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Why Your Backtest Looks Better Than Your Account

Why Your Backtest Looks Better Than Your Account — Investing 101 guide cover

Idea clave

  • A backtest run on the data you designed the rule from is not evidence. It is a description of that data.
  • The tell for a fitted parameter is fragility: if 19 and 21 fail where 20 works, 20 was noise.
  • Every rule you tried and discarded is part of the experiment. Report the number of attempts, or the best result means nothing.

Basado en Evidence-Based Technical Analysis David Aronson, 2006

The gap that shows up on the day you start

The backtest keeps rising right up to the last day of the tested period, then stops. That boundary is not a coincidence.

David Aronson's Evidence-Based Technical Analysis takes an unusual position for a book about chart rules: most published technical rules, tested properly, do not survive. Not because charts are useless, but because the testing that produced them cannot distinguish a real pattern from an accident.

A rising backtest curve that goes flat where the test data endsA line climbs smoothly across the left half of the chart, then a vertical divider marks the end of the tested period and the line moves sideways and slightly down after it.tuned on thismet this laterThe left half is not evidence. It is the data the rules wereshaped to fit.
The line changes character exactly where the tested data ends. Everything left of the divider was available while the rules were being chosen, which is what makes it uninformative.

This is not dishonesty. It happens to careful people, because the process that produces it — try a rule, adjust it, try again — feels exactly like research. The adjusting is the problem.

The fastest test: check the neighbours

A real effect is broad. If only one setting works, you have found a feature of your sample, not of the market.

A single narrow performance spike beside a broad flat-topped regionTwo small charts. On the left, results peak sharply at one parameter value and collapse on either side. On the right, a wide range of values all produce a similar good result.one lucky settinga whole neighbourhooddiscardkeepIf the neighbours fail, the peak was noise
Two ways a parameter can look good. The spike on the left is the one people ship, because its peak is higher. The plateau on the right is the one that keeps working.

So when a 20-day average is the rule, test 15 and 25 as well. If they are roughly as good, the effect is probably about medium-term trend and 20 is an arbitrary point inside it. If they collapse, the 20 was reading one particular sequence of past prices.

The rules you threw away still count

This is the part Aronson is most insistent about, and the part that is easiest to skip because it feels like bookkeeping.

A grid of tested rules with a single one highlightedSixty small squares in a grid, all dim except one, which is bright. The bright square is labelled as the best result out of the whole set.60 rules tested, best one reportedWith 60 tries, a 1-in-60 fluke is the expected result,not a discovery. Report how many you tried.
Test sixty variations and the best one will look impressive whether or not any of them work. A one-in-sixty result is what sixty attempts are supposed to produce.

The instinct is to report only the survivor — the version that finally worked. But the survivor's result is only meaningful relative to how many attempts it beat. A rule that wins after three tries and one that wins after three hundred are very different claims, and the backtest output looks identical.

What gets written down

  • The final rule and its result
  • The parameter that worked
  • The chart of the good period

What makes it evidence

  • How many variants were tried first
  • How the neighbouring parameters did
  • The result on data never used to choose anything

Testing so that something is always unseen

Hold back data, fit on the rest, score only on the held-back part — then roll the window and do it again.

Four stacked rows, each with a training block followed by a test blockEach row shifts further right than the one above it. In every row a long shaded training block is followed immediately by a shorter test block.Fit here, then score on what comes nextfitscored
Walk-forward testing. Each row fits on the shaded block and is scored only on the block after it, so every score comes from data that had no influence on the rule.

This costs you something real: the scored sample is much smaller than the full history, so the result is noisier and usually worse. That is the honest version. A backtest that uses all the data and reports a beautiful number has spent its entire sample on fitting and has nothing left to test with.

  1. Split the history before you look at it. The last third is off limits.
  2. Build and tune on the first two-thirds until you are done changing things.
  3. Run once on the held-back third. Once — a second run after an adjustment makes it in-sample too.
  4. If the result is much worse, that is the finding. Do not go back and tune until it improves.

If you are not running a backtest at all

Most beginners never open backtesting software, and this still applies to them — because looking at a chart and deciding a pattern works is a backtest, just an unrecorded one with no discipline about attempts.

Eyeballing history has every flaw described above plus one more: you cannot see the cases you skipped past. The practical translation is to write the rule down before you scan for examples, then count the failures with the same energy you spent finding the successes. That is also what a trading journal is for, and why pattern reliability is a measured number rather than a felt one.

Prueba esta semana

  • Take one rule you believe in and write down its parameters before testing anything.
  • Test the two neighbouring values on each parameter. If they fail, mark the rule as unproven.
  • Count how many variations you have tried on this idea in total, including the ones you abandoned.
  • Set aside the most recent six months of data and do not look at it until the rule is final.

Preguntas frecuentes

What is curve fitting in trading?

Adjusting a strategy's rules or parameters until they fit the historical data used to develop them. The result describes that specific history rather than any repeating market behaviour, so it does not carry forward.

What is the difference between in-sample and out-of-sample testing?

In-sample data is what the rules were built and tuned on, so good results there are expected and prove nothing. Out-of-sample data is held back and used once, after the rules are final, which is the only part that tests anything.

How do I know if my backtest is overfitted?

Check the parameters either side of the ones you chose. A robust rule performs similarly across a range of nearby settings; an overfitted one falls apart as soon as you move a parameter slightly.

How much data do I need for a reliable backtest?

Enough to contain many independent examples of the thing you are testing, and more than one kind of market. A rule tested only across a long bull run has not been tested against the conditions that break it, however many years it covers.

Is walk-forward testing better than a single backtest?

Yes, because every score comes from data that had no role in choosing the rules, and it produces several such scores rather than one. It reports lower returns than a single all-data backtest, and that lower number is the more honest estimate.

Reading about a system is not having one.

Plutux is where you write your rules down, test them against real data, and keep the record your memory would otherwise rewrite. Join the waitlist for early access.

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