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no
경험
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11
제품
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4
데몬 버전
|
|
0
작업
|
1
거래 신호
|
0
구독자
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8년 이상의 시장 경험을 가진 숙련된 개발자 겸 트레이더가 만든 신경망 전문가 어드바이저입니다. 이 전문가는 오랜 기간 동안 시장을 연구하고 다양한 전략을 테스트하며 축적한 노하우를 바탕으로 이 로봇을 개발했습니다. 수많은 반복 학습과 최적화 작업을 거쳐 완성된 이 로봇은 정교하고 정밀한 매매 시스템을 갖추고 있습니다. 이 고성능 로봇은 신경망 기술을 사용하여 시장 데이터를 심층 분석하고 매매 의사결정을 내립니다. 신경망 모델은 2000년부터 2010년까지의 EURCHF 통화쌍 방대한 역사적 데이터로 훈련되어 다양한 시장 환경에 대한 적응력이 뛰어납니다. 역사적 데이터에 대한 백테스트 결과, 이 로봇은 인상적인 성과를 보였습니다. ✅최대 Drawdown은 7%로 낮은 수준이며 ✅샤프 비율은 3 이상의 높은 수치를 기록했습니다. ✅수익 요인 2.44, ✅회복 요인 약 3.76 등 우수한 지표를 달성했습니다. 높은 기대수익 메트릭스와 적절한 위험 수준을 결합하여 이 로봇은
Revolutionary Forex expert advisor, built on the principles of the unique Recovery trading system. The key to the effectiveness of this algorithm is the accurate analysis of price behavior near psychologically important round levels. Moreover, the system uses a special price representation in the form of coordinates different from traditional candlestick charts. Main advantages of the advisor: Recovery strategy, based on price recovery High accuracy in identifying key support and resistance
This is an expert advisor that combines advanced automated trading technologies. A unique algorithm based on machine learning and artificial intelligence models allows real-time market analysis and well-balanced trading decisions. Key features: System that adapts to changing market conditions Utilization of a averaging strategy to minimize risks High entry accuracy, confirmed by successful testing since 2008 Stable profitability, 67% profitable trades Sharpe ratio over 1, indicating efficient
Скорость майнинга: 0.83 гигахэшей в секунду
Оставшееся время при обычном переборе хэшей: 53595377703241.02 дней
Текущий хэш: bab82dc74aedc6a4ee2ded6f9e0f0ee3a1a192ed0c7cc4b035d0f370727c22c0
AI Grid Forex Advisor - The Power of AI Trading We are proud to introduce an innovative trading advisor, developed with the application of artificial intelligence (AI) technologies. At the core of our AI advisor lies a unique neural network model, mathematically based on a classification method developed by Soviet mathematicians. This model allows for the identification of complex nonlinear patterns in the currency market and the formulation of highly effective trading decisions. A key feature
Trader Zoya's Advisor - The Power of AI Trading We are proud to introduce Zoya, a cutting-edge trading advisor powered by advanced machine learning algorithms. Designed for automated AI trading on the EUR/USD currency pair, this expert advisor has demonstrated impressive results over the course of more than 24 years of testing. At the heart of Zoya's advisor lies a unique machine learning model, trained on historical market data starting from 2007. Despite facing major market disruptions, such
План до начала лета : обучить минимум 21 модель, по 5-7 на каждой паре, на разных парах. Целевая доходность в год у каждой по 2%, целевая просадка до 1% в год, и совокупная по всем парам не более 4-5%. Каждую из моделей я буду загружать в маркет MQL5, средняя цена по всем моделям будет 800 долларов.
А 1 июня начнется жара. Мы с другом возьмём проп, большой счёт. Проп израильский, мы так страхуемся от санкций, евреи никогда не будут вводить санкции против русскоязычных там, где русские могут помочь им заработать.
Все Вагнера торгуют там, кто понял тот понял) Я уже получил подключение к их реальному серверу, посмотрел там все, мои модели там успешно торгуют, все супер!
И сразу же в июне я запущу реалити шоу на 2 миллиарда тенге. Смысл этого пропа в масштабировании счета. Берете вы допустим счет на 50 000 долларов, при успешной торговле проп обязуется увеличивать ваш счёт на 30-50% за каждые 10% на счёте, вплоть до 4 000 000$.
Я сделаю публичный сигнал MQL5. Буду рассказывать о том, как идёт торговля, предоставлять отчётность. Все это будет в рамках реалити, и цель дойти до планки счета в 4 миллиона долларов за 2-3 года.
Как вам идея?
Hello! The Al trading model was trained on Australian dollar data, with a forward test starting in 2008. Eight years of training data correspond to 16 years of test data. The Al trading model uses 100 features and employs one XGBoost model and one CatBoost model. The Al trading model was trained on data up to 2008, enabling it to effectively predict future prices. The forward test out of the training segment can be observed after 2008. The Al trading model utilizes 100 features, allowing it to
Hello! The Al trading model was trained on Australian dollar data with a forward test starting in 2015. Six years of training data correspond to 18 years of test data. The Al trading model uses 100 features and one XGBoost model and one CatBoost model. The Al trading model was trained on data up to 2015, allowing it to effectively predict future prices. A forward test outside the training section can be viewed after 2015. The Al trading model uses 100 features that allow it to effectively
Hello! The Al trading model was trained on Australian dollar data with a forward test starting in 2010. Six years of training data correspond to 18 years of test data. The Al trading model uses 400 features and one XGBoost model and one CatBoost model. The Al trading model was trained on data up to 2010, allowing it to effectively predict future prices. A forward test outside the training section can be viewed after 2010. The Al trading model uses 400 features that allow it to effectively
Я сменил подход, сменил картинку. Сделал 10 лучших метрик оценки моделей классификации, научно обоснованных метрик, таких как точность, полнота, меткость, F1, и т.п. Вывел на основе этих метрик среднюю, сделал формулу.
Сразу же появилась новая проблема, дисбаланс классов. Если цена на исторических данных росла в течение 20-30 лет, лучшие метрики укажут на модели бычьего рынка, которые будут в итоге прогнозировать только покупки и все. Я с этим уже сталкивался, когда делал компьютерное зрение для анализа графиков. Пришлось внедрять балансировку классов по новому для меня методу, с весами классов.
Итог всей этой работы будет известен завтра, после тысячи обученных моделей и выбора одной лучшей. Тружусь для маркета, систему охлаждения ноутбука пожалуй надо уже менять)
Больше не придется мучиться, обучая сотни моделей, когда в прибыль идёт одна из сотни. Научные метрики нормально оценивают модели.
Hello! The Al trading model was trained on data in Australian dollars, and forward testing began in 2006. Six years of training data correspond to 18 years of test data. The Al trading model uses 99 features, one XGBoost model, and one CatBoost model. The Al trading model was trained on data up to 2006, which allowed it to effectively predict future prices. The forward test outside the training section can be viewed after 2006. The Al trading model uses 99 features that allow it to effectively
Hello! The Al trading model was trained on data in Australian dollars, and forward testing began in 2006. Six years of training data correspond to 18 years of test data. The Al trading model uses 99 features, one XGBoost model, and one CatBoost model. The Al trading model was trained on data up to 2006, which allowed it to effectively predict future prices. The forward test outside the training section can be viewed after 2006. The Al trading model uses 99 features that allow it to effectively
This Al trading expert advisor (EA) uses two machine learning models - Chinese XGBoost and Russian CatBoost, working in pairs and enhancing each other's signals. The Al trading advisor is specifically designed for the euro-dollar pair and trained on data from the RoboForex broker. By using two models, the Al trading advisor can effectively analyze the market and provide more accurate trading signals. The model is trained on data up to 2010, which allows it to effectively predict future prices
Разработка торгового робота на основе машинного обучения: подробное руководство. В первой статье цикла осуществлен сбор и подготовка данных и признаков. Для реализации проекта используется язык программирования Python и библиотеки, а также платформа MetaTrader 5.
How do market makers work? Let's consider this issue and create a primitive market-making algorithm.
We will analyze the question of what quantitative analysis is and how it is used by major players. We will create one of the quantitative analysis algorithms in the MQL5 language.