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FinRL: The first open-source project for financial reinforcement learning

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Re: FinRL: The first open-source project for financial reinforcement learning

#51

Earlier quoted context omitted.

https://www.amazon.co.uk/Quantitative-Equity-Portfolio-Manag... Caveat: it’s not exactly “easy reading”, and you might want to have the “three blue one brown” YouTube channel on standby.

Watched every video of them, I am okay with math. Thank you very much! Btw, since quant industry is full of secrets - is there any source on some actual money figures - how much firms make, what are the sums that are required to be shuffled, what are the frequencies of trades, profitability figures? Anything to get a slight glimpse into inner workings of this. Unfortunately googling turns out nothing or scams

> is there any source on some actual money figures - actual money figures - how much firms make

Yes there are a number of public sources, especially for UCITS funds that have some regulatory requirements to post publish their portfolio composition.

But you probably won't be able to make much sense of these figures, as it needs to be interpreted relative to the orientation of the fund.

See, a hedge fund is not selling "performance" per se, it's selling a reward for assuming a specific kind of risk on your behalf. Lots of people don't understand that nuance, and that's where you see all these messages like "haha my index fund outperformed hedge fund X".

Say you're a super wealthy company, idk, let's take an insurance company for the sake of the example. You have 1 billion dollar lying around and you would like to earn some passive money on it.

You can't just give that money to whatever hedge fund manager will give you the most performance, simply because you, as an investor, already have exposure to a bunch of factors. You cannot afford to have that money exposed to the same risks.

- Suppose your insured clients are mostly from Europe, it would be a bad investment to place that money on something that is exposed to the European economy. It would mean that if some bad macroeconomic factor impacts the European economy, thus making Europeans companies at risk, your dear investment would fall at the same time.

- You probably will need to be able to withdraw a part of that money at some point. Even if not, you will need to have some sort of balanced books to keep track of. Something along the lines of "the overall company reserve should cover 20% of the insured goods of clients". That means you cannot invest on whatever yields good performance, you also have to make sure the volatility of this investment won't put you at risk.

There are thousands of considerations like that, each client will have a different set. This leads to a bunch of different "orientations" of funds. Each client will typically have some kind of preferred allocation that he will balance between multiple funds accordingly.

Some examples:

- CTAs (trend followers) will provide good performance, at the expense of a strong market exposure and volatility.

- Market neutrals will provide pure alpha (no market exposure) but with lower volatility and returns.

- Arbitragers will provide very good returns, very low volatility, but with very small capacity.

- Macro funds will provide returns uncorrelated to the market, but suffer very low vol in certain circumstances.

There is no point in comparing the returns of different orientations of funds. Even comparing the returns of same kind of funds is not really relevant, to have a full picture you would need to k ow exactly what this kind of fund is supposed to deliver in terms of volatility, exposure, returns, etc.

> what are the sums that are required to be shuffled

Usually this is an output of your strategy, not something you decide a priori. Quantitative funds win "on average", so you want to trade as much as you can for the law of large numbers to kick in, until the trading costs catches up.

> what are the frequencies of trades

That completely depends on the kind of fund, and the regulatory enveloppe with which the fund is sold.

Typically that will go from a few microseconds for the best arbitragers to weekly/monthly rebalancing for large fundental funds.

Re: FinRL: The first open-source project for financial reinforcement learning

#52
post #14

```Technical indicators: 'macd', 'boll_ub', 'boll_lb', 'rsi_30', 'dx_30', 'close_30_sma', 'close_60_sma'.``` lulz

Why lulz? Bad indicators, too few?

These are naive and unrefined technical indicators.

- there's only price/volume, it's very poor in terms of information. Smells like an academic with only access to a free database.

- it's really the classical technical analysis 101 indicators, studied 10000 times in research articles.

- you would need wayyyy more indicators to get a meaningful model.

Re: FinRL: The first open-source project for financial reinforcement learning

#53
post #28

Earlier quoted context omitted.

What's the best way for someone doing and ML and optimisation PhD to get into quant research/Trading? I've been playing with paper trading, trying to use creative data sources to find alpha and algotrading micro amounts of Cryptos for a while now and am seriously considering doing this for my post PhD life if I can't find a nice tenure track (a complication is that I do not want to leave Switzerland until I have citi…

Get an internship at a reputable prop shop or hedge fund (that specialize in quant finance). There are plenty in Switzerland.

Do you have a list/advice to find them? I only found a few with offices in Switzerland, and not being in the industry I don't fully trust my judgement of what is "reputable"

Re: FinRL: The first open-source project for financial reinforcement learning

#54
post #51

Earlier quoted context omitted.

Watched every video of them, I am okay with math. Thank you very much! Btw, since quant industry is full of secrets - is there any source on some actual money figures - how much firms make, what are the sums that are required to be shuffled, what are the frequencies of trades, profitability figures? Anything to get a slight glimpse into inner workings of this. Unfortunately googling turns out nothing or scams

> is there any source on some actual money figures - actual money figures - how much firms make Yes there are a number of public sources, especially for UCITS funds that have some regulatory requirements to post publish their portfolio composition. But you probably won't be able to make much sense of these figures, as it needs to be interpreted relative to the orientation of the fund. See, a hedge fund is not selling…

Much appreciated for the information!

Re: FinRL: The first open-source project for financial reinforcement learning

#55
post #43

Earlier quoted context omitted.

Watched every video of them, I am okay with math. Thank you very much! Btw, since quant industry is full of secrets - is there any source on some actual money figures - how much firms make, what are the sums that are required to be shuffled, what are the frequencies of trades, profitability figures? Anything to get a slight glimpse into inner workings of this. Unfortunately googling turns out nothing or scams

Hedge funds are a very diverse group: from small shops managing tens of millions to the large funds managing tens of billions. The very good ones probably make around 20% a year (meaning that they earn around $40M per year per $1B that’s under management). HFT companies are a different story. The biggest ones make around $1B/year (e.g Virtu, Flow Traders which are public), with a few of them even bigger. There are ma…

Thank you!

Re: FinRL: The first open-source project for financial reinforcement learning

#56
post #50

Earlier quoted context omitted.

Quants: So much fancy vocabulary just to consistently underperform your favorite mutual index fund :)

Do you really believe the world is that simple? It poses you no problem to live in a world where your outsider opinion on a topic you probably just read about on the internet, is right versus a whole industry of very smart people managing hundreds of billions? At no point do you wonder that if thousands of smart, successful people and companies do something, then maybe that something is not as absurd as some internet…

I have done research in financial modeling and indeed have a family-member that manages his own successful hedge fund, so the comment was somewhat tongue-in-cheek. I do consider myself an outsider, though, as I’ve been out of the field for sometime.

Still, if you’ve worked in hedge funds you’ll know two things: connections and luck account for almost everything. What remains are mostly linear models that almost never outperform your basic mutual index funds.

Those very smart people moving billions do get things very wrong, though often at an imbalanced risk to themselves (an imbalance in their favor, mind you), so I don’t put much stock in such sweeping appraisals.

Re: FinRL: The first open-source project for financial reinforcement learning

#57
post #51

Earlier quoted context omitted.

> is there any source on some actual money figures - actual money figures - how much firms make Yes there are a number of public sources, especially for UCITS funds that have some regulatory requirements to post publish their portfolio composition. But you probably won't be able to make much sense of these figures, as it needs to be interpreted relative to the orientation of the fund. See, a hedge fund is not selling…

Much appreciated for the information!

And thank you for asking the question that allowed for such a great answer.

Re: FinRL: The first open-source project for financial reinforcement learning

#58
post #48
post #15

I really can't help but wonder - say I want to create a trading strategy that I actually want to use. Is there really any point in learning an open source framework, where I can assume that the most profitable strategies it can produce are already being used by someone? I think I'd rather start by learning the underlying frameworks like TensorFlow, that this one seems to wrap, and produce something that has at least…

> Is there really any point in learning an open source framework, where I can assume that the most profitable strategies it can produce are already being used by someone? In practice, yes, because the methodology itself is only as good as the data you feed it. There's only so much you can do with e.g. ML. The framework gives you that methodology. You can fiddle with it, fine tune it, etc, but that's often just margin…

Hmm, but seen under this angle, it seems even worse, no? The tool lists about a dozen well-known public data sources that it's compatible to work with, you can't expect to gain any edge from those.

Sure, you could learn how to fit your proprietary data into this tool, but that goes back to my point, why not learn how to work with TensorFlow or whatever ML framework directly? They're not that difficult that you'd need a wrapper.

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