Financial Data Engineering

Transform market data into time-series pipelines with OHLCV and indicators.

1|Updated Apr 16, 2026

Search Tags:#time-series #market-data #indicators #data-pipelines #ohlcv #financial-analysis #b3

Ready to run

npx skills add https://github.com/melissa-pereira-deel/creative-technologist-agent --skill financial-data-engineering

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: Financial Data Engineering
Source: https://github.com/melissa-pereira-deel/creative-technologist-agent/tree/main/skills/financial-data
Command: npx skills add https://github.com/melissa-pereira-deel/creative-technologist-agent --skill financial-data-engineering

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Transform messy, high-velocity market data into clean, reliable time-series pipelines.

Core Features & Use Cases

OHLCV modeling across multiple timeframes (1m, 5m, 1D, 1W, etc.)
Market data sources integration (Brapi, CVM, BCB) and data provenance
Indicator computation (MA, RSI, MACD) and Investment Clock logic
Scalable storage and governance for regulatory-ready analytics

Quick Start

Ingest a sample market feed and compute a 20-period moving average and RSI for a chosen ticker.

Frequently Asked Questions about Financial Data Engineering

High-intent search queries and answers about installing and using this skill.

FAQPage Schema

How do I build data pipelines for high-velocity market data?▼

Financial data pipelines ingest raw market feeds and transform them into clean, reliable time-series structures. This process handles messy inputs to compute technical indicators and store OHLCV feeds across multiple timeframes for fast analysis.

How do I compute technical indicators like RSI and MACD from OHLCV feeds?▼

You compute technical indicators like RSI and MACD by processing modeled OHLCV feeds within a data pipeline. The pipeline ingests market data, calculates moving averages, and outputs indicator values across configurable timeframes such as 1m or 1D.

Can I integrate Brazilian market data sources like Brapi, CVM, and BCB into time-series pipelines?▼

Yes, you can integrate Brazilian market data sources like Brapi, CVM, and BCB into time-series pipelines. The pipeline supports configurable data sources, ingesting external feeds while maintaining data provenance for regulatory-ready analytics.

Does this market data pipeline approach work with Timescale and PostgreSQL for scalable storage?▼

Yes, this market data pipeline approach works with Timescale and PostgreSQL for scalable storage. It stores computed indicators and OHLCV feeds in scalable relational databases, ensuring data governance and reliable time-series retrieval.

What is the best way to model OHLCV data across multiple timeframes?▼

The best way to model OHLCV data across multiple timeframes is to ingest raw market feeds and aggregate them into structured time-series pipelines. This ensures clean data modeling for intervals like 1m, 5m, 1D, and 1W.

How do I track data provenance for regulatory-ready financial analytics?▼

You track data provenance for regulatory-ready financial analytics by integrating market data sources and storing them in scalable Timescale or PostgreSQL databases. This ensures clean, governed time-series pipelines for compliance.