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.
Raw Input (Card Data / Image Metadata)
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Structured Ingestion & Normalization
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Validation & Heuristic Scoring
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Pricing & ROI Computation
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Relational Storage
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Dashboard Interface
- 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
Cleans and normalizes raw card rows prior to downstream processing.
Functions:
trim_outliers()blended_price()roi_pct()
Returns normalized grading scores and classification bands.
pip install -r requirements.txtpython app/cardtracker_app.pyOpen dashboard:
http://127.0.0.1:5099/dash
pytest -qThis 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.
Released under the MIT License.