Great project — the automated equity research pipeline is impressive, especially the multi-source data aggregation.
I built Chart Library, an API that finds historically similar chart patterns and returns what happened next. It could add a "historical pattern context" section to generated research reports — e.g. "the current NVDA chart most closely resembles 10 prior setups, 8 of which were higher 5 days later."
Adding it as a data source would be straightforward:
import requests
# Returns similar patterns, forward returns, and market regime context
data = requests.get("https://chartlibrary.io/api/v1/intelligence/NVDA").json()There's also an MCP server (pip install chartlibrary-mcp) with 19 tools for more granular queries — individual pattern matches, regime analysis, specific timeframes, etc.
Free tier: 200 calls/day, no signup. Covers 15K stocks with 10 years of minute-bar data and 24M pre-computed pattern embeddings.
This is historical pattern data, not predictions — so it fits well in a research report context where you want to present what has happened before and let the reader draw conclusions. Happy to help if you want to explore it.
Great project — the automated equity research pipeline is impressive, especially the multi-source data aggregation.
I built Chart Library, an API that finds historically similar chart patterns and returns what happened next. It could add a "historical pattern context" section to generated research reports — e.g. "the current NVDA chart most closely resembles 10 prior setups, 8 of which were higher 5 days later."
Adding it as a data source would be straightforward:
There's also an MCP server (
pip install chartlibrary-mcp) with 19 tools for more granular queries — individual pattern matches, regime analysis, specific timeframes, etc.Free tier: 200 calls/day, no signup. Covers 15K stocks with 10 years of minute-bar data and 24M pre-computed pattern embeddings.
This is historical pattern data, not predictions — so it fits well in a research report context where you want to present what has happened before and let the reader draw conclusions. Happy to help if you want to explore it.