ACTUAL RESEARCH EXAMPLE: This page uses a validated historical SATS research set. It demonstrates how BackTest Forensics turns strategy performance into conditional research questions. It is exploratory research only and has no live trade or risk authority.
BackTest Forensics · SATS MNQ Research

When did the strategy perform best — and why?

A three-way state study of 161 actual SATS MNQ trades, comparing baseline results with market-state conditions observed between July 20 and August 26, 2026.

InstrumentMNQ
Sample161 Trades
Research windowJul 20–Aug 26, 2026
Study typeExploratory
Executive finding

The strategy's edge was not uniform across market states.

The strongest observed subset was QFR2 CLEAR_TREND + GEX UNANIMOUS.

The full 161-trade sample was profitable, but the conditional subset materially improved average trade value and profit factor. That does not establish causation. It identifies a state combination worth challenging with holdout data and trade-path analysis.

Working conclusion · Research lead
Baseline P&L+$3,961
Baseline avg/trade+$24.60
Baseline PF1.765
Trades161
Evidence 01

Baseline strategy performance.

The first job is to establish what the strategy actually produced before adding market-state context.

MeasureObserved result
Total trades161
Total P&L+$3,961
Average per trade+$24.60
Average per contract+$16.89
Raw profit factor1.765
Per-contract profit factor1.671
Evidence 02

The strongest observed state improved both expectancy and profit factor.

ConditionTradesDaysAvg / TradeAvg / ContractPFPF / Contract
QFR2 CLEAR_TREND + GEX UNANIMOUS7021+$39.99+$33.122.3612.546
Full sample161+$24.60+$16.891.7651.671
01
The conditional improvement is economically meaningful inside this sample.Average trade value increased from +$24.60 to +$39.99, while raw profit factor increased from 1.765 to 2.361.
02
Per-contract results improved as well.Average per contract rose from +$16.89 to +$33.12, and per-contract profit factor rose from 1.671 to 2.546.
03
That still does not prove the labels created the edge.The subset may also differ in direction, time of day, volatility, trade duration, excursion path or other correlated conditions.
Evidence 03

The next question is mechanism, not another filter.

ENTRY

Was signal quality actually better?

Compare initial Maximum Adverse Excursion (MAE)—how far the trade moved against the position—and early follow-through. If the state reduced immediate heat, the improvement may begin at entry.

PATH

Did trades travel differently?

Measure Maximum Adverse Excursion (MAE), Maximum Favorable Excursion (MFE), spike magnitude, pullback and giveback. A stronger environment may simply allow more favorable excursion before the exit logic engages.

EXIT

Did the strategy capture more available opportunity?

Compare realized P&L with Maximum Favorable Excursion (MFE)—the best move in the trade's favor—and test whether exit efficiency changed under the stronger state.

Counter-test

Do not convert an exploratory state into a live trading rule yet.

The correct next step is chronological validation.

Discover the relationship in one period, then test it in later untouched data. Match trades on direction, session and volatility where possible. If the advantage survives those controls, the state becomes a stronger candidate for risk scaling or contextual interpretation.

Action status · Validate first
Recommended next tests

What BackTest Forensics would investigate next.

A
Maximum Adverse Excursion (MAE) / Maximum Favorable Excursion (MFE) decompositionDetermine whether the stronger state reduced adverse excursion, increased favorable excursion, improved capture, or some combination.
B
Matched controlsCompare trades with similar direction, session and volatility to test whether the state labels add incremental explanatory value.
C
Chronological holdoutTest the state relationship in later untouched data rather than optimizing a threshold on the discovery sample.
D
Risk-scaling testIf the relationship survives validation, test probability-weighted risk scaling before considering hard trade blocks.
Forensic conclusion

The useful finding is not “GEX makes SATS profitable.”

The defensible conclusion is narrower: within this 161-trade historical sample, SATS performed materially better when QFR2 was CLEAR_TREND and GEX was UNANIMOUS. That relationship deserves mechanism testing and out-of-sample validation. BackTest Forensics is designed to continue from that point rather than stop at the correlation.

Actual historical SATS research example. Exploratory research only; no live trade or risk authority. Historical and backtested performance does not guarantee future results. Nothing on this page is investment advice or a recommendation to trade.