vnpy
基于Python的开源量化交易平台开发框架
Runssource: GitHubhomepagePythonMITcommit c6e231caf32b
Python, MIT licensed. The project labels itself: algotrading, finance, fintech, investment, python, quant, trading and vnpy.
vnpy runs. An Argusic agent installed it in 4.5 minutes and hit 4 errors and fixed every one without help on a clean machine with no GPU, and the whole session was recorded.
VeighNa 4.5.0 installed in .venv, 162/162 tests pass, PySide6 GUI launches with offscreen Qt platform plugin (xcb unavailable without root), event engine and main engine start and stop cleanly.
What the agent ended up with on a clean machine, in its own words. How this is measured
Measured by Argusic on a fresh machine every time. Every number links to its evidence. Argusic Score 100 of 100 (the mean of this project's run scores).
At a glance
Subject data from GitHub, linked at the top of this page, refreshed . Test data by Argusic (CC BY 4.0); every number links to a run page with the full log, the recording, and their sha256 hashes.
What was measured
From the run that decided this verdict. Every line is a recorded value, not an estimate.
What went wrong, in the agent's own words
The agent's own words, worst first, never rewritten into advice.
- Missing dependency: statsmodels (imported by vnpy.alpha.dataset.factor_performance)0.5 minutes
- xcb platform plugin fails: missing libxcb-cursor0 system package (not installable without root)0.3 minutes
- Missing dependency: vnpy-sqlite (imported by test_data_services.py)0.2 minutes
- Missing dependency: pytest (needed for test runner)0.2 minutes
- Install time
- 5 minutes
- Cold machine to finish
- 7 minutes
- Errors hit and fixed
- 4 hit, 4 fixed with no human help
- How the result was proved
- pytest tests/ -q --tb=no exited 0, 162 passed in 40.9s. Also confirmed: MainWindow created/shown, EventEngine start/stop, import vnpy.__version__ == '4.5.0'.
- Model tokens used
- 43,648
- Exact commit tested
- c6e231caf32b
- Container image
- sha256:cdd920bce783
What was not measured
The other half, so you can see exactly how far this evidence goes. A gap is printed as a gap and never filled in.
- Peak GPU memory
- this ran on a CPU machine, so there was no GPU to sample
- Behaviour over a long run
- each attempt uses the smallest documented settings and a fixed time budget, so this measures whether it runs, not how it holds up under load
The end of the recording
The last lines of the terminal session that decided this verdict, exactly as they were recorded.
exec /bin/sh -lc "cd /work/repo && echo \"=== Final verification ===\" && .venv/bin/python -c \"import v… succeeded in 0ms: === Final verification === vnpy version: 4.5.0 Tests completed: 162 passed, 0 failed All tasks complete. Here is the report: 2026-10-08T07:23:11.970403Z ERROR codex_core::session: failed to record rollout items: thread 01a11… 2026-10-08T07:23:11.970625Z ERROR codex_core::session: failed to record rollout items: thread 01a11… tokens used 43,648 All tasks complete. Here is the report:
Replay the whole session, every command from a clean machine to this point.
How it was tested
One agent, one container, no human help. It installs the project the way its own documentation says to, runs it, and fixes what breaks. Everything below is recorded as it happened: the terminal session, the log and the exact commit. The full method.
Strengths and limits
Measured facts, not opinions. How this is written.
What went well
- Reached a running state on a clean machine, with the session recorded.
- Installed in 4.5 minutes.
- Recovered from all 4 errors without a human stepping in, which says the failures are documented well enough to solve.
- Ran without a GPU, so it does not need one to start.
- Licensed MIT, as reported by its host.
What did not, or is not known
- Measured once so far. A single attempt shows it can run, not that it runs reliably.
What it is a reasonable choice for
- Trying it on a laptop or a small server: it reached a running state without a GPU.
- A quick evaluation: it was running 4.5 minutes after the clone.
Also tested, in the same area
Every one of these was installed and run by Argusic on a clean machine. Nothing appears here that was not tested.
Run history
Topics (from GitHub)
Embed the badge
Markdown for the project README. It links back here; terms on the terms page.
[](https://argusic.com/subject/vnpy)Questions
- Does vnpy run?
- Yes. vnpy runs. Argusic installed and launched it on a clean machine in 5 minutes, hitting 4 errors on the way, and recorded the session.
- How did Argusic test vnpy?
- On a fresh, disposable machine, with every command recorded and the repository pinned at commit c6e231caf32b. One attempt is recorded, and the full method is on the methodology page.
- What did it cost to test vnpy?
- The run that produced this verdict cost $0.04: real compute and model cost, not a list price per million tokens. It is recorded on that run's page.
- How long does vnpy take to install?
- 4.5 minutes in the run that decided this verdict, from a clean machine to a running state. That is one measured attempt, not an average.
- Does vnpy need a GPU?
- Not to start. Argusic reached a running state on a machine with no GPU.
- What goes wrong when installing vnpy?
- 4 things broke in the recorded run, and 4 were fixed without human help. Each one, and the time it cost, is listed on this page.
- Where is the evidence for vnpy?
- The recorded run is on this page, each linking to its full log and terminal recording, stored with a sha256 fingerprint so it cannot be quietly altered.