🚀 I build financial data pipelines, apply machine learning for quantitative investing/trading and develop API-ETL workflows for real-world portfolio insights.

💼 Financial Data Engineer | Quant Investing | API Workflows & ETL | Portfolio Optimization | Algo Trading

I'm passionate about turning raw financial data into intelligent, actionable investing strategies.
I specialize in:

• 🏗️ Designing ETL workflows that pull and structure financial data from APIs into SQL DB
• 🤖 Applying machine learning for portfolio construction
• 📊 Building scalable tools for data-driven investing/trading & quantitative research

• ⚙️ Enhancing & optimizing existing data pipeline repositories
• 🔌 Developing new API-to-SQL ETL frameworks
• 🧠 Creating ML-based portfolio analytics and investing/trading strategies
• 🧪 Testing robust methods for quant signals, optimization and backtesting

🌟 sws-api-etl-orchestration
ETL pipeline that extracts structured financial data from financial API, loads it into PostgreSQL, and enables further downstream stock analysis.

🌟 sws-api-etl-nlp-bi-end2end
A complete workflow: from API data acquisition to SQL storage, NLP/Regex data enhancement, portfolio analytics/optimization & BI visualization dashboards.

🌟 yahooquery-etl-postgresql
Flexible Python ETL project that fetches historical financial market data from Yahoo Finance, transforms and stores it in SQL DB for further analysis.

💡 ml-pipeline-sharpe_ratio-universe Linear price targets for various window settings plus universe of Sharpe ratio in & out of sample.

Languages:

Libraries & Data Handling

Machine Learning & Optimization

Database & ETL

Visualization & BI

Other:

• APIs (GraphQL/REST)
• Financial Modeling
• Time Series Analysis
• Machine Learning for Quant
• Data Warehousing & ETL

📧 Email: nicholas.papadimitris@gmail.com
💼 LinkedIn: nicholas-papadimitris

"Vibe coding my way to the top 🚀"