Investment hedging
When the volatility of a given asset rises, the system suggests reallocating part of the portfolio to reduce direct exposure, which reduces cash withdrawals in periods of sharp decline.
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Time differences close the decision window before you open your screen. An intermittent internet connection interrupts you from the latest price update. Data scattered between multiple applications makes every decision based on incomplete information.
A manual decision in these circumstances carries additional risk, not because the investor is incompetent, but because the information itself is late or incomplete.
Technical engine
ArabTotu connects the APIs of multiple trading and conversion platforms, cleans and standardizes data before passing it to predictive analysis models. The result is continuous, real-time optimization and reduced risk exposure without repetitive manual intervention.
Models do not predict absolutely, but rather monitor recurring patterns in price movement and liquidity, and transform them into digital indicators that can be measured and compared.
Mechanism of action
The system pulls price, volume and liquidity data from each connected platform every minute, and stores it in a standardized format that is comparable between different sources.
The models examine the recent history of an asset, compare it to similar past patterns, and assign a numerical confidence score to each pattern detected.
The result is presented to you as a clear recommendation: entry point, risk level, and suggested stop limit. The final decision always remains with you.
Use cases
When the volatility of a given asset rises, the system suggests reallocating part of the portfolio to reduce direct exposure, which reduces cash withdrawals in periods of sharp decline.
The system monitors the instantaneous price differences between two different platforms on the same asset, and identifies a window of opportunity before prices converge again.
Instead of entering with a single amount, the system calculates tiered entry points based on historical support levels, to optimize the average entry price over time.
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