Description
The linear gradient (or gradient in general) is a fundamental concept in calculus and linear algebra, used to describe the direction and the fastest rate of change of a function. In this case, the approach used for the Financial Market is to define greater gains as volatility increases in a range. We know that in the Forex market, price variation is constant. A solution was thought of to make gains from this inconstancy of the Forex market. the Gradient UMl pro robot specifically was adapted to several methods and filters with the aim of maximizing gains. The algorithm constantly monitors the price through the implemented filters, to position pending orders adapted to the target range according to the level of volatility. The machine learning function monitors and evolves according to the market progress, reducing risk by adjusting the stop and targets according to the price movement. The only disadvantage of this algorithm is a strong trending market, which can impact risk. However, if you know the market cycles and identify when a trend ends, when the accumulation and distribution period begins, you can maximize the performance of the algorithm by connecting to these periods.
Time chart m5 usdjpy, gbpusd, eurusd, optimized and configured, for low risk leave the adx filter on, turned off opens more positions but greater risk.
Expected Results
Style: Hybrid Scalping and Swing Trading
Average monthly income: Variable (from 2% to 14%, depending on conditions)
Trade Time: Active during the selected time
For better performance, adjust the parameter length = 20 from the 1st to the 15th of the month. From the 16th to the 30th, length = 12. Length = 0 remains in the automatic aggressive ML mode, recommended for the range market.
Summary
The bot incorporates a machine learning function that continuously adjusts stop-loss and target levels based on evolving market conditions to manage risk. Users can enable an ADX filter to reduce risk by limiting trade openings, or disable it to increase position frequency with higher risk. Parameter adjustments are recommended monthly to optimize performance: setting the length parameter to 20 during the first half of the month and 12 during the second half, or using an automatic aggressive machine learning mode for range-bound markets.
Trading activity is active during selected times, with expected average monthly returns varying between 2% and 14%, depending on market conditions. The system is best suited for markets exhibiting cyclical behavior, where trend endings and accumulation/distribution phases can be identified to maximize effectiveness.
