6/ Advanced Risk Management

Core Risk Management Features

1. Rug Pull Detection:

   • A rug pull occurs when liquidity is suddenly withdrawn from a token’s pool, leaving investors unable to sell their holdings.

   • Detection Mechanism:

     • Dash monitors liquidity shifts in real-time across multiple decentralized exchanges (DEXs).

     • Liquidity Drain Formula: Ldrop=ΔLLinitialL_{\text{drop}} = \frac{\Delta L}{L_{\text{initial}}}Ldrop​=Linitial​ΔL​ Where:

       • Ldrop>50%L_{\text{drop}} > 50\%Ldrop​>50% within a 5-minute window triggers an alert.

     • Example:

       • Initial liquidity (LinitialL_{\text{initial}}Linitial​): $500,000.

       • Liquidity after 5 minutes (LfinalL_{\text{final}}Lfinal​): $200,000.

       Ldrop=500,000−200,000500,000=60%L_{\text{drop}} = \frac{500,000 - 200,000}{500,000} = 60\%Ldrop​=500,000500,000−200,000​=60%

       • A rug pull alert is generated with high priority.

2. Pump-and-Dump Prevention:

   • Pump-and-dump schemes involve artificially inflating a token’s price before selling large holdings, causing a crash.

   • Anomaly Detection:

     • Dash uses volume-price divergence (VPDVPDVPD) analysis: VPD=ΔPΔVVPD = \frac{\Delta P}{\Delta V}VPD=ΔVΔP​ Where:

       • High VPDVPDVPD values (>3> 3>3) indicate unusual price increases compared to volume growth.

     • Example:

       • Price increases by 100%, but volume grows by only 10%.

       VPD=10010=10VPD = \frac{100}{10} = 10VPD=10100​=10

       • Dash flags the token as a potential pump.

3. Bot Activity Monitoring:

   • Dash identifies bot-driven transactions that can manipulate token prices or exploit DEXs.

   • Behavioral Analysis:

     • Detects high-frequency, low-latency transactions (<10ms<10ms<10ms).

     • Flags wallets executing identical trades in rapid succession.

Scam Token Analysis

Dash evaluates the legitimacy of new tokens using a multi-dimensional analysis framework:

1. Contract Security:

   • Checks for unverified contracts or those with suspicious functions (e.g., minting or transfer ownership).

   • Flags contracts where:

     • Ownership is not renounced.

     • Functions like setOwner() or mint() remain active.

2. Holder Distribution:

   • Monitors token distribution to identify centralization risks: Wtop=Top 10 Wallets’ HoldingsTotal SupplyW_{\text{top}} = \frac{\text{Top 10 Wallets' Holdings}}{\text{Total Supply}}Wtop​=Total SupplyTop 10 Wallets’ Holdings​

     • Example:

       • If the top 10 wallets hold 80% of supply: Wtop=80100=0.8 (80%)W_{\text{top}} = \frac{80}{100} = 0.8 \, (80\%)Wtop​=10080​=0.8(80%)

       • The token is flagged for excessive centralization.

3. Social and Website Analysis:

   • Analyzes a project’s online presence for inconsistencies or red flags:

     • Fake social media followers (e.g., low engagement-to-follower ratio).

     • Cloned or incomplete websites (e.g., lacking SSL/TLS certificates).

Wallet Risk Tracking

Dash Wallet Risk Tracking module analyzes wallet activity to detect potentially harmful behavior:

1. Whale Monitoring:

   • Identifies wallets with significant holdings capable of influencing the market.

   • Example:

     • A wallet holding 10% of liquidity executes a sell order:

       • Dash issues an alert predicting potential price impacts.

2. Coordinated Wallets:

   • Detects wallets acting in concert (e.g., multiple wallets executing trades at identical timestamps).

   • Flags clusters of wallets with high transaction interconnectivity using graph analysis.

Fraud Scoring and Alerts

Dash assigns a Fraud Risk Score (FRS) to tokens and wallets based on observed behaviors:

FRS=w1⋅Rrug+w2⋅Rpump+w3⋅RwalletFRS = w_1 \cdot R_{\text{rug}} + w_2 \cdot R_{\text{pump}} + w_3 \cdot R_{\text{wallet}}FRS=w1​⋅Rrug​+w2​⋅Rpump​+w3​⋅Rwallet​

Where:

• RrugR_{\text{rug}}Rrug​: Rug pull risk score.

• RpumpR_{\text{pump}}Rpump​: Pump-and-dump risk score.

• RwalletR_{\text{wallet}}Rwallet​: Wallet risk score.

• w1,w2,w3w_1, w_2, w_3w1​,w2​,w3​: Weights assigned based on risk priority.

Risk Levels:

• Low Risk (FRS<0.3FRS < 0.3FRS<0.3): Token or wallet behavior appears normal.

• Medium Risk (0.3≤FRS<0.70.3 \leq FRS < 0.70.3≤FRS<0.7): Potential anomalies detected.

• High Risk (FRS≥0.7FRS \geq 0.7FRS≥0.7): Strong evidence of fraudulent activity.

Example: Comprehensive Scam Alert

Scenario: A newly launched token shows suspicious activity:

1. Contract Analysis:

   • Ownership is not renounced, and minting functions are active.

2. Holder Distribution:

   • Top 5 wallets hold 90% of the supply.

3. Liquidity Monitoring:

   • A 60% liquidity drop is detected within 3 minutes.

4. Fraud Risk Score:

   • Rrug=0.8R_{\text{rug}} = 0.8Rrug​=0.8, Rwallet=0.7R_{\text{wallet}} = 0.7Rwallet​=0.7, Rpump=0.6R_{\text{pump}} = 0.6Rpump​=0.6.

   FRS=0.4⋅0.8+0.3⋅0.7+0.3⋅0.6=0.71FRS = 0.4 \cdot 0.8 + 0.3 \cdot 0.7 + 0.3 \cdot 0.6 = 0.71FRS=0.4⋅0.8+0.3⋅0.7+0.3⋅0.6=0.71

Result: The token is flagged as High Risk, and users are alerted immediately.

Future Enhancements

1. Real-Time Scam Reports:

   • Integration with community-driven platforms to validate suspicious tokens.

2. AI-Powered Rug Pull Predictions:

   • Advanced ML models to predict rug pulls before they occur based on liquidity patterns.

3. Enhanced Scam Visualizations:

   • Interactive bubble maps for tracking coordinated wallet activity.