Hi, I’m Lucas, aka the author of the Quantreo quant trading blog.

I am quantitative trader and I jumped into this amazing field 7 years ago. It took me a while to truly understand the science behind the quant trading strategy building process… Even today, the reliable resources are rare, so I let you imagine seven years ago.

My journey was hard but it’s worth the effort. That’s why I created the Quant Trading Blog from Quantreo: Made by a quant trader, 100% Free, the tutorials will shape your journey learning the quant science!

The entire blog is sponsored by the Alpha Quant Program.

face of a man with chart graphics that show its trading performance.


The first thing to explain is the difference between quant analyst, quant researcher and quant trader. Indeed, when we say quant which one we are referring to? To all of them… We do different jobs but the boundary between them is quite blurred.

Many other people can take action in the quant trading strategy building process, to manage the data for example or programming engineer to reduce the latency of the trading strategies. But anyway, let me explain the three best known.

Quant analyst

The quant analyst job is necessary to implement mathematical models to find interesting relationships on the market. Its outputs can be used by several people: risk management, portfolio management, trading…

👉🏻 If a quant analyst finds a good way to predict the trend of an asset for example, you can use it to minimize the risk, to optimize a portfolio and to trade in a more efficient way.

Quant Researcher

The quant researcher is dedicating most of its time to improve the quant trading strategy building process, our “trading strategies factory”. The quant research will work on an efficient way to backtest a strategy, new machine learning technic to extract interesting patterns that can be used later by the quant analyst or the quant trader.

Quant Trader

The quant trader is here to create quant trading strategies. Indeed, the quant analyst has created many interesting features that help us understand the market. On the other hand, the quant research brings us cutting edge methods. Mixing all of that, the quant trader will elaborate a trading strategy based on an economic thinking.

👉🏻 For example, if the quant analyst found a way to predict the next market condition (bearish, consolidation or bullish) with a high probability of success, the quant trader can imagine the following strategy: when we switch from one market condition to another and the volatility is high, we will enter in position in the new market condition way (if we switch from bearish to bullish, we take a buy position).
The beginners will move their stop losses because they think we are still in the same trend and the fluctuation are just because of the volatility. The more they will increase their stop loss, the more we will earn money.

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logo of the 2 biggest quant trading firms


The two mastodons in quant trading are the Renaissance fund created by Jim Simon, the father of quant finance and Citadel. To give you an idea, from 1988 to 2018, investing in its own fund, Simon generated an average annual return of 39.1% with a drawdown that was never higher than 1 or 2% (source: Nasdaq). I was not able to find any information about the Citadel drawdown but I do not think, it is far away.

What is the secret of these firms? Math. They do not trade using manual traders that follow their feeling. They hire PhDs in math, physics, and other scientific fields, that know nothing about trading. The goal is to find a statistically significant relationship and incorporate them into the trading strategies.

Of course, after hearing these performances, you would like to jump into quant trading but, of course, obtaining such performance and especially on a long period is nearly impossible. From my own experience, if you achieve between 20%/year with a 5%-10% max drawdown , it is already a good performance, especially if you are an individual quant trader. Indeed, the power of quant traders come when they work together. That’s why I created the Alpha Quant club integrated into the alpha quant program: we work together to create a trading bot portfolio and learn from each other. “When the snows fall and the white winds blow, the lone wolf dies but the pack survives.”


Quantitative trading or algorithmic trading? Many people confuse both. There are complementary but at the same time very different. Algorithmic trading is the art of automating a trading strategy.

Indeed, most of the time, it is trading strategies based on news, technical indicators, or chart patterns. The math behind all of that don’t demand a very high level: be conformable with percentages and the basic concept of statistic is clearly enough (mean, standard deviation…)

On the other hand, quant trading is based on quantitative models. By the way, you can definitely quant trading without any automation: for example, you can create market sentiment indicators based on natural language processing, you can create a screener based on time-series models (ARMA, ARIMA…) or machine learning models. So, it demands more knowledge than basic statistics even with all the libraries you can find because you need to 100% of all the models you are using.

Here, on the quantreo’s blog, we will talk about quantitative trading and algorithmic trading because most of the quant strategies are automated. Just below you can check our latest posts.


An infinity of strategies to achieve the same goal… Indeed, the first goal of creating quant strategies is to obtain an interesting return reducing your risk. You ALWAYS need to consider these two aspects of a quantitative trading strategy: return and risk.

The advantage that the quants have over the algorithmic traders or the manual traders is their ability to manage huge datasets to extract information. Indeed, they can use very unusual data like satellite images, credit card information or very difficult to handle data like ticks or book orders’ data.

It seems obvious but, of course, the more you are working on difficult to handle and unusual data, the longer you will keep your edge once you find one because the other traders use simpler datasets.

represent quant trading with in the top the broker dashboard with the transactions and in the bottom a trading backtesting

Moreover, quant trading strategies have no limits. You can use the “basics” strategies: momentum, mean reversion, or arbitrage strategies. But you can also pimp up your analysis using machine learning and artificial intelligence.

However, the complexity of your analysis is not correlated to the profitability of your strategy: generally, it is even the opposite. You need to take time to find interesting data, clean them and do some features engineering: this part is nearly 70% of the work and much more important than the strategy building part, in my opinion.


In this section, I think, we need to ask two questions: “How to become a quant analyst?” and “How to stay a quant analyst?”. A small difference but a very important one. Generally, a quant holds a master or a PhD in a scientific field: math, physics, compute science…

But things are moving… Of course, it is easier to obtain a job or to be individual quant trader with a strong background in math and programming. However, we see more and more talents that do not have anything of that! Of course, they have strong knowledge in trading, math and programming, but they didn’t obtain them through a university: they are self-learners. So, if you do not have any degree in science, in doesn’t mean you can’t be a quant.

Moreover, the best thing to become a quant is to speak with other quants: it seems obvious, but it can make you save weeks of work in one conversation: it was what boost my quant knowledge. But I advise you to wait to have some skills and to ask specific questions. No quants will take its own time to help someone not really motivated which ask non-precise questions. When you contact a quant, create a relation, and ask a specific question, “What type of robustness testing are you using on your strategies” for example. Certainly not something like “I would like to be a quant, help me”.

The second question is how to stay a quant? The answer is simple, continue to learn, always! My daily routine is working on my bots and my community and also, I have some times in the week where I surf on quant LinkedIn profiles looking for interesting articles, check some YouTube videos from quants, etc. It is mandatory! Methods that worked well in 2000, do not work anymore now and the method that currently work will not in a few years…


We know a lot of things about quants now, but we do not know the quant trading strategy building process. Of course, we can write an infinity of articles just talking about that, so I will try to keep it simple.
diagram representing the trading strategy building process

1. Data curator – models extract information and data are food for models, the quant needs to understand the specificity of each data and market: a stock doesn’t have the same characteristic as a Forex asset or than an option on a bond, for example.

2. Data analysis – once we have clear and clean data, we need to extract information from them (and not strategies at this step). It can be information on the trend, on the volatility, on anything that we can use to create a trading strategy. For example, a quant analyst can find that once we have a consolidation zone and a decreasing volatility, the randomness of the market is really high, so we shouldn’t take any trade now. Indeed, doing nothing is still taking action… Never forget it!

3. Trading Strategy development – Thanks to the data analyst, we have interesting information, now as explained below in the quant trader section, we need to convert this information into a real strategy with clear entry and exit points, with clear money management, with a clear volume to invest…

4. The backtesting– One of the most important and one of the most misunderstood steps. That’s why, the first series of tutorials of this blog talked about that. Backtesting is here to validate your strategy, not to optimize it.

5. Live trading – Once the strategy is validated through all the robustness tests done in the backtest, we can put it in incubation: in live trading with a demo account or with a small capital. Once, it performs well in the incubation period, we can add more capital.

6. Portfolio management – A trading strategy is exactly like a derivative product or a stock: when you create a stock portfolio, you need to optimize it to reduce your risk and stabilize your return. For a trading bot portfolio, it is the same! 


Learning quant trading only with a course is clearly… not enough! And you should be aware of that if you have read the previous section. The question is so “how to learn quant trading?”. With a course, yes! But being part of a community also, with a support, with projects and much more…
And that’s why, I created the alpha quant program, to give you access to all you need:
  • E-learning section
  • A quant traders’ community
  • Monthly projects to help you apply your skills on real-life projects
  • Templates included creating your first bot as quick as possible
  •  Trading strategies included helping you understand each step of the process
  •  7D/7 support from the instructor
If you are interested in joining a real community of traders helping each other’s, even if you are a beginner, you are welcomed!

It was quite dense with a lot of things to assimilate. The best article to read now, should be the deeper explanation about the trading strategies building process. If you have any questions, feel free to contact me on Discord, we have a public community or directly on LinkedIn in private messages!

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