nifty, Banknifty, USDINR and High Liquid Stock Derivatives. Trading the Markets Since 2006 onwards. Using Market Profile and Orderflow for more than a decade. Designed and published 100+ open source trading systems on various trading tools. Strongly believe that market understanding and robust trading frameworks are the key to the trading success. Writing about Markets, Trading System Design, Market Sentiment, Trading Softwares & Trading Nuances since 2007 onwards. Author of Marketcalls.in)

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