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FxPouya
2025-12-26 15:19:15 +03:30
committed by GitHub
parent b54fbf0aa8
commit 185ec2ed5d
7 changed files with 603 additions and 17 deletions
+171
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@@ -815,3 +815,174 @@ tbody tr:hover {
.loading {
animation: pulse 2s ease-in-out infinite;
}
/* Walk-Forward Analysis Badges */
.wf-badge {
display: inline-flex;
align-items: center;
gap: 6px;
padding: 6px 12px;
border-radius: 20px;
font-size: 12px;
font-weight: 600;
margin-left: 10px;
}
.wf-badge-pass {
background: rgba(72, 187, 120, 0.15);
color: var(--accent-success);
border: 1px solid rgba(72, 187, 120, 0.3);
}
.wf-badge-fail {
background: rgba(237, 137, 54, 0.15);
color: var(--accent-warning);
border: 1px solid rgba(237, 137, 54, 0.3);
}
.wf-icon {
font-size: 14px;
font-weight: 700;
}
.wf-text {
font-size: 11px;
text-transform: uppercase;
letter-spacing: 0.5px;
}
.wf-score {
background: rgba(255, 255, 255, 0.1);
padding: 2px 6px;
border-radius: 10px;
font-size: 11px;
font-weight: 700;
}
/* Walk-Forward Results Tab */
.wf-summary {
display: flex;
gap: 30px;
margin-bottom: 30px;
align-items: center;
}
.robustness-gauge {
text-align: center;
padding: 25px;
background: linear-gradient(135deg, var(--accent-primary) 0%, var(--accent-secondary) 100%);
border-radius: 15px;
color: white;
min-width: 160px;
box-shadow: var(--shadow);
}
.gauge-value {
font-size: 52px;
font-weight: 700;
line-height: 1;
}
.gauge-label {
font-size: 13px;
opacity: 0.95;
margin-top: 8px;
text-transform: uppercase;
letter-spacing: 1px;
}
.wf-status {
flex: 1;
padding: 20px 25px;
border-radius: 12px;
font-size: 16px;
}
.status-pass {
background: rgba(72, 187, 120, 0.15);
border-left: 4px solid var(--accent-success);
color: var(--accent-success);
}
.status-fail {
background: rgba(245, 101, 101, 0.15);
border-left: 4px solid var(--accent-danger);
color: var(--accent-danger);
}
.comparison-table {
margin: 25px 0;
background: var(--bg-secondary);
border-radius: 12px;
overflow: hidden;
}
.comparison-table table {
width: 100%;
border-collapse: collapse;
}
.comparison-table th,
.comparison-table td {
padding: 15px 20px;
text-align: left;
border-bottom: 1px solid var(--border-color);
}
.comparison-table th {
background: var(--bg-card);
font-weight: 600;
color: var(--text-secondary);
text-transform: uppercase;
font-size: 11px;
letter-spacing: 1px;
}
.comparison-table tbody tr:hover {
background: var(--bg-hover);
}
.comparison-table .good {
color: var(--accent-success);
font-weight: 600;
}
.comparison-table .warning {
color: var(--accent-warning);
font-weight: 600;
}
.comparison-table .bad {
color: var(--accent-danger);
font-weight: 600;
}
.wf-charts {
display: grid;
grid-template-columns: 1fr 1fr;
gap: 25px;
margin-top: 30px;
}
.wf-chart-container {
background: var(--bg-secondary);
padding: 20px;
border-radius: 12px;
}
.wf-chart-container h4 {
margin-bottom: 15px;
color: var(--accent-primary);
font-size: 16px;
}
@media (max-width: 768px) {
.wf-charts {
grid-template-columns: 1fr;
}
.wf-summary {
flex-direction: column;
align-items: stretch;
}
}
+38 -14
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@@ -211,6 +211,29 @@
<small>Minimum trade count (10-200)</small>
</div>
</div>
<!-- Walk-Forward Analysis -->
<div class="config-card">
<h3>Walk-Forward Analysis</h3>
<div class="form-group">
<label>
<input type="checkbox" id="enable-walkforward" checked>
Enable Walk-Forward Validation
</label>
<small>Validate strategies on out-of-sample data to detect overfitting</small>
</div>
<div class="form-group" id="walkforward-settings">
<label>Training Period: <span id="training-percent">70</span>%</label>
<input type="range" id="training-ratio" min="50" max="90" value="70" step="5"
oninput="updateTrainingSplit()">
<div class="split-info"
style="display: flex; justify-content: space-between; margin-top: 5px; font-size: 12px; color: #718096;">
<span>Training: <span id="training-bars">0</span> bars</span>
<span>Testing: <span id="testing-bars">0</span> bars</span>
</div>
<small>Higher ratio = more data for training, less for validation</small>
</div>
</div>
</div>
<div class="action-buttons">
@@ -297,21 +320,22 @@
<!-- Scripts -->
<script src="js/utils.js"></script>
<script src="js/strategy.js"></script>
<script src="js/strategy.js?v=2.2"></script>
<script src="js/backtester.js"></script>
<script src="js/ga-engine.js"></script>
<script src="js/rule-parser.js"></script>
<script src="js/mq4-converter.js"></script>
<script src="js/mq5-converter.js"></script>
<script src="js/ctrader-converter.js"></script>
<script src="js/pine-converter.js"></script>
<script src="js/mq4-generator.js"></script>
<script src="js/mq5-generator.js"></script>
<script src="js/report-generator.js"></script>
<script src="js/strategy-details.js"></script>
<script src="js/html-report-generator.js"></script>
<script src="js/ui-controller.js"></script>
<script src="js/main.js?v=2.0"></script>
<script src="js/walk-forward.js"></script>
<script src="js/ga-engine.js?v=2.2"></script>
<script src="js/rule-parser.js?v=2.1"></script>
<script src="js/mq4-converter.js?v=2.1"></script>
<script src="js/mq5-converter.js?v=2.1"></script>
<script src="js/ctrader-converter.js?v=2.1"></script>
<script src="js/pine-converter.js?v=2.1"></script>
<script src="js/mq4-generator.js?v=2.1"></script>
<script src="js/mq5-generator.js?v=2.1"></script>
<script src="js/report-generator.js?v=2.1"></script>
<script src="js/strategy-details.js?v=2.1"></script>
<script src="js/html-report-generator.js?v=2.1"></script>
<script src="js/ui-controller.js?v=2.1"></script>
<script src="js/main.js?v=2.1"></script>
</body>
</html>
+35 -1
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@@ -14,9 +14,15 @@ class GeneticOptimizer {
minTrades: config.minTrades || 30,
minPF: config.minPF || 1.5,
maxDD: config.maxDD || 25,
minWR: config.minWR || 45
minWR: config.minWR || 45,
// Walk-Forward Analysis settings
enableWalkForward: config.enableWalkForward || false,
trainingRatio: config.trainingRatio || 0.7
};
// Debug: Log walk-forward config
console.log('🔧 GA Config - Walk-Forward:', this.config.enableWalkForward, 'Ratio:', this.config.trainingRatio);
this.population = [];
this.foundStrategies = [];
this.isRunning = false;
@@ -40,11 +46,39 @@ class GeneticOptimizer {
* Evaluate fitness for all strategies
*/
evaluatePopulation() {
// Log walk-forward status (only once per generation)
if (this.currentGeneration === 0) {
console.log('🔬 Walk-Forward Analysis:', this.config.enableWalkForward ? 'ENABLED' : 'DISABLED');
if (this.config.enableWalkForward) {
console.log('📊 Training Ratio:', (this.config.trainingRatio * 100) + '%');
}
}
for (const strategy of this.population) {
// Regular backtest on full data
const backtester = new Backtester(this.data, strategy, this.symbol);
strategy.metrics = backtester.run();
// Walk-forward analysis (if enabled)
if (this.config.enableWalkForward) {
const wfAnalysis = new WalkForwardAnalysis(
this.data,
this.config.trainingRatio || 0.7
);
strategy.walkForward = wfAnalysis.analyze(strategy, this.symbol);
// Penalize strategies that fail walk-forward validation
if (!strategy.walkForward.passed) {
strategy.fitness = this.calculateFitness(strategy.metrics) * 0.5; // 50% penalty
} else {
// Bonus for high robustness
const robustnessBonus = 1 + (strategy.walkForward.robustness / 200); // Up to 50% bonus
strategy.fitness = this.calculateFitness(strategy.metrics) * robustnessBonus;
}
} else {
strategy.fitness = this.calculateFitness(strategy.metrics);
}
}
// Sort by fitness (descending)
this.population.sort((a, b) => b.fitness - a.fitness);
+56 -1
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@@ -281,7 +281,10 @@ async function startGeneration() {
minPF: parseFloat(document.getElementById('min-pf').value),
minWR: parseFloat(document.getElementById('min-wr').value),
maxDD: parseFloat(document.getElementById('max-dd').value),
minTrades: parseInt(document.getElementById('min-trades').value)
minTrades: parseInt(document.getElementById('min-trades').value),
// Walk-Forward Analysis settings
enableWalkForward: document.getElementById('enable-walkforward').checked,
trainingRatio: parseInt(document.getElementById('training-ratio').value) / 100
};
// Show progress section
@@ -469,9 +472,30 @@ function createStrategyCard(strategy, number) {
const card = document.createElement('div');
card.className = 'strategy-card';
// Generate walk-forward badge HTML (only if walk-forward was enabled and data exists)
let wfBadgeHTML = '';
if (strategy.walkForward && typeof strategy.walkForward === 'object') {
const wf = strategy.walkForward;
if (wf.robustness !== undefined && wf.passed !== undefined) {
const badgeClass = wf.passed ? 'wf-badge-pass' : 'wf-badge-fail';
const badgeIcon = wf.passed ? '✓' : '⚠';
const badgeText = wf.passed ? 'Robust' : 'Overfitted';
wfBadgeHTML = `
<div class="wf-badge ${badgeClass}" title="Robustness Score: ${wf.robustness}/100">
<span class="wf-icon">${badgeIcon}</span>
<span class="wf-text">${badgeText}</span>
<span class="wf-score">${wf.robustness}</span>
</div>
`;
}
}
const name = `Strategy #${number}`;
card.innerHTML = `
<div class="strategy-header">
<div class="strategy-name">${name}</div>
${wfBadgeHTML}
<input type="checkbox" class="strategy-select" onchange="toggleStrategySelection(${number - 1}, this.checked)">
</div>
<div class="strategy-metrics">
@@ -750,10 +774,41 @@ function formatTime(seconds) {
return `${minutes}m ${secs}s`;
}
/**
* Update training/testing split display
*/
function updateTrainingSplit() {
const ratio = parseInt(document.getElementById('training-ratio').value);
document.getElementById('training-percent').textContent = ratio;
// Update bar counts if data is loaded
if (appData.csvData) {
const totalBars = appData.csvData.length;
const trainingBars = Math.floor(totalBars * (ratio / 100));
const testingBars = totalBars - trainingBars;
document.getElementById('training-bars').textContent = trainingBars.toLocaleString();
document.getElementById('testing-bars').textContent = testingBars.toLocaleString();
}
}
/**
* Toggle walk-forward settings visibility
*/
function toggleWalkForwardSettings() {
const enabled = document.getElementById('enable-walkforward').checked;
const settings = document.getElementById('walkforward-settings');
if (settings) {
settings.style.display = enabled ? 'block' : 'none';
}
}
// Explicitly expose functions to global scope for inline onclick handlers
window.downloadStrategy = downloadStrategy;
window.downloadAllStrategies = downloadAllStrategies;
window.toggleStrategySelection = toggleStrategySelection;
window.showComparison = showComparison;
window.updateTrainingSplit = updateTrainingSplit;
window.toggleWalkForwardSettings = toggleWalkForwardSettings;
window.hideComparison = hideComparison;
window.toggleCustomBarsInput = toggleCustomBarsInput;
+2
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@@ -100,6 +100,8 @@ class Strategy {
newStrategy.closeAtOpposite = this.closeAtOpposite;
newStrategy.metrics = this.metrics ? JSON.parse(JSON.stringify(this.metrics)) : {};
newStrategy.fitness = this.fitness || 0;
// Copy walk-forward analysis results
newStrategy.walkForward = this.walkForward ? JSON.parse(JSON.stringify(this.walkForward)) : undefined;
return newStrategy;
}
+203
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@@ -0,0 +1,203 @@
/**
* Walk-Forward Analysis Module
* Validates strategies on out-of-sample data to detect overfitting
*/
class WalkForwardAnalysis {
constructor(data, trainingRatio = 0.7) {
this.data = data;
this.trainingRatio = trainingRatio;
this.splitIndex = Math.floor(data.length * trainingRatio);
}
/**
* Split data into training and testing periods
* @returns {Object} { training: Array, testing: Array }
*/
split() {
return {
training: this.data.slice(0, this.splitIndex),
testing: this.data.slice(this.splitIndex),
splitIndex: this.splitIndex,
trainingBars: this.splitIndex,
testingBars: this.data.length - this.splitIndex
};
}
/**
* Run walk-forward analysis on a strategy
* @param {Object} strategy - Strategy to analyze
* @param {string} symbol - Trading symbol
* @returns {Object} Analysis results
*/
analyze(strategy, symbol = '') {
const { training, testing } = this.split();
// Backtest on training data
const trainingBacktest = new Backtester(training, strategy, symbol);
const trainingResults = trainingBacktest.run();
// Backtest on testing data
const testingBacktest = new Backtester(testing, strategy, symbol);
const testingResults = testingBacktest.run();
// Calculate degradation
const degradation = this.calculateDegradation(trainingResults, testingResults);
// Calculate robustness score
const robustnessScore = this.calculateRobustness(degradation, trainingResults, testingResults);
// Determine if strategy passed
const passed = robustnessScore >= 60;
return {
training: trainingResults,
testing: testingResults,
degradation: degradation,
robustness: robustnessScore,
passed: passed,
splitInfo: {
trainingBars: training.length,
testingBars: testing.length,
trainingRatio: this.trainingRatio
}
};
}
/**
* Calculate performance degradation between training and testing
* @param {Object} train - Training results
* @param {Object} test - Testing results
* @returns {Object} Degradation metrics
*/
calculateDegradation(train, test) {
return {
profitFactor: this.calcPercentDegradation(train.profitFactor, test.profitFactor),
winRate: train.winRate - test.winRate,
maxDrawdown: test.maxDrawdown - train.maxDrawdown,
netProfit: this.calcPercentDegradation(train.netProfit, test.netProfit),
totalTrades: test.totalTrades,
avgWin: this.calcPercentDegradation(train.avgWin, test.avgWin),
avgLoss: this.calcPercentDegradation(train.avgLoss, test.avgLoss)
};
}
/**
* Calculate percentage degradation
* @param {number} trainValue - Training value
* @param {number} testValue - Testing value
* @returns {number} Degradation percentage
*/
calcPercentDegradation(trainValue, testValue) {
if (trainValue === 0) return 0;
return ((trainValue - testValue) / trainValue * 100);
}
/**
* Calculate robustness score (0-100)
* Higher score = more robust strategy
* @param {Object} degradation - Degradation metrics
* @param {Object} train - Training results
* @param {Object} test - Testing results
* @returns {number} Robustness score
*/
calculateRobustness(degradation, train, test) {
// Profit Factor score (40% weight)
// Lower degradation = higher score
const pfDeg = Math.abs(degradation.profitFactor);
let pfScore = 100;
if (pfDeg > 50) pfScore = 0;
else if (pfDeg > 30) pfScore = 30;
else if (pfDeg > 15) pfScore = 60;
else pfScore = 100;
// Win Rate score (30% weight)
const wrDeg = Math.abs(degradation.winRate);
let wrScore = 100;
if (wrDeg > 20) wrScore = 0;
else if (wrDeg > 10) wrScore = 40;
else if (wrDeg > 5) wrScore = 70;
else wrScore = 100;
// Drawdown score (30% weight)
// For drawdown, increase is bad
const ddDeg = degradation.maxDrawdown;
let ddScore = 100;
if (ddDeg > 15) ddScore = 0;
else if (ddDeg > 10) ddScore = 40;
else if (ddDeg > 5) ddScore = 70;
else if (ddDeg < -5) ddScore = 100; // Better drawdown in testing
else ddScore = 85;
// Additional checks
// Penalize if testing has too few trades
let tradesPenalty = 0;
if (test.totalTrades < 20) {
tradesPenalty = 20;
} else if (test.totalTrades < 30) {
tradesPenalty = 10;
}
// Bonus if testing performs better
let bonus = 0;
if (test.profitFactor > train.profitFactor && test.winRate >= train.winRate) {
bonus = 10;
}
// Weighted average
const score = (pfScore * 0.4) + (wrScore * 0.3) + (ddScore * 0.3) - tradesPenalty + bonus;
return Math.max(0, Math.min(100, Math.round(score)));
}
/**
* Get degradation severity level
* @param {number} value - Degradation value
* @param {boolean} inverse - If true, higher is worse (for drawdown)
* @returns {string} 'good', 'warning', or 'bad'
*/
getDegradationLevel(value, inverse = false) {
const absValue = Math.abs(value);
if (inverse) {
// For drawdown increase
if (value < 0) return 'good'; // Improved
if (absValue < 5) return 'good';
if (absValue < 10) return 'warning';
return 'bad';
} else {
// For profit factor, win rate degradation
if (absValue < 10) return 'good';
if (absValue < 20) return 'warning';
return 'bad';
}
}
/**
* Generate summary text for walk-forward results
* @param {Object} results - Walk-forward results
* @returns {string} Summary text
*/
generateSummary(results) {
const { robustness, passed, degradation } = results;
if (passed) {
if (robustness >= 80) {
return 'Excellent! Strategy shows strong robustness with minimal overfitting.';
} else if (robustness >= 70) {
return 'Good! Strategy performs well on out-of-sample data.';
} else {
return 'Acceptable. Strategy shows reasonable robustness but monitor performance.';
}
} else {
if (robustness < 40) {
return 'Warning! High overfitting risk. Strategy may not perform well in live trading.';
} else {
return 'Caution. Strategy shows some overfitting. Consider re-optimization or more data.';
}
}
}
}
// Export for use in other modules
window.WalkForwardAnalysis = WalkForwardAnalysis;
+97
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@@ -191,6 +191,7 @@
<li><a href="#export">Exporting Data from MT4/MT5</a></li>
<li><a href="#upload">Uploading Data</a></li>
<li><a href="#config">Configuring Strategy Generation</a></li>
<li><a href="#walkforward">Walk-Forward Analysis</a></li>
<li><a href="#generate">Generating Strategies</a></li>
<li><a href="#results">Viewing Results</a></li>
<li><a href="#download">Downloading Strategies</a></li>
@@ -338,6 +339,102 @@
</tbody>
</table>
<h3 id="walkforward">🔬 Walk-Forward Analysis</h3>
<p>Walk-forward analysis validates strategies on out-of-sample data to detect overfitting and ensure real-world
performance.</p>
<div class="alert alert-info">
<strong>What is Overfitting?</strong> A strategy that performs well on historical data but fails in live
trading due to being too closely fitted to past price patterns.
</div>
<h4>How It Works</h4>
<ol>
<li><strong>Data Splitting:</strong> Historical data is divided into training and testing periods</li>
<li><strong>Training Period:</strong> Strategy is optimized on this data (e.g., 70% of bars)</li>
<li><strong>Testing Period:</strong> Strategy is validated on unseen data (e.g., 30% of bars)</li>
<li><strong>Performance Comparison:</strong> Metrics are compared between training and testing</li>
<li><strong>Robustness Score:</strong> A score (0-100) indicates how well the strategy generalizes</li>
</ol>
<h4>Configuration</h4>
<ul>
<li><strong>Enable Walk-Forward Validation:</strong> Check this box to activate the feature</li>
<li><strong>Training Period:</strong> Adjust the slider (50-90%, default: 70%)
<ul>
<li>Higher ratio = More data for training, less for validation</li>
<li>Lower ratio = Less training data, more for validation</li>
<li>Recommended: 70% for most cases</li>
</ul>
</li>
</ul>
<h4>Understanding the Badges</h4>
<p>Each strategy card displays a walk-forward badge showing its robustness:</p>
<table>
<thead>
<tr>
<th>Badge</th>
<th>Score Range</th>
<th>Meaning</th>
<th>Recommendation</th>
</tr>
</thead>
<tbody>
<tr>
<td>✓ ROBUST (Green)</td>
<td>60-100</td>
<td>Strategy passed validation</td>
<td>Safe to use for live trading</td>
</tr>
<tr>
<td>⚠ OVERFITTED (Orange)</td>
<td>0-59</td>
<td>Strategy failed validation</td>
<td>Avoid - likely curve-fitted</td>
</tr>
</tbody>
</table>
<h4>Robustness Score Interpretation</h4>
<ul>
<li><strong>80-100:</strong> Excellent - Minimal degradation, high confidence</li>
<li><strong>60-79:</strong> Good - Acceptable degradation, suitable for trading</li>
<li><strong>40-59:</strong> Moderate - Concerning degradation, needs review</li>
<li><strong>0-39:</strong> Poor - High overfitting risk, avoid</li>
</ul>
<h4>What Gets Measured</h4>
<p>The robustness score is calculated based on degradation in:</p>
<ul>
<li><strong>Profit Factor:</strong> How much profit factor drops in testing vs. training</li>
<li><strong>Win Rate:</strong> Percentage point decrease in win rate</li>
<li><strong>Max Drawdown:</strong> Increase in maximum drawdown</li>
<li><strong>Trade Count:</strong> Sufficient trades in testing period</li>
</ul>
<div class="alert alert-warning">
<strong>Important:</strong> A strategy with excellent training performance but poor testing performance is
likely overfitted and should be avoided.
</div>
<h4>Best Practices</h4>
<ul>
<li>Use at least 1,000 bars of data (minimum 300 for testing)</li>
<li>Keep training ratio at 70% for balanced results</li>
<li>Only trade strategies with robustness score ≥ 60</li>
<li>Prefer strategies with scores above 80 for higher confidence</li>
<li>If all strategies fail, try different data or settings</li>
</ul>
<h4>Impact on Strategy Generation</h4>
<p>When walk-forward is enabled:</p>
<ul>
<li><strong>Failed strategies:</strong> Receive 50% fitness penalty (less likely to be selected)</li>
<li><strong>Robust strategies:</strong> Receive up to 50% fitness bonus (prioritized)</li>
<li><strong>Result:</strong> GA naturally evolves toward robust, non-overfitted strategies</li>
</ul>
<h2 id="generate">🎯 Generating Strategies</h2>
<ol>
<li>Review your settings</li>