Возможно ты попал по адресу! ))
Объявление простое.
Есть наработки в области [парного] кросс-трейдинга.
Последняя реализация ТС на С# для крипты. Надёжна и проста как "Теория всего."
Не ясна пока доходность, т.к. тестера не имеется.
Много граблей посчитано, шишки получены, достигнута определённая ясность сознания.
Могу поделиться опытом со страждущими!)
А так же идеями...
Взамен - взаимность, участие, энтузиазм, интерес к предмету.
Так же, в листе ожидания зависла задачка для программиста среднего уровня. Просто нет на это времени.
Задачка не очень сложная, но ставящая точку в представлении о доходности существующей ТС (кросс-трейдинг).
и вносящая ясность по её адаптации для форекс. Что потребует сосредоточенности и понимания mql-специфики.
Кому интересно, кликните Алексея в тг-группе: https://t.me/+1FQItKhhFtlhYjEy ( парадный вход: https://t.me/Equilibrium_liq/165 )
и пообщаемся.
#кросс_трейдинг #парный_трейдинг
MJR CatBoost Clusters Dream is an EA designed for major Forex currency pairs, built on the CatBoost machine learning algorithm and cluster analysis techniques applied to market data. During operation, the EA analyzes a large number of parameters derived from price action and moving averages. This data is processed by the CatBoost model, which identifies the most probable market entry opportunities. The following groups of features are used for market analysis: Distances Between Price and Moving
XAG CatBoost Clusters Dream EA is built on the CatBoost algorithm with market data clustering techniques. At its core, the model uses a set of technical features based on silver price data and moving averages (MA). As inputs, it takes distances between price and different MAs across multiple timeframes, as well as the distances between the moving averages themselves. This helps the model figure out how far price is stretched away from its average and how strong the current trend
GOLD CatBoost Clusters Dream EA operates on the CatBoost algorithm using clustering methods. A set of features is fed into the CatBoost model, constructed based on price data and moving averages (MA). These include distances between the price and MAs across different periods, as well as between the MAs themselves, which help capture deviation and trend strength. The features are normalized—by dividing by the price or MA—to make them scale-invariant. Additionally, rolling window
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