How do HFT firms and quant traders consistently generate absolute returns that are not affected by crazy swings of cryptocurrencies?
In this day and age, cryptocurrency markets are highly fragmented, strongly affected by shock and don’t provide enough liquidity for newly listed assets. Empirical evidence show that sufficient market inefficiencies exist to permit consistent trading success. Find out how top quantitative traders consistently exploit these opportunities to secure profits that are not affected by market direction and protect themselves from wild volatility of cryptocurrencies.
Let’s have a look at the daily prices of Bitcoin on two different exchanges. While BTC has been largely in the green lately, it has lost more than 15 percent in just a couple of hours!
Although the price is the same most of the time, there are times when noticeable divergence persists. That means you buy ETH on one exchange and sell it on the second exchange at the same time and profit from the spread between two exchanges. Let’s examine it a little bit closer:
While most of the time, the spread isn’t high enough to cover transaction costs, there were moments where you could’ve made up to six percent (minus the transaction costs) almost risk-free. That is the simplest form of arbitrage. There are more complex forms of this strategy, generally called “statarb” (statistical arbitrage). For instance, the relationship between prices of two assets may be stationary (i.e. doesn’t change with time in the long run), so if one of the assets has risen/fallen by the greater amount than the other, you can profit from that relationship. Now, let’s examine challenges that prevent you from making the most amount of money with this strategy:
-While you monitor the price of the token T with your capital locked on exchanges A and B, nothing happens. But at the same time, a spread opens between exchanges C and D where you have no money and you miss the opportunity. Should you split your capital between all four exchanges, you’d have made proportionally less money on each deal, hence you need to allocate your capital optimally over 1000+ tokens/coins and 100+ exchanges!
-While you may have decided that spread of 2% is enough for you to make profit, you may secure that amount, but if spread increases to 4% and your capital is already locked at 2%, you missed a much more profitable deal! Alternatively, if you wait for a 4% spread but it only reaches 2% and then goes back to 0%, you just have missed a 2% profit.
We will get into more details on how do quant algorithms optimally execute arbitraging strategies in upcoming articles,so stay tuned!
Now let’s look at another popular trading strategy which is widely used in HFT (high frequency trading). It’s called “market making”: when you provide liquidity to the market (and some exchanges even pay you rebates for that!) by quoting bid and ask prices simultaneously (i.e. buying and selling the same asset), collecting the price difference (called “bid-ask spread”). Let’s have a look at daily prices of Bitcoin in Japanese yen and the bid-ask spread:
The trick here is to avoid holding your position for too long, because the price may change significantly (most HFT algorithms hold position for only fractions of a second!) and to control the size of your position, so, for example, if the price is going down, you’re not buying more and more. These strategies are usually very complex and require some advanced math, but we will explain what they do in futurearticles. For now, let’s see how a typical high frequency market making strategy works:
It makes a very small amount of money per trade, but it makes thousands of trades per day and shows consistent profit that is not affected by market direction.
There are also purely data-driven algorithms that use machine learning methods to spot more subtle patterns and inefficiencies in the market data.
As you can see, there are ways to consistently profit from cryptocurrencies, regardless of the market direction.
As a group of “quants” with academic background in Numerical Methods, Computational Mathematics, Game Theory and hands-on experience in High Frequency Trading and Machine Learning, our interest was in exploring opportunities in cryptocurrency markets, with the goal of exploiting various market inefficiencies to generate steady absolute returns (not correlated with market movements) with low volatility, or simply put, steady profit without major drawdowns. For more information please visit http://www.TensorBox.com or read about our Initial Token Offering and the Early Bird Promotion here: https://medium.com/tensorbox/tensorbox-initial-token-offering-details-oct-2nd-2017-ce391140d86e
