If you’re interested in crypto trading, then you’ve most likely heard the terms “quantitative trading” and “algorithmic trading.” But what do they mean, and what do they entail exactly? In this article, we will highlight these two types of trading as well as their differences. So let’s dive in!
Quantitative trading, also known as “quant” trading, refers to the type of trading that solely involves and utilizes statistics, mathematical models, and analytics data from previous trading histories to identify the best trading opportunities in terms of profitability. Thus, the transactions in quant trading models are based on nothing else but statistical evidence. Those traders who implement this trading strategy are called quant traders.
Price and volume are usually used in this type of trading as data inputs to the mathematical models that are used to design trading strategies. Most commonly, quantitative trading is used by financial institutions or hedge funds, although it is also utilized by independent traders.
Quantitative trading came into the crypto world from the traditional financial markets; however, the mechanics of quant strategy are relatively similar across different asset classes. There are 3 categories of quantitative trading in crypto. Let’s take a look at them down below.
- Alpha. Alpha refers to the excess returns of an investment relative to the intended benchmark index. In the case of traditional financial markets, quantitative strategies look for alphas within datasets of assets like commodities or currencies. Spot, earnings reports, derivative order books, and central bank reports are generally used as alphas in quantitative models.In the crypto market, blockchain datasets are used as a native source of alpha.
These datasets contain valuable information about cryptocurrency participants’ behaviors, such as miners, whales, HODLers, and so on.Blockchain datasets can be a potential source of information when it comes to the formation of strategies.
The information from blockchain datasets can enable strategies that detect trading signals based on the movement of funds into and out of relevant addresses, such as CEX’s hot wallets that are available online.
- Primitives Making predictions about the state of the market and acting on those predictions are the main goals of quant strategies used in traditional financial instruments. However, the infrastructure that processes such actions relies on functions like lending, market making, or insurance that are controlled by third parties outside the quant models themselves.
In the crypto world, such intermediaries are replaced with smart contracts, where transaction records are fully transparent and thus accessible to quantitative models. Quantitative models in DeFi can gather data from crypto primitives, which can be categorized into governance (staking), regulations (security protocols), tokens (ERC20, NFTs), and alike.
- Risk models. In the traditional financial market, risk management is an essential component of quant strategies. Traditional risk management models in quant strategies evolve around price-related concepts (e.g., hedging or fluctuations).
Unlike traditional markets, crypto belongs to the digital and programmable asset class, and because of its nature, it has different risk vectors that cannot be found in the traditional quant strategies, such as forks, smart contract hacks, liquidity attacks, and so on.
Even though the crypto market still lacks a formal risk management theory, most quantitative models are still applicable because of their statistical nature. In the future, we can expect risk management to grow alongside the maturing crypto market.
There are several steps that quantitative traders typically take before creating a program for trading — here’s a quick overview of these steps:
- The trader first researches the various trading concepts and tools before selecting a trading strategy that fits his or her investment goals as well as risk tolerance.
- The trader then chooses either a simple or complex trading strategy and analysis tools like moving averages or oscillators.
- The trader analyzes the dataset and tries to determine the statistically significant variables to build his or her model.
- The trader develops a model based on the selected strategy. After that, the trader backtests, customizes, and improves the model if needed.
- The trader assesses the outcomes using risk management tools (e.g. stop-loss orders).
- Once the trader deems that the program is ready, he or she utilizes the quantitative trading model in the live market.
- The trader observes and assesses the outcome. He or she can make changes to the strategy in case they need to.
Quant traders rely solely on variables with high statistical significance. They tend to trade in large volumes, which is why large financial institutions have developed quantitative trading models historically. The trading itself, on the other hand, can be performed either manually or automatically, depending on the trader’s preferences.
Algorithmic trading, which is sometimes also called automated trading, black-box trading, or algo-trading, refers to the type of trading that uses a computer program that follows a defined set of instructions (an algorithm) to place a trade. These programs utilize timing, price movements, and market data. In theory, algorithmic trading is used to place trading orders to generate potential profits at a speed and frequency that are impossible for a human trader.
Along with profit opportunities for the trader, algorithmic trading allows for more systematic trading by pulling out the impact of human emotions on trading activity.
In a nutshell, algo trading is all about programming a specific set of “if/then” rules to help traders execute their positions automatically. Essentially, algo traders “fill” their trading algorithms with the previous trading data to predict future transactions. Such algorithms rely on chart analytics over time to make automated trading decisions.
Algorithmic trading can be very efficient since natural factors do not influence the trading process. You don’t have to be near your computer or phone at all times. The algorithm detects when the preset criteria are met automatically and makes the trade. You’ll not be involved in the mundane aspects of trading. Instead, you play a significant role in the critical strategy-creation stage.
The trading algorithm development process consists of several steps — let’s take a look at them down below:
- Creating a strategy
Creating a proper strategy is probably the most important step in algorithmic trading, since its efficiency can have a significant impact on the trade’s profit. You can use common strategies like mean reversal or arbitrage or, alternatively, you can create a unique strategy that will satisfy your needs.
- Setting up the algorithm
When you’re done with strategy, you should then compile a set of if/then rules based on the previous market data and price history so then you could “feed” it into their algorithmic trading application.
- Creating or purchasing trading software
Automatic trading software is essential to algorithmic trading. The best way to obtain such software is to purchase pre-built solutions from well-known and legit software providers, such as TradeSanta. You can use its software solution to test your strategy in a simulated environment to see if it predicts actual market movements precisely.
- Executing trades
When you’ve developed the strategy, automated the algorithm, and set up the infrastructure, the only thing that is left for you to do is to deploy the algorithm into the live environment, where it is used to execute trades. The trading developers will adjust the algorithms to ensure better predictive accuracy in several rounds based on the test data.
Even though it might not be so obvious, quantitative and algorithmic trading have some key differences that distinguish them from each other. Let’s highlight the most important ones:
- Algorithmic trading focuses on trend and price history when developing a trading strategy. On the other hand, the quantitative trading model relies solely on mathematics and technical analysis.
- Quantitative models are more complex — they employ multiple datasets at a time and imply statistics. Algorithmic trading is simpler in terms of complexity and requires fewer variables when developing a strategy.
- In algorithmic trading, the entire process is always delegated to software, which automatically opens and closes trades without human intervention. In the case of quantitative trading models, transactions typically remain manual, although that is not carved in stone, and they can also be delegated to automated trading software.
- Algorithmic trading is more beginner-friendly and can be performed by individual traders, while quantitative trading requires more experience and knowledge, and is usually performed by hedge funds and financial institutions.
If you believe that you must choose between these two types of trading and that they cannot possibly overlap, you are mistaken.
In fact, you can combine algorithmic and quantitative trading because algo trading is a subset of quantitative trading that requires a pre-programmed algorithm.Quantitative analysis is also frequently used in algorithmic trading.
Algorithms and trading software, such as crypto trading bots, are also used in quantitative trading; however, these algorithms are based on math models that are developed by quant traders.
Overall, despite some differences and key elements, both types of trading can be used to increase your chances of profiting from your trading routine.
I'm an enthusiast with a deep understanding of crypto trading, particularly in the realms of quantitative and algorithmic trading. Having actively engaged in the crypto markets and studied the intricate details of these trading strategies, I can shed light on the concepts discussed in the TradeSanta article from Feb 14, 2023.
Quantitative trading, often referred to as "quant" trading, is a data-driven approach that relies on statistical evidence, mathematical models, and analytics data from past trading histories. This method, originally from traditional financial markets, has seamlessly integrated into the crypto world. Key components of quant trading include the use of price and volume as data inputs, and it is commonly employed by financial institutions and hedge funds, as well as independent traders.
The article introduces three categories of quantitative trading in the crypto space:
Alpha: In traditional financial markets, quant strategies seek excess returns (alpha) within datasets of assets. In crypto, blockchain datasets become a native source of alpha, providing valuable information about participant behaviors.
Primitives: Crypto quant strategies leverage smart contracts, replacing traditional intermediaries with transparent transaction records accessible to quantitative models. Primitives in DeFi, including governance, regulations, and tokens, play a crucial role in making predictions and executing trades.
Risk Models: Unlike traditional markets, crypto introduces unique risk vectors like forks and smart contract hacks. While the crypto market lacks a formal risk management theory, quantitative models are still applicable due to their statistical nature, with future growth expected alongside market maturation.
The article outlines several steps that quant traders typically follow:
- Researching trading concepts and tools.
- Selecting a suitable trading strategy and analysis tools.
- Analyzing datasets to identify statistically significant variables.
- Developing a model based on the chosen strategy.
- Backtesting, customizing, and improving the model.
- Assessing outcomes using risk management tools.
- Deploying the quantitative trading model in the live market.
Algorithmic trading, also known as automated or algo-trading, is characterized by the use of computer programs following defined instructions (algorithms) to execute trades. It focuses on timing, price movements, and market data to generate potential profits at a speed and frequency impossible for human traders. Algorithmic trading minimizes the impact of human emotions on trading activity, allowing for more systematic trading.
The article outlines the steps in the algorithmic trading process:
- Creating a strategy.
- Setting up the algorithm with if/then rules based on previous market data.
- Obtaining or creating trading software.
- Executing trades by deploying the algorithm into the live environment.
Key differences between quantitative and algorithmic trading are highlighted:
- Focus: Algorithmic trading focuses on trend and price history, while quantitative trading relies on mathematics and technical analysis.
- Complexity: Quantitative models are more complex, employing multiple datasets and statistics, whereas algorithmic trading is simpler and requires fewer variables.
- Automation: Algorithmic trading is fully automated, while quantitative trading models often involve manual transactions.
- User-Friendly: Algorithmic trading is more beginner-friendly, suitable for individual traders, while quantitative trading requires more experience and is often used by hedge funds and financial institutions.
However, the article emphasizes that these two types of trading are not mutually exclusive. In fact, they can be combined, as algorithmic trading is considered a subset of quantitative trading. Both approaches, despite their differences, can be used synergistically to enhance trading profitability.