An Introductory Guide into Quantitative / Algorithmic Trading in Crypto

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For many readers, the idea of trading has often been influenced by the sort of thing you see in movies about WallStreet. While this type of trading is obviously real, to some extent at least, it does not tell the full story.

This post attempts to shed light on the true aspects of trading in the financial markets and where the industry is heading.


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In the world of analysis trading, there are basically three styles or approaches to consider:

  • Fundamental analysis is where a trader bases investment decisions on fundamental factors of a target asset such as earnings, market cap, sales growth, and general macroeconomic market conditions.
  • Technical analysis is where a trader attempts to determine a market mood by looking and price and volume patterns in order to predict their directions.
  • Quantitative analysis is the newest kid on the block. This strategy is largely deployed by large hedge funds, investment firms, and sophisitcated investors and it heavily relies on mathematics and mathematical models to making trading decisions.

Think of quantitative trading (quant trading) as a combination of both fundamental and technical analysis with trades executed electronically by computers.

As mentioned in a previous post, quantitative analysis is the future of crypto trading and for very good reasons.

Components of a Quant Trading System

The most basic quantitative analyzer involves checking 2 basic data inputs:

  • Price
  • Volume

By plugging the above two inputs into a mathematical formula, a quant trader can make a fairly accurate prediction on where a crypto asset will go next.

There are 4 key components that make for a successful quant trading system:

  • Strategy Identification – Identify a strategy (find or create your own) that exploits an edge and then decide the frequency of trading
  • Strategy Backtesting – Look at historic market conditions and determine how the strategy hould have performed in the past
  • Execution System – Link the strategy to an exchange to automatic the trading
  • Risk Management – Optimize capital allocation and consistently tweak and improve the system to manage risk

Quant trading takes on a scientific approach by analyzing data inputs and what those inputs have historically meant for markets. Based on results, a trader is able to make predictions based on that analysis and give a predictive report.

Such a counter-intuitive conclusion would, for example, say that a particular strategy has a 40% or a 70% percentage return on invested funds.

In short, a good quant trader can only provide a predictive analysis of the future but not a 100% guarantee. However, a good quant system is more often right that its wrong and as a result, is able to create consistent profits over time.

There is no doubt that quantitative trading outperforms ‘passive HODLing strategies’ and other discretionary long/short and multi-strategies.

According to some reports, the average crypto hedge fund performance by strategy has much higher success rate by comparison as shown below:

  • Quantitative (+58%)
  • Discretionary Long/Short (+33%)
  • Discretionary Long Only (+42%)
  • Multi-strategy (+19%)

An increasing number of investors are investing in systematic crypto hedge funds and crypto quants exclusively with a majority of these quants being less than 3 years old.

Clearly, we are still very early in the space, and as more hedge funds enter the crypto space to manage institutional investors, the potential for quant trading to surpass other strategies is looking increasingly probable.

NB: This post was contributed by Loibon – An African-based quant trading firm


RECOMMENDED READING: 87% of Bitcoin Investments in 2020 Came from Institutional Investors, Dominated By Asset Managers, Latest Grayscale Report Reveals


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