Main Quantitative Finance Definitions You Should Absolutely Know


Hey, let’s summarize the main quantitative finance terms and see explanations that would be useful for anyone who wants to become a quantitative trader. People who are reading the articles and case studies of traders and have issues with the jargon can also benefit from learning the basics.

So we’ve gone through some research and books, and here are the main terms you can check first before you break into the space of quantitative trading!



Quantitative Finance & Trading

Algorithmic Trading

Automated Trading


Ultra-High & Ultra-Low Latency Trading

Recommending Materials on Quantitative Trading

Summing Up


First, it is important to touch upon what quantitative finance and trading are, so before we get into other definitions let’s understand the overall finance industry. The market categorizes the finance industry into two main broad aspects:

  • Transactional finance ─ predominantly consists of accounting where one maintains books of accounts of day-to-day activity,
  • Decision-making finance ─ it is that part of finance based on experience and various analysis decisions. The decision making finance involves qualitative and quantitative analysis to decide what is the future course of action.


Qualitative finance is more about subjective judgments using qualitative factors which cannot be quantified in a real sense. On the other side, there is Quantitative Finance, let’s describe the term in detail and check what is trading.


Quantitative Finance & Trading

Quantitative Finance is the usage of complex mathematical models and extremely large data sets to analyze financial markets, prices, manage risks, etc. It’s more data-driven and more quantifiable, that is one can measure the impact of variables on the occurrence of a given event.


For example, interest rates around the world are falling so what is the impact of falling interest rates on the risk exposure a bank carries. Or what would be the impact of interest rates falling on the value of my assets or the value of my liability?


Quantitative finance means using mathematical models and data sets to:

  • Predict a future event or what's the likelihood of occurrence of a particular event,
  • Predict what would be the impact if once this event occurs to the profit or loss,
  • See the impact on the balance sheet or to any other financial aspect of the business.


Quantitative finance is the arm in finance trading which you know takes large data sets into considerations to predict future events and that's where quantitative finance is at the tipping point.


Trading is a fundamental financial concept that involves purchasing and selling products or services. When we talk about financial trading, we mean buying or selling securities, for example, business shares. Most trading was done these days online. Advancements in technology have even allowed everyday individuals to take up trading at home to try and make money. Even though trading involves stocks and other investments it is often seen as a very different practice from investing. When you buy or sell stocks, you seem to be a trader whether professionally or as a hobby.


It’s different from being an investor, in layman terms, investing in trading is the act of spending money with the hope of generating some larger benefit or return in the future while trading is the act of buying or selling investments. Making money for an investor means that someone who placed their money in something is looking to profit from that asset growth over time. A trader is someone who makes money in the short term by buying and selling stocks frequently. In other words, while one relies on gradual appreciation the other focuses on market volatility. Today, trading can mean several different things, many companies hire traders to help them carry out their investment decisions.


Algorithmic Trading

The algorithm is a definite set of the well and clearly described instructions that experts are using to solve a class of specific issues and make calculations. For example, the Euclidean algorithm is an effective method to see the greatest common divisor of two positive integers, a and b.


In Algorithmic Trading (aka algo-trading or black-box trading), you can use an algorithm that automatically performs different actions such as buying or selling stocks. Besides this, you have to follow the market trends with buy or sell orders generated based on a set of conditions fulfilled by technical indicators. You have to base your algorithmic trading strategy on the market trends which you determined by using statistics. This strategy can also compare historical and current data in predicting where the trends are likely to continue or reverse.


Another algorithmic trading strategy is using a reversion system which operates under the assumption that markets are ranging all the time. Experts who employ this strategy typically calculate an average asset price using historical data and take trades in anticipation of the current price returning to the average price


When we talk about algorithmic trading, we cannot pass computer programming as it is used to design sophisticated trading algorithms. So Algorithmic trading is the process of converting a trading logic or strategy into an algorithm or computer code. This strategy takes input data, processes it and generates trading signals.


Quantitative analysts or quants are typically trained in Python C or Java programming languages to come up with algorithmic trading systems and mathematical models that help to quantify human and economical behaviours. Now the quants are at the heart of yet another technological revolution in finance: trading at the speed of light. Many quants are using Google algorithms to check what people are searching for to recognize what is valuable and what the market is all about.


Automated Trading

Trading decisions should not be based on sound research, emotions and excess risks in the hope to get rich quickly. To trade profitably in the market, the role of luck should be minimized or removed by using thoroughly tested methods which are based on sound mathematical concepts and statistical models. These methods often involve automated execution of trades to remove the role of emotion from trading. In trading parlance, this is called automated trading.


By using Automated Trading systems set by the user, you can execute automated tasks independently. Automated trading automates all trading strategies and includes:

  • Quantitative modelling and
  • Tracking indicators
  • Portfolio risk monitoring


Let's say you are a quantitative trader. You hypothesize that the stock that has positive returns over the past one year is likely to give positive returns over the next 1 month. As a quant trader, you won't rely just on intuition. You will use automated trading to extract a large amount of historical data for multiple stocks. You calculate the past 1 year returns of the stocks, check whether the returns are positive or negative. If positive, then you check for the future 1-month returns of the stocks.


You track indicators, analyze the performance and monitor risks. If everything works well then you place the orders to a broker. It can involve a combination of various tech indicators in such a way that the portfolio gives maximum return at a given risk. All of these techniques and modelling methods help to predict future security's price and volatility in the market.



If you choose to work in finance you can automate your transactions. How? You can enable trading deals to occur in milliseconds which means you can deal with a few trading strategies, involving buying and selling securities at maximum speed. This is called High-Frequency Trading or HFT. It has nothing to do with the economy. This is how the networks work.


HFT includes automated decision-making and execution and relies on algorithmic trading. For example, let’s imagine that a user wishes to purchase a share at €10.00 at 2:00:00pm from the New York MarketStock Exchange. But in the same seconds, the unit costs €10.02 on the London Stock Exchange. And 400 milliseconds after, the stock price for a unit in London Exchange levels up to €00.06. So 400 milliseconds is a moment that takes a person to blink but for the HFT system, it is a moment to grab the opportunity and make a profit. The system knows what happens in the financial markets, to the milliseconds.


Low-Latency trading is described as the signal speed of trading systems. Latency is the delay before the data transfer starts. It’s the amount of time the data signals travel from trading venues to processing apps, brokerage and other financial institutions. Low latency trading systems can be used in algorithmic and high-frequency trading as the systems help reduce signal latency which is useful for all automated trading types.


Ultra-High & Ultra-Low Latency Trading

Ultra-low latency trading is a version of ultra-low-latency trading systems. It is crucial to enable and deploy strategies that minimize trading latency to directly faster execution and get bigger profits. To minimize latency in your trading model and maximize contribution, a new upgraded version of low-latency trading systems has been in use. The infrastructure components enable the low latency capabilities that the users need today.


Ultra-high frequency trading, according to experts, is used by traders who pay for access to an exchange that shows quotes earlier than the rest of the systems and market. It is computerized algorithms that use sophisticated tech tools to trade ultra-high securities she system is engaging with financial services and firms including exchanges, funds and leading market data suppliers, and many of whom are reviewing acceleration strategies to quickly assist technology that can improve analytics and market data performance through the program.


Recommending Materials on Quantitative Trading

Quantitative researchers and quantitative traders that work at trading citadels read certain books that they believe will benefit them. For example, you can start with the below list which contains the main information you need to learn:

  • Option Volatility & Pricing: Advanced Trading Strategies and Techniques by Sheldon Natenberg
  • Dynamic Hedging by Nassim Nicholas Taleb 
  • Frequently Asked Questions in Quantitative Finance by Paul Wilmott (two editions)
  • Python for Data Analysis by Wes McKinney
  • Technical Analysis: Is Mostly Bullshit - Why Flipping a Coin is a Better Strategy than Using Technical Analysis in the Financial, Stock, and Forex Markets by Tim Morris


Summing Up

There are a lot of other terms which have to be learned by people in trading and quantitative analysis. Other crucial points and terms that are known to most traders who are experts in quantitative finance are quantitative momentum strategy and model, machine learning in trading, investment banking analysis, risk management in finance, data science and analytics. You can start reading books on quantitative trading which will help you to get a certain background and learn other essential terms and quant finance jargon.

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