What do you want to do?
3 use cases
-
Run the offline research sample and open its workbench
Start the local app on bundled fixtures and inspect the staged research workbench without any external services.
View prompt
In the finathink repo, set up the environment as described in its Quick Start, then run the local app with --sample on port 8765. Report the /workbench stage list, each stage status, and the manifest fingerprint shown. Verify that the sample run reports the stages from the research flow without requiring any external credentials.
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Normalize your own data API into point-in-time batches
Save a data connection, map its fields, run a trial pull and check PIT status and fingerprints before research uses it.
View prompt
In the finathink repo, follow docs/USER_DATA_API_GUIDE.md to map the fields of <data API name> and run one explicit trial connection. Report the DataBatch fingerprint, field quality summary and PIT status. Verify that no credential value appears in any saved research record or log.
-
Trace why a paper decision card cannot be executed
Follow the risk, portfolio and quant gates to see which stage kept a paper decision card in a non-executable state.
View prompt
In the finathink repo, open the HTML report from a local sample run and list each risk, portfolio and quant stage with its status and evidence reference. Report whether the paper decision card is executable. Verify that the card is non-executable if any upstream stage failed, and state that the repo does not place broker orders.
How to use Finathink
- Check you have what it needs: Python 3.11 or later; A local copy of the repo (inferred: git clone https://github.com/ceyyy427/finathink.git); No broker, live trading account or real vendor credentials are needed for the offline sample run.
- Open ceyyy427/finathink on GitHub and read the README and license.
- Run
python3 -m venv .venv - Run
source .venv/bin/activate - Run
python3 -m pip install -e '.[dev]' - Run
PYTHONPATH=src python3 -m pytest -qto confirm the test suite passes
These steps come from our top use case, Run the offline research sample and open its workbench, which also has a copy-paste agent prompt.
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Get them freeAbout this repo
- Who it is forIndividual quant learners and researchers who want auditable, point-in-time research runs, and AI agents coordinating that research.
- Why it is hot
- LanguagePython
- Sourcegithub.com/ceyyy427/finathink · Back to Hot repos
Requirements
- Python 3.11 or later
- A local copy of the repo (inferred: git clone https://github.com/ceyyy427/finathink.git)
- No broker, live trading account or real vendor credentials are needed for the offline sample run
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Questions about Finathink
What is Finathink?
Finathink is a local-first Python workbench that turns user data, evidence, factors, backtests, risk checks and paper portfolios into a traceable research record with offline HTML reports. It is an open-source project on GitHub: https://github.com/ceyyy427/finathink.
Who is Finathink for?
Individual quant learners and researchers who want auditable, point-in-time research runs, and AI agents coordinating that research.
What can I do with Finathink?
3 use cases so far, for example: Run the offline research sample and open its workbench; Normalize your own data API into point-in-time batches; Trace why a paper decision card cannot be executed. Each has steps and a copy-paste agent prompt.
What do I need to run Finathink?
Python 3.11 or later; A local copy of the repo (inferred: git clone https://github.com/ceyyy427/finathink.git); No broker, live trading account or real vendor credentials are needed for the offline sample run.
What are alternatives to Finathink?
Other hot AI repos in Money: Northcinder, Qanat, DSH Cost Meter, Grandet, Itr Wala.
Is Finathink safe to run?
Deduped does not own, audit or vouch for it. Read the README and license, run it on your own systems first and review every command before you run it.