Market Data Reporting Pipeline

Problem

Performance tracking that lives in a spreadsheet is a recurring manual task that silently rots: someone forgets a week, a formula gets dragged wrong, and the history is no longer trustworthy. I wanted the reporting to happen whether or not anyone remembered to do it.

Approach

The same shape as any production reporting system, scaled down:

Ingest → store → compute → render → deliver → monitor

• Ingest — pull prices and corporate actions from [source] on a schedule, with retries and a raw-response cache so a failed run never loses a day.
• Store — append-only price history plus a positions table, so any past report can be regenerated exactly.
• Compute — time-weighted returns, contribution by holding, drawdown, and benchmark comparison in pandas.
• Render — a Jinja2 HTML email with inline charts; the same templates render the weekly summary at a different aggregation.
• Deliver — scheduled send, with per-recipient scoping so multiple accounts can be reported separately from the same pipeline.
• Monitor — a heartbeat and a failure email, because a silent pipeline is indistinguishable from a working one.

Architecture

scheduler ──► fetchers ──► raw cache ──► normalize ──► metrics ──► templates ──► email
    │                                                                            │
    └──────────────────── run log + failure alert ◄──────────────────────────────┘

Why this matters for analytics work

Most of the value in an analytics role is not the model — it's building the thing that produces the same correct number every morning without a human in the loop. This project is that, end to end: idempotent ingestion, replayable history, templated reporting, and alerting on its own failure.

Results

The automated pipeline has been running on a daily schedule for about two months. It's still in active development — the ingestion, reporting, and alerting paths are working end to end, and the current focus is hardening the data-quality checks and moving scheduling onto a dedicated task runner before I put real runtime and uptime figures behind it.

Next

• Move scheduling to a proper task runner and containerize the deployment
• Add a data-quality check that blocks a report on suspicious price moves