I trade this strategy in the Principle Analysis Portfolio. The GBP/JPY is a very volatile pair and can be tough to trade manually as a result. Trying to find viable large timeframe strategies on this pair would be a challenge in my view as this is more of a short term, sharp moving, pivoting pair. So I used StrategyQuant Pro v3.8 to develop small timeframe range method that uses the Average True Range volatility indicator to catch big moves in a short period of time, and sustain a profit over a long period of time.
These reports are traded based on a $5,000 account with the smallest lot size of MT4's .01 lot size to allow everyone to easily do the relevant math to translate that to their own trading lot sizes. This strategy made 18,426 pips in about 8 years with a minimal drawdown of $273 (5%), and a Return to Drawdown ratio of 5.72. This means that it returns 5.72 times more money than the money lost during drawdowns. You can see from the above chart that although 2008 was a stagnate year for the strategy, all other years have a nice smooth and steady increase.
This is quite simply the monthly performance of the strategy returned in US dollars per every .01 lots.
The above charts breakdown the trading timeframes a little more. I like strategies that can trade well and in the profit every year and every day of the week. 2008 was not a profitable year, however the loss was small, and anomalies in market behavior should be allowed at some degree. In forex, I allow for strategy success during a specific timeframe because some pairs are more active in certain sessions than others. And some strategies rely on volatility and volume to be successful, and so picking a specific session in the 24 hour cycle is definitely an acceptable method that does not reduce the robustness of the strategy. As you can see with this strategy, this pair trades well during the European/London session (timezones in chart are GMT +0), This makes sense since this pair includes the British Pound, and the most profitable period surrounds the opening hours of the session.
This the Monte Carlo analysis I ran on the strategy. This tests strategy robustness, which basically determines if the strategy is form fitted to specific set of data and/or timeframe, or if it's able to be successful in any period and data selected, which is how future market movement would play out. For this test I randomized the order of the trades, strategy parameters with a probability of 20% and a max change of 20%, starting bars with a max change of 100, and I randomized history data with a probabiliy of 20% and a max price change of 10% of the Average True Range volatility indicator.
The results are what I could call decent, but definitely not outstanding. The test returns a 95% degree of confidence that the strategy will not produce a result below $1097. This is quite a bit beneath the original strategy result, which is something I have to watch for moving forward. But again, this currency pair is extremely wild and volatile, and I'm trading it on the 5min timeframe. So any amount of stability in the strategy is something to hang on to. You can see that all the robustness tests did perform fairly well (different colored lines on the chart), so regardless of the reduction in profit from the original strategy, every test still produced a solid profit nonetheless.
Walk Forward Matrix Optimization is a very rigorous optimization and robustness test for your strategy. Because of this, it is very difficult to pass this test. Walk Forward Matrix Optimization is a set of Walk Forward optimizations performed with a different set of optimization periods, parameters and out of sample percentages (robustness tests). The results will tell if your strategy will benefit from periodic optimizations, and if the strategy is robust enough to sustain a solid profit in the future.
You can see from the results that this strategy performed well during Walk Forward Matrix Optimization, and actually had a small increase in Net Profit from $1,566 to $1,626. The report shows that 8 out of 18 parameter combinations passed, the best grouping of combinations passed 7 out of 9 times, and all runs produced solid in-sample and out of sample net profit results. Lastly, the test tells me that this strategy is best reoptimized every 321 days with a history of 747 days on 7 runs with 30% out of sample. Basically what this means is that every 2 years I will reoptimize the strategy on about a year's worth of history data.
I feel that with the parameters, trading method and timeframe established for this pair, along with the timeframe and currency pair used, this strategy is robust enough to trade in my portfolio and is now an active part of my portfolio.
Follow the performance of this strategy and others at Principle Analysis Portfolio.
Find and develop your own strategy: StrategyQuant Pro v3.8
PLEASE NOTE: THIS IS JUST AN ANALYSIS BLOG AND IN NO WAY GUARANTEES OR IMPLIES ANY PROFIT OR GAIN. THE DATA HERE IS MERELY AN EXPRESSED OPINION. TRADE AT YOUR OWN RISK.
