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Hello Everyone,
I have recently been learning a bit more about financial analysis and decided to take a Udemy course on Python for Financial Analysis and Algorithmic Trading. The course is great and I recommend it to anyone interested, but it is also nearly 3 years old now and based off the Quantopian platform which has seen some major changes since the course's release.
My question now is what are the most popular tools/frameworks being used today and for what purposes? I would really appreciate some insight on a full live traders software setup just to further my understanding of the topic. For example, is it more common to use an online platform, or do more live traders install the needed libraries directly to their machines? Do people use Quantopian at all to research their data in a Notebook before porting the algorithm over here to trade with it? I also read a little about Alpaca Market, but they appear to be more of a broker than an algorithm platform?
I am just looking to broaden my understanding of the topic, and learn about what current technologies are out there. Any response is greatly appreciated since I know this is a bit of a vague question.
Thanks all and happy coding
Derek Melchin
Hi Kyle,
The specific tools/frameworks used can vary from trader to trader. QC users have the option to utilize our data feeds and co-located servers to deploy their live trading algorithms. Alternatively, our users can setup their own trading server with our open-source trading engine, LEAN. Our Medium blog post provides a great walk-through on how this can be done.
It is possible to do research on the Quantopian platform before implementing the strategy on our platform. However, we recommend using our research environment as researching on one data set and trading on another may lead to performance issues.
Alpaca provides a paper & live brokerage API. It is in beta right now on QC, but we allow our users to select the Alpaca brokerage for live trading their algorithms. For more information regarding their services, consult their documentation.
To learn more about algorithmic trading technologies, we recommend our users complete our Introduction to Financial Python tutorial series. It covers some of the popular trading libraries, including numpy and pandas.
Best,
Derek Melchin
The material on this website is provided for informational purposes only and does not constitute an offer to sell, a solicitation to buy, or a recommendation or endorsement for any security or strategy, nor does it constitute an offer to provide investment advisory services by QuantConnect. In addition, the material offers no opinion with respect to the suitability of any security or specific investment. QuantConnect makes no guarantees as to the accuracy or completeness of the views expressed in the website. The views are subject to change, and may have become unreliable for various reasons, including changes in market conditions or economic circumstances. All investments involve risk, including loss of principal. You should consult with an investment professional before making any investment decisions.
Kyle Davey
The material on this website is provided for informational purposes only and does not constitute an offer to sell, a solicitation to buy, or a recommendation or endorsement for any security or strategy, nor does it constitute an offer to provide investment advisory services by QuantConnect. In addition, the material offers no opinion with respect to the suitability of any security or specific investment. QuantConnect makes no guarantees as to the accuracy or completeness of the views expressed in the website. The views are subject to change, and may have become unreliable for various reasons, including changes in market conditions or economic circumstances. All investments involve risk, including loss of principal. You should consult with an investment professional before making any investment decisions.
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