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quant-proj

quant-proj is the single active code repository for this personal quantitative research workspace. Mutable databases, raw downloads, caches, backups, and credentials live outside Git under QUANT_DATA_ROOT.

The project deliberately keeps one small runtime surface:

  • built-in defaults plus one optional external configuration file and environment-variable overrides;
  • one CLI (quant);
  • one central DuckDB access layer;
  • small A-share and US market-semantics modules;
  • one deterministic backtest core;
  • one test suite and one CI workflow.

Legacy repositories and completed experiments are historical evidence, not runtime dependencies. The complete pre-convergence tree is preserved at the GitHub Release/tag archive/pre-governance-convergence-20260721.

Local setup

python -m venv .venv
.venv/bin/pip install -e '.[dev]'
.venv/bin/pytest
.venv/bin/ruff check .

The default settings are built into the package, so quant info also works from a non-editable wheel without a copied config/ directory. The default project root is the source checkout when present and otherwise the current directory. Override paths when needed:

export QUANT_DATA_ROOT=/home/rongyu/workspace/quant-data
quant info

Select an external TOML file with quant --config PATH ... or QUANT_CONFIG.

Market execution is fail-closed: callers must explicitly attest that bar and event inputs are complete and available, and settlement/tax rules are selected from the trade date. US settlement also carries the exact accepted-session sequence; A-share custom commissions never disable statutory dated stamp tax. This is deliberately a small contract rather than a data governance subsystem.

Production market-data retrieval and broker execution are intentionally not part of this rebuild.

Repository shape

The active branch contains the runtime, generic research primitives, their tests, and the minimal architecture. Manager roadmaps, task packets, mechanism registries, terminal experiment reports, and one-off runners are intentionally kept out of the active tree.

About

Lightweight research-only quantitative system with one Python package, one CLI, deterministic backtesting, and fail-closed market/data semantics.

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