TradingAgents/docs/agent/decisions/012-portfolio-no-orm.md

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---
type: decision
status: active
date: 2026-03-20
agent_author: "claude"
tags: [portfolio, database, supabase, orm, prisma]
related_files:
- tradingagents/portfolio/supabase_client.py
- tradingagents/portfolio/repository.py
- tradingagents/portfolio/migrations/001_initial_schema.sql
---
## Context
When designing the Portfolio Manager data layer (Phase 1), the question arose:
should we use an ORM (specifically **Prisma**) or keep the raw `supabase-py`
client that the scaffolding already plans to use?
The options considered were:
| Option | Description |
|--------|-------------|
| **Raw `supabase-py`** (chosen) | Direct Supabase PostgREST client, builder-pattern API |
| **Prisma Python** (`prisma-client-py`) | Code-generated type-safe ORM backed by Node.js |
| **SQLAlchemy** | Full ORM with Core + ORM layers, Alembic migrations |
## The Decision
**Use raw `supabase-py` without an ORM for the portfolio data layer.**
The data access layer (`supabase_client.py`) wraps the Supabase client directly.
Our own `Portfolio`, `Holding`, `Trade`, and `PortfolioSnapshot` dataclasses
provide the type-safety layer; serialisation is handled by `to_dict()` /
`from_dict()` on each model.
## Why Not Prisma
1. **Node.js runtime dependency**`prisma-client-py` uses Prisma's Node.js
engine at code-generation time. This adds a non-Python runtime requirement
to a Python-only project.
2. **Conflicts with Supabase's migration tooling** — the project already uses
Supabase's SQL migration files (`migrations/001_initial_schema.sql`) and the
Supabase dashboard for schema changes. Prisma's `prisma migrate` maintains
its own shadow database and migration state, creating two competing systems.
3. **Code generation build step** — every schema change requires running
`prisma generate` before the Python code works. This complicates CI, local
setup, and agent-driven development.
4. **Overkill for 4 tables** — the portfolio schema has exactly 4 tables with
straightforward CRUD. Prisma's relationship traversal and complex query
features offer no benefit here.
## Why Not SQLAlchemy
1. **Not using a local database** — the database is managed by Supabase (hosted
PostgreSQL). SQLAlchemy's connection-pooling and engine management are
designed for direct database connections, which bypass Supabase's PostgREST
API and Row Level Security.
2. **Extra dependency** — SQLAlchemy + Alembic would be significant new
dependencies for a non-DB-heavy app.
## Why Raw `supabase-py` Is Sufficient
- `supabase-py` provides a clean builder-pattern API:
`client.table("holdings").select("*").eq("portfolio_id", id).execute()`
- Our dataclasses already provide compile-time type safety and lossless
serialisation; the client only handles transport.
- Migrations are plain SQL files — readable, versionable, Supabase-native.
- `SupabaseClient` is a thin singleton wrapper that translates HTTP errors into
domain exceptions — this gives us the ORM-like error-handling benefit without
the complexity.
## Constraints
- **Do not** add an ORM dependency (`prisma-client-py`, `sqlalchemy`, `tortoise-orm`)
to `pyproject.toml` without revisiting this decision.
- **Do not** bypass `SupabaseClient` by importing `supabase` directly in other
modules — always go through `PortfolioRepository`.
- If the schema grows beyond ~10 tables or requires complex multi-table joins,
revisit this decision and consider SQLAlchemy Core (not the ORM layer) with
direct `asyncpg` connections.
## Actionable Rules
- All DB access goes through `PortfolioRepository``SupabaseClient`.
- Migrations are `.sql` files in `tradingagents/portfolio/migrations/`, run via
the Supabase SQL Editor or `supabase db push`.
- Type safety comes from dataclass `to_dict()` / `from_dict()` — not from a
code-generated ORM schema.