Keep CALM and Trade SMART

This EA is only for serious traders, who wants really robust strategy for longterm and sustainable profits. 

Strategy uses MACD direction change for finding trading opportunities. It is fully automatic and doesn't require any action from you. Just attach it to the EURUSD H1 chart and let this EA works for you.

This EA has passed 9 robustness tests, indicating a quality strategy.

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: you can see clear results thanks to 99% model quality, great profit over the years with only 100$ risk, used default MoneyManagement, used data from broker Admirals.

· 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.