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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

  #21 (permalink)
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Hi guys,

just wondering, did any of you guys actually tried to run Ran's notebook? Perhaps below happens only sometimes ... In Anaconda (Windows 10x64 - latest&greatest versions), "import talib as ta" returns module not found error ...
doing "conda info ta-lib" after adding <https://anaconda.org/Quantopian/> channel gives dependencies: numpy 1.9 & python 2.7 ... which kind of gets us back to the never ending package inter-dependency time-consuming discussion.
Was Ran running on python 3.6.0 (Anaconda 4.3.1 x64) or something else?

I think it was Python 3.5.2 (Anaconda 4.2.0).

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  #22 (permalink)
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andby View Post
Hi guys,

just wondering, did any of you guys actually tried to run Ran's notebook? Perhaps below happens only sometimes ... In Anaconda (Windows 10x64 - latest&greatest versions), "import talib as ta" returns module not found error ...
doing "conda info ta-lib" after adding <https://anaconda.org/Quantopian/> channel gives dependencies: numpy 1.9 & python 2.7 ... which kind of gets us back to the never ending package inter-dependency time-consuming discussion.
Was Ran running on python 3.6.0 (Anaconda 4.3.1 x64) or something else?

Keep in mind that Anaconda is just used as a convenience. There's no reason why you can't simply install each package as required into a virtualenv. That way you're not doing a system wide update to your installed python packages, and you're also not installing a massive library of packages when all you need is a handful of them.

It's fairly quick (and clean) to just create a virtualenv per project and only install what you need into it.

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Hi guys,

It is my pleasure to welcome back Ran Aroussi on Wednesday, May 24th @ 1:00PM Eastern US for another webinar.

This webinar is titled "Backtesting & Optimization Strategies w/Python", and bullet points include:

- Different types of backtesting
- How to prepare your data
- Backtesting pitfalls and how to avoid them
- Optimizing strategy parameters
- Monte Carlo optimization

Register for the event:
https://futures.io/webinars/

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Last edited by Big Mike; May 22nd, 2017 at 05:01 PM.
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Please note tomorrow's webinar is at 1 PM EDT. Not 4:30 PM EDT.

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Awesome webinar. Many thanks.
Cant wait for 3th and 4th.

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Webinar recording:



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)
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  #27 (permalink)
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What did everyone think of the webinar?

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)
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If you want
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  #28 (permalink)
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Ran posted his slides and code to github.

https://github.com/ranaroussi/futuresio-webinars

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excellent work, thanks for sharing

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Excellent Webinar Ran, Thank You, even if I am an R guy and not Python.

FYI I think when you do this...
Please register on futures.io to view futures trading content such as post attachment(s), image(s), and screenshot(s).

I think your selecting all strategies that have a minimuim equity rather than a max drawdown, hence why on your later slide where you look at MaxDD your getting values as high as -75%.

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