portfolio_app/toronto/loaders/__init__.py

@@ -10,7 +10,6 @@ from .dimensions import (
     load_time_dimension,
     load_trreb_districts,
 )
-from .trreb import load_trreb_purchases, load_trreb_record
 
 __all__ = [
     # Base utilities
@@ -25,8 +24,6 @@ __all__ = [
     "load_neighbourhoods",
     "load_policy_events",
     # Fact loaders
-    "load_trreb_purchases",
-    "load_trreb_record",
     "load_cmhc_rentals",
     "load_cmhc_record",
 ]

portfolio_app/toronto/loaders/trreb.py

@@ -1,129 +0,0 @@
-"""Loader for TRREB purchase data into fact_purchases."""
-
-from sqlalchemy.orm import Session
-
-from portfolio_app.toronto.models import DimTime, DimTRREBDistrict, FactPurchases
-from portfolio_app.toronto.schemas import TRREBMonthlyRecord, TRREBMonthlyReport
-
-from .base import get_session, upsert_by_key
-from .dimensions import generate_date_key
-
-
-def load_trreb_purchases(
-    report: TRREBMonthlyReport,
-    session: Session | None = None,
-) -> int:
-    """Load TRREB monthly report data into fact_purchases.
-
-    Args:
-        report: Validated TRREB monthly report containing records.
-        session: Optional existing session.
-
-    Returns:
-        Number of records loaded.
-    """
-
-    def _load(sess: Session) -> int:
-        # Get district key mapping
-        districts = sess.query(DimTRREBDistrict).all()
-        district_map = {d.district_code: d.district_key for d in districts}
-
-        # Build date key from report date
-        date_key = generate_date_key(report.report_date)
-
-        # Verify time dimension exists
-        time_dim = sess.query(DimTime).filter_by(date_key=date_key).first()
-        if not time_dim:
-            raise ValueError(
-                f"Time dimension not found for date_key {date_key}. "
-                "Load time dimension first."
-            )
-
-        records = []
-        for record in report.records:
-            district_key = district_map.get(record.area_code)
-            if not district_key:
-                # Skip records for unknown districts (e.g., aggregate rows)
-                continue
-
-            fact = FactPurchases(
-                date_key=date_key,
-                district_key=district_key,
-                sales_count=record.sales,
-                dollar_volume=record.dollar_volume,
-                avg_price=record.avg_price,
-                median_price=record.median_price,
-                new_listings=record.new_listings,
-                active_listings=record.active_listings,
-                avg_dom=record.avg_dom,
-                avg_sp_lp=record.avg_sp_lp,
-            )
-            records.append(fact)
-
-        inserted, updated = upsert_by_key(
-            sess, FactPurchases, records, ["date_key", "district_key"]
-        )
-        return inserted + updated
-
-    if session:
-        return _load(session)
-    with get_session() as sess:
-        return _load(sess)
-
-
-def load_trreb_record(
-    record: TRREBMonthlyRecord,
-    session: Session | None = None,
-) -> int:
-    """Load a single TRREB record into fact_purchases.
-
-    Args:
-        record: Single validated TRREB monthly record.
-        session: Optional existing session.
-
-    Returns:
-        Number of records loaded (0 or 1).
-    """
-
-    def _load(sess: Session) -> int:
-        # Get district key
-        district = (
-            sess.query(DimTRREBDistrict)
-            .filter_by(district_code=record.area_code)
-            .first()
-        )
-        if not district:
-            return 0
-
-        date_key = generate_date_key(record.report_date)
-
-        # Verify time dimension exists
-        time_dim = sess.query(DimTime).filter_by(date_key=date_key).first()
-        if not time_dim:
-            raise ValueError(
-                f"Time dimension not found for date_key {date_key}. "
-                "Load time dimension first."
-            )
-
-        fact = FactPurchases(
-            date_key=date_key,
-            district_key=district.district_key,
-            sales_count=record.sales,
-            dollar_volume=record.dollar_volume,
-            avg_price=record.avg_price,
-            median_price=record.median_price,
-            new_listings=record.new_listings,
-            active_listings=record.active_listings,
-            avg_dom=record.avg_dom,
-            avg_sp_lp=record.avg_sp_lp,
-        )
-
-        inserted, updated = upsert_by_key(
-            sess, FactPurchases, [fact], ["date_key", "district_key"]
-        )
-        return inserted + updated
-
-    if session:
-        return _load(session)
-    with get_session() as sess:
-        return _load(sess)

portfolio_app/toronto/parsers/__init__.py

@@ -7,10 +7,8 @@ from .geo import (
     TRREBDistrictParser,
     load_geojson,
 )
-from .trreb import TRREBParser
 
 __all__ = [
-    "TRREBParser",
     "CMHCParser",
     # GeoJSON parsers
     "CMHCZoneParser",

portfolio_app/toronto/parsers/trreb.py

@@ -1,82 +0,0 @@
-"""TRREB PDF parser for monthly market watch reports.
-
-This module provides the structure for parsing TRREB (Toronto Regional Real Estate Board)
-monthly Market Watch PDF reports into structured data.
-"""
-
-from pathlib import Path
-from typing import Any
-
-from portfolio_app.toronto.schemas import TRREBMonthlyRecord, TRREBMonthlyReport
-
-
-class TRREBParser:
-    """Parser for TRREB Market Watch PDF reports.
-
-    TRREB publishes monthly Market Watch reports as PDFs containing:
-    - Summary statistics by area (416, 905, Total)
-    - District-level breakdowns
-    - Year-over-year comparisons
-
-    The parser extracts tabular data from these PDFs and validates
-    against the TRREBMonthlyRecord schema.
-    """
-
-    def __init__(self, pdf_path: Path) -> None:
-        """Initialize parser with path to PDF file.
-
-        Args:
-            pdf_path: Path to the TRREB Market Watch PDF file.
-        """
-        self.pdf_path = pdf_path
-        self._validate_path()
-
-    def _validate_path(self) -> None:
-        """Validate that the PDF path exists and is readable."""
-        if not self.pdf_path.exists():
-            raise FileNotFoundError(f"PDF not found: {self.pdf_path}")
-        if not self.pdf_path.suffix.lower() == ".pdf":
-            raise ValueError(f"Expected PDF file, got: {self.pdf_path.suffix}")
-
-    def parse(self) -> TRREBMonthlyReport:
-        """Parse the PDF and return structured data.
-
-        Returns:
-            TRREBMonthlyReport containing all extracted records.
-
-        Raises:
-            NotImplementedError: PDF parsing not yet implemented.
-        """
-        raise NotImplementedError(
-            "PDF parsing requires pdfplumber/tabula-py. "
-            "Implementation pending Sprint 4 data ingestion."
-        )
-
-    def _extract_tables(self) -> list[dict[str, Any]]:
-        """Extract raw tables from PDF pages.
-
-        Returns:
-            List of dictionaries representing table data.
-        """
-        raise NotImplementedError("Table extraction not yet implemented.")
-
-    def _parse_district_table(
-        self, table_data: list[dict[str, Any]]
-    ) -> list[TRREBMonthlyRecord]:
-        """Parse district-level statistics table.
-
-        Args:
-            table_data: Raw table data extracted from PDF.
-
-        Returns:
-            List of validated TRREBMonthlyRecord objects.
-        """
-        raise NotImplementedError("District table parsing not yet implemented.")
-
-    def _infer_report_date(self) -> tuple[int, int]:
-        """Infer report year and month from PDF filename or content.
-
-        Returns:
-            Tuple of (year, month).
-        """
-        raise NotImplementedError("Date inference not yet implemented.")

portfolio_app/toronto/schemas/__init__.py

@@ -13,12 +13,8 @@ from .dimensions import (
     TimeDimension,
     TRREBDistrict,
 )
-from .trreb import TRREBMonthlyRecord, TRREBMonthlyReport
 
 __all__ = [
-    # TRREB
-    "TRREBMonthlyRecord",
-    "TRREBMonthlyReport",
     # CMHC
     "CMHCRentalRecord",
     "CMHCAnnualSurvey",

portfolio_app/toronto/schemas/trreb.py

@@ -1,52 +0,0 @@
-"""Pydantic schemas for TRREB monthly market data."""
-
-from datetime import date
-from decimal import Decimal
-
-from pydantic import BaseModel, Field
-
-
-class TRREBMonthlyRecord(BaseModel):
-    """Schema for a single TRREB monthly summary record.
-
-    Represents aggregated sales data for one district in one month.
-    """
-
-    report_date: date = Field(description="First of month (YYYY-MM-01)")
-    area_code: str = Field(
-        max_length=3, description="District code (W01, C01, E01, etc.)"
-    )
-    area_name: str = Field(max_length=100, description="District name")
-    area_type: str = Field(max_length=10, description="West / Central / East / North")
-    sales: int = Field(ge=0, description="Number of transactions")
-    dollar_volume: Decimal = Field(ge=0, description="Total sales volume ($)")
-    avg_price: Decimal = Field(ge=0, description="Average sale price ($)")
-    median_price: Decimal = Field(ge=0, description="Median sale price ($)")
-    new_listings: int = Field(ge=0, description="New listings count")
-    active_listings: int = Field(ge=0, description="Active listings at month end")
-    avg_sp_lp: Decimal = Field(
-        ge=0, le=200, description="Avg sale price / list price ratio (%)"
-    )
-    avg_dom: int = Field(ge=0, description="Average days on market")
-
-    model_config = {"str_strip_whitespace": True}
-
-
-class TRREBMonthlyReport(BaseModel):
-    """Schema for a complete TRREB monthly report.
-
-    Contains all district records for a single month.
-    """
-
-    report_date: date
-    records: list[TRREBMonthlyRecord]
-
-    @property
-    def total_sales(self) -> int:
-        """Total sales across all districts."""
-        return sum(r.sales for r in self.records)
-
-    @property
-    def district_count(self) -> int:
-        """Number of districts in report."""
-        return len(self.records)