Writing an algorithm to trade one stock is easy. You get the price, check a condition, and buy. But what happens when you want to scan the entire Nifty 500 for breakouts? Or monitor 200 Option Strikes for premium decay?

If you use a simple for loop, your code will fail.

# The detailed way (Synchronous)
for symbol in symbols:
    price = get_price(symbol) # Takes 0.2 seconds
    check_strategy(price)
# Total time for 500 symbols = 100 seconds!

By the time you reach the 500th symbol, the market has moved 100 seconds ahead. Your data is ancient. You need Concurrency.

Understanding the Python GIL

Python is notorious for its Global Interpreter Lock (GIL), which prevents it from running multiple CPU threads perfectly in parallel. However, for trading, we are mostly I/O Bound (waiting for network data), not CPU Bound.

This means we don't necessarily need "Parallelism" (multiple cores); we need "Concurrency" (handling multiple tasks at once while waiting).

The Solution: AsyncIO

AsyncIO is a library to write concurrent code using the async/await syntax. It allows your program to pause one task (while waiting for data) and work on another task instantly.

It turns your 100-second loop into a near-instant blaze of requests.

The Concurrent Way

import asyncio
import aiohttp

async def check_symbol(session, symbol):
    async with session.get(f'https://api.broker.com/{symbol}') as resp:
        price = await resp.json()
        check_strategy(price)

async def main():
    async with aiohttp.ClientSession() as session:
        tasks = [check_symbol(session, sym) for sym in symbols]
        await asyncio.gather(*tasks)

# Total time for 500 symbols = 0.5 seconds

Scaling with Producer-Consumer Pattern

For a production-grade Order Management System (OMS), we go a step further. We separate the "Market Data" part from the "Strategy" part.

• Producer (WebSockets): Connects to the broker and pushes 5000+ ticks per second into a Queue or Redis.
• Consumer (Workers): Multiple worker processes pull ticks from the Queue and process them independently.

This decoupling ensures that even if your strategy logic is heavy (e.g., calculating Option Greeks), it never blocks the incoming market data stream.

Why This Matters

In the Indian markets, F&O execution speed is everything. If your scanner takes 5 seconds to cycle through the Option Chain, you will miss the gamma spike.

Implementing true concurrency allows you to have a "Market Eye" that sees everything, everywhere, all at once.