Compare commits
2 Commits
ad6ee3d37f
...
2a6db2a252
| Author | SHA1 | Date | |
|---|---|---|---|
| 2a6db2a252 | |||
| 140d3085bf |
120
README.md
120
README.md
@@ -1,2 +1,120 @@
|
||||
# personal-portfolio
|
||||
# Analytics Portfolio
|
||||
|
||||
A data analytics portfolio showcasing end-to-end data engineering, visualization, and analysis capabilities.
|
||||
|
||||
## Projects
|
||||
|
||||
### Toronto Housing Dashboard
|
||||
|
||||
An interactive choropleth dashboard analyzing Toronto's housing market using multi-source data integration.
|
||||
|
||||
**Features:**
|
||||
- Purchase market analysis from TRREB monthly reports
|
||||
- Rental market analysis from CMHC annual surveys
|
||||
- Interactive choropleth maps by district/zone
|
||||
- Time series visualization with policy event annotations
|
||||
- Purchase/Rental mode toggle
|
||||
|
||||
**Data Sources:**
|
||||
- [TRREB Market Watch](https://trreb.ca/market-data/market-watch/) - Monthly purchase statistics
|
||||
- [CMHC Rental Market Survey](https://www.cmhc-schl.gc.ca/professionals/housing-markets-data-and-research/housing-data/data-tables/rental-market) - Annual rental data
|
||||
|
||||
**Tech Stack:**
|
||||
- Python 3.11+ / Dash / Plotly
|
||||
- PostgreSQL + PostGIS
|
||||
- dbt for data transformation
|
||||
- Pydantic for validation
|
||||
- SQLAlchemy 2.0
|
||||
|
||||
## Quick Start
|
||||
|
||||
```bash
|
||||
# Clone and setup
|
||||
git clone https://github.com/lmiranda/personal-portfolio.git
|
||||
cd personal-portfolio
|
||||
|
||||
# Install dependencies and configure environment
|
||||
make setup
|
||||
|
||||
# Start database
|
||||
make docker-up
|
||||
|
||||
# Initialize database schema
|
||||
make db-init
|
||||
|
||||
# Run development server
|
||||
make run
|
||||
```
|
||||
|
||||
Visit `http://localhost:8050` to view the portfolio.
|
||||
|
||||
## Project Structure
|
||||
|
||||
```
|
||||
portfolio_app/
|
||||
├── app.py # Dash app factory
|
||||
├── config.py # Pydantic settings
|
||||
├── pages/
|
||||
│ ├── home.py # Bio landing page (/)
|
||||
│ └── toronto/ # Toronto dashboard (/toronto)
|
||||
├── components/ # Shared UI components
|
||||
├── figures/ # Plotly figure factories
|
||||
└── toronto/ # Toronto data logic
|
||||
├── parsers/ # PDF/CSV extraction
|
||||
├── loaders/ # Database operations
|
||||
├── schemas/ # Pydantic models
|
||||
└── models/ # SQLAlchemy ORM
|
||||
|
||||
dbt/
|
||||
├── models/
|
||||
│ ├── staging/ # 1:1 source tables
|
||||
│ ├── intermediate/ # Business logic
|
||||
│ └── marts/ # Analytical tables
|
||||
```
|
||||
|
||||
## Development
|
||||
|
||||
```bash
|
||||
make test # Run tests
|
||||
make lint # Run linter
|
||||
make format # Format code
|
||||
make ci # Run all checks
|
||||
```
|
||||
|
||||
## Data Pipeline
|
||||
|
||||
```
|
||||
Raw Files (PDF/Excel)
|
||||
↓
|
||||
Parsers (pdfplumber, pandas)
|
||||
↓
|
||||
Pydantic Validation
|
||||
↓
|
||||
SQLAlchemy Loaders
|
||||
↓
|
||||
PostgreSQL + PostGIS
|
||||
↓
|
||||
dbt Transformations
|
||||
↓
|
||||
Dash Visualization
|
||||
```
|
||||
|
||||
## Environment Variables
|
||||
|
||||
Copy `.env.example` to `.env` and configure:
|
||||
|
||||
```bash
|
||||
DATABASE_URL=postgresql://user:pass@localhost:5432/portfolio
|
||||
POSTGRES_USER=portfolio
|
||||
POSTGRES_PASSWORD=<secure>
|
||||
POSTGRES_DB=portfolio
|
||||
DASH_DEBUG=true
|
||||
```
|
||||
|
||||
## License
|
||||
|
||||
MIT
|
||||
|
||||
## Author
|
||||
|
||||
Leo Miranda - [GitHub](https://github.com/lmiranda) | [LinkedIn](https://linkedin.com/in/yourprofile)
|
||||
|
||||
@@ -1,12 +1,31 @@
|
||||
"""Plotly figure factories for data visualization."""
|
||||
|
||||
from .choropleth import create_choropleth_figure
|
||||
from .summary_cards import create_metric_card_figure
|
||||
from .time_series import create_price_time_series, create_volume_time_series
|
||||
from .choropleth import (
|
||||
create_choropleth_figure,
|
||||
create_district_map,
|
||||
create_zone_map,
|
||||
)
|
||||
from .summary_cards import create_metric_card_figure, create_summary_metrics
|
||||
from .time_series import (
|
||||
add_policy_markers,
|
||||
create_market_comparison_chart,
|
||||
create_price_time_series,
|
||||
create_time_series_with_events,
|
||||
create_volume_time_series,
|
||||
)
|
||||
|
||||
__all__ = [
|
||||
# Choropleth
|
||||
"create_choropleth_figure",
|
||||
"create_district_map",
|
||||
"create_zone_map",
|
||||
# Time series
|
||||
"create_price_time_series",
|
||||
"create_volume_time_series",
|
||||
"create_market_comparison_chart",
|
||||
"create_time_series_with_events",
|
||||
"add_policy_markers",
|
||||
# Summary
|
||||
"create_metric_card_figure",
|
||||
"create_summary_metrics",
|
||||
]
|
||||
|
||||
@@ -231,3 +231,119 @@ def create_market_comparison_chart(
|
||||
)
|
||||
|
||||
return fig
|
||||
|
||||
|
||||
def add_policy_markers(
|
||||
fig: go.Figure,
|
||||
policy_events: list[dict[str, Any]],
|
||||
date_column: str = "event_date",
|
||||
y_position: float | None = None,
|
||||
) -> go.Figure:
|
||||
"""Add policy event markers to an existing time series figure.
|
||||
|
||||
Args:
|
||||
fig: Existing Plotly figure to add markers to.
|
||||
policy_events: List of policy event dicts with date and metadata.
|
||||
date_column: Column name for event dates.
|
||||
y_position: Y position for markers. If None, uses top of chart.
|
||||
|
||||
Returns:
|
||||
Updated Plotly Figure object with policy markers.
|
||||
"""
|
||||
if not policy_events:
|
||||
return fig
|
||||
|
||||
# Color mapping for policy categories
|
||||
category_colors = {
|
||||
"monetary": "#1f77b4", # Blue
|
||||
"tax": "#2ca02c", # Green
|
||||
"regulatory": "#ff7f0e", # Orange
|
||||
"supply": "#9467bd", # Purple
|
||||
"economic": "#d62728", # Red
|
||||
}
|
||||
|
||||
# Symbol mapping for expected direction
|
||||
direction_symbols = {
|
||||
"bullish": "triangle-up",
|
||||
"bearish": "triangle-down",
|
||||
"neutral": "circle",
|
||||
}
|
||||
|
||||
for event in policy_events:
|
||||
event_date = event.get(date_column)
|
||||
category = event.get("category", "economic")
|
||||
direction = event.get("expected_direction", "neutral")
|
||||
title = event.get("title", "Policy Event")
|
||||
level = event.get("level", "federal")
|
||||
|
||||
color = category_colors.get(category, "#666666")
|
||||
symbol = direction_symbols.get(direction, "circle")
|
||||
|
||||
# Add vertical line for the event
|
||||
fig.add_vline(
|
||||
x=event_date,
|
||||
line_dash="dot",
|
||||
line_color=color,
|
||||
opacity=0.5,
|
||||
annotation_text="",
|
||||
)
|
||||
|
||||
# Add marker with hover info
|
||||
fig.add_trace(
|
||||
go.Scatter(
|
||||
x=[event_date],
|
||||
y=[y_position] if y_position else [None], # type: ignore[list-item]
|
||||
mode="markers",
|
||||
marker={
|
||||
"symbol": symbol,
|
||||
"size": 12,
|
||||
"color": color,
|
||||
"line": {"width": 1, "color": "white"},
|
||||
},
|
||||
name=title,
|
||||
hovertemplate=(
|
||||
f"<b>{title}</b><br>"
|
||||
f"Date: %{{x}}<br>"
|
||||
f"Level: {level.title()}<br>"
|
||||
f"Category: {category.title()}<br>"
|
||||
f"<extra></extra>"
|
||||
),
|
||||
showlegend=False,
|
||||
)
|
||||
)
|
||||
|
||||
return fig
|
||||
|
||||
|
||||
def create_time_series_with_events(
|
||||
data: list[dict[str, Any]],
|
||||
policy_events: list[dict[str, Any]],
|
||||
date_column: str = "full_date",
|
||||
value_column: str = "avg_price",
|
||||
title: str = "Price Trend with Policy Events",
|
||||
) -> go.Figure:
|
||||
"""Create a time series chart with policy event markers.
|
||||
|
||||
Args:
|
||||
data: Time series data.
|
||||
policy_events: Policy events to overlay.
|
||||
date_column: Column name for dates.
|
||||
value_column: Column name for values.
|
||||
title: Chart title.
|
||||
|
||||
Returns:
|
||||
Plotly Figure with time series and policy markers.
|
||||
"""
|
||||
# Create base time series
|
||||
fig = create_price_time_series(
|
||||
data=data,
|
||||
date_column=date_column,
|
||||
price_column=value_column,
|
||||
title=title,
|
||||
)
|
||||
|
||||
# Add policy markers at the top of the chart
|
||||
if policy_events:
|
||||
fig = add_policy_markers(fig, policy_events)
|
||||
|
||||
return fig
|
||||
|
||||
20
portfolio_app/pages/health.py
Normal file
20
portfolio_app/pages/health.py
Normal file
@@ -0,0 +1,20 @@
|
||||
"""Health check endpoint for deployment monitoring."""
|
||||
|
||||
import dash
|
||||
from dash import html
|
||||
|
||||
dash.register_page(
|
||||
__name__,
|
||||
path="/health",
|
||||
title="Health Check",
|
||||
)
|
||||
|
||||
|
||||
def layout() -> html.Div:
|
||||
"""Return simple health check response."""
|
||||
return html.Div(
|
||||
[
|
||||
html.Pre("status: ok"),
|
||||
],
|
||||
id="health-check",
|
||||
)
|
||||
263
portfolio_app/pages/toronto/methodology.py
Normal file
263
portfolio_app/pages/toronto/methodology.py
Normal file
@@ -0,0 +1,263 @@
|
||||
"""Methodology page for Toronto Housing Dashboard."""
|
||||
|
||||
import dash
|
||||
import dash_mantine_components as dmc
|
||||
from dash import html
|
||||
|
||||
dash.register_page(
|
||||
__name__,
|
||||
path="/toronto/methodology",
|
||||
title="Methodology | Toronto Housing Dashboard",
|
||||
description="Data sources, methodology, and limitations for the Toronto Housing Dashboard",
|
||||
)
|
||||
|
||||
|
||||
def layout() -> dmc.Container:
|
||||
"""Render the methodology page layout."""
|
||||
return dmc.Container(
|
||||
size="md",
|
||||
py="xl",
|
||||
children=[
|
||||
# Header
|
||||
dmc.Title("Methodology", order=1, mb="lg"),
|
||||
dmc.Text(
|
||||
"This page documents the data sources, processing methodology, "
|
||||
"and known limitations of the Toronto Housing Dashboard.",
|
||||
size="lg",
|
||||
c="dimmed",
|
||||
mb="xl",
|
||||
),
|
||||
# Data Sources Section
|
||||
dmc.Paper(
|
||||
p="lg",
|
||||
radius="md",
|
||||
withBorder=True,
|
||||
mb="lg",
|
||||
children=[
|
||||
dmc.Title("Data Sources", order=2, mb="md"),
|
||||
# TRREB
|
||||
dmc.Title("Purchase Data: TRREB", order=3, size="h4", mb="sm"),
|
||||
dmc.Text(
|
||||
[
|
||||
"The Toronto Regional Real Estate Board (TRREB) publishes monthly ",
|
||||
html.Strong("Market Watch"),
|
||||
" reports containing aggregate statistics for residential real estate "
|
||||
"transactions across the Greater Toronto Area.",
|
||||
],
|
||||
mb="sm",
|
||||
),
|
||||
dmc.List(
|
||||
[
|
||||
dmc.ListItem("Source: TRREB Market Watch Reports (PDF)"),
|
||||
dmc.ListItem("Geographic granularity: ~35 TRREB Districts"),
|
||||
dmc.ListItem("Temporal granularity: Monthly"),
|
||||
dmc.ListItem("Coverage: 2021-present"),
|
||||
dmc.ListItem(
|
||||
[
|
||||
"Metrics: Sales count, average/median price, new listings, ",
|
||||
"active listings, days on market, sale-to-list ratio",
|
||||
]
|
||||
),
|
||||
],
|
||||
mb="md",
|
||||
),
|
||||
dmc.Anchor(
|
||||
"TRREB Market Watch Archive",
|
||||
href="https://trreb.ca/market-data/market-watch/market-watch-archive/",
|
||||
target="_blank",
|
||||
mb="lg",
|
||||
),
|
||||
# CMHC
|
||||
dmc.Title(
|
||||
"Rental Data: CMHC", order=3, size="h4", mb="sm", mt="md"
|
||||
),
|
||||
dmc.Text(
|
||||
[
|
||||
"Canada Mortgage and Housing Corporation (CMHC) conducts the annual ",
|
||||
html.Strong("Rental Market Survey"),
|
||||
" providing rental market statistics for major urban centres.",
|
||||
],
|
||||
mb="sm",
|
||||
),
|
||||
dmc.List(
|
||||
[
|
||||
dmc.ListItem("Source: CMHC Rental Market Survey (Excel)"),
|
||||
dmc.ListItem(
|
||||
"Geographic granularity: ~20 CMHC Zones (Census Tract aligned)"
|
||||
),
|
||||
dmc.ListItem(
|
||||
"Temporal granularity: Annual (October survey)"
|
||||
),
|
||||
dmc.ListItem("Coverage: 2021-present"),
|
||||
dmc.ListItem(
|
||||
[
|
||||
"Metrics: Average/median rent, vacancy rate, universe count, ",
|
||||
"turnover rate, year-over-year rent change",
|
||||
]
|
||||
),
|
||||
],
|
||||
mb="md",
|
||||
),
|
||||
dmc.Anchor(
|
||||
"CMHC Housing Market Information Portal",
|
||||
href="https://www.cmhc-schl.gc.ca/professionals/housing-markets-data-and-research/housing-data/data-tables/rental-market",
|
||||
target="_blank",
|
||||
),
|
||||
],
|
||||
),
|
||||
# Geographic Considerations
|
||||
dmc.Paper(
|
||||
p="lg",
|
||||
radius="md",
|
||||
withBorder=True,
|
||||
mb="lg",
|
||||
children=[
|
||||
dmc.Title("Geographic Considerations", order=2, mb="md"),
|
||||
dmc.Alert(
|
||||
title="Important: Non-Aligned Geographies",
|
||||
color="yellow",
|
||||
mb="md",
|
||||
children=[
|
||||
"TRREB Districts and CMHC Zones do ",
|
||||
html.Strong("not"),
|
||||
" align geographically. They are displayed as separate layers and "
|
||||
"should not be directly compared at the sub-regional level.",
|
||||
],
|
||||
),
|
||||
dmc.Text(
|
||||
"The dashboard presents three geographic layers:",
|
||||
mb="sm",
|
||||
),
|
||||
dmc.List(
|
||||
[
|
||||
dmc.ListItem(
|
||||
[
|
||||
html.Strong("TRREB Districts (~35): "),
|
||||
"Used for purchase/sales data visualization. "
|
||||
"Districts are defined by TRREB and labeled with codes like W01, C01, E01.",
|
||||
]
|
||||
),
|
||||
dmc.ListItem(
|
||||
[
|
||||
html.Strong("CMHC Zones (~20): "),
|
||||
"Used for rental data visualization. "
|
||||
"Zones are aligned with Census Tract boundaries.",
|
||||
]
|
||||
),
|
||||
dmc.ListItem(
|
||||
[
|
||||
html.Strong("City Neighbourhoods (158): "),
|
||||
"Reference overlay only. "
|
||||
"These are official City of Toronto neighbourhood boundaries.",
|
||||
]
|
||||
),
|
||||
],
|
||||
),
|
||||
],
|
||||
),
|
||||
# Policy Events
|
||||
dmc.Paper(
|
||||
p="lg",
|
||||
radius="md",
|
||||
withBorder=True,
|
||||
mb="lg",
|
||||
children=[
|
||||
dmc.Title("Policy Event Annotations", order=2, mb="md"),
|
||||
dmc.Text(
|
||||
"The time series charts include markers for significant policy events "
|
||||
"that may have influenced housing market conditions. These annotations are "
|
||||
"for contextual reference only.",
|
||||
mb="md",
|
||||
),
|
||||
dmc.Alert(
|
||||
title="No Causation Claims",
|
||||
color="blue",
|
||||
children=[
|
||||
"The presence of a policy marker near a market trend change does ",
|
||||
html.Strong("not"),
|
||||
" imply causation. Housing markets are influenced by numerous factors "
|
||||
"beyond policy interventions.",
|
||||
],
|
||||
),
|
||||
],
|
||||
),
|
||||
# Limitations
|
||||
dmc.Paper(
|
||||
p="lg",
|
||||
radius="md",
|
||||
withBorder=True,
|
||||
mb="lg",
|
||||
children=[
|
||||
dmc.Title("Limitations", order=2, mb="md"),
|
||||
dmc.List(
|
||||
[
|
||||
dmc.ListItem(
|
||||
[
|
||||
html.Strong("Aggregate Data: "),
|
||||
"All statistics are aggregates. Individual property characteristics, "
|
||||
"condition, and micro-location are not reflected.",
|
||||
]
|
||||
),
|
||||
dmc.ListItem(
|
||||
[
|
||||
html.Strong("Reporting Lag: "),
|
||||
"TRREB data reflects closed transactions, which may lag market "
|
||||
"conditions by 1-3 months. CMHC data is annual.",
|
||||
]
|
||||
),
|
||||
dmc.ListItem(
|
||||
[
|
||||
html.Strong("Geographic Boundaries: "),
|
||||
"TRREB district boundaries were manually digitized from reference maps "
|
||||
"and may contain minor inaccuracies.",
|
||||
]
|
||||
),
|
||||
dmc.ListItem(
|
||||
[
|
||||
html.Strong("Data Suppression: "),
|
||||
"Some cells may be suppressed for confidentiality when transaction "
|
||||
"counts are below thresholds.",
|
||||
]
|
||||
),
|
||||
],
|
||||
),
|
||||
],
|
||||
),
|
||||
# Technical Implementation
|
||||
dmc.Paper(
|
||||
p="lg",
|
||||
radius="md",
|
||||
withBorder=True,
|
||||
children=[
|
||||
dmc.Title("Technical Implementation", order=2, mb="md"),
|
||||
dmc.Text("This dashboard is built with:", mb="sm"),
|
||||
dmc.List(
|
||||
[
|
||||
dmc.ListItem("Python 3.11+ with Dash and Plotly"),
|
||||
dmc.ListItem("PostgreSQL with PostGIS for geospatial data"),
|
||||
dmc.ListItem("dbt for data transformation"),
|
||||
dmc.ListItem("Pydantic for data validation"),
|
||||
dmc.ListItem("SQLAlchemy 2.0 for database operations"),
|
||||
],
|
||||
mb="md",
|
||||
),
|
||||
dmc.Anchor(
|
||||
"View source code on GitHub",
|
||||
href="https://github.com/lmiranda/personal-portfolio",
|
||||
target="_blank",
|
||||
),
|
||||
],
|
||||
),
|
||||
# Back link
|
||||
dmc.Group(
|
||||
mt="xl",
|
||||
children=[
|
||||
dmc.Anchor(
|
||||
"← Back to Dashboard",
|
||||
href="/toronto",
|
||||
size="lg",
|
||||
),
|
||||
],
|
||||
),
|
||||
],
|
||||
)
|
||||
257
portfolio_app/toronto/demo_data.py
Normal file
257
portfolio_app/toronto/demo_data.py
Normal file
@@ -0,0 +1,257 @@
|
||||
"""Demo/sample data for testing the Toronto Housing Dashboard without full pipeline.
|
||||
|
||||
This module provides synthetic data for development and demonstration purposes.
|
||||
Replace with real data from the database in production.
|
||||
"""
|
||||
|
||||
from datetime import date
|
||||
from typing import Any
|
||||
|
||||
|
||||
def get_demo_districts() -> list[dict[str, Any]]:
|
||||
"""Return sample TRREB district data."""
|
||||
return [
|
||||
{"district_code": "W01", "district_name": "Long Branch", "area_type": "West"},
|
||||
{"district_code": "W02", "district_name": "Mimico", "area_type": "West"},
|
||||
{
|
||||
"district_code": "W03",
|
||||
"district_name": "Kingsway South",
|
||||
"area_type": "West",
|
||||
},
|
||||
{"district_code": "W04", "district_name": "Edenbridge", "area_type": "West"},
|
||||
{"district_code": "W05", "district_name": "Islington", "area_type": "West"},
|
||||
{"district_code": "W06", "district_name": "Rexdale", "area_type": "West"},
|
||||
{"district_code": "W07", "district_name": "Willowdale", "area_type": "West"},
|
||||
{"district_code": "W08", "district_name": "York", "area_type": "West"},
|
||||
{
|
||||
"district_code": "C01",
|
||||
"district_name": "Downtown Core",
|
||||
"area_type": "Central",
|
||||
},
|
||||
{"district_code": "C02", "district_name": "Annex", "area_type": "Central"},
|
||||
{
|
||||
"district_code": "C03",
|
||||
"district_name": "Forest Hill",
|
||||
"area_type": "Central",
|
||||
},
|
||||
{
|
||||
"district_code": "C04",
|
||||
"district_name": "Lawrence Park",
|
||||
"area_type": "Central",
|
||||
},
|
||||
{
|
||||
"district_code": "C06",
|
||||
"district_name": "Willowdale East",
|
||||
"area_type": "Central",
|
||||
},
|
||||
{"district_code": "C07", "district_name": "Thornhill", "area_type": "Central"},
|
||||
{"district_code": "C08", "district_name": "Waterfront", "area_type": "Central"},
|
||||
{"district_code": "E01", "district_name": "Leslieville", "area_type": "East"},
|
||||
{"district_code": "E02", "district_name": "The Beaches", "area_type": "East"},
|
||||
{"district_code": "E03", "district_name": "Danforth", "area_type": "East"},
|
||||
{"district_code": "E04", "district_name": "Birch Cliff", "area_type": "East"},
|
||||
{"district_code": "E05", "district_name": "Scarborough", "area_type": "East"},
|
||||
]
|
||||
|
||||
|
||||
def get_demo_purchase_data() -> list[dict[str, Any]]:
|
||||
"""Return sample purchase data for time series visualization."""
|
||||
import random
|
||||
|
||||
random.seed(42)
|
||||
data = []
|
||||
|
||||
base_prices = {
|
||||
"W01": 850000,
|
||||
"C01": 1200000,
|
||||
"E01": 950000,
|
||||
}
|
||||
|
||||
for year in [2024, 2025]:
|
||||
for month in range(1, 13):
|
||||
if year == 2025 and month > 12:
|
||||
break
|
||||
|
||||
for district, base_price in base_prices.items():
|
||||
# Add some randomness and trend
|
||||
trend = (year - 2024) * 12 + month
|
||||
price_variation = random.uniform(-0.05, 0.05)
|
||||
trend_factor = 1 + (trend * 0.002) # Slight upward trend
|
||||
|
||||
avg_price = int(base_price * trend_factor * (1 + price_variation))
|
||||
sales = random.randint(50, 200)
|
||||
|
||||
data.append(
|
||||
{
|
||||
"district_code": district,
|
||||
"full_date": date(year, month, 1),
|
||||
"year": year,
|
||||
"month": month,
|
||||
"avg_price": avg_price,
|
||||
"median_price": int(avg_price * 0.95),
|
||||
"sales_count": sales,
|
||||
"new_listings": int(sales * random.uniform(1.2, 1.8)),
|
||||
"active_listings": int(sales * random.uniform(2.0, 3.5)),
|
||||
"days_on_market": random.randint(15, 45),
|
||||
"sale_to_list_ratio": round(random.uniform(0.95, 1.05), 2),
|
||||
}
|
||||
)
|
||||
|
||||
return data
|
||||
|
||||
|
||||
def get_demo_rental_data() -> list[dict[str, Any]]:
|
||||
"""Return sample rental data for visualization."""
|
||||
data = []
|
||||
|
||||
zones = [
|
||||
("Zone01", "Downtown"),
|
||||
("Zone02", "Midtown"),
|
||||
("Zone03", "North York"),
|
||||
("Zone04", "Scarborough"),
|
||||
("Zone05", "Etobicoke"),
|
||||
]
|
||||
|
||||
bedroom_types = ["bachelor", "1_bedroom", "2_bedroom", "3_bedroom"]
|
||||
|
||||
base_rents = {
|
||||
"bachelor": 1800,
|
||||
"1_bedroom": 2200,
|
||||
"2_bedroom": 2800,
|
||||
"3_bedroom": 3400,
|
||||
}
|
||||
|
||||
for year in [2021, 2022, 2023, 2024, 2025]:
|
||||
for zone_code, zone_name in zones:
|
||||
for bedroom in bedroom_types:
|
||||
# Rental trend: ~5% increase per year
|
||||
year_factor = 1 + ((year - 2021) * 0.05)
|
||||
base_rent = base_rents[bedroom]
|
||||
|
||||
data.append(
|
||||
{
|
||||
"zone_code": zone_code,
|
||||
"zone_name": zone_name,
|
||||
"survey_year": year,
|
||||
"full_date": date(year, 10, 1),
|
||||
"bedroom_type": bedroom,
|
||||
"average_rent": int(base_rent * year_factor),
|
||||
"median_rent": int(base_rent * year_factor * 0.98),
|
||||
"vacancy_rate": round(
|
||||
2.5 - (year - 2021) * 0.3, 1
|
||||
), # Decreasing vacancy
|
||||
"universe": 5000 + (year - 2021) * 200,
|
||||
}
|
||||
)
|
||||
|
||||
return data
|
||||
|
||||
|
||||
def get_demo_policy_events() -> list[dict[str, Any]]:
|
||||
"""Return sample policy events for annotation."""
|
||||
return [
|
||||
{
|
||||
"event_date": date(2024, 6, 5),
|
||||
"effective_date": date(2024, 6, 5),
|
||||
"level": "federal",
|
||||
"category": "monetary",
|
||||
"title": "BoC Rate Cut (25bp)",
|
||||
"description": "Bank of Canada cuts overnight rate by 25 basis points to 4.75%",
|
||||
"expected_direction": "bullish",
|
||||
},
|
||||
{
|
||||
"event_date": date(2024, 7, 24),
|
||||
"effective_date": date(2024, 7, 24),
|
||||
"level": "federal",
|
||||
"category": "monetary",
|
||||
"title": "BoC Rate Cut (25bp)",
|
||||
"description": "Bank of Canada cuts overnight rate by 25 basis points to 4.50%",
|
||||
"expected_direction": "bullish",
|
||||
},
|
||||
{
|
||||
"event_date": date(2024, 9, 4),
|
||||
"effective_date": date(2024, 9, 4),
|
||||
"level": "federal",
|
||||
"category": "monetary",
|
||||
"title": "BoC Rate Cut (25bp)",
|
||||
"description": "Bank of Canada cuts overnight rate by 25 basis points to 4.25%",
|
||||
"expected_direction": "bullish",
|
||||
},
|
||||
{
|
||||
"event_date": date(2024, 10, 23),
|
||||
"effective_date": date(2024, 10, 23),
|
||||
"level": "federal",
|
||||
"category": "monetary",
|
||||
"title": "BoC Rate Cut (50bp)",
|
||||
"description": "Bank of Canada cuts overnight rate by 50 basis points to 3.75%",
|
||||
"expected_direction": "bullish",
|
||||
},
|
||||
{
|
||||
"event_date": date(2024, 12, 11),
|
||||
"effective_date": date(2024, 12, 11),
|
||||
"level": "federal",
|
||||
"category": "monetary",
|
||||
"title": "BoC Rate Cut (50bp)",
|
||||
"description": "Bank of Canada cuts overnight rate by 50 basis points to 3.25%",
|
||||
"expected_direction": "bullish",
|
||||
},
|
||||
{
|
||||
"event_date": date(2024, 9, 16),
|
||||
"effective_date": date(2024, 12, 15),
|
||||
"level": "federal",
|
||||
"category": "regulatory",
|
||||
"title": "CMHC 30-Year Amortization",
|
||||
"description": "30-year amortization extended to all first-time buyers and new builds",
|
||||
"expected_direction": "bullish",
|
||||
},
|
||||
{
|
||||
"event_date": date(2024, 9, 16),
|
||||
"effective_date": date(2024, 12, 15),
|
||||
"level": "federal",
|
||||
"category": "regulatory",
|
||||
"title": "Insured Mortgage Cap $1.5M",
|
||||
"description": "Insured mortgage cap raised from $1M to $1.5M",
|
||||
"expected_direction": "bullish",
|
||||
},
|
||||
]
|
||||
|
||||
|
||||
def get_demo_summary_metrics() -> dict[str, dict[str, Any]]:
|
||||
"""Return summary metrics for KPI cards."""
|
||||
return {
|
||||
"avg_price": {
|
||||
"value": 1067968,
|
||||
"title": "Avg. Price (2025)",
|
||||
"delta": -4.7,
|
||||
"delta_suffix": "%",
|
||||
"prefix": "$",
|
||||
"format_spec": ",.0f",
|
||||
"positive_is_good": True,
|
||||
},
|
||||
"total_sales": {
|
||||
"value": 67610,
|
||||
"title": "Total Sales (2024)",
|
||||
"delta": 2.6,
|
||||
"delta_suffix": "%",
|
||||
"format_spec": ",.0f",
|
||||
"positive_is_good": True,
|
||||
},
|
||||
"avg_rent": {
|
||||
"value": 2450,
|
||||
"title": "Avg. Rent (2025)",
|
||||
"delta": 3.2,
|
||||
"delta_suffix": "%",
|
||||
"prefix": "$",
|
||||
"format_spec": ",.0f",
|
||||
"positive_is_good": False,
|
||||
},
|
||||
"vacancy_rate": {
|
||||
"value": 1.8,
|
||||
"title": "Vacancy Rate",
|
||||
"delta": -0.4,
|
||||
"delta_suffix": "pp",
|
||||
"suffix": "%",
|
||||
"format_spec": ".1f",
|
||||
"positive_is_good": False,
|
||||
},
|
||||
}
|
||||
52
scripts/db/init_schema.py
Normal file
52
scripts/db/init_schema.py
Normal file
@@ -0,0 +1,52 @@
|
||||
#!/usr/bin/env python3
|
||||
"""Initialize database schema.
|
||||
|
||||
Usage:
|
||||
python scripts/db/init_schema.py
|
||||
|
||||
This script creates all SQLAlchemy tables in the database.
|
||||
Run this after docker-compose up to initialize the schema.
|
||||
"""
|
||||
|
||||
import sys
|
||||
from pathlib import Path
|
||||
|
||||
# Add project root to path
|
||||
sys.path.insert(0, str(Path(__file__).parent.parent.parent))
|
||||
|
||||
from portfolio_app.toronto.models import create_tables, get_engine # noqa: E402
|
||||
|
||||
|
||||
def main() -> int:
|
||||
"""Initialize the database schema."""
|
||||
print("Initializing database schema...")
|
||||
|
||||
try:
|
||||
engine = get_engine()
|
||||
|
||||
# Test connection
|
||||
with engine.connect() as conn:
|
||||
result = conn.execute("SELECT 1")
|
||||
result.fetchone()
|
||||
print("Database connection successful")
|
||||
|
||||
# Create all tables
|
||||
create_tables()
|
||||
print("Schema created successfully")
|
||||
|
||||
# List created tables
|
||||
from sqlalchemy import inspect
|
||||
|
||||
inspector = inspect(engine)
|
||||
tables = inspector.get_table_names()
|
||||
print(f"Created tables: {', '.join(tables)}")
|
||||
|
||||
return 0
|
||||
|
||||
except Exception as e:
|
||||
print(f"Error: {e}", file=sys.stderr)
|
||||
return 1
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
sys.exit(main())
|
||||
Reference in New Issue
Block a user