Price Action With Python?

[entropytrading]

I would probably agree with your assessment that the amount of information is limited. However, that is mostly due to the fact that the Python PEPs and documentation are enough. I read through this my first go around (though for version 2): https://docs.python.org/3/tutorial/index.html

Very little I have had problems with language-wise, most to do with use of libraries.

What do you use C++ for nowadays?

 

[nooby_mcnoob]

What do you use C++ for nowadays?

For anything I want to distribute in executable form. Pretty sure Python has figured out that story now after all these years but who knows.

 

[entropytrading]

For anything I want to distribute in executable form. Pretty sure Python has figured out that story now after all these years but who knows.

But why do you need to distribute anything when trading your own account and I assume your programs are command-based.

 

[nooby_mcnoob]

But why do you need to distribute anything when trading your own account and I assume your programs are command-based.

I have more than one source of income

 

[entropytrading]

Do you have a github thing that i can take a look at your old stuff?

 

[nooby_mcnoob]

Do you have a github thing that i can take a look at your old stuff?

Nope.

 

[entropytrading]

I tried, but the copyList doesn't affect the original list.

 

[rudi20]

The big advantage of Python is the business logic reads very clearly, and that is important when you're developing systems. You don't want the code and syntax to get in the way of actually doing the job.

I've built my trading systems in Python using the following packages:
Pandas (mainly for getting data in/out and human analysis)
Numpy (for processing of data once loaded into memory, I tend not to use Pandas)
Numba (When I have to iterate through large datasets that would take too long using native Python)
scikit-learn (for data analysis)
matplotlib (for visualising and charting)

Regarding Pandas, I don't tend to use it for the actual algorithmic part. Instead I find it easier to convert everything to integers and use a numpy array. However, after my backtesting has run, all the results are put into a Pandas dataframe and then I can analyse that, usually in a Jupyter notebook.

As an aside, Quantopian uses Python so if you ever wanted to code on there, Python is a good language to learn.

 

[akine]

Question for experienced Python programmers.

I'm only a couple of weeks into learning Python and aside from using it for Web Development, I want to build technical trading systems as well (among other things). So, my question is whether or not it's possible to code discretionary technical analysis methods. By discretionary, I'm referring to price action methods such as trend lines/channels, support and resistance levels, volume and spread, etc. Basically, visual chart patterns that are usually charted by lines of some sort.

Is it possible to not only code such discretionary methods and but also develop an automated trading system based on that sort of a strategy? I've saw the algorithmic trading libraries for Python, by the way, but I'm asking about a form of visual analysis.

If you are a newbie youtube videos of "Sentdex"(for python in finance) and "Darwinex"(live data feed from MT4 to your python program) would be very useful.

https://pasteboard.co/IR9FAJY.jpg

That is what i come up with thanks to those channels...

 

[darthtraderbeta]

Been there, done that. What you will find is python is more than suitable for this task IF you define with precision what you mean by "price action".
If you do this exercise though it will be like peeling an onion. You just strip off layers and layers until there is nothing left of what you started with as far as "price action" because the concept is vague and lacking precision.
Then you either start looking for ideas that can be precisely defined or start reading trader psychology books and other types of magical thinking.