Webinar: Prototyping Trading Strategies in Python w/Ran Aroussi
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Webinar: Prototyping Trading Strategies in Python w/Ran Aroussi


Matlab, R project and Python

Created February 22nd 2017 by Big Mike
Updated January 24th 2019 by Mtype
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Webinar: Prototyping Trading Strategies in Python w/Ran Aroussi

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Webinar: Prototyping Trading Strategies in Python w/Ran Aroussi

Hi guys,

It is my pleasure to welcome Ran Aroussi on Thursday, February 23rd @ 1PM Eastern US (note the special time).

The webinar title is "Prototyping Trading Strategies in Python", and bullet points include:

- The Python Toolbox: Platforms and Libraries for Trading
- Data Wrangling and Working with Market Data
- Using Technical Indicators with Python
- Coming Up with Trading Ideas
- Writing and Testing your first Trading Strategy

You can register for the event here:
https://futures.io/webinars/

Mike

Due to time constraints, please do not PM me if your question can be resolved or answered on the forum.

Need help?
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2) Start a journal and post to it daily with the trades you made to show your strengths and weaknesses.
3) Set goals for yourself to reach daily. Make them about how you trade, not how much money you make.
4) Accept responsibility for your actions. Stop looking elsewhere to explain away poor performance.
5) Where to start as a trader? Watch this webinar and read this thread for hundreds of questions and answers.
6)
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  #3 (permalink)
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Ran Aroussi will be continuing this webinar series in the future on FIO, this is the first of four webinars planned:

Prototyping Trading Strategies
Backtesting & Optimization (TBA)
Live Trading (TBA)
Using Machine Learning in Trading (TBA)

You can find Ran on his website:
Aroussi.com

Mike

Due to time constraints, please do not PM me if your question can be resolved or answered on the forum.

Need help?
1) Stop changing things. No new indicators, charts, or methods. Be consistent with what is in front of you first.
2) Start a journal and post to it daily with the trades you made to show your strengths and weaknesses.
3) Set goals for yourself to reach daily. Make them about how you trade, not how much money you make.
4) Accept responsibility for your actions. Stop looking elsewhere to explain away poor performance.
5) Where to start as a trader? Watch this webinar and read this thread for hundreds of questions and answers.
6)
Help using the forum? Watch this video to learn general tips on using the site.

If you want
to support our community, become an Elite Member (see why)

Follow me on Twitter Visit my Facebook Visit my futures io Trade Journal Reply With Quote
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  #4 (permalink)
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Reminder that today's event is at a special time, 1PM Eastern US.

Mike

Due to time constraints, please do not PM me if your question can be resolved or answered on the forum.

Need help?
1) Stop changing things. No new indicators, charts, or methods. Be consistent with what is in front of you first.
2) Start a journal and post to it daily with the trades you made to show your strengths and weaknesses.
3) Set goals for yourself to reach daily. Make them about how you trade, not how much money you make.
4) Accept responsibility for your actions. Stop looking elsewhere to explain away poor performance.
5) Where to start as a trader? Watch this webinar and read this thread for hundreds of questions and answers.
6)
Help using the forum? Watch this video to learn general tips on using the site.

If you want
to support our community, become an Elite Member (see why)

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  #5 (permalink)
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This was a great webinar. Looking forward to the remaining 3.

"It does not matter how slowly you go, as long as you do not stop." Confucius
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some more questions

Great webinar indeed!

Some more questions if possible:

1) Is there any library specialised to automatically handle futures (big-point-value, min-move, etc)?

2) Is there a library out there for python to handle walk-forward testing?

3) The same goes for portfolio testing - is there anything more or less specialised for testing a high nr. of instruments in parallel?

4) What would typical reasons be for using R when python doesn't meet the needs ... have you ever been in such situation?

Thanks!

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  #7 (permalink)
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Ran has provided his slides to the site so everyone has access to the code prototyping-trading-strategies-with-python slides

On the last slide is a recommended reading and tutorial list.

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have not watched webinar ..will do so later. but i went to the site. I love his "About" page and how he starts it "I was born to be awesome ...not perfect." He uses the tag on twtr too....nice !!

i cannot code and may not. will check if he helps with setup/code else will keep tabs especially for DOM stuff. There's a lot to learn in this life..

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I'm not the presenter so these are my own answers .

1/ No but it's easy to do it yourself

2/ No need: assuming you're using similar principles shown in the webinar you'll have a Pandas DataFrame with all the information needed to backtest. So you can backtest the full length of the DataFrame minus the rows you want to use for the forward testing. From the webinar slides the forward tests will be done with spy[-100:] (assuming you'll use the 100 last "rows" for the forward tests)

3/ I'm not sure, maybe Pyfolio can deal with it.

4/ R is for maths guys, not for coders. I found very hard to code in R if you previously learned and worked with classical coding language (C, C++, Java, Pascal, ...). I don't think there's anything you can do in R which can't be done in Python with the huge amount of libraries available.


andby View Post
Great webinar indeed!

Some more questions if possible:

1) Is there any library specialised to automatically handle futures (big-point-value, min-move, etc)?

2) Is there a library out there for python to handle walk-forward testing?

3) The same goes for portfolio testing - is there anything more or less specialised for testing a high nr. of instruments in parallel?

4) What would typical reasons be for using R when python doesn't meet the needs ... have you ever been in such situation?

Thanks!


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Not the presenter either but just wanted to chip in on @sam028's points.

1/ You can do it in any language.

2/ Same as above.

4/ It depends on what you're looking to do. Python is definitely newbie-friendly, however functional R is a lot more expressive and has a much faster development cycle for both CS and math-educated users. Almost everyone whom I've worked with that is familiar with both languages would use Python only for simple IO tasks and use R for the data exploration. For example, if you want to infer the distribution of model parameters with MCMC:
 
Code
reducer <- function(acc,x) {
    ...
    trans.matrix <- acc[[1]]
    state.history <- acc[[2]]
    ...
}
final <- Reduce(reducer, splitter(data[-1,]), state)
sequence <- final[[2]]
sequence <- sapply(sequence, as.character)
mcfit <- markovchainFit(data=sequence, byrow=TRUE)
mcmc <- new('markovchain', states=..., transitionMatrix=mcfit$estimate@transitionMatrix...)
Python does not have an idiomatic way to do this. And also since it's natural to write models in a reduction tree in R, it's convenient to translate your exploratory code to another language for high-performance production trading or to parallelize on a large cluster, whereas the object-oriented style of data analysis in Python (pandas etc.) isn't suitable for a concurrent environment. And when you're working in a mature trading environment, you spend a lot more of your time tackling with this type of problem than the kind that pandas/Tensorflow etc. are designed for.

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