1. Sisyphean Quants 6:53 2. Integer Differentiation 15:26 3. Inefficient Sampling 24:32 4. Wrong Labeling 30:32 5. Weighting of non-IID samples 38:29 6. Cross-Validation leakage 45:33 7. Backtest Overfitting 48:31 8. QnA 1:00:30 Great lecture. Very Insightful. Thank you for sharing :) I'm looking forward to reading the book.
@junal278 күн бұрын
A former student of UNED and the UCM, two public Spanish universities make me proud of what our system was. Excellent Marcos, never ever forget your origins. Excellent presentation
@rtkevans5 жыл бұрын
Outstanding presentation, the best I've seen in quant finance.
@sc0tty3195 жыл бұрын
i wished i read his books or papers two years ago...i read lots of financial research papers and replicated them. But not a single paper works in real life. but I now know the exact reason.
@ahsabour4883 жыл бұрын
You took the words right out of my mouth! :). I've been replicating some research papers recently and its really amazing that none of them actually work :) . It seems people are just interested in publishing rather than producing knowledge. But this talk was really helpful in giving me a direction to move forward. cheers and good luck on your research
@kevinshen32212 жыл бұрын
can relate ! this lecture should be the first thing you watch before you read any other researches
@drek2736 ай бұрын
@@ahsabour488 so true. academia is plagued with impracticality for the sake to appear fancy
@VOLightPortal6 ай бұрын
How do you reproduce something that doesn't work? Technically then it has failed replication.
@marcosadelino69904 жыл бұрын
Metalabeling can also be used to differentiate between exit strategies and entry strategies - the first one has to be greedy and have an opinion for any given moment when you are in the market, whereas the second one needs to balance the degree of certainty with the expected outcome (given the exit strategy) minus commissions / slippage.
@adokoka3 жыл бұрын
Marcos Lopez de Prado, you should get a Nobel Prize for your contributions to Finance!
@dl6622 жыл бұрын
Not meant to be disrespectful to his work, but really most he’s talked about is ml101, the kind of problems ppl in control n medical research comm have been dealing with on a daily basis. A Nobel prize feels a bit humorous to me.
@adokoka2 жыл бұрын
@@dl662, do you understand what he is saying? Are you experienced with Finance ML?
@W-HealthPianoExercises10 ай бұрын
@@dl662 You are right. But sometimes Nobel prizes have been really humorous too 🥰
@mynameisawesomeman4 жыл бұрын
Why does no one in the hedge fund space seem to like this guy? I think he's got a lot of great ideas that make sense from a theoretical point of view, especially when you consider the low signal to noise ratio of the statistical modeling problem in finance. Everyone seems to obsess over details but never think about the big picture like MLDP does.
@thetagang68543 жыл бұрын
Who exactly doesn't like this guy?
@alejandrorodriguezdomingue1743 жыл бұрын
Is not that they do not like him. He is one of the smartest guys on the street and usually likes to patent or safe credit for his ideas. Hedge Funds do not like to patent or publish anything because the minute they published the stretegy does not work anymore. So there is a conflict of interest between MLP and any hedge fund. If it were not the case he will be an unknown HF billionaire. Now he is not a HF billionaire but he is known worldwide
@abhimanyuchoudhary7902 жыл бұрын
Hes got some good ideas, but most of his ideas don't really do much in practice. Like fractional diffwrencing is pretty much useless - regular differencing works fine, and if it doesn't, fractional differencing is usually not going to help. He also has a huge ego - it's really exhausting reading his books because he comes across as so dogmatic and egotistical. And ultimately he states a bunch of obvious truths like they're new ideas giants haven't been aware of, and he doesn't have a track record to show for it. Just a bunch of degrees.
@dl6622 жыл бұрын
@@alejandrorodriguezdomingue174 I didn’t know much about his work but I liked his talk, even though I think most problem he presents here are quite basic to the research community. But the single fact that he patents his idea is against my values n turns me off.
@OskarBienko Жыл бұрын
@@dl662 why?
@fzigunov5 жыл бұрын
Someone is thinking, finally.
@mindingthedata42183 жыл бұрын
Could someone help me out with understanding the Integer Differentiation section? If I'm understanding correctly, when d=0, the time series is simply the original prices themselves and when d=1, the series is the returns from one price to the next. What would happen to the series if you had d=.5 for example? Let's say that the original price series was [32.45, 31.25, 33.61, 33.15, 33.40, 32.97]. Thanks in advance!
@OskarBienko Жыл бұрын
Check the python libraries for fractional differencing bruv
@michaelwoythaler Жыл бұрын
There's a nugget or two in this talk. Thanks for sharing.
@诗雨黄-m3r3 жыл бұрын
Amazing!it's just surprised to me,every time i only learn how to use a ML model on some datasets, however overlooking that which conditions we use it in a wrong way
@alrey722 жыл бұрын
I had difficulty understanding: - what's the thing about memory .. even technical indicators are looking at history prices at n periods. Neural networks are using time series to not only look at historical prices but also the sequence. - small groups are also ideal in developing trading algorithms. Max is 3 per group: 1 with the trading/model idea, 1 programmer, 1 tester. In some cases, 1 person can do all. - all available data should be given to all groups and its up to the groups to select what to use.
@nonserviam249 ай бұрын
iI am not 100% sure if my idea is right but I think with memory the problem is related to the differentiation. The time series data of stocks resembles "non-stationary-data", meaning its mean, variance and autocorrelation changes over time. To make the data more digestible for the predictive models this data is transformed into stationary data by differentiating the whole data, effectively stabilizing the mean. This differentiation leads to a loss in memory. So even if you feed the model with data of the last 100 days there is NO (or low) memory within this data due to the differentiation and therefore the model can not leverage its memory supportive architecture
@thequantartist4 жыл бұрын
This talk is gold!
@Memetovideo3 жыл бұрын
The link for the presentation is not valid anymore:( please helpppppp
@alexCh-ln2gw Жыл бұрын
i'm wading through the first couple chapters of his book and skimming/previewing latter chapters. Feels like the book is telling us to machine learn on autocorrelated features. I don't think this actually works in practice and the book focuses a lot on "management" (building a pipeline of "discovery") rather than the actual hard results that he says matters more than back testing or whatever else he says in his book. He even mentions there are no concrete strategies revealed. He seems more like an academic who doesn't actually trade anything, but writes books. The ultimate ultimate of a financial guru fraud with fancy degrees in the end. A lot of things are made up out of thin air. It's just like when people make up patterns and have funny names for them ("double monkey ass bottom top flag"). He does this in his book a lot; "triple-barrier-method" for example. Does he present any sort of study that shows this method is effective for entering or closing trades? no. It's just thrown out there like he pulled something out of his a**.
@pimpXBT9 ай бұрын
yeah the ideas are abundant but i didnt see any live or forward test or proof. The way he discredits efficient market theory and flat out saying markets are predictable is insane.
@sc0tty3195 жыл бұрын
24:33 Pitfall #3: Inefficient Sampling
@chubulu984216 күн бұрын
What’s the title of his book?
@yaelolivercarmonachavando48624 ай бұрын
buenas tardes sr marcos mentor financiero una pregunta el machine learning es usado también en las acciones meme como game stop y amc? gracias por la respuesta
@junal278 күн бұрын
creo que estas en el lugar equivovado, menuda pregunta
James Simmons seems to had been running closed shop with free information flow. But in that variant one had to pay employees enough to incentivise them not to leave.
@markymark62293 жыл бұрын
Lol I love the jab at economists being an example of a “high recall algorithm”
@coolcatool2 жыл бұрын
@@anshupandey6260 I think it is high R2
@thiagovitordrumond1844 Жыл бұрын
@@anshupandey6260 some measure of true positives, false negatives and so on
@QuassaKE Жыл бұрын
higher recall = higher true positives (0.5-1.0)
@draganmil5 жыл бұрын
so frustrating when no one listening when I say how important data cleaning is
@stefano893625 күн бұрын
10:30 He formalized what I tried to explain to my "scientists" colleagues, several times. But yeah I'm just a MSc, not a PhD so why should I have opinions?
@zhangrenjie82923 жыл бұрын
Thanks for sharing, excellent!
@jamesstat3 жыл бұрын
Is Numeraire the final solution here?
@jeremytan10513 жыл бұрын
I have had no luck with fractional differentiation, that is, I haven't got any useful results from this. Anyone got any different results?
@gsm74906 ай бұрын
Most of that is waste of time
@kunalroy419829 күн бұрын
I don't understand what he means by returns have no memory, returns just like prices have repeating patterns over the course of multiple years and those patterns can be learned by a model. And modeling returns can tell you a lot about price direction
@junal278 күн бұрын
if can exploit what you know you are already billionaire
@cyiannaki70354 жыл бұрын
Thank you Marcos!
@jaco6yR5 жыл бұрын
Good presentation although it's basically just a synopsis of his book.
@divyeshpoddar29724 жыл бұрын
Whats the name of his book?
@willrocksBR3 жыл бұрын
@@divyeshpoddar2972 "Never gonna give you up"
@klingon533 жыл бұрын
@@willrocksBR oooooohhh that's dirty
@marcogelsomini76552 жыл бұрын
liked the three barrier method!!
@gogae223 жыл бұрын
What does it mean fractionally differentiated?
@tomkamikaze3 жыл бұрын
Taking integer number of differences to stationarize a time series
@MinhTran-freespirit Жыл бұрын
Massive respect for Marcos
@keith27742 жыл бұрын
so basically when the price is moving up - I press the green button?
@randobianco Жыл бұрын
Is green good?
@arfa96012 жыл бұрын
38:00 false breakout vol filtering
@aonutube3 жыл бұрын
This dude is good in finance and good at yapping people too.
@ahsamv19922 жыл бұрын
this man is my neighbor
@EdritKolotit3 жыл бұрын
Edward Norton is very smart!
@newsmansuper2925 Жыл бұрын
2023, ChatGTP enters the ..... Chat.
@Hamromerochannel Жыл бұрын
Exactly! Who would have though so?
@dankkush56783 жыл бұрын
59:00 so he run many trials and got good results? Maybe he overfitted the results!
@roc788028 күн бұрын
poor data, imperfect machines, unreliable algorithms. the output is obviously substandard.
@6388-s2n3 жыл бұрын
48:37
@jackymarcel4108Ай бұрын
Moore Nancy Thompson Brenda Wilson Lisa
@loela15633 жыл бұрын
존나어렵네
@K_23_Z11 ай бұрын
4 years later MLLF or MILF as i call it, are doing fine.
@csp103 Жыл бұрын
Whole presentation is a load of rubbish. Each of his points can be countered with an opposite example. I doubt this man has made even one profitable trade in his entire life.
@OskarBienko Жыл бұрын
Could you elaborate?
@michaelwoythaler Жыл бұрын
@@OskarBienkoI doubt that he is capable of elaborating his assertions.
@drek2736 ай бұрын
i love how you made such a loaded comment without elaborating especially when the topic is complex. Brilliant. You added value to the discussion