Struct Metrics 

#[repr(C)]
pub struct Metrics {
    pub last_updated: i64,
    pub weekly_active_users: u32,
    pub weekly_activity_count: u32,
    pub weekly_retention_rate: u16,
    pub community_score: u16,
    pub user_weight: u16,
    pub activity_weight: u16,
    pub retention_weight: u16,
    pub customer_reward_share: u16,
    pub merchant_reward_share: u16,
    pub _padding: [u8; 2],
}

Metrics stores weekly community health data used for reward decay calculation.

This struct implements the Enhanced Community-Driven Decay model which adjusts daily rewards based on community health metrics. The community score is calculated using a weighted geometric mean approach combining weekly active users, weekly activity count, and weekly retention rate with dynamic weights based on community age.

Formula

user_score = min((weekly_active_users / TARGET_WEEKLY_USERS) * 10000, 10000)
activity_score = min((weekly_activity_count / TARGET_WEEKLY_ACTIVITY) * 10000, 10000)
retention_score = min((weekly_retention_rate / TARGET_RETENTION_RATE) * 10000, 10000)
weights = oracle_provided_weights (no longer fixed phases)
community_score = weighted_geometric_mean([user_score, activity_score, retention_score], weights)

decay_factor = smooth_function(community_score) // 0.5 + 0.5 * (1 - (score/10000)^2)
daily_rewards = BASE_DAILY_REWARDS * decay_factor * time_decay / 100_000_000
customer_pool = daily_rewards * customer_reward_share / 10000
merchant_pool = daily_rewards * merchant_reward_share / 10000

Targets

• TARGET_WEEKLY_USERS: 10,000 active users per week
• TARGET_WEEKLY_ACTIVITY: 50,000 transactions per week (replaces volume-based target)
• TARGET_RETENTION_RATE: 70% retention rate (7000 basis points)

Dynamic Weight Strategy (Oracle Configurable)

• Launch Phase: [6000, 3000, 1000] - User focus, encourage activity
• Growth Phase: [4000, 3500, 2500] - Balanced metrics
• Maturity Phase: [3000, 3000, 4000] - Quality focus

Customer/Merchant Split

• Launch: 70% customers, 30% merchants (drive adoption)
• Growth: 60% customers, 40% merchants (balanced)
• Maturity: 50% customers, 50% merchants (equal partnership)

Decay Factors

• Smooth Decay: Gradual transition from 100% to 50% rewards based on community score
• Time Decay: Linear decay over 5 years, reaching 10% minimum
• Formula: 0.5 + 0.5 * (1 - (score/10000)^2)
• No sharp thresholds, eliminates gaming incentives

Daily Rewards Impact

• Healthy Community (score = 100%): 50,000 MIRACLE/day (50% of 100,000)
• Growing Community (score = 50%): 87,500 MIRACLE/day (87.5% of 100,000)
• Struggling Community (score = 0%): 100,000 MIRACLE/day (100% of 100,000)

Edge Cases

• Initial Launch (score = 0%): 100,000 MIRACLE/day (100% of base rewards)
• Zero Activity (no users/activity): 100,000 MIRACLE/day (100% of base rewards)
• Oracle Failure (stale data): Uses last known community score
• New Project: Full rewards encourage early adoption and community growth

Memory Layout

Optimized for 8-byte alignment with explicit padding to ensure consistent cross-platform behavior and minimal account size (48 bytes total).

Fields

last_updated: i64

Last update timestamp (Unix timestamp). Tracks when the metrics were last updated by the oracle.

weekly_active_users: u32

Weekly active users count. Used to calculate user score component of community health.

weekly_activity_count: u32

Weekly activity count (number of transactions, not amounts). Used to calculate activity score component of community health. Replaces volume-based counting for fair mediator platform rewards.

weekly_retention_rate: u16

Weekly retention rate in basis points (0-10000, 0-100%). Percentage of users who return week-over-week.

community_score: u16

Calculated community score (0-10000 basis points). Represents overall community health as percentage (0-100%).

user_weight: u16

User weight in basis points (0-10000) for weighted geometric mean. Determines importance of user count in community score calculation.

activity_weight: u16

Activity weight in basis points (0-10000) for weighted geometric mean. Determines importance of transaction count in community score calculation.

retention_weight: u16

Retention weight in basis points (0-10000) for weighted geometric mean. Determines importance of retention rate in community score calculation.

customer_reward_share: u16

Customer reward share in basis points (0-10000, 0-100%). Percentage of daily rewards allocated to customers.

merchant_reward_share: u16

Merchant reward share in basis points (0-10000, 0-100%). Percentage of daily rewards allocated to merchants. Must sum to 10000 with customer_reward_share.

_padding: [u8; 2]

Padding to ensure proper alignment (2 bytes to maintain 48-byte struct size)

Implementations

impl Metrics

pub fn to_bytes(&self) -> &[u8]

Trait Implementations

impl AccountValidation for Metrics

fn assert<F>(&self, condition: F) -> Result<&Self, ProgramError>

where F: Fn(&Self) -> bool,

fn assert_err<F>( &self, condition: F, err: ProgramError, ) -> Result<&Self, ProgramError>

where F: Fn(&Self) -> bool,

fn assert_msg<F>(&self, condition: F, msg: &str) -> Result<&Self, ProgramError>

where F: Fn(&Self) -> bool,

fn assert_mut<F>(&mut self, condition: F) -> Result<&mut Self, ProgramError>

where F: Fn(&Self) -> bool,

fn assert_mut_err<F>( &mut self, condition: F, err: ProgramError, ) -> Result<&mut Self, ProgramError>

where F: Fn(&Self) -> bool,

fn assert_mut_msg<F>( &mut self, condition: F, msg: &str, ) -> Result<&mut Self, ProgramError>

where F: Fn(&Self) -> bool,

impl Clone for Metrics

fn clone(&self) -> Metrics

Returns a duplicate of the value.

fn clone_from(&mut self, source: &Self)

Performs copy-assignment from source.

impl Debug for Metrics

fn fmt(&self, f: &mut Formatter<'_>) -> Result

Formats the value using the given formatter.

impl Discriminator for Metrics

fn discriminator() -> u8

impl PartialEq for Metrics

fn eq(&self, other: &Metrics) -> bool

Tests for self and other values to be equal, and is used by ==.

fn ne(&self, other: &Rhs) -> bool

Tests for !=. The default implementation is almost always sufficient, and should not be overridden without very good reason.

impl Zeroable for Metrics

fn zeroed() -> Self

Calls zeroed.

impl Copy for Metrics

impl Pod for Metrics

impl StructuralPartialEq for Metrics