Hey I just bought your paid template, this is helping me with some concepts I was having difficulty to come up with on a solution I'm developing, doesn't have anything to do with stocks, but the general concepts are very similar. Thanks a lot!
Hi Kyle, thanks for joining my course on Udemy. There's so many interesting insights that can be incorporated into the analysis. I'm working on adding a module to the course that includes the financial metrics in the analysis as well as insights from analysts questions and management answers from latest earnings call. Would be very interested in your input on what might provide alpha especially if you have access to information such as this: site.financialmodelingprep.com/developer/docs
Just yesterday I did them (n8n) know I liked your work made with this. Good one mate. As interested in automation, AI and trading. Definitely it catched my attention
Ive found in my experience that vector data stores are very unreliable. Hit and miss as to when they work,.you need rhe chunking correct, embedding amiunt etc all to be perfect for the vector store to be able to retrieve all the info. Have you looked into this as well? Whats you experience with vector DB?
good point. I've found that larger chunk sizes work best. The improvements in LLMs with 32K and greater context windows makes larger chunk sizes more feasible. Additionally, the lower costs per token for the models also enable this. Additionally, there are techniques like reranking and using a hyde retriever helps with quality.
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Hey I just bought your paid template, this is helping me with some concepts I was having difficulty to come up with on a solution I'm developing, doesn't have anything to do with stocks, but the general concepts are very similar. Thanks a lot!
Thanks very much!
Vey excited to join your course on Udemy. Come from traditional buy side. Really excited to learn to automate the process.
Hi Kyle, thanks for joining my course on Udemy. There's so many interesting insights that can be incorporated into the analysis. I'm working on adding a module to the course that includes the financial metrics in the analysis as well as insights from analysts questions and management answers from latest earnings call. Would be very interested in your input on what might provide alpha especially if you have access to information such as this: site.financialmodelingprep.com/developer/docs
Just yesterday I did them (n8n) know I liked your work made with this. Good one mate.
As interested in automation, AI and trading. Definitely it catched my attention
Thanks Alex for your support
Ive found in my experience that vector data stores are very unreliable. Hit and miss as to when they work,.you need rhe chunking correct, embedding amiunt etc all to be perfect for the vector store to be able to retrieve all the info. Have you looked into this as well? Whats you experience with vector DB?
good point. I've found that larger chunk sizes work best. The improvements in LLMs with 32K and greater context windows makes larger chunk sizes more feasible. Additionally, the lower costs per token for the models also enable this. Additionally, there are techniques like reranking and using a hyde retriever helps with quality.
Thanks bro for sharing that, How can we implement agents in n8n with hierarchical process?
I can look at that next. Could you share a use case that I can start with?