FIRST GATE · EDGE DIAGNOSTIC
Does the strategy have an edge worth explaining?
We examine expectancy, profit factor, consistency, drawdown behavior, concentration and robustness before moving into deeper market-state research.
Actual historical results: Past results are not necessarily indicative of future results.
The forensic framework
We don't just measure the backtest. We reconstruct what happened inside it.
A conventional report tells you net profit, win rate, drawdown, Maximum Adverse Excursion (MAE) and Maximum Favorable Excursion (MFE). BackTest Forensics asks what those numbers were doing through time, in what sequence, under which market conditions, and whether the explanation survives a counter-test.
That is the difference between measuring a backtest and investigating one.
How we investigate
Start with the edge. Then bring in the engines that can explain it.
BackTest Forensics first asks whether the strategy shows a credible edge worth investigating. When deeper context is justified, EdgeQuery applies specialized research engines to different parts of the market environment surrounding your trades.
Five proprietary research engines
Each engine investigates a different part of what was happening around the trade.
We run the trades reported by your backtest through our research engines to identify what conditions were present when the strategy performed well, failed, or changed behavior.
GAMMA EXPOSURE
Gamma Exposure Engine
Tests how long and short trades behaved across different GEX and structural environments.
MARKET STRUCTURE
Volume Profile & Market Structure Engine
Measures strategy behavior in trending, rotational, sideways and choppy markets, including location relative to developing value and important volume structure.
LIQUIDITY AUTHENTICITY
Order Flow & Spoof Detection Engine
Examines whether trades were supported by genuine liquidity and order flow—or exposed to liquidity that disappeared when it mattered.
REGIME
Trend Quality & Regime Engine
Measures how performance changes as independent trend conditions strengthen, weaken, disagree or transition.
ECONOMIC EVENTS
Economic Event Engine
Tests whether the strategy was actually exposed to scheduled economic or central-bank events—and how those trades performed during the event window.
The purpose is explanation: identify the conditions where your strategy strengthens, weakens or begins to break down.
Trade-path reconstruction
One Maximum Adverse Excursion (MAE) number can hide an entire story.
Maximum Adverse Excursion (MAE) is the largest move against a position while the trade is open. It is only the starting point. The useful information is in the path: when the heat arrived, how long it lasted, whether the trade recovered, what happened next, and whether that behavior repeated under the same conditions.
Maximum Adverse Excursion (MAE)
How did the trade behave while it was wrong?
Measure maximum heat, time underwater, time to Maximum Adverse Excursion (MAE), repeated adverse waves, recovery time, and whether the worst adverse move occurred before or after favorable excursion. Then compare that path across winners, losers, direction, session, volatility and market state.
Maximum Favorable Excursion (MFE)
How much opportunity did the strategy create versus monetize?
Maximum Favorable Excursion (MFE) is the largest move in the trade's favor while it is open. Measure time to MFE, realized-P&L-to-MFE capture, post-MFE giveback, and whether the exit occurred during a normal retracement or after genuine deterioration. Two +$80 winners can be radically different if one reached only +$110 and the other reached +$500 first.
SEQUENCE
What happened first?
Maximum Adverse Excursion (MAE) → Maximum Favorable Excursion (MFE) is different from MFE → MAE. A trade that suffers early, recovers and trends is not the same setup as one that moves immediately in favor and later gives everything back. Sequence can expose entry quality, stop pressure and exit inefficiency.
A real forensic question
“What stop would improve the curve?” is usually the wrong first question.
Your Backtest Optimizer shows which threshold improves historical results. Backtest Forensics first investigates what kind of trades the threshold would remove—and what winning trades it would accidentally destroy.
Example from historical strategy research
Weak long trades absorbed roughly twice as much adverse movement as favorable opportunity.
In one historical strategy study, losing long trades averaged about 107 points of Maximum Adverse Excursion (MAE) against only about 54 points of Maximum Favorable Excursion (MFE). In plain English, those losing longs moved almost twice as far against the position as they ever moved in its favor. That was more useful than simply knowing the longs lost money. It suggested a recurring trade path: substantial heat with limited opportunity.
Did maximum heat arrive immediately after entry or later?
How long did those trades remain underwater?
Which losing longs recovered after deep Maximum Adverse Excursion (MAE)?
Would a tighter stop also eliminate the rare large long winners?
Did the weak path concentrate in one market state?
Did aligned longs behave differently from conflict longs?
The next test is not “optimize the stop.” It is: what was different about the trades that immediately went underwater, and can that condition be identified without destroying the long trades that later became major winners?
Maximum Favorable Excursion (MFE) as exit forensics
Profit alone cannot tell you whether the exit was good.
A backtester can tell you a trade made $80. We ask how much opportunity existed before that $80 was realized, how long it existed, and how much was surrendered.
Capture efficiencyWhat percentage of available Maximum Favorable Excursion (MFE) became realized P&L?
Time to opportunityDid Maximum Favorable Excursion (MFE) arrive quickly, slowly, or only after enduring large Maximum Adverse Excursion (MAE) first?
Post-MFE givebackHow much open profit disappeared before the strategy exited?
Exit-state dependencyDoes capture efficiency change under trend, chop, volatility, session or other market-state conditions?
What separates your successful breakout trades from the failures? Is genuine order-flow participation missing when the breakout fails—or is the trade running into a Call Wall, Put Wall, unfavorable GEX condition, weak trend quality, or another market-state constraint?
The goal is not just to explain the failure. It is to identify conditions you can test as filters, confirmations, or exclusions in future strategy development.
The goal is not just to explain the failure. It is to identify conditions you can test as filters, confirmations, or exclusions in future strategy development.
Loss clustering
Are the losses random—or are they repeating the same failure path?
We group trade paths, not just losing outcomes. The goal is to find whether the same adverse behavior keeps appearing under related conditions.
DirectionDo longs and shorts fail differently? Does one side spend more time underwater or recover less often?
Session & timingDoes early-session Maximum Adverse Excursion (MAE) behave differently from midday MAE? Are late failures mostly giveback rather than bad entries?
Volatility & regimeDoes the same setup behave differently under GEX, trend, or chop?
Sequence & drawdownDo losses cluster after prior losses, during drawdown phases, or after a measurable deterioration in trade-path behavior?
Before filtering out a losing condition, investigate whether it also produces some of your best trades. The answer may be smaller size, different exits, or no change at all—not removing the condition entirely.
Market-state attribution
Did the strategy change, did the market change, or did both change together?
Trade-path evidence becomes more useful when attached to the environment surrounding the trade. Where data supports it, EdgeQuery IQ compares excursion behavior with trend, volatility, session, macro and structural market-state context.
Market behavior changed
The same entry logic may encounter more adverse excursion, less follow-through or more giveback because the surrounding environment changed.
Trade behavior changed
If Maximum Adverse Excursion (MAE), Maximum Favorable Excursion (MFE), recovery time or capture efficiency deteriorates even inside comparable market states, the evidence points back toward strategy-side behavior.
Both changed together
A strategy can keep firing the same nominal setup while its trade path changes because the environment changed. We test the interaction rather than forcing a one-cause story.
Research that challenges the first answer
The finding is only useful if the explanation survives a challenge.
Internal futures research repeatedly produced results that changed the question rather than merely changing a parameter.
Hidden liquidity conditionMBO-derived liquidity behavior isolated a recurring failure state not present in the submitted strategy data. The research then asked whether that condition could be mitigated without eliminating winners.
Path-quality diagnosisLosing longs showed roughly 107 MAE versus 54 MFE, while losing shorts took similar heat but generated more favorable movement. That helped distinguish directional path weakness from a universal exit defect.
Hypothesis rejectionTrend conflict amplified the damage, but aligned longs also remained negative. The obvious “conflict caused the problem” story did not survive scrutiny.
Forward-hypothesis disciplineA strong regime/propagation interaction remained positive in a later portion of the sample, but because the grouping was chosen after inspection, it stayed a forward hypothesis rather than becoming a live rule.
The first explanation is not always the right one. Backtest Forensics keeps testing until the evidence either supports the explanation—or forces the investigation in a different direction.
The five-question forensic standard
What happened → Where → Why → Necessary conditions → What would invalidate it.
Every serious investigation should progress from description to explanation and then to falsification. The goal is not merely to find a pattern. It is to understand what supports the edge and what evidence would make us stop believing the explanation.
Subscriber-facing example
“The strategy made $42,860” is only the beginning.
73% of total profit came from 19% of trades occurring during persistent directional sessions. When those sessions are removed, expectancy falls from $84 per trade to $17.
That changes the research question. The strategy may not have a broadly distributed edge; it may have a concentrated dependency on directional regimes. The next job is to test whether that dependency survives across time, volatility states and out-of-sample data.
Finding → Evidence → Meaning → Next experiment. This is the standard BackTest Forensics should deliver to subscribers.
01 · RECONSTRUCT
Rebuild the trade path.
Maximum Adverse Excursion (MAE), Maximum Favorable Excursion (MFE), time underwater, recovery, sequencing, giveback, duration and realized outcome.
02 · COMPARE
Find what changes with the path.
Direction, time, volatility, regime, market structure, macro context and other available states.
03 · CHALLENGE
Try to break the explanation.
Use matched controls, chronology, holdouts and out-of-sample checks where the dataset permits them.
Your Backtest Optimizer does this: “Filters conditions to improve the historical equity curve.”
Backtest Forensics does this: “Investigates whether the condition is actually causing the problem, what other conditions are present, and what still holds up after those are tested.”
Backtest Forensics does this: “Investigates whether the condition is actually causing the problem, what other conditions are present, and what still holds up after those are tested.”