Referential Labs Blog
Practitioner-written analysis on backtest integrity, market data quality, and execution slippage. Depth over frequency, drawn from real infrastructure work.
backtesting, backtest-validation, property-based-testing, quant-infrastructureMetamorphic Relations for Backtests: Testing the Engine, Not the Strategy
Backtests have a testing problem: you don't know the correct output. Metamorphic testing sidesteps that by asking whether outputs change correctly under known input transformations. Fee monotonicity and cash invariance as sanity checks for backtest engines.
backtesting, backtest-validation, quant-infrastructure, strategy-validationA Taxonomy of Backtest Lies: Survival Bias, Lookahead Bias, and the Rest
Every backtest is biased. The question is how badly and in which direction. A working taxonomy of the biases that quietly corrupt backtesting results, with detection methods and mitigation strategies for each.
backtesting, backtest-validation, strategy-validation, quant-infrastructureWalk-Forward Optimization: Anchored vs. Rolling Windows and When Each Fails
Walk-forward validation is the backbone of out-of-sample testing for trading strategies. Anchored versus rolling windows, meta-parameter overfitting, purging and embargo, and the disciplines that stop walk-forward from becoming another way to lie to yourself.
data-engineering, market-data, quant-infrastructureMarket Data Hygiene Part 3: Reference Data and Historical Integrity
Your price data is clean, but are your corporate actions applied correctly? Is your historical data truly point-in-time? Reference data errors and historical revisionism corrupt backtests in ways that statistical checks can't catch.
data-engineering, market-data, quant-infrastructureMarket Data Hygiene Part 2: Cross-Validation and Contextual Analysis
Statistical methods catch obvious errors, but context catches the subtle ones. Learn how cross-asset validation, time-based patterns, venue awareness, and multi-source triangulation reveal data problems that outlier detection misses.
data-engineering, market-data, quant-infrastructureMarket Data Hygiene Part 1: Statistical Methods for Detecting Bad Data
Bad data doesn't announce itself. Learn the statistical methods and domain-specific heuristics that separate clean market data from the noise that corrupts your strategies.
backtesting, quant-research, methodologyAvoiding Common Backtesting Pitfalls: A Practical Guide
Your backtest shows 40% annual returns. But will it hold up in live trading? Learn to identify and fix the most common backtesting errors that inflate expectations and destroy live performance.
backtesting, paper-trading, quant-researchBacktesting vs Paper Trading: What Really Matters
Should you trust your backtest or run paper trading first? Neither fully prepares you for live trading, but each has specific use cases. Learn when to use each and how to bridge the gap.
execution, slippage, trading-infrastructureOrder Execution Slippage Analysis: Measuring and Mitigating
Your strategy makes money in backtests but loses in live trading. The difference is often execution slippage. Learn how to measure, analyze, and reduce the hidden cost of trading.
data-engineering, architecture, quant-infrastructureReal-Time Data Pipeline Architecture for Trading Systems
Your trading system needs market data with sub-second latency. Learn how to architect data pipelines that reliably deliver real-time market data from exchange to strategy.
