“If your phone can understand your voice and recognize who you are, why can’t it recognize and trade charts?
I think in the next few years, all types of trading, high frequency, technical analysis, value enhancement, arbitrage, anything really, and in all different markets, equities, stocks, commodities, cryptocurrencies,
will be affected by Artificial Intelligence (AI). or even completely dominated by Artificial Intelligence.
Human managers will have to learn to co-exist with this.”
– Marshall Chang, speaking during a Ted Talk (2018)
There is a shift in trading that is taking place and it is happening faster and faster – quant or algorithmic trading is what they call it – and it will change the entire landscape of trading, not just in cryptocurrencies, but in all markets.
The rise of algorithmic trading is undisputed and continues unabated especially in the crypto markets that continue to see more institutional investors, who largely play on the algorithmic trading level, enter this fast-growing space.
This is a first of a series of articles that we shall be publishing that looks at the world of algorithmic trading and why, as a trader, you should be seriously thinking about entering this space if you are looking at having a profitable crypto trading career over the long-term.
OK, to begin, let’s put the world of algorithmic trading into perspective.
From a World of Seconds to Milliseconds
The human decision making ecosystem operates in seconds, minutes, and hours. For example, a chess player will take a few seconds, and sometimes minutes, to make a decision on the best moves in a game.
Chess playing however is only one pawn in this decision making process. The human brain takes approximately this long to do the following:
- Read a tweet – 3 seconds
- Read a BitcoinKE post – 3-5 minutes
- Read a scientific Journal – 2-3 hours
According to science, the limit of human decision making process is limited to 1,000ms. The ability to tap into this milliseconds world however becomes challenging when you put several parameters into the decision making process. In the process, decision-making tends to get slower.
The human eye refreshes every 5-10 milliseconds
The ability to measure with microsecond accuracy however is no longer a luxury, but a requirement in this day and age in order to remain competitive and relevant, and this cannot be any truer in crypto than any other market.
Enter Quant Trading
Quantitative traders or quants for short, use mathematical models to identify trading opportunities. Quant trading is generally used by financial institutions and hedge funds who usually have to manage large transactions. However, quant trading is also becoming more commonly used by individual investors and these tools are becoming more readily available as more of these traders seek a competitive edge.
Quantitative trading techniques can be summarized as below:
- High-frequency trading – Use of complex computer algorithms to analyze multiple markets and execute orders based on market conditions. Typically, fastest traders are more profitable due to high execution speeds
- Algorithmic trading – Utilization of automated and pre-programmed trading instructions to account for such variables as price, timing, and volume. This is largely applied in order execution, arbitrage, and trend trading strategies
- Statistical arbitrage – Utilization of mean reversion analysis to invest in diverse portfolios for short periods of time with the goal of reducing exposure. Traders exploit tiny inefficiencies lasting milliseconds to generate profits from minuscule price movements
In the context of crypto markets and this series, we shall be looking at quant trading by taking advantage of mathematics and the availability of comprehensive crypto databases over the last 7 years to make rational trading decisions.
We shall also be looking at how these models are back-tested and optimized by putting them through various trading scenarios until a favorable trading strategy is developed and implemented in real time markets using real-time capital.
One advantage of quant trading is in enabling individual users to use back-tested data and remove emotional decision-making during crypto trading. Due to its ineffectiveness once market conditions change, quant trading strategies need to be continuously tested for optimal results.
Overcoming emotions of fear and greed is one of the biggest challenges for individual traders and this often stifles rational thinking, which, in turn, usually leads to losses. Quant trading helps eliminate the problem of bringing emotions into a trade thereby helping traders gain most value.
In the next post, we are going to dive deeper into what it takes to build a successful quant strategy.
NB: This post was contributed by Loibon – An African-based quant trading firm
Loibon is a quant development and trading service providing tested algorithmic and discretionary trading systems across major cryptocurrency markets.
- Join the Loibon beta: https://loibon.com/
- Join the Loibon Telegram community here: https://t.me/loibon
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