this is eye opening , very fresh perspective on using ML to tackle one of the toughest challenge - finance .

This is gold. Fantastic crystal clear presentation!!! (y)

Something that you find after tens of scrolls of your monitor after very specific query. Kind of bonus level, here it is ;).

The high frequency example of flash crash is classic fat tail. When many algorithm act the same way you get extreme events. Without the common models, their actions would cancel. But if many players come to same conclusion, you get the black swan. Machine learning is causing the extreme events, the black swans, as it correlates activities.

Crystal clear presentation. Amazing

great presentation !

this is mind blowing

The video could have divulged the returns and risks generated by leading ML based quant funds over the years and also returns generated by some major trading algos over the years, to bring out theshelf life of specific algos and ability to generate sustained higher sharp ratios.

Where is the presentation that Marco is discussing? Can we download it somewhere?

epic

@Onnti

Жыл бұрын

epicx2

the problem is that we lack financial market data, especially bearish market data. If the company focuses on HFT, it surely has enough data to deal with regime change. However, for medium-term trading, ML cannot help us to predict regime change because of lacking data.

@omegasigma4500

4 жыл бұрын

not true due to the existence of alternative data and the opportunity to generate synthetical data

@TheCheukhin

4 жыл бұрын

@@omegasigma4500 yes, you can find more data. Also, it is easy to overfit a medium term trading strategy

@omegasigma4500

4 жыл бұрын

@@TheCheukhin did you even watch the video? it's also about avoiding backtest overfitting...

@bartenderbob8875

4 жыл бұрын

Not sure what you mean by not enough data. We have nearly 15 years of decent T1 data for all asset classes - a lot of it is free too! Data Quality tends to increase with the year it was collected (aka more gaps, missing data from 2000’s than 2019) Besides, increasing your training set doesn’t necessarily reduce OOS variance.

@bartenderbob8875

4 жыл бұрын

Omega Sigma is right. We can easily create synthetic data sets that is seeded from historical data to fill in gaps or simply create more similar data. Just read the scikit-learn documentation on it

i don't think that statistical approach cannot deal with outliers but yes maybe empirical alghoritms can do better

@max0x7ba

Жыл бұрын

The empirical results speak for themselves. Your opinion is anti-scientific.

De Prado's logic: since poor models fit badly I don't use any model. Unfortunately this approach is not going to work when data are not enough to tell you anything close to the truth, e.g. in finance where returns are fat tailed and most observations are just noise

@max0x7ba

Жыл бұрын

You failed to grasp Marcos' message and the fundamental idea of ensembling.

@albertosantangelo6872

Жыл бұрын

@@max0x7ba you lack understanding of statistical properties of returns

@max0x7ba

Жыл бұрын

@@albertosantangelo6872 You said that you don't have enough data and some return distribution has fat tails. And you believe your data is noise. You failed to grasp Marcos' message to maximize recall instead of precision. Recall can be converted to precision by the ensemble model. That's machine learning fundamentals, you statistical genius.

@albertosantangelo6872

Жыл бұрын

@@max0x7ba study before commenting, eg read Taleb’s book on applied statistics (freely available on arxiv)

@max0x7ba

Жыл бұрын

@@albertosantangelo6872 You should read his book beyond "fat tails" on the cover.

I don't believe in hedging. Why not just lessen the position size if you're not that confident. On some occasions, both the original position and hedge can go against you.

@leimococ

2 жыл бұрын

Not believing in hedging is like not believing in clouds. It's true that in some occasions position and hedge can go against you; that doesn't invalidate the fact that good hedging strategies will lower the probability of losing by diversifying exposure.

@max0x7ba

Жыл бұрын

Hedging is based on the scientific method. Your beliefs are irrelevant.

@juggernautuci8253

Жыл бұрын

Hedging is like the insurance. Your clients may dislike the risk