Position & P&L Data Models — Real-time & Batch
Practical, systems-focused course to design, implement and operate position and P&L data models for trading platforms. Covers event-driven position streaming, intraday P&L waterfalls, batch reconciliation, attribution, storage patterns and governance.
At a glance
- • Real-time position aggregation (streaming) and batch reconciliation
- • P&L waterfall: MTM, cash, accruals, fees, FX, funding
- • Storage patterns: event store, OLTP state, time-series & warehouse
- • Observability, SLAs, SLOs and operational runbooks
Course overview
Position and P&L are the lingua franca of trading operations and risk. This course teaches data model design and the engineering required to produce accurate, auditable position and P&L views both intraday (real-time) and for end-of-day / historical analytics (batch).
Audience
Quant engineers, data engineers, risk analysts, ETRM implementers, platform architects, and ops engineers.
Outcomes
Design canonical models, implement streaming and batch pipelines, produce P&L waterfalls, and operationalize reconciliation and monitoring.
Prereqs
Comfort with SQL, basic programming, familiarity with trade lifecycle and valuations recommended.
Curriculum — Modules & Topics
Core modules with practical design patterns and implementation examples.
Module 1 — Canonical Position Model
- Position identity: position id, trade links, portfolio, book, instrument
- Position state vs event store (delta vs snapshot)
- Canonical schema examples (JSON/Avro) and versioning
Module 2 — P&L Waterfall & Attribution
- Define waterfall components: MTM, realized/unrealized, cash, accruals, fees, FX, financing
- Attribution to trades, strategies, desks and risk factors
- Handling corporate actions, rollovers and exercise events
Module 3 — Real-time Streaming Patterns
- Event sourcing: trade events, lifecycle events, market ticks
- Streaming aggregation strategies: tumbling vs session windows, incremental updates
- Exactly-once semantics, idempotency, deduplication and ordering
Module 4 — Batch & Warehouse Patterns
- EOD snapshot design, history tables, partitioning and clustering
- Trade-to-position ETL, SCD patterns for reference data
- Materialized views for P&L rollups and reporting
Module 5 — Valuations & Market Data Linking
- Link trades to valuations: valuation id, scenario id, curve snapshot id
- Time-series storage for valuations & risk cubes
- Handling late-arriving market data and recalculation strategies
Module 6 — Reconciliation & Exception Management
- Reconciliation types: trade-level, position-level, cash & settlement
- Auto-resolution rules, tolerances and exception queues
- Operational workflows, ticketing and audit trails
Module 7 — Operationalization & Observability
- Metrics: throughput, latency, freshness; tracing & correlation ids
- SLOs for P&L freshness and position accuracy; alerts and runbooks
- Testing: contract tests, scenario replay and synthetic trade generators
Module 8 — Storage & Performance Tradeoffs
- OLTP state vs event store vs columnar analytics
- Hot-warm-cold storage strategies, compression and retention
- Design for scale: sharding, leaderboards, and aggregation tiers
Reference architectures & patterns
High-level blueprints for real-time + batch co-existence.
Streaming-first (Hot path)
Trade events → Kafka topics → stream processors (Flink/ksql/Faust) produce incremental position & intraday P&L results, materialized into a fast-serving store (Redis/ClickHouse/ksql materialized views) for downstream apps and dashboards.
Batch reconciliation & analytics (Cold path)
End-of-day snapshots and valuation jobs into a warehouse (Snowflake/Databricks/ClickHouse) for historical P&L, attribution, regulatory reports and backtesting. Reconciliations run nightly with exception queues surfaced to ops UIs.
Hybrid: deterministic curves & versioning
Use deterministic valuation ids (market-snapshot + algorithm + version) so both hot and cold paths compute identical MTM and attribution results for auditability.
Idempotency & Ordering
Design producers & connectors with correlation ids, sequence numbers and idempotent consumers. Strategies for out-of-order events and late-arrivals (compaction, tombstones, replays).
Hands-on labs & exercises
Practical labs to build real artifacts you can run in sandboxes or cloud environments.
Lab 1 — Event Store & Real-time Position Stream
Ingest trade events (NEW/AMEND/CANCEL) to Kafka, build a streaming job that maintains incremental position per instrument/book/portfolio and materializes snapshots every 1 minute.
Lab 2 — Intraday P&L Waterfall
Implement P&L waterfall: MTM changes from market ticks, realized P&L from trades, accruals and fees. Produce per-trade and per-book attribution in streaming processor.
Lab 3 — Batch EOD Snapshot & Warehouse
Run an EOD job to persist snapshot positions, valuations into Snowflake/ClickHouse, and build materialized rollups for daily P&L reports.
Lab 4 — Reconciliation Engine
Implement reconciliation rules comparing upstream ETRM positions vs downstream accounting & settlement records; create tolerance rules and exception workflow that generates tickets (sample UI stub).
Lab 5 — Scenario Replay & Backfill
Build replay harness to reprocess a historical window through streaming jobs (recompute P&L) and validate deterministic outputs vs archived EOD results.
Lab 6 — Observability & SLOs
Instrument events with tracing and metrics (OpenTelemetry), create Grafana dashboards and configure alerts for P&L freshness and reconciliation drift thresholds.
Capstone — End-to-end Position & P&L Pipeline
Deliver a full pipeline: trade ingestion → canonicalization → streaming aggregation → intraday P&L → EOD warehouse snapshot → reconciliation report. Provide schema, streaming code (sample), SQL views and runbook.
Deliverables & materials
- Canonical position & P&L JSON/Avro schemas and ERD for batch tables
- Streaming job examples (Flink/ksql/pyFlink/Faust) and SQL views for warehouses
- Reconciliation playbook, exception UI stub, and runbooks
- Replay & backfill harness, test-data generator and solution notebooks
- Certificate: Yukti Certified Position & P&L Engineer
Pricing & delivery options
Self-paced
Recorded modules, code samples and lab guides.
Cohort (Instructor-led)
6-week cohort with live labs, code reviews and capstone feedback.
Enterprise
Private workshops, on-site integration and production runbook creation.
Contact & Custom Requests
Want an enterprise quote, private cohort, or a customized syllabus? Tell us about team size, preferred delivery and target outcomes.