Algorithmic Trading In Python Using Open Tools And Open Data
June 16th, 2019
50 mins 43 secs
About this Episode
Algorithmic trading is a field that has grown in recent years due to the availability of cheap computing and platforms that grant access to historical financial data. QuantConnect is a business that has focused on community engagement and open data access to grant opportunities for learning and growth to their users. In this episode CEO Jared Broad and senior engineer Alex Catarino explain how they have built an open source engine for testing and running algorithmic trading strategies in multiple languages, the challenges of collecting and serving currrent and historical financial data, and how they provide training and opportunity to their community members. If you are curious about the financial industry and want to try it out for yourself then be sure to listen to this episode and experiment with the QuantConnect platform for free.
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- Your host as usual is Tobias Macey and today I’m interviewing Jared Broad and Alex Catarino about QuantConnect, a platform for building and testing algorithmic trading strategies on open data and cloud resources
- How did you get introduced to Python?
- Can you start by explaining what QuantConnect is and how the business got started?
- What is your mission for the company?
- I know that there are a few other entrants in this market. Can you briefly outline how you compare to the other platforms and maybe characterize the state of the industry?
- What are the main ways that you and your customers use Python?
- For someone who is new to the space can you talk through what is involved in writing and testing a trading algorithm?
- Can you talk through how QuantConnect itself is architected and some of the products and components that comprise your overall platform?
- I noticed that your trading engine is open source. What was your motivation for making that freely available and how has it influenced your design and development of the project?
- I know that the core product is built in C# and offers a bridge to Python. Can you talk through how that is implemented?
- How do you address latency and performance when bridging those two runtimes given the time sensitivity of the problem domain?
- What are the benefits of using Python for algorithmic trading and what are its shortcomings?
- How useful and practical are machine learning techniques in this domain?
- Can you also talk through what Alpha Streams is, including what makes it unique and how it benefits the users of your platform?
- I appreciate the work that you are doing to foster a community around your platform. What are your strategies for building and supporting that interaction and how does it play into your product design?
- What are the categories of users who tend to join and engage with your community?
- What are some of the most interesting, innovative, or unexpected tactics that you have seen your users employ?
- For someone who is interested in getting started on QuantConnect what is the onboarding process like?
- What are some resources that you would recommend for someone who is interested in digging deeper into this domain?
- What are the trends in quantitative finance and algorithmic trading that you find most exciting and most concerning?
- What do you have planned for the future of QuantConnect?
Keep In Touch
- @jaredbroad on Twitter
- AlexCatarino on GitHub
- @AlexCatx on Twitter
- @QuantConnect on Twitter
- Good Omens book and miniseries
- Chernobyl HBO Series
- LEAN algorithm engine
- Alpha Streams
- Google Spanner
- Visual Studio Code
The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA