🧩Feature Mechanics & AI Formulas

💻 Core algorithms powering real-time DeFi threat detection

1. DeFiWatch: Real-Time DeFi Threat Detection

def defi_watch(defi_data):
    price_change = defi_data["priceChange"]
    market_liquidity = defi_data["marketLiquidity"]
    token_volume = defi_data["tokenVolume"]

    risk_factor = (price_change / market_liquidity) * token_volume
    alert_threshold = 0.5

    # Calculate risk factor based on price change, liquidity, and token volume
    if risk_factor > alert_threshold:
        return "Alert: Potential DeFi Threat Detected"
    else:
        return "DeFi Market Stable"

Description: DeFiWatch continuously scans token markets and calculates a dynamic risk factor using three key parameters: price change, market liquidity, and token volume. This helps detect anomalies such as pump-and-dumps, low-liquidity traps, or sudden shifts in market sentiment. If the computed factor crosses the danger line, an instant alert is generated.

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2. WakeAlert: Token Behavior Analysis

function wakeAlert(tokenData) {
  const fluctuationRatio = Math.abs(tokenData.currentPrice - tokenData.previousPrice) / tokenData.previousPrice;
  const volumeImpact = tokenData.transactionVolume / tokenData.marketLiquidity;

  // Evaluate the alert based on fluctuation ratio and volume impact
  if (fluctuationRatio > 0.1 && volumeImpact > 0.5) {
    return 'Alert: Token Behavior Out of Normal Range';
  } else {
    return 'Token Behavior Normal';
  }
}

Description: WakeAlert focuses on sudden price movements and how they relate to current liquidity. When price volatility exceeds normal thresholds — especially during low liquidity phases — this often signals manipulation or coordinated wallet activity. WakeAlert helps users detect tokens acting “off-script” before it becomes obvious to the market.

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3. TokenSense: Suspicious Activity Monitoring

def token_sense(token_data):
    price_change = token_data["priceChange"]
    previous_price = token_data["previousPrice"]
    token_volume = token_data["tokenVolume"]
    market_liquidity = token_data["marketLiquidity"]

    price_impact = price_change / previous_price
    volume_ratio = token_volume / market_liquidity

    # Monitor for suspicious activity based on price impact and volume ratio
    if abs(price_impact) > 0.2 and volume_ratio > 1:
        return "Alert: Suspicious Token Activity Detected"
    else:
        return "Token Activity Normal"

Description: TokenSense detects irregular market behavior by evaluating how aggressively a token’s price is moving in relation to its environment. A spike in volume with disproportionate price movement can indicate wash trading, bot swarms, or stealth launches with malicious intent. TokenSense is calibrated to flag these moments before users fall into a trap.

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4. WalletGuard: Risk Alert System

function walletGuard(walletData) {
  const walletRiskScore = walletData.totalVolume / walletData.activeTokens;
  const alertThreshold = 0.75;

  // Calculate wallet risk score based on transaction volume and active token count
  if (walletRiskScore > alertThreshold) {
    return 'Alert: High-Risk Wallet Detected';
  } else {
    return 'Wallet Activity Normal';
  }
}

Description: WalletGuard identifies wallets acting with suspicious intensity, such as whales rotating through low-cap tokens, multi-token exploits, or sniper contracts. By comparing total transaction volume to the diversity of token holdings, it highlights wallets that pose a potential threat — especially useful in pre-launch and early market phases.

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5. DeFiShield: Multi-Chain Risk Detection

function defiShield(defiData) {
  const multiChainRisk = (defiData.priceVolatility * defiData.totalVolume) / defiData.marketLiquidity;
  const riskThreshold = 1.0;

  // Detect multi-chain risk based on volatility, volume, and liquidity
  if (multiChainRisk > riskThreshold) {
    return 'Alert: Multi-Chain Risk Detected';
  } else {
    return 'DeFi Safe';
  }
}

Description: DeFiShield is BitwakeScan’s upcoming module for cross-chain risk intelligence. It evaluates tokens with bridges or presence on multiple chains, calculating a composite risk level based on volatility, volume surges, and liquidity depth. The model is designed to catch events like synchronized exploits, cross-chain rug setups, and early-stage flashloan attacks before they ripple across ecosystems.