This makes me wonder if you could get a safe and extremely useful oracle if you only allow it to output a few bits (eg buy/sell some specific stock).
“Muddled thinking but very interesting direction”
Seems fine as a practical solution. But it’s still nice to do the math to figure out the formula, just like we have a formula for gravity.
Thanks for the update! I’m glad to hear there was some traction. Certainly more than I expected.
Ok, I’m gonna read this book. Just reread this summary; so good! Thanks again for writing it up! As they say, sorry I only have one super upvote to give you.
In two weeks or so can you please post an update about how many people reached out. This might partially demonstrate how much actual demand there’s for something like this. Also, thank you!
I really like the analogous questions translation. That was illuminating. Thanks for writing this up.
There’s a good number of people for whom utility is almost linear in amount of money they have.
Good question. I don’t know.
In this case the latency is not a big issue because you're trading on day bars. So if it takes you a few minutes to get into the position, that seems fine. (But that's something you'd want to measure and track.)
In these strategies you'd be holding a position every bar (long or short). So at the end of the day, once the day bar closes, you'd compute your signal for the next day and then enter that position. If you're going to do stop-losses that's something you'd want to backtest before implementing.
Overall, you'll want to start trading some amount of capital (may be 0, which is called paper trading) using any new strategy and track its performance relative to your backtest results + live results. A discrepancy with backtest results might suggest overfit (most likely) or market conditions changing. Discrepancy with live results might be a result of order latency, slippage, or other factors you haven't accounted for.
Yes to everything Satvik said, plus: it helps if you've tested the algorithm across multiple different market conditions. E.g. in this case we've looked at 2017 and 2018 and 2019, each having a pretty different market regime. (For other assets you might have 10+ years of data, which makes it easier to be more confident in your findings since there are more crashes + weird market regimes + underlying assumptions changing.)
But you're also getting at an important point I was hinting at in my homework question:
We're predicting up bars, but what we ultimately want is returns. What assumptions are we making? What should we consider instead?
Basically, it's possible that we predict the sign of the bar with a 99% accuracy, but still lose money. This would happen if every time we get the prediction right the price movement is relatively small, but every time we get it wrong, the price moves a lot and we lose money.
Stop losses can help. Another way to mitigate this is to run a lot of uncorrelated strategies. Then even if the market conditions becomes particularly adversarial for one of your algorithms, you won't lose too much money because other algorithms will continue to perform well: https://www.youtube.com/watch?v=Nu4lHaSh7D4
Crypto quant trading: Naive Bayes
2019-05-07T19:29:40.507Z · score: 30 (7 votes)If you would prefer the universe to be in ... If I was to make Evan's argument, that's the point I'd try to make.
My own intuition supporting Evan's line of argument comes from the investing world: it's much better to run a lot of uncorrelated positive EV strategies than a few really good ones, since the former reduces your volatility and drawdown, even while at the expense of EV measured in USD.
I'm actually very glad you wrote this up, because I have had a similar thought for a while now. And my intuition is roughly similar to yours. I wouldn't use terms like "decision theory," though, since around here that has very specific mathematical connotations. And while I do think my intuition on this topic is probably incorrect, it's not yet completely clear to me how.
I’ll try to post it this weekend. :)
Swarm AI (tool)
2019-05-01T23:39:51.553Z · score: 18 (4 votes)Yeah, I had the same occurrence + feeling recently when I wrote the quant trading post. It felt like: "Wait, who would downvote this post...??" It's probably more likely that someone just retracted an upvote.
price_change=price/old_price, so no. pct_change is what you're thinking.
Basically support is some line (horizontal or angled) which predicts when the price going down will stop and revert to going up. It's acting as a kind of "support". Resistance is the opposite. I'll probably end up writing a post on this sometime.
I’ve stopped using Facebook since December of 2018. I currently have no desire to go back to it. I don’t feel like I’m missing anything either. After being off it for two months or so I went and checked all my notifications. There wasn’t a single post that I felt like “wow, I can’t believe I almost missed this.” (I still use Messenger and FB events.)
Can't comment on market making; that's not something we do.
I agree that linear models usually perform much better.
Well, I was learning together with the rest of our team. So there were a lot of conversations and an inflow of information from all sorts of places: books, blogs, Quora, advisors, internet research, etc... Honestly, it's all a little helpful to get you thinking along the right lines. So I can't recommend any specific resource, but I can recommend learning broadly.
Okay, one particular resource I'll recommend is: https://www.youtube.com/channel/UC-dLWl8etTtPSGdbbcYffGw
If you have things that you found helpful, please share them here too.
Depending on how complex your strategies are, may be something like this could work: https://www.youtube.com/watch?v=P_YmXyUsb9A
Hmm, that's a good question. I haven't looked into it too much, but I'd definitely try to leverage a 3rd party platform for hosting or at least use a 3rd party library to do the trading. https://enigma.co/catalyst/ was kind of decent when I used it for a while a year ago. By now, if they're still improving it, it's probably pretty good. One advantage there is that they have the data too. https://github.com/ccxt/ccxt is a wonderful API that generalizes how you interact with exchanges.
Hosting wise, AWS (what we use) or Google Cloud would both work. I personally like the latter a lot more.
This comment was written in response to you feeling confused about what strategies to explore. I might write a fuller post about it, but for now here're the thoughts off the top of my head:
Calling marking anti-inductive is correct, but it's not helpful when trying to find strategies (as you've just noticed). I'd break down the strategy research process steps into:
1) Can you find a strategy (algorithm + data) that historically has performed well?
2) Can you find this strategy in such a way so as not to find a ton of other strategies that worked by random chance?
3) What % of the market has figured out this strategy?
From Eliezer's post:
Let's say you see me flipping a coin. It is not necessarily a fair coin. It's a biased coin, and you don't know the bias. I flip the coin nine times, and the coin comes up "heads" each time. I flip the coin a tenth time. What is the probability that it comes up heads?
If you answered "ten-elevenths, by Laplace's Rule of Succession", you are a fine scientist in ordinary environments, but you will lose money in finance.
In finance the correct reply is, "Well... if everyone else also saw the coin coming up heads... then by now the odds are probably back to fifty-fifty."
Right. But if it's slightly more complicated than just looking at the coin, then suddenly we can have an edge:
1) May be not everyone can write the code to compute which way the coin is facing (algorithm). May be not everyone can see the coin (data).
2) May be other people are looking at the weather and the weather has been sunny nine days in a row.
3) May be not everyone can run their algorithm fast enough to make the trading decision in time. May be others figured out this strategy, but they're not confident in it enough to deploy a lot of money.
So once you find your strategy, you might be in a pretty small group of people who have discovered it. So you'll be fine in proportion to how much money is allocated to this strategy vs how much capacity it has.
And the lesson is: aim for a strategy complexity that's simple enough to pass 2), but complicated enough that most people haven't found it. And the bar for that is actually not that high (at least in crypto).
Crypto quant trading: Intro
2019-04-17T20:52:53.279Z · score: 60 (23 votes)Awesome! I’ve really enjoyed reading this. Quite a lot of ideas resonated in a surprising new way. I might actually read this book. Thanks for writing up the summary!
Reminds me of this: https://youtu.be/vl-44jDYDJQ
Ruby recently wrote this, which seems relevant: https://www.lesswrong.com/posts/k2KwBEq4ToXnvZSbz/on-the-nature-of-agency
I think overall I just appreciate that you guys did something for April 1st. It made the website / community feel a bit more alive.
Having studied and achieved stream entry (in the Vipassana tradition), I very much doubt many people have stumbled into it. Although for clarity, what % of the population are we taking about? Quick fermi: from what I’ve seen in the spiritual community about 1/1000 have achieved stream entry spontaneously / easily. Out of my bubble, I’d say 1/100 is spiritually inclined. Then I’d add another factor of at least 1/100 to control for my bubble being the Bay Area.
The reason why I doubt it is because most people will tell you (and have written) that it has take them (and people they know) many years and intense practice to get it.
I do think a lot of people have gotten A&P though.
Not long after Lantz made Bostrom’s ideas playable, a young filmmaker named Alberto Roldán followed a link to the game on Facebook and was entranced. He contacted Lantz about making Universal Paperclips into a feature-length film. Roldán is close to finishing a draft of the script, and he has already curried the interest of several producers. His version will be much more explicitly peopled than the game itself is. “It has to involve characters who feel like it’s their story,” he told me. “As you play, you keep getting these increasingly large multimillion dollar gifts to sort of placate your ostensible keepers. We know who these characters are in our modern world: hyper-rich tech titans who are not super interested in questioning the thing that has enriched them—with the justification that they are healing the world at large.”
One way I’ve experienced this was playing poker in a casino. I was pretty decent and could see a lot of the the mistakes other people were making. I thought that meant I should just be able to come in and win their money, but I was sorely mistaken. It was only after a full week of training and practice that I was able to get to that level. (A week sounds like not a lot of time, but it took me a few years of playing on and off to get to that level + realization,)
Yes! For me one area where it’s super clear is relationships. On first dates I’m 100% active curiosity. I’ll ask lots of questions, trying to get to the heart of who the person across the dinner table is. But after a while, once I’ve “accepted” them, I switch more to open curiosity. It feels more playful. I’m less interested in their version and more interested in exploring together. There’s trust.
Note that the post you linked by Johnicholas contains a mistake that the author admits invalidates his point.
[Link] OpenAI LP
2019-03-12T23:22:59.861Z · score: 15 (5 votes)Actually I take that last part back. I could see people who love to vibe about rationality. I think I actually do too, just at a certain level of depth / casually,
Yes, but also once you get good at vibing you want to go vibe with people on your favorite frequency / topic. Also I think some topics are more “vibable“ than others, and rationality is just not one of them.
Also I think this post gets at part of what you’re pointing at: https://www.lesswrong.com/posts/4QemtxDFaGXyGSrGD/other-people-are-wrong-vs-i-am-right
Camel: you have a piece of data, then someone asks if you’ve seen a camel, then you realize that fits with your data. Elephant: you have a piece of data and so you imagine what the “thing” might be. But nobody walked in and asked if someone has seen an elephant.
Why do you think you should be reading / learning more vs going and doing / making something?
Seems like it would throw a brick at you, because it wanted to throw a brick, not caring that in 2 seconds it’ll hit your face. (You can probably come up with a better example with a slightly longer timeframe.)
RAIN is easiest and most memorable.
I learned recently that some states used to offer an equivalent of "forever stamps" for education. Meaning you pay $X at any time and you've guaranteed your payment for a state university in the future. Obviously, they discontinued it, since the costs rose and they lost money. But if you wanted to short *education cost*, you'd basically want to sell these guarantees yourself.
I don’t think I’ve read this view before, or if I have, I’ve forgotten it. Thanks for writing this up!
I’ve been reading basically every post for the past few months. I don’t usually leave comments though, unless it’s to support and thank the author. (Thanks for writing this! Funny enough I also noticed recently how few comments there are, and it seemed worth bringing up.) I guess I feel like I just don’t have much to add to most posts.
“view them as a set of open-ended concepts that have yet to reach a satisfactory conclusion“ is a good description of how I feel about this post.
I call it “making an executive decision.” And I used that term before getting into startups.
Link: That Time a Guy Tried to Build a Utopia for Mice and it all Went to Hell
2019-01-23T06:27:05.219Z · score: 15 (6 votes)Thanks for writing your thoughts out loud. Curious to follow your progress.
Of course, truly effective tea plus a well-conveyed story about its great properties will generate more sales than effective tea or a good story alone.
Sometimes not, I think. It's almost like a measure of the efficiency / effectiveness of the given market. If the market is really good at recognizing reality, then you don't need to tell a story. (Basic software libraries are like that: do they give the compute the right thing? If yes, then it's good.) If the market is not at recognizing reality, then creating stories is often way cheaper than then doing the real thing. (And also transfers better across domains.)
Is there anything in the world that we know of that does alignment for something else? Can we say that humans are doing "coherent extrapolated volition" for evolution? Keeping in mind that under this view, evolution itself would evolve and change into something more complex and may be better.
I think there's a bias when we consider optimizing for X's values to consider only X without its environment. But the environment gave rise to X and actually much of X doesn't make sense without it. So I think to some extent we also would need to find ways to do alignment on the environment itself. And that means to some extent helping evolution.
First off, great thought experiment! I like it, and it was a nice way to view the problem.
The most obvious answer is: “Wow, we sure don’t know how to help. Let’s design a smarter intelligence that’ll know how to help better.”
At that point I think we’re running the risk of passing the buck forever. (Unless we can prove that process terminates.) So we should probably do at least something. Instead of trying to optimize, I’d focus on doing things that are most obvious. Like helping it not to die. And making sure it has food.
Just wanted to say I’ve been reading all your recent posts. And I really like all of them, the ideas you lay out, and how you talk about them. You really make LW worth coming to almost every day. Thank you! FWIW, if you continue down this path, I could see you having your own SSC-sized community.
I love that game too. Thanks for the write up, it’s been fun to read your take on it. I haven’t played it recently, so didn’t know they added the ending. I’ll come back and play it again.