ETRM Modeling | Market Data, Curves & Time-Series Modeling | 12-module full course

Learn how professional trading and risk systems handle market data, curve building, and time-series modeling — from ingestion to real-time analytics.

Format: Self-paced + Cohort · Duration: 4–8 weeks · Level: Advanced (201/301)
https://durgaanalytics.com/etrm_marke...

Cohort - Hands-on Lab 3 — Build a Forward Curve (Bootstrapping)
Objective:
Construct a simple yield and forward curve from sample market instruments, compare interpolation
techniques, and visualize differences.
Tools:
Python / Databricks SQL / Jupyter Notebook
Steps:
1. Load rate data (Deposits, Futures, Swaps) from provided CSV.
2. Implement bootstrapping to derive discount factors.
3. Build a forward curve using interpolated forward rates.
4. Compare linear, spline, and log-linear interpolation.
5. Visualize the curves and identify smoothness differences.
6. Incorporate holiday adjustments and day count conventions.
Deliverables:
Python notebook implementing bootstrapping and interpolation
Curve visualization chart (rates vs maturity)
Validation report comparing interpolation errors (RMSE)
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This continuous 12-module course takes you step-by-step through the architecture, math, and engineering patterns behind modern market-data platforms used in banks, ETRM systems, and fintech firms.

Build real expertise in:
📊 Data ingestion pipelines (real-time + batch)
🧠 Curve construction & interpolation methods
📈 Volatility surfaces & time-series forecasting
🧪 Backtesting, validation & stress testing
🏗️ Production governance, lineage & reproducibility
⚡ Streaming, OLAP, hybrid, and caching architectures

Each lesson includes clear explanations, architecture diagrams, and real-world examples using tools like Kafka, Databricks, Snowflake, Redis, and FastAPI. By the end, you’ll be able to design your own market-data and curve-management platform with full reproducibility and low-latency analytics.

Chapters
00:00 - Course Introduction
08:57 - Module 1 — Market Data Ingestion & Pipelines
16:42 - Module 2 — Time-Series Storage Patterns
24:50 - Module 3 — Curve Construction (Rates & Commodity Forwards)
32:04 - Module 4 — Interpolation & Smoothing Techniques
42:14 - Module 5 — Volatility Surfaces & Smile Modeling
49:59 - Module 6 — Time-Series Analysis & Forecasting
58:23 - Module 7 — Backtesting & Validation
1:07:00 - Module 8 — Production Considerations & Governance
1:15:55 - Module 9 — Streaming-First Pipeline Design
1:26:05 - Module 10 — Batch + OLAP Pipeline
1:36:15 - Module 11 — Hybrid (Real-Time + Historical) Pipeline
1:42:37 - Module 12 — Curve Service & Caching

📘 About the Course

This is part of the Yukti Advanced Market Data Engineering Series — designed for professionals in data engineering, quant development, and risk technology.
Follow along to gain hands-on skills that connect data modeling, analytics, and production governance in real trading environments.

#MarketData #TimeSeries #CurveModeling #FinancialEngineering #ETRM #QuantAnalytics #Snowflake #Kafka #Databricks #RiskTechnology #DataGovernance #VolatilitySurface

market data modeling, curve construction, volatility surface, time-series forecasting, financial data engineering, databricks, snowflake, kafka streaming, dbt pipelines, ETRM, quantitative analytics, risk modeling, backtesting, data governance, hybrid pipelines, real-time data, financial systems architecture