The first question about any set of price levels isn't how long it took to build. It's whether you could have known the levels in advance, whether they'd have been the same later, and whether trading them survives costs. Time spent developing something is not evidence of an edge.
The problem a levels system tries to solve
Before a session, you want reference points where price behavior can be planned for, instead of guessing in real time. KPL is YMI's version. The levels are fixed reference prices anchored to the regular session (RTH) opening print, and the standard ES and NQ map has four levels based on full-session and after-hours price travel. They don't move intraday. They aren't AI-predicted prices, institutional order-flow levels or secret support and resistance predictions, and model context is separate from the KPL calculation. The framework has been in development for some time, but I won't use a timeline as proof of anything. Only tested results count.
A fixed anchor has a practical effect. The opening print isn't known before RTH opens (9:30am ET for the cash session, holidays excepted), so a finished anchored map can't exist before then. Traders plan first and mark levels after the open. For the standard map, see the KPL strategy page.
Why fixed levels are testable
Your next step
Build the daily map before the session.
See how ES and NQ Key Price Levels fit the daily plan. Follow the room, practice in SIM, and review what happened around each level.
A system that redraws itself after the fact can look right in hindsight. Levels that are set once, by a defined rule, and then stay put can be recorded and checked afterward. That's the property you want from any levels tool. Ask these before trusting one:
- Are the levels set before you trade them, and do they stay the same for the session?
- Is the rule that generates them written down, or does it change when results disappoint?
- Is a touch defined in ticks, and is a reaction defined in points?
- Are results counted after commissions and slippage?
- Were they tested on sessions that weren't used to design the rule?
A hypothetical test, with the costs left in
Say you log 80 touches of a level system and trade each one. Hypothetically, 45% win at an average of 7 ES points and 55% lose an average of 4 points.
| Step | Points | ES dollars |
|---|---|---|
| Expectancy per trade, before costs: 0.45 x 7 - 0.55 x 4 | 0.95 | $47.50 |
| Hypothetical costs and slippage per trade | 0.50 | $25.00 |
| Expectancy after costs | 0.45 | $22.50 |
Half the apparent edge went to costs. Now the sample. If the standard deviation of a single trade is 5 points, the standard error over 80 trades is 5 / sqrt(80), about 0.56 points. A rough 95% range on the 0.45 average is about -0.65 to +1.55, which includes zero. Eighty trades did not prove anything here. And all of that is hypothetical, assuming independent trades, which markets don't deliver.
Out of sample and regimes
Split your records by time. Settle the definitions on the first part, then score the second part without changing anything. If you retune after seeing the second part, it's no longer out of sample. Also look at how results differ between trending and quiet days, and between high and low volatility, since a rule can work in one and fail in the other. Simulated fills don't validate queue position or slippage, and 50 SIM trades is not live proof. For real money, repeat the test with small size.
What this means for using KPL
KPL gives you a published list of reference prices. What you do at them, how you size, and whether it works for you are separate questions. Use the edge calculator to check the breakeven win rate for your stop and target, and keep your own log of touches. If you want the daily levels and course, the Intro trial is 7 days free, then $97/month, and includes daily ES and NQ KPL and the community.
About the Author
Founder, Young Money Investments · Quant Trader
Cameron trades ES, NQ, and futures across multiple market cycles. He founded Young Money Investments to teach systematic, data-driven trading and manages Magnum Opus Capital. His work emphasizes documented rules, risk controls, and review over outcome promises.
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