What is Quantitative Trading?

Quant trading is now popular in the last several decades. Today, on the other hand, a lot of men and women don’t know what quant trading is, how it works or how to implement quant trading analysis strategies.

We want to help. In this guide, we’ll explain some of the most important things you need to know about quantitative trading so everyone can understand.

Quantitative trading is a trading method that involves using quantitative analysis to determine when to purchase or sell. Quantitative analysis involves crunching numbers and running data through mathematical formulas.

Based on the outcome of your quantitative analysis, you might determine that a specific stock is going to rise or fall in amount.

Some people call it quantitative trading, while others call it algorithmic trading.

In many cases, quantitative analysis is as simple as analyzing two of the most basic trading numbers: amount and volume. In more complicated cases, a quantitative analysis could require an analysis of hundreds – even thousands – of different factors.

Today, some of the world’s largest investors use quantitative analysis to make informed trading decisions. A hedge fund might have a quant trading division, for example, dedicated to analyzing every trade. The hedge fund might make billion-dollar trades based on this quantitative analysis.

At a more basic level, an average investor might read quant trading analyses on the internet before making a trade. Thanks to the proliferation of quant trading guides on the internet, it’s easy for ordinary investors to implement quant trading strategies on portfolios of all sizes.

At an even more basic level, all trades involve some type of quantitative analysis. Any time you’re using math, statistics, or numbers to make a prediction about future performance, you’re engaging in quantitative analysis.

How Does Quantitative Trading Work?

The most basic quantitative analyzer involves checking two basic data inputs: amount and volume. These are the two most common data inputs used in quantitative analysis.

A quantitative trading analyst might plug amount and volume into a mathematical formula, for example, to make a prediction on where the stock will go next.

Picture quantitative trading as a combination of mathematics, modern technology, and comprehensive databases. Quantitative trading throws all of these things into a blender, then extracts useful information from the resulting numbers.

Quantitative trading systems consist of four key components:

Strategy Identification: The before all else step is to identify a method. Find a method or conceive your own. Exploit an edge, then decide how frequently the system will trade.

Strategy Backtesting: Next, test that method on historic store conditions. How well would that method perform over the course of 2018? How well would it have performed in 1948?

Execution System: The next step is to link to a brokerage, automate trading, and minimize deal costs.

Risk Management: Once the system begins executing, the goal is to optimize capital allocation and manage risk while consistently tweaking and improving the quant trading system.

Quantitative trading is a broad field. It is combined with multiple other trading strategies. Common quantitative trading techniques can include high-frequency trading, for example, or algorithmic trading and statistical arbitrage. All of these techniques rely on quantitative analysis to make informed decisions.

What Does a Quantitative Trader Do?

A quantitative trader will take a trading capacity and conceive a model of it using mathematics. The quant trader will execute that capacity using mathematical formulas.

Then, the quant trader will conceive a computer program that applies the model to historical store data. The model is backtested (using historical data) then optimized. If the quant trader is satisfied with the outcome, then the system is implemented in real-time stores using real capital.

In many cases, the quantitative trader uses programming languages like C or Python to execute these trading strategies. C is particularly popular for high-frequency trading, although Python and R might be used for lower frequency trading.

Quant Trading is Like Meteorology: An Analogy

Our friends at Investopedia recommend thinking of quant trading like meteorology.

It’s the job of a meteorologist to analyze weather patterns, current inputs, and historical data for a specific region and then make predictions based on that information.

Just like a meteorologist, a quant trader checks various inputs, analyzes what those inputs have historically meant for stores, then makes predictions based on that analysis.

A meteorologist might release a weather report stating there’s a 90% chance of rain – even though it’s currently sunny outside. The meteorologist arrived at this counterintuitive conclusion after analyzing climate data from sensors throughout the area.

Although it’s not currently raining, historical data shows that it rains 90% of the time when similar data is detected from sensors. The sensors might have deducted a 15% drop in pressure, for example. 90% of the time when a 15% drop in pressure is detected, it’s going to rain in the next 24 hours.

A quant trader might release a similar analysis. Bitcoin’s amount might be reaching $20,000, for example. The store is in full bull mode, and everyone is optimistic that amounts will go on to develop. The quant trader might check underlying numbers, however, to predict that the end of the bull run is coming.

Examples of Quantitative Trading

A good quantitative trader will conceive a program that predicts the future.

No quantitative trading program can predict the future 100% of the time. However, a quant trading program that is right more often than it’s defame may be able to conceive consistent benefits.

Let’s say an investor wants to predict the future amount of a share. That investor believes in momentum investing. She writes a simple program that identifies winning shares during an upward momentum swing in the stores. During the next store upturn, this investor’s program buys those shares to consistently earn a benefit. This is a simple example of the power of quantitative trading.

Typically, a trader will use an assortment of techniques to identify winning shares. To complement her quantitative analysis, for example, the trader might also use technical analysis, fundamental analysis, and value investing techniques. By carefully considering all of these strategies, the trader has the best chance of picking winning shares and maximizing returns.

Pros and Cons of Quantitative Trading

If quantitative trading was correct 100% of the time, then every hedge fund in the world would only use quantitative analysis. Quant trading, like any trading method, is not perfect.


Remove Emotion from Trading: Quantitative trading is all about numbers, inputs, mathematics, and formulas. A quant analysis formula has no place for emotional inputs. It’s just data.

Works Great in Conjunction with Other Trading Strategies: The best traders use a blend of strategies to inform their trading decisions. Quantitative analysis works particularly well for this purpose. It complements other trading strategies well.

Make Informed Decisions on Multiple Assets: Quant trading can quickly analyze multiple shares. Just plug the inputs into the formula to instantly obtain a quant analysis.

It Doesn’t Have to Be Right 100 percent of their TimeNo trading method on the planet will become 100% correct 100 percent of their time. But that’s perhaps not the goal of quant trading; the single purpose is to create more correct trades than erroneous trades.


Too Much Data: Quantitative traders have access to the immense number of information. You’re able to glance at store statistics for 1000s of days of asset trading actions, as an instance, then establish trading strategies according to that info. Some times, with a great deal of data, it is just a fantastic thing. In different scenarios, too a lot of data is overwhelming for most traders.

Good Quant Trading Requires Constant Adaptation: Financial stores are amazingly lively. Even a quant trading method should be quite dynamic to maintain up. A hedge fund could conceive a fruitful qualitative trading formula, simply to have that particular formula eventually become obsolete within a month or two. A quant trader could move to a winning streak if their formula will be consistently delivering benefits, simply to move to the losing streak if their formula suddenly doesn’t work for store conditions.

You’re Competing Against Hedge Funds: Hedge funds have the money to establish a full-fledged quant trading division. They hire dozens of programmers, analysts, and statisticians to develop the best possible quantitative trading model. If you want to become a quantitative trader, you’re going to compete with these people.

How to Find or Create Quantitative Trading Strategies

Up above, we mentioned that method identification is the before all else step for implementing a quantitative trading method.

Finding (or creating) the right quant trading method today is the before all else step towards consistently earning benefit from stores.

Fortunately, finding a good quant trading method isn’t hard. You are able to readily discover profitable quant trading plans through public sources. Academics regularly publish theoretical trading effects, as an instance, dependent on several different formulas and investigations. Financial industry books and trade journals may underline the trading procedures used by now leading hedge funds.

You may ask: Why would anyone share a successful qualitative trading method? why wouldn’t a hedge fund keep this method to themselves? If everybody is using a specific trading method, then won’t it prevent the method from working longterm if the other audience the store?

That’s a pretty fantastic question, however, there’s also a fantastic answer. Hedge funds will talk about the fundamental specifics of their plans, however, they also won ‘t discuss the exact parameters and tuning methods they use to execute the trading method. These optimizations are crucial for turning an average method into a profitable one.

Here are some of the best free resources for identifying trading strategies today:

Social Science Research Network – www.ssrn.com

arXiv Quantitative Finance – arxiv.org/archive/q-fin

Seeking Alpha – www.seekingalpha.com

Elite Trader – www.elitetrader.com

These websites feature tens of thousands of trading strategies. You’ll see strategies separated into different categories, including “mean-reversion” and “Trend following ” or “momentum” strategies.

You’ll also see trading strategies separated based on their frequency. Some strategies are designed for low-frequency trading (LFT), for example, which typically means you hold shares for partially a day. Other strategies are built for high-frequency trading (HFT), which means you purchase and sell shares throughout the trading day.

You can also find “ultra-high-frequency trading” (UHFT) strategies, which involve holding shares for just seconds or milliseconds.

How to Backtest a Quantitative Trading Strategy

Backtesting is a crucial part of developing a quant trading method. After identifying your method, you want to see how that method performs on real store conditions. Fortunately, there’s a wealth of data at your fingertips, making it easy to test your method in historical crypto stores, asset stores, and other stores.

Many newbie quantitative traders will use the free historical trading data offered by Yahoo Finance, for example. However, more professional or advanced traders may want to pay for better data.

Free Data Versus Paid Data: Why You Should Consider Paying for Market Data

The convenience of free data is obvious: you’re getting a wealth of historical store data at your fingertips free of charge. However, there are significant downsides to free data, including:

Accuracy Issues: Free data might have errors. The data provider has no incentive to correct these errors because they’re not getting paid. Professional traders will draw data from two or more sources, then check the data against one another (say, using a spike filter) to eliminate inconsistencies.

Survivorship Bias: Many of the companies listed on asset stores in 1967 are no longer trading today. Some have been acquired. Others went bankrupt. Unfortunately, some datasets only include companies that survived the decades. This introduces survivorship bias into your method. You’re only analyzing companies that survived. Your trading method backtest will inevitably go better than it would have performed under actual store conditions.

Corporate Actions, Stock Splits, Etc.: Free datasets may also ignore certain corporate actions and how these actions affect shares. They may not include adjustments for asset splits and returns, for example. More professional data providers will implement adjustments into their data, but free data providers will not.

How to Setup an Execution System for your Quant Trading Strategy

Quant trading execution systems vary. Some execution systems are fully automated: the system makes trades with no manual intervention. Other execution systems are manual, with operators executing each trade.

Generally, HFT and (especially) UHFT trading strategies are fully automated, while LFT strategies are manual or semi-manual.

Some of the important things to consider when establishing an execution system include:

Interface to the Brokerage: Some people call up their broker by telephone to execute a trade. Others set up a fully-automated high-performance Application Programming Interface (API). Generally, you want your interactions with the brokerage to be automated so you can concentrate on optimizing the trading method.

Minimization of Transaction Costs: When making hundreds of trades in a short period of time, minimizing deal costs is crucial. What fees does the brokerage charge? Are you paying a flat fee per trade or a percentage fee? Does the exchange charge separate fees from the brokerage? What about slippage? What’s the difference between what you intended your order to be filled at and what it was actually filled at? What about the spread? What’s the difference between the bid and ask amount of the security being traded? For an average at-home investor making a few trades a month, these things don’t matter. To get quant traders – specially HFT trades – small fees can quickly accumulate.

Divergence of Strategy Performance out of Backtested Performance: Some quant trading systems do the job perfectly in real store states. They repeat their backtested victory and achieve fantastic outcomes. Many trading strategies, though, can very quickly diverge, using backtested performance fast-dividing itself out of real-world performance. Bugs can appear. Market conditions could vary. Precisely the equal inputs which contributed to certain outputs before might well not result in all those baits.

Latency: Latency is the total amount of time that you lose when sending a purchase. How much time does it take for the order to make it to the market or broker? Latency can considerably affect fertility – notably for HFT or even UHFT strategies.

FAQs About Quantitative Trading

Q: Don’t all trading strategies involve some type of quantitative analysis?

A: By definition, quantitative analysis involves using inputs – like amount and trading volume – to make predictions. Many trading strategies – even the most basic strategies – involve looking at numbers to make predictions about the future. In that sense, many trading strategies is considered quantitative trading strategies in some sense.

Q: What’s the difference between quantitative trading and algorithmic trading?

A: Algorithmic trading and quant trading might seem like two names for the equal thing. They’re closely intertwined but slightly different. Algorithmic trading is one specific part of quant trading. An algorithm developer will conceive an algorithm that the quant trader can use to generate benefits. Without an algorithm, the quant trading developer could conceive a quant trading system, but it won’t work without a program. That said, a few individuals (like Wikipedia) utilize the terms qualitative algorithm and trading.

Q: Which level should I obtain when I wish to turn into a quant trader or quant programmer?

A: Quantitative traders result in many diverse backgrounds. There’s not any particular amount used by nearly all qualitative traders. But some amounts are very popular than some others. Computer science and mathematics degrees, as an instance, are especially common. Lots of people who have a background in application development try to go into the organizational trading area.

Q: How do historical trading statistics become ‘good’ or’ ‘bad’. Isn’t all trading data equal?

A: Certain historical store data is good or bad. Some data is inaccurate. Some data have survivorship bias (it only includes companies that survived to the present data). Free data sources might be good for beginner quantitative trading developers, but more serious developers will want to pay for data.

Q: Which programming language is used for quantitative trading?

A: Setting up algorithmic trading systems requires strong programming skills. Generally, C is the preferred language because it is the fastest, which is important when every microsecond counts. Some developers use R and Python to backtest and evaluate trading strategies, although the code in C for fast execution and high-frequency trading. For medium and low-frequency trading, any of the languages should be fine.

Q: Where I can find a trading algorithm that gives me high, risk-free returns?

A: Quant trading is advanced, but there are no guarantees of returns. If someone is trying to sell you a trading algorithm with high, risk-free returns, then you’re probably being scammed.

Palm Beach Quant Quantitative Trading Final Word

Quantitative trading is the process of using statistics and math to predict what will happen based on what has happened in the past.

Today, everyone from cryptocurrency traders to hedge fund managers use quantitative analysis to make informed decisions. Some exclusively use quant analysis to predict their next moves, while others use quantitative analysis as part of a broader toolkit.

If you’re good at analyzing data, then you may want to obtain into quantitative trading. If you’re not good at analyzing data, then you can find plenty of quantitative trading resources available online where you can read quantitative analysis reports for all types of stores.

As always, happy trading!