# Learning and applying Quantitative Finance successfully as an individual instead of a team

In the past few months, I became really interested in using machine learning techniques in the realm of quantitative finance and trading. I made a few rudimentary models and I immediately realized how difficult it is.

I picked up a book "Advances in Financial Machine Learning" by Marcos Prado. It is an amazing source of information but is highly challenging. The author prefaces the book by saying that you have to work in a team to be successful at it and the book was written in the same way.

This is a bit discouraging and I am wondering if the only way to successfully pull it off is by finding a group of individuals with PhDs who are as motivated as I am in tackling this domain?

Are there individuals who have read this book or others such as these on their own and been successful in making models with predictive powers?

• It is very hard on your own, I did it for a long time and eventually the competition was too fierce. I intend to do the same thing I would advise you: join one of these larger research groups that benefit from having all the resources! Jul 14, 2020 at 21:43

Welcome to Quant-Stackexchange Sleepy Panda, this is an interesting question and it also seems to be an interesting book.

It depends on your goal and your definition of success. If you intend to learn a lot about an interesting topic and deepen your understanding of financial market dynamics, study companions and individuals who work through the book or similar topics and who you can have discussions with are certainly going to be very helpful.

If however you intent to make a living of algorithmic trading using machine learning, it will probably not even be enough to have a team of motivated and capable individuals. Investing and trading is an arms race that requires not only incredible talent but also adequate infrastructure and upfront investment. Those who make a lot of money spend millions on fast connections and exclusive information.

In any way, you should not let that discourage you. Instead just give it a try and if you get stuck, put the book down, study whatever slows you down (programming, financial market theory, ...) and then continue.

• What would motivate individual researchers to learn something so grueling if they can't even make money out of it? And if they are not even academic researchers but doing it as a side hobby? Perhaps some hope to get a job in this area in the future and fewer still are doing it because it is challenging and fun. Jul 9, 2020 at 17:22
• Exactly, @SleepyPanda! I'm also doing this on the side, having studied finance and working as a data scientist. Eventually you will build up expertise and can do that kind of work either by consulting or by working for a bank or a large financial institution which has the required capital and talent. Doing it on your own is close to impossible if you ask me. Jul 9, 2020 at 18:48
• @SleepyPanda Not sure why you think they "can't even make money out of it". Yes, you need to work as part of a team (i.e. company) to realistically engage in algo trading for profit. But good quants are exceptionally well-paid by the companies that they work for. Jul 10, 2020 at 15:40

This r/answers post can assist with your second question. The short answer is no. An individual will probably not succeed at making models with predictive powers.

Even if you are a successful quant (extremely hard and rare), to be so you need expensive resources not available to individuals.

Knowledge and learning are always super helpful in building ones skill set but what I will say after 15 years in finance is that people really overestimate the possibility of “winning” the markets especially if you are retail investor sitting at home. Unless you are an exceptionally rare breed and an undiscovered genius-level savant (in which case you should find a job at a real hedge fund to monetize it), you will not pick up a meaningful edge *that can be monetized/applied”.

Here is a non-exhaustive list of reasons why, some practical, some theoretical, etc.

1. In order to properly gain an edge as a quant in the market, you need access to very large clean databases and those can cost a lot. Real quant investors are spending millions on these datasets and you are unlikely to find comparable data from non-institutional providers. So at home, you will absolutely have cost-scaling issues. Not to mention computing power and data analysts needed to maintain this.

Some hedge funds use a “server farm” of 100+ blade servers to run analyses overnight.

So much of quant investing is focused on high-frequency trading and unless you are paying for an institutional data feed (\$mms / year) and are focusing on millisecond-level execution, you will lose this as well.

1. Another trick used is expert networks, aka former executives of companies who after a period of say 12 months are legally deemed to possess no material public information. Hedge funds will call them and ask them (for a hefty fee) about the realities of running these businesses, their thoughts on what the company will try to do, etc. All of this is crucial information which is not available to you as a quant.
1. Quantitative finance is model driven and is therefore based on historical correlations and relationships repeating themselves. As of June 2020, this is being destroyed by things like insane technical demand as a result of Fed policies, intervention, etc. Understanding policy outcomes is the most important thing right now. Not a knock on quantitative finance, but I'm just pointing out it’s a bad environment for such strategies.
1. Just to put things into perspective, even the best best best institutional investors have success rates $$<70\%$$. Most actually lose money for investors when comparing after-fee returns versus benchmarks, which is why passive investing has grown in popularity, and rightfully so.

The soul-crushing self-doubt is half the fun!

I would say the most important things to understand are pot odds, comparative advantage, adverse selection and market structure (microstructure and macro players).

As an individual, it is my personal belief that it is necessary to find a niche in which you have a comparative advantage, where the major players cannot effectively compete or don’t care to, looking for opportunities where the pot odds outweigh transaction costs, and where you can estimate and account for adverse selection in execution. Microstructure will then help you at the margins to improve profitability.

Throwing a basket of ML bananas in a blender is unlikely to be fruitful.

surrounding yourself with like minded people is a proven route to success.

now doing research on your own is ok, but make sure you cross check your research and findings with others on forums, quantopian for instance has a community, tests datasets and competitions, things are always different when seen throughout another person's paradigm, and backtesting only gets you so far and doesn't replace another human's feedback.

For a more individual approach, I would recommend the books of Michael Halls Moore from quantstart, which are very abordable and ensure the average retail reader is handheld throughout.

You have to first come up with something really clever. Next step is to convince someone with tons of money to back your ideas because that is going to make them a lot of money. Without significant financial resources, the chance is slim.

My opinion... it's possible to do it on your own. But you need to be very strict, doing your research without any deviation from the scientific approach.

You'll need a source of historical data at tick level, and a solid backtesting system for your ideas.

The amount of time required is huge.

But regardless of the field, if you want to success... effort+time it's everywhere

As the accepted answer states, it really depends on your definition of success. Most tends to focus on the high frequency / market making models, but I think if you pick your battles I don't think it's impossible to make some profits as an individual.

IMHO while it would be highly unlikely for one person to build a truly robust algo trading system (automated pipelines, consistent alpha, high quality of execution, robust risk controls, etc.), there are enough off-the-shelf infrastructure available to research and execute a decent system or idea.

For something long-term like an asset allocation or macro rotation strategy, these can be tested and executed through platforms like Quantopian relatively easily (or even manually if your planned frequency is low). Your relative outperformance will likely be small, but not bad as something "part time".

Alternatively, if you want to do this full-time for yourself as a day trader, I do think there are market opportunities out there (although you would be better off working somewhere :p).

However, you would really need to understand the limits and underlying assumptions of what you are doing and to limit your risk exposure. For example, the leveraged inverse volatility trades were immensely popular and profitable in 2017 and 2018, but then suffered from the big shocks in 2018 and 2019.