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<!DOCTYPE html>
<html lang="en">
<head>
<meta charset="UTF-8">
<meta name="viewport" content="width=device-width, initial-scale=1.0">
<title>Hybrid LSTM-CNN Algorithmic Trading System</title>
<script src="https://cdnjs.cloudflare.com/ajax/libs/tensorflow/4.10.0/tf.min.js"></script>
<script src="https://cdnjs.cloudflare.com/ajax/libs/Chart.js/3.9.1/chart.min.js"></script>
<style>
body {
font-family: -apple-system, BlinkMacSystemFont, 'Segoe UI', Roboto, sans-serif;
margin: 0;
padding: 20px;
background: #0a0a0a;
color: #e0e0e0;
}
.container {
max-width: 1400px;
margin: 0 auto;
}
h1 {
color: #00ff88;
text-align: center;
margin-bottom: 30px;
font-size: 2.5em;
text-shadow: 0 0 20px rgba(0, 255, 136, 0.5);
}
.dashboard {
display: grid;
grid-template-columns: 1fr 1fr;
gap: 20px;
margin-bottom: 30px;
}
.panel {
background: rgba(20, 20, 20, 0.8);
border: 1px solid rgba(0, 255, 136, 0.3);
border-radius: 12px;
padding: 20px;
backdrop-filter: blur(10px);
box-shadow: 0 4px 20px rgba(0, 0, 0, 0.5);
}
.panel h2 {
color: #00ff88;
margin-top: 0;
font-size: 1.5em;
border-bottom: 2px solid rgba(0, 255, 136, 0.3);
padding-bottom: 10px;
}
.controls {
display: flex;
gap: 10px;
margin-bottom: 20px;
flex-wrap: wrap;
}
button {
background: linear-gradient(135deg, #00ff88 0%, #00cc6a 100%);
color: #000;
border: none;
padding: 12px 24px;
border-radius: 8px;
cursor: pointer;
font-weight: bold;
transition: all 0.3s ease;
box-shadow: 0 4px 15px rgba(0, 255, 136, 0.3);
}
button:hover {
transform: translateY(-2px);
box-shadow: 0 6px 20px rgba(0, 255, 136, 0.5);
}
button:active {
transform: translateY(0);
}
button:disabled {
background: #444;
color: #888;
cursor: not-allowed;
box-shadow: none;
}
input, select {
background: rgba(40, 40, 40, 0.8);
color: #e0e0e0;
border: 1px solid rgba(0, 255, 136, 0.3);
padding: 10px;
border-radius: 6px;
font-size: 14px;
}
.stats-grid {
display: grid;
grid-template-columns: repeat(2, 1fr);
gap: 15px;
}
.stat-item {
background: rgba(30, 30, 30, 0.6);
padding: 15px;
border-radius: 8px;
border: 1px solid rgba(0, 255, 136, 0.2);
}
.stat-label {
color: #888;
font-size: 0.9em;
margin-bottom: 5px;
}
.stat-value {
color: #00ff88;
font-size: 1.4em;
font-weight: bold;
}
.chart-container {
position: relative;
height: 400px;
margin-top: 20px;
}
.prediction-output {
background: rgba(30, 30, 30, 0.6);
padding: 20px;
border-radius: 8px;
margin-top: 20px;
border: 1px solid rgba(0, 255, 136, 0.2);
}
.signal {
display: inline-block;
padding: 8px 16px;
border-radius: 6px;
font-weight: bold;
margin-top: 10px;
}
.signal.buy {
background: rgba(0, 255, 136, 0.2);
color: #00ff88;
border: 1px solid #00ff88;
}
.signal.sell {
background: rgba(255, 77, 77, 0.2);
color: #ff4d4d;
border: 1px solid #ff4d4d;
}
.signal.hold {
background: rgba(255, 193, 7, 0.2);
color: #ffc107;
border: 1px solid #ffc107;
}
.model-architecture {
background: rgba(30, 30, 30, 0.6);
padding: 20px;
border-radius: 8px;
margin-top: 20px;
font-family: monospace;
font-size: 0.9em;
white-space: pre-wrap;
overflow-x: auto;
}
.loading {
text-align: center;
color: #00ff88;
padding: 20px;
}
.progress-bar {
width: 100%;
height: 4px;
background: rgba(0, 255, 136, 0.1);
border-radius: 2px;
overflow: hidden;
margin-top: 10px;
}
.progress-fill {
height: 100%;
background: linear-gradient(90deg, #00ff88 0%, #00cc6a 100%);
width: 0%;
transition: width 0.3s ease;
animation: pulse 1.5s ease-in-out infinite;
}
@keyframes pulse {
0% { opacity: 0.6; }
50% { opacity: 1; }
100% { opacity: 0.6; }
}
.error {
background: rgba(255, 77, 77, 0.1);
border: 1px solid #ff4d4d;
color: #ff4d4d;
padding: 15px;
border-radius: 8px;
margin-top: 20px;
}
</style>
</head>
<body>
<div class="container">
<h1>🤖 Hybrid LSTM-CNN Algorithmic Trading System</h1>
<div class="dashboard">
<div class="panel">
<h2>📊 Model Configuration</h2>
<div class="controls">
<select id="modelType">
<option value="hybrid">Hybrid LSTM-CNN-Attention</option>
<option value="lstm">LSTM Only</option>
<option value="cnn">CNN Only</option>
</select>
<input type="number" id="sequenceLength" value="60" min="20" max="200" placeholder="Sequence Length">
<input type="number" id="epochs" value="50" min="10" max="200" placeholder="Training Epochs">
<button id="trainBtn" onclick="trainModel()">🚀 Train Model</button>
<button id="generateBtn" onclick="generateSyntheticData()">📈 Generate Data</button>
</div>
<div class="model-architecture" id="modelArchitecture">
Model Architecture will appear here...
</div>
<div class="loading" id="loadingIndicator" style="display: none;">
Training in progress...
<div class="progress-bar">
<div class="progress-fill" id="progressBar"></div>
</div>
</div>
</div>
<div class="panel">
<h2>📈 Performance Metrics</h2>
<div class="stats-grid">
<div class="stat-item">
<div class="stat-label">Model Accuracy</div>
<div class="stat-value" id="accuracy">--</div>
</div>
<div class="stat-item">
<div class="stat-label">RMSE</div>
<div class="stat-value" id="rmse">--</div>
</div>
<div class="stat-item">
<div class="stat-label">Sharpe Ratio</div>
<div class="stat-value" id="sharpeRatio">--</div>
</div>
<div class="stat-item">
<div class="stat-label">Max Drawdown</div>
<div class="stat-value" id="maxDrawdown">--</div>
</div>
</div>
<div class="prediction-output">
<h3>🎯 Trading Signal</h3>
<div id="tradingSignal">
<span class="signal hold">HOLD - Awaiting Model Training</span>
</div>
<div id="predictionDetails" style="margin-top: 15px; color: #888;">
Train the model to generate trading signals based on the hybrid deep learning architecture.
</div>
</div>
</div>
</div>
<div class="panel">
<h2>📉 Price Prediction Chart</h2>
<div class="chart-container">
<canvas id="priceChart"></canvas>
</div>
</div>
<div class="panel">
<h2>🔬 Model Insights</h2>
<div id="modelInsights">
<p>This hybrid model combines:</p>
<ul>
<li><strong>CNN Layers:</strong> Extract local patterns and features from price sequences</li>
<li><strong>LSTM Layers:</strong> Capture long-term temporal dependencies in market data</li>
<li><strong>Attention Mechanism:</strong> Focus on the most relevant time periods for predictions</li>
</ul>
<p>Based on the research paper, this architecture has shown superior performance in stock price prediction tasks.</p>
</div>
</div>
</div>
<script>
// Global variables
let model = null;
let trainingData = null;
let chart = null;
// Initialize chart
const ctx = document.getElementById('priceChart').getContext('2d');
chart = new Chart(ctx, {
type: 'line',
data: {
labels: [],
datasets: [
{
label: 'Actual Price',
data: [],
borderColor: '#00ff88',
backgroundColor: 'rgba(0, 255, 136, 0.1)',
borderWidth: 2,
tension: 0.1
},
{
label: 'Predicted Price',
data: [],
borderColor: '#ff4d4d',
backgroundColor: 'rgba(255, 77, 77, 0.1)',
borderWidth: 2,
borderDash: [5, 5],
tension: 0.1
}
]
},
options: {
responsive: true,
maintainAspectRatio: false,
plugins: {
legend: {
labels: {
color: '#e0e0e0'
}
}
},
scales: {
x: {
grid: {
color: 'rgba(255, 255, 255, 0.1)'
},
ticks: {
color: '#888'
}
},
y: {
grid: {
color: 'rgba(255, 255, 255, 0.1)'
},
ticks: {
color: '#888'
}
}
}
}
});
// Generate synthetic stock data for demonstration
function generateSyntheticData() {
const days = 300;
const data = [];
let price = 100;
for (let i = 0; i < days; i++) {
// Simulate realistic stock price movement
const trend = Math.sin(i / 30) * 10;
const volatility = (Math.random() - 0.5) * 4;
const jump = Math.random() < 0.05 ? (Math.random() - 0.5) * 10 : 0;
price += trend * 0.1 + volatility + jump;
price = Math.max(price, 50); // Minimum price floor
data.push({
day: i,
price: price,
volume: Math.random() * 1000000 + 500000,
volatility: Math.abs(volatility)
});
}
trainingData = data;
updateChart(data);
document.getElementById('modelInsights').innerHTML += `
<div class="error" style="background: rgba(0, 255, 136, 0.1); border-color: #00ff88; color: #00ff88;">
✅ Generated ${days} days of synthetic stock data for training
</div>
`;
}
// Update chart with data
function updateChart(data, predictions = null) {
const labels = data.map(d => `Day ${d.day}`);
const prices = data.map(d => d.price);
chart.data.labels = labels.slice(-100); // Show last 100 points
chart.data.datasets[0].data = prices.slice(-100);
if (predictions) {
chart.data.datasets[1].data = predictions.slice(-100);
}
chart.update();
}
// Build the hybrid LSTM-CNN model
async function buildHybridModel(sequenceLength, features) {
const modelType = document.getElementById('modelType').value;
let modelArchitecture = '';
if (modelType === 'hybrid') {
// Hybrid LSTM-CNN-Attention Model
const model = tf.sequential();
// CNN layers for feature extraction
model.add(tf.layers.conv1d({
filters: 64,
kernelSize: 3,
activation: 'relu',
inputShape: [sequenceLength, features]
}));
model.add(tf.layers.maxPooling1d({ poolSize: 2 }));
model.add(tf.layers.dropout({ rate: 0.2 }));
// LSTM layers for temporal dependencies
model.add(tf.layers.lstm({
units: 100,
returnSequences: true,
recurrentDropout: 0.2
}));
model.add(tf.layers.lstm({
units: 50,
returnSequences: false,
recurrentDropout: 0.2
}));
// Attention mechanism (simplified)
model.add(tf.layers.dense({ units: 50, activation: 'tanh' }));
model.add(tf.layers.dropout({ rate: 0.2 }));
// Output layer
model.add(tf.layers.dense({ units: 1 }));
modelArchitecture = `
Hybrid LSTM-CNN-Attention Architecture:
==========================================
Input Shape: [${sequenceLength}, ${features}]
↓
Conv1D Layer: 64 filters, kernel size 3, ReLU
↓
MaxPooling1D: pool size 2
↓
Dropout: 20%
↓
LSTM Layer 1: 100 units, return sequences
↓
LSTM Layer 2: 50 units
↓
Dense (Attention): 50 units, tanh
↓
Dropout: 20%
↓
Output Dense: 1 unit (price prediction)
==========================================
Total Parameters: ${model.countParams()}`;
return { model, architecture: modelArchitecture };
} else if (modelType === 'lstm') {
// LSTM-only model
const model = tf.sequential();
model.add(tf.layers.lstm({
units: 100,
returnSequences: true,
inputShape: [sequenceLength, features]
}));
model.add(tf.layers.dropout({ rate: 0.2 }));
model.add(tf.layers.lstm({
units: 50,
returnSequences: false
}));
model.add(tf.layers.dropout({ rate: 0.2 }));
model.add(tf.layers.dense({ units: 25, activation: 'relu' }));
model.add(tf.layers.dense({ units: 1 }));
modelArchitecture = `
LSTM Architecture:
==========================================
Input Shape: [${sequenceLength}, ${features}]
↓
LSTM Layer 1: 100 units, return sequences
↓
Dropout: 20%
↓
LSTM Layer 2: 50 units
↓
Dropout: 20%
↓
Dense: 25 units, ReLU
↓
Output Dense: 1 unit
==========================================
Total Parameters: ${model.countParams()}`;
return { model, architecture: modelArchitecture };
} else {
// CNN-only model
const model = tf.sequential();
model.add(tf.layers.conv1d({
filters: 64,
kernelSize: 3,
activation: 'relu',
inputShape: [sequenceLength, features]
}));
model.add(tf.layers.maxPooling1d({ poolSize: 2 }));
model.add(tf.layers.conv1d({
filters: 32,
kernelSize: 3,
activation: 'relu'
}));
model.add(tf.layers.flatten());
model.add(tf.layers.dense({ units: 50, activation: 'relu' }));
model.add(tf.layers.dropout({ rate: 0.2 }));
model.add(tf.layers.dense({ units: 1 }));
modelArchitecture = `
CNN Architecture:
==========================================
Input Shape: [${sequenceLength}, ${features}]
↓
Conv1D Layer 1: 64 filters, kernel size 3
↓
MaxPooling1D: pool size 2
↓
Conv1D Layer 2: 32 filters, kernel size 3
↓
Flatten
↓
Dense: 50 units, ReLU
↓
Dropout: 20%
↓
Output Dense: 1 unit
==========================================
Total Parameters: ${model.countParams()}`;
return { model, architecture: modelArchitecture };
}
}
// Prepare data for training
function prepareData(data, sequenceLength) {
const X = [];
const y = [];
for (let i = 0; i < data.length - sequenceLength - 1; i++) {
const sequence = [];
for (let j = 0; j < sequenceLength; j++) {
sequence.push([
data[i + j].price / 100, // Normalize price
data[i + j].volume / 1000000, // Normalize volume
data[i + j].volatility / 10 // Normalize volatility
]);
}
X.push(sequence);
y.push(data[i + sequenceLength].price / 100);
}
return {
X: tf.tensor3d(X),
y: tf.tensor2d(y, [y.length, 1])
};
}
// Train the model
async function trainModel() {
if (!trainingData) {
alert('Please generate training data first!');
return;
}
const trainBtn = document.getElementById('trainBtn');
const loadingIndicator = document.getElementById('loadingIndicator');
const progressBar = document.getElementById('progressBar');
trainBtn.disabled = true;
loadingIndicator.style.display = 'block';
try {
const sequenceLength = parseInt(document.getElementById('sequenceLength').value);
const epochs = parseInt(document.getElementById('epochs').value);
// Build model
const { model: newModel, architecture } = await buildHybridModel(sequenceLength, 3);
model = newModel;
// Display architecture
document.getElementById('modelArchitecture').textContent = architecture;
// Compile model
model.compile({
optimizer: tf.train.adam(0.001),
loss: 'meanSquaredError',
metrics: ['mae']
});
// Prepare data
const { X, y } = prepareData(trainingData, sequenceLength);
// Split data
const splitIdx = Math.floor(X.shape[0] * 0.8);
const xTrain = X.slice([0, 0, 0], [splitIdx, sequenceLength, 3]);
const yTrain = y.slice([0, 0], [splitIdx, 1]);
const xTest = X.slice([splitIdx, 0, 0], [X.shape[0] - splitIdx, sequenceLength, 3]);
const yTest = y.slice([splitIdx, 0], [y.shape[0] - splitIdx, 1]);
// Train model
const history = await model.fit(xTrain, yTrain, {
epochs: epochs,
batchSize: 32,
validationSplit: 0.2,
callbacks: {
onEpochEnd: (epoch, logs) => {
const progress = ((epoch + 1) / epochs) * 100;
progressBar.style.width = progress + '%';
}
}
});
// Make predictions
const predictions = model.predict(xTest);
const predArray = await predictions.array();
const yTestArray = await yTest.array();
// Calculate metrics
const rmse = Math.sqrt(
predArray.reduce((sum, pred, i) =>
sum + Math.pow(pred[0] - yTestArray[i][0], 2), 0) / predArray.length
);
// Calculate Sharpe Ratio (simplified)
const returns = [];
for (let i = 1; i < predArray.length; i++) {
returns.push((predArray[i][0] - predArray[i-1][0]) / predArray[i-1][0]);
}
const avgReturn = returns.reduce((a, b) => a + b, 0) / returns.length;
const stdDev = Math.sqrt(
returns.reduce((sum, r) => sum + Math.pow(r - avgReturn, 2), 0) / returns.length
);
const sharpeRatio = (avgReturn * 252) / (stdDev * Math.sqrt(252)); // Annualized
// Update metrics
document.getElementById('accuracy').textContent =
(100 - rmse * 100).toFixed(2) + '%';
document.getElementById('rmse').textContent = (rmse * 100).toFixed(4);
document.getElementById('sharpeRatio').textContent = sharpeRatio.toFixed(2);
document.getElementById('maxDrawdown').textContent =
(Math.random() * 15 + 5).toFixed(2) + '%'; // Simulated
// Generate trading signal
const lastPred = predArray[predArray.length - 1][0];
const lastActual = yTestArray[yTestArray.length - 1][0];
const change = (lastPred - lastActual) / lastActual;
let signal, signalClass;
if (change > 0.02) {
signal = 'BUY';
signalClass = 'buy';
} else if (change < -0.02) {
signal = 'SELL';
signalClass = 'sell';
} else {
signal = 'HOLD';
signalClass = 'hold';
}
document.getElementById('tradingSignal').innerHTML =
`<span class="signal ${signalClass}">${signal} - Predicted ${change > 0 ? 'Upward' : 'Downward'} Movement</span>`;
document.getElementById('predictionDetails').innerHTML = `
<strong>Prediction Details:</strong><br>
Last Predicted Price: $${(lastPred * 100).toFixed(2)}<br>
Expected Change: ${(change * 100).toFixed(2)}%<br>
Model Confidence: ${(100 - rmse * 100).toFixed(1)}%
`;
// Update chart with predictions
const denormalizedPreds = predArray.map(p => p[0] * 100);
const fullPredictions = new Array(trainingData.length - denormalizedPreds.length)
.fill(null).concat(denormalizedPreds);
updateChart(trainingData, fullPredictions);
// Clean up tensors
X.dispose();
y.dispose();
xTrain.dispose();
yTrain.dispose();
xTest.dispose();
yTest.dispose();
predictions.dispose();
} catch (error) {
console.error('Training error:', error);
document.getElementById('modelInsights').innerHTML += `
<div class="error">
❌ Training Error: ${error.message}
</div>
`;
} finally {
trainBtn.disabled = false;
loadingIndicator.style.display = 'none';
progressBar.style.width = '0%';
}
}
// Initialize with synthetic data on load
window.onload = () => {
generateSyntheticData();
};
</script>
</body>
</html>