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Python For Trading : The Benefits

Python For Trading : The Benefits

In the world of burgers, there aren’t really many buzzwords. Maybe for the very fancy burgers, they might have wagyu beef or something exotic like that (and no, those burgers aren’t any better). However, by and large there’s only so much you can do to a burger to make it different. 

Ultimately it needs a bread bun, a burger patty and a few extras, like cheese. If any of these are missing, well, it’s not really a burger. 

Finance, alas, is full of buzzwords. Many of these are banded around without too much thought. When it comes to quant, some of the most common are Python, alternative data and machine learning. If we purely focus on the coding angle, Python is becoming increasingly popular in financial institutions as a way of number crunching financial data. 

Of course, Excel is still probably number one for most traders, but Python is becoming a must have skill for graduates. At least judging by my classes, I always seem to get more students coming to my Python courses compared to VBA/Excel (VBA is still a useful skill, given all the legacy code out there!). However, if you’re not a graduate, and you’re more experienced do you need to know Python? 

First thing I would say is that not everyone needs to become a developer in financial institution. In the same way, not everyone needs to become a trader! There are many different roles within any financial firm, which all compliment one another. 

There is so much complexity, not everyone can know everything in detail. However, at the same time, having some understanding of what Python can do at various levels, even if they aren’t necessarily going to sit down and code a lot every day. 

If a trader has some understanding of what Python can do, then it make it easier for them, to know what can be accomplished in Python, and the various types of questions they can ask, which perhaps in Excel would have been too challenging. If we go back to my burger analogy, I don’t necessarily need to be an excellent cook to understand that burgers can be very tasty. 

However, if I have some understanding of the process which creates that burger, and all the various condiments I can better order a burger which fits my individual tastes. 

My point is that if coding is just a black box, it makes more challenging to know the types of questions you can ask. 

If we can peek inside that black box, it makes it a lot easier. Python is also a transferable skill to many other disciplines, so a knowledge of Python can be useful if you want to solve problems more broadly in data science (combined with an understanding of statistics).

Written by Cuemacro CEO Saeed Amen

Seeking the cues in macro markets

What are the signals we can use to trade macro markets? Cuemacro is a company focused on understanding macro markets from a quantitative perspective, in particular currency markets. Our goal is understand how data can be used to deepen understanding of macro markets. We use both existing and innovative data sources to create systematic trading strategies, analytics and data indices. We build our analytics using Python and our open source libraries chartpy, findatapy and finmarketpy. We offer several services for clients which include:

  • Alt Data Products / Creating exciting new datasets for clients to improve their own trading decisions and understand financial markets better
  • Research Consulting / Writing bespoke quant research papers and developing bespoke models for clients
  • Monetising Alt Data / Helping data companies and corporate institutions monetise their datasets through research and marketing services and aiding financial institutions to get into the alternative data age
  • Software / Developing bespoke market analytics to be deployed on clients’ systems, building on our open source Python frameworks, including for backtesting, visualisation and TCA (transaction cost analysis)
  • Teaching / We offer workshops for clients which include Python for finance and alternative data. We have taught at a number of large banks and funds.

Why the name Cuemacro?

Cue is defined as “a thing said or done that serves as a signal to an actor or other performer to enter or to begin their speech or performance.” In a trading context, market participants seek to understand the cues to enter into a trade. We seek to find these signals. Given our focus on macro markets, it was natural to put the two ideas to name our company Cuemacro.


Client Portfolio

Below we give examples of some of the client projects we have done at Cuemacro. Our clients have ranged from data vendors, to asset managers to quant hedge funds over the years, and have been based in both the US and also Europe. Our bespoke projects can range from delivering innovative new quant research for clients to developing analytics platforms for clients to run (such as TCA/transaction cost analysis).

  • Bloomberg / We were commissioned by Bloomberg to write a research paper to show how their machine readable news could be used to trade FX. The project involved using a large dataset consisting of text, which we processed to construct sentiment scores and FX based trading signals. We also discussed how the dataset could be used to understand FX volatility around ECB and FOMC meetings. The paper was published on Bloomberg’s website and we also discussed the paper on Bloomberg TV.
  • Investopedia / Investopedia commissioned us to conduct research to examine how web search data to their site could be used by investors. Their investor anxiety index is based on searches around subjects such as “short selling” which are consistent with investors concerns. We showed how the index could be used to trade equities. We talked about the project on Bloomberg TV.
  • Freepoint Commodities / We were commissioned to examine how to apply a quant approach to commodities trading.
  • A large European asset manager / We were commissioned by the firm to develop a Python based FX TCA library. Over nearly 2 years, we wrote the specifications with our client, and later implemented the framework, both a web based front end and also a back end for computation. Through the course of the project, we solved some crucial issues associated with the computation of large datasets. In particular, we worked on functionality to allow the computation to be distributed efficiently, to ensure the library was fast and could scale to the hardware. Elements of the project grew into our tcapy software product. Enterprise licences are available to purchase for tcapy.
  • A Chicago proprietary trading firm / We were commissioned to develop an intraday FX trading strategy for the firm, which was later run profitably with real capital.


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