Trading Analytics Research
A personal research project for quantitative analysis: market data pipelines, signal generation, and comprehensive backtesting with systematic risk controls.
Note: This is a personal research prototype, not a commercial product. Past performance does not indicate future results. Trading involves substantial risk.
The Challenge
Quantitative trading research requires rapid iteration on signal ideas with rigorous backtesting. Most retail tools lack the flexibility for custom signals or the rigor for proper evaluation.
The goal was to build a research framework that enables fast prototyping while maintaining statistical discipline—avoiding common pitfalls like look-ahead bias and overfitting.
Design Goals
- Clean separation between signal research and execution
- Rigorous backtesting with proper train/test splits
- Transaction cost modeling for realistic P&L
- Risk controls: position limits, drawdown stops
- Reproducible experiments with version control
System Architecture
Key Components
Data Pipeline
Automated ingestion of market data with quality checks, corporate action adjustments, and efficient storage in columnar format for fast analytics.
Signal Framework
Modular signal generation with standardized interfaces. Each signal is a pure function: data in, predictions out. Easy to test and combine.
Backtesting Engine
Event-driven simulation that respects causality (no look-ahead). Models market impact, slippage, and transaction costs for realistic P&L.
Evaluation Harness
Comprehensive metrics: Sharpe, Sortino, max drawdown, win rate. Statistical tests for significance. Visualization of equity curves and drawdowns.
Research Outcomes
The framework has been used to research and evaluate dozens of signal ideas. Key learnings include:
- •Most signals that look good in backtests don't survive transaction costs
- •Proper out-of-sample testing eliminates 80%+ of "alpha"
- •Risk management is more important than signal quality
- •Simple signals often beat complex ML models after costs
Need data infrastructure or analytics?
The same rigor that goes into quantitative research applies to any data-intensive system.
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