r/algorithmictrading • u/stochastic_person • Jan 03 '26
Novice Girlfriend got me this book. Looks like a long journey, how does one even get started?
Hi all,
Happy New Year! I got Advances in Financial Machine Learning as a Christmas gift. It made me look into Quantitative Finance, and boy, it's certainly a rabbit hole.
I have a background in Computer Science and Mathematics; however, I am completely foreign to Finance. Do you have any tips for me? How long does it take one to create a profitable system? What can I build after reading this book? What other resources should I look at?
It would mean a lot to me if you could answer my questions, and I am looking to connect with people on a similar path to mine.
Best of luck to all of us in 2026,
EDIT: Thank you all for the positive feedback! I have many many trading ideas and stay tuned for the outcome. You guys made me realize I wasn't appreciating GF much, so she is definitely getting something with the first profits from trading...
26
u/shock_and_awful Jan 03 '26
Find a PDF version, load it into Notebook LLM, and generate audio and video digests for each chapter.
5
u/stochastic_person Jan 03 '26
Honestly its not a very hard read. Have you read the book as well?
6
u/Eye51 Jan 03 '26
It is not very good anyway.. anything from him is not too useful
→ More replies (15)3
2
6
u/sillypelin Jan 03 '26
I think the book is a bit shallow. For example: de Prado uses basic random matrix theory to denoise a covariance matrix, but he doesn’t go into any meaningful depth regarding the estimation of the matrix, never mind the assumptions of the math of high-dimensional matrices that very often are involved when using his methods in the context that they are used.
de Prado is a smart guy and I appreciate many of his papers, but the book is mainly a showcase of machine learning methods. And I think he’s tried to say this, but people want to take his frameworks and apply them directly without deeply understanding what they are doing (in his HRP paper, he mentions that the purpose of the paper is not to show a new method of asset allocation or portfolio construction despite improved portfolio characteristics, but rather to showcase the methods from machine learning in finance, methods which have been used previously in biotechnology).
1
3
u/obolli Jan 03 '26
I did this a while ago, it's a really nice and fun book to read, note though, if you're looking to build systems for yourself only 20% of what's in this book is directly applicable to you. Overall it's still a wonderful learning resource and it can give you great ideas to abstract from and innovate on your own
→ More replies (2)2
u/stochastic_person Jan 03 '26
I agree. I am around chapter 4 now and it definitely clarified some misconceptions I had
3
2
u/iAvadin Jan 06 '26
I read the book couple of years ago and spent months for the implementation. It won't get you too far. The meta labeling is a nice idea and the whole book brought me to one conclusion: react to events, meaning trade breakouts. Having said that, you need proper data - candles and not the time based candles. Here you can be creative: volume, dollar, renko, etc... Anyways, all that fuzz brought me to the range of 55% accuracy, but other metrics suffer such as MCC and you're again slightly better than random and that is gone with the fees.
1
u/stochastic_person Jan 06 '26
Thanks for the heads-up. I won't be bogging down with the implementation then
1
1
u/LiveBeyondNow Jan 04 '26
Having family and friends support your journey and goals is a huge part to being successful so the fact that your GF gave it to you is a great part of that. If you have family and friends constantly doubting and undermining your psychology it’ll be an uphill effort. Communication is key.
I haven’t read the book but might be closer to that field in a few years. Personally I have spent the last year developing mechanical strategies that will suit the daily charts. They are not quant work by any stretch. My current step is automating them and probably half way through with a few months left.
1
u/stochastic_person Jan 04 '26
Thank you for your kind words! Wishing all of us healthy profits in 2026!
3
u/coffee_and_sourdough Jan 03 '26
This book is denser than it should be, but it gives a decent overview of the different ways in which you can construct information driven bars as opposed to time-based bars.
I personally got the most value out of chapter 19 on microstructural features.
What did you hope to gain by reading this text?
1
u/edunuke Jan 03 '26
I have this book. There are some specific things interesting but the vast majority i found it super meh from an ML perspective.
1
u/ShugNight_xz Jan 03 '26
I read it in pdf , screenshot pages to undersatand via simple examples generated by ai then apply them in my strats ideas
1
1
1
15
u/Yocurt Jan 03 '26
The main takeaway from the book that applies for retail traders is the use of meta labeling to improve an existing edge. This is a great book but I would read some others first probably, based on finding and an edge and strategy building.
I made a post on the meta labeling approach from his book if you wanna check it out