Definition Resource Design Patterns
Guide for designing Definition resources that serve as reusable plugin templates. Definitions are the "Law" that Instances must follow.
When to Use
Apply when:
- •Creating new plugin types (PipelineDef, StoreDef, etc.)
- •Designing the interface contract a plugin must implement
- •Specifying input/output schemas for validation
- •Defining compatibility rules for upstream/downstream connections
- •Embedding guardrail contracts that enforce data quality
Core Concept: The Law
Definition resources contain the rules that Instance resources must follow:
code
Definition = The Law ├── interface_spec # Abstract interface to implement ├── input_schema # Expected input format ├── output_schema # Expected output format ├── compatibility_rules # What can connect upstream/downstream └── guardrail_contracts # Validation rules (enforced by Engine)
Definition Types
| Type | Purpose | Example Implementations |
|---|---|---|
PipelineDef | Data ingestion/transform | BloombergPipeline, FREDPipeline, ExpressionPipeline |
StoreDef | Storage backend | ParquetStore, SQLiteStore, VirtualStore |
AccessorDef | Data access pattern | SimpleAccessor, PITAccessor, FieldAccessor |
OpDef | Mathematical operator | REF, DELTA, MEAN, CORR, RANK |
OpMacroDef | Saved expression | User-defined formulas |
MLModuleDef | ML model template | XGBoostModule, LSTMModule |
PortfolioOptimizerDef | Optimization algo | MVO, HRP, BlackLitterman, RiskParity |
Schema Structure
See references/schema-structure.md for complete JSON schema.
Minimal Definition
python
definition_metadata = {
"interface_spec": "optaic.interfaces.BasePipeline",
"input_schema": {"datasets": {"type": "array"}},
"output_schema": {"type": "DataFrame", "columns": ["date", "entity", "value"]},
}
Full Definition with Contracts
python
definition_metadata = {
"interface_spec": "optaic.interfaces.SignalPipeline",
"version": "1.0",
"input_schema": {
"datasets": {"type": "array", "items": {"$ref": "#/DatasetInstance"}},
"parameters": {"type": "object", "properties": {...}}
},
"output_schema": {
"type": "DataFrame",
"columns": ["date", "entity", "value"],
"value_range": {"min": -1, "max": 1}
},
"compatibility_rules": {
"upstream_types": ["DatasetInstance"],
"downstream_types": ["SignalInstance", "BacktestInstance"]
},
"guardrail_contracts": [
{"kind": "signal.bounds", "config": {"min": -1, "max": 1, "allow_nan": False}},
{"kind": "pit.policy", "config": {"knowledge_date_required": True}},
]
}
Implementation Checklist
- • Define
interface_specpointing to abstract base class - • Specify
input_schemawith JSON Schema format - • Specify
output_schemawith expected structure - • Define
compatibility_rulesfor resource graph connections - • Embed
guardrail_contractsfor validation - • Create extension table in
libs/db/models/ - • Add to
ResourceTypeenum
Reference Files
- •Schema Structure - Complete JSON schema spec
- •Examples - Example Definition implementations