540 lines
21 KiB
HTML
540 lines
21 KiB
HTML
<div class="card training-panel">
|
|
<div class="card-header">
|
|
<h6 class="mb-0">
|
|
<i class="fas fa-graduation-cap"></i>
|
|
Training
|
|
</h6>
|
|
</div>
|
|
<div class="card-body p-2">
|
|
<!-- Model Selection -->
|
|
<div class="mb-3">
|
|
<label for="model-select" class="form-label small">Model</label>
|
|
<select class="form-select form-select-sm" id="model-select">
|
|
<option value="">Loading models...</option>
|
|
</select>
|
|
</div>
|
|
|
|
<!-- Training Controls -->
|
|
<div class="mb-3">
|
|
<button class="btn btn-primary btn-sm w-100" id="train-model-btn">
|
|
<i class="fas fa-play"></i>
|
|
Train Model
|
|
</button>
|
|
</div>
|
|
|
|
<!-- Training Status -->
|
|
<div id="training-status" style="display: none;">
|
|
<div class="alert alert-info py-2 px-2 mb-2">
|
|
<div class="d-flex align-items-center mb-1">
|
|
<div class="spinner-border spinner-border-sm me-2" role="status">
|
|
<span class="visually-hidden">Training...</span>
|
|
</div>
|
|
<strong class="small">Training in progress</strong>
|
|
</div>
|
|
<div class="progress mb-1" style="height: 10px;">
|
|
<div class="progress-bar progress-bar-striped progress-bar-animated" id="training-progress-bar"
|
|
role="progressbar" style="width: 0%"></div>
|
|
</div>
|
|
<div class="small">
|
|
<div>Epoch: <span id="training-epoch">0</span>/<span id="training-total-epochs">0</span></div>
|
|
<div>Loss: <span id="training-loss">--</span></div>
|
|
</div>
|
|
</div>
|
|
</div>
|
|
|
|
<!-- Training Results -->
|
|
<div id="training-results" style="display: none;">
|
|
<div class="alert alert-success py-2 px-2 mb-2">
|
|
<strong class="small">
|
|
<i class="fas fa-check-circle"></i>
|
|
Training Complete
|
|
</strong>
|
|
<div class="small mt-1">
|
|
<div>Final Loss: <span id="result-loss">--</span></div>
|
|
<div>Accuracy: <span id="result-accuracy">--</span></div>
|
|
<div>Duration: <span id="result-duration">--</span></div>
|
|
</div>
|
|
</div>
|
|
</div>
|
|
|
|
<!-- Real-Time Inference -->
|
|
<div class="mb-3">
|
|
<label class="form-label small">Real-Time Inference</label>
|
|
<button class="btn btn-success btn-sm w-100" id="start-inference-btn">
|
|
<i class="fas fa-play"></i>
|
|
Start Live Inference
|
|
</button>
|
|
<button class="btn btn-danger btn-sm w-100 mt-1" id="stop-inference-btn" style="display: none;">
|
|
<i class="fas fa-stop"></i>
|
|
Stop Inference
|
|
</button>
|
|
</div>
|
|
|
|
<!-- Inference Status -->
|
|
<div id="inference-status" style="display: none;">
|
|
<div class="alert alert-success py-2 px-2 mb-2">
|
|
<div class="d-flex align-items-center mb-1">
|
|
<div class="spinner-border spinner-border-sm me-2" role="status">
|
|
<span class="visually-hidden">Running...</span>
|
|
</div>
|
|
<strong class="small">🔴 LIVE</strong>
|
|
</div>
|
|
<div class="small">
|
|
<div>Signal: <span id="latest-signal" class="fw-bold">--</span></div>
|
|
<div>Confidence: <span id="latest-confidence">--</span></div>
|
|
<div class="text-muted" style="font-size: 0.7rem;">Charts updating every 1s</div>
|
|
</div>
|
|
</div>
|
|
</div>
|
|
|
|
<!-- Test Case Stats -->
|
|
<div class="small text-muted">
|
|
<div class="d-flex justify-content-between">
|
|
<span>Test Cases:</span>
|
|
<span id="testcase-count">0</span>
|
|
</div>
|
|
<div class="d-flex justify-content-between">
|
|
<span>Last Training:</span>
|
|
<span id="last-training-time">Never</span>
|
|
</div>
|
|
</div>
|
|
</div>
|
|
</div>
|
|
|
|
<script>
|
|
// Load available models on page load with polling for async loading
|
|
let modelLoadingPollInterval = null;
|
|
|
|
function loadAvailableModels() {
|
|
fetch('/api/available-models')
|
|
.then(response => response.json())
|
|
.then(data => {
|
|
const modelSelect = document.getElementById('model-select');
|
|
|
|
if (data.loading) {
|
|
// Models still loading - show loading message and poll
|
|
modelSelect.innerHTML = '<option value="">🔄 Loading models...</option>';
|
|
|
|
// Start polling if not already polling
|
|
if (!modelLoadingPollInterval) {
|
|
console.log('Models loading in background, will poll for completion...');
|
|
modelLoadingPollInterval = setInterval(loadAvailableModels, 2000); // Poll every 2 seconds
|
|
}
|
|
} else {
|
|
// Models loaded - stop polling
|
|
if (modelLoadingPollInterval) {
|
|
clearInterval(modelLoadingPollInterval);
|
|
modelLoadingPollInterval = null;
|
|
}
|
|
|
|
modelSelect.innerHTML = '';
|
|
|
|
if (data.success && data.models.length > 0) {
|
|
// Show success notification
|
|
if (window.showSuccess) {
|
|
window.showSuccess(`✅ ${data.models.length} models loaded and ready for training`);
|
|
}
|
|
|
|
data.models.forEach(model => {
|
|
const option = document.createElement('option');
|
|
option.value = model;
|
|
option.textContent = model;
|
|
modelSelect.appendChild(option);
|
|
});
|
|
|
|
console.log(`✅ Models loaded: ${data.models.join(', ')}`);
|
|
} else {
|
|
const option = document.createElement('option');
|
|
option.value = '';
|
|
option.textContent = 'No models available';
|
|
modelSelect.appendChild(option);
|
|
}
|
|
}
|
|
})
|
|
.catch(error => {
|
|
console.error('Error loading models:', error);
|
|
const modelSelect = document.getElementById('model-select');
|
|
modelSelect.innerHTML = '<option value="">Error loading models</option>';
|
|
|
|
// Stop polling on error
|
|
if (modelLoadingPollInterval) {
|
|
clearInterval(modelLoadingPollInterval);
|
|
modelLoadingPollInterval = null;
|
|
}
|
|
});
|
|
}
|
|
|
|
// Load models when page loads
|
|
if (document.readyState === 'loading') {
|
|
document.addEventListener('DOMContentLoaded', loadAvailableModels);
|
|
} else {
|
|
loadAvailableModels();
|
|
}
|
|
|
|
// Train model button
|
|
document.getElementById('train-model-btn').addEventListener('click', function () {
|
|
const modelName = document.getElementById('model-select').value;
|
|
|
|
if (appState.annotations.length === 0) {
|
|
showError('No annotations available for training');
|
|
return;
|
|
}
|
|
|
|
// Get annotation IDs
|
|
const annotationIds = appState.annotations.map(a => a.annotation_id);
|
|
|
|
// Start training
|
|
startTraining(modelName, annotationIds);
|
|
});
|
|
|
|
function startTraining(modelName, annotationIds) {
|
|
// Show training status
|
|
document.getElementById('training-status').style.display = 'block';
|
|
document.getElementById('training-results').style.display = 'none';
|
|
document.getElementById('train-model-btn').disabled = true;
|
|
|
|
// Reset progress
|
|
document.getElementById('training-progress-bar').style.width = '0%';
|
|
document.getElementById('training-epoch').textContent = '0';
|
|
document.getElementById('training-loss').textContent = '--';
|
|
|
|
// Start training request
|
|
fetch('/api/train-model', {
|
|
method: 'POST',
|
|
headers: { 'Content-Type': 'application/json' },
|
|
body: JSON.stringify({
|
|
model_name: modelName,
|
|
annotation_ids: annotationIds
|
|
})
|
|
})
|
|
.then(response => response.json())
|
|
.then(data => {
|
|
if (data.success) {
|
|
// Start polling for training progress
|
|
pollTrainingProgress(data.training_id);
|
|
} else {
|
|
showError('Failed to start training: ' + data.error.message);
|
|
document.getElementById('training-status').style.display = 'none';
|
|
document.getElementById('train-model-btn').disabled = false;
|
|
}
|
|
})
|
|
.catch(error => {
|
|
showError('Network error: ' + error.message);
|
|
document.getElementById('training-status').style.display = 'none';
|
|
document.getElementById('train-model-btn').disabled = false;
|
|
});
|
|
}
|
|
|
|
function pollTrainingProgress(trainingId) {
|
|
const pollInterval = setInterval(function () {
|
|
fetch('/api/training-progress', {
|
|
method: 'POST',
|
|
headers: { 'Content-Type': 'application/json' },
|
|
body: JSON.stringify({ training_id: trainingId })
|
|
})
|
|
.then(response => response.json())
|
|
.then(data => {
|
|
if (data.success) {
|
|
const progress = data.progress;
|
|
|
|
// Update progress bar
|
|
const percentage = (progress.current_epoch / progress.total_epochs) * 100;
|
|
document.getElementById('training-progress-bar').style.width = percentage + '%';
|
|
document.getElementById('training-epoch').textContent = progress.current_epoch;
|
|
document.getElementById('training-total-epochs').textContent = progress.total_epochs;
|
|
document.getElementById('training-loss').textContent = progress.current_loss.toFixed(4);
|
|
|
|
// Check if complete
|
|
if (progress.status === 'completed') {
|
|
clearInterval(pollInterval);
|
|
showTrainingResults(progress);
|
|
} else if (progress.status === 'failed') {
|
|
clearInterval(pollInterval);
|
|
showError('Training failed: ' + progress.error);
|
|
document.getElementById('training-status').style.display = 'none';
|
|
document.getElementById('train-model-btn').disabled = false;
|
|
}
|
|
}
|
|
})
|
|
.catch(error => {
|
|
clearInterval(pollInterval);
|
|
showError('Failed to get training progress: ' + error.message);
|
|
document.getElementById('training-status').style.display = 'none';
|
|
document.getElementById('train-model-btn').disabled = false;
|
|
});
|
|
}, 1000); // Poll every second
|
|
}
|
|
|
|
function showTrainingResults(results) {
|
|
// Hide training status
|
|
document.getElementById('training-status').style.display = 'none';
|
|
|
|
// Show results
|
|
document.getElementById('training-results').style.display = 'block';
|
|
document.getElementById('result-loss').textContent = results.final_loss.toFixed(4);
|
|
document.getElementById('result-accuracy').textContent = (results.accuracy * 100).toFixed(2) + '%';
|
|
document.getElementById('result-duration').textContent = results.duration_seconds.toFixed(1) + 's';
|
|
|
|
// Update last training time
|
|
document.getElementById('last-training-time').textContent = new Date().toLocaleTimeString();
|
|
|
|
// Re-enable train button
|
|
document.getElementById('train-model-btn').disabled = false;
|
|
|
|
showSuccess('Training completed successfully');
|
|
}
|
|
|
|
// Real-time inference controls
|
|
let currentInferenceId = null;
|
|
let signalPollInterval = null;
|
|
|
|
document.getElementById('start-inference-btn').addEventListener('click', function () {
|
|
const modelName = document.getElementById('model-select').value;
|
|
|
|
if (!modelName) {
|
|
showError('Please select a model first');
|
|
return;
|
|
}
|
|
|
|
// Start real-time inference
|
|
fetch('/api/realtime-inference/start', {
|
|
method: 'POST',
|
|
headers: { 'Content-Type': 'application/json' },
|
|
body: JSON.stringify({
|
|
model_name: modelName,
|
|
symbol: appState.currentSymbol
|
|
})
|
|
})
|
|
.then(response => response.json())
|
|
.then(data => {
|
|
if (data.success) {
|
|
currentInferenceId = data.inference_id;
|
|
|
|
// Update UI
|
|
document.getElementById('start-inference-btn').style.display = 'none';
|
|
document.getElementById('stop-inference-btn').style.display = 'block';
|
|
document.getElementById('inference-status').style.display = 'block';
|
|
|
|
// Show live mode banner
|
|
const banner = document.getElementById('live-mode-banner');
|
|
if (banner) {
|
|
banner.style.display = 'block';
|
|
}
|
|
|
|
// Start polling for signals
|
|
startSignalPolling();
|
|
|
|
showSuccess('Real-time inference started - Charts now updating live');
|
|
} else {
|
|
showError('Failed to start inference: ' + data.error.message);
|
|
}
|
|
})
|
|
.catch(error => {
|
|
showError('Network error: ' + error.message);
|
|
});
|
|
});
|
|
|
|
document.getElementById('stop-inference-btn').addEventListener('click', function () {
|
|
if (!currentInferenceId) return;
|
|
|
|
// Stop real-time inference
|
|
fetch('/api/realtime-inference/stop', {
|
|
method: 'POST',
|
|
headers: { 'Content-Type': 'application/json' },
|
|
body: JSON.stringify({ inference_id: currentInferenceId })
|
|
})
|
|
.then(response => response.json())
|
|
.then(data => {
|
|
if (data.success) {
|
|
// Update UI
|
|
document.getElementById('start-inference-btn').style.display = 'block';
|
|
document.getElementById('stop-inference-btn').style.display = 'none';
|
|
document.getElementById('inference-status').style.display = 'none';
|
|
|
|
// Hide live mode banner
|
|
const banner = document.getElementById('live-mode-banner');
|
|
if (banner) {
|
|
banner.style.display = 'none';
|
|
}
|
|
|
|
// Stop polling
|
|
stopSignalPolling();
|
|
|
|
currentInferenceId = null;
|
|
showSuccess('Real-time inference stopped');
|
|
}
|
|
})
|
|
.catch(error => {
|
|
showError('Network error: ' + error.message);
|
|
});
|
|
});
|
|
|
|
function startSignalPolling() {
|
|
signalPollInterval = setInterval(function () {
|
|
// Poll for signals
|
|
fetch('/api/realtime-inference/signals')
|
|
.then(response => response.json())
|
|
.then(data => {
|
|
if (data.success && data.signals.length > 0) {
|
|
const latest = data.signals[0];
|
|
document.getElementById('latest-signal').textContent = latest.action;
|
|
document.getElementById('latest-confidence').textContent =
|
|
(latest.confidence * 100).toFixed(1) + '%';
|
|
|
|
// Update chart with signal markers
|
|
if (appState.chartManager) {
|
|
displaySignalOnChart(latest);
|
|
}
|
|
}
|
|
})
|
|
.catch(error => {
|
|
console.error('Error polling signals:', error);
|
|
});
|
|
|
|
// Update charts with latest data
|
|
updateChartsWithLiveData();
|
|
}, 1000); // Poll every second
|
|
}
|
|
|
|
function updateChartsWithLiveData() {
|
|
// Fetch latest chart data
|
|
fetch('/api/chart-data', {
|
|
method: 'POST',
|
|
headers: { 'Content-Type': 'application/json' },
|
|
body: JSON.stringify({
|
|
symbol: appState.currentSymbol,
|
|
timeframes: appState.currentTimeframes,
|
|
start_time: null,
|
|
end_time: null
|
|
})
|
|
})
|
|
.then(response => response.json())
|
|
.then(data => {
|
|
if (data.success && appState.chartManager) {
|
|
// Update each chart with new data
|
|
Object.keys(data.chart_data).forEach(timeframe => {
|
|
const chartData = data.chart_data[timeframe];
|
|
if (appState.chartManager.charts[timeframe]) {
|
|
updateSingleChart(timeframe, chartData);
|
|
}
|
|
});
|
|
}
|
|
})
|
|
.catch(error => {
|
|
console.error('Error updating charts:', error);
|
|
});
|
|
}
|
|
|
|
let liveUpdateCount = 0;
|
|
|
|
function updateSingleChart(timeframe, newData) {
|
|
const chart = appState.chartManager.charts[timeframe];
|
|
if (!chart) return;
|
|
|
|
try {
|
|
// Update candlestick data
|
|
Plotly.update(chart.plotId, {
|
|
x: [newData.timestamps],
|
|
open: [newData.open],
|
|
high: [newData.high],
|
|
low: [newData.low],
|
|
close: [newData.close]
|
|
}, {}, [0]);
|
|
|
|
// Update volume data
|
|
const volumeColors = newData.close.map((close, i) => {
|
|
if (i === 0) return '#3b82f6';
|
|
return close >= newData.open[i] ? '#10b981' : '#ef4444';
|
|
});
|
|
|
|
Plotly.update(chart.plotId, {
|
|
x: [newData.timestamps],
|
|
y: [newData.volume],
|
|
'marker.color': [volumeColors]
|
|
}, {}, [1]);
|
|
|
|
// Update counter
|
|
liveUpdateCount++;
|
|
const counterEl = document.getElementById('live-update-count');
|
|
if (counterEl) {
|
|
counterEl.textContent = liveUpdateCount + ' updates';
|
|
}
|
|
} catch (error) {
|
|
console.error('Error updating chart:', timeframe, error);
|
|
}
|
|
}
|
|
|
|
function stopSignalPolling() {
|
|
if (signalPollInterval) {
|
|
clearInterval(signalPollInterval);
|
|
signalPollInterval = null;
|
|
}
|
|
}
|
|
|
|
function displaySignalOnChart(signal) {
|
|
// Add signal marker to chart
|
|
if (!appState.chartManager || !appState.chartManager.charts) return;
|
|
|
|
// Add marker to all timeframe charts
|
|
Object.keys(appState.chartManager.charts).forEach(timeframe => {
|
|
const chart = appState.chartManager.charts[timeframe];
|
|
if (!chart) return;
|
|
|
|
// Get current annotations
|
|
const currentAnnotations = chart.element.layout.annotations || [];
|
|
|
|
// Determine marker based on signal
|
|
let markerText = '';
|
|
let markerColor = '#9ca3af';
|
|
|
|
if (signal.action === 'BUY') {
|
|
markerText = '🔵 BUY';
|
|
markerColor = '#10b981';
|
|
} else if (signal.action === 'SELL') {
|
|
markerText = '🔴 SELL';
|
|
markerColor = '#ef4444';
|
|
} else {
|
|
return; // Don't show HOLD signals
|
|
}
|
|
|
|
// Add new signal marker
|
|
const newAnnotation = {
|
|
x: signal.timestamp,
|
|
y: signal.price,
|
|
text: markerText,
|
|
showarrow: true,
|
|
arrowhead: 2,
|
|
ax: 0,
|
|
ay: -40,
|
|
font: {
|
|
size: 12,
|
|
color: markerColor
|
|
},
|
|
bgcolor: '#1f2937',
|
|
bordercolor: markerColor,
|
|
borderwidth: 2,
|
|
borderpad: 4,
|
|
opacity: 0.8
|
|
};
|
|
|
|
// Keep only last 10 signal markers
|
|
const signalAnnotations = currentAnnotations.filter(ann =>
|
|
ann.text && (ann.text.includes('BUY') || ann.text.includes('SELL'))
|
|
).slice(-9);
|
|
|
|
// Combine with existing non-signal annotations
|
|
const otherAnnotations = currentAnnotations.filter(ann =>
|
|
!ann.text || (!ann.text.includes('BUY') && !ann.text.includes('SELL'))
|
|
);
|
|
|
|
const allAnnotations = [...otherAnnotations, ...signalAnnotations, newAnnotation];
|
|
|
|
// Update chart
|
|
Plotly.relayout(chart.plotId, {
|
|
annotations: allAnnotations
|
|
});
|
|
});
|
|
|
|
console.log('Signal displayed:', signal.action, '@', signal.price);
|
|
}
|
|
</script> |