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Распределение
| Символ | Сделки | Sell | Buy | |
|---|---|---|---|---|
| WINQ24 | 291 | |||
| WINM24 | 166 | |||
| WINV24 | 57 | |||
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| Символ | Общая прибыль, USD | Убыток, USD | Прибыль, USD | |
|---|---|---|---|---|
| WINQ24 | 1K | |||
| WINM24 | 756 | |||
| WINV24 | -724 | |||
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| Символ | Общая прибыль, pips | Убыток, pips | Прибыль, pips | |
|---|---|---|---|---|
| WINQ24 | 3.6K | |||
| WINM24 | 5K | |||
| WINV24 | -3K | |||
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- Загрузка депозита
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Среднее проскальзывание на основе статистики исполнения на реальных счетах разных брокеров указано в пунктах. Зависит от разницы между котировками поставщика с "XPMT5-PRD" и подписчика, а также от задержек в исполнении ордеров. Чем меньше значение, тем лучше качество копирования.
|
XPMT5-PRD
|
1.76 × 2175 | |
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GenialInvestimentos-PRD
|
1.98 × 48 | |
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Rico-PRD
|
5.64 × 439 | |
ThrusterLABS executes an exclusive optimization method, called WFNSA - Walk Forward Neighborhood Statistical Analysis, which basically consists of:
1. Sequential macro-meso-micro optimization to the exhaustion of the trading parameters of the Robotizz experts on the historical IBOV data;
2. Construction of a big data sample of hundreds of thousands to millions of backtests for each robot, including different operating modes such as long and/or short, study of best times/days for trading, risk management, going through the core parameters of algorithm operation (averages, indicators, price action) up to the fine adjustment of take profit/stop loss.
3. All simulations, in their different stages of execution, use a training period (in-sample) vs. walk-forward (out-of-sample) of at least 30 to 50%. A combinatorial statistical criterion is applied to obtain the best configurations, taking into account both results obtained.
4. The best average performance set - a cluster of positive results, with relative variability of the parameters under analysis, is defined, by its average values, as the configuration setup for the next stage. The best result is not adopted, but rather those average values that are representative of the cluster, aiming to eliminate overfitting.
5. Once the average value of the winning cluster has been determined, the configuration ranges in relation to the average adopted in the previous step are expanded between 5 and 15%, in addition to testing on a lower and higher timeframe. This approach - Statistical Neighborhood Analysis - aims to evaluate the robustness and reliability of the average configuration obtained, stressing its values by expanding the possible ranges, and testing all possibilities again. If all iterations of the neighborhood resulting from the average setup obtained in the previous step are victorious (minimum Van Tharp SQN of 2.0 and average greater than 3.5, both in-sample and out-of-sample), the configuration for optimal operation of the robot under study is approved and is applied in a real account.
6. Finally, the methodology submits a random sample and also the best, average and worst results of the neighborhood to an analysis of Equity Chart and Trade Analysis in Quant Analyzer, seeking valuable and specific insights from this tool.
https://www.mql5.com/en/market/product/111740
Active (timeframe): WIN (M1).