What Makes Python Most Preferred Language For Algorithmic Traders

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For people who wish to thrive in the competitive market of quantitative trading programming expertise in Python, C++ or Java is a must. The core concepts behind using these programming languages for algorithmic trading are same.

One of the most common questions that we receive at QuantInsti® is: “Which programming language should I learn for algorithmic trading?”
The answer to this question is: There is nothing like a “BEST” language for algorithmic trading.
But you could always look at Python, which is an entirely different game changer when it comes to Algo Trading.

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What is Python?

Python is a free open-source and cross-platform language which has a rich library for almost every task imaginable and specialized research environment.

It is an excellent choice for automated trading in case of low/medium trading frequency, i.e. for trades which do not last less than a few seconds. It has multiple APIs/Libraries that can be linked to make it optimal, cheaper and allow greater exploratory development of multiple trade ideas.

What makes python preferred language of Algo Traders

What Makes Python Most Preferred Language For Algorithmic Traders?

Open Source:

Helps them build their own:

  • Data Connectors,
  • Execution Mechanisms,
  • Backtesting,
  • Risk and Order Management,
  • Walkforward, and
  • Optimization Testing Modules.

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Functional Programming:

  • Easier to write and evaluate algo trading structures
  • The code can be easily extended to dynamic algorithms for trading
  • Creating trading platforms takes lesser time as compared to C/C++
  • Ease of using as compared to other languages like R

Shorter Codes:

  • Using Python for trading requires fewer lines of code due to the availability of extensive libraries
  • Quant traders can skip various steps which other languages like C or C++ might require
  • This brings down the overall cost of maintaining the trading system

Less Backtesting Worries:

  • Python makes use of high-performance libraries like Pandas or NumPy for back-testing to maintain competitiveness with its compiled equivalents
  • Python trading language is ideal for 5-minute bars

Easier To Use:

  • Comparatively easier to fix new modules to Python language and make it expansive
  • The existing modules also make it easier for algo traders to share functionality amongst different programs by decomposing them into individual modules which can be applied to various trading architectures

Next Step

If you are seeking more information on Python libraries for Algo Trading, check out our blog-post. If you are a coder or a tech professional looking to learn automated trading from experts like Dr. Yves Hilpisch, have a look at our course Executive Programme in Algorithmic Trading (EPAT™). The course covers training modules like Statistics & Econometrics, Financial Computing & Technology, and Algorithmic & Quantitative Trading. EPAT™ equips you with the required skill sets to be a successful trader. Enroll now!

You can also check out our interactive course ‘Python for Trading‘  where you’ll get a hands-on experience on Python coding by coding and backtesting your own strategy plus a joint certification from QuantInsti® and MCX.

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One thought on “What Makes Python Most Preferred Language For Algorithmic Traders

  1. May 30, 2016

    Mastazi Reply

    I disagree with the “functional programming” bit.

    Guido Van Rossum, the creator of Python, once wrote: “I have never considered Python to be heavily influenced by functional languages, no matter what people say or think. I was much more familiar with imperative languages such as C and Algol 68 and although I had made functions first-class objects, I didn’t view Python as a functional programming language” [1].

    While Python has some functional programming features (lambdas, higher-order functions) it’s not really a functional language (it lacks many functional programming features such as tail call optimization). I would describe Python as an object-oriented language or, maybe, as a multi-paradigm one.

    Or perhaps you are using the expression “functional programming” with a different meaning than the one it has in computer science?

    [1] http://python-history.blogspot.com.au/2009/04/origins-of-pythons-functional-features.html

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