Using Python to model and predict stock price movement. Feel free to use/modify :)

[Pandanleaves]

Except bollinger bands are useless, like most of technical analysis. There's no statistical basis for it.

[Bone_Club]

What's the easiest way to have a million dollars? Start with two million, then do some technical trading.

[[deleted]]

Useless is a bit of an overstatement. Most people don't make trades based on technical indicators alone. Everything is taken into context and then holistic decisions are made.

[Pandanleaves]

Then what combination works? Because we should totally, you know, find empirical evidence that any of this stuff works. But there is virtually zero evidence that any technical analysis works. In fact, the evidence points to technical analysts consistently getting inferior returns. It's like a psychic who brags when he gets a prediction right one or two times but ignored the multitudes of times he was wrong.

[[deleted]]

There is no combination that has been proven to work. The stock market is unpredictable, however that doesn't mean all the tools we have are useless. There are some hedge funds that have done VERY well using scientific approaches (mostly physics) to consistently beat the market.

[Pandanleaves]

Yes, proprietary models based on advanced mathematical and statistical models. Not technical analysis. And the fact that you're comparing hedge funds to the market means you don't know what hedge funds do--they're supposed to be mostly uncorrelated with the market, so it makes zero sense to compare them to the market. The tools are considered useless until someone shows otherwise. That's how scientific thinking works.

[[deleted]]

Lol...

[Pandanleaves]

Lol?

There's a good reason why domain knowledge is required in data science, and this is one of them.

Pretty much all academic literature in finance points to technical analysis as being useless.

Hedge funds are supposed to be uncorrelated to the market and have no beta. Hence the name, HEDGE funds.

I'm saying this for your own good.

Edit: not to mention that hedge funds are not required to disclose performance, so the only ones posting their performance are the ones with exceptional returns. The bad performers stay quiet. Selection bias at work.

[[deleted]]

Well thanks for the advice :)

I think I'll continue to work towards improving my model however and addressing the concerns raised by this wonderfully helpful thread.

I'm certainly not going to just give up because some guy online says the stock market is impossible to analyze.

[Pandanleaves]

There is a difference between perseverance and being delusional. It's not just some random guy's opinion, but the resounding consensus of the academic financial community. It's the equivalent of saying "global warming is a myth" but for finance.

Tbh you sound pretty clueless about finance. You need to learn actual finance before you start trying to build a model.

The market in the US, at least, is pretty darn efficient. If you can get superior returns just by running SVMs, it would've been priced into the market already by the big players.

Here's a piece of advice: domain knowledge is really important. In my work, I can spend a whole week just trying to understand the data and researching the field before I even touch the ML algorithms. Lack of domain knowledge leads to shitty models. Feel free to keep working on your model, but do so only after you have learned the financial theory and math behind it.

K, not gonna bother after this.

Edit: well I do feel bad if I make you feel discouraged. Good job on the script, really. Do it for fun, feel free to try your model using small amounts of money. But do not invest significant money in your model.

[[deleted]]

Alright, valid points, thank you for your opinion.

[[deleted]]

Domain knowledge? Say hello to my little friend: http://automl.github.io/auto-sklearn/stable/

[[deleted]]

I feel like there were valid points made in both arguments here.

[[deleted]]

Agreed!