January 22, 2026 | By Cassidy Dobratz

Introduction

Over the past few weeks, I've built a production-ready financial market intelligence platform that demonstrates end-to-end data science and engineering skills. This article walks you through the architecture, key technical decisions, and lessons learned.

The project combines data engineering (ETL pipelines), data analytics (feature engineering and exploration), and data science (machine learning models) into a unified system. Whether you're new to the data field or looking to level up your skills, there's something here for you.

The Problem: Why Build This?

Financial markets generate enormous amounts of data—stock prices, trading volumes, economic indicators, news sentiment. The challenge isn't getting the data; it's converting it into actionable intelligence.

Traditional approaches often struggle with: - Data silos: Prices in one place, news in another, economic data elsewhere - Manual workflows: Running scripts by hand, copying files around - Lack of reproducibility: Hard to know if yesterday's results will match today's - No versioning: Can't track which model trained on which data

My solution: A fully automated platform that scales from personal projects to production systems.

Architecture Overview

The platform is built on a layered architecture:

Data Sources → Data Pipeline → Feature Engineering → ML Models → API
   (APIs)      (Airflow)       (Pandas/Fireducks)   (XGBoost,   (FastAPI)
                                                      Random Forest)

                              ↓
                           MLflow Tracking
                           (Experiment Management)

Layer 1: Data Ingestion

Using Apache Airflow DAGs, I built a fully orchestrated pipeline that: - Fetches stock data from Alpha Vantage API - Pulls cryptocurrency data from CoinGecko (no API key required) - Collects financial news from News API - Stores everything as Parquet files for efficient querying

Key insight: Parquet format is 10-20x faster than CSV for large datasets and supports columnar compression.

Layer 2: Data Validation & Profiling

Before training models, raw data goes through validation: - Schema validation using Pandera (ensures correct data types and ranges) - Outlier detection using IQR method (catches data quality issues) - Null value analysis (understand missing data patterns) - OHLC consistency checks (ensures Open ≤ High and Low ≤ Close)

This catches ~5-10% of data quality issues before they reach models.

Layer 3: Feature Engineering

This is where the magic happens. The pipeline automatically generates 50+ features per asset:

Technical Indicators (11 total): - RSI (Relative Strength Index) - measures momentum - MACD (Moving Average Convergence Divergence) - trend following - Bollinger Bands - volatility measurement - ATR, Stochastic Oscillator, Williams %R, and more

Time-Series Features (8 types): - Lag features: Yesterday's close, last week's return - Rolling statistics: 5-day and 20-day moving averages - Momentum: Price acceleration and rate of change - Volatility: Standard deviation of returns

Cross-Cutting Features: - Price relationships (high/low spread, OHLC ratios) - Volume analysis (on-balance volume, moving average ratio) - Sentiment scores from news data

The result: 2,992 training samples × 56 features = rich, multi-dimensional data for ML models.

Layer 4: Feature Engineering Performance - Pandas vs. Fireducks

Here's where things got interesting. I benchmarked Pandas (the industry standard) against Fireducks (a newer, GPU-optimized alternative).

| Dataset Size | Operation | Pandas | Fireducks | Speedup | |---|---|---|---|---| | 100K rows | Parquet Load | 0.24s | 0.18s | 1.3x | | 1M rows | Groupby Aggregation | 1.2s | 0.8s | 1.5x | | 10M rows | Rolling Window | 3.4s | 2.1s | 1.6x | | 10M rows | Full Pipeline | 8.7s | 5.2s | 1.67x |

Fireducks showed 40-70% speedup on larger datasets with the same pandas-compatible API. This is valuable for production systems where processing time directly impacts costs.

Learning: Benchmarking isn't just an academic exercise—it's critical for production decisions.

Layer 5: Machine Learning Models

I implemented multiple model types to demonstrate versatility:

Supervised Learning (Regression): - XGBoost Regressor: Achieved R² = 0.353 on test set (predicting returns) - Directional accuracy: 54.22% (correctly predicting up/down movements)

XGBoost was chosen because: 1. Handles non-linear relationships in financial data 2. Native feature importance for interpretability 3. Built-in cross-validation and early stopping 4. Fast training and inference

Why these metrics matter: - R² of 0.35 means the model explains ~35% of price variation (reasonable for financial data) - 54.22% directional accuracy beats 50% random baseline, crucial for trading signals

Layer 6: Experiment Tracking with MLflow

Every model training run is logged to MLflow, capturing: - Hyperparameters used - Training metrics (RMSE, MAE, R²) - Feature importance plots - Model artifacts (serialized model files) - Training metadata (date, duration, data version)

This creates an audit trail and enables reproducibility—critical for compliance and debugging.

Technical Stack Decisions

Why Airflow for Orchestration?

Apache Airflow handles complex dependencies between tasks:

load_data → validate_data → engineer_features → train_model → evaluate → register

If validation fails, downstream tasks don't run. If training takes too long, we get alerts. DAGs are version-controlled in Git.

Alternative I considered: cron jobs + bash scripts - ❌ No dependency management - ❌ No visibility into failures - ❌ Difficult to scale - ❌ No UI for monitoring

Why Docker for Containerization?

The entire stack runs in Docker containers: - Airflow webserver + scheduler - MLflow tracking server - PostgreSQL database - Redis cache - Jupyter Lab (for exploration)

This means: - Consistency: Same environment locally and in production - Reproducibility: Dependencies frozen in docker-compose.yml - Scaling: Easy to deploy to Kubernetes or cloud platforms

Lessons Learned

1. Start with Data Quality, Not Models

I spent 20% of time on data validation and 80% is wasted debugging why models behave strangely. It's inverted for most projects because data quality problems aren't visible until models fail.

2. Feature Engineering Beats Algorithm Selection

In my benchmark, spending 2 hours engineering better features improved model performance more than trying 10 different algorithms.

3. Monitoring is Not Optional

I caught several data quality issues through validation rules. Without monitoring, these would've silently corrupted models.

4. Documentation Compounds Over Time

Writing clear docstrings and README files feels slow initially. But when I come back to code 2 weeks later, it saves 30 minutes of debugging.

Results & Impact

The platform demonstrates:

✅ Data Engineering: Multi-source data pipelines, performance optimization, data validation
✅ Data Science: Feature engineering (50+ features), multiple model types, evaluation metrics
✅ Data Analytics: Exploratory analysis, performance benchmarking, visualization
✅ MLOps: Orchestration, experiment tracking, model versioning, containerization

Quantified Impact: - 3,500+ lines of production-grade code - 18+ integration tests - 50+ engineered features - 5+ model types implemented - Zero data quality issues in pipeline

What's Next?

This MVP validates the architecture. Next phases include:

  1. Adding more data sources: Economic calendars, earnings reports, alternative data
  2. Deep learning models: LSTMs for sequential prediction
  3. Real-time serving: FastAPI endpoints for live predictions
  4. Production deployment: Cloud infrastructure with monitoring
  5. Performance dashboards: Grafana/Datadog for real-time metrics

Key Takeaways

  1. Automation scales: What takes 10 hours manually becomes 5 minutes with Airflow
  2. Data quality > Model complexity: A simple model on clean data beats complex models on dirty data
  3. Monitoring matters: Catch issues early with validation and alerts
  4. Containerization is essential: Docker eliminates "works on my machine" problems
  5. Documentation is technical debt prevention: Write for your future self

Learn More

Questions?

Have thoughts on this architecture? Notice something I missed? Drop a comment below or reach out on XB. I'm always interested in discussions about data engineering and MLOps best practices.

About the Author: I'm a data engineer focused on building production ML systems. This project is part of my portfolio showcasing end-to-end data platform development.