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Sinergy Al Model AudUsdH1

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 analyze the market and provide more accurate trading signals.

To choose the lot size, an improved Kelly formula is used, taking the signal from one of the Al trading models. The stop, take profit, and risk values can be optimized according to personal preferences and risk level. The Al trading model overcomes the overfitting problem, and in the future, new improved models will be presented for other assets.

For training the Al trading model, such technologies as cross-validation, feature engineering, feature clustering, and data preprocessing were used. The Al trading model uses a hybrid of regression and classification models, which allows it to more effectively predict prices. Several new versions of the Al trading model are developed daily to achieve better results. In the future, it is planned to develop one model for each popular asset. The developer of the Al trading advisor is an author with over 8 years of experience in the market, an expert in financial markets.


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Hola, soy   NESCO   . Soy un experto en robots totalmente automáticos y analizo el mercado de forma independiente y tomo decisiones comerciales. Algunas de mis funciones están escritas usando   GPT-4_COPILOT   y optimizadas por   MQL5_CLOUD_NETWORK   . Tengo mi propio servidor para recibir eventos financieros en el mundo. Puedo trabajar para ti 24 horas al día, 5 días a la semana sin tu intervención y notificarte con un mensaje por teléfono si se necesita tu atención; Mi característica pr
Otros productos de este autor
MQL5 - a machine learning model for prop trading, developed by a real trader with over 8 years of experience and extensive experience in algorithmic trading. The model works on the euro/dollar pair in the hourly timeframe. The model was trained on a history segment from 2000 to 2010 and has been successfully operating on a real prop firm account with live monitoring. Our model is excellently stable to new data, avoids over-optimization and overfitting, passes shuffle tests, walk-forward tests, M
Stenco Recover EA
Yevgeniy Koshtenko
5 (1)
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 leve
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We present to you ShtencoNeuralLink - a revolutionary trading advisor with a unique idea that has been under development since 2022. ShtencoNeuralLink is based on a neural network, fully written in the MQL5 language. Its idea is absolutely new and has no analogues on the market, operating in completely different price and time coordinate systems, going beyond the classical Japanese candlesticks or bars. The heart of the advisor is a trainable neural network, whose weights are automatically selec
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Here is a quantitative analysis indicator for any market. Its essence is simple - it calculates the magnitude of the waves of price movement from top to bottom, and gives the median value of the magnitude of the non-recoil trend. If the current value of the movement has exceeded this threshold by more than 10% (110% of the indicator indicator), you can enter a trade against the trend and work with a large margin of action based on ATR. Transactions are obtained with a high risk-reward ratio and
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.
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 analyz
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 analyz
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
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, s
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 fe
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 ri
Asesor Experto de Red Neuronal de un desarrollador y trader experimentado con 8 años en el mercado. Este robot de alto rendimiento está creado utilizando tecnología de redes neuronales para analizar datos del mercado y tomar decisiones de trading. El modelo de red neuronal fue entrenado con datos históricos del par de divisas EURCHF desde el 2000 hasta el 2010.   Según las pruebas retrospectivas en datos históricos, el robot demuestra resultados impresionantes: Drawdown Máximo - 7% Ra
The classification model for MQL5 is a highly efficient tool for stable trading, based on a quality neural network and a variety of multidimensional features for model training. This model has ensured stable trading since 2007 and has passed a forward test since 2015, confirming its reliability and effectiveness. The use of a variety of multidimensional features allows for high accuracy in forecasts and optimal trading decisions. At the core of the robot is a quality neural network that ensures
MQL5 - a machine learning model for prop trading, developed by a real trader with over 8 years of experience and extensive experience in algorithmic trading. The model works on the euro/dollar pair in the hourly timeframe. The model was trained on a history segment from 2000 to 2010 and has been successfully operating on a real prop firm account with live monitoring. Our model is excellently stable to new data, avoids over-optimization and overfitting, passes shuffle tests, walk-forward tests, M
MQL5 - a machine learning model for prop trading, developed by a real trader with over 8 years of experience and extensive experience in algorithmic trading. The model works on the euro/dollar pair in the hourly timeframe. The model was trained on a history segment from 2000 to 2010 and has been successfully operating on a real prop firm account with live monitoring. Our model is excellently stable to new data, avoids over-optimization and overfitting, passes shuffle tests, walk-forward tests, M
MQL5 - a machine learning model for prop trading, developed by a real trader with over 8 years of experience and extensive experience in algorithmic trading. The model works on the euro/dollar pair in the hourly timeframe. The model was trained on a history segment from 2000 to 2010 and has been successfully operating on a real prop firm account with live monitoring. Our model is excellently stable to new data, avoids over-optimization and overfitting, passes shuffle tests, walk-forward tests, M
MQL5 - a machine learning model for prop trading, developed by a real trader with over 8 years of experience and extensive experience in algorithmic trading. The model works on the euro/dollar pair in the hourly timeframe. The model was trained on a history segment from 2000 to 2010 and has been successfully operating on a real prop firm account with live monitoring. Our model is excellently stable to new data, avoids over-optimization and overfitting, passes shuffle tests, walk-forward tests, M
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