Adverse excursion: a key concept for risk managementAs a professional trader, I can tell you about the adverse excursion and its crucial importance in the world of trading.
Adverse excursion: a key concept for risk management
Adverse excursion refers to the unfavorable movement of the price of an asset after a position is opened. More precisely, it is the difference between the entry price and the worst point the price reaches before the position becomes profitable again or is closed.
Maximum Adverse Excursion (MAE)
The concept of Maximum Adverse Excursion (MAE), developed by John Sweeney, is particularly useful. It measures the maximum floating loss suffered by a position before it turns in your favor or is closed. The MAE is a powerful statistical tool for analyzing drawdowns in an open position.
Trading Efficiency
Using MAE has several benefits for traders:
Optimizing Stop-Loss: By analyzing MAE over a series of trades, the optimal level for placing stop-loss orders can be statistically determined.
Evaluating Trading Systems: MAE helps evaluate the performance of trading systems and identify areas for improvement.
Refining Risk Management Strategies: By understanding the maximum adverse moves, traders can refine their strategies to better preserve their capital.
Improving Trading Efficiency: MAE analysis can help improve decision-making and execute trades with greater accuracy and confidence.
Practical Application
To effectively use the concept of adverse excursion, it is crucial to collect data on a large number of trades. For example, if you observe a series of MAEs like this: 15, 23, 18, 16, 0, 11, 31, 17, 8, 0, 19, 26, 0, 38, 22, you can deduce valuable information about the behavior of your trades and adjust your stop-loss levels accordingly.
In conclusion, the adverse excursion and especially the MAE are powerful tools for any serious trader. They allow to optimize risk management, improve the performance of strategies and make more informed decisions. As they say in the trade, "who controls his risks, controls his profits".
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Using the Maximum Adverse Excursion (MAE) has several significant advantages over traditional stop-loss placement methods:
Data-driven optimization
The MAE allows for a more precise and data-driven approach to stop-loss placement:
Statistical analysis: By examining the distribution of the MAE over a large number of trades, the optimal level for placing stop-loss orders can be statistically determined.
Performance visualization: The graphical representation of the MAE provides a clear overview of trade performance, allowing the most effective stop-loss levels to be visually identified.
Balancing protection and performance
The MAE helps to find an optimal balance between capital protection and trading performance:
Retention of winning trades: The stop-loss can be placed to retain 75-85% of winning trades, thus avoiding prematurely cutting potentially profitable positions.
Elimination of large losses: At the same time, this approach eliminates trades that suffer large losses, thus protecting capital.
Adaptation to the specific strategy
The MAE adapts to the unique characteristics of each trading strategy:
Customization: Unlike generic methods, the MAE takes into account the specific behavior of the trades of a given strategy.
Flexibility: This approach can be applied to a variety of strategies, whether short-term trading, swing trading, or long-term positions3.
Improved risk management
Using the MAE contributes to better overall risk management:
Deep understanding: The MAE provides a more nuanced understanding of how trades evolve, allowing for better risk assessment.
Reduced stress: By having a solid basis for placing stop-losses, traders can reduce the stress associated with real-time decision-making.
Complementarity with other tools
The MAE can be used in conjunction with other techniques:
Combination with the MFE: The analysis of the Maximum Favorable Excursion (MFE) in parallel can help to optimize not only the stop-losses, but also the profit-taking.
Cross-validation: The results obtained by the MAE analysis can be compared with those of traditional parameter optimization methods for greater confidence in the strategy.
