Getting Familiar with Quantitative Trading in Crypto: A Beginners’ Guide

There is a good reason for the growing hype about quantitative trading in crypto, mainly because it efficiently combines past market data with accurate mathematical algorithms.

By analyzing historical data of a market, quantitative investors can shape formulas that have proven to predict future crypto market movement.

There is excellent success in quantitative trading, as it relies on the thorough development of specific formulas. And applying these formulas to automated algorithms carefully avoids the psychological defects of being human.

Why Use Quantitative Crypto Trading?

Quant – or quantitative – analysis is a relatively new trading strategy frequently deployed by hedge funds, large investment firms, and sophisticated investors. It relies heavily on mathematics.

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There are several advantages that computer algorithms have over human traders. The first and most prominent of them is that they can run perpetually. Even after human traders have called it a day, these computer algorithms can keep running around the clock as long as the cryptocurrency markets are open.

Secondly, these trading algorithms can place trade orders with speed by using bots. These bots typically run on high-performance computer servers capable of opening and closing trades faster than humans.

However, an algorithm’s most important benefit is that it has no emotion as it is run entirely by code. This enables them to consistently and objectively process the numbers and execute the trade irrespective of how you feel.

Indeed, feelings of fear and greed are often some of the direct causes of significant trading losses. A trader will divert from a tried and tested strategy merely because of how they feel.

How Quant Trading Works

If you have a strategy that relies purely on crypto-asset price relations, it is possible to develop an algorithm. Indeed, numerous methods can be employed with quantitative trading.

Market algorithms are coded in well-known programming languages, including Python, Nodejs, and C++, and then run on dedicated servers that connect to an exchange API. Once connected, the algorithm uses price feeds as the model’s inputs to quantify the data and create orders as outputs.

We presently have all the essential ingredients to create and operate quantitative trading algorithms in the crypto markets. Essentially, users can access strong liquidity across the top ten market cap digital assets and open access from various cryptocurrency exchanges with robust API systems.

As the markets become more saturated and competitive, they could be followed by a range of high-frequency trading firms and quantitative Hedge funds.

Genesis Trading Acquires Quant Investment Firm Qu Capita

Cryptocurrency trading can be risky, but a new acquisition could make it more predictable and competitive.

Genesis, a crypto trading and lending startup, is now providing the quantitative trading proficiencies of Qu Capital to its users. In September of last year, Genesis announced that it had acquired the New York-based investment company specializing in quantitative crypto trading.

Genesis CEO Michael Moro remarked that his company acquired Qu Capital to integrate its in-house team and expand its trading and lending dealings. Qu Capital was created in 2017 and has since endeavored to develop crypto trading tech, which comprises exchange connectivity, order steering, and trade execution tools.

After its crypto-related lending business saw $746M in loans in the 2nd quarter of 2019, Genesis embarked on this remarkable acquisition agreement, growing its total originations to $2.3B since its launch in 2018.

The company provides big individual traders and institutional players with easy over-the-counter (OTC) cryptocurrency trading and leading services.

Trend Following in Quant Trading

Trend following in quantitative trading is based on the notion that markets have momentum, and you want to be on top of that momentum. One of the most recognized technical indicators is trends, and many such indicators can be deployed to map emerging trends.

Some of the most used indicators in crypto trading are Moving Average (MA) Cross Overs. These usually occur when a “faster” and shorter-term MA indicator crosses over the longer-term or “slow” indicator.

The graph below shows an example of a conventional 50-day MA crossover of the 200-day MA indicator. In this case, the crossover indicates a bearish trend, and BTC should be shorted.

The opposite will occur if the fast indicator crosses over the slow indicator from the bottom. In this case, you should go long BTC. Usually, this is one of the simplest indicators, and traders will usually combine it with a range of others.

You could develop a simple trading algorithm that will execute the trade for you with the functionality to place stop losses and halt limit orders when the execution order is given.

Pairs Trading

The notion of pairs trading is that if two assets have been trading near lockstep in the past. If there is a reversion away in that historical relationship, it indicates that the two assets are expected to revert. You will then sell the ” overpriced ” asset and buy the underpriced one.

In cryptocurrency trading, the two digital coins will have a high correlation with general crypto market movements, which means that you are pretty hedged against adverse market moves.

In the graph below, you can observe the ratio of the value of ZCash (ZEC) to that of Monero (XMR), along with an elaborate model of the Bollinger Bands of these sequences.

There were two instances where the ratio exceeded the two standard deviations, indicating that it could ultimately revert. Therefore, you will short ZEC and buy XMR, hoping that the latter will surge in price and the former will decline.

You can also observe the Bollinger Bands and use that to indicate that the spread between the two coins’ prices has increased/declined further than historically justifiable numbers.

Arbitrage Trades

With arbitrage trading, you try to exploit market mispricing and earn a risk-free profit. However, to take advantage of these opportunities, you need to act quickly, as they only last for a few seconds before the market recognizes that there is mispricing and closes the gap.

In the cryptocurrency markets, arbitrage trades are usually the most profitable for the differences in price between digital tokens on numerous exchanges.

This will require the bot developer to account for both exchanges and link the algorithm’s orders to their API systems.

Some bots can take advantage of mispricing on an exchange itself. For example, a bot called “Agent Smith” made quite a bit of money during the bull market as it traded mispricings on the Poloniex exchange.

Should You Consider Quant Trading?

Here are some considerations for traders wishing to apply quantitative trading:

1. First, there are still going to be standard trading risks involved.
2. Past performance does not indicate future performance, and traders must monitor the process and adjust frequently.
3. You should be a sophisticated trader with solid experience to evaluate a particular strategy effectively and know if it suits you.
4. You shouldn’t trade more money than you can lose.
5. Fees can quickly kill profits if quant trading bots execute orders too regularly.

Conclusion

Quantitative trading in the digital asset markets is becoming more competitive, but opportunities are still available, especially with technical indicators and reversion strategies.

If you have a strategy that works, there’s a good possibility that it can be coded into a mathematical formula to trade automatically. As long as you’re diligent, quant trading can be a brilliant way to approach the crypto markets and make substantial profits.

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Preferably, it would help if you considered taking the time to build trust with a quant trading system before adopting it. This could entail observing the system’s performance in hypothetical simulations before putting real money in it. You can do this with a paper trading account to guarantee your numbers track well in many cases.