save/load data anotations
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Dobromir Popov
@@ -354,6 +354,31 @@ class AnnotationDashboard:
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# Save annotation
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self.annotation_manager.save_annotation(annotation)
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# Automatically generate test case with ±5min data
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try:
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test_case = self.annotation_manager.generate_test_case(
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annotation,
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data_provider=self.data_provider,
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auto_save=True
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)
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# Log test case details
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market_state = test_case.get('market_state', {})
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timeframes_with_data = [k for k in market_state.keys() if k.startswith('ohlcv_')]
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logger.info(f"Auto-generated test case: {test_case['test_case_id']}")
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logger.info(f" Timeframes: {timeframes_with_data}")
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for tf_key in timeframes_with_data:
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candle_count = len(market_state[tf_key].get('timestamps', []))
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logger.info(f" {tf_key}: {candle_count} candles")
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if 'training_labels' in market_state:
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logger.info(f" Training labels: {len(market_state['training_labels'].get('labels_1m', []))} labels")
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except Exception as e:
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logger.error(f"Failed to auto-generate test case: {e}")
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import traceback
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traceback.print_exc()
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return jsonify({
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'success': True,
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'annotation': annotation.__dict__ if hasattr(annotation, '__dict__') else annotation
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@@ -477,17 +502,35 @@ class AnnotationDashboard:
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data = request.get_json()
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model_name = data['model_name']
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annotation_ids = data['annotation_ids']
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annotation_ids = data.get('annotation_ids', [])
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# Get annotations
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annotations = self.annotation_manager.get_annotations()
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selected_annotations = [a for a in annotations
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if (a.annotation_id if hasattr(a, 'annotation_id')
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else a.get('annotation_id')) in annotation_ids]
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# If no specific annotations provided, use all
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if not annotation_ids:
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annotations = self.annotation_manager.get_annotations()
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annotation_ids = [
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a.annotation_id if hasattr(a, 'annotation_id') else a.get('annotation_id')
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for a in annotations
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]
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# Generate test cases
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test_cases = [self.annotation_manager.generate_test_case(ann)
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for ann in selected_annotations]
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# Load test cases from disk (they were auto-generated when annotations were saved)
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all_test_cases = self.annotation_manager.get_all_test_cases()
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# Filter to selected annotations
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test_cases = [
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tc for tc in all_test_cases
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if tc['test_case_id'].replace('annotation_', '') in annotation_ids
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]
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if not test_cases:
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return jsonify({
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'success': False,
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'error': {
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'code': 'NO_TEST_CASES',
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'message': f'No test cases found for {len(annotation_ids)} annotations'
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}
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})
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logger.info(f"Starting training with {len(test_cases)} test cases for model {model_name}")
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# Start training
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training_id = self.training_simulator.start_training(
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@@ -497,7 +540,8 @@ class AnnotationDashboard:
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return jsonify({
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'success': True,
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'training_id': training_id
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'training_id': training_id,
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'test_cases_count': len(test_cases)
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})
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except Exception as e:
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@@ -572,6 +616,107 @@ class AnnotationDashboard:
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'message': str(e)
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}
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})
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@self.server.route('/api/realtime-inference/start', methods=['POST'])
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def start_realtime_inference():
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"""Start real-time inference mode"""
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try:
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data = request.get_json()
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model_name = data.get('model_name')
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symbol = data.get('symbol', 'ETH/USDT')
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if not self.training_simulator:
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return jsonify({
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'success': False,
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'error': {
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'code': 'TRAINING_UNAVAILABLE',
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'message': 'Training simulator not available'
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}
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})
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# Start real-time inference
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inference_id = self.training_simulator.start_realtime_inference(
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model_name=model_name,
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symbol=symbol,
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data_provider=self.data_provider
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)
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return jsonify({
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'success': True,
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'inference_id': inference_id
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})
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except Exception as e:
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logger.error(f"Error starting real-time inference: {e}")
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return jsonify({
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'success': False,
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'error': {
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'code': 'INFERENCE_START_ERROR',
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'message': str(e)
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}
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})
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@self.server.route('/api/realtime-inference/stop', methods=['POST'])
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def stop_realtime_inference():
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"""Stop real-time inference mode"""
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try:
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data = request.get_json()
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inference_id = data.get('inference_id')
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if not self.training_simulator:
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return jsonify({
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'success': False,
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'error': {
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'code': 'TRAINING_UNAVAILABLE',
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'message': 'Training simulator not available'
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}
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})
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self.training_simulator.stop_realtime_inference(inference_id)
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return jsonify({
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'success': True
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})
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except Exception as e:
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logger.error(f"Error stopping real-time inference: {e}")
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return jsonify({
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'success': False,
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'error': {
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'code': 'INFERENCE_STOP_ERROR',
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'message': str(e)
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}
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})
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@self.server.route('/api/realtime-inference/signals', methods=['GET'])
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def get_realtime_signals():
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"""Get latest real-time inference signals"""
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try:
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if not self.training_simulator:
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return jsonify({
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'success': False,
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'error': {
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'code': 'TRAINING_UNAVAILABLE',
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'message': 'Training simulator not available'
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}
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})
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signals = self.training_simulator.get_latest_signals()
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return jsonify({
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'success': True,
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'signals': signals
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})
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except Exception as e:
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logger.error(f"Error getting signals: {e}")
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return jsonify({
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'success': False,
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'error': {
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'code': 'SIGNALS_ERROR',
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'message': str(e)
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}
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})
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def run(self, host='127.0.0.1', port=8051, debug=False):
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"""Run the application"""
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