How to Become a ‘Quant’ Overnight

The secret to gaining quant insights without quant training.

By James Ross, Sales Director EMEA, BMLL.

DISCLAIMER: No sales people or execution consultants were harmed, and no code was written in the making of this analysis.

In the Capital Markets world, it is generally accepted that it is necessary to have Python proficiency and access to certain third-party terminals to construct, identify and articulate vital deep market microstructure insights. Even for the quickest bit of research, full quant expertise has been a necessary trading desk skill set. But will this still be the case in the future? In this article, I explore why this is no longer the case.

Deep market insights are only for sophisticated market participants …

... or so used to be the case.

BMLL, the leading provider of historical data and analytics for Equities, ETFs and Futures. Wow, that is an impressive title indeed. But why does this matter? And why should it matter to the average market participant in the European Capital Markets?

Over the last almost two years, I have been very fortunate to be on the sales team front line at BMLL in London, attempting to answer this very question and communicate the multiple explanations to the market.

It will be of no news to you reading this, that market data is not only a hot commodity and moot point, but a necessity in today’s market.

But, it doesn’t stop there. Market data is incredibly important to a multitude of market participants, from Global Exchanges to Sell-Side Banks, Hedge Funds, and everywhere in between. However, in industry news, or in meetings you don’t hear about the quest for any old data. Rather, it is the need for high-quality, reliable and easy-to-use data that everyone is talking about.

So for someone like myself, who is an active market participant with an innate interest in why the trading landscape behaves the way it does and when, but doesn’t possess Python or coding expertise, you’d forgive me for thinking that many (if not, all) of the granular insights required to truly and deeply understand this thing we call the market microstructure, resides in the hands of the few highly sophisticated institutions/players today.

Key questions you always hear In the news, at conferences or in the boardroom…

  1. How can I better identify the distribution of liquidity over time?

  2. How much liquidity is addressable vs. non-addressable?

  3. How do I go beyond simply the breakdown of Dark vs. Lit to see more granular trade classifications?

These are just some of the questions I struggled to answer comprehensively before having access to BMLL Vantage, a powerful market microstructure visualisation tool.

With that in mind, here is how I, a non-quant, can go about trying to shed light on some of these questions, but this time, with access to market-leading historical Level 1, 2 and 3 data.


Part 1: What did the breakdown of liquidity look like during H1 2024?

Figure 1: H1 2024 - Daily Classified Trades - Source: BMLL Vantage

  • To go beyond simple Dark vs. Lit classifications, Figure 1 illustrates how the use of BMLL’s European OPOL indices and flagship metrics Daily Classified Trades (DCT) enables you to generate a more granular view of the market.

  • The DCT shows the concentration of liquidity on every European trading venue across 22 (instead of 2) trading mechanisms/venues using a single API or data source.

  • ‘So What?’ and why is this important? Without this, you would have to source data from different exchanges in different formats and munge them together in a way that makes sense and is easy to derive insights from. Good luck with that!

  • Leveraging these metrics can support a variety of use cases such as SOR optimisation, understanding addressable liquidity to better navigate trading conditions, and tracking market share across multiple execution venues.

  • Figure 1 reveals insights you otherwise would have missed e.g., the significant drop-off in ‘Special Price’ in May and June or the growth in relative proportion of liquidity coming from the Lit Closing Auction in Q2.

  • With access to these metrics, you can 1) Highlight granular trends, 2) Pose more questions to your team and clients and 3) Look a little smarter in the process!

Part 2: What proportion of liquidity is Addressable vs. Non-Addressable?

Figure 2a: & 2b: YTD Pre & Post FCA Regulation - Addressable vs. Non-Addressable Liquidity - Source: BMLL Vantage

  • Figure 2a & 2b are self-explanatory. Which is probably a breath of fresh air when it comes to figuring out how much liquidity you can interact with or not.

  • Using BMLL Daily Classified Trades, Figure 2a and 2b illustrate changes in the ratio of Addressable vs. Non-Addressable liquidity pre (left chart) and post-FCA post-trade reporting change (right chart).

  • According to FIX, Addressable Liquidity can be defined as ‘Liquidity that one could in theory interact with.’ In other words, any liquidity that can be used as part of the price discovery process to form an execution. With this in mind, you can probably guess what Non-Addressable Liquidity means ….

Why is this useful and what can we see?

  1. It shows how much of the total liquidity you can and can’t actually interact with.

  2. Figures 2a and 2b seem to show a 16.2% uptick in addressable liquidity pre and post-regulatory change.

  3. However, the absolute level of addressable liquidity remained more or less the same, with the driving factor behind this supposed increase being a significant portion of non-addressable trades no longer required to be reported - in reference to the following article (see article on recent FCA change here).

Part 3: Visualising Recent Market Events - GameStop

If it isn’t Systematic Internalisers, Consolidated Tape, Closing Auction, or industry favourite, Fragmentation, then it almost definitely will be GameStop and the ascendance of Retail Trading.

The GameStop phenomenon, often referred to as the "GameStop short squeeze," unfolded in early 2021 and became a landmark event in financial markets. GameStop has been in the minds of many ever since and recently saw its shares jump again by almost 23% in mid-June of this year.

The sheer magnitude of GameStop’s impact on financial markets can be difficult to quantify, but it certainly highlighted the power of retail investors and social media, raising questions about market manipulation, short-selling and the democratisation of investing.

What can we see from the most recent GameStop rally?

Figure 3: 1/5/2024 - 13/6/2024 - Retail Trades Breakdown S&P 500 + GME - Source: BMLL Vantage

  • GameStop’s (GME:XNYS) proportion of retail liquidity grew as high as 18.4% on June 7th, 2024.
  • Compared to 5.64%, 2.93% and 1.92% for Nvidia, Tesla and Apple respectively on the same day.

Part 4: Closing Auction, Closing Auction, Closing Auction …

All I ever hear about is the closing auction.

Pre-Covid, there was even a market-wide consultation based on potentially extending European trading hours, with many citing the possibility of reducing the concentration of liquidity in the open and closing auctions and more evenly distributing liquidity across the trading day, as a possible benefit of the proposed change.

The closing auction has garnered a lot of attention over the last few years, mainly in part to the sheer amount of liquidity residing there.

But just how significant is the closing auction in comparison to other phases of the trading day? And is the amount of press coverage it receives justified?

Figure 4a: 2024 YTD - Closing Auction as a Proportion of Daily Classified Trades - Source: BMLL Vantage

  • Figure 4a clearly illustrates that the closing auction is the recipient of a significant proportion of daily traded volumes in Europe.

  • YTD, Figure 4b shows The closing auction has an average of 11.54% of European liquidity and the 4th largest amongst the different trade classifications.

  • So I think it is safe to say that perhaps the closing auction is worthy of the amount of press it gets and is showing no signs of fading anytime soon.

Figure 4b: 2024 YTD - Closing Auction as a Proportion of Daily Classified Trades - Source: BMLL Vantage

Granular historical data and analytics now available to all

The market microstructure views shown in this article are just scratching the surface of what you can do!

If you’re worried because you didn’t study computer science, have zero Python skills, or don’t have a team of quants on hand, relax! BMLL brings high-quality, granular historical data and analytics to all market participants. With the visualisation tool, BMLL Vantage, you too, can become a quant overnight.

Register for BMLL Vantage and start your free trial today