What Statistics Are Important in Forex?

When you are testing a Forex system or method, you are testing it to see if it can provide consistent, repeatable profits. The only way for you to see this objectively is through statistical evidence. There is a tendency for new traders to assume that there is one all important statistic which they should work on, but this isn’t a re`listic way of going about developing a system that works. Many people think that if they create a Forex system with a high win percentage, for example, they will be profitable. But what if your losses are all huge and your wins are tiny? You might still have a losing system.

The best approach is to cultivate a number of Forex statistics which provide you with important information about your trading. Well-gathered statistical evidence will not only demonstrate to you whether a system is profitable, but give you information which you can use to improve that system. Here are some important statistics to focus on during your Forex backtests and demo tests.

• Win/loss ratio (or win percentage). Obviously you want to get a high percentage of wins and a low percentage of losses. This is one of the most important things you can aim for, but as mentioned already, it isn’t everything.
• Size of wins and losses. You want larger wins and smaller losses if possible.
• Net pips. How many pips have you made, total, over the course of the Forex test?
• Number of breakeven Forex trades. How many trades did you break even on? The reason this is important to calculate is because you will usually lose a little money on breakeven trades since you must still pay the spread for the trade. You will need to add up the costs of your breakeven trades and subtract that cost from your net profit. It can be significant if you have a lot of breakeven trades.
• Worst losing streak. How many losses in a row did you incur during your worst losing streak?
• Average number of winning trades per day/week/month/year. If you’re eventually going to trade for a living you need to get some feel for how much money your system might actually make you in a given real life time period.
• Number of winning/losing trades for various types of Forex trades. If your method involves multiple entry methods, tally up data on every single one of them. You may also want to take notes on context. You may discover a price pattern or indicator which works great in one context works poorly in another. You can then make the adjustment to your next test.
• SOL Quotient. This term comes from well known Forex trader Rob Booker. Your SOL Quotient = your net profit/maximum loss. The resulting number is how many of your worst losing trades you’d be able to withstand in a row before blowing your net profit. Naturally you want this to be a large number.

You can profit with any one of these statistics being poor — if the others make up for it. There is no one golden statistic which determines statistics — but taken together these statistics can help you to succeed at Forex. If you are using MetaTrader platform to backtest or demo test your trading strategy, you can then use a report analysis tool to get all these important statistics.

If you want to share your opinion on the importance of various FX trading statistics, please use the commentary form below to post it.

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What Is Moving Average?

One type of indicator which you'll see time and again as you are learning about Forex is the moving average (MA). Moving averages are lagging indicators—this means they don't predict price direction, but rather are calculated from past prices. There are four popularly used types of moving averages: Simple, Exponential, Weighted, and Smoothed. You will rarely see Weighted moving averages used in Forex, but we'll go over them anyway. Most traders prefer to stick with Simple and Exponential moving averages. The default is to calculate moving averages using closing prices, but you can also choose to calculate using High, Low, Open, Median, Typical, and Weighted prices. You'll be able to choose how to calculate your moving averages in your charting platform, unless you wish to calculate them manually.

Let's start with simple moving averages (SMA). A simple moving average is calculated by adding up the most recent N number of prices and then dividing that sum by N. What is "N"? It's called a period of moving average and you can set it in your charting software. It can take any positive integer value from 1 to infinity. It indicates how many days (or hours, or weeks, or any other chosen periods of time) you’ll be adding up. The general formula for calculating the simple moving average for a given moment of time is:



For example, you chose 5 as the number of days (period), and the Close prices for those are (from oldest to most recent): 1.3345, 1.3348, 1.3350, 1.3374 and 1.3325. Then the simple moving average can be calculated as:

SMA = (1.3325 + 1.3374 + 1.3350 + 1.3348 + 1.3345) / 5 = 1.33484

With an exponential moving average (EMA), the calculation is more or less the same, but the difference is that exponentially less weight is given to the older data. This is done to reduce lag. Here's a general formula to calculate the EMA:



Which means that the EMA for today is calculated based on the EMA value yesterday, today's price and the special multiplier α, which can be anything from 0 to 1 (the higher it is, the sharper is the exponential decline of the weight of the older data). In Forex, α for exponential moving averages is usually calculated as 2 / (N + 1), where N is the period of the MA.

For example, we have the same data and period as in the above example for SMA. Let's calculate α:

α = 2 / (5 + 1) = ~0.33

The EMA of the first day is considered equal to the price of that day:

EMA₁ = 1.3345

EMA₂ = 1.3345 + 0.33 × (1.3348 - 1.3345) = 1.3346

EMA₃ = 1.3346 + 0.33 × (1.3350 - 1.3346) = 1.33473

EMA₄ = 1.33473 + 0.33 × (1.3374 - 1.33473) = 1.33561

EMA₅ = 1.33561 + 0.33 × (1.3325 - 1.33561) = 1.33458

As you see, it's quite different from the result obtained using the simple moving average calculation.

A weighted moving average (WMA) is similar, except that in the case of an EMA, the weight given to each older point of data decreases exponentially. In the case of a weighted moving average, the weight decreases incrementally. In general case the WMA is calculated as follows:



If, for example, we choose the same data and period as in the examples above, we'll get the following result for the weighted moving average:

WMA = (5 × 1.3325 + 4 × 1.3374 + 3 × 1.3350 + 2 × 1.3348 + 1 × 1.3345) / (5 + 4 + 3 + 2 + 1) = 1.33475

Once again, the result is somewhat different from both SMA and EMA.

A smoothed moving average (SMMA) is like a mix of a simple moving average and an exponential moving average. In general, it's calculated the same way as the EMA except that the multiplier α = 1 / N:



Consider the same example with the same 5 pieces of data. Let's calculate the multiplier:

α = 1 / 5 = 0.2

The SMMA of the first day is taken as the price of that day:

SMMA₁ = 1.3345

SMMA₂ = 1.3345 + 0.2 × (1.3348 - 1.3345) = 1.33456

SMMA₃ = 1.33456 + 0.2 × (1.3350 - 1.33456) = 1.33465

SMMA₄ = 1.33465 + 0.2 × (1.3374 - 1.33465) = 1.3352

SMMA₅ = 1.3352 + 0.2 × (1.3325 - 1.3352) = 1.33466

Although it's different from all of three previous variants of the MA, as you see, it's closer to the result obtained with the EMA calculation.

The nice thing about charting software is that you don't have to learn all these formulas; your charting platform will do your calculations for you. All you have to do is choose the periods you want to calculate the moving averages across and let the software display them for you. Here is a MetaTrader chart of the daily GBP/USD showing all four types of moving averages applied to the close for a period of 14:



Simple Moving Average: Red
Exponential Moving Average: Blue
Weighted Moving Average: Green
Smoothed Moving Average: Orange

As you can see in the chart, exponential and weighted moving averages are faster than simple moving averages, and smoothed moving averages are the slowest of all. The longer the period of any moving average, the greater the lag will be.

What can you do with moving averages? Most people who trade moving averages use them either to provide context for other systems or on their own in crossover systems. Moving averages tend to act as support and resistance levels; a lot of people like to place a slower moving average and a faster moving average on their chart, and then wait for the faster moving average to cross under nr over the slower one. This can indicate an opportunity to sell or buy respectively. But one should also remember that moving average is not some magic trading tool and it will often fail.

If you have any questions or want to share some useful info regarding various types of moving averages, please feel free to reply using the commentary link below.