stockcast 0.2.0

pip install --upgrade stockcast

Documentation: https://filtheo.github.io/stockcast/

Rolling forecast targets from one table, a decision-level order-variance
metric, and faster, leaner runs. No breaking change: every 0.1.0 call gives
the same result.

What's new

• Rolling targets from one table. rolling_schedule(policy, targets, inventory, ...) fits one copy of the policy per forecast origin and
  returns the policy_schedule a run needs. It checks that every origin
  falls on a decision and that every product has its target there. See
  refresh targets as forecasts roll.
• One target per decision, when you ask for it. With
  allow_target_reuse=False, run and run_comparison require a fitted
  target for every decision and name the first one without its own. By
  default a policy keeps its target until the schedule replaces it, as
  before.
• Gaps in the targets. With missing_targets="no_order", a product
  without a target at a decision places no order there, and the event table
  marks that row in the new target_missing_flag column. A constraint or
  callback that would order such a product stops the run. The default,
  "raise", stops the run before it starts, as before.
• Order variance. order_variance is the variance of order quantities
  over the decisions, zero orders included, per SKU and then averaged over
  SKUs: the order variance of
  Zhou, Arik and Wang (2023). See
  evaluation and metrics.
• Group by one column name. InventoryEvaluator.evaluate(..., groupby="unique_id") now works like groupby=["unique_id"].
• Large panels. A new recipe, run large panels,
  runs SKUs in chunks and checks that the results equal one big run.
• Faster and leaner runs. Runs that refit their targets at every
  decision are faster, and runs hold less memory. Event tables,
  histories, order frames and every fingerprint in the run manifest are
  unchanged; the manifest records the new package version.

Compatibility

Every 0.1.0 call gives the same result. The new event column
target_missing_flag appears only in runs with missing_targets="no_order",
and the new run_settings keys appear only when used:
allow_target_reuse when it is False, and missing_targets with
"no_order".

stockcast 0.1.0

pip install stockcast

Documentation: https://filtheo.github.io/stockcast/

The first public release of Stockcast: DataFrame-first inventory decisions,
simulation, and evaluation for many SKUs at once.

The 0.1.x line keeps the documented import namespaces, public outputs,
metrics, and scientific behaviour stable. Backward-compatible additions and
corrections will be recorded here. A breaking redesign will come with a new
release line and a migration note.

What's included

• Timing you can reason about. Decisions are made
  before demand, lead time may be
  zero, and a DecisionSchedule (periodic, one-time, explicit, or your own)
  says when a policy may order, separately from how much it orders.
• Synthetic demand from any distribution. DemandGenerator builds
  seeded, validated demand tables from built-in models or from your own
  sampler function, with one sampler for the whole panel or one per SKU. See
  demand and calendars.
• Policies. Order-up-to policies, ReorderPointPolicy for (s,Q) and
  (s,S) rules on any schedule, with levels from fixed values, a forecast, or
  your own target provider, single-order (newsvendor)
  decisions, and BasePolicy for your own rules. Targets can come from any
  forecaster or from a planner.
• Open orders and suppliers. Declare an opening pipeline with
  with_open_orders(...), and split orders across Supplier objects with
  fixed or random lead times, partial deliveries, and custom allocations. See
  suppliers and open orders.
• Unreliable deliveries. A DeliveryOutcome decides how much of a due
  delivery arrives, is delayed, or never arrives. See
  unreliable deliveries.
• Inventory processes. Add physical stock flows such as damage or
  inspection loss at defined phases, with FIFO shelf life built in. See
  shelf life and inventory processes.
• Comparisons in one call. run_comparison runs every option on the same
  demand, from its own copy of the same opening stock, with the same random
  supplier lead times. Every result carries a run manifest. See
  compare forecasts and policies.
• Operational rules. Ordering constraints (minimum, multiple, maximum,
  shelf space) and callbacks for planned interventions, each with an audit
  table.
• From backtest to production. The same state, policy and constraint
  objects run a periodic plan-and-close job. See
  use Stockcast in production.
• Evidence you can check. Every run produces a checked event table, a
  run manifest, callback audits, order and process-flow frames, and service
  and cost metrics.
• Documentation. A Quickstart, a nine-step Walkthrough, a Guide to
  every building block, recipes, 24 runnable example notebooks,
  and a full API reference. Every Python example in the docs is executed in
  the test suite.

Stockcast supports Python 3.10 to 3.13, NumPy 1.23 or later, pandas 1.5 or
later, and Matplotlib 3.6 or later.