A Message From Larry Connors
Over the past 15 years, we’ve made a significant investment both in time and money into Amibroker. As a whole, this has been one of the better investments we’ve ever made. Along the way, many hundreds and likely thousands of our customers followed our lead and moved onto Amibroker, especially for strategy testing.
Over the past two years, I’ve been reading more and more about how the top quant trading firms were moving onto Python. Python is an open source programming language developed with the intent of simplicity. It’s widely used in many industries by many of the largest organizations including Amazon, Google. and NASA. In 2018 it was selected as programming language of the year because of its vast use and ability. This use now includes the financial markets industry and trading.
In 2008 AQR Capital Management, now the second largest hedge fund in the world, introduced pandas. Pandas is a software library written for Python specifically designed for data manipulation and analyses.
In layman’s terms, it’s basically a better, more powerful version of Excel. This opened the door for professionals in the trading industry to do their research on data faster and more efficiently.
AQR made pandas open source. This means there’s more than a decade of programming available for Python users that’s been developed by some of the best minds in the financial markets. And this code is free for everyone to use.
After researching Python further, I’ve made the decision to begin moving all our programming and testing onto Python. I’ve hired Chris Cain to lead the move for us. I believe this move will pay off in our ability to build more and even better trading strategies in the future at a much faster pace, especially because we now have access to the programming code of many of the best and most brilliant quant programmers who use Python and pandas at their trading firms.
We’ll continue to support Amibroker and TradeStation. I know many people have put time into it. But as we move ahead, I’m excited by the many possibilities Python brings and we’ll be joining the hundreds of quant firms who are already using it.
Chris has written a two-part series on what Python is, why Python for Traders is different, and how you can apply Python in the future as the best quant trading firms in the world have done.
I hope you enjoy this series. if you have any questions on this, please feel free to email me at firstname.lastname@example.org or Chris at email@example.com.
As mentioned in the previous article, the Python coding language is becoming the dominant coding language for the finance and trading industries. It’s a necessary skill for many jobs at the largest and most sophisticated hedge funds and trading desks in the world – including job titles such as quantitative researcher, algorithmic trader, systematic portfolio manager, and risk manager.
Python is not limited to professionals. Because it’s so simple to learn and easy to use compared to the other programming languages, many top individual traders have also migrated away from EasyLanguage and Amibroker to Python. In my opinion, these traders have a large advantage over the traders who are programming in antiquated languages.
In the future, these advantages will only become larger because Python is open source which means thousands of traders worldwide will make the language even more powerful and widely used.
The Advantages of Python
Why has Python become the language of choice? What advantages does it offer over other languages? How can learning Python improve both your personal trading performance as well as your prospects for breaking into the lucrative investment and trading industry? Below I outline a number of the main benefits of Python:
- Python is easy to learn. The developers of the Python programming language attempted to make it as “English like” as possible. This greatly reduces the learning curve, especially for those new to computer programming.
- Python requires fewer lines of code. Compared to other programming languages like EasyLanguage, Amibroker, C, and Java, equivalent functionality can be accomplished using far fewer lines of code. This was by design, as Python was built from the ground up for both readability and brevity.
- The Libraries Make the Language: Python has the most popular and well-established libraries used in data analysis and quantitative trading/research.
What are ‘libraries’, you might ask? Think of them as “add-ons” to the language, which extends the functionality of the language in huge and important ways. Being that Python is an open source technology (more on that in a second), libraries are written for Python continuously and from any developer in the world. This means with Python, you’ll have access to the same tools developed by many of the best programming minds in the financial markets!
- Python is an open source technology. What does “open source” mean? Open source refers to a program in which the source code is available to the public, free of cost, for use or modification from its original design.
Think of this as a way for multiple traders and entities to collaborate openly to further the development of the technology. This has a number of key advantages:
- Being open source, any developer can write libraries for Python and extend the functionality of the language. This stands in contrast to a technology such as TradeStation’s EasyLanguage, which would require the owner of the language (in this case TradeStation) to extend the functionality of the language.
- Permission to use Python is not required; you’ll have permanent access. Python is free to everyone!
- Python can do more things than many languages including EasyLanguage and Amibroker’s AFL. The list is endless. I’ll go deeper into this in the webinar I’m holding next week. (link here)
- You can analyze any data you can get your hands on. This stands in contrast with non-open source languages, which often makes you use their data or one of their data partners.
- Python already has a handful of backtesting programs written into the language. The most advanced, powerful, and functional backtester on the market comes from Quantopian, which is what I primarily use.
I’m partial to Quantopian as are many other traders globally. In fact, Larry is going to be a panelist at Quantopian’s annual conference being held in April in Boston this year.
- Python is already very widely used by many of the best trading firms in the world. This brings with it many advantages. First, it is a very well documented language. If you are stuck on a coding problem, no doubt many others have been stuck on the same problem. A quick google search will solve the issue. Also, the website “stackoverflow” (stackoverflow.com) is a popular forum where people ask questions regarding programming languages. When you get stuck on a Python problem, the answer will most likely be found there. Finally, most Python libraries come with their own well-developed documentation should you need further assistance.
- Python can be used for many things, not just backtesting. While backtesting trading strategies is a fundamental job function of any quantitative trader, there will be times when you want to do other things. Some of these “other things” can include top-level research, portfolio reporting and analysis, making visualizations, market monitoring, and many others. Python can help you do all of this, and more.
Simply put, Python skills will lead you on a faster path to finding more profitable trading strategies. Learning Python for quantitative finance and trading has the additional benefit of greatly increasing one’s career prospects, as these skills are in high demand from the largest, most successful trading desks, and hedge funds in the world.
The bottom line is that Python will make you a better trader and researcher almost immediately.
If you’d like more information on learning how to program in Python, I will be holding a free webinar on Tuesday, April 16 and Thursday, April 18 at 1:00 pm EST. Click here to sign up for my free webinar.
Connors Research, LLC.
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