4. How It Works?

AI-Driven Data Collection The protocol utilizes GAME SDK to aggregate real-time data from decentralized exchanges and market feeds.
Example formula for risk-adjusted return: [ R_a = \frac{\mu_a - R_f}{\sigma_a} ] where: ( R_a ) = risk-adjusted return ( \mu_a ) = expected portfolio return ( R_f ) = risk-free rate ( \sigma_a ) = standard deviation of portfolio returns
Dynamic Asset Rebalancing Using predictive algorithms, the AI agent reallocates assets dynamically:
Rebalancing weight formula: [ w_i^* = \frac{\mu_i / \sigma_i}{\sum_{j=1}^{n} \mu_j / \sigma_j} ] where: ( w_i^* ) = optimal weight of asset ( i ) ( \mu_i ) = expected return of asset ( i ) ( \sigma_i ) = risk of asset ( i )
User Dashboard The protocol provides a transparent dashboard displaying real-time portfolio performance and transaction history.

Last updated 5 months ago

AI-Driven Data Collection The protocol utilizes GAME SDK to aggregate real-time data from decentralized exchanges and market feeds.
Example formula for risk-adjusted return: [ R_a = \frac{\mu_a - R_f}{\sigma_a} ] where: ( R_a ) = risk-adjusted return ( \mu_a ) = expected portfolio return ( R_f ) = risk-free rate ( \sigma_a ) = standard deviation of portfolio returns
Dynamic Asset Rebalancing Using predictive algorithms, the AI agent reallocates assets dynamically:
Rebalancing weight formula: [ w_i^* = \frac{\mu_i / \sigma_i}{\sum_{j=1}^{n} \mu_j / \sigma_j} ] where: ( w_i^* ) = optimal weight of asset ( i ) ( \mu_i ) = expected return of asset ( i ) ( \sigma_i ) = risk of asset ( i )
User Dashboard The protocol provides a transparent dashboard displaying real-time portfolio performance and transaction history.

Last updated 5 months ago