Store

skills/store owns reusable file storage for market data and generic research artifacts. The public project uses explicit strategy universes and does not maintain a central universe registry.

DataManager

from skills.store.data_manager import DataManager, DataQualityReport, validate_ohlcv
from skills.store.workspace import resolve_workspace_paths

The data root comes from QUANTSPACE_DATA_ROOT, otherwise the repository data/ directory.

resolve_workspace_paths() centralizes QUANTSPACE_WORKSPACE_ROOT, QUANTSPACE_DATA_ROOT, and QUANTSPACE_REPORTS_ROOT without creating any directories. Runtime entrypoints use it instead of modifying sys.path or independently guessing the repository root.

Supported layout:

data/market/<frequency>/<symbol>.parquet
data/adj_factor/<symbol>.parquet
data/factors/<namespace>/
data/factor_test/<namespace>/
data/correlation/
data/backtest/
data/models/
data/export/

The subdirectory below data/market/ is a storage directory/data-set key. Its base part is the real bar frequency, while an optional _adj suffix records the price-adjustment state:

Directory key	Real freq	Meaning
1d	1d	Daily unadjusted (raw) bars
1d_adj	1d	Adjusted daily bars
5m	5m	5-minute unadjusted bars
5m_adj	5m	Adjusted 5-minute bars

Therefore data/market/1d_adj/ does not represent a frequency named 1d_adj; its freq is still 1d, and 1d_adj is only the directory name indicating adjusted 1d data. The same convention applies to other bar intervals: <freq>_adj stores adjusted bars whose actual frequency is <freq>.

The current DataManager API names its directory-selector parameter frequency. Pass the full directory key to read_symbol / read_symbols / save_symbol (for example, frequency="1d_adj") so it resolves the intended path. Do not reuse that suffixed value as the semantic freq or pass it to an upstream market-data API; use freq="1d" there.

Main methods:

• read_symbol, read_symbols, save_symbol
• import_symbol_csv, import_combined_csv, list_symbols
• save_factor, read_factor, factor_namespace_dir, factor_filename
• save_factor_test, read_factor_test_summary
• save_factor_correlation, read_factor_correlation
• save_backtest_run, read_backtest_summary, read_backtest_run
• list_models, read_model_metadata

read_symbols returns a MultiIndex (symbol, eob) panel and reports every missing symbol in one FileNotFoundError.

Factor-mining Phase 02 persists research artifacts under data/factors/<namespace>/artifacts/ through DataManagerArtifactStore (path segments are validated; resolved paths must stay under that namespace) and may reuse save_factor / factor_filename for explicit wide-pivot caches keyed by content-addressed params such as cache_key. Phase 04 additionally uses DataManager.namespaced_artifact_dir / get_by_identity for controller snapshots and append-only event payloads under the same factors namespace root (no second catalog).

from skills.store.data_manager import DataManager

panel = DataManager().read_symbols(
    ["SHSE.510300", "SHSE.510500"],
    frequency="1d_adj",
)

Boundary Rules

• Keep market and generic research files here.
• Pass strategy universes as explicit symbol lists owned by the caller.
• Treat factor/backtest/model subdirectory names as artifact namespaces, not centrally managed instrument universes.
• Do not add strategy identity schemas or a second experiment catalog.