⚙️ SenaraFlow – Under the Hood
🧠 Core Functions Powering the Stream
Below are the essential JavaScript functions that simulate the logic behind SenaraFlow’s AI modules. While these are simplified examples, they illustrate how we process liquidity data, detect market anomalies, and generate adaptive signals. These functions serve as the conceptual skeleton for our machine learning systems.
1. 🌀 LiquidityFlow: Real-Time Monitoring
def liquidity_flow(market_data):
liquidity_ratio = market_data['tokenVolume'] / market_data['marketLiquidity']
# If liquidity ratio is too low, flag the transaction as a risk
if liquidity_ratio < 0.1:
return 'Alert: Low Liquidity Detected'
else:
return 'Liquidity Flow Stable'Description: LiquidityFlow monitors the real-time health of a token's liquidity by comparing its trading volume to overall market liquidity. If the ratio is too low, it may signal slippage risk, a thin market, or deceptive volume.
Use Case:
Scanning tokens before entry
Spotting unstable liquidity environments
Early alerts on low-exit conditions
2. 📡 SignalBoost: Market Signal Evaluation
function signalBoost(signalData) {
const signalStrength = signalData.signalValue * signalData.reliabilityFactor;
const signalThreshold = 200;
// Evaluate if the market signal is strong enough
if (signalStrength > signalThreshold) {
return 'Alert: Strong Market Signal Detected';
} else {
return 'Market Signal Weak';
}
}Description: SignalBoost evaluates the strength and credibility of market signals by multiplying signal intensity with a reliability coefficient. When both align, the function triggers a high-priority alert.
Use Case:
Finding early-stage token trends
Filtering false signals
Detecting breakout patterns before volume peaks
3. 🔮 DataStream: Machine Learning Liquidity Forecasting
function dataStream(marketData) {
const liquidityRiskFactor = marketData.totalVolume / marketData.marketLiquidity;
const liquidityThreshold = 0.4;
// Predict liquidity risk by analyzing volume-to-liquidity ratio
if (liquidityRiskFactor > liquidityThreshold) {
return 'Alert: High Liquidity Risk Predicted';
} else {
return 'Liquidity Risk Low';
}
}Description: DataStream emulates an ML-driven prediction model that evaluates potential liquidity threats based on volume-to-liquidity dynamics. When trading volume exceeds what the market can reasonably support, the system flags elevated risk.
Use Case:
Anticipating liquidity traps or exit slippage
Detecting sudden over-inflated trading behavior
Preventing entry into potentially manipulated assets
4. 🛡 FlowGuard: Predictive Risk Management
def flow_guard(market_data):
liquidity_factor = market_data['tokenVolume'] / market_data['totalLiquidity']
risk_level = liquidity_factor * market_data['marketDepth']
# If the risk level is too high, trigger a liquidity alert
if risk_level > 0.5:
return 'Alert: High Liquidity Risk'
else:
return 'Liquidity Management Stable'Description: FlowGuard takes liquidity analysis deeper by incorporating market depth — a measurement of how strong the order book is behind the volume. The risk score reflects how well liquidity holds under pressure.
Use Case:
Testing if a token can support large exits
Flagging artificial volume without depth
Identifying tokens vulnerable to manipulation
5. 🔁 DynamicSignals: Adaptive Signal Intelligence
function dynamicSignals(signalData) {
const reliabilityScore = signalData.reliabilityFactor * signalData.frequency;
const threshold = 100;
// Evaluate if the signal strength and reliability justify an alert
if (reliabilityScore > threshold) {
return 'Alert: Significant Market Signal Detected';
} else {
return 'Signal Not Significant';
}
}Description: DynamicSignals adapts to evolving signal patterns by considering both the frequency and reliability of alerts over time. It reduces false positives and increases precision by learning what matters most in live trading conditions.
Use Case:
Identifying sustained trends vs. noisy pumps
Surfacing only high-confidence events
Tailoring signal sensitivity over time
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