SnapBuyMarket — Sanitized Architecture Sample

Flask-based backend system implementing structured ingestion, normalization, pricing analysis, and ROI evaluation workflows for large-scale trading card datasets.

Sanitized engineering sample only — no private data, API keys, proprietary assets, or production logic included.

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Architecture Pipeline

Raw Input (Card Data / Image Metadata)
        ↓
Structured Ingestion & Normalization
        ↓
Validation & Heuristic Scoring
        ↓
Pricing & ROI Computation
        ↓
Relational Storage
        ↓
Dashboard Interface

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Technical Highlights

• Structured ingestion and normalization pipelines
• JSON/data validation and malformed input handling
• Outlier detection for pricing reliability
• Blended market price computation
• ROI calculation workflows
• Heuristic grading and classification logic
• Layered separation of ingest, pricing, and grading systems
• Deterministic Pytest validation suite
• Batch-oriented processing and structured persistence enforcement

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Core Modules

app/ingest/session.py

Cleans and normalizes raw card rows prior to downstream processing.

app/roi/pricing.py

Functions:

• trim_outliers()
• blended_price()
• roi_pct()

app/grading/heuristics.py

Returns normalized grading scores and classification bands.

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Getting Started

Install Dependencies

pip install -r requirements.txt

Run Application

python app/cardtracker_app.py

Open dashboard:

http://127.0.0.1:5099/dash

Run Tests

pytest -q

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Sanitization Notice

This repository intentionally excludes:

• Production datasets
• API credentials
• Proprietary pricing sources
• Sensitive business logic
• Private collection assets

The purpose of this repository is to demonstrate backend architecture, data workflows, and engineering structure only.

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License

Released under the MIT License.