Same entry rule, very different trade
A price tests a reference level at 10:15 a.m. ET after a clear morning move, with a nearby target and clean structure. Another test of the same level happens at 12:45 p.m. ET in a choppy range with a target barely larger than the stop. Both meet the entry rule. They are not the same trade. A scoring checklist forces you to look at what differs before you click, so marginal setups stop sneaking in as routine ones.
This is a process tool, not a validated edge. The points and thresholds below are a starting template. Whether the scores predict results for you is something to test with your own journal.
Five factors, 0 to 2 points each
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.
1. Structural confluence
0 if the setup sits at a minor level with nothing behind it. 1 if it sits at a KPL reference price or another visible level such as a prior session high or low. 2 if it sits at a KPL level and a second independent level lines up nearby. Remember that KPL levels are fixed reference prices anchored to the regular session opening print, and the standard ES and NQ map has four levels. The opening anchor is not known before the regular session opens at 9:30 a.m. ET, so you cannot score a completed anchored map before the open.
2. Trend alignment
0 if the trade is against the trend on both the 1-hour and 4-hour charts. 1 if it agrees on one. 2 if it agrees on both. Define trend in advance (for example, higher highs and higher lows, or price versus a moving average) so the score is not a mood.
3. Time of day
Score from your own volume data. As a placeholder: 2 for the first two hours after the 9:30 a.m. ET cash open, 1 for 1:30 p.m. to 4:00 p.m. ET, 0 for the midday stretch. These windows are a convention, not a guarantee, and holiday sessions break them. Replace them with what your own trades and the volume profile show.
4. Reward to risk
0 if the first logical target is under 1.5 times your stop distance, 1 for 1.5 to 2.5, 2 for 2.5 or more. The target has to be realistic for the session: do not count a 20-point target if the day has not moved that far in comparable sessions.
5. Market context
0 for choppy, directionless conditions. 1 for a directional bias with mixed signals. 2 for a clear trending regime that matches your trade direction. If you want a rules-based regime view, see how the Marty bot specs describe their own behavior instead of guessing at its logic.
Two worked examples
| Factor | Setup A | Setup B |
|---|---|---|
| Structural confluence | 2 (KPL plus prior day high) | 1 (KPL only) |
| Trend alignment | 2 | 1 |
| Time of day | 2 (10:15 a.m. ET) | 0 (12:45 p.m. ET) |
| Reward to risk | 2 (2.8 to 1) | 1 (1.6 to 1) |
| Market context | 1 | 0 |
| Total | 9 | 3 |
Both of these are hypothetical. Setup A is a candidate for your normal size. Setup B is a skip, or at most reduced size with a firm exit plan.
Turning scores into decisions
A simple starting rule: 8 to 10 is a full-size candidate, 5 to 7 is normal size, 3 to 4 is skip or half size, 0 to 2 is no trade. These are position-sizing conventions that you choose, not probabilities. No score level makes a trade more likely to win on its own; it only organizes the evidence you have.
Test the scores against your results
- Record the score and each factor for every trade you take, plus the ones you skip if you can.
- After at least 100 trades, group by score band and compare average result per trade, after costs.
- Check each factor on its own. If one never separates good trades from bad ones, drop it or redefine it.
- Watch for small bands: three trades in a band tells you nothing.
If the bands do not separate in your own data, the checklist is not adding information, and the right move is to change it, not to trust it harder.
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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