A Basic Guide to Quantitative (Quant) Trading
High-frequency trading (HFT) tools have been available to large investors like institutions and hedge funds for some time. Using algorithmic strategies, “HFTs are buying when the price is below trend and selling when the price is above trend. This tends to reduce the price fluctuations” (Conerly, 2014).
Individual investors have been looking for resources to gain the same advantages. This has led to an increased popularity in quant and algo trading. Many seem to look at the sector as an undiscovered territory that is too complex or difficult to understand when it is quite the opposite. Getting a brief overview of what it is and what it means can be helpful to the average trader or investor.
Quant trading
Quant trading can be seen as a trading methodology using computer-like and mathematical models to execute decisions. Quantitative trading isn’t limited to pure written mathematical models, in fact, many regard modern-day quant-trading as a blend of programming and utilization of statistical and probability-related models to deliver above-normal returns. Quant trading involves analyzing suspected bits of data to find correlations, deviations, and outliers with the goal of obtaining an advantage in the market.
According to James E. McWhinney, writing for Investopedia, “Unlike traditional qualitative investment analysts, quants don’t visit companies, meet the management teams or research the products the firms sell in an effort to identify a competitive edge. They often don’t know or care about the qualitative aspects of the companies they invest in, relying purely on math to make investment decisions.” — this is partially true. While some quantitative models can be purely-math inspired, it doesn’t mean that you’re not allowed to look at other factors. The basis is that quant trading is highly analytical, but what happens when other factors are incorporated?
Quant trading optimizes data to pick assets, dates, and times to trade. In many cases, four core factors are taken into consideration:
- Strategy Choice — Finding the trading method, and optimal quantitative analysis approach to trading. This means looking at the overall atmosphere, and figuring out how to emulate the most advantageous methodologies. With cryptocurrencies and digital assets in particular, these can be analyzed and tested differently.
- Backtesting — Backtesting simulates the performance of a model based on data history. Using data alone, it can analyze performance without bias. Backtesting is essential when testing a quantitative model and allows a theoretical outcome. The greatest backtesting events are those that take into consideration there is huge unpredictability in any market.
- Operating Link — Link to a brokerage or exchange to automate the trades and minimize transaction costs. This will differ depending on what assets you’re trading. With digital assets, application programming interfaces are linked with exchanges of choice.
- Optimize Allocation — Input like The Kelly Criterion calculator helps set the size of the trade. Allocation is essential for each trade.
Quantitative analysis is a mindset as well as a tool that identifies historic data patterns to execute profitable trades. Quant analysis also helps reduce the uncertainty with analytics that also identifies the extent of unpredictability in a specific trade. The quant trading system weighs predictive analytics to choose between risks and buys or sells as the data demands. Mixing and matching certain qualities to these analysis mindsets is what differentiates each quant methodology.
Advantages in quant trading
Quant trading can be a decision-making system that eschews the historic and qualitative context to make unbiased investments. If chosen, only data patterns matter. Detached from historic predisposition and personal feelings, quant trading can be reliant on executes of buy/sell trades consistently.
Data-based as it is, the quant trading strategy works without reliance on analysts, managers, and advisers.
Algo trading
Algorithmic trading is a form of high-frequency trading using programmed strategies and computers to make large orders in securities markets. This is where the true opportunity can reside.
Large orders can typically come from institutional investors, hedge funds, and Wall Street trading desks, but recently tools allow for individual traders have to enter the market and enable a new playing field for those with more analytical minds.
An algorithm is a procedure that solves a mathematical problem. It lays out the specific sequence of instructions that tell a computer what to do. It’s a program coding system that instructs the computer to act on a system of IF, AND, and OR choices. In the digital currency market, there are new tools to be utilized. The integration of API’s allow for new arbitrage-related opportunities. Buying on one exchange at $1 and selling on another at $1.1 is an entirely new game now since digital assets can be moved to anywhere in the world in mere minutes.
Algorithms help to determine when you should buy and sell something to make the best return on investment with the least amount of uncertainty; if you program it correctly. Otherwise, it could be a tragic indicator. Algorithms are only as powerful as the mind that programs it. It is worth noting that trading is a zero-sum game which means if you’re making money, someone else is losing money. With that in mind, code that you find online that seem too good to be true, is in most cases, too good to be true.
As a convenience, traders can now, with digital asset exchanges, load algos into personal computers to execute trades based on your criteria.
Pros in Algo Trading
Algo trading helps maximize profits by controlling market risk and reducing execution costs. NASDAQ says, “Since algorithms are written beforehand and are executed automatically, the main advantage is speed. The speed at which these trades are made is measured in fractions of a second, faster than humans can perceive.”
In addition to the transaction speed, algo trading enables research and analysis at a higher speed than human ability. Because algo trading takes investment selection out of the investor’s hands, it makes trades with accuracy and without human error. And, that removes personal emotion, fear, greed, and bias to make the trade totally rational. Imagine analyzing data that is released within seconds then trading almost immediately after said analysis is complete. That’s possible now.
With all these factors in mind, backtesting is typically done on an algorithm to test its efficiency and return in a previous market. Investors can simulate a model based on data to see if and how it has worked. Backtesting helps you identify and remove flaws in their system and readjust their strategy.
Without an extensive team of analysts, managers, and advisers, traders can, therefore, save money in time, fees, and charges.
Conclusions
Studies have connected market volatility and flash crashes with high-frequency trading systems. Yet, those concerns have not deterred investors who figure that some noise in the market is a good thing.
This has created a market for software developers who are creating software programs traders can use or subscribe to. No one is giving away their algorithms, but these programs offer strategic options for traders to launch algorithms that appear to serve their investment goals. Those with the most complex systems are those that come in first place in the markets.
As Jeremy Bogaisky, an editor at Forbes, writes, “Cheap technology and sophisticated engineers have disrupted almost every other aspect of Wall Street. Now we get hedge fund tools for the masses.”
