Backtesting, and the four ways it lies to you

Look-ahead bias

Using information the strategy could not have had at that moment. It creeps in subtly: a daily close used for an intraday decision, an index membership list as it stands today, a data point revised after the fact. It is the most common and the most flattering error, because the strategy is effectively being told the answer.

Survivorship bias

Testing against instruments that still exist. Every asset that went to zero, delisted or was quietly removed is absent from your history, so the backtest measures how a strategy did among the winners. In crypto, where the failure rate is high and delisting is common, this is enormous and largely invisible.

Overfitting

Tuning parameters until the past looks good. Any sufficiently flexible strategy can be fitted to any history, and the more you tune, the more you are describing what happened rather than what tends to happen. The tell is a strategy that degrades sharply when a parameter moves slightly: real edges are not that delicate.

Ignoring costs

Fees, spread, slippage, and the fact that your own order moves the price. A strategy trading frequently on small edges is almost always profitable before costs and unprofitable after, and the gap is widest in exactly the thin markets that show the best backtests. Modelling the spread is not a refinement, it is the difference between a result and a fantasy.

What a backtest is actually good for

Rejecting strategies, not selecting them. A strategy that fails a backtest is genuinely dead; a strategy that passes one has merely not been eliminated yet. Treating it as a filter rather than a forecast is the honest use, and it is why forward-testing on live data with real costs matters more than another decade of history.

Live, right now, on this page

Read from a live market as this page rendered, through a public endpoint that needs no key. Read at 2026-10-18 06:12 UTC; accurate as of that time and not afterwards.

Common questions

See the number itself

Pages that read a live market and show what they read, updated while you look at them.

• S&P 500, live, as a continuously traded contract, the benchmark a strategy has to beat, quoted continuously rather than daily.
• Nvidia stock price, live, with the tape underneath it, realised volatility from hourly closes, the input a position sizer actually needs.
• Tesla stock price, live, with the week behind it, a name whose moves cluster around scheduled events, which is what breaks a naive backtest.
• Meta stock price, live, with the week's tape, a share count that shrinks through buybacks, the classic way a backtest overstates a return.
• Micron Technology stock price, live, with memory-cycle context, a commodity cycle long enough to fit entirely inside a short sample and fool it.
• SanDisk stock, live, after the split from Western Digital, a listing with almost no history, where there is nothing to backtest against yet.
• Exxon Mobil stock, live, through the tokenised share, a total return dominated by dividends, the single most common backtest error.
• Merck stock, live, through the tokenised share, a dated patent cliff, a known future step that a naive sample will not contain.