Edge Ratio

Edge ratio — Maximum Favorable Excursion divided by Maximum Adverse Excursion — is one of the few performance metrics that measures your setup’s structural price-movement advantage before your exits distort the data. Formalized by John Sweeney in Campaign Trading (1996), it answers a question win rate and profit factor cannot: does price actually move further in your favor than against you before the trade resolves? This makes edge ratio a leading indicator of strategy health, sitting in the performance category alongside metrics like expectancy and profit factor.

Formula & Calculation

Edge Ratio = Sum of MFEs / Sum of MAEs

Where:

• MFE = Maximum Favorable Excursion — the largest unrealized gain a trade reaches before close
• MAE = Maximum Adverse Excursion — the largest unrealized loss a trade reaches before close
• Sum of MFEs = Total MFE across all trades in the sample
• Sum of MAEs = Total MAE across all trades in the sample

To calculate, record the intraday high (for longs) and intraday low reached during each trade. MFE is the distance from entry to the most favorable extreme; MAE is the distance to the most adverse extreme — both measured in dollars per position. Sum each column across your sample and divide.

A ratio above 1.0 means trades, on average, travel further in your favor than against you before resolution. An edge ratio of 2.0 means the average trade reaches twice as favorable a price as it reaches an adverse price. See Maximum Favorable Excursion and Maximum Adverse Excursion for detailed explanations of each component.

Benchmarks

Level	Range	What It Means
Strong	above 2.0	Trades move more than twice as far in your favor as against you — reliable structural edge
Tradeable	1.5 – 2.0	Consistent structural advantage; setup is worth trading in appropriate conditions
Marginal	1.0 – 1.5	Edge exists but is thin — dependent on precise execution and favorable conditions
No Edge	below 1.0	Setup moves against you as far or further than it moves for you; structural disadvantage

Practical Example

A trader runs 40 SPY breakout trades over two months with 100-share positions. Summing MFE across all 40 trades yields $12,480 total; summing MAE yields $5,920 total.

Edge Ratio = $12,480 / $5,920 = 2.11

Average MFE per trade: $12,480 / 40 = $312. Average MAE: $5,920 / 40 = $148. This is a strong structural edge — the setup moves, on average, $164 further in the trader’s favor than against them before resolving.

The trader then segments by VIX regime. In February (VIX averaging 16), the edge ratio was 2.4. In March (VIX averaging 28), the same setup produced edge ratio 0.82: average MFE $190, average MAE $232. March P&L looked roughly flat because tighter stops limited realized losses — but the structural edge had collapsed entirely. Without edge ratio segmentation, the warning would have gone unnoticed until losses accumulated. The setup works; it just doesn’t work in elevated-volatility environments, and knowing that is the difference between selective filtering and a strategy blowup.

How to Track Edge Ratio

1. Record MFE and MAE at trade close — log the most favorable and most adverse price reached during each trade relative to your entry price, converted to dollar value per position size
2. Group by setup type — maintain separate edge ratio calculations for each distinct setup (VWAP reclaim, breakout, mean reversion) rather than mixing them
3. Accumulate at least 30 samples per category — fewer trades produce unstable ratios; a single outlier can shift the result by 0.5 or more
4. Segment by condition — track edge ratio separately for high-VIX vs. low-VIX days, trending vs. range-bound sessions, and morning vs. midday entries
5. Monitor as a rolling 30-trade average — plot edge ratio over time to catch degradation trends 2–4 weeks before they show up in net P&L

How to Improve Edge Ratio

1. Filter by volume tier — if edge ratio drops below 1.0 on low-volume days, restrict the setup to sessions where average volume exceeds your defined threshold; this alone can raise edge ratio from 0.9 to 1.6 on the same setup
2. Widen initial stops on high-edge-ratio setups — if MFE averages $312 but you’re stopping out at $80 MAE, you’re not letting favorable excursion develop; aligning stop placement with typical MAE allows the structural edge to play out
3. Cut setups with sustained edge ratio below 1.0 — if the last 30 trades show edge ratio under 1.0, the structural advantage is gone; pause the setup and review the conditions that changed
4. Restrict time-of-day windows — if edge ratio is 2.3 in the first 90 minutes of the session but 0.8 midday, eliminate midday entries rather than averaging down the quality of the setup

Common Mistakes

1. Calculating on too few trades — edge ratio on 10 trades is noise, not signal; one large outlier distorts the result by 40% or more; require 30+ trades before acting on the number
2. Blending all setups into one ratio — a blended edge ratio of 1.4 can mask one setup at 2.1 and another at 0.6; always calculate separately by setup type, as the setup accuracy and edge ratio often diverge across setup categories
3. Ignoring condition segmentation — a setup’s overall edge ratio of 1.8 can conceal an edge ratio of 0.7 in high-VIX regimes; trading that setup without the VIX filter exposes capital to a condition where it has no statistical advantage
4. Treating edge ratio as static — edge ratio is a rolling signal, not a one-time badge; a setup that showed 2.1 eighteen months ago but has trended to 0.9 over the last 30 trades is a regime-change alert, not a minor fluctuation

How JournalPlus Calculates Edge Ratio

JournalPlus automatically calculates MFE and MAE for every imported trade using intraday price data, then displays edge ratio on the analytics dashboard segmented by setup type. The trade log filters let you isolate any condition — VIX range, session time, instrument — and recalculate edge ratio on the filtered subset in real time, without exporting to a spreadsheet. The performance charts include a rolling 30-trade edge ratio overlay so degradation trends are visible before they reach P&L. When a setup’s edge ratio drops below 1.0 across the trailing sample, the dashboard flags it alongside related metrics like profit factor and average R:R so you can diagnose whether the issue is structural or execution-related.