When you backtest a strategy on Well Street, you're asking one question: if I had followed these exact rules in the past, what would have happened? The backtester answers it by replaying market history at daily resolution. It steps through each trading day in order, checks your rules against the data that existed up to that day, and records every buy, sell, and portfolio value along the way.
Nothing is estimated after the fact. The simulation holds positions, applies your entry and exit rules, and compounds the results forward — the same bookkeeping you'd have done yourself, just across decades instead of weeks.
Each simulated day follows the same loop. First, the strategy's conditions are evaluated — a screen, a signal, a rebalance schedule, whatever rules you set. Second, the resulting trades are applied to the simulated portfolio at that day's prices. Third, the portfolio's value is marked and stored, building the equity curve you see when the run finishes.
Because the loop only ever sees data available on the simulated date, a rule can't accidentally peek at the future. That discipline matters more than any other design choice in the system — a backtest that leaks tomorrow's information into today's decision will look brilliant and mean nothing.
A finished backtest reports growth and risk together, because either one alone can mislead. Total return and CAGR (compound annual growth rate) tell you how fast the strategy grew. Maximum drawdown tells you the worst peak-to-trough fall along the way — the number that decides whether a real person could have stayed in the strategy. Volatility and risk-adjusted measures like the Sharpe ratio relate the reward to the ride.
Read drawdown first. Two strategies can both show 12% CAGR while one lost 20% at its worst moment and the other lost 55%. A 55% loss needs a 122% gain just to get back to even, and very few people hold on through that. The equity curve tells you what the strategy did; the drawdown tells you what it would have cost you to live through it.
Real trading isn't free, so honest simulation can't be either. Backtests should always be read with trading costs in mind: commissions, bid-ask spreads, and slippage — the gap between the price a signal saw and the price a real order would have filled at. Even small per-trade costs compound: a strategy that trades monthly and gives up a fraction of a percent per round trip can quietly lose several percentage points of annual return to friction alone.
When you compare two backtest results, check that they make the same cost assumptions. And when a high-turnover strategy narrowly beats a patient one, assume friction closes the gap until proven otherwise.
A backtest is evidence, not a promise. The clearest danger is overfitting: tune enough knobs and any strategy can be made to fit the past perfectly — and fitting the past perfectly is exactly what makes it fragile in the future. Fewer rules, tested across long periods and different market regimes, deserve more trust than a many-parameter strategy that looks flawless on one stretch of history.
Markets also change. A pattern that worked for twenty years can fade once it's widely known. That's why the honest way to hold a backtest is as a filter — it can tell you an idea would have failed, and it can tell you an idea survived history. It cannot tell you the future will cooperate. Past performance does not guarantee future results, on Well Street or anywhere else.
Well Street exists so people can share strategies and check them against evidence instead of vibes. That only works if the checking is trustworthy — so the backtester favors honest defaults over flattering ones: daily-resolution simulation, point-in-time data underneath (see the point-in-time methodology page), and risk reported next to return, never hidden behind it.
Well Street is a research and analytics platform. It doesn't execute trades, it isn't a broker, and nothing here is investment advice. The goal is simpler: when you see a strategy's history on Well Street, you can trust that history was computed fairly.
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