Credit Engine

HyperTrend’s proprietary on-chain credit engine redefines trust in Web3 through a multi-dimensional analysis, drawing insights from on-chain transaction data, social networks, and ecosystem engagement. It distills traders’ capability profiles and, leveraging trading proficiency as a credibility foundation, integrates social connections and on-chain interactions to generate dynamic credit scores. Dubbed the “digital blueprint of trading prowess and social trust,” this model provides a quantifiable trust cornerstone for core financial services.

Gravity Index

The Gravity Index is the most essential component of the HyperTrend credit engine. It evaluates a user’s trading behavior and performance based on over 300 on-chain data features, including time windows, trading pairs, risk exposure, and behavioral patterns. The index quantifies six key dimensions of trading ability, reflecting genuine profitability, strategy maturity, capital efficiency, and market adaptability, producing a composite score ranging from 0 to 1000.

Regular users can use the Gravity Index to identify outstanding traders to follow or learn from, while traders themselves can use it as a “quantitative mirror” to refine their strategies.

Design Principles

Unlike traditional centralized metrics that rely on single-dimensional profit data, the Gravity Index is multi-dimensional, time-sensitive, and behavior-driven. It integrates feature engineering, machine learning clustering and regression, hierarchical normalization, time-decay weighting, anomaly detection, and denoising techniques to produce a stable, interpretable, and manipulation-resistant scoring system.

Six Evaluation Dimensions

Dimension	Description
Profit Quality	Measures real profitability and sustainability, excluding luck or short-term variance.
Risk Control	Evaluates position management, stop-loss discipline, drawdown control, and capital protection ability.
Win Matrix	Analyzes win rate and profit/loss ratio under different market regimes to assess precision and adaptability.
Leverage Artistry	Assesses leverage utilization skills, evaluating ratio selection and margin efficiency for risk-reward optimization.
Market Phase	Gauges trend alignment, analyzing performance in ranging or trending markets to assess adaptive trading.
On-chain Footprint	Measures multi-chain activity, tracking interaction breadth across DeFi protocols and cross-chain frequency.

Weighting and Scoring Mechanism

The Gravity Index $S = \sum_{i=1}^{6} w_i D_i$ (where $\sum w_i = 1$) combines six dimension scores $D_i$(0-1000), with weights $w_i$dynamically optimized via backtesting and cross-validation to ensure long-term stability, short-term responsiveness, and resistance to manipulation.

Outputs and Interpretation

The model outputs two main results:

• Overall Score: A 0–1000 index representing total trading competence.

• Skill Map: A six-dimensional radar visualization showing performance strengths and weaknesses.

Social Credit

HyperTrend’s Social Credit mechanism innovatively incorporates social relationships into credit evaluation and risk management, enabling credit linkage and risk sharing through “invitation relationships,” fostering community growth and mutual trust networks.

Core Mechanism

Positive Incentive

When a high-credit user A invites a new user B and A’s credit score S_A exceeds the threshold S_th, B receives a bonus on their initial credit score:

$S_B = S_0 + α·min(S_A, S_max).$

If $S_A < S_th$, then $S_B = S_0$.

Risk Sharing

If the invited user B defaults, the inviter A receives a penalty according to:

$ΔS_A = -β·D_B·λ^t, with S_A’ = S_A + ΔS_A$

The inviter must also bear a proportional loss:

Loss_inviter = $β × L_B$

Parameters

$α$ is the bonus coefficient, $β$ is the risk-sharing coefficient, $λ$ is the time-decay factor, $D_B$ is B’s default ratio, and $L_B$ is the default loss amount.

Mechanism Balance

Positive incentives and risk sharing synergize to optimize user behavior, balancing credit propagation and risk distribution.

Implementation Details

Smart contracts record invitation links, automate credit boosts, and monitor defaults, with time and tier limits applied.

• Relationship Binding: Invitation codes or links are recorded and written to the blockchain.

• Bonus Limits: Prevent excessive reward amplification by applying tiered caps.

• Default Checks: The system periodically reviews invite chains; defaults trigger upper-layer score deductions.

• Tier Limitation: Only one direct invitation layer is traced; penalty magnitude is capped to prevent cascading loss.

Periodic Credit Update

During each update cycle, credit scores evolve according to:

$S_i(t+1) = S_i(t) + Σ α·min(S_j,S_max)·R_{j,i} - Σ β·D_k·λ^t·R_{i,k}$

Thus, user i’s social credit rises when invited by high-credit users and falls if their invitees default, forming a dynamic equilibrium network.

Ecosystem Engagement Score

The Ecosystem Engagement Score measures a user’s breadth and depth of participation across the Web3 ecosystem. It captures cross-chain activity, protocol engagement, asset management, and governance behavior, forming the most decentralized and tamper-resistant component of the entire credit engine.

Evaluation Logic

Dimension	Description
Cross-chain Activity	Assesses asset and interaction frequency across chains like Ethereum and Arbitrum.
Protocol Engagement	Tracks interaction types and frequency with DeFi, NFT, and DAO protocols.
Asset Health	Analyzes stablecoin ratios and asset distribution for risk management maturity.
Governance Behavior	Measures DAO voting, POAP claims, and ENS/DID binding as indicators of identity credibility.

Scoring Model

The system aggregates on-chain behavioral data to compute a composite ecosystem engagement score:

$F = Σ wj·Xj$

Here, $Xj$ denotes the j-th behavioral indicator (frequency, diversity, or governance activity), and $wj$ denotes its learned weight.

On-Chain Identity Credibility

To mitigate Sybil attacks and false identities, HyperTrend introduces an identity consistency factor $C_id$:

$C_id = 1 / (1 + e^(-θ(E - μ)))$

$E$ represents behavioral entropy, $μ$ is the threshold mean, and $θ$ is the sensitivity coefficient.

Higher $C_id$ values indicate natural, consistent user behavior and higher trustworthiness.

Credit Scoring and Influence

Dynamic Credit Update

Credit updates across periods follow:

$S_i(t+1) = S_i(t) + ρ1·G_i + ρ2·N_i + ρ3·F_i·C_id,i$

Where:

$S_i$ is the total credit score, $G_i$ is the Gravity Index, $N_i$ is the Social Credit, $F_i$ is the Ecosystem Engagement Score, $C_idi$ is the identity consistency factor, and $ρ1, ρ2, ρ3$ are adaptive weights.

Model Dynamics and Significance

• Cyclic Updates: The system recalculates all sub-scores in each cycle

• Feedback Loop: Positive behavior boosts scores; anomalies trigger reductions.

• Equilibrium: Weights balance individual risk, social collaboration, and ecosystem contribution.

Platform Privileges

The credit engine serves as the foundation for multiple product features.

High-credit users enjoy:

• Lower borrowing interest rates.

• Higher loan limits.

• Eligibility to apply for “Lead Trader” mentorship roles.

Low-credit users face:

• Reduced loan and leverage limits.

• Temporary restrictions until they rebuild reputation through responsible trading and participation.