Learning Algorithmic Trading: A Deep Dive with Brian from QuantLabs.net

Listen to this podcast about learning algorithmic trading. Geared towards those seeking to enter the exciting world of quantitative trading, Brian dives deep into insightful forum posts from quant.stackexchange.com.

Welcome, quantitative enthusiasts, to another episode featuring Brian from QuantLabs.net! Recorded on April 22nd, this episode focuses on Brian’s exploration of valuable resources for aspiring algo traders and quantitative developers. Listen to this podcast about learning algorithmic trading. Geared towards those seeking to enter the exciting world of quantitative trading, Brian dives deep into insightful forum posts from quant.stackexchange.com.


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Sparkling the Flame: A Developer’s Journey into Quant Development

The discussion ignites with a user’s query on the forum. This individual, a seasoned stack developer with three years of experience, seeks to embark on a new path – the world of algorithmic trading and quantitative development. Eager to learn, they inquire about resources, books, and the most suitable programming languages for this pursuit.

Brian promptly unveils an impressive resource – Wilmot.com. While it may initially appear solely as a job-posting platform, Brian sheds light on its hidden gem: detailed information about the companies seeking these quantitative talents. From industry giants like JP Morgan and Goldman Sachs to HSBC, the list on Wilmot.com offers a glimpse into the kind of professionals sought after in the quant space. This information proves invaluable for anyone aspiring to delve deeper into the quantitative finance industry.

Navigating the Book Maze: Top Picks for Quant Learning

Shifting gears, Brian tackles the vast collection of books recommended on quant.stackexchange.com. He acknowledges the potential for overwhelm with such an extensive list. To guide listeners through this literary labyrinth, Brian unveils his top picks. Names like Mark Joshi and Dr. Ernie Chan find their place on Brian’s recommendation list, hinting at upcoming interviews with these industry experts on his YouTube channel and podcast – exciting news for those eager to learn from the best.

Beyond the List: Essential Reads for Quantitative Finance

Brian doesn’t stop there. He expands the resource pool by recommending three additional books for those serious about quantitative finance:

  • The Concept of Practiced Mathematical Finance by Mark Joshi: This book lays a strong foundation in the mathematical concepts used extensively in quantitative finance.
  • Paul Wilmott on Quantitative Finance by Paul Wilmott: As Brian suggests, this series by Paul Wilmott serves as a comprehensive guide, encompassing various crucial aspects of quantitative finance.
  • Options, Futures, and Other Derivatives by John Hull: Understanding derivatives is instrumental in quantitative trading, and John Hull’s book provides a clear and concise explanation of these financial instruments.

For those new to the field, Brian recommends starting with Paul Wilmott’s series, offering a well-rounded introduction to the core components of quantitative finance.

Fast-Track Your Algorithmic Journey: TradersPost.io

Recognizing that not everyone may have extensive programming experience, Brian introduces TradersPost.io. This service caters to individuals seeking a quicker entry point into algorithmic trading. By offering pre-built infrastructure and tools, TradersPost.io allows users to hit the ground running with minimal coding requirements.

Join the Community, Stay Informed

Brian, ever the advocate for community building, invites listeners to join the discussion on his Discord server. This platform fosters interaction and knowledge exchange among aspiring and experienced quantitative traders. Additionally, he encourages listeners to sign up for his email list, promising exciting announcements in the pipeline.

The episode concludes with Brian expressing his hope to welcome Dr. Ernie Chan back to the show for another interview, this time focusing on his latest venture in machine learning. This prospect offers a tantalizing glimpse into the future of quantitative finance and the potential applications of machine learning in this ever-evolving field.

By combining Brian’s insights with the recommended resources and the potential of the QuantLabs.net community, listeners are well-equipped to embark on their journey into the fascinating world of algorithmic trading and quantitative development. Remember, stay tuned for Brian’s upcoming interview with Dr. Ernie Chan to gain even deeper insights into this dynamic field.





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