To survive in the age of robots, it is necessary to learn a programming language that makes your trading algorithms smarter and not just faster. Having knowledge of a popular programming language is the building block to becoming a professional algorithmic trader. It is not just enough if a person has a love for numbers. Professionals need to put the logic using numbers into a software program to perform a successful transaction. Programming languages are an important contributing factor for trading systems. To build a concrete trading platform- knowledge of various programming languages is a must as it helps control the volatile and multi-faceted market conditions.
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. If an individual acquires expertise in any one language then switching over to the other programming language for algorithmic trading should not be a tough task.
With rapid advancements in technology every day- it is difficult for programmers to learn all the programming languages. 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 that there is nothing like a “BEST” language for algorithmic trading. There are many important concepts taken into consideration in the entire trading process before choosing a programming language –cost, performance, resiliency, modularity and various other trading strategy parameters.
Each programming language has its own pros and cons and a balance between the pros and cons based on the requirements of the trading system will affect the choice of programming language an individual might prefer to learn. Every organization has a different programming language based on their business and culture.
- What kind of trading system will you use?
- Are you planning to design an execution based trading system?
- Are you in need of a high-performance back tester?
Algorithmic trading developers are often confused whether to choose an open source technology or a commercial/proprietary technology. Before deciding on this it is important to consider the activity of the community surrounding a particular programming language, the ease of maintenance, ease of installation, documentation of the language and the maintenance costs. Python for trading has become a preferred choice recently as Python is an open source and all the packages are free for commercial use.
Python algorithmic trading has gained traction in the quant finance community as it makes it easy to build intricate statistical models with ease due to the availability of sufficient scientific libraries like Pandas, NumPy, PyAlgoTrade, Pybacktest and more.
Benefits of Using Python in Algorithmic Trading
- Parallelization and huge computational power of Python trading give scalability to the portfolio.
- Algorithmic trading python makes it easier to write and evaluate algo trading structures because of its functional programming approach. The code can be easily extended to dynamic algorithms for trading. Python can be used to develop some great trading platforms where using C or C++ is a hassle and time-consuming job. Trading with Python is an ideal choice for people who want to become pioneers with dynamic algo trading platforms.
- For individuals new to algorithmic trading, python code is easily readable and accessible. So, if you are stepping into the world of algorithmic trading then QuantInsti’s executive program will help you implement your strategies in the live environment through Python trading platforms.
- It is 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.
- When using Python for trading it 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.
- With a wide range of scientific libraries in Python, algorithmic traders can perform any kind of data analysis at an execution speed that is comparable to compiled languages like C++.
The Drawback of Using Python in Algorithmic TradingJust like every coin has two faces, there are some drawbacks of using Python for trading. However, the pros of using python for trading exceed the drawbacks making it a supreme choice of programming language for algorithmic trading platforms.
In python, every variable is considered as an object, so every variable will store unnecessary information like size, value and reference pointer. Usually, the size of python variables is 3 times more than the size of C language variables. When storing millions of variables if memory management is not done effectively, it could lead to memory leaks and performance bottlenecks.
Algorithmic Trading - Python vs. C++
- A compiled language like C++ is often an ideal programming language choice if the backtesting parameter dimensions are large. However, Python makes use of high-performance libraries like Pandas or NumPy for backtesting to maintain competitiveness with its compiled equivalents. Python or C++ - the language to be used for backtesting and research environments will be decided based on the requirements of the algorithm and the available libraries.
- Choosing C++ or Python will depend on the trading frequency. Python trading language is ideal for 5-minute bars but when moving downtime sub-second time frames this might not be an ideal choice.
- If speed is a distinctive factor to compete with your competent then using C++ is a better choice than using Python for Trading.
- C++ is complicated language, unlike Python which even beginners can easily read, write and learn.
Next StepPython algorithmic trading has gained traction in the quant finance community as it makes it easy to build intricate statistical models with ease due to the availability of sufficient scientific libraries like Pandas, NumPy, PyAlgoTrade, Pybacktest and more.
In case you are looking to master the art of using Python to generate trading strategies, backtest, deal with time series, generate trading signals, predictive analysis and much more, you can enroll for our course on Python for Trading!