Select Page

# Using Maximum Adverse Excursion for Stop Loss Placement

Jul 26, 2021

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.

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:

1. Closed profit/loss
2. MAE

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.

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.

## Wrapping Up

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.

If you want to experiment with StrategyQuant’s cool features, don’t forget to check out the 14-day free trial!

Want to develop a portfolio of automated trading strategies?

Supercharge your strategy development with StrategyQuant

Access 14-day FREE trial here!

Get up to USD 300 discount!

Strategies need improvement?

Use QuantAnalyzer’s powerful analysis tools

Try the FREE version here!

Get 20% Discount here!

## Automated MACD Divergence Forex Trading Strategy

The MACD is a simple and effective momentum indicator. Here’s how you can save screen time by programming a MACD divergence strategy for the GBPJPY!

## Laguerre RSI Trend Following Strategy

The Laguerre RSI attempts to improve the responsiveness of the regular RSI, whilst keeping whipsaw trades to a minimum. Let’s see how well it detects short-term pullbacks for a trend following strategy!

## What Is the Kaufman Adaptive Moving Average?

The Kaufman Adaptive Moving Average is a unique indicator that automatically adapts to the market’s noise. Here I explain its inner workings and show you how to build a trend following strategy around it.

## What is Fixed Ratio Money Management?

Have you heard of fixed ratio money management? How does it compare to the popular fixed fractional approach? Here I’ll explain how fixed ratio works, and see how it stacks up against fixed fractional money management.

## Build a Diversified Portfolio With QuantAnalyzer

The ability to efficiently trade a diversified portfolio of strategies is one of the biggest advantages of algorithmic trading. Here we will use QuantAnalyzer’s Portfolio Master to build a portfolio consisting of high performing, uncorrelated strategies.

## What Is the QQE Indicator?

The QQE is a mysterious indicator that sometimes pops up in trading forums. Does it deserve a place alongside the more traditional momentum indicators like the RSI and CCI? Let’s add it to a trend following strategy to find out!

## Do Bollinger Bands + Candlestick Patterns Work?

Bollinger Bands are great at detecting overbought and oversold conditions. Let’s use them to develop a countertrend strategy, and then refine our entries using limit entries and candlestick patterns.

## How Good Are The Bollinger Bands’ Trailing Stops?

Trailing stop losses are a popular feature in many trend following systems. Bollinger Bands, the ever-popular technical indicator among retail traders, actually contain two inbuilt trailing stops. Are these any good? Let’s find out!

Hey there, Wayne here! I’m on a mission to develop robust algorithmic trading strategies for the forex markets. Trading Tact is where I share my trading methods and insights.

###### Have a Question?

1. Do you have the code for excel for creating the MAE/MFE chart?

• no it was done manually. Just a quick scatter plot

Want to develop a portfolio of automated trading strategies?

Supercharge your strategy development with StrategyQuant

Access 14-day FREE trial here!

Get up to USD 300 discount!

Strategies need improvement?

Use QuantAnalyzer’s powerful analysis tools

Try the FREE version here!

Get 20% Discount here!

## Forex Weekend Gaps: Can You Exploit Them?

Have you noticed that forex weekend gaps usually reverse within 3 days? Here I’ll program a mean reversion strategy to exploit gaps over the last 18 years!

## Dynamic Position Sizing: Is It Time to Go Big?

Should you increase your lot sizes for higher probability trades? Let’s code a dynamic position sizing scheme to capture more outsized wins!

## Pivot Points: A Reliable Support & Resistance Indicator

Pivot points are the perfect tool if you trade using support & resistance. Here’s how to develop an automated pivot points forex strategy!

## Money Flow Index: An Improved RSI?

The Money Flow Index is sometimes called the volume-weighted RSI. Can it outperform the RSI in this trend following strategy?

## Trade Slippage: How Can You Simulate and Minimize It?

Are you a victim of excessive trade slippage? Here’s how you can minimize slippage, and more realistically simulate it in your backtests!

## Edge Ratio: A Unique Way to Quantify Entry Profitability

Selecting a profitable entry is a critical step in strategy development. Here I’ll demonstrate how to use the Edge Ratio to maximize your profit potential.

## Automated Bollinger Bands Squeeze Forex Strategy

StrategyQuant’s BBWR indicator is the perfect tool to detect a Bollinger Bands squeeze. Here I explain how it’s calculated, and use it to program a breakout strategy for the AUDJPY!

## Automated Schaff Trend Cycle Forex Strategy

The Schaff Trend Cycle is a unique combination of the MACD and Stochastic indicators. Here’s how you can use it to improve your trend following results!

## Should You Use the Kelly Criterion for Forex Trading?

The Kelly criterion is a famous mathematical formula that attempts to maximize your long-term capital growth. In this post, I’ll apply it to a EURUSD breakout strategy and explain some of its potential shortcomings when applied to forex trading.

## Forex Intermarket Correlations: How Do You Exploit Them?

Knowledge of intermarket correlations can improve your forex trading win rate. Here I explain three important types of correlations, and how you can use them to benefit your trading.

## 5 Forex Day Trading Challenges & How to Overcome Them

Forex day trading seems to have a particular appeal to new traders. Here I highlight five hidden challenges of day trading, and offer some suggestions on how to overcome them.

A temporary trading pause can improve your win rate if you’re trend following a volatile market. Here I’ll program a trading pause into a simple breakout strategy, and test its effectiveness on the Widow Maker – the GBPJPY.

## Can Partial Profit Taking Benefit Trend Followers?

Partial profit taking is a dilemma often faced by long-term trend followers. Could this benefit your overall strategy performance?

Let’s test!

## Multiple Timeframe Backtesting – A Quick Robustness Test

Multiple timeframe backtesting can be a valuable addition to your strategy development workflow. Here I explain why you should do it, and how to conveniently do it in MT4 and StrategyQuant.

Hey there, Wayne here! I’m on a mission to develop robust algorithmic trading strategies for the forex markets. Trading Tact is where I share my trading methods and insights.