EUR 1 of 8 MT4
- 专家
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Tomas Michalek
I am constantly developing Expert Advisors with focus on high sustainability, which can't be proven only by nice looking backtest. I always do robustness tests, as I want to offer only quality strategies.
Contact me in the PM if you want to develop strategy for specific market and/or timeframe. - 版本: 1.0
- 激活: 10
Plug & Play portfolio - series of high-quality EURUSD H1 strategies for maximized success.
This EA alone can bring you great profits, but for the best results check also rest of the EUR portfolio.
Strategy is using CCI indicator for finding suitable trade and then sets the pending order according to daily extremes.
No grid, no martingale, no tuned backtest, no fairytales, but real results.
This EA has passed 9 robustness tests, indicating good strategy quality.
Benefits for you
Plug & Play system, so you don't have to read long blog or study excessive technical description, only to be able to run the strategy. Just attach it to the chart and let this strategy works for you.
Every position has predefined stoploss with configurable fixed amount (you can risk fixed percentage of your initial balance).
Strategy is developed by genetic algorithms on long data period and it passed all 9 robustness tests, so the quality of the strategy is verified.
Robustness tests are more important, than a nice looking backtest. Nicely looking backtest is useless if the strategy won't perform the same in the future, don't you think?
Backtest is only preview of past results, but robustness tests provide a realistic view of whether a strategy can perform well in the future or not. Strategy, which passes the tests is robust, stable, and with sustainable results.
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 stoploss amount, so you can risk only portion of your initial balance. 100 $ by default.
Screenshots
· 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.
