EUR 6 of 8
Plug & Play portfolio - series of high-quality EURUSD H1 strategies for maximized success.
The sixth strategy from the EUR-8 portfolio. Your only work here is to buy it, attach to the EURUSD on H1, set your risk and wait for results.
This fully automatic EA was Triple tested - backtest on 'Out of sample data', robustness tests, portfolio correlation.
No tunned backtest or over-fitted strategy. This is professionaly developed EA.
Benefits for you
Amazing Plug & Play system - studying configuration and finding the best optimization is history. This work is included in the price and I did it for you already.
Every position has predefined configurable stoploss as a fixed amount per trade (you can risk fixed percenage of your initial balance).
Strategy is developed by genetic algorithms on long data period and as always, it passed all 9 robustness tests, so you know what you can expect from future trades.
|New to robustness tests? Learn why I do extensive Monte Carlo saimulations and Walk-forward matrix test on all my EA's before I publish them. |
· CustomComment - choose your comment to distinguish strategy, or keep default
· MagicNumber - choose your number to distinguish strategy, or keep default
· mmRiskedMoney - configurable fixed stoploss amount, default amount is 100 USD
· Portfolio equity: combined backtest of all strategies together, for years 2003 to 2020.
· Portfolio parts equity: particular equities of strategies from the portfolio EUR-8, for years 2003 to 2020.
· Portfolio statistics: see the portfolio statistics from 2003 to 2020. You can see details like number of trades, return to drawdown ration and other parameters.
· Portfolio correlation: if two or more strategies have losses at the same month, it is not good for the portfolio. Portfolio correlation has to be taken seriously - see that no strategies correlate above 0.5, which means low correlation.
· Strategy equity: backtest of the strategy, tested on the data from Dukascopy, from 2003 to 2020.
· Strategy statistics: see the detailed statistics of the strategy backtest from 2003 to 2020.
· 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, which proves, that strategy is not over-fitted, as it has great backtest results even with changed parameters.
· 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.
· Classic Metatrader 5 report for 10 years: you can see clear results of the backtest with 99% model quality.