Max Adverse Excursion (MAE)

Max Adverse Excursion (MAE) measures the largest unrealized loss experienced during a trade before it is closed. It is a helpful metric for evaluating risk, helping traders understand how much a trade moves against them before turning profitable or being stopped out. By analyzing MAE across multiple trades, traders can identify patterns in risk exposure, refine stop-loss levels, and improve position sizing. Incorporating MAE into a trading strategy can enhance discipline and ensure a data-driven approach to managing risk.

Trading Systems and Methods (6th Edition, 2020, Wiley) — Perry J. Kaufman

The key to mastering risk isn't avoiding loss but knowing how much loss you can afford to endure before the market proves you right.

- Anonymous

How to Calculate MAE?

Maximum Adverse Excursion (MAE) measures the largest unrealized drawdown during a trade. Expressing MAE as a percentage allows fair comparison across assets, while averaging across trades provides insights for stop-loss optimization.

MAE% for a Single Trade

For a long trade:

MAE_{\text{long}\%} = \frac{EntryPrice - LowestPrice}{EntryPrice} \times 100

For a short trade:

MAE_{\text{short}\%} = \frac{HighestPrice - EntryPrice}{EntryPrice} \times 100

MAE% with Multiple Orders (Scaling In)

If you add positions at different prices, use a Volume-Weighted Average Price (VWAP):

VWAP = \frac{\sum(EntryPrice_i \times Size_i)}{\sum Size_i}

Then:

MAE_{\%} = \frac{|VWAP - ExtremePrice|}{VWAP} \times 100

Where:

ExtremePrice = Lowest price (for long) or highest price (for short) while the position was open
VWAP = blended entry price based on order sizes

For conservative stress testing, calculate MAE% from the first entry price.

Average MAE% Across Multiple Trades

To evaluate your overall stop efficiency, average MAE% over N trades:

AvgMAE\% = \frac{\sum_{i=1}^{N} MAE_{i}\%}{N}

Where:

$MAE_{i}\%$ = MAE% for trade i
N = total number of trades analyzed

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Use Average MAE% segmented by time periods (1M, 2M, 3M…) or strategy types to fine-tune stop-loss placement and improve consistency.

Maximum Adverse Excursion: Analyzing Price Fluctuations for Trading Management (1996, Wiley) — John Sweeney

Importance of MAE in Trading

Maximum Adverse Excursion (MAE) is important because it provides valuable insights into the risk profile of a trading strategy. It helps traders identify how much a trade moves against them before recovering or hitting their stop-loss, allowing for better calibration of risk parameters like position sizing and stop levels. By analyzing MAE, traders can spot patterns of excessive drawdowns and optimize strategies to minimize unnecessary risk. It also aids in distinguishing between trades that are worth holding through adverse moves and those that require stricter risk controls.

Trading Systems and Methods (6th Edition, 2020, Wiley) — Perry J. Kaufman

Trading Examples

The following examples demonstrate how to calculate MAE (Maximum Adverse Excursion) using percentage-based metrics, scaling in, and averaging across trades. These scenarios help traders evaluate both individual trade risk and overall strategy performance.

Single Trade MAE%

A trader enters a long position at $100. During the trade, the price dips to $97 before recovering and closing at $108.

Metric	Value
Asset	Stock XYZ
Entry Price	$100
Lowest Price Reached	$97
Exit Price	$108 (Profit Target Hit)

Calculation:

MAE_{\text{long}\%} = \frac{100 - 97}{100} \times 100 = 3\%

Analysis:

The trade endured a 3% drawdown before becoming profitable.
Stops set tighter than 3% could have caused a premature exit.

Losing Trade MAE%

A trader enters a long position at $50.
The price falls to $44, triggering a stop-loss at $45.

Metric	Value
Asset	Stock ABC
Entry Price	$50
Lowest Price Reached	$44
Exit Price	$45 (Stop Loss Hit)

Calculation:

MAE_{\text{long}\%} = \frac{50 - 44}{50} \times 100 = 12\%

Analysis:

The trade experienced a 12% adverse move, far exceeding a typical 5% stop-loss.
This suggests poor entry timing or higher-than-expected volatility.

Scaling In with VWAP

A trader scales into a position:

Buys 50 shares at $100
Buys 50 shares at $95

The lowest price reached during the trade is $90.

VWAP Calculation:

VWAP = \frac{(100 \times 50) + (95 \times 50)}{50 + 50} = 97.5

MAE% Calculation:

MAE_{\%} = \frac{97.5 - 90}{97.5} \times 100 \approx 7.7\%

Analysis:

The position endured a 7.7% drawdown based on the blended entry.
If calculated from the first entry ($100), MAE% would be 10%, showing a stricter view of trade heat.

Average MAE% Across Multiple Trades

Trade #	Entry Price	Extreme Price	MAE%
1	$100	$97	3%
2	$50	$44	12%
3	$97.5 (VWAP)	$90	7.7%

Average MAE% Calculation:

AvgMAE\% = \frac{3 + 12 + 7.7}{3} \approx 7.6\%

Analysis:

On average, trades endured ~7.6% drawdown before resolution.
Stops placed closer than 7% would likely result in frequent early exits.
This metric helps calibrate stop-loss placement to match real trade behavior.

Trade Comparison: Profitable vs Losing

Profitable Trade

Entry Price$100

Stop Loss$95

Lowest Price Reached$97

Exit Price$108 (Profit Hit)

MAE%3%

Losing Trade

Entry Price$50

Stop Loss$45

Lowest Price Reached$44

Exit Price$45 (Stop Hit)

MAE%12%

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MAE is Not a Fixed Rule: Market conditions evolve. Regularly revisit and recalibrate MAE expectations based on recent performance.

Combining MAE with Other Tools

Average True Range (ATR): Use ATR to set stop-loss levels based on market volatility, complementing MAE Potential.
Risk-Reward Ratio: Pair MAE with profit targets to ensure Risk-Reward Ratio aligns with trading goals.
Win rate Analysis: Evaluate MAE alongside trade outcomes to identify profitable patterns.
Position sizing: Combine MAE with position-sizing strategies to limit capital exposure.

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Don’t Ignore Outliers: Outliers in MAE can teach you about rare events or weaknesses in your system. Learn from them, but don’t base all decisions on extremes.

Key Points

Risk Assessment Tool: Max Adverse Excursion (MAE) measures the largest Drawdown a trade experiences while it is open, helping to assess risk exposure.
Trade Evaluation: MAE highlights how much a position moves against you before recovering or closing, providing insights into trade quality and execution.
Stop-loss Optimization: Use MAE data to refine stop-loss levels, ensuring they are neither too tight (causing premature exits) nor too loose (increasing losses).
Risk Management Insight: Trades with high MAE relative to profit indicate excessive risk and may require adjustments to entry timing or position sizing.
Benchmark for Strategy Testing: Comparing MAE across trades reveals patterns in strategy performance and helps identify areas for improvement.
Volatility Awareness: Higher MAE values are often associated with volatile markets or poorly timed entries, emphasizing the importance of market context.
Complement to MFE: Combine MAE with Max Favorable Excursion to evaluate the balance between risk and reward for each trade.
Emotional Discipline: Understanding MAE encourages traders to stick to predefined risk limits and avoid emotional reactions to temporary drawdowns.
Portfolio-Level Analysis: Aggregating MAE across a portfolio provides a broader view of risk exposure and helps optimize overall risk management.
Backtesting and Monitoring: Include MAE in backtesting to identify historical risk patterns and continuously monitor it in live trading for ongoing strategy refinement.

Trading Systems and Methods (6th Edition, 2020, Wiley) — Perry J. Kaufman

Conclusion

Understanding and utilizing Max Adverse Excursion (MAE) empowers traders to optimize risk management and improve the consistency of their strategies. By analyzing historical MAE data, traders can make informed adjustments to stop-loss levels and position sizing, fostering disciplined and sustainable trading practices.

R-Multiple Max Favorable Excursion