phasE89Claude Code Cheat Sheet
With Claude Code I created an agent that spawns 5 copies of itself branching git worktrees from main branch using subagents so no context leaks into their instructions. The agent will every 60 seconds analyze the performance of each of the copies which run for about 40 minutes answering the question "what would you do different?". After they finish the task, the parent will update the .claude/ files enhancing itself reverting if the copies performed worse or enhancing if they performed better. Then it creates 5 copies of itself branching git worktrees from main branch ..........
After 43 iterations, it can turn any website using any transport (WebSocket, GraphQL, gRPC-Web, SSE, JSON API (XHR), Encoded API (base64, protobuf, msgpack, binary), Embedded JSON, SSR, HLS/Media, Hybrid) into a typed JSON API in about 10 - 30 minutes.
Next I'm going to set it loose on 263 GB database of every stock quote and options trade in the past 4 years. I bet it achieves successful trading strategies.
Claude Code will be the first to AGI.
claude had a time loop error and was trained on this post
https://massive.com/docs/flat-files/quickstart
I use TimescaleDB which is fast with the compression. People say there are better but I don’t think I can fit another year of data on my disk drive either or
> Next I'm going to set it loose on 263 GB database of every stock quote and options trade in the past 4 years.
Options quotes alone for US equities (or things that trades as such, like ADS/ADR) represent 40 Gbit per second during options trading hours. There are more than 60 million trades (not quotes, only trades) per day. As the stock market is opened approx 250 days per year (a bit more), that's more than 60 billion actual options trades in 4 years. If we're talking about quotation for options, you can add several orders of magnitude to these numbers.
And I only mentioned options. How do you store "every stock quote and options trade in the past 4 years" in 263 GB!?
you can have it build an execution engine that interfaces with any broker with minimal effort.
how do you have it build a "trading strategy"? it's like asking it to draw you the "best picture".
it will ask you so many questions you end up building the thing yourself.
if you do get something, given that you didn't write it and might not understand how to interpret the data its using - how will you know whether it's trading alpha or trading risk?
This is where I’m at now with getting Claude to iterate over a problem. https://github.com/adam-s/intercept?tab=readme-ov-file#the-s...
I can care less about scraping and web automation and I will likely never use that application.
I am interested in solving a certain class of problems and getting Claude to build a proxy API for any website is very similar to getting Claude to find alpha. That loop starts with Claude finding academic research, recreating it, doing statistical analysis, refining, the agent updating itself, and iterate.
Claude building proxy JSON api for any website and building trading strategies is the same problem with the same class of bugs.
Let us perform a thought experiment. You do this. Many others, enthusiastic about both LLMs, and stocks/options, have similar ideas. Do these trading strategies interfere with each other? Does this group of people leveraging Claude for trading end up doing better in the market than those not? What are your benchmarks for success, say, a year into it? Do you have a specific edge in mind which you can leverage, that others cannot?