Statistics simply means collecting data to make informed decisions. In quantitative trading, using statistics is inevitable because the basis of quant trading relies on statistics.
Quantitative trading can be defined as a trading strategy formed on quantitative analysis, which uses mathematical techniques to seek out trading opportunities. Big financial corporations mainly use quant trading, as transactions involve a large volume of assets. These days though, quant trading is now commonly used by private investors.
While quant trading may not be ideal for beginners, it is still worth learning about, as you could combine some techniques with your already existing trading strategies. After reading this article, you will get to know what quantitative trading is, how it is used in crypto, how you can get started, and different modern quant trading strategies you can use.
What Is Quantitative Trading?
As the intro explains, quantitative trading uses mathematical formulas and statistics to create trading strategies and identify trading opportunities. Price and volume are the primary data used in quantitative analysis as inputs to the mathematical models applied.
Quant traders use tech, math, and statistics to make trading decisions by recreating an existing technique using mathematical formulas, then developing computer programs that utilize the model with existing market data. After the model is tested and optimized, it is applied in the real markets using tangible assets if it yields favorable results.
How Is Quant Trading Used In Crypto?
The rise of modern technological trends has thrown the world of investment into growth mode. These trends include improving upon existing technology in the form of innovation.
In crypto, quantitative strategies in the form of machine learning are used by trading companies to maximize the chances of making more profit. Quant trading is used in the crypto world in the following ways.
Some crypto platforms use trading algorithms and bots to lock in buying and selling cryptocurrency positions for investors, at the right time and in the right amount.
Machine learning practices are employed to help analyze big cryptocurrency data and spot new trading opportunities.
Arbitraging is made more accessible by using quant trading models. They help carry out multitudes of trades simultaneously in different markets to profit from price differences. It carries a considerable amount of risks, but if you do it right, you could mitigate some of the risks.
Artificial Intelligence (AI)
AI is used to develop and implement unique trading systems that make trading better.
How Does Quantitative Trading Work?
To start, you must decide on steps to take regarding strategy, system execution, risk management, and backtesting.
Essentially, there’s a basic format to follow when making trading decisions. These formats are as follows;
- The trading company or trader has to do thorough research to find an investment strategy that best suits their investment at a risk they can tolerate.
- Tools for analysis and complex trading are selected, e.g., oscillators. The trader then creates a system on the chosen strategy using the tools.
- Backtesting and customization have to be done to improve this system. However, it is essential to note that a positive outcome from backtesting doesn’t necessarily mean it’ll always work.
- Risk management tools are used to assess the outcome of the prototype. If satisfactory, they can then use it in the quant trading system.
In conclusion, traders can use this system to generate huge profits. It is also super efficient because it’s easier to learn from data statistics. Hence, they can implement better for extra accuracy.
How to get Started with Quant Trading?
Quantitative trading is different from regular trading since the winning strategies are done by the trader either manually or through machine learning in the hopes of making a profit using accurate data. It would be best to have a few business, scientific, and programming skills before starting quant trading. The six most crucial knowledge areas are listed in no particular order.
You can better understand how the market works by learning about it through finance. Without financial knowledge, it would be challenging to identify lucrative market opportunities. Depending on the type of trading you want to start, it would be beneficial to understand the concepts you want to use.
You need a knowledge of statistics to calculate whether an opportunity is well worth it. To spot a good opportunity, you can use a simulation to get optimum results or the Expected Value and Kelly Criterion.
In quant trading, programming is essential as new strategies require you to test them regularly to improve them. Programming language expertise and other technical skills are required during strategy development’s initial stages.
Research and Adaptability
You must be able to read independently, research on your own, and evaluate the trading market. A successful trader must also be adaptable enough to make money continuously over the long term while using risk control methods.
To comprehend gathered data and improve strategy, one needs to be familiar with data research, mining, and analysis.
Apply to a reputable trading company if you provide the necessary value. It will help if you recognize opportunities that can teach you more than you already know to be a good quant trader. It opens up an avenue for free mentoring, connections, and information, slaying several birds with one stone.
In summary, quant trading requires you to understand how the market works, spot an opportunity, and improve your current knowledge using a specific skill set.
You must recognize opportunities that can teach you more than you already know to be a good quant trader. Applying to a reputable trading company might give you an extra edge as you receive free mentoring, connections, and information, thus improving your skills and experience exponentially.
Quantitative Trading Strategies That Work in 2022
The trading market is ever-changing and sophisticated. Strategies that used to work a decade ago are no longer profitable as it is a volatile market, and things are bound to change. Here are some quant trading strategies everyone is talking about in 2022.
This method is not the typical trading strategy that involves price and supply. In this tech age, alternative data strategies are nontraditional methods to predict profits.
- Using satellite imaging in the Walmart parking lot to determine price changes.
- Social media foot traffic data to determine sales in a bookstore.
Obscure markets are unpopular, less efficient, and less regulated than larger markets. The reason to trade in these hidden markets is that there are a lot of opportunities that remain profitable for a long time. However, as the market grows and gains popularity, you will witness a change in options.
High-Frequency Trading (HFT)
HFT is characterized by high trade volume, high communication, more significant computing speeds, expensive software, and low profit per trade. The superpower these traders use to beat their competition is simply high communication speed, which is why they spend a lot on hardware and software that boost their computing speeds. Categories of HFT strategies include news analysis, arbitrage, and investment in infrastructure.
As the name implies, with machine learning, the computer learns through data analysis or on its own. These techniques ensure that computers do what they’re supposed to without explicit instructions.
Quant trading is a technique that focuses on quantitative analysis using mathematics and statistics to seek out trading opportunities. Using quant trading might make trading a lot easier, but it is essential to know how to do it well by being skilled or hiring skilled quant traders. A quant trader must be well aware of trends and pay attention when creating trading strategies. It is also worth knowing that no method is 100% efficient, so calculate risks before investing to know your chances of making profits instead of losses.
Diana is the CTO of Vestinda.
She’s an engineer with extensive experience in the payments space, passionate about mathematics and artificial intelligence.