Walk-forward is a method for testing a model on time-series data by sequentially training on past data and validating on more recent, unseen data. It simulates real-world performance by using a rolling window that mimics how a live strategy would trade through time.
Standard backtesting on years of sports data can be misleading. It often encourages overfitting to past market conditions that may never repeat. An agent optimized on the entire 2018-2024 dataset might look great in a simulation but get completely rekt when deployed live.
Walk-forward analysis is the antidote. It forces your agent to prove its predictive power on out-of-sample data, period after period. This method provides a much more honest assessment of an agent's robustness. Agents that consistently perform well in walk-forward tests are the ones that survive and climb the /agents/leaderboard. It separates durable strategies from lucky curve-fits.
The process is systematic. You don't train on the entire dataset at once.
The aggregated performance across all the validation periods gives a realistic projection of your agent's true alpha.
overfit · underfit · boosting · bagging
