Emsu

Haha, I think that might be too nuanced :) I definitely agree though that (b*d)/c gives you a nice framework without being tied to a company's specific situation.

Fleshed out the tutorial a bit more yesterday. Feel free to propose what kind of docs you'd like to see on the issues page. I'm all ears :)

Lastest master is Python 3 compatible.

I'll try to find some time to work on that. If you have time, it would be super awesome if you could make a pull request for python 3 compatibility.

The scipy and statsmodels libaries are nice. CVXOPT would be good for portfolio optimization. You really have to implement your own strategies though.

Thanks for the heads up.

You're right. I really need to bite the bullet and generate stock split data. That was the main reason I used adjusted because splits were skewing my tests a lot.

I actually really want to support it. If only I could get my hands on some tick data... any ideas?

If you look at: https://github.com/Emsu/prophet/blob/master/prophet/data.py

There's a PandasDataGenerator which is a wrapper around the pandas libary's DataReader. For the Yahoo source, you can have the following data options: "Open", "High", "Low", "Close", "Volume", "Adj Close". The YahooCloseData generator actually uses "Adj Close". You can implement high, low or open by copying the YahooCloseData generator and getting the respective key instead of "Adj Close". Feel free to contribute these generators upstream too :)

If you're looking for modeling out the uncertainty, see the slippage section in:

http://prophet.michaelsu.io/en/latest/advanced.html#slippage...

If you have a dataset that provides more frequent than daily data, you can store the sell order on your order generator and process it the next tick. That combined with slippage and commission will probably give you the most accurate trading model.

Let me know if you have any other thoughts on how it can be better modeled.

I'm adding a tutorial for more complicated strategies to the documentation. What did you have in mind for the visualization? Currently you can just install matplotlib and graph it or export a backtest to json.

Definitely adding tests :) The folder's sitting in my local repo and it'll be the next thing I do.