Linear Gradiente v3
cBot
الإصدار 1.0، Aug 2025
Windows, Mac, Mobile, Web
صورة "Linear Gradiente v3" المحملةصورة "Linear Gradiente v3" المحملة
75
المبيعات
3.77K
التثبيتات المجانية

Statistics live data updated weekly. 5-year test sample


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.

Better performance in accumulation and distribution market cycles


Adjust your order according to your investor profile: minimum 2 orders , maximum 30


These are the parameters already optimized for EURUSD,GBPUSD

ON, GRAPH H1 LOW RISK ADX FILTER OFF.



Expected Results

Style: Hybrid Scalping and Swing Trading

Average monthly income: Variable (from 2% to 10%, depending on conditions)

Trade Time: Active during the selected time

Operation: Totally automated

ملف تعريف التداول
0.0
التقييمات: 0
تقييمات العملاء
لا توجد تقييمات لهذا المنتج حتى الآن. هل جرَّبته بالفعل؟ كن أول من يخبر الآخرين!
Stocks
Forex

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