Compounding My Interests by Elliot Turner

"Logic and Intent: Shaping Today's Financial Markets"

Started program trading with a spread algo between Deere and Caterpillar, under the assumption that fundamental drivers were similar and spreads will revert to mean.
- In executing this algo, felt orders were being copied by someone else.
Today, 70% of total US volume is algos.
How do algos introduce risks?
- Problems occur when you can’t predict.
The algo ecosystem: the number of possibilities grow exponentially when algos interact with other algos.
- 1 runaway algo problem. Example-on Amazon there was a $1 million book. Someone raised the price in marketplaces of another ever so slightly and that triggered a cascade where this book ended up listed for $20 million (the story of how this happened is fascinating and told here)
- 2 Flash crash – unpredictable interaction of algos
What is an algorithm? It is a sequence of logic statements. All algos are created by humans. They do what people intend them to do. Intent=important. Humans are driven by incentives, algorithms are driven by human intent.
- The technologist needs to understand the human goal, or else risk is introduced into the system.
IEX introduced a 350 microsecond delay on an order reaching the exchange.
The broker’s dilemma: brokers were matching orders between buyers and sellers, so brokers created dark pools. Broker A gets the buy, Broker B gets the sell, what’s the incentive for Broker A to trade with B?
In today’s market there are 11 exchanges, 40+ dark pools (IEX right now is a dark pool, but will try to become an exchange eventually).
Exchange dilemma: exchanges facilitate issuers with investors. Exchanges are supposed to be neutral to all participants, but now are for-profit companies who build services for specific customers. This is not the intended purpose of exchanges, and biases these exchanges towards one kind of participant (HFTs) over another.
There have been three generations of market algos so far:
- 1 automatic traders flow, algos execute upon traders’ ideas, helping these traders focus on “their work” as opposed to execution
- 2 gaming automatic trader-based algos. These algos took advantage of transparent inefficiencies in the first generations functionality.
- 3 counteract generation 2. A trader who wants to buy size needs to game level two algos in order to hide intent and execute efficiently.
Participants send orders, but they don’t arrive at the actual exchange at the same time.
At the micro level, markets are deterministic (opposite of physics).
Latency arb—in a distributed system, race conditions matter. HFT aims to exploit the race. Exchanges need to know where the market is before pricing a transaction. Introducing the 350 microsecond delay through a fishing-line like fiber. In doing so, assume the order is not fast. And then figure out where the market is.
Resistance to IEX so far has come from 2nd generation algo programmers.