I really like the analogous questions translation. That was illuminating. Thanks for writing this up.
Alexei
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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
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)
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_changeis 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).
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!