A catastrophic stop loss is a vital risk management tool for many traders. Here I’ll show you how to optimize your stop loss distance using maximum adverse excursion.
In the diverse world of strategy development, there are often multiple approaches to the same problem.
Traditional parameter optimization is often used to determine your strategy’s stop loss placement.
Here I’ll demonstrate a more visual method using the concept of maximum adverse excursion, and compare the results from both methods.
What is Maximum Adverse Excursion?
Maximum adverse excursion (MAE) is the largest floating loss during a trade. It measures the furthest that prices moved against you.
Let’s say you enter a long trade at $100, and the market progresses as shown:
Your MAE in this case would be the $20, the difference between the entry price and the lowest market price during the trade.
If you never experience a floating loss during a trade, your MAE will be zero.
Plotting Maximum Adverse Excursion
Here’s a case study:
Suppose you have a reversal trend following strategy for the GBPJPY. Since the strategy is always in the market, you decide to add a catastrophic stop loss to limit risk.
To determine stop loss placement using MAE, you first need to plot your individual trade distribution from the backtest.
MT4’s Strategy Tester doesn’t calculate MAE; you can use StrategyQuant to run the backtests instead. For each trade, there are two metrics you’ll need:
- Closed profit/loss
I like to use pips instead of $ values, because currency pip values fluctuate over time.
With these two values, you can plot the following MAE chart using Excel:
For every backtest trade, the chart displays the closed profit/loss in relation to the MAE during the trade.
Green dots are winning trades, while red dots are losing trades.
Notice that the vast majority of wining trades have low MAE values. Winning trades are usually profitable quickly, experiencing only small floating losses.
Also notice the ‘Loss Diagonal’ consisting of losing trades. These trades were exited close to their lowest equity point.
Let’s look at trades A and B. Although they’re located in the same area, they progressed very differently.
Trade A had a MAE of 580 pips, and was eventually closed at a 580-pip loss.
Trade B had a MAE of 600 pips, but managed to recover, eventually closing with a 500-pip win.
Let’s now use this chart for stop loss placement.
Using Maximum Adverse Excursion for Stop Loss Placement
By analyzing the distribution of MAE in relation to the eventual profit/loss, you can estimate how much floating loss a trade can incur before it is unlikely to recover.
You can place your catastrophic stop loss at this MAE level, because the risks associated with the trade are no longer justified.
Adding a stop creates a vertical boundary at a particular MAE value. Once this value is hit, the trade is immediately closed at a loss. Below you can see the same MAE chart, but with a hypothetical 200-pip stop loss.
All trades to the right of this 200-pip stop loss will be shifted onto this line. This seems great because you’ll be removing the big losses, but you’ll also sacrifice a portion of your winning trades.
These winning trades experienced a MAE of 200 pips or more, but managed to recover to close in profit.
An optimal stop loss thus removes the big losses, without choking off too many trades that eventually became profitable.
From the MAE chart, you can estimate that the ideal stop loss would be in the 50-150 pip range.
As a first pass, I recommend placing your stop such that you retain 75-85% of your winning trades. I’ll demonstrate this using an 85% cut-off.
Using the backtest statistics, I’ll place the stop such that any trade that hits the stop level has only a 15% chance of recovery.
The reversal strategy above contains 442 winning trades. This means I need a stop level that retains 376 (85% of 442) winning trades. This corresponds to about a 100-pip stop.
Here’s how the MAE chart looks with a 100-pip stop.
There is a concentration of losses at the 100-pip MAE value where the trades are taken out by the stop loss.
Note that many trades have a MAE of 101 pips because my backtest models a 1-pip slippage. In actual trading, during times of extreme volatility, stop loss slippage can increase drastically.
Comparison with Stop Loss Optimization
How does the above MAE method compare with traditional parameter optimization?
In parameter optimization, a parameter of interest is varied across a wide range, with the optimal value producing the best strategy performance. This versatile method can be used to select anything from trading entries to profit targets and trailing stops.
Here I’ll perform stop loss optimization using StrategyQuant’s Optimizer.
The stop loss was varied from 10 to 250 pips, in steps of 1 pip.
The return/drawdown ratio is negative for very small stop values; excessively tight stops are often a recipe for disaster for trend following systems. You need to be willing to take bigger risks to overcome transaction costs in the form of spread, slippage and commissions.
There is a high plateau in the 90 to 120-pip stop loss region. I would pick the middle of this plateau (105 pips), which is pretty close to the value obtained from the MAE chart.
Stop losses are great at limiting your capital downside and market exposure; many traders thus consider them to be mandatory.
Using MAE to determine stop loss placement is a viable alternative to traditional parameter optimization. Obtaining similar results from both methods gives you confidence that your strategy is conceptually sound.
Complementary to MAE is maximum favourable excursion (MFE). MFE is the largest floating profit during a trade, and can be used to study whether profit targets would be beneficial for your strategy.
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