The Edge Ratio helps you compare entry signals by measuring the open profits and losses they generate.

This post will cover:

- Maximum favourable excursion and maximum adverse excursion
- Edge Ratio calculations
- Quantifying entry profitability with the Edge Ratio
- Benefits & drawbacks of the Edge Ratio

Traders are perpetually engaged in the search for better entry signals.

With today’s backtesting software, we can quickly quantify an entry’s profitability over years of historical data.

The Edge ratio, first proposed by Curtis Faith in Way of the Turtle, is a unique metric that can help with entry selection.

The ratio is derived from each trade’s maximum favourable excursion (MFE) and maximum adverse excursion (MAE). Let’s briefly discuss these concepts.

## Maximum Favourable Excursion & Maximum Adverse Excursion

MFE is the largest open gain during a trade. It measures the furthest that prices moved in your favour.

MAE is the largest open *loss* during a trade. It measures the furthest that prices moved *against* you.

MAE and MFE are calculated relative to your entry price. Suppose you have a trade that progresses as shown:

The trade was closed at breakeven, but had a MFE of $20 and a MAE of $30.

MFE is often used to optimize profit target placement, while MAE is used for stop loss placement.

MT4’s backtester does not calculate MFE/MAE by default, so I recommend backtesting your strategy using StrategyQuant.

I prefer to display MFE/MAE in pips since this removes the effects of fluctuating currency values.

## Edge Ratio Calculation

The Edge Ratio is the MFE/MAE ratio normalized by the prevailing market volatility when the trade is opened. It essentially measures the entry’s ability to accrue profits.

This normalization is necessary because volatile markets tend to produce higher MFE and MAE values. The prevailing 14-period ATR at the trade open will be used for normalization.

StrategyQuant automatically calculates the Edge Ratio for you. Here I’ll use a hypothetical five-trade backtest to demonstrate the calculation.

For each trade, you’ll need the MFE, MAE and the 14-period ATR at the trade open.

- Normalize volatility by dividing the MFE and MAE by the ATR. There is no need to convert the ATR to pips.
- Obtain the average normalized MFE and MAE values.
- Obtain the Edge Ratio by dividing the average MFE by the average MAE.

An Edge Ratio of 1.00 indicates equal amounts of favourable and adverse price movement.

The 3.80 ratio above indicates the strategy is far more likely to produce open profits. This correlates with the 200-pip gain after the five trades.

You can use the ratio to evaluate complete strategies, or any strategy element of interest. Since MFE and MAE are often used to track trade progression without any exits, I think the Edge Ratio is best suited to evaluate entry profitability.

That’s what we’ll do now.

## Quantifying Entry Profitability With the Edge Ratio

I’ll evaluate the performance of two popular indicators: Bollinger Bands and Keltner Channels.

These indicators are visually similar. Each consists of a middle moving average surrounded by a pair of upper and lower bands.

The Bollinger Band width is +-2 standard deviations, whereas the Keltner Channels use +-2 ATR. Standard deviation accounts for directional volatility, unlike the ATR. This makes the Bollinger Bands expand more during market trends.

Each indicator will be used to generate trend following entries for the 30-minute GBPJPY. Trading logic is as follows:

- Buy when price closes above the upper Bollinger Band/Keltner Channel
- Sell when price closes below the lower Bollinger Band/Keltner Channel
- Exit each position after 50 bars have elapsed

The 50-bar time stop, which equals about one day on M30, gives each signal equal time to generate profits.

This strategy was quickly programmed in AlgoWizard:

I’ll optimize each indicator’s lookback period from 10-100, in steps of 1. Plotting the Edge Ratio across a large range of lookback periods allows a more comprehensive assessment of entry profitability.

The strategy was then loaded into StrategyQuant’s Optimizer and setup as shown:

### Optimization Results

Here’s the Edge Ratios from 2003-2021.

Even with these basic entry signals, it’s reassuring that both indicators produce an average Edge Ratio exceeding 1.0.

The Bollinger Bands start to outperform the Keltner Channels for lookback periods above 50. I would pick a period in the 80-100 range for further development. Periods above 100 are feasible as long as you don’t mind the lower trading frequency.

I previously did another Bollinger Bands vs. Keltner Channels comparison, where I plotted each indicator’s optimization profile across a wide range of lookback periods and band multiples. The Bollinger Bands produced higher profits despite having less market exposure.

I suspect the Bollinger Bands’ faster and larger expansion during market trends is the reason behind this performance improvement. Their widespread popularity is certainly justified.

## Edge Ratio Benefits

### Considers Open-Trade Performance

Unlike most performance metrics, the Edge Ratio takes both open profits and losses into account. This can help you determine optimal trade exits.

For example, if your entry has mediocre closed-trade metrics but a high Edge Ratio, it’s likely that you’re giving back a chunk of open profits. A profit target would be handy in such cases.

Likewise, if your Edge Ratio is surprisingly low, you may choose to implement tighter stop losses.

### Versatile

Since the Edge Ratio is volatility-normalized using the ATR, you can use it to compare performance across different markets, or across different regimes for the same market.

## Edge Ratio Drawbacks

### Doesn’t Consider Trading Frequency

Since we use the *average* normalized MFE and MAE values, the Edge Ratio is not affected by trading frequency. Higher trading frequencies are usually preferred because they generate profits faster.

To alleviate this, you can plot profits vs. lookback period instead. A simple reversal strategy, which is always in the market, will suffice for such cases.

You can use both methods concurrently to get a more comprehensive overview of your entry performance.

### Affected By Backtest Precision

MFE and MAE values continually evolve as your trade progresses. Your backtest thus needs to be precise enough to capture the open profits/losses within each bar.

If your backtest engine evaluates performance at the open of each 30-minute bar, your MFE/MAE values will only be updated every 30 minutes.

For best results, you should backtest with tick precision or at least with 1-minute OHLC prices.

## Wrapping Up

Hopefully this post has given you several ideas on how to quantify entry profitability.

The Edge Ratio is a unique metric that considers the open profits/losses generated by your entries, possibly giving it an edge (pun intended) over more conventional metrics.

Plotting the Edge Ratio against your indicator lookback period is a good way to rank different entry signals and select an optimal lookback.

On a related note, you can also plot the Edge Ratio against the number of bars in trade. This illustrates how your entry’s edge evolves as the trade progresses.

If you need help with this, check out my post on how to plot expectancy vs. number of bars in trade.

Ultimately, every method has its benefits and drawbacks; there is no universal or best way. What matters is that you attempt to quantify your strategy’s performance using a method you’re confident in.

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