Backtest method and results (2001 - 2013)
Last updated on 22/12/2012
The forex beginner strategy is an easy learning tool that allows you to practice trading in a simple and beginner-friendly way. We recommend that you trade on a demo account with play money, until you are experienced enough to make a conscious and risk-aware decision whether to trade for real money.
At the same time you can explore different trading concepts, other ways of analysing charts and start developing your own strategies and ways of trading.
In this lesson, we will show you the backtesting results of the forex beginner strategy for the EUR/USD currency pair since January 2001. A backtest simulates the performance of a strategy based on historical data, to estimate how a strategy would have performed if it had been used.
Things to bear in mind when looking at backtesting results
There are key points that you must be aware of before looking into backtest results.
Past performance is not indicative of future results
The forex market environment can change, and this can significantly impact the performance of a strategy. Thus, a trading strategy that would have been very successful in the past years might not be successful going forward. Experienced forex traders are often able to judge under which overall marketing conditions a strategy can be expected to do well. Until you have gained such knowledge, be careful before relying on any one strategy.
Backtesting is not an exact science
There are different factors that influence the outcome of a backtest. For example, different forex brokers will have different spreads, and so using a variety of brokers will produce variations in the results. Another thing to bear in mind is that price feeds also vary between brokers, which means that indicators such as fractals could appear at different spots depending on the data used.
Then, there will also be difference based on whether a backtest is done algorithmically or whether it is done by a real person going over historical price data. In our case, we used an algorithmic approach to reduce the impact of human error.
Finally, there is a difference between backtesting a trading strategy and applying a strategy in a real environment. In a real environment you are more likely to make mistakes or react a bit too slowly to changes in price. Also, depending on your broker, factors such as execution speed and slippage might impact the results.
Trading large positions can yield different results
The larger an account gets, the more you can risk and hence more volume you can trade. However, trading very large amounts can actually produce a unique problems, because you can actually move the price of the asset you are trading, simply by putting on a trade.
This tends not to be an issue when trading very small sizes, however you should note that when you start to approach a large enough volume, you may actually move the price. This could potentially give you slower execution as well as less desirable prices in order for your entire order to be filled.
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The backtesting method used
As the rules of the basic beginner strategy are purely mechanical, we created an algorithm that applies the strategy to real historical EUR/USD data ranging from 1st January 2001 until 30th November 2012.
The algorithm was instructed to only consider trades that happen in the EUR/USD prime time, which is from 8:00 GMT until 17:30 GMT. After 17:30 GMT, no new trades were opened. All remaining open trades were closed at 18:00 GMT.
For the test, we assumed an average spread of 1.5 pips. The algorithm worked on the bid prices, with the spread being substracted from the end result of the trade. Note that when trading for real, one needs to take into account the spread when setting up the trades.
We also assumed a 0.4 pip distance between any candle high/low and the fractal tip.
The money management method used was not the general method given in the beginner strategy. Instead we used the advance method of determining the pip size, called the optimal position size.
We used our best efforts to make the backtesting as accurate as possible using the above method, but can give no guarantee that our backtest has been error free.
The above method generated 7000 trades in the time period. You can download an excel file giving the details of each trade here:
The 7000 trades in this period of time were then analysed:
How does the profitability in pips translate to returns?
In order to determine how a profit in pips translates into an actual profit, the position sizes used are key. Larger position sizes mean higher risk, but also higher returns.
We have simulated the fictional returns of the beginner strategy backtest using the following simulation:
We took a fictional starting capital of 100,000 USD.
For each trade, we determined the position size based on risking 2% of the total equity on any single trade.
This means that if the stop loss was hit in any trade, exactly 2% of the capital would be lost.
To determine the amount of capital gained or lost per pip in a given trade we used the following formula:
Capital gained or lost per pip = (Max risk per trade)/(Distance entry price to initial stop loss in pip)
Where Risk per trade = Trading capital * 0.02.
As each trade affects our trading capital – it will increase, decrease or stay the same depending on the result of the trade – it is recalculated after each trade by adding the respective profit or loss.
Using this approach, and again assuming a spread of 1.5 pip, we get the following results:
If you would like to look at the charts for each year, then you can check the following lesson.
Again, we would like to remind you that past performance of a backtest is not indicative of future results. The forex beginner strategy is a learning tool that allows you to practice trading in a simple and beginner-friendly way.
We recommend you to trade on a demo account with play money until you are experienced enough to make a conscious and risk-aware decision about whether to trade for real money. If you decide to use the beginner strategy for real money trading, you do so at your own risk.