I designed and built a private real-time backend for collecting, validating, storing, and monitoring minute-level market data. The project is not presented as a trading-profit system. The value is in reliable data infrastructure: source trust, time-series integrity, recovery, observability, and repeatable operational reports.

Raw data ingestion is not enough for analytical or AI workflows. A live pipeline can silently fail in many ways:

duplicate rows;

missing time ranges;

stale runtime buffers;

broken WebSocket reconnects;

corrupted calculated fields;

partial history after restart;

no clear proof that downstream data is safe to use.

For AI analytics, reports, or decision-support interfaces, this is dangerous. If the data layer is unreliable, every model, dashboard, or report above it becomes questionable.

The system ingests external market data, stores both base and calculated rows, maintains ML-ready history, and exposes health/status surfaces for operation and review.

Core flow:

External WS/REST source -> Redis runtime buffers -> MongoDB collections -> Parquet / ML-ready storage -> reports and review UI

I implemented the pipeline around practical reliability concerns:

WebSocket and REST source handling.

Redis runtime buffers for live state.

MongoDB storage for base and derived time-series rows.

Parquet snapshots for offline analysis and ML-ready workflows.

Duplicate/gap controls.

Recovery and recalculation paths after restart or missing history.

Data freshness checks.

Health monitoring for API, WebSocket, Redis, MongoDB, calculation services, and runtime state.

Machine-generated reports for pipeline status and review.

The system treats the external source as a trust reference. If stored base candles fail validation against source truth, downstream calculations are blocked and recovery/recalculation starts instead of silently continuing with questionable data.

Startup checks verify that runtime dependencies are available before the system is considered healthy:

Redis readiness.

MongoDB readiness.

storage coverage.

service status.

data freshness.

time/sequence continuity.

This creates an operational contour where problems are visible early and can be repaired before they contaminate later analysis.

Python FastAPI Asyncio REST API WebSocket Redis MongoDB / Motor Parquet Docker Linux / systemd Prometheus / Grafana / Loki concepts ZeroMQ AI-assisted documentation and review

I handled the architecture and implementation as a systems architect, backend developer, and data pipeline operator.

My work included:

data lifecycle design;

pipeline architecture;

backend/API implementation;

validation and recovery logic;

monitoring and health surfaces;

operational reports;

documentation and run logic.

The result is a continuous time-series backend with source-trust guards, anti-duplicate controls, gap recovery, ML-ready storage, validation views, and operational reporting.

The same engineering pattern is useful outside market data:

API data synchronization;

analytics backends;

internal reporting pipelines;

AI/LLM data preparation;

monitoring and freshness checks;

workflow automation where silent data errors are expensive.

This project directly supports the services I offer:

AI/API Integration Diagnostic.

OpenAI / FastAPI MVP Fix.

Python Automation for Reports, Docs, or Data Checks.

Backend / Data Pipeline Monitoring Audit.

Trading rules, strategy thresholds, proprietary formulas, private calculations, credentials, account data, and sensitive strategy details are intentionally not disclosed.