Do you know Python?
It's something you'll need if you're serious about financial markets and algorithmic trading. Python is a computer programming language that is used on a daily basis by both institutions and investors for a variety of objectives, including quantitative research (data exploration and analysis) and designing, testing, and executing trading algorithms. Only large institutional players had the money and technical know-how to take advantage of the benefits of algorithmic trading in the past, but times are changing. Let's take a vacation back in time before we get into the finer elements of Python and how to get started with algorithmic trading.
The Paleolithic Era
It was the 1960s. Television in black and white. Radio that is still analog. Trades over the phone. It was a halcyon era built on human-to-human interactions: a wealthy investor with a hot tip would phone his broker, who would enter the order into his own system. Done and dusted. Those were the good old days. Was that the case?
Trading became more sophisticated in the 1970s and 1980s. The New York Stock Exchange (NYSE) developed the "Designated Order Turnaround" (DOT) system in 1976, which allowed brokers to route 100-share orders directly to floor specialists. By 1984, the NYSE had developed a more advanced "SuperDOT" system that allowed orders of up to 100,000 shares to be delivered straight to the floor. It was no longer man versus. man, but man vs. machine all of a sudden.
Let's fast forward to the present day
Then, in the new millennium, came the fintech disruptors. Decimalization, algorithmic trading, and high-frequency trading are all terms used to describe the process of decimalization. Faster, more complex hardware allowed programmers to construct more complicated algorithms, which allowed computers to determine the timing, pricing, and quantity of trades based on pre-determined rules. Traders could now create hundreds of small orders instead of one large order. High-frequency trading was made possible by more complex algorithms. Consider millions of trades every day, executed at breakneck rates. Machine vs. machine's future had arrived.
Why Python for Trading Algorithms?
You must study a language in order to discover the secrets of a particular culture or country. The same may be said for automated trading. However, which programming language is best for the job? After all, you can't study them all at once, so you'll have to choose one, with factors like cost, performance, robustness, modularity, and other trading strategy elements influencing your choice. Python, C++, Java, C#, and R are the five programming languages that an aspiring trader might pick from.
While we'll go into deeper detail on three of these (Python, C++, and R) later in this post, a few comments about Python at this point should be helpful. Python's functional programming style makes it easy to write and evaluate algorithmic trading structures, which is one of the aspects that makes it particularly useful. In fact, one of Python's primary selling features is its relative ease and simplicity of usage.
Beautiful is better than ugly; explicit is better than implicit; simple is better than complex; the complex is better than complicated, and readability counts — there's even a book called "The Zen of Python." Python's Advantages and Disadvantages in Algorithmic Trading
Okay, I know what you're thinking: enough with the Zen and Python also trading. What are the advantages and disadvantages of utilizing it?
Python code is intelligible and accessible to users who are new to algorithmic trading. There is just less of it in Python than in other coding languages, which means that trading with Python requires fewer lines of code thanks to the comprehensive libraries available.
Python is a programming language that is “interpreted.” Unlike a compiler, which executes code as its whole and lists all possible errors at once, an interpreter executes code statements "one by one." Python debugging is extensive and complete because it allows live modifications to code and data, resulting in faster execution because single errors (rather than several ones) emerge and may be cleared.
In a nutshell, popularity. Python is likely to be used in the algorithmic trading platforms and tools you're already aware of.
The culture of algorithmic trading is based on the Python programming language, making it easy to cooperate, trade code, and crowdsource help. Scalability is provided by parallelization and Python's enormous computational capability. Python makes it easier to add new modules and expand them when compared to other languages. It's also easy for traders to transfer functionality between different applications thanks to the existing modules.
Python's broad, comprehensive support libraries imply that most often performed programming activities are already scripted, reducing the amount of code that needs to be created.
Python is a programming language
One of Python's greatest assets is also one of its greatest flaws. Python users may find it difficult to learn and operate in other programming languages due to its simplicity of use, features, and huge libraries.
According to some users, Python shines in desktop and server applications, but not so much in mobile computing.
Because variables are considered objects, improper memory management can result in memory leaks and performance bottlenecks (i.e. millions of variables being stored).
C++ vs. R vs. Python
Python is a user-friendly language that has shown to be a winner for traders beginning to write as well as more skilled users fine-tuning their crypto trading bots when compared to C++ and R. Unlike C++, which is a difficult language to learn, Python is a confidence builder, making it simple for beginners to read, write, and learn with a relatively short learning curve. It can be used to create some wonderful trading algorithms that would be difficult or time-consuming to implement in C++.
Python is slower than C++, but because it is a high-level language, it is extensively employed in quant trading. Python's high-performance libraries make tasks like research and prototyping considerably easier to complete. Additionally, because Python libraries use C++/C or Fortran code, Algo traders can execute nearly any type of data analysis at a speed equivalent to compiled languages such as C++.
The language utilized for trade execution will be largely determined by the trading frequency. If the trade frequency is less than one second, a compiled language such as C++ is usually the best option. Backtesting and research, on the other hand, are unaffected because execution times are irrelevant in a backtesting scenario. In addition, high-performance libraries such as Pandas and NumPy can be used to reduce backtest computing time.
When you first start out, your crypto trading algorithm may be simple, but as you advance, you may want to try out some more advanced techniques, such as optimizing parameters with deep learning using cutting-edge techniques and neural networks, for which Python is the most popular language (with R and C++ a close second and third).
By now, you've probably noticed that R hasn't been addressed much. In the eyes of some, R and Python were on par a few years ago, but Python today offers better support for modern software development tools and processes. Python comes out on top since its package libraries have met or excelled R in practically every way, not to mention its ease of use. R simply does not have the appearance or feel of a fully-fledged, organized language with a clean and consistent syntax, object-oriented capabilities, and easily extensible packages.
Python in the Financial Sector
Python has become one of the most popular programming languages for fintech organizations, combining economics, finance, and data science. It consistently ranks among the top three most popular languages in financial services. In fact, Python is one of only a few computer languages that offer the most career prospects in the banking industry in absolute numbers. In 2020, according to studies, there were approximately 1,500 Python positions available, with 14 additional Python programmers vying for each one. Citigroup, for example, now offers Python coding lessons as part of its continuing education program for financial analysts and traders. Python has a lot to offer traders, analysts, and researchers for many of the reasons outlined previously in this article.
Python should pique your attention if you're looking for a job in banking. The Quartz program at Bank of America is written in Python. “Everyone at JPMorgan now needs to know Python, and there are roughly 5,000 developers using it at Bank of America,” says former BoA IT guru Kirat Singh, adding, "There are close to 10 million lines of Python code in Quartz, and we received close to 3,000 commits a day." One of the reasons we chose it in the first place was because it's a good scripting language that's easy to incorporate into both the front and back ends.”
Python is commonly used in quantitative finance due to its analytical features. Python users benefit from easier data visualization and sophisticated statistical analyses thanks to modules like Pandas. With Python-based systems that use libraries like Scikit or PyBraing, financial services organizations may harness strong machine-learning algorithms and their predictive analytics.
Closer to home, traders need reliable tools to do thorough market analysis, identify patterns and insights, and then make predictions and projections based on their findings. Python algorithmic traders can construct extremely imaginative trading methods and gain predictive analytical insights into specific market circumstances.
Python isn't simply a great programming language for algorithmic traders, though. It's the language that drives some of today's largest brands, as well as the stars of the future, from multibillion-dollar enterprises to start-ups. Python is used by Google, Facebook, and Microsoft for online applications, data science, artificial intelligence, machine learning, deep learning, and task automation, while Instagram, Spotify, and Uber utilize it to run their websites.
What characteristics distinguish a skilled algorithmic trader?
Algorithmic trading is a lot like being a triathlete: you sprint, swim, and cycle. I know what you're thinking: another one of those motivational sports metaphors...
Traders, like triathletes, must master three vital talents to be successful: math, finance, and code. You can be a natural mathematician and an expert coder, but if you don't know much about finance, you'll struggle to get to the finish line. You must develop innovative trading ideas, be able to translate those ideas into mathematical models, and then implement those models in code.
However, developing technical abilities is only part of the equation. Learning to swim is something that anyone can do. Alternatively, you may improve your running skills. Alternatively, you could ride a bike like a pro. Those are the things that will get you into the race after qualifying. But, in order to truly surpass others or exceed your own expectations, you must enjoy the sensation of water and ground beneath your feet, and that metal frame, with its gears, pedals, and wheels, must become an extension of your body.
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