4 ms·

How's the (fictitious) ROI on that? :)

1337biz 12y ago
I'd like to see an April 1st only algorithm. Just one that knows how to spot a good joke and making money out of it.
crazypyro 12y ago
I was simply using it to predict relatively short-term (between a few hours to 2-3 weeks) movements in the stock. I wasn't using it to even predict the magnitude of the rise/fall beyond a few "tiers". Think like small/medium/large magnitudes and that's not far off. With that said, I had between 60-75% success rate on predicting the direction, depending on how I set different things like requiring multiple sources, confidence required to execute prediction, NLP tweaking, etc. The tiers was worse, but I think it was around 40-45% on the last iteration. Obviously, I would want to get better at predicting magnitude before I used it in any real life trading. It also was highly dependent on the company/industry. It performed better on something like Apple or Google, but worse on some smaller companies in industries that are not near as news heavy. The main goal of the project was just to satisfy my curiosity with algo trading, web scraping and data analysis while giving me another cool project to talk about in interviews, as I'm a college student. I used scrapy, NLTK, pandas mostly.
- nether 12y ago
  I think a 60% success rate is phenomenal, given that card counting only nets a 0.5-1.0% edge over the house. You've got yourself a 20% edge.
  - notahacker 12y ago
    Don't forget that as the OP pointed out, they tested on direction rather than magnitude; if the losses from the predictions where it got the direction of movement wrong are big enough it might be no edge at all, or a negative ROI.
- lordnacho 12y ago
  If it's really 60% and it works over the general universe of shares, you've built a money machine.
  - omegaham 12y ago
    Remember that the general direction of the market right now is upward. This means that these day-trading algorithms need to beat the typical rate of return for, say, a diversified index fund. Many of them don't.
    - lordnacho 12y ago
      Well I'm assuming the guy has thought about that, and his dataset is long enough to be meaningful. He didn't say how much the return varied, which you need for the comparison with the index to make sense.
  - kasey_junk 12y ago
    Nope because it doesn't account for risk.
    - lordnacho 12y ago
      The fact that it works over the universe accounts for the risk. If you have a 60% chance of being right, doing it over a few thousand stocks will make your daily return extremely steady, thanks to the law of large numbers. Of course there are correlations and such things to think about, but it's all controllable with that kind of edge.
      - kasey_junk 12y ago
        No it still doesn't. Because you have to allocate resources to the positional bet and one side of that bet has virtually limitless risk attached to it and the other has limited upside.
        lordnacho 12y ago

I'd like to see an April 1st only algorithm. Just one that knows how to spot a good joke and making money out of it.

I was simply using it to predict relatively short-term (between a few hours to 2-3 weeks) movements in the stock. I wasn't using it to even predict the magnitude of the rise/fall beyond a few "tiers". Think like small/medium/large magnitudes and that's not far off. With that said, I had between 60-75% success rate on predicting the direction, depending on how I set different things like requiring multiple sources, confidence required to execute prediction, NLP tweaking, etc. The tiers was worse, but I think it was around 40-45% on the last iteration. Obviously, I would want to get better at predicting magnitude before I used it in any real life trading. It also was highly dependent on the company/industry. It performed better on something like Apple or Google, but worse on some smaller companies in industries that are not near as news heavy. The main goal of the project was just to satisfy my curiosity with algo trading, web scraping and data analysis while giving me another cool project to talk about in interviews, as I'm a college student. I used scrapy, NLTK, pandas mostly.

nether 12y ago
I think a 60% success rate is phenomenal, given that card counting only nets a 0.5-1.0% edge over the house. You've got yourself a 20% edge.
- notahacker 12y ago
  Don't forget that as the OP pointed out, they tested on direction rather than magnitude; if the losses from the predictions where it got the direction of movement wrong are big enough it might be no edge at all, or a negative ROI.
lordnacho 12y ago
If it's really 60% and it works over the general universe of shares, you've built a money machine.
- omegaham 12y ago
  Remember that the general direction of the market right now is upward. This means that these day-trading algorithms need to beat the typical rate of return for, say, a diversified index fund. Many of them don't.
  - lordnacho 12y ago
    Well I'm assuming the guy has thought about that, and his dataset is long enough to be meaningful. He didn't say how much the return varied, which you need for the comparison with the index to make sense.
- kasey_junk 12y ago
  Nope because it doesn't account for risk.
  - lordnacho 12y ago
    The fact that it works over the universe accounts for the risk. If you have a 60% chance of being right, doing it over a few thousand stocks will make your daily return extremely steady, thanks to the law of large numbers. Of course there are correlations and such things to think about, but it's all controllable with that kind of edge.
    - kasey_junk 12y ago
      No it still doesn't. Because you have to allocate resources to the positional bet and one side of that bet has virtually limitless risk attached to it and the other has limited upside.
      - lordnacho 12y ago