“Great things are done by a series of small things brought together.” – Vincent Van Gogh
In late 2001, $1.1 million in member contributed capital is dropped into Northtown Trading, LLC. By 2005, this operation becomes Tradebot Systems, Inc. and the rest has become a notable component of high-frequency trading history – and mythology.
In comparison to much of our other modeling, we don’t know much about Tradebot. However, given the contextual evidence provided by that other modeling, we can leverage the data we do have on Tradebot much further than we’d otherwise be able to.
For the trained eye, the exhibit below – wherein Alphacution presents the total assets and a summation of cash and net receivables found in Form X17A5 for the years beginning 2001 and ending 2018 – shows that this trading operation is mostly holding cash and receivables from brokers. This means that they’re position exposure is limited, which further means that they are trading fast enough – and small enough – to be (mostly) flat at the end of each day.
Alphacution has often written that, in a perfect world, trading firms would much prefer to be flat by the end of each day (since risk-adjusted returns are highly correlated with holding period). However, it is the amount strategy scaling desired relative to available liquidity in the target security universe that ultimately forces position accumulation to form.
This position accumulation hasn’t happened here – certainly not to any degree that we have observed among a broad spectrum of peers and competitors – as we see that a vast majority of the assets here are cash or near-cash. Topping out around $100 million in total assets since 2009, there are a few possible reasons for this plateau:
First, there could be simple capacity constraints preventing the core strategy from growing further. Second, since $100 in assets under management (or, portfolio value) is the minimum trigger for filing a 13F report, perhaps Tradebot is purposely staying under that threshold. And, third – a reason we don’t often consider – perhaps the team at Tradebot is happy just staying around this level and enjoying the cash that it throws off. Now, based on the fact that Tradebot has launched other affiliates – like, Tradebot Ventures, Tradebot Properties and others – that is usually a strong sign that the core strategy is experiencing capacity constraints.
Anyway, since we only have the FOCUS reports to go off of, we don’t know the mix of trading in stocks and ETFs. (Chances are, there is no option trading in the book.) So, while we don’t know the product mix, nor the number of names Tradebot would be active in, we can home in on some of the position sizing limitations that would allow a trading firm like this to remain flying under the 13F radar.
In the chart below, Alphacution present the money-shot exhibit from the Feed post, “Ranking Strategy Speed by Top Quants, Market Makers,” wherein we propose that average position size is related to strategy turnover frequency (or, average position holding period).
As you can see, the smallest average position size in shares is represented by the market maker, Latour Trading, and its affiliated proprietary quant operation, Tower Research Capital (TRC). Until now, Alphacution viewed this tandem as the fastest / smallest in the observable universe of players. Yet, even with a small sample of evidence, Tradebot changes that.
In the next two charts, Alphacution isolates the average stock and ETF positions (in shares) for both Latour and TRC over the 31-quarter period beginning Q4 2011 and ending Q2 2019.
With these boundary conditions as reference, the point we are making here is simply this: Assuming that they are trading in similar names as Latour/TRC (which is a high probability), Alphacution estimates that Tradebot would need to keep intraday positions averaging less than 1,000-2,000 shares for stocks and less than 5,000 shares for ETFs in order to expect to be able to get flat by the end of each trading session.
As usual, its a fascinating puzzle, and we’re always gratified to add another player to the model library that appears to represent a new boundary condition, thereby bringing greater contextual clarity to everything else that is connected to it…
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