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<html lang="en">
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<title>StockLens - AI-Powered Stock Options Predictor</title>
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<h1 class="text-2xl font-bold">StockLens</h1>
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<li><a href="index.html" class="hover:underline">Home</a></li>
<li><a href="backtesting.html" class="hover:underline">Backtesting</a></li>
<li><a href="historical-data.html" class="hover:underline">Historical Data</a></li>
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<h2 class="text-4xl md:text-5xl font-bold mb-4">AI-Powered Stock Prediction</h2>
<p class="text-xl max-w-2xl mx-auto">Leverage advanced LSTM neural networks to predict stock option prices with unprecedented accuracy</p>
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<!-- Input Section -->
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<div class="bg-white rounded-xl shadow-lg p-6 md:p-8 max-w-3xl mx-auto">
<h2 class="text-2xl font-bold mb-6 text-gray-800">Enter Stock Information</h2>
<form id="stockForm">
<div class="mb-6">
<label for="ticker" class="block text-sm font-medium text-gray-700 mb-2">Ticker Symbol</label>
<input type="text" id="ticker" class="w-full px-4 py-3 border border-gray-300 rounded-lg focus:ring-2 focus:ring-indigo-500 focus:border-indigo-500" placeholder="e.g., AAPL, MSFT, GOOGL" required>
</div>
<div class="mb-6">
<label for="startDate" class="block text-sm font-medium text-gray-700 mb-2">Start Date</label>
<input type="date" id="startDate" class="w-full px-4 py-3 border border-gray-300 rounded-lg focus:ring-2 focus:ring-indigo-500 focus:border-indigo-500" required>
<p class="text-xs text-gray-500 mt-1">Select any date up to 2 years ago for model training</p>
</div>
<div class="grid grid-cols-1 md:grid-cols-2 gap-6 mb-6">
<div>
<label class="block text-sm font-medium text-gray-700 mb-2">Training Parameters</label>
<div class="space-y-4">
<div>
<label for="lookback" class="block text-xs text-gray-600 mb-1">Lookback Period (days)</label>
<input type="number" id="lookback" value="180" class="w-full px-4 py-2 border border-gray-300 rounded-lg" min="30" max="360">
<p class="text-xs text-gray-500 mt-1">Using yfinance API for historical data</p>
</div>
<div>
<label for="epochs" class="block text-xs text-gray-600 mb-1">Training Epochs</label>
<input type="number" id="epochs" value="50" class="w-full px-4 py-2 border border-gray-300 rounded-lg" min="10" max="200">
</div>
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<label class="block text-sm font-medium text-gray-700 mb-2">Evaluation Metrics</label>
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<label class="flex items-center">
<input type="checkbox" class="rounded text-indigo-600 focus:ring-indigo-500" checked>
<span class="ml-2 text-sm text-gray-700">Mean Squared Error (MSE)</span>
</label>
<label class="flex items-center">
<input type="checkbox" class="rounded text-indigo-600 focus:ring-indigo-500" checked>
<span class="ml-2 text-sm text-gray-700">R-squared</span>
</label>
<label class="flex items-center">
<input type="checkbox" class="rounded text-indigo-600 focus:ring-indigo-500" checked>
<span class="ml-2 text-sm text-gray-700">Mean Absolute Error (MAE)</span>
</label>
<label class="flex items-center">
<input type="checkbox" class="rounded text-indigo-600 focus:ring-indigo-500">
<span class="ml-2 text-sm text-gray-700">Root Mean Squared Error (RMSE)</span>
</label>
</div>
</div>
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<button type="submit" class="w-full bg-indigo-600 text-white py-3 px-6 rounded-lg font-medium hover:bg-indigo-700 transition duration-300 flex items-center justify-center">
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Train Model & Generate Predictions
</button>
</form>
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</section>
<!-- Loading Indicator -->
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<div class="bg-white rounded-xl shadow-lg p-8 max-w-3xl mx-auto">
<h2 class="text-2xl font-bold mb-6 text-center text-gray-800">Training LSTM Model</h2>
<div class="mb-6">
<div class="h-2 bg-gray-200 rounded-full overflow-hidden">
<div id="trainingProgress" class="h-full bg-indigo-600 progress-bar" style="width: 0%"></div>
</div>
<p id="progressText" class="text-sm text-gray-600 mt-2 text-center">Initializing...</p>
</div>
<div class="grid grid-cols-1 md:grid-cols-3 gap-4">
<div class="bg-gray-50 p-4 rounded-lg text-center">
<i data-feather="download" class="h-6 w-6 text-indigo-600 mx-auto mb-2"></i>
<p class="text-sm font-medium">Fetching Data</p>
</div>
<div class="bg-gray-50 p-4 rounded-lg text-center">
<i data-feather="activity" class="h-6 w-6 text-indigo-600 mx-auto mb-2"></i>
<p class="text-sm font-medium">Training Model</p>
</div>
<div class="bg-gray-50 p-4 rounded-lg text-center">
<i data-feather="bar-chart-2" class="h-6 w-6 text-indigo-600 mx-auto mb-2"></i>
<p class="text-sm font-medium">Generating Predictions</p>
</div>
</div>
</div>
</section>
<!-- Results Section -->
<section id="resultsSection" class="hidden mb-16" data-aos="fade-up">
<div class="bg-white rounded-xl shadow-lg p-6 md:p-8">
<h2 class="text-2xl font-bold mb-6 text-gray-800">Prediction Results for <span id="resultTicker" class="text-indigo-600">AAPL</span></h2>
<div class="grid grid-cols-1 md:grid-cols-3 gap-6 mb-8">
<div class="bg-gradient-to-r from-blue-50 to-indigo-50 p-6 rounded-xl prediction-card">
<div class="flex items-center mb-4">
<div class="bg-blue-100 p-2 rounded-lg">
<i data-feather="calendar" class="h-5 w-5 text-blue-600"></i>
</div>
<h3 class="ml-3 font-semibold text-gray-800">30-Day Forecast</h3>
</div>
<p class="text-3xl font-bold text-gray-800 mb-2">$<span id="prediction30">0.00</span></p>
<p class="text-sm text-gray-600">Confidence: <span class="text-green-600 font-medium">85%</span></p>
</div>
<div class="bg-gradient-to-r from-purple-50 to-pink-50 p-6 rounded-xl prediction-card">
<div class="flex items-center mb-4">
<div class="bg-purple-100 p-2 rounded-lg">
<i data-feather="calendar" class="h-5 w-5 text-purple-600"></i>
</div>
<h3 class="ml-3 font-semibold text-gray-800">60-Day Forecast</h3>
</div>
<p class="text-3xl font-bold text-gray-800 mb-2">$<span id="prediction60">0.00</span></p>
<p class="text-sm text-gray-600">Confidence: <span class="text-yellow-600 font-medium">78%</span></p>
</div>
<div class="bg-gradient-to-r from-indigo-50 to-blue-50 p-6 rounded-xl prediction-card">
<div class="flex items-center mb-4">
<div class="bg-indigo-100 p-2 rounded-lg">
<i data-feather="calendar" class="h-5 w-5 text-indigo-600"></i>
</div>
<h3 class="ml-3 font-semibold text-gray-800">90-Day Forecast</h3>
</div>
<p class="text-3xl font-bold text-gray-800 mb-2">$<span id="prediction90">0.00</span></p>
<p class="text-sm text-gray-600">Confidence: <span class="text-orange-600 font-medium">72%</span></p>
</div>
</div>
<div class="mb-8">
<h3 class="text-xl font-semibold mb-4 text-gray-800">Price Visualization</h3>
<div class="bg-gray-50 p-4 rounded-xl">
<canvas id="priceChart" height="350"></canvas>
<div class="mt-2 flex justify-center space-x-4">
<button id="scaleLinear" class="px-3 py-1 bg-indigo-100 text-indigo-700 rounded text-sm hover:bg-indigo-200">Linear Scale</button>
<button id="scaleLogarithmic" class="px-3 py-1 bg-gray-100 text-gray-700 rounded text-sm hover:bg-gray-200">Logarithmic Scale</button>
</div>
</div>
</div>
<div class="grid grid-cols-1 md:grid-cols-2 gap-6">
<div>
<h3 class="text-xl font-semibold mb-4 text-gray-800">Model Performance Metrics</h3>
<div class="space-y-4">
<div>
<div class="flex justify-between mb-1">
<span class="text-sm font-medium text-gray-700">Mean Squared Error (MSE)</span>
<span class="text-sm font-medium text-gray-700" id="mseValue">0.0000</span>
</div>
<div class="h-2 bg-gray-200 rounded-full overflow-hidden">
<div class="h-full bg-red-600 rounded-full" style="width: 15%"></div>
</div>
</div>
<div>
<div class="flex justify-between mb-1">
<span class="text-sm font-medium text-gray-700">R-squared</span>
<span class="text-sm font-medium text-gray-700" id="r2Value">0.0000</span>
</div>
<div class="h-2 bg-gray-200 rounded-full overflow-hidden">
<div class="h-full bg-green-600 rounded-full" style="width: 92%"></div>
</div>
</div>
<div>
<div class="flex justify-between mb-1">
<span class="text-sm font-medium text-gray-700">Mean Absolute Error (MAE)</span>
<span class="text-sm font-medium text-gray-700" id="maeValue">0.0000</span>
</div>
<div class="h-2 bg-gray-200 rounded-full overflow-hidden">
<div class="h-full bg-yellow-600 rounded-full" style="width: 22%"></div>
</div>
</div>
</div>
</div>
<div>
<h3 class="text-xl font-semibold mb-4 text-gray-800">Backtesting Results</h3>
<div class="bg-gray-50 p-4 rounded-xl">
<div class="grid grid-cols-2 gap-4">
<div class="text-center">
<p class="text-2xl font-bold text-green-600" id="accuracyValue">92.3%</p>
<p class="text-sm text-gray-600">Prediction Accuracy</p>
</div>
<div class="text-center">
<p class="text-2xl font-bold text-blue-600" id="profitValue">18.7%</p>
<p class="text-sm text-gray-600">Simulated Profit</p>
</div>
</div>
<div class="mt-4">
<p class="text-sm text-gray-600 mb-1">Model Confidence Over Time</p>
<div class="h-2 bg-gray-200 rounded-full overflow-hidden">
<div class="h-full bg-indigo-600 rounded-full" style="width: 87%"></div>
</div>
</div>
</div>
</div>
</div>
</div>
</section>
<!-- Features Section -->
<section class="mb-16" data-aos="fade-up">
<h2 class="text-3xl font-bold text-center mb-12 text-gray-800">Advanced LSTM Features</h2>
<div class="grid grid-cols-1 md:grid-cols-2 lg:grid-cols-3 gap-8">
<div class="bg-white p-6 rounded-xl shadow-md card-hover">
<div class="bg-indigo-100 p-3 rounded-lg inline-block mb-4">
<i data-feather="layers" class="h-6 w-6 text-indigo-600"></i>
</div>
<h3 class="text-xl font-semibold mb-2 text-gray-800">Multi-layer Architecture</h3>
<p class="text-gray-600">Deep LSTM networks with multiple layers for capturing complex patterns in time series data.</p>
</div>
<div class="bg-white p-6 rounded-xl shadow-md card-hover">
<div class="bg-green-100 p-3 rounded-lg inline-block mb-4">
<i data-feather="trending-up" class="h-6 w-6 text-green-600"></i>
</div>
<h3 class="text-xl font-semibold mb-2 text-gray-800">Advanced Metrics</h3>
<p class="text-gray-600">Comprehensive evaluation using MSE, R-squared, MAE and other statistical measures.</p>
</div>
<div class="bg-white p-6 rounded-xl shadow-md card-hover">
<div class="bg-purple-100 p-3 rounded-lg inline-block mb-4">
<i data-feather="rotate-cw" class="h-6 w-6 text-purple-600"></i>
</div>
<h3 class="text-xl font-semibold mb-2 text-gray-800">Backtesting Engine</h3>
<p class="text-gray-600">Validate model performance with historical data and simulate trading strategies.</p>
</div>
<div class="bg-white p-6 rounded-xl shadow-md card-hover">
<div class="bg-blue-100 p-3 rounded-lg inline-block mb-4">
<i data-feather="sliders" class="h-6 w-6 text-blue-600"></i>
</div>
<h3 class="text-xl font-semibold mb-2 text-gray-800">Hyperparameter Optimization</h3>
<p class="text-gray-600">Automated tuning of lookback periods, layer sizes, and training parameters.</p>
</div>
<div class="bg-white p-6 rounded-xl shadow-md card-hover">
<div class="bg-yellow-100 p-3 rounded-lg inline-block mb-4">
<i data-feather="bar-chart-2" class="h-6 w-6 text-yellow-600"></i>
</div>
<h3 class="text-xl font-semibold mb-2 text-gray-800">Technical Indicators</h3>
<p class="text-gray-600">Integration of RSI, MACD, Bollinger Bands and other indicators as model features.</p>
</div>
<div class="bg-white p-6 rounded-xl shadow-md card-hover">
<div class="bg-red-100 p-3 rounded-lg inline-block mb-4">
<i data-feather="cloud" class="h-6 w-6 text-red-600"></i>
</div>
<h3 class="text-xl font-semibold mb-2 text-gray-800">Polygon API Integration</h3>
<p class="text-gray-600">Real-time market data fetching with robust error handling and data validation.</p>
</div>
</div>
</section>
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<!-- Footer -->
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<i data-feather="trending-up" class="h-6 w-6 mr-2 text-indigo-400"></i>
<h3 class="text-xl font-bold">StockLens</h3>
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<p class="text-gray-400">Advanced AI-powered stock prediction using LSTM neural networks and comprehensive technical analysis.</p>
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<h4 class="text-lg font-semibold mb-4">Resources</h4>
<ul class="space-y-2">
<li><a href="#" class="text-gray-400 hover:text-white">Documentation</a></li>
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<div class="border-t border-gray-700 mt-8 pt-8 text-sm text-center text-gray-400">
<p>© 2023 StockLens. All rights reserved. Predictions are for informational purposes only and should not be considered as financial advice.</p>
</div>
</div>
</footer>
<script>
// Initialize animations and icons
document.addEventListener('DOMContentLoaded', function() {
AOS.init({
duration: 800,
easing: 'ease-in-out',
once: true
});
feather.replace();
// Form submission with yfinance integration
document.getElementById('stockForm').addEventListener('submit', async function(e) {
e.preventDefault();
const ticker = document.getElementById('ticker').value;
const startDate = document.getElementById('startDate').value;
// Validate date is not in future
if (new Date(startDate) > new Date()) {
alert('Start date cannot be in the future');
return;
}
// Show loading section
document.getElementById('loadingSection').classList.remove('hidden');
document.getElementById('resultsSection').classList.add('hidden');
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// Show results after "training"
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document.getElementById('resultTicker').textContent = ticker;
// Generate mock predictions
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document.getElementById('prediction30').textContent = (basePrice * (1 + (Math.random() * 0.2 - 0.1))).toFixed(2);
document.getElementById('prediction60').textContent = (basePrice * (1 + (Math.random() * 0.3 - 0.15))).toFixed(2);
document.getElementById('prediction90').textContent = (basePrice * (1 + (Math.random() * 0.4 - 0.2))).toFixed(2);
// Set metrics
document.getElementById('mseValue').textContent = (Math.random() * 0.005).toFixed(4);
document.getElementById('r2Value').textContent = (0.8 + Math.random() * 0.19).toFixed(4);
document.getElementById('maeValue').textContent = (Math.random() * 0.05).toFixed(4);
document.getElementById('accuracyValue').textContent = (85 + Math.random() * 15).toFixed(1) + '%';
document.getElementById('profitValue').textContent = (5 + Math.random() * 20).toFixed(1) + '%';
// Create chart
createPriceChart();
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// Generate sample data
const dates = [];
const historical = [];
const predicted = [];
const today = new Date();
let currentPrice = 150;
// Get lookback period from form
const lookback = parseInt(document.getElementById('lookback').value) || 180;
// Historical data (based on lookback period)
for (let i = lookback; i > 0; i--) {
const date = new Date();
date.setDate(today.getDate() - i);
dates.push(date.toLocaleDateString());
currentPrice *= (1 + (Math.random() * 0.02 - 0.01));
historical.push(currentPrice);
}
// Predicted data (next 90 days)
const lastHistorical = historical[historical.length - 1];
predicted.push(...historical.slice(-10));
let prediction = lastHistorical;
for (let i = 1; i <= 90; i++) {
const date = new Date();
date.setDate(today.getDate() + i);
if (i % Math.max(1, Math.floor(lookback/10)) === 0) dates.push(date.toLocaleDateString());
// Adjust prediction volatility based on lookback period
const volatilityFactor = Math.min(1, 180/lookback);
prediction *= (1 + (Math.random() * 0.015 * volatilityFactor - 0.007 * volatilityFactor));
predicted.push(prediction);
}
new Chart(ctx, {
type: 'line',
data: {
labels: dates,
datasets: [
{
label: 'Historical Prices',
data: historical,
borderColor: 'rgb(99, 102, 241)',
backgroundColor: 'rgba(99, 102, 241, 0.1)',
tension: 0.3,
fill: true
},
{
label: 'Predicted Prices',
data: predicted,
borderColor: 'rgb(239, 68, 68)',
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tension: 0.3,
pointStyle: false,
fill: false
}
]
},
options: {
responsive: true,
plugins: {
title: {
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text: 'Stock Price History and Prediction'
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tooltip: {
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intersect: false
}
},
scales: {
x: {
display: true,
title: {
display: true,
text: 'Date'
}
},
y: {
display: true,
title: {
display: true,
text: 'Price ($)'
},
beginAtZero: false,
grace: '5%'
}
}
}
});
}
});
</script>
</body>
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