b5da424dfe
The portfolio data, risk and mean-CVaR optimisation service, prepared for students who run it on their own DGX Spark without root, Docker or Tailscale. - deploy/apptainer/qc.sh: install, start/stop/status/logs, cli/api/python, data import/export and Yahoo setup/update. cuOpt and quantcore run as two Apptainer instances of the user and talk over 127.0.0.1; the API and the test bench listen on 127.0.0.1 only. Builds quantcore.sif without root, or falls back to a plain Python image plus a virtualenv when the Spark allows no unprivileged builds. Detects taken ports and suggests free ones. - deploy/apptainer/quantcore.def + constraints.txt: the image, with the library versions of the verified production container pinned. - cuOpt under Apptainer: NVIDIA's image sets HOME=/opt/cuopt, read-only in a SIF, so the solver died on its first job; qc.sh gives it a writable home. - solvers: a cuOpt that disappears during a solve now falls back to HiGHS instead of answering 500 (requests' ConnectionError escaped the fallback). - client: idempotent GETs retry a dropped keep-alive connection, which made `quantcore-api update --wait` fail when a poll met uvicorn's 5 s timeout. - config: cuOpt defaults to http://127.0.0.1:5000 and `quantcore serve` to 127.0.0.1; the Docker images keep passing their own values. - docs/SPARK_APPTAINER.md: the students' guide; README, USAGE, ARCHITECTURE and SPARK_DEPLOYMENT point to it. Tested on a DGX Spark (GB10, Apptainer 1.5.4, unprivileged user) in both modes: selftest on every endpoint, optimisation on cuOpt, Yahoo update, port conflicts; 139 tests pass in the container against the live cuOpt. Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>
50 lines
1.3 KiB
TOML
50 lines
1.3 KiB
TOML
[build-system]
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requires = ["setuptools>=68", "wheel"]
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build-backend = "setuptools.build_meta"
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[project]
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name = "quantcore"
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version = "0.1.0"
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description = "Deterministic quant core of the AI-Augmented Portfolio Intelligence Platform: data, risk/performance measurement, stock-price models, Black-Litterman expected returns and mean-CVaR MILP optimisation on NVIDIA cuOpt."
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readme = "README.md"
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requires-python = ">=3.11"
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license = { text = "MIT" }
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authors = [{ name = "Florian Herzog" }]
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dependencies = [
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"numpy>=1.26",
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"pandas>=2.1",
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"scipy>=1.11",
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"pyarrow>=14",
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"yfinance>=0.2.50",
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"requests>=2.31",
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"lxml>=4.9",
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"pydantic>=2.5",
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"pydantic-settings>=2.1",
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"fastapi>=0.109",
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"uvicorn[standard]>=0.27",
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"msgpack>=1.0",
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]
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[project.optional-dependencies]
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dev = ["pytest>=7.4", "pytest-cov", "httpx>=0.25", "ruff"]
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[project.scripts]
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quantcore = "quantcore.cli:main"
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quantcore-api = "quantcore.apicli:main"
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[tool.setuptools.packages.find]
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include = ["quantcore*"]
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[tool.setuptools.package-data]
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"quantcore.data" = ["static/*.csv", "static/*.json"]
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"quantcore.api" = ["ui/*.html", "ui/*.css", "ui/js/*.js", "ui/js/tabs/*.js"]
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[tool.pytest.ini_options]
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testpaths = ["tests"]
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addopts = "-q"
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filterwarnings = ["ignore::DeprecationWarning", "ignore::FutureWarning"]
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[tool.ruff]
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line-length = 110
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target-version = "py311"
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