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Lessons learned building an ML trading system

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Re: Lessons learned building an ML trading system

#61

Post by traK6Dcm »

cco wrote: post #9

And in the end, what value was created? "Liquidity in the BTC market"?

Author here. Actually, the system is mostly taking liquidity from the market so it's not even doing that :) Perhaps, "opportunity for others to create more liquidity"

Jokes aside, it's actually something I am thinking about a lot. Such systems don't create value, but they extremely intellectually interesting and I've learned a lot. You can say the same for many other projects, for example academic research in many fields. Most of it is just noise to promote the author and does not create value in the world. But it's intellectually interesting, so people work on it.

Other people write compilers for fun to learn something new without creating value. I don't think this is fundamentally different.

Re: Lessons learned building an ML trading system

#62

Post by account73466 »

traK6Dcm wrote: post #56
gricardo99 wrote: post #2

Great post! Very refreshing to hear about a) the honest level of effort involved in this type of endeavor, b) the amount of nonsense trading advice out there. Maybe in a future post you could discuss the security and banking side of this in more detail? In the 6ish years I’ve played around with crypto trading (and I really mean play, nothing close to your level), I’ve had 2 exchanges hacked and lose all customer fund…

Author here. Honestly, I don't have a good answer. I spread my capital across enough exchanges so that if one runs away with it gets hacked it doesn't ruin me. It's just a risk I'm taking. I'm also not trading much capital. Because the system is more on the HFT side, the actively traded capital isn't that high, and I don't care about losing it. Any profit I try to get out of the exchanges regularly. I wouldn't feel c…

Apart from Binance who else you trust? (of course overall no crypto exchange can be really trusted) I assume you trade alts vs (BTC or USDT).

Also, when you said "market neutral", did you mean you also short (only few pairs have margin on Binance and it appeared recently).

Re: Lessons learned building an ML trading system

#63

Post by account73466 »

traK6Dcm wrote: post #61
cco wrote: post #9

And in the end, what value was created? "Liquidity in the BTC market"?

Author here. Actually, the system is mostly taking liquidity from the market so it's not even doing that :) Perhaps, "opportunity for others to create more liquidity" Jokes aside, it's actually something I am thinking about a lot. Such systems don't create value, but they extremely intellectually interesting and I've learned a lot. You can say the same for many other projects, for example academic research in many fi…

So you enter with market orders and exit with trailing limit orders?

Re: Lessons learned building an ML trading system

#64

Post by drawkbox »

cco wrote: post #9

And in the end, what value was created? "Liquidity in the BTC market"?

Depends on what the gains are used for.

Ultimately all investors from the hedge funds down to the long sucker, it is all skimming for returns. HFT just skims from other skimmers returns. Yes those investments may have created value, but ultimately people invest because they want returns.

The money made will be used for more skimming but also some for investment in other possibly beneficial projects.

Extracting financial value via investment (skimming) may allow someone to start a company, support a community or family and have valuable time to make those things better. Just as an investment in a company/idea creates value in that company, the value gained from trading where the first step really is skimming, can be used to create value in the real world or even just time which may lead to more real actual value created.

Re: Lessons learned building an ML trading system

#65

Post by jackschultz »

> The biggest edge probably comes from the effort put into building the infrastructure.

I feel like this should be in bold, but either way, I love reading that in these posts. In every way, from research to confirm your models are correct, to be able to trust real time trades, you need a solid architecture. This thought isn't only for trading remember, where it's the same in tons of solutions to problems. If comment readers have other examples, I'd love to hear them in responses.

Re: Lessons learned building an ML trading system

#66

Post by jstanley »

criddell wrote: post #60
nickreese wrote: post #11

After having spent an insane amount of time in late 2017/2018 building an HFT bot for Binance I can say this is a pretty solid article. In our case we were doing triangle trading between BTC/ETH/USDT pairs and had our buys/sell delay down to 3-7ms. At one point moving 0.3-0.7% of Binance’s daily volume. Few notes: * Finding an objective point of truth for value when all of the currencies are floating is hard but vita…

What's the value of HFT? If exchanges were required to add a random delay to very trade to work against high frequency traders, would anything of value be lost?

If exchanges added a random delay, that would mean traders have to plan for a longer time horizon, which means they need to offer more conservative prices. That means when you're selling, you get less money, and when you're buying, you get less stuff.

Some people have this notion that if their trade got matched against a HFT that they have somehow lost out. The exact opposite is true. If the party on the other side of your trade was a HFT, then that implies that all other parties were offering worse prices than the HFT was. If the HFT had not been there, you would have got a worse price (whether on the buy side or the sell side). The presence of high-frequency traders (or any traders, for that matter) reduces spreads and increases liquidity.

The only people who get worse deals as a result of high-frequency trading are people who would sell you the same stuff for more money.

Re: Lessons learned building an ML trading system

#67

Post by [deleted] »

criddell wrote: post #60
nickreese wrote: post #11

After having spent an insane amount of time in late 2017/2018 building an HFT bot for Binance I can say this is a pretty solid article. In our case we were doing triangle trading between BTC/ETH/USDT pairs and had our buys/sell delay down to 3-7ms. At one point moving 0.3-0.7% of Binance’s daily volume. Few notes: * Finding an objective point of truth for value when all of the currencies are floating is hard but vita…

What's the value of HFT? If exchanges were required to add a random delay to very trade to work against high frequency traders, would anything of value be lost?

[deleted]

Re: Lessons learned building an ML trading system

#68

Post by traK6Dcm »

criddell wrote: post #60
nickreese wrote: post #11

After having spent an insane amount of time in late 2017/2018 building an HFT bot for Binance I can say this is a pretty solid article. In our case we were doing triangle trading between BTC/ETH/USDT pairs and had our buys/sell delay down to 3-7ms. At one point moving 0.3-0.7% of Binance’s daily volume. Few notes: * Finding an objective point of truth for value when all of the currencies are floating is hard but vita…

What's the value of HFT? If exchanges were required to add a random delay to very trade to work against high frequency traders, would anything of value be lost?

Many HFT systems provide liquidity. If there is no liquidity, retail investors like you or me cannot buy or sell.

Just imagine you want to exchange a currency because you go traveling and the exchange tells you "Sorry, nothing available right now, gotta come back in a few weeks". That's what would happen if there is no liquidity.

Re: Lessons learned building an ML trading system

#69

Post by onlyrealcuzzo »

Akababa wrote: post #41
onlyrealcuzzo wrote: post #7

Is this the author? I would love to know how this fared recently in the large sell-off. What he says about some markets possibly being predicable rings true to me. But the article was far from convincing that the BTC market is actually predicable. The natural assumption should be that the author was in the right place at the right time. Although he went through great lengths, I'm not convinced this is anything other…

Through hypothesis testing you can estimate the probability that this was due to luck is very low. Assuming that a monkey would have a 50% chance of profiting on a day, the chance of going a month without a losing day is less than 1 in a billion.

Wouldn't it be better to look at how much this deviates from average market returns during the time frame?

Re: Lessons learned building an ML trading system

#70

Post by jstanley »

bhl wrote: post #38
nickreese wrote: post #11

After having spent an insane amount of time in late 2017/2018 building an HFT bot for Binance I can say this is a pretty solid article. In our case we were doing triangle trading between BTC/ETH/USDT pairs and had our buys/sell delay down to 3-7ms. At one point moving 0.3-0.7% of Binance’s daily volume. Few notes: * Finding an objective point of truth for value when all of the currencies are floating is hard but vita…

> Best we could tell someone was front running us due to an artificial delay for our account (delay between trades went to ~20ms up from our prior steady speed of 3-7ms) and/or a bunch of the trades in the orderbook were bogus. I wouldn't be surprised, given that traditional HFT companies are building cryptotrading desks and they have a lot more capital to play with too.

You can't simply front-run someone else by having deep pockets.

The only way that is possible is if you gain privileged information about that person's orders before they actually hit the book. The most practical way to do this is to be the exchange.

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