Market Data, Curves & Time-Series Modeling
Practical, engineering-focused course on ingesting market data, building / maintaining curves, modeling time-series, volatility surfaces and production architectures that power pricing, risk and analytics for finance & energy domains.
Snapshot
- • Market data pipelines (real-time & batch), cleansing and enrichment
- • Curve construction (discount, forward, commodity forward curves) & interpolation
- • Time-series modeling: resampling, gaps, seasonality, rolling windows
- • Volatility surfaces, scenario generation, and backtesting
Why this course
Market-data engineering and time-series modeling are foundational for pricing, risk, trading signals and downstream analytics. This course balances statistical techniques (interpolation, smoothing, forecasting) with production engineering (schema design, throughput, latency, governance).
Who should attend
Data engineers, quant engineers, risk/market data teams, ETRM/Front-office integrators, and data architects.
Outcomes
Deliver robust market-data pipelines, constructed curves and volatility surfaces, time-series store patterns and backtested models ready for production use.
Prereqs
Comfortable with SQL, basic statistics, and familiarity with time-series concepts (timestamps, resampling).
Curriculum — Modules & Topics
Modular syllabus — each module contains lessons, hands-on labs, code samples and suggested readings.
Module 1 — Market Data Ingestion & Pipelines
- Data sources (exchanges, vendors, brokers), real-time vs snapshot feeds
- Message formats (FIX, CSV, JSON), schema evolution and registry
- Preprocessing: normalization, deduplication, timestamp alignment
- Latency & throughput tradeoffs; batching, micro-batching and streaming
Module 2 — Time-Series Storage Patterns
- TSDB vs columnar OLAP vs data lake: tradeoffs and when to use each
- Schema design: partitioning, clustering, retention (micro-partitions)
- Compression, downsampling, retention & rollups
Module 3 — Curve Construction (Rates & Commodity Forwards)
- Discount curves, forward curves, swap strip construction
- Bootstrapping methods, interpolation (linear, spline, log-linear), extrapolation
- Seasonality, holiday calendars, business-day conventions
Module 4 — Interpolation & Smoothing Techniques
- Linear, cubic spline, monotone cubic, Hermite, and piecewise methods
- Smoothing (LOESS), kernel regression, penalized splines
- Numerical stability, boundary behavior and choosing the right method
Module 5 — Volatility Surfaces & Smile Modeling
- Implied volatility surfaces: strikes, deltas, maturities
- Surface smoothing, arbitrage-free constraints, parameterizations (SABR)
- Local volatility and interpolated grids for pricing
Module 6 — Time-Series Analysis & Forecasting
- Resampling, missing values, forward/backward fill, interpolation
- Seasonality decomposition, rolling windows, EWMA, ARIMA basics
- Feature engineering for ML models — lags, returns, realized volatility
Module 7 — Backtesting & Validation
- Backtest frameworks, walk-forward validation, look-ahead bias avoidance
- Metrics: RMSE, MAE, hit-rate, calibration and stability checks
- Scenario tests, stress scenarios and sensitivity analysis
Module 8 — Production Considerations & Governance
- Schema governance, lineage, provenance and data quality checks
- Monitoring: data completeness, freshness SLAs, alerts and dashboards
- Performance tuning, caching strategies and reproducible curve generation
Reference Architectures & Patterns
Blueprints for real-world market-data systems.
Streaming-first pipeline
Use Kafka (topics per instrument/series) → stream processors (ksql/Fluent/Beam) → materialized views → time-series store for real-time curves and alerts.
Batch + OLAP pipeline
Daily vendor snapshots → ETL (dbt/Databricks) → micro-partitioned warehouse (Snowflake/ClickHouse) → serving layer for pricing and analytics.
Hybrid (real-time + historical)
Hot path in TSDB for last-N days, cold path in columnar warehouse; materialize stitched views for wide-range queries and backtests.
Curve service & caching
Stateless curve builder service (inputs: market snapshot id + params) producing deterministic curve ids; cache results with TTL & versioning for reproducibility.
Hands-on Labs & Capstone
Practical exercises to build skills and deliverables you can reuse in production.
Lab 1 — Ingest & Normalize Tick/Snapshot Data
Ingest sample tick and end-of-day snapshots, normalise symbols, align timestamps and store into partitioned tables.
Lab 2 — Build Forward Curve (Bootstrapping)
Implement bootstrapping for a simple yield curve and for a commodity forward strip using Python/SQL; compare interpolation methods.
Lab 3 — Volatility Surface Construction
Construct an implied volatility surface from option quotes, apply smoothing and ensure arbitrage-free checks.
Lab 4 — Time-Series Feature Engine
Create lag features, rolling realized volatility and momentum features for a price series and persist them for model consumption.
Lab 5 — Backtest Curve Robustness
Backtest curve stability across vendor feeds and rebasing dates; produce reports and change alerts.
Capstone — Curve-as-a-Service
Deliver a deterministic service that produces curves given market snapshot id and parameters, with tests, caching and monitoring.
Deliverables & Materials
- Market-data ingestion templates & schema catalog
- Curve construction notebook (Python) covering bootstrapping & interpolation
- Volatility surface builder and smoothing utilities
- Time-series feature engine and backtest notebooks
- Operational playbooks: freshness SLAs, alerts, governance checklist
Pricing & Delivery Options
Self-paced
Recorded modules, notebooks, and lab guides.
Cohort (Instructor-led)
6-week cohort with live labs, code reviews and capstone feedback.
Enterprise
Private cohorts, on-site integration support and sandbox setup.
Contact & Custom Requests
Want an enterprise quote, private cohort, or a customized syllabus? Tell us about team size, preferred delivery and target outcomes.