Quantitative trading is the love child of Trading techniques, and the brilliant science computers have created for us. It is the math where the trends are calculated on the basis of how the markets have been behaving in the past with similar conditions. What the quantitative traders do is make a model of the previous behavior of the market and then backtest in similar conditions.

Suppose the model predicts a similar pattern or a similar outcome in most of those hypothetical situations. In that case, the same results are made the foundation stones of the speculations, which then drive the bets made in the market.

Quantitative analysis is a branch of computer science that deals with a  lot of data and crunching it to find a pattern. Everything that we see around us. Every invention that has ever walked the face of the earth is because of this ability of the human brain—pattern-recognition.

We see a bacteria or a virus acting a specific way; we make a pattern out of it, and then we create a vaccine. Mentally, we have patterns too. The people who are going through a lot of stress. they develop a certain pattern in their day-to-day lives just to suppress that stress.

The doctors try and recognise this pattern and let the patients know so that they can get out of that loop. Even up in the sky, we look at the stars and all of the heavenly bodies and make a pattern out of those to understand things a little better.

Quantitative trading is the exact same thing. All it revolves around is pattern recognition. The traders don’t have to do that independently, but they deploy mathematical models to process historical data. The model is deployed in the real world once the backtesting has efficiently surpassed the harshest constraints.

Quantitative trading usually deals with large volumes of data, like the data of a hedge fund (the data in the example of a hedge fund would be money).

Since there is so much data involved, every quant trader needs to have some basic knowledge of its terminologies.

Let us look at some thighs that can make every quant a better trader:

## Understand the languages:

The programming languages like Python and React have their own single-handed impact on finance and quant trading. Python lets you create models in an IDE( integrated development environment).

Whereas React has more than applicable applications in the finance industry. Having an upper hand in any of these two languages can let you become a better quant and will put you ahead of the crowd that is just trying to own the claims that they are a quant trader.

## Be comfortable around math that sounds alien:

The math like stochastic calculation, optimisation techniques, and Fourier transforms along a couple of more hard names is what you will mostly see while you begin quantitative trading. This is not just math but, Engineering math, The kind of math that every quant trader should vary of.

As  a quant trader, if you are looking for a job, then you will mostly be hired by banks given the fact that you have a doctorate degree in a specific and applicable science that the industry needs. This is because of the fact that the banks and big hedge funds need people who can teach themselves new math very easily.

Being a math-head alone will not cut it. No matter how good of a programmer you are, how good of a mathematician you are, understanding the basics of trading and trading strategies is as important in quantitative trading as it is for any other form of trading; being able to work the strategies quickly can make you move up in the trader lot. Integration of math, Programming and trading strategies is what makes the best quant traders.

## Why is there a need for quantitative trading?

Quantitative trading is something that basically keeps manual trading at bay for a number of reasons. When there is a fresh event in the market, the traders will study it and plan their trades accordingly. What if a method would do the same for you and even take positions on your behalf? What if those positions were precise to the level of a CPU calculated approach?

That is the exact reason why Quant trading is needed. Quantitative trading is needed to make quick decisions whenever the market is subjected to some sort of event or news.

Quantitative trading and the best quantitative trading strategies work perfectly for people who want to time the market before the majority of traders around the globe can do that.

### Can I personally run a quantitative trading strategy?

If you are thinking of building an HFT (high-frequency trading) strategy for yourself, you will face goliath constraints under the names of equipment and infra costings.

## With all those things being said, let us have a look at the best quantitative trading strategies:

### Alternative Data Strategy:

Quantitative trading is more than just reading the market. It is more about reading the consumer data. Different data poses different pictures. For example, if the people are buying more apple products then there can be a possibility of the firm seeing a better stock potential for that quarter. This is called an alternative data strategy: When the traders look at different data than the market data. Market data is more like how the firm has done in the past few years and how it will do in the next quarter or whatever the time window to study it is taken under consideration.

There are different data types that are analyzed in the alternate data trading strategy.

These include

1. Weather data
2. Consumer data
3. Location data.

There are different data types than these as well but these are the ones that are required to fulfill a good alternate data trading strategy.

The data is important, and hence, it plays hard to get. The hedge fund providers have to be part of an online auction to hold any type of data. When they buy the data, this gives the institutions a step ahead accelerator than a majority of traders because when it comes to data, you need to be creative with the sources and keep changing them since you are looking at making trading decisions that the masses have not yet completed, but will for sure make in the coming days or weeks.

### Playing in smaller markets:

There are markets that have fewer people that trade on them and since that is the case, the competition is lower and the opportunity to make money is exponentially high.

Quants can  build the best quantitative trading strategy in these markets because of the fact that there are a lot less people looking for entry and exit positions.

Since the number of people is less, there are many more opportunities for the system to calculate a better entry and exit position for the quant. If this strategy is integrated with the alternative data trading strategy then good results can be seen.

For example, if there’s a new cryptocurrency in the market then the traders can deploy their trading models around that crypto and earn as much as they want. All that depends on how dedicated the trader is and how correctly has he or she programmed the model and how well is the execution in the correct market.

### Trade at a high frequency:

This is also called HFT or high-frequency trading. To be a high-frequency trader, the person needs to be involved with a good trading firm.  There are certain strategies that lie within the High-frequency trading strategy:

Let us have a look at those:

This is about finding similar characteristics about a group of stocks. For example, all the cars fall under the automobile sector and are all a witness to similar market conditions. In That case, it would be easy to study that sector and find a good entry or exit position in terms of High frequency trading.

#### 2. Arbitrage in Latency:

A traditional hedge fund might buy a lot of stocks for a particular asset. An HFT hedge fund will detect this and in return, it will buy all the stocks of that asset on all the exchanges and then sell it to the traditional hedge fund for profit. The traditional hedge funds are generally lower than the HFTs. Since the high frequency trading firms do the same maneuver millions of times in a day, they automatically book the profits.

#### 3. Statistical arbitrage:

There is a chance that a lot of similar stocks move ina similar way. When these stocks diverge, the high frequency trader will either  go long on one and short on the other. This is completely subjective as to what the trader does in such a situation but that is one sure way to book profits in the case of similar and diverging stocks.

Quantitative trading is the process where concrete trading strategies are developed that are completely based on math and modelling. The quant trader will look at the  data and then apply stats and modelling tonit in order to look for better entry and exit positions. Quantitative trading is looking at data and then making an entry and exit position or a speculation.
The trading strategy can be applied manually or automatically with the help of computers as well.

Algorithmic trading is changing the idea of a trade into an algorithm and then giving that algorithm to a coder who will then write a code for it in a coding language that will later be deployed in the market by the involved firm. Algorithmic trading is more about the computing knowledge than the trading knowledge. The writers who write the algorithm need to be essentially good at it. The entry and exit positions in algorithmic trading are done via representing signals. In this whole process, there is no need for human  intervention and the system does everything whereas in quantitative trading, the results of data analysis can be applied by people or computers.

The quants are more into using sophisticated techniques to achieve their goals, whereas the algorithmic traders will almost always use simpler strategies to work in the market because there has to be a code that has to be written around that algo and the simpler the algo. Simpler and optimised is the code.