🧬 Aelira Intelligence Functions

Core mechanisms powering Aelira’s perception engine

This section reveals the core intelligence formulas behind AeliraSense. Each function represents a unique method of perceiving risk, built with transparent logic — no black boxes, no mystery thresholds. These mechanisms form the predictive backbone of the system, scanning volatility, anomalies, and hidden threats in real time.

What you see here is exactly what the system sees.

1. AnomalyVision — Detecting Hidden Crypto Risks

function anomalyVision(transactionData) {
  const priceFluctuationRatio = Math.abs(transactionData.currentPrice - transactionData.previousPrice) / transactionData.previousPrice;
  const transactionWeight = transactionData.amount / transactionData.totalVolume;

  const anomalyScore = priceFluctuationRatio * transactionWeight;

  if (anomalyScore > 0.25) {
    return 'Alert: Hidden Risk Detected';
  } else {
    return 'Transaction Safe';
  }
}

🧠 What it does: AnomalyVision assesses price fluctuation intensity relative to the transaction's size. If a small token volume suddenly spikes in price and volume weight, it could indicate early-stage manipulation or insider movement. The function flags any transaction that produces an anomaly score greater than 0.25.

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2. InstabilityAlert — Predicting Market Instability

def instability_alert(market_data):
    volatility_score = market_data["price_change"] / market_data["previous_price"]
    liquidity_impact = market_data["total_volume"] / market_data["market_liquidity"]

    instability_risk = volatility_score * liquidity_impact

    if instability_risk > 0.6:
        return "Alert: Market Instability Predicted"
    else:
        return "Market Stable"

🌪 What it does: InstabilityAlert evaluates volatility pressure vs liquidity strength. High volume on weak liquidity can destabilize tokens fast. This AI-layer gives early warning before flash dumps or erratic price behavior.

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3. RiskPulse — AI-Based Risk Forecasting

def risk_pulse(asset_data):
    price_volatility = asset_data["price_change"] / asset_data["previous_price"]
    sentiment_score = asset_data["market_trend"]  # Range: 0 to 1

    risk_factor = price_volatility * sentiment_score

    if risk_factor > 0.75:
        return "Alert: High Risk Forecasted"
    else:
        return "Low Risk"

⚠️ What it does: RiskPulse blends volatility metrics with a market sentiment indicator (based on off-chain inputs like social signals, AI-labeled trends). The higher the volatility in a trending environment, the more likely the asset is overhyped or primed for correction.

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4. SafeGuard — Real-Time Asset Protection

function safeGuard(assetData) {
  const protectionIndex = assetData.price * assetData.demandFactor;
  const riskFactor = assetData.volatility / assetData.marketLiquidity;

  const assetProtectionScore = protectionIndex / riskFactor;

  if (assetProtectionScore < 2) {
    return 'Alert: Asset Protection Needed';
  } else {
    return 'Asset Safe';
  }
}

🛡 What it does: SafeGuard models how stable and protected a token appears under pressure. If demand is low, volatility is high, and liquidity is weak — the asset becomes vulnerable. This function acts like a "shield strength scanner" across assets.

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5. CryptoGuard — Multi-Layer Risk Detection

def crypto_guard(tx_data):
    complexity_score = (tx_data["amount"] * tx_data["token_age"]) / 1000
    market_risk = (tx_data["price_change"] * tx_data["market_depth"]) / tx_data["volume"]

    multi_layer_risk = complexity_score * market_risk

    if multi_layer_risk > 0.8:
        return "Alert: High Multi-Layer Risk Detected"
    else:
        return "Transaction Low Risk"

🧬 What it does: CryptoGuard aggregates temporal complexity (e.g., older tokens being suddenly moved in large size) with market friction (depth vs volume vs volatility). Useful in identifying complex setups like dormant whale wakes, bridge flows, or stealth accumulations.