Here’s What an ACTUAL Trading Edge Looks Like
Using data to build out a real-world information advantage.
In sum, markets are a reflection of changes in information. Price discovery comes not from obscure technical factors, but rather from changes in information. So naturally, if one wants to succeed in markets, one must have a robust information framework.
The goal of this will be to walk you through a real-life, hands-on implementation of this approach.
The “Who’s Next?” Approach
For this example, assume we are an investment manager that wants to find new stocks to invest in. We want our approach to capture an information “lag”, so when a news event or award directly influences Stock A, we want to know how that news event will influence Stock B, before news for Stock B breaks out on its own.
There are millions of sources of information, so it’s tough to know where to start, but we will start by looking at what the government is doing. Every day, contractors and companies from all over the country bid on contracts from departments of the federal government. And every day, new contracts are handed out.
These range from $100,000 contracts to supply the U.S. Army with bulk napkins over 2 years, to things like multi-million dollar deals for supplying pharmacy medication to inmates at a federal prison.
To start our search, we leverage tools like OpenBB and get information of all latest contracts awarded:
from openbb_terminal.sdk import openbb
Latest_Contracts = openbb.stocks.gov.lastcontracts()This returns the following:
Taking BSX (Boston Scientific Corporation) for example, we see that in February 2023, it was awarded ~$785,000 from the Department of Veterans Affairs for Yttrium Microspheres and Watchman Closure Devices. Without turning this into a dive on niche medical technologies, I’ll briefly explain what each of those are:
- TheraSphere™ Y-90 Glass Microspheres: Yttrium-90 microspheres are radioactive particles which are mainly used for treating patients with hepatocellular carcinoma (HCC).
- Watchman Closure Device: A Watchman is a heart implant meant for preventing blood clots and reducing the risk of strokes.
Great, so now we‘ve narrowed down to a company that just received a large order from the federal government. But this information was likely priced-in when the award news came out, so it won’t do us that much good. However, we know that Boston Scientific just received this new order, but who else does that effect?
We know that they will be manufacturing, transporting, and storing for this order, but who will they be buying the pieces from? If we are able to know who their biggest supplier is, we can deduce that they will get a portion of the contract award by proxy. So let’s get that info:
Suppliers = openbb.stocks.dd.supplier("BSX")This returns:
All of these stocks are associated with the supply chain of Boston Scientific. Going down the list, you’d see that they are all essentially chemical supply companies. But that makes sense, if every company had to build their tools in-house from scratch, there wouldn’t be very many companies.
It’s tedious, but the next part would be to do a brief dive into each company on the list until you find the one(s) that specializes in the chemical/material used to make the 2 products. Once you have that, then you can go even deeper and find out who the suppliers and customers of that company are.
Going through a framework like this is how opportunities are found in niche, bizarre situations that people don’t generally think of. Just from reading this alone, you now are aware of a list of chemical companies that provide supplies to Boston Scientific and you also know more about heart implants and radioactive therapy. But most importantly, you have more information than the general public who would read about that award on some outlet like CNBC.
That’s edge.
Taking it Further
With a framework like this, there is essentially no limit to how deep you can dive and what interesting things you’d find.
In the example above, possible further steps are:
- Cross-referencing the supplier company with insider/congress trades:
Relevant_Congress_Trades = openbb.stocks.gov.gtrades("Supplier Company")- See what that company lobbied for in the past to estimate future contract awards:
Lobby_History = openbb.stocks.gov.lobbying("Supplier Company")And so much more. The only limit is your creativity as an investor/trader.
Final Thoughts
Hopefully this was useful and taught you something that you didn’t know. Knowing how to access and apply this kind of research is extraordinarily important in being an informed market participant.
Information like this has really only been available at an institutional level from companies like Bloomberg, but that’s been changing since the advent of open-source code. I used OpenBB (SDK) for this and it acts as an information aggregator from various sources, so the cost to the end-user is nothing. There is a manual terminal available for those who do not code. This is something I highly recommend checking out.
If this article piqued your interest, you’d likely enjoy some of my other posts just like this one:
- Predicting The Market Might Be Easier Than I Thought
- Capturing The Sales Seasonality Effect
- My Sports Betting Algorithm Might Be Bringing In Serious Dollars.
Happy trading!
