New Genius AI FTMO

New Genius AI FTMO

The trading method is based on the algorithm of the Iron Man trading advisor and the methodology for calculating the trading system from the Thomas advisor
AI NEW GENIUS  – has 5 stages of trade management (search, analysis, control, entry, exit) and 3 stages of trading decisions (adapted mathematical model of the Fourier and Laplace transforms), combining all this into one automatic full-cycle trading scheme. Using innovative trading methods with artificial and neural networks, it is a massive use for fast Forex trading.

Requirements for trading advisor:
Trade only instrument: XAUUSD
Minimum deposit 1000$
Working timeframe M5
ECN account type (use recommended partner broker with partner code to get spread from 0 pips)

AI algorithms can analyze vast amounts of data, including historical prices, trading volumes, news events, and technical indicators, to make decisions to buy or sell assets. The core of the trading bot includes reworking the trading strategy every 2 weeks, which allows you to find the best entry points.

Accuracy and speed improvement:
AI-powered trading bots are able to analyze data with unparalleled accuracy and speed, allowing them to make decisions instantly and respond to market changes in real time. AI can process large amounts of data and perform complex calculations

Functionality of a trading advisor

  • Core AI
    Remote control
    Trading panel
    Virtual order system

Core AI – determines the future possible price movement.
Upon receipt of a deviation, the native begins to simulate a movement with 25 possible entry points.
part of the code describing – Core AI

The EA goes through a process of optimization and training to achieve maximum efficiency. We use historical market data to train and evaluate the performance of the Expert Advisor. The learning process includes the following steps:

Data collection: We collect extensive historical data about the Forex market, including open and close prices, highs and lows, and trading volumes. This data serves as the basis for training our neural networks.

Neural Network Training: We apply supervised learning to train our neural networks where the input consists of historical prices and technical indicators and the output contains future price predictions. Neural networks go through several training periods, adjusting their weights to improve the accuracy of their predictions.

Parameter Optimization: We also optimize the parameters of our EA using various methods such as genetic algorithms or gradient descent. This allows us to fine-tune the EA’s parameters for optimal performance and risk reduction.

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