Hey everyone! It's my first post, so this is fun. I've been building a pipeline for a project I really want to start. On the surface, it's basically a Financial Analyst for myself. What I am really trying to do is push it to be as accurate as it can be through systems development. So this is the current pipeline by far:
Input the top 50 stocks of the S&P ->
Filter stocks ( process still in design ) ->
Pass all filtered stocks (if any) to Haiku ->
Filter again using qualitative data such as news and market trends ->
Stock data can be discarded, saved in a JSON file as potentially useful, or passed along ->
Pass all filtered stocks and qualitative data (if any) to a more powerful model (Sonnet or higher) ->
Use all provided data, including pattern records from the backtesting stage, to output a clear decision with a reason ->
Pass the output to another model for critiquing. If the critique would be minor, skip. Otherwise, return the critique to the previous model to revise and repeat ->
Replicate the decision in paper trading during backtesting to record performance results ->
Pass the results to an AI model to output an explanation for why this happened, along with a confidence rating (which can also be passed back into the model for a quick critique pass) ->
Record the explanation and result permanently in a JSON file ->
If the same result occurs several times and the explanation is similar, pass the data back to Sonnet to modify the core strategy.
I'm also planning to add a backtesting mechanic that uses all of the data to search for correlations between two variables, test them across several historical periods, log them if they're consistent, and forward-test them in the current market until they're fully validated as a usable pattern for the current model.
Can you list all of your suggestions and critiques? Try being helpful, not rude, etc.
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