Category: Article

What Makes a Backtest Reliable? Learn how strategy fidelity, environmental consistency, realistic frictions, fair benchmarks, and clear limits shape research.

Backtested Strategies matters more than ever in the age of AI because BTS turns AI-aggregated trading rules into backtests under a common methodology.

Learn why your backtest fails in live trading when data, timing, costs, liquidity, accounting, benchmarks, or overfitting are ignored in the simulation.

Backtest headlines can be useful, but serious investors need the methodology, structure, evidence layer, and growing research library underneath them.

Proxy backtests can answer useful questions when original markets, data, or rules cannot be replicated directly—if substitutes and limits are disclosed.

A signal can point to a pattern, but a strategy needs rules, timing, sizing, costs, accounting, benchmarks, and failure-mode review before it deserves trust.

Learn why BTS treats backtests as proving grounds, testing signals against costs, execution, portfolio accounting, benchmarks, failure modes, and uncertainty.

See why standardized backtesting methodology matters—and how clear assumptions make strategy results easier to inspect, compare, challenge, and trust.

Learn how choosing the right benchmark changes the question a backtest answers, from opportunity set and control portfolio to active overlay and trade-off.

Review the 2026 split between the January Barometer and January Confirmation Indicator, including breadth, volatility, and confirmation context for risk.