Build batch and streaming market data pipelines in Python with Kafka, PostgreSQL and Parquet, covering normalization, bar aggregation, quality checks and replay.
Duration7 weeks
LevelIntermediate
Modules7
Lessons28
This course moves from foundational data handling to full market data pipelines. You will ingest tick and bar data from batch files and streaming sources, normalize it into consistent schemas, aggregate ticks into bars, and store results in both time-series tables and columnar files. The course stresses correctness: sequence gaps, late data, replay, backfills and monitoring are built into every stage.
7 modules · 28 lessons · 7 weeks. Curriculum preview; the final syllabus may change before enrollment opens.
Build a streaming pipeline that normalizes simulated trade and quote messages through Kafka
Implement a bar aggregation service that produces time, volume and dollar bars
Create a backfill and replay tool with gap detection and a monitoring dashboard
Quantzivo certificates are completion credentials issued by Quantzivo, not accredited qualifications. Courses teach technical and research skills; they do not promise investment returns or provide investment advice.
5 weeks5 modulesLearn the foundations of financial data engineering: data types, schemas, SQL, ingestion, cleaning, corporate actions and reliable batch pipelines in Python.
10 weeks10 modulesStudy and build low-latency market data systems: feed handlers, order book reconstruction, efficient serialization, lock-free design, measurement and resilient distribution.
8 weeks8 modulesDesign and build an event-driven trading system architecture with message queues, state management, risk services and replayable simulations shared between research and paper trading.
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