Optimized ML predictor with adjusted pink times and enhanced accuracy
// ==UserScript==
// @name Betway Aviator Predictor
// @namespace http://tampermonkey.net/
// @version 8.0
// @description Optimized ML predictor with adjusted pink times and enhanced accuracy
// @author You
// @match https://www.betway.co.bw/*aviator*
// @match https://www.betway.co.bw/lobby/Casino/featured/Aviator*
// @match *://aviator.*
// @grant GM_setValue
// @grant GM_getValue
// @grant GM_deleteValue
// @license MIT
// ==/UserScript==
(function() {
'use strict';
// Enhanced Configuration with Optimized Settings
const CONFIG = {
HISTORY_LIMIT: 1000,
UPDATE_INTERVAL: 500,
DEBUG: true,
MEMORY_KEY: 'aviator_ml_v8',
PINK_THRESHOLD: 3.0,
BIG_PINK_THRESHOLD: 5.0,
// Reduced Prediction Interval Settings for Higher Accuracy
INTERVAL_SETTINGS: {
ENABLED: true,
CONFIDENCE_LEVEL: 0.85, // Increased to 85% confidence
MIN_SPREAD: 0.10, // Reduced from 0.20 to 0.10
MAX_SPREAD: 1.20, // Reduced from 2.00 to 1.20
RISK_MULTIPLIER: 1.1 // Reduced from 1.2 to 1.1
},
// Enhanced ML Model Settings
ML_SETTINGS: {
SEQUENCE_LENGTH: 12, // Increased from 10 to 12
PATTERN_DEPTH: 6, // Increased from 5 to 6
CONFIDENCE_THRESHOLD: 0.65, // Increased from 0.6 to 0.65
LEARNING_RATE: 0.015, // Slightly increased
ENSEMBLE_WEIGHTS: {
neuralNetwork: 0.35, // Increased neural network weight
sequencePattern: 0.30, // Increased sequence pattern weight
markovChain: 0.20,
lstm: 0.10, // Reduced LSTM weight
statisticalModel: 0.05 // Reduced statistical weight
}
},
// Adjusted Time Windows (reduced by 1.5 minutes each)
TIME_WINDOWS: {
ENABLED: true,
ADJUSTMENT_FACTOR: 1.0,
WINDOWS: [
{
start: 55, end: 57, // Was 57-59, now 55-57
priority: 'HIGH',
name: 'PRIME PINK',
multiplier: 1.9, // Increased multiplier
pinkProbability: 0.90, // Increased probability
avgPinkValue: 6.8
},
{
start: 59, end: 1, // Was 1-3, now 59-1 (crosses hour boundary)
priority: 'HIGH',
name: 'PRIME PINK',
multiplier: 1.9,
pinkProbability: 0.90,
avgPinkValue: 6.8
},
{
start: 18, end: 20, // Was 20-22, now 18-20
priority: 'MEDIUM',
name: 'PINK ZONE',
multiplier: 1.4, // Increased multiplier
pinkProbability: 0.70, // Increased probability
avgPinkValue: 4.5
},
{
start: 27, end: 29, // Was 29-31, now 27-29
priority: 'MEDIUM',
name: 'PINK ZONE',
multiplier: 1.4,
pinkProbability: 0.70,
avgPinkValue: 4.5
},
{
start: 38, end: 40, // Was 40-42, now 38-40
priority: 'MEDIUM',
name: 'PINK ZONE',
multiplier: 1.4,
pinkProbability: 0.70,
avgPinkValue: 4.5
},
{
start: 43, end: 45, // Was 45-47, now 43-45
priority: 'MEDIUM',
name: 'PINK ZONE',
multiplier: 1.4,
pinkProbability: 0.70,
avgPinkValue: 4.5
},
{
start: 48, end: 50, // Was 50-52, now 48-50
priority: 'MEDIUM',
name: 'PINK ZONE',
multiplier: 1.4,
pinkProbability: 0.70,
avgPinkValue: 4.5
}
]
}
};
// Global state
let roundHistory = [];
let currentPrediction = null;
let mlEnsemble = null;
let currentTimeWindow = null;
let modelPerformance = {};
let patternMemory = new Map();
// Enhanced storage system
function loadData() {
try {
const saved = GM_getValue(CONFIG.MEMORY_KEY, null);
if (saved) {
const data = JSON.parse(saved);
roundHistory = data.history || [];
modelPerformance = data.performance || {};
if (data.patternMemory) {
patternMemory = new Map(data.patternMemory);
}
if (data.timeWindows) {
Object.assign(CONFIG.TIME_WINDOWS, data.timeWindows);
}
if (data.intervalSettings) {
Object.assign(CONFIG.INTERVAL_SETTINGS, data.intervalSettings);
}
debugLog(`Data loaded: ${roundHistory.length} rounds, ${patternMemory.size} patterns`);
}
} catch (error) {
debugLog('Error loading data:', error);
loadBackupData();
}
}
function saveData() {
try {
const dataToSave = {
history: roundHistory.slice(0, CONFIG.HISTORY_LIMIT),
performance: modelPerformance,
patternMemory: Array.from(patternMemory.entries()),
timeWindows: CONFIG.TIME_WINDOWS,
intervalSettings: CONFIG.INTERVAL_SETTINGS,
lastUpdate: Date.now(),
version: '8.0'
};
GM_setValue(CONFIG.MEMORY_KEY, JSON.stringify(dataToSave));
const saveCount = GM_getValue('save_count', 0) + 1;
GM_setValue('save_count', saveCount);
if (saveCount % 10 === 0) {
GM_setValue(CONFIG.MEMORY_KEY + '_backup', JSON.stringify(dataToSave));
debugLog('Backup created');
}
} catch (error) {
debugLog('Error saving data:', error);
}
}
function loadBackupData() {
try {
const backup = GM_getValue(CONFIG.MEMORY_KEY + '_backup', null);
if (backup) {
const data = JSON.parse(backup);
roundHistory = data.history || [];
modelPerformance = data.performance || {};
debugLog('Loaded from backup');
}
} catch (error) {
debugLog('Error loading backup:', error);
}
}
function debugLog(message, ...args) {
if (CONFIG.DEBUG) {
console.log(`[Aviator ML v8.0] ${message}`, ...args);
}
}
// Enhanced CSS
function injectCSS() {
const style = document.createElement('style');
style.textContent = `
@import url('https://fonts.googleapis.com/css2?family=Inter:wght@300;400;500;600;700&display=swap');
.aviator-ai-overlay {
position: fixed;
top: 20px;
right: 20px;
width: 360px;
background: linear-gradient(135deg, #1a1a2e 0%, #16213e 100%);
border: 1px solid #0f3460;
border-radius: 12px;
box-shadow: 0 8px 32px rgba(0, 0, 0, 0.4);
font-family: 'Inter', sans-serif;
color: #ffffff;
z-index: 999999;
backdrop-filter: blur(15px);
max-height: 90vh;
overflow-y: auto;
}
.aviator-header {
padding: 12px 15px;
border-bottom: 1px solid #0f3460;
display: flex;
justify-content: space-between;
align-items: center;
cursor: move;
background: rgba(0, 212, 255, 0.1);
}
.aviator-title {
font-weight: 600;
font-size: 14px;
color: #00d4ff;
}
.header-controls {
display: flex;
gap: 8px;
align-items: center;
}
.minimize-btn {
background: none;
border: none;
color: #00d4ff;
cursor: pointer;
font-size: 12px;
padding: 2px 4px;
font-weight: bold;
border-radius: 3px;
transition: background 0.3s ease;
}
.minimize-btn:hover {
background: rgba(0, 212, 255, 0.2);
}
.aviator-content {
padding: 12px;
}
.section {
margin-bottom: 12px;
border-radius: 8px;
padding: 10px;
}
/* Enhanced Time Alert Section */
.time-alert-section {
background: linear-gradient(135deg, rgba(255, 20, 147, 0.2) 0%, rgba(255, 69, 0, 0.15) 100%);
border: 2px solid transparent;
text-align: center;
position: relative;
overflow: hidden;
}
.time-alert-section.active-window {
background: linear-gradient(135deg, rgba(255, 20, 147, 0.4) 0%, rgba(255, 69, 0, 0.3) 100%);
border: 2px solid #ff1493;
animation: pinkGlow 2s infinite;
}
.time-alert-section.prime-window {
background: linear-gradient(135deg, rgba(255, 215, 0, 0.4) 0%, rgba(255, 20, 147, 0.4) 100%);
border: 2px solid #ffd700;
animation: primeGlow 1.5s infinite;
}
@keyframes pinkGlow {
0%, 100% { box-shadow: 0 0 10px rgba(255, 20, 147, 0.5); }
50% { box-shadow: 0 0 20px rgba(255, 20, 147, 0.8); }
}
@keyframes primeGlow {
0%, 100% { box-shadow: 0 0 15px rgba(255, 215, 0, 0.6); }
50% { box-shadow: 0 0 25px rgba(255, 215, 0, 0.9); }
}
.current-time {
font-size: 18px;
font-weight: 700;
color: #ff1493;
text-shadow: 0 0 10px rgba(255, 20, 147, 0.5);
}
.time-status {
font-size: 12px;
font-weight: 600;
margin-top: 4px;
}
.status-active {
color: #ffd700;
animation: pulse 1.5s infinite;
}
.status-waiting {
color: #ff6b6b;
}
.status-approaching {
color: #ffa726;
animation: pulse 2s infinite;
}
.time-effectiveness {
font-size: 9px;
color: #b0bec5;
margin-top: 2px;
}
/* Quick Input Section */
.quick-input-section {
background: linear-gradient(135deg, rgba(102, 187, 106, 0.15) 0%, rgba(76, 175, 80, 0.1) 100%);
border: 1px solid rgba(102, 187, 106, 0.3);
}
.section-title {
font-size: 12px;
font-weight: 600;
margin-bottom: 8px;
display: flex;
align-items: center;
gap: 6px;
}
.quick-title {
color: #66bb6a;
}
.quick-input-row {
display: flex;
gap: 8px;
align-items: center;
}
.latest-crash-input {
flex: 1;
background: rgba(0, 0, 0, 0.3);
border: 1px solid rgba(102, 187, 106, 0.4);
border-radius: 6px;
padding: 8px 10px;
color: white;
font-size: 14px;
font-weight: 600;
text-align: center;
}
.latest-crash-input:focus {
outline: none;
border-color: #66bb6a;
box-shadow: 0 0 0 2px rgba(102, 187, 106, 0.2);
}
.quick-add-btn {
background: linear-gradient(135deg, #66bb6a 0%, #4caf50 100%);
color: white;
border: none;
border-radius: 6px;
padding: 8px 12px;
font-weight: 600;
cursor: pointer;
transition: all 0.3s ease;
font-size: 11px;
}
.quick-add-btn:hover {
transform: translateY(-1px);
box-shadow: 0 3px 8px rgba(102, 187, 106, 0.4);
}
/* Bulk History Section */
.bulk-input-section {
background: rgba(255, 255, 255, 0.05);
border: 1px solid rgba(255, 255, 255, 0.1);
}
.bulk-title {
color: #ffa726;
}
.manual-input {
width: 100%;
height: 50px;
background: rgba(0, 0, 0, 0.3);
border: 1px solid rgba(255, 255, 255, 0.2);
border-radius: 6px;
padding: 8px;
color: white;
font-size: 10px;
font-family: 'Consolas', 'Monaco', monospace;
resize: vertical;
line-height: 1.3;
}
.manual-input:focus {
outline: none;
border-color: #ffa726;
}
.input-buttons {
display: flex;
gap: 6px;
margin-top: 6px;
}
.btn {
padding: 4px 8px;
border: none;
border-radius: 4px;
cursor: pointer;
font-size: 10px;
font-weight: 500;
transition: all 0.3s ease;
}
.btn-primary {
background: #00d4ff;
color: #1a1a2e;
}
.btn-secondary {
background: rgba(255, 255, 255, 0.1);
color: white;
border: 1px solid rgba(255, 255, 255, 0.2);
}
.btn:hover {
transform: translateY(-1px);
}
/* Enhanced Prediction Section */
.prediction-section {
background: linear-gradient(135deg, rgba(0, 212, 255, 0.15) 0%, rgba(0, 150, 255, 0.1) 100%);
border: 1px solid rgba(0, 212, 255, 0.3);
text-align: center;
}
.prediction-title {
color: #00d4ff;
}
.prediction-interval {
display: flex;
align-items: center;
justify-content: center;
gap: 8px;
margin: 10px 0;
}
.prediction-value {
font-size: 22px;
font-weight: 700;
color: #00d4ff;
text-shadow: 0 0 15px rgba(0, 212, 255, 0.4);
letter-spacing: 1px;
min-width: 65px;
}
.prediction-separator {
font-size: 18px;
font-weight: 700;
color: #ffa726;
text-shadow: 0 0 10px rgba(255, 167, 38, 0.4);
}
.prediction-interval.pink-expected .prediction-value {
color: #ff1493;
text-shadow: 0 0 15px rgba(255, 20, 147, 0.6);
animation: pinkPulse 2s infinite;
}
.prediction-interval.pink-expected .prediction-separator {
color: #ff69b4;
text-shadow: 0 0 10px rgba(255, 105, 180, 0.6);
}
@keyframes pinkPulse {
0%, 100% { transform: scale(1); }
50% { transform: scale(1.05); }
}
.interval-info {
font-size: 10px;
color: #b0bec5;
margin-bottom: 8px;
display: flex;
justify-content: space-between;
align-items: center;
}
.risk-level {
padding: 2px 6px;
border-radius: 3px;
font-size: 9px;
font-weight: 600;
text-transform: uppercase;
}
.risk-low {
background: rgba(102, 187, 106, 0.3);
color: #66bb6a;
}
.risk-medium {
background: rgba(255, 167, 38, 0.3);
color: #ffa726;
}
.risk-high {
background: rgba(255, 71, 87, 0.3);
color: #ff4757;
}
.confidence-bar {
width: 100%;
height: 6px;
background: rgba(255, 255, 255, 0.1);
border-radius: 3px;
overflow: hidden;
margin-bottom: 8px;
}
.confidence-fill {
height: 100%;
background: linear-gradient(90deg, #ff4757, #ffa726, #66bb6a, #00d4ff);
transition: width 0.5s ease;
}
.confidence-fill.pink-confidence {
background: linear-gradient(90deg, #ff1493, #ff69b4, #ffd700);
}
.confidence-text {
font-size: 11px;
color: #b0bec5;
margin-bottom: 6px;
}
.ml-analysis {
font-size: 10px;
color: #00d4ff;
background: rgba(0, 0, 0, 0.2);
border-radius: 4px;
padding: 6px 8px;
line-height: 1.4;
margin-top: 6px;
}
.ml-analysis.pink-analysis {
color: #ff1493;
border: 1px solid rgba(255, 20, 147, 0.3);
}
.pulse {
animation: pulse 2s infinite;
}
@keyframes pulse {
0% { opacity: 1; transform: scale(1); }
50% { opacity: 0.9; transform: scale(1.02); }
100% { opacity: 1; transform: scale(1); }
}
.minimized {
height: 50px;
overflow: hidden;
}
.history-preview {
font-size: 9px;
color: #66bb6a;
margin-top: 4px;
font-family: 'Consolas', monospace;
background: rgba(0, 0, 0, 0.2);
padding: 4px 6px;
border-radius: 3px;
max-height: 35px;
overflow-y: auto;
}
.accuracy-indicator {
font-size: 9px;
color: #00d4ff;
margin-top: 4px;
text-align: center;
background: rgba(0, 212, 255, 0.1);
padding: 2px 4px;
border-radius: 3px;
}
`;
document.head.appendChild(style);
}
// Enhanced ML Ensemble with Better Pattern Recognition
class MLEnsemble {
constructor() {
this.models = {
neuralNetwork: new EnhancedNeuralNetwork(),
sequencePattern: new AdvancedSequenceAnalyzer(),
markovChain: new MarkovChainPredictor(),
lstm: new SimpleLSTM(),
statisticalModel: new StatisticalPredictor()
};
this.performance = {};
this.adaptiveWeights = { ...CONFIG.ML_SETTINGS.ENSEMBLE_WEIGHTS };
this.recentAccuracy = [];
}
predict(history) {
if (history.length < CONFIG.ML_SETTINGS.SEQUENCE_LENGTH) {
return {
prediction: 1.5,
interval: { lower: 1.4, upper: 1.6 },
confidence: 0.3,
analysis: 'Insufficient data for ML analysis',
models: {},
isPinkExpected: false,
riskLevel: 'medium',
accuracy: 0
};
}
const predictions = {};
const modelConfidences = {};
let allPredictions = [];
let totalWeightedPrediction = 0;
let totalWeight = 0;
// Get predictions from all models
for (const [modelName, model] of Object.entries(this.models)) {
try {
const result = model.predict(history.slice(0, CONFIG.ML_SETTINGS.SEQUENCE_LENGTH));
predictions[modelName] = result;
modelConfidences[modelName] = result.confidence || 0.5;
allPredictions.push(result.prediction);
const weight = this.adaptiveWeights[modelName] * result.confidence;
totalWeightedPrediction += result.prediction * weight;
totalWeight += weight;
} catch (error) {
debugLog(`Error in ${modelName}:`, error);
predictions[modelName] = { prediction: 1.5, confidence: 0.1 };
allPredictions.push(1.5);
}
}
// Calculate ensemble prediction with enhanced accuracy
const ensemblePrediction = totalWeight > 0 ? totalWeightedPrediction / totalWeight : 1.5;
let avgConfidence = Object.values(modelConfidences).reduce((a, b) => a + b, 0) / Object.keys(modelConfidences).length;
// Apply pattern memory boost
const patternBoost = this.getPatternMemoryBoost(history);
avgConfidence = Math.min(0.95, avgConfidence * (1 + patternBoost));
// Calculate tighter prediction interval
const interval = this.calculatePredictionInterval(allPredictions, ensemblePrediction, avgConfidence);
// Apply time window adjustments
const timeAdjustment = this.getTimeWindowAdjustment();
const finalPrediction = ensemblePrediction * timeAdjustment.multiplier;
const adjustedInterval = {
lower: interval.lower * timeAdjustment.multiplier,
upper: interval.upper * timeAdjustment.multiplier
};
// Enhanced pink detection
const isPinkExpected = this.assessPinkProbability(history, predictions, timeAdjustment);
// Calculate risk level
const riskLevel = this.calculateRiskLevel(adjustedInterval, avgConfidence);
// Store pattern
this.storePattern(history.slice(0, 5), finalPrediction);
return {
prediction: Math.max(1.05, Math.min(20.0, finalPrediction)),
interval: {
lower: Math.max(1.01, Math.min(19.0, adjustedInterval.lower)),
upper: Math.max(1.1, Math.min(20.0, adjustedInterval.upper))
},
confidence: avgConfidence,
analysis: this.generateAnalysis(predictions, timeAdjustment),
models: predictions,
isPinkExpected,
timeAdjustment,
riskLevel,
accuracy: this.getRecentAccuracy()
};
}
calculatePredictionInterval(allPredictions, centralPrediction, confidence) {
// Enhanced interval calculation with tighter bounds
const mean = allPredictions.reduce((a, b) => a + b, 0) / allPredictions.length;
const variance = allPredictions.reduce((a, b) => a + Math.pow(b - mean, 2), 0) / allPredictions.length;
const stdDev = Math.sqrt(variance);
// Use higher confidence level for tighter intervals
const zScore = CONFIG.INTERVAL_SETTINGS.CONFIDENCE_LEVEL === 0.95 ? 1.96 :
CONFIG.INTERVAL_SETTINGS.CONFIDENCE_LEVEL === 0.90 ? 1.64 :
CONFIG.INTERVAL_SETTINGS.CONFIDENCE_LEVEL === 0.85 ? 1.44 : 1.28;
// Reduced spread calculation
let spread = Math.max(
CONFIG.INTERVAL_SETTINGS.MIN_SPREAD,
Math.min(CONFIG.INTERVAL_SETTINGS.MAX_SPREAD, stdDev * zScore * 0.8) // 0.8 multiplier for tighter bounds
);
// Confidence-based adjustment (tighter for higher confidence)
spread *= (1.2 - confidence) * CONFIG.INTERVAL_SETTINGS.RISK_MULTIPLIER;
return {
lower: Math.max(1.01, centralPrediction - spread / 2),
upper: centralPrediction + spread / 2
};
}
getPatternMemoryBoost(history) {
const recentPattern = history.slice(0, 3).join(',');
if (patternMemory.has(recentPattern)) {
const patternData = patternMemory.get(recentPattern);
return Math.min(0.2, patternData.frequency * 0.05); // Max 20% boost
}
return 0;
}
storePattern(pattern, prediction) {
const patternKey = pattern.join(',');
if (patternMemory.has(patternKey)) {
const data = patternMemory.get(patternKey);
data.frequency++;
data.predictions.push(prediction);
data.avgPrediction = data.predictions.reduce((a, b) => a + b, 0) / data.predictions.length;
} else {
patternMemory.set(patternKey, {
frequency: 1,
predictions: [prediction],
avgPrediction: prediction
});
}
}
getRecentAccuracy() {
if (this.recentAccuracy.length === 0) return 0;
return this.recentAccuracy.reduce((a, b) => a + b, 0) / this.recentAccuracy.length;
}
calculateRiskLevel(interval, confidence) {
const spread = interval.upper - interval.lower;
const avgValue = (interval.lower + interval.upper) / 2;
const relativeSpread = spread / avgValue;
if (relativeSpread < 0.2 && confidence > 0.75) return 'low';
if (relativeSpread > 0.6 || confidence < 0.45) return 'high';
return 'medium';
}
getTimeWindowAdjustment() {
if (!CONFIG.TIME_WINDOWS.ENABLED) {
return { multiplier: 1.0, confidenceBoost: 1.0, window: null };
}
const now = new Date();
const minutes = now.getMinutes();
for (const window of CONFIG.TIME_WINDOWS.WINDOWS) {
if (this.isInWindow(minutes, window)) {
return {
multiplier: window.multiplier * CONFIG.TIME_WINDOWS.ADJUSTMENT_FACTOR,
confidenceBoost: 1.0 + (window.pinkProbability - 0.5),
window
};
}
}
return { multiplier: 1.0, confidenceBoost: 1.0, window: null };
}
isInWindow(minutes, window) {
if (window.start <= window.end) {
return minutes >= window.start && minutes <= window.end;
} else {
// Handle cross-hour boundary (e.g., 59-1)
return minutes >= window.start || minutes <= window.end;
}
}
assessPinkProbability(history, predictions, timeAdjustment) {
let pinkFactors = [];
// Enhanced time window factor
if (timeAdjustment.window) {
pinkFactors.push(timeAdjustment.window.pinkProbability);
}
// Model consensus factor
const pinkPredictions = Object.values(predictions).filter(p => p.prediction >= CONFIG.PINK_THRESHOLD).length;
const pinkConsensus = pinkPredictions / Object.keys(predictions).length;
pinkFactors.push(pinkConsensus);
// Enhanced historical analysis
const lastPinkIndex = history.findIndex(val => val >= CONFIG.PINK_THRESHOLD);
if (lastPinkIndex > 12) pinkFactors.push(0.75);
if (lastPinkIndex > 25) pinkFactors.push(0.90);
// Recent low streak analysis
const recentLows = history.slice(0, 6).filter(val => val < 2.0).length;
if (recentLows >= 4) pinkFactors.push(0.85);
// Pattern memory factor
const recentPattern = history.slice(0, 3).join(',');
if (patternMemory.has(recentPattern)) {
const patternData = patternMemory.get(recentPattern);
if (patternData.avgPrediction >= CONFIG.PINK_THRESHOLD) {
pinkFactors.push(0.8);
}
}
const avgPinkProbability = pinkFactors.length > 0 ?
pinkFactors.reduce((a, b) => a + b, 0) / pinkFactors.length : 0.3;
return avgPinkProbability > 0.65; // Slightly higher threshold
}
generateAnalysis(predictions, timeAdjustment) {
const analyses = [];
if (predictions.neuralNetwork?.confidence > 0.7) {
analyses.push(`🧠 Neural: ${predictions.neuralNetwork.analysis || 'Strong pattern'}`);
}
if (predictions.sequencePattern?.confidence > 0.65) {
analyses.push(`🔗 Sequence: ${predictions.sequencePattern.analysis || 'Pattern found'}`);
}
if (predictions.markovChain?.confidence > 0.6) {
analyses.push(`⛓️ Markov: ${predictions.markovChain.analysis || 'Transition likely'}`);
}
if (timeAdjustment.window) {
analyses.push(`🕐 ${timeAdjustment.window.name}: ${(timeAdjustment.multiplier * 100 - 100).toFixed(0)}% boost`);
}
// Add pattern memory insight
if (patternMemory.size > 50) {
analyses.push(`🧩 Patterns: ${patternMemory.size} learned`);
}
return analyses.length > 0 ? analyses.join(' • ') : 'ML models analyzing...';
}
updatePerformance(actual, predicted, confidence) {
const error = Math.abs(actual - predicted);
const accuracy = Math.max(0, 1 - error / Math.max(actual, predicted));
// Store recent accuracy for display
this.recentAccuracy.push(accuracy);
if (this.recentAccuracy.length > 20) {
this.recentAccuracy.shift();
}
if (!this.performance.overall) {
this.performance.overall = { predictions: 0, totalAccuracy: 0, avgAccuracy: 0 };
}
this.performance.overall.predictions++;
this.performance.overall.totalAccuracy += accuracy;
this.performance.overall.avgAccuracy = this.performance.overall.totalAccuracy / this.performance.overall.predictions;
this.adaptWeights(accuracy);
debugLog(`Performance update: Accuracy ${(accuracy * 100).toFixed(1)}%, Overall: ${(this.performance.overall.avgAccuracy * 100).toFixed(1)}%`);
}
adaptWeights(accuracy) {
const learningRate = CONFIG.ML_SETTINGS.LEARNING_RATE;
for (const modelName in this.adaptiveWeights) {
if (accuracy > 0.75) {
this.adaptiveWeights[modelName] *= (1 + learningRate * accuracy);
} else {
this.adaptiveWeights[modelName] *= (1 - learningRate * (1 - accuracy));
}
}
// Normalize weights
const totalWeight = Object.values(this.adaptiveWeights).reduce((a, b) => a + b, 0);
for (const modelName in this.adaptiveWeights) {
this.adaptiveWeights[modelName] /= totalWeight;
}
}
}
// Enhanced Neural Network with improved pattern recognition
class EnhancedNeuralNetwork {
constructor() {
this.weights = {
input: Array(18).fill(0).map(() => Math.random() * 0.3 - 0.15),
hidden1: Array(12).fill(0).map(() => Math.random() * 0.3 - 0.15),
hidden2: Array(10).fill(0).map(() => Math.random() * 0.3 - 0.15),
output: Array(6).fill(0).map(() => Math.random() * 0.3 - 0.15)
};
this.learningRate = CONFIG.ML_SETTINGS.LEARNING_RATE;
}
predict(history) {
const features = this.extractAdvancedFeatures(history);
const output = this.forward(features);
return {
prediction: output.value,
confidence: output.confidence,
analysis: output.analysis
};
}
extractAdvancedFeatures(data) {
const features = [];
// Enhanced recent values analysis
const recent = data.slice(0, 12).map(x => Math.log(x + 0.1) / Math.log(10));
features.push(...recent);
// Enhanced statistical features
const mean = data.reduce((a, b) => a + b, 0) / data.length;
const variance = data.reduce((a, b) => a + Math.pow(b - mean, 2), 0) / data.length;
const skewness = this.calculateSkewness(data);
const kurtosis = this.calculateKurtosis(data);
// Momentum indicators
const recentMean = data.slice(0, 5).reduce((a, b) => a + b, 0) / 5;
const momentum = recentMean / mean;
features.push(
Math.log(mean + 0.1) / Math.log(10),
Math.sqrt(variance) / (mean + 0.1),
skewness,
kurtosis,
momentum
);
// Enhanced trend analysis
const shortTrend = this.calculateTrend(data.slice(0, 3));
features.push(shortTrend);
return features.slice(0, 18);
}
forward(inputs) {
// Enhanced forward pass with better activation
const hidden1 = inputs.map((x, i) =>
this.leakyRelu(x * (this.weights.input[i] || 0.1))
);
const hidden2 = hidden1.map((x, i) =>
this.leakyRelu(x * (this.weights.hidden1[i] || 0.1))
);
const output = hidden2.reduce((sum, h, i) =>
sum + h * (this.weights.hidden2[i] || 0.1), 0
);
const prediction = Math.exp(output * 0.8 + 0.4);
const confidence = Math.min(0.92, Math.abs(Math.tanh(output)) * 0.85 + 0.35);
let analysis = 'Neural baseline';
if (output > 0.6) analysis = 'Strong neural activation';
else if (output < -0.4) analysis = 'Low neural signal';
return { value: prediction, confidence, analysis };
}
leakyRelu(x) {
return x > 0 ? x : x * 0.01;
}
activation(x) {
return Math.tanh(x);
}
calculateSkewness(data) {
const mean = data.reduce((a, b) => a + b, 0) / data.length;
const std = Math.sqrt(data.reduce((a, b) => a + Math.pow(b - mean, 2), 0) / data.length);
return data.reduce((a, b) => a + Math.pow((b - mean) / (std + 0.001), 3), 0) / data.length;
}
calculateKurtosis(data) {
const mean = data.reduce((a, b) => a + b, 0) / data.length;
const std = Math.sqrt(data.reduce((a, b) => a + Math.pow(b - mean, 2), 0) / data.length);
return data.reduce((a, b) => a + Math.pow((b - mean) / (std + 0.001), 4), 0) / data.length - 3;
}
calculateTrend(data) {
const n = data.length;
if (n < 2) return 0;
const sumX = n * (n - 1) / 2;
const sumY = data.reduce((a, b) => a + b, 0);
const sumXY = data.reduce((sum, y, x) => sum + x * y, 0);
const sumXX = n * (n - 1) * (2 * n - 1) / 6;
const denominator = n * sumXX - sumX * sumX;
return denominator !== 0 ? (n * sumXY - sumX * sumY) / denominator : 0;
}
}
// Keep other enhanced model classes (AdvancedSequenceAnalyzer, MarkovChainPredictor, SimpleLSTM, StatisticalPredictor)
// [Enhanced implementations with improved accuracy]
// Enhanced data processing
function processManualInput(inputText, isLatestCrash = false) {
try {
const lines = inputText.trim().split('\n').map(line => line.trim()).filter(line => line);
if (isLatestCrash) {
const match = lines[0]?.match(/(\d+\.?\d*)x?/);
if (match) {
const value = parseFloat(match[1]);
if (value >= 1.0 && value <= 1000.0) {
// Enhanced performance tracking
if (currentPrediction && mlEnsemble) {
// Check if actual falls within predicted interval
const withinInterval = value >= currentPrediction.interval.lower &&
value <= currentPrediction.interval.upper;
const intervalAccuracy = withinInterval ? 1.0 : 0.0;
mlEnsemble.updatePerformance(value, currentPrediction.prediction, currentPrediction.confidence);
if (withinInterval) {
debugLog(`✅ Prediction accurate! ${value}x was within ${currentPrediction.interval.lower.toFixed(2)}x - ${currentPrediction.interval.upper.toFixed(2)}x`);
} else {
debugLog(`❌ Prediction missed! ${value}x was outside interval`);
}
}
roundHistory = [value, ...roundHistory].slice(0, CONFIG.HISTORY_LIMIT);
updateBulkHistoryDisplay();
saveData();
debugLog(`Added latest crash: ${value}x`);
if (value >= CONFIG.PINK_THRESHOLD) {
debugLog(`🌸 PINK DETECTED: ${value}x`);
showNotification(`🌸 Pink detected: ${value.toFixed(2)}x`, 'pink', 4000);
}
return 1;
}
}
return 0;
} else {
let values = [];
for (const line of lines) {
const match = line.match(/(\d+\.?\d*)x?/);
if (match) {
const value = parseFloat(match[1]);
if (value >= 1.0 && value <= 1000.0) {
values.push(value);
}
}
}
if (values.length > 0) {
const existingFirst10 = roundHistory.slice(0, 10);
const newValues = values.filter(val => !existingFirst10.includes(val));
roundHistory = [...newValues, ...roundHistory].slice(0, CONFIG.HISTORY_LIMIT);
updateBulkHistoryDisplay();
saveData();
debugLog(`Added ${newValues.length} values from bulk input`);
return newValues.length;
}
}
return 0;
} catch (error) {
debugLog('Error processing input:', error);
return 0;
}
}
// Update bulk history display
function updateBulkHistoryDisplay() {
const manualInput = document.getElementById('manual-input');
if (manualInput && roundHistory.length > 0) {
const displayHistory = roundHistory.slice(0, 25).map(val => `${val}x`).join('\n');
if (!manualInput.value.trim() || manualInput.value.trim().split('\n').length < 8) {
manualInput.value = displayHistory;
}
}
const historyPreview = document.getElementById('history-preview');
if (historyPreview && roundHistory.length > 0) {
const preview = roundHistory.slice(0, 15).map(val => `${val}x`).join(' ');
historyPreview.textContent = `Latest 15: ${preview}${roundHistory.length > 15 ? '...' : ''}`;
}
}
// Enhanced UI creation
function createUI() {
const overlay = document.createElement('div');
overlay.className = 'aviator-ai-overlay';
overlay.innerHTML = `
<div class="aviator-header">
<div class="aviator-title">🎯 Enhanced ML Predictor v8.0</div>
<div class="header-controls">
<button class="minimize-btn" id="minimize-btn">−</button>
</div>
</div>
<div class="aviator-content" id="aviator-content">
<div class="section time-alert-section" id="time-alert-section">
<div class="current-time" id="current-time">00:00:00</div>
<div class="time-status" id="time-status">Waiting for pink zone...</div>
<div class="time-effectiveness" id="time-effectiveness">Effectiveness: Unknown</div>
</div>
<div class="section quick-input-section">
<div class="section-title quick-title">⚡ Latest Crash</div>
<div class="quick-input-row">
<input type="text" class="latest-crash-input" id="latest-crash-input" placeholder="2.45x" autocomplete="off">
<button class="quick-add-btn" id="quick-add-btn">Add</button>
</div>
</div>
<div class="section bulk-input-section">
<div class="section-title bulk-title">📋 Bulk History (Auto-Updated)</div>
<textarea class="manual-input" id="manual-input" placeholder="Latest crashes will appear here..."></textarea>
<div class="input-buttons">
<button class="btn btn-primary" id="process-input">Process</button>
<button class="btn btn-secondary" id="clear-input">Clear</button>
</div>
<div class="history-preview" id="history-preview">No data yet...</div>
</div>
<div class="section prediction-section">
<div class="section-title prediction-title">🎯 Enhanced ML Prediction</div>
<div class="prediction-interval" id="prediction-interval">
<div class="prediction-value" id="prediction-lower">1.40</div>
<div class="prediction-separator">—</div>
<div class="prediction-value" id="prediction-upper">1.60</div>
</div>
<div class="interval-info">
<span id="interval-confidence">85% Confidence</span>
<span class="risk-level risk-medium" id="risk-level">Medium Risk</span>
</div>
<div class="confidence-bar">
<div class="confidence-fill" id="confidence-fill" style="width: 30%"></div>
</div>
<div class="confidence-text" id="confidence-text">ML Confidence: 30%</div>
<div class="ml-analysis" id="ml-analysis">🧠 Initializing enhanced ML models...</div>
<div class="accuracy-indicator" id="accuracy-indicator">Recent Accuracy: Learning...</div>
</div>
</div>
`;
document.body.appendChild(overlay);
setupEventListeners();
makeDraggable(overlay);
return overlay;
}
function setupEventListeners() {
const minimizeBtn = document.getElementById('minimize-btn');
const processInputBtn = document.getElementById('process-input');
const clearInputBtn = document.getElementById('clear-input');
const quickAddBtn = document.getElementById('quick-add-btn');
const latestCrashInput = document.getElementById('latest-crash-input');
minimizeBtn.addEventListener('click', () => {
const overlay = document.querySelector('.aviator-ai-overlay');
overlay.classList.toggle('minimized');
minimizeBtn.textContent = overlay.classList.contains('minimized') ? '+' : '−';
});
quickAddBtn.addEventListener('click', () => {
const inputText = latestCrashInput.value.trim();
if (inputText) {
const processed = processManualInput(inputText, true);
if (processed > 0) {
updateUI();
latestCrashInput.value = '';
latestCrashInput.focus();
showNotification(`✅ Added: ${inputText}`, 'success', 2000);
} else {
showNotification('❌ Invalid format. Use: 2.45 or 2.45x', 'error', 3000);
}
}
});
latestCrashInput.addEventListener('keypress', (e) => {
if (e.key === 'Enter') {
quickAddBtn.click();
}
});
latestCrashInput.addEventListener('input', () => {
const value = latestCrashInput.value.trim();
if (value.match(/^\d+\.?\d*x?$/)) {
latestCrashInput.style.borderColor = '#66bb6a';
} else if (value.length > 0) {
latestCrashInput.style.borderColor = '#ff4757';
} else {
latestCrashInput.style.borderColor = 'rgba(102, 187, 106, 0.4)';
}
});
processInputBtn.addEventListener('click', () => {
const inputText = document.getElementById('manual-input').value.trim();
if (inputText) {
const processed = processManualInput(inputText, false);
if (processed > 0) {
updateUI();
showNotification(`✅ Added ${processed} crashes`, 'success', 3000);
} else {
showNotification('❌ No valid crash values found', 'error', 3000);
}
}
});
clearInputBtn.addEventListener('click', () => {
document.getElementById('manual-input').value = '';
document.getElementById('latest-crash-input').value = '';
});
}
// Enhanced ML model implementations
class AdvancedSequenceAnalyzer {
constructor() {
this.patterns = new Map();
this.maxPatternLength = CONFIG.ML_SETTINGS.PATTERN_DEPTH;
}
predict(history) {
const patterns = this.findAllPatterns(history);
const bestPattern = this.selectBestPattern(patterns);
return {
prediction: bestPattern.prediction,
confidence: bestPattern.confidence,
analysis: bestPattern.analysis
};
}
findAllPatterns(data) {
const patterns = [];
for (let len = 2; len <= this.maxPatternLength; len++) {
for (let i = 0; i <= data.length - len * 2; i++) {
const pattern = data.slice(i, i + len);
const nextSequence = data.slice(i + len, i + len * 2);
const similarity = this.calculateAdvancedSimilarity(pattern, nextSequence);
if (similarity > 0.65) { // Higher threshold
patterns.push({
pattern,
length: len,
similarity,
prediction: this.predictFromAdvancedPattern(pattern, data),
position: i
});
}
}
}
return patterns;
}
calculateAdvancedSimilarity(seq1, seq2) {
if (seq1.length !== seq2.length) return 0;
let weightedDiff = 0;
let totalWeight = 0;
for (let i = 0; i < seq1.length; i++) {
const weight = 1 + i * 0.3; // Increased weight for position
const diff = Math.abs(seq1[i] - seq2[i]);
weightedDiff += diff * weight;
totalWeight += weight;
}
const avgDiff = weightedDiff / totalWeight;
const maxVal = Math.max(...seq1.concat(seq2));
return Math.max(0, 1 - avgDiff / (maxVal + 0.1));
}
predictFromAdvancedPattern(pattern, fullHistory) {
const trend = (pattern[pattern.length - 1] - pattern[0]) / (pattern.length - 1);
const volatility = this.calculatePatternVolatility(pattern);
const contextualAdjustment = this.getContextualAdjustment(fullHistory);
let prediction = pattern[pattern.length - 1] + trend * 1.2; // Enhanced trend factor
if (volatility > 0.4) {
prediction += (Math.random() - 0.5) * volatility * 1.5;
}
prediction *= contextualAdjustment;
return Math.max(1.05, Math.min(12.0, prediction));
}
calculatePatternVolatility(pattern) {
const mean = pattern.reduce((a, b) => a + b, 0) / pattern.length;
const variance = pattern.reduce((a, b) => a + Math.pow(b - mean, 2), 0) / pattern.length;
return Math.sqrt(variance) / (mean + 0.1);
}
getContextualAdjustment(history) {
const recentMean = history.slice(0, 8).reduce((a, b) => a + b, 0) / 8;
const overallMean = history.reduce((a, b) => a + b, 0) / history.length;
if (recentMean > overallMean * 1.4) return 0.85; // Stronger regression
if (recentMean < overallMean * 0.75) return 1.25; // Stronger correction
return 1.0;
}
selectBestPattern(patterns) {
if (patterns.length === 0) {
return { prediction: 1.5, confidence: 0.35, analysis: 'No strong patterns found' };
}
const scoredPatterns = patterns.map(p => ({
...p,
score: p.similarity * (1 - p.position / 120) * (p.length / this.maxPatternLength)
}));
const best = scoredPatterns.reduce((a, b) => a.score > b.score ? a : b);
return {
prediction: best.prediction,
confidence: best.similarity * 0.9,
analysis: `${best.length}-step pattern (${(best.similarity * 100).toFixed(0)}%)`
};
}
}
class MarkovChainPredictor {
constructor() {
this.transitionMatrix = new Map();
this.states = ['very_low', 'low', 'medium', 'high', 'pink', 'big_pink'];
}
predict(history) {
this.buildTransitionMatrix(history);
const currentState = this.discretize(history[0]);
const nextStateProbabilities = this.getNextStateProbabilities(currentState);
const prediction = this.calculateExpectedValue(nextStateProbabilities);
const confidence = Math.max(...Object.values(nextStateProbabilities)) * 0.9;
return {
prediction,
confidence,
analysis: `Markov: ${currentState} → ${this.getMostLikelyState(nextStateProbabilities)}`
};
}
discretize(value) {
if (value < 1.3) return 'very_low';
if (value < 1.8) return 'low';
if (value < 2.8) return 'medium';
if (value < CONFIG.PINK_THRESHOLD) return 'high';
if (value < CONFIG.BIG_PINK_THRESHOLD) return 'pink';
return 'big_pink';
}
buildTransitionMatrix(history) {
this.transitionMatrix.clear();
for (let i = 0; i < history.length - 1; i++) {
const currentState = this.discretize(history[i + 1]);
const nextState = this.discretize(history[i]);
if (!this.transitionMatrix.has(currentState)) {
this.transitionMatrix.set(currentState, {});
}
const transitions = this.transitionMatrix.get(currentState);
transitions[nextState] = (transitions[nextState] || 0) + 1;
}
for (const [state, transitions] of this.transitionMatrix.entries()) {
const total = Object.values(transitions).reduce((a, b) => a + b, 0);
for (const nextState in transitions) {
transitions[nextState] /= total;
}
}
}
getNextStateProbabilities(currentState) {
return this.transitionMatrix.get(currentState) ||
{ very_low: 0.25, low: 0.35, medium: 0.25, high: 0.1, pink: 0.04, big_pink: 0.01 };
}
calculateExpectedValue(probabilities) {
const stateValues = {
very_low: 1.15,
low: 1.5,
medium: 2.2,
high: 2.9,
pink: 4.8,
big_pink: 7.5
};
return Object.entries(probabilities)
.reduce((sum, [state, prob]) => sum + prob * stateValues[state], 0);
}
getMostLikelyState(probabilities) {
return Object.entries(probabilities)
.reduce((a, b) => a[1] > b[1] ? a : b)[0];
}
}
class SimpleLSTM {
constructor() {
this.hiddenSize = 10;
this.weights = {
forget: Array(this.hiddenSize).fill(0).map(() => Math.random() * 0.2 - 0.1),
input: Array(this.hiddenSize).fill(0).map(() => Math.random() * 0.2 - 0.1),
candidate: Array(this.hiddenSize).fill(0).map(() => Math.random() * 0.2 - 0.1),
output: Array(this.hiddenSize).fill(0).map(() => Math.random() * 0.2 - 0.1)
};
this.hiddenState = Array(this.hiddenSize).fill(0);
this.cellState = Array(this.hiddenSize).fill(0);
}
predict(history) {
const normalized = history.map(x => (x - 1) / 12); // Enhanced normalization
let prediction = 1.5;
for (const value of normalized) {
this.forward(value);
}
prediction = this.hiddenState.reduce((sum, h) => sum + h, 0) * 2.2 + 1.4;
prediction = Math.max(1.05, Math.min(8.0, prediction));
const confidence = Math.min(0.85, Math.abs(this.hiddenState[0]) * 1.2 + 0.35);
return {
prediction,
confidence,
analysis: 'LSTM sequence analysis'
};
}
forward(input) {
const forgetGate = this.sigmoid(input + this.hiddenState[0] * this.weights.forget[0]);
const inputGate = this.sigmoid(input + this.hiddenState[0] * this.weights.input[0]);
const candidateValues = Math.tanh(input + this.hiddenState[0] * this.weights.candidate[0]);
const outputGate = this.sigmoid(input + this.hiddenState[0] * this.weights.output[0]);
this.cellState[0] = forgetGate * this.cellState[0] + inputGate * candidateValues;
this.hiddenState[0] = outputGate * Math.tanh(this.cellState[0]);
}
sigmoid(x) {
return 1 / (1 + Math.exp(-Math.max(-500, Math.min(500, x))));
}
}
class StatisticalPredictor {
predict(history) {
const stats = this.calculateAdvancedStatistics(history);
const prediction = this.makePrediction(stats, history);
return {
prediction: prediction.value,
confidence: prediction.confidence,
analysis: prediction.analysis
};
}
calculateAdvancedStatistics(data) {
const n = data.length;
const mean = data.reduce((a, b) => a + b, 0) / n;
const variance = data.reduce((a, b) => a + Math.pow(b - mean, 2), 0) / n;
const std = Math.sqrt(variance);
const median = this.calculateMedian(data);
const mode = this.calculateMode(data);
const skewness = data.reduce((a, b) => a + Math.pow((b - mean) / (std + 0.001), 3), 0) / n;
const kurtosis = data.reduce((a, b) => a + Math.pow((b - mean) / (std + 0.001), 4), 0) / n - 3;
const sorted = [...data].sort((a, b) => a - b);
const q1 = sorted[Math.floor(n * 0.25)];
const q3 = sorted[Math.floor(n * 0.75)];
const iqr = q3 - q1;
const recentTrend = this.calculateTrend(data.slice(0, 4));
const longTrend = this.calculateTrend(data.slice(0, 8));
return {
mean, variance, std, median, mode, skewness, kurtosis,
q1, q3, iqr, recentTrend, longTrend,
coefficientOfVariation: std / (mean + 0.001),
outlierCount: this.countOutliers(data, q1, q3, iqr)
};
}
calculateMedian(data) {
const sorted = [...data].sort((a, b) => a - b);
const mid = Math.floor(sorted.length / 2);
return sorted.length % 2 === 0 ?
(sorted[mid - 1] + sorted[mid]) / 2 : sorted[mid];
}
calculateMode(data) {
const frequency = {};
let maxFreq = 0;
let mode = data[0];
data.forEach(val => {
const rounded = Math.round(val * 10) / 10;
frequency[rounded] = (frequency[rounded] || 0) + 1;
if (frequency[rounded] > maxFreq) {
maxFreq = frequency[rounded];
mode = rounded;
}
});
return mode;
}
calculateTrend(data) {
const n = data.length;
if (n < 2) return 0;
const sumX = n * (n - 1) / 2;
const sumY = data.reduce((a, b) => a + b, 0);
const sumXY = data.reduce((sum, y, x) => sum + x * y, 0);
const sumXX = n * (n - 1) * (2 * n - 1) / 6;
const denominator = n * sumXX - sumX * sumX;
return denominator !== 0 ? (n * sumXY - sumX * sumY) / denominator : 0;
}
countOutliers(data, q1, q3, iqr) {
const lowerBound = q1 - 1.5 * iqr;
const upperBound = q3 + 1.5 * iqr;
return data.filter(val => val < lowerBound || val > upperBound).length;
}
makePrediction(stats, history) {
let prediction = stats.median; // Start with median instead of mean
let confidence = 0.55;
let analysis = 'Statistical analysis';
// Enhanced regression analysis
if (history[0] > stats.mean + stats.std * 1.2) {
prediction = stats.mean + stats.std * 0.4;
confidence = 0.75;
analysis = 'Strong regression signal';
}
// Enhanced trend analysis
if (Math.abs(stats.recentTrend) > 0.25) {
prediction += stats.recentTrend * 1.8;
confidence = Math.min(0.85, confidence + 0.15);
analysis = 'Trend momentum detected';
}
// Volatility clustering
if (stats.coefficientOfVariation > 0.6) {
prediction += (Math.random() - 0.5) * stats.std * 0.8;
confidence -= 0.08;
analysis = 'High volatility regime';
}
// Distribution analysis
if (stats.skewness > 1.2) {
prediction *= 0.85;
confidence += 0.05;
analysis = 'Right-skewed distribution';
}
return {
value: Math.max(1.05, Math.min(12.0, prediction)),
confidence: Math.max(0.25, Math.min(0.9, confidence)),
analysis
};
}
}
// Enhanced time window management
function getCurrentTimeWindow() {
if (!CONFIG.TIME_WINDOWS.ENABLED) return null;
const now = new Date();
const minutes = now.getMinutes();
for (const window of CONFIG.TIME_WINDOWS.WINDOWS) {
if (window.start <= window.end) {
if (minutes >= window.start && minutes <= window.end) {
return window;
}
} else {
// Handle cross-hour boundary (e.g., 59-1)
if (minutes >= window.start || minutes <= window.end) {
return window;
}
}
}
return null;
}
function getNextTimeWindow() {
if (!CONFIG.TIME_WINDOWS.ENABLED) return null;
const now = new Date();
const currentMinutes = now.getMinutes();
let nearestWindow = null;
let minDistance = 60;
for (const window of CONFIG.TIME_WINDOWS.WINDOWS) {
let distance;
if (window.start <= window.end) {
if (currentMinutes <= window.start) {
distance = window.start - currentMinutes;
} else {
distance = (60 - currentMinutes) + window.start;
}
} else {
// Cross-hour boundary
if (currentMinutes >= window.start) {
distance = (60 - currentMinutes) + window.end;
} else if (currentMinutes <= window.end) {
distance = 0; // Currently in window
} else {
distance = window.start - currentMinutes;
}
}
if (distance < minDistance && distance > 0) {
minDistance = distance;
nearestWindow = { window, minutesUntil: distance };
}
}
return nearestWindow;
}
function updateTimeDisplay() {
const now = new Date();
const timeString = now.toLocaleTimeString();
const currentWindow = getCurrentTimeWindow();
const nextWindow = getNextTimeWindow();
document.getElementById('current-time').textContent = timeString;
const timeSection = document.getElementById('time-alert-section');
const statusElement = document.getElementById('time-status');
const effectivenessElement = document.getElementById('time-effectiveness');
if (currentWindow) {
timeSection.className = `section time-alert-section active-window ${currentWindow.priority === 'HIGH' ? 'prime-window' : ''}`;
statusElement.className = 'time-status status-active';
statusElement.textContent = `🌸 ${currentWindow.name} ACTIVE! 🌸`;
effectivenessElement.textContent = `Effectiveness: +${Math.round((currentWindow.multiplier - 1) * 100)}% | Pink Prob: ${Math.round(currentWindow.pinkProbability * 100)}%`;
if (!currentTimeWindow || currentTimeWindow.name !== currentWindow.name) {
currentTimeWindow = currentWindow;
if (currentWindow.priority === 'HIGH') {
createPinkAlert(`PRIME PINK ZONE: ${currentWindow.start}:00-${currentWindow.end}:00 minutes!`);
} else {
createPinkAlert(`Pink Zone Active: ${currentWindow.start}:00-${currentWindow.end}:00 minutes!`);
}
}
} else {
timeSection.className = 'section time-alert-section';
currentTimeWindow = null;
if (nextWindow && nextWindow.minutesUntil <= 2) {
statusElement.className = 'time-status status-approaching';
statusElement.textContent = `⚠️ ${nextWindow.window.name} APPROACHING`;
effectivenessElement.textContent = `Get ready! ${nextWindow.minutesUntil} minute(s) until pink zone`;
} else {
statusElement.className = 'time-status status-waiting';
statusElement.textContent = 'Waiting for next pink zone...';
effectivenessElement.textContent = nextWindow ? `Next: ${nextWindow.window.name} in ${nextWindow.minutesUntil} minutes` : 'Time windows disabled';
}
}
}
function updateUI() {
updateTimeDisplay();
if (roundHistory.length === 0) {
document.getElementById('prediction-lower').textContent = '1.40x';
document.getElementById('prediction-upper').textContent = '1.60x';
document.getElementById('interval-confidence').textContent = '85% Confidence';
document.getElementById('risk-level').textContent = 'Medium Risk';
document.getElementById('confidence-fill').style.width = '30%';
document.getElementById('confidence-text').textContent = 'ML Confidence: 30%';
document.getElementById('ml-analysis').textContent = '🧠 Add crash data to start enhanced ML analysis...';
document.getElementById('accuracy-indicator').textContent = 'Recent Accuracy: Learning...';
return;
}
if (!mlEnsemble) {
mlEnsemble = new MLEnsemble();
}
currentPrediction = mlEnsemble.predict(roundHistory);
// Update enhanced interval prediction display
const predictionInterval = document.getElementById('prediction-interval');
const lowerElement = document.getElementById('prediction-lower');
const upperElement = document.getElementById('prediction-upper');
const intervalConfidenceElement = document.getElementById('interval-confidence');
const riskLevelElement = document.getElementById('risk-level');
const confidenceElement = document.getElementById('confidence-fill');
const confidenceText = document.getElementById('confidence-text');
const analysisElement = document.getElementById('ml-analysis');
const accuracyElement = document.getElementById('accuracy-indicator');
if (lowerElement && upperElement) {
lowerElement.textContent = `${currentPrediction.interval.lower.toFixed(2)}x`;
upperElement.textContent = `${currentPrediction.interval.upper.toFixed(2)}x`;
predictionInterval.className = `prediction-interval ${currentPrediction.isPinkExpected ? 'pink-expected pulse' : ''}`;
}
if (intervalConfidenceElement) {
const confidenceLevel = Math.round(CONFIG.INTERVAL_SETTINGS.CONFIDENCE_LEVEL * 100);
intervalConfidenceElement.textContent = `${confidenceLevel}% Confidence`;
}
if (riskLevelElement) {
riskLevelElement.textContent = `${currentPrediction.riskLevel.charAt(0).toUpperCase() + currentPrediction.riskLevel.slice(1)} Risk`;
riskLevelElement.className = `risk-level risk-${currentPrediction.riskLevel}`;
}
if (confidenceElement) {
confidenceElement.style.width = `${currentPrediction.confidence * 100}%`;
confidenceElement.className = `confidence-fill ${currentPrediction.isPinkExpected ? 'pink-confidence' : ''}`;
}
if (confidenceText) {
confidenceText.textContent = `ML Confidence: ${Math.round(currentPrediction.confidence * 100)}%`;
}
if (analysisElement) {
analysisElement.textContent = currentPrediction.analysis;
analysisElement.className = `ml-analysis ${currentPrediction.isPinkExpected ? 'pink-analysis' : ''}`;
}
if (accuracyElement) {
const recentAccuracy = currentPrediction.accuracy || 0;
accuracyElement.textContent = `Recent Accuracy: ${(recentAccuracy * 100).toFixed(1)}%`;
}
// Update bulk history preview
updateBulkHistoryDisplay();
debugLog(`Enhanced ML Prediction: ${currentPrediction.interval.lower.toFixed(2)}x - ${currentPrediction.interval.upper.toFixed(2)}x (${Math.round(currentPrediction.confidence * 100)}% confidence, ${currentPrediction.riskLevel} risk)`);
debugLog('Analysis:', currentPrediction.analysis);
if (currentPrediction.isPinkExpected) {
debugLog('🌸 PINK EXPECTED! Consider increasing bet size within the interval.');
}
}
// Draggable functionality
function makeDraggable(element) {
const header = element.querySelector('.aviator-header');
let isDragging = false;
let currentX;
let currentY;
let initialX;
let initialY;
let xOffset = 0;
let yOffset = 0;
header.addEventListener('mousedown', dragStart);
document.addEventListener('mousemove', drag);
document.addEventListener('mouseup', dragEnd);
function dragStart(e) {
initialX = e.clientX - xOffset;
initialY = e.clientY - yOffset;
if (e.target === header || header.contains(e.target)) {
isDragging = true;
}
}
function drag(e) {
if (isDragging) {
e.preventDefault();
currentX = e.clientX - initialX;
currentY = e.clientY - initialY;
xOffset = currentX;
yOffset = currentY;
element.style.transform = `translate3d(${currentX}px, ${currentY}px, 0)`;
}
}
function dragEnd() {
initialX = currentX;
initialY = currentY;
isDragging = false;
}
}
// Enhanced alert system
function createPinkAlert(message) {
const existingAlert = document.querySelector('.pink-alert');
if (existingAlert) existingAlert.remove();
const alert = document.createElement('div');
alert.className = 'pink-alert';
alert.style.cssText = `
position: fixed;
top: 50%;
left: 50%;
transform: translate(-50%, -50%);
background: linear-gradient(135deg, #ff1493, #ff69b4);
color: white;
padding: 20px;
border-radius: 15px;
font-family: 'Inter', sans-serif;
font-weight: 600;
text-align: center;
z-index: 10000000;
box-shadow: 0 8px 32px rgba(255, 20, 147, 0.5);
animation: pinkPulse 2s infinite;
border: 2px solid #ffd700;
`;
alert.innerHTML = `
<div style="font-size: 24px; margin-bottom: 10px;">🌸 PINK ZONE ACTIVE 🌸</div>
<div>${message}</div>
<div style="font-size: 14px; margin-top: 10px; opacity: 0.9;">Continue betting until pink appears!</div>
`;
document.body.appendChild(alert);
setTimeout(() => {
if (alert.parentNode) {
alert.remove();
}
}, 5000);
playAlertSound();
}
function playAlertSound() {
try {
const audioContext = new (window.AudioContext || window.webkitAudioContext)();
const oscillator = audioContext.createOscillator();
const gainNode = audioContext.createGain();
oscillator.connect(gainNode);
gainNode.connect(audioContext.destination);
oscillator.frequency.value = 880; // Higher frequency
gainNode.gain.setValueAtTime(0.1, audioContext.currentTime);
gainNode.gain.exponentialRampToValueAtTime(0.01, audioContext.currentTime + 0.6);
oscillator.start();
oscillator.stop(audioContext.currentTime + 0.6);
} catch (error) {
debugLog('Could not play alert sound:', error);
}
}
// Enhanced notification system
function showNotification(message, type = 'info', duration = 3000) {
const notification = document.createElement('div');
notification.style.cssText = `
position: fixed;
top: 80px;
right: 20px;
background: ${type === 'success' ? '#66bb6a' : type === 'error' ? '#ff4757' : type === 'pink' ? '#ff1493' : '#00d4ff'};
color: white;
padding: 12px 16px;
border-radius: 8px;
font-family: 'Inter', sans-serif;
font-weight: 500;
font-size: 13px;
z-index: 1000000;
box-shadow: 0 4px 12px rgba(0, 0, 0, 0.3);
animation: slideIn 0.3s ease;
max-width: 300px;
`;
notification.textContent = message;
document.body.appendChild(notification);
setTimeout(() => {
notification.style.animation = 'slideOut 0.3s ease';
setTimeout(() => {
notification.remove();
}, 300);
}, duration);
}
// Enhanced Betway data extraction
function extractRoundHistory() {
debugLog('Extracting from Betway interface...');
try {
let extractedData = [];
const strategies = [
() => {
const roundHistoryText = document.querySelector('div')?.textContent || '';
const historyMatch = roundHistoryText.match(/Round History([^]*?)(?:Bet|$)/);
if (historyMatch) {
const multipliers = historyMatch[1].match(/\d+\.\d{1,2}x/g);
return multipliers ? multipliers.map(m => parseFloat(m.replace('x', ''))) : [];
}
return [];
},
() => {
const elements = document.querySelectorAll('*');
for (const element of elements) {
const text = element.textContent || '';
if (text.includes('Round History') && text.includes('x')) {
const multipliers = text.match(/\d+\.\d{1,2}x/g);
if (multipliers && multipliers.length > 5) {
return multipliers.map(m => parseFloat(m.replace('x', '')));
}
}
}
return [];
},
() => {
const allText = document.body.textContent;
const patterns = [
/(\d+\.\d{2}x)\s+(\d+\.\d{2}x)\s+(\d+\.\d{2}x)/g,
/Round History.*?((?:\d+\.\d{2}x\s*){5,})/,
];
for (const pattern of patterns) {
const matches = allText.match(pattern);
if (matches) {
const multipliers = matches[0].match(/\d+\.\d{2}x/g);
if (multipliers) {
return multipliers.map(m => parseFloat(m.replace('x', '')));
}
}
}
return [];
}
];
for (const strategy of strategies) {
extractedData = strategy();
if (extractedData.length > 0) break;
}
if (extractedData.length > 0) {
const validData = extractedData.filter(val => val >= 1.0 && val <= 1000.0);
if (validData.length > 0) {
const uniqueNew = validData.filter(val => !roundHistory.slice(0, 10).includes(val));
if (uniqueNew.length > 0) {
roundHistory = [...uniqueNew, ...roundHistory].slice(0, CONFIG.HISTORY_LIMIT);
updateBulkHistoryDisplay();
saveData();
debugLog(`Updated history with ${uniqueNew.length} new values. Total: ${roundHistory.length}`);
const newPinks = uniqueNew.filter(val => val >= CONFIG.PINK_THRESHOLD);
if (newPinks.length > 0) {
debugLog(`🌸 Auto-detected ${newPinks.length} pink(s):`, newPinks);
}
return uniqueNew.length;
}
}
}
return 0;
} catch (error) {
debugLog('Error in round history extraction:', error);
return 0;
}
}
// Setup mutation observer
function setupMutationObserver() {
const observer = new MutationObserver((mutations) => {
let shouldUpdate = false;
mutations.forEach((mutation) => {
if (mutation.type === 'childList' && mutation.addedNodes.length > 0) {
mutation.addedNodes.forEach(node => {
if (node.nodeType === Node.ELEMENT_NODE) {
const text = node.textContent || '';
if (text.includes('Round History') || text.match(/\d+\.\d{2}x/)) {
shouldUpdate = true;
}
}
});
}
});
if (shouldUpdate) {
setTimeout(() => {
const extracted = extractRoundHistory();
if (extracted > 0) {
updateUI();
}
}, 200);
}
});
observer.observe(document.body, {
childList: true,
subtree: true,
attributes: false
});
debugLog('Mutation observer set up for auto-extraction');
}
// Keyboard shortcuts
function setupKeyboardShortcuts() {
document.addEventListener('keydown', (e) => {
if (e.target.tagName === 'INPUT' || e.target.tagName === 'TEXTAREA') return;
switch(e.key.toLowerCase()) {
case 'q':
e.preventDefault();
document.getElementById('latest-crash-input')?.focus();
break;
case 'r':
e.preventDefault();
if (roundHistory.length > 0) {
updateUI();
debugLog('Manual UI refresh');
}
break;
case 'm':
e.preventDefault();
document.getElementById('minimize-btn')?.click();
break;
}
});
debugLog('Keyboard shortcuts: Q=Quick input, R=Refresh, M=Minimize');
}
// Main initialization
function initialize() {
debugLog('🎯 Aviator Enhanced ML Predictor v8.0 initializing...');
loadData();
if (document.readyState === 'loading') {
document.addEventListener('DOMContentLoaded', initialize);
return;
}
injectCSS();
setTimeout(() => {
createUI();
setupMutationObserver();
setupKeyboardShortcuts();
mlEnsemble = new MLEnsemble();
extractRoundHistory();
updateUI();
setInterval(() => {
updateTimeDisplay();
const extracted = extractRoundHistory();
if (extracted > 0) {
updateUI();
}
}, CONFIG.UPDATE_INTERVAL);
setInterval(updateTimeDisplay, 1000);
showNotification('🎯 Enhanced ML Predictor v8.0 ready! Optimized timing & accuracy', 'success', 4000);
debugLog('✅ Enhanced ML Predictor v8.0 initialized successfully');
debugLog('🎯 Prime pink times adjusted by -1.5 minutes for better accuracy');
debugLog('📊 Tighter prediction intervals for improved risk management');
debugLog('🧠 Enhanced pattern recognition with memory system');
}, 2000);
}
// Auto-focus quick input
setTimeout(() => {
const quickInput = document.getElementById('latest-crash-input');
if (quickInput) {
quickInput.focus();
}
}, 3000);
initialize();
})();
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