Trend is friend X
- 专家
- Tomas Michalek
- 版本: 1.0
- 激活: 10
Trade the trend
Amazing trend strategy for GBPUSD H1, which uses KeltnerChannel and daily extremes.
No grid, martingale, tunned backtest or fairytails, but real results.
This EA has passed 9 robustness tests, indicating a quality strategy.
Benefits for you
Plug & Play system - you don't have to read long blog or study excessive technical description, only to be able to run the strategy. EA should be simple to use without demands on customer.
Every position has predefined stoploss with configurable fixed amount (you can risk fixed percenage of your initial balance).
Strategy is developed by genetic algorithms on long data period and it passed all 9 robustness tests, so the quallity of the strategy is verified.
Backtest shows history, but robustness tests indicate the future results, so they are more important, than a nice looking backtest.
Backtest is indicator of strategy performance - robustness tests tells you how probable these results are in the future.
Technical parameters
· CustomComment - choose your comment to distinguish strategy, or keep default
· MagicNumber - choose your number to distinguish strategy, or keep default
· mmRiskedMoney - configurable fixed amount, so you can risk portion of your initial balance
Screenshots
· Classic Metatrader 5 report for 10 years: great Profit Factor over time, perfect modeling quality, clear results, used 300$ fixed amount for every position
· Strategy equity for 17 years: nicely looking equity curve of backtest, which was done on precise data from Dukascopy from 2003 to 2020. Used default MM (300$).
· Strategy statistics for 17 years: see the results over 17 years long backtest - on historical data strategy had great Return/DD ratio, Profit Factor and stability.
· Monte Carlo analysis - randomized slippage, spread and historical data: simulation of real market conditions and test of strategy sensitivity to market volatility and liquidity. Lines similar to original backtest means good robustness of the strategy.
· Monte Carlo analysis - randomized trades order: test, which tells us whether the strategy is sensitive to specific market cycles. According to the picture, the strategy is not sensitive to the specific order of trades.
· Monte Carlo analysis - randomized strategy parameters: test against over-fitted strategy, even with randomly changed indicator parameters the strategy showed profitable results.
· Walk-forward matrix - complex series of simulations, where we optimize strategy parameters based on one period and then do the backtest on another period, comparing whether results are profitable. These steps are then repeated for the next time periods, which leads to the creation of a matrix of executed tests. The goal of this test is to find out, whether the strategy is over-fitted. If strategy won't work with slightly different parameters, it is most probably over-fitted and won't work in the future. You can see on the screenshot that the strategy was profitable for a lot of various optimization iteration on historical data.
