Portfolios

Construct an equal-weights portfolio end to end. The workflow runs in two stages: a schema-preparation step that provisions the interpolated price storage, then a run step that publishes prices, computes weights, and stores the portfolio DataNode result. It reuses the calendar from Calendars as Portfolio.calendar_uid.

For the runtime model behind these row APIs, see Core Concepts.

Two-stage equal-weights workflow

Run the portfolio workflow in two stages:

python examples/msm_portfolios/portfolio_equal_weights_prepare_schema.py
python examples/msm_portfolios/portfolio_equal_weights_run.py

The preparation script derives the configured interpolated price storage from the registered ExternalPricesStorage table and the example interpolation policy, finds or generates the real dynamic Alembic revision under the active migration namespace, and runs the dynamic provider upgrade before portfolio DataNodes write. If an older registered ExternalPricesStorage table is missing cadence metadata, the preparation step repairs that source metadata before deriving the dynamic interpolation table. The run script creates the optional portfolio Index, publishes example OHLCV source bars to ExternalPricesStorage, interpolates prices, runs SignalWeights, PortfolioWeights, and PortfoliosDataNode, creates or reuses the crypto CRYPTO_24_7 calendar, and stores the calendar, index, and DataNode UIDs on the Portfolio row. The price configuration stores the ExternalPricesStorage TimeIndexMetaTable UID on InterpolatedPricesConfig, so the explicit upstream interpolation node can recover the price source through the SDK APIDataNode lookup path. The portfolio configuration receives that InterpolatedPrices node as valuation_source_instance and sets valuation_column="close"; PortfoliosDataNode does not create interpolation storage internally. Real portfolio extensions can pass any compatible asset-indexed valuation DataNode or APIDataNode and choose any numeric valuation column, such as fair_value or nav, without reshaping the source into OHLC bars. A focused configuration example is available at examples/msm_portfolios/portfolio_custom_valuation_column_example.py:

python examples/msm_portfolios/portfolio_custom_valuation_column_example.py \
  --source-time-index-meta-table-uid <fair-value-time-index-meta-table-uid>

The source bar frequency is read from the registered source table's cadence metadata, then used with __metatable_extra_hash_components__ to select a configured output storage table, so different source cadence, upsample frequency, and interpolation rule combinations do not collide inside one price table. The script prints the workflow steps, created row UIDs, source valuation row counts, explicit valuation-source dependency details, and published DataNode storage UIDs.

Next → Pricing Instruments