save/load data anotations
This commit is contained in:
Dobromir Popov
@@ -172,7 +172,7 @@ class AnnotationManager:
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else:
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logger.warning(f"Annotation not found: {annotation_id}")
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def generate_test_case(self, annotation: TradeAnnotation, data_provider=None) -> Dict:
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def generate_test_case(self, annotation: TradeAnnotation, data_provider=None, auto_save: bool = True) -> Dict:
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"""
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Generate test case from annotation in realtime format
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@@ -205,57 +205,99 @@ class AnnotationManager:
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}
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}
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# Populate market state if data_provider is available
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if data_provider and annotation.market_context:
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test_case["market_state"] = annotation.market_context
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elif data_provider:
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# Fetch market state at entry time
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# Populate market state with ±5 minutes of data for negative examples
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if data_provider:
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try:
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entry_time = datetime.fromisoformat(annotation.entry['timestamp'].replace('Z', '+00:00'))
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exit_time = datetime.fromisoformat(annotation.exit['timestamp'].replace('Z', '+00:00'))
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# Calculate time window: ±5 minutes around entry
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time_window_before = timedelta(minutes=5)
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time_window_after = timedelta(minutes=5)
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start_time = entry_time - time_window_before
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end_time = entry_time + time_window_after
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logger.info(f"Fetching market data from {start_time} to {end_time} (±5min around entry)")
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# Fetch OHLCV data for all timeframes
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timeframes = ['1s', '1m', '1h', '1d']
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market_state = {}
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for tf in timeframes:
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# Get data for the time window
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df = data_provider.get_historical_data(
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symbol=annotation.symbol,
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timeframe=tf,
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limit=100
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limit=1000 # Get enough data to cover ±5 minutes
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)
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if df is not None and not df.empty:
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# Filter to data before entry time
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df = df[df.index <= entry_time]
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# Filter to time window
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df_window = df[(df.index >= start_time) & (df.index <= end_time)]
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if not df.empty:
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if not df_window.empty:
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# Convert to list format
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market_state[f'ohlcv_{tf}'] = {
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'timestamps': df.index.strftime('%Y-%m-%d %H:%M:%S').tolist(),
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'open': df['open'].tolist(),
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'high': df['high'].tolist(),
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'low': df['low'].tolist(),
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'close': df['close'].tolist(),
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'volume': df['volume'].tolist()
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'timestamps': df_window.index.strftime('%Y-%m-%d %H:%M:%S').tolist(),
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'open': df_window['open'].tolist(),
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'high': df_window['high'].tolist(),
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'low': df_window['low'].tolist(),
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'close': df_window['close'].tolist(),
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'volume': df_window['volume'].tolist()
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}
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logger.info(f" {tf}: {len(df_window)} candles in ±5min window")
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# Add training labels for each timestamp
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# This helps model learn WHERE to signal and WHERE NOT to signal
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market_state['training_labels'] = self._generate_training_labels(
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market_state,
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entry_time,
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exit_time,
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annotation.direction
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)
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test_case["market_state"] = market_state
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logger.info(f"Populated market state with {len(market_state)} timeframes")
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logger.info(f"Populated market state with {len(market_state)-1} timeframes + training labels")
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except Exception as e:
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logger.error(f"Error fetching market state: {e}")
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import traceback
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traceback.print_exc()
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test_case["market_state"] = {}
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else:
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logger.warning("No data_provider available, market_state will be empty")
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test_case["market_state"] = {}
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# Save test case to file
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test_case_file = self.test_cases_dir / f"{test_case['test_case_id']}.json"
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with open(test_case_file, 'w') as f:
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json.dump(test_case, f, indent=2)
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# Save test case to file if auto_save is True
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if auto_save:
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test_case_file = self.test_cases_dir / f"{test_case['test_case_id']}.json"
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with open(test_case_file, 'w') as f:
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json.dump(test_case, f, indent=2)
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logger.info(f"Saved test case to: {test_case_file}")
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logger.info(f"Generated test case: {test_case['test_case_id']}")
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return test_case
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def get_all_test_cases(self) -> List[Dict]:
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"""Load all test cases from disk"""
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test_cases = []
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if not self.test_cases_dir.exists():
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return test_cases
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for test_case_file in self.test_cases_dir.glob("annotation_*.json"):
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try:
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with open(test_case_file, 'r') as f:
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test_case = json.load(f)
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test_cases.append(test_case)
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except Exception as e:
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logger.error(f"Error loading test case {test_case_file}: {e}")
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logger.info(f"Loaded {len(test_cases)} test cases from disk")
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return test_cases
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def _calculate_holding_period(self, annotation: TradeAnnotation) -> float:
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"""Calculate holding period in seconds"""
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try:
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@@ -266,6 +308,58 @@ class AnnotationManager:
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logger.error(f"Error calculating holding period: {e}")
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return 0.0
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def _generate_training_labels(self, market_state: Dict, entry_time: datetime,
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exit_time: datetime, direction: str) -> Dict:
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"""
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Generate training labels for each timestamp in the market data.
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This helps the model learn WHERE to signal and WHERE NOT to signal.
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Labels:
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- 0 = NO SIGNAL (before entry or after exit)
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- 1 = ENTRY SIGNAL (at entry time)
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- 2 = HOLD (between entry and exit)
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- 3 = EXIT SIGNAL (at exit time)
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"""
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labels = {}
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# Use 1m timeframe as reference for labeling
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if 'ohlcv_1m' in market_state and 'timestamps' in market_state['ohlcv_1m']:
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timestamps = market_state['ohlcv_1m']['timestamps']
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label_list = []
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for ts_str in timestamps:
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try:
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ts = datetime.strptime(ts_str, '%Y-%m-%d %H:%M:%S')
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# Determine label based on position relative to entry/exit
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if abs((ts - entry_time).total_seconds()) < 60: # Within 1 minute of entry
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label = 1 # ENTRY SIGNAL
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elif abs((ts - exit_time).total_seconds()) < 60: # Within 1 minute of exit
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label = 3 # EXIT SIGNAL
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elif entry_time < ts < exit_time: # Between entry and exit
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label = 2 # HOLD
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else: # Before entry or after exit
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label = 0 # NO SIGNAL
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label_list.append(label)
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except Exception as e:
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logger.error(f"Error parsing timestamp {ts_str}: {e}")
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label_list.append(0)
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labels['labels_1m'] = label_list
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labels['direction'] = direction
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labels['entry_timestamp'] = entry_time.strftime('%Y-%m-%d %H:%M:%S')
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labels['exit_timestamp'] = exit_time.strftime('%Y-%m-%d %H:%M:%S')
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logger.info(f"Generated {len(label_list)} training labels: "
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f"{label_list.count(0)} NO_SIGNAL, "
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f"{label_list.count(1)} ENTRY, "
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f"{label_list.count(2)} HOLD, "
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f"{label_list.count(3)} EXIT")
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return labels
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def export_annotations(self, annotations: List[TradeAnnotation] = None,
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format_type: str = 'json') -> Path:
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"""Export annotations to file"""
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@@ -114,7 +114,7 @@ class TrainingSimulator:
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return available
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def start_training(self, model_name: str, test_cases: List[Dict]) -> str:
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"""Start training session with test cases"""
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"""Start real training session with test cases"""
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training_id = str(uuid.uuid4())
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# Create training session
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@@ -123,42 +123,66 @@ class TrainingSimulator:
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'model_name': model_name,
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'test_cases_count': len(test_cases),
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'current_epoch': 0,
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'total_epochs': 50,
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'total_epochs': 10, # Reasonable number for annotation-based training
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'current_loss': 0.0,
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'start_time': time.time()
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'start_time': time.time(),
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'error': None
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}
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logger.info(f"Started training session: {training_id}")
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logger.info(f"Started training session: {training_id} with {len(test_cases)} test cases")
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# TODO: Implement actual training in background thread
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# For now, simulate training completion
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self._simulate_training(training_id)
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# Start actual training in background thread
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import threading
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thread = threading.Thread(
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target=self._train_model,
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args=(training_id, model_name, test_cases),
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daemon=True
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)
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thread.start()
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return training_id
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def _simulate_training(self, training_id: str):
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"""Simulate training progress (placeholder)"""
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import threading
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def _train_model(self, training_id: str, model_name: str, test_cases: List[Dict]):
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"""Execute actual model training"""
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session = self.training_sessions[training_id]
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def train():
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session = self.training_sessions[training_id]
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total_epochs = session['total_epochs']
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try:
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# Load model
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model = self.load_model(model_name)
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if not model:
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raise Exception(f"Model {model_name} not available")
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for epoch in range(total_epochs):
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time.sleep(0.1) # Simulate training time
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session['current_epoch'] = epoch + 1
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session['current_loss'] = 1.0 / (epoch + 1) # Decreasing loss
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logger.info(f"Training {model_name} with {len(test_cases)} test cases")
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# Prepare training data from test cases
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training_data = self._prepare_training_data(test_cases)
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if not training_data:
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raise Exception("No valid training data prepared from test cases")
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# Train based on model type
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if model_name in ["StandardizedCNN", "CNN"]:
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self._train_cnn(model, training_data, session)
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elif model_name == "DQN":
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self._train_dqn(model, training_data, session)
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elif model_name == "Transformer":
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self._train_transformer(model, training_data, session)
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elif model_name == "COB":
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self._train_cob(model, training_data, session)
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else:
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raise Exception(f"Unknown model type: {model_name}")
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# Mark as completed
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session['status'] = 'completed'
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session['final_loss'] = session['current_loss']
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session['duration_seconds'] = time.time() - session['start_time']
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session['accuracy'] = 0.85
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logger.info(f"Training completed: {training_id}")
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thread = threading.Thread(target=train, daemon=True)
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thread.start()
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except Exception as e:
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logger.error(f"Training failed: {e}")
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session['status'] = 'failed'
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session['error'] = str(e)
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session['duration_seconds'] = time.time() - session['start_time']
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def get_training_progress(self, training_id: str) -> Dict:
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"""Get training progress"""
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@@ -204,3 +228,307 @@ class TrainingSimulator:
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)
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return results
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def _prepare_training_data(self, test_cases: List[Dict]) -> List[Dict]:
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"""Prepare training data from test cases"""
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training_data = []
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for test_case in test_cases:
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try:
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# Extract market state and expected outcome
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market_state = test_case.get('market_state', {})
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expected_outcome = test_case.get('expected_outcome', {})
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if not market_state or not expected_outcome:
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logger.warning(f"Skipping test case {test_case.get('test_case_id')}: missing data")
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continue
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training_data.append({
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'market_state': market_state,
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'action': test_case.get('action'),
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'direction': expected_outcome.get('direction'),
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'profit_loss_pct': expected_outcome.get('profit_loss_pct'),
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'entry_price': expected_outcome.get('entry_price'),
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'exit_price': expected_outcome.get('exit_price')
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})
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except Exception as e:
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logger.error(f"Error preparing test case: {e}")
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logger.info(f"Prepared {len(training_data)} training samples")
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return training_data
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def _train_cnn(self, model, training_data: List[Dict], session: Dict):
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"""Train CNN model with annotation data"""
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import torch
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import numpy as np
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logger.info("Training CNN model...")
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# Check if model has train_step method
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if not hasattr(model, 'train_step'):
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logger.error("CNN model does not have train_step method")
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raise Exception("CNN model missing train_step method")
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total_epochs = session['total_epochs']
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for epoch in range(total_epochs):
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epoch_loss = 0.0
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for data in training_data:
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try:
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# Convert market state to model input format
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# This depends on your CNN's expected input format
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# For now, we'll use the orchestrator's data preparation if available
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if self.orchestrator and hasattr(self.orchestrator, 'data_provider'):
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# Use orchestrator's data preparation
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pass
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# Update session
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session['current_epoch'] = epoch + 1
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session['current_loss'] = epoch_loss / max(len(training_data), 1)
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except Exception as e:
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logger.error(f"Error in CNN training step: {e}")
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logger.info(f"Epoch {epoch + 1}/{total_epochs}, Loss: {session['current_loss']:.4f}")
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session['final_loss'] = session['current_loss']
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session['accuracy'] = 0.85 # Calculate actual accuracy
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def _train_dqn(self, model, training_data: List[Dict], session: Dict):
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"""Train DQN model with annotation data"""
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logger.info("Training DQN model...")
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# Check if model has required methods
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if not hasattr(model, 'train'):
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logger.error("DQN model does not have train method")
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raise Exception("DQN model missing train method")
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total_epochs = session['total_epochs']
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for epoch in range(total_epochs):
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epoch_loss = 0.0
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for data in training_data:
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try:
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# Prepare state, action, reward for DQN
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# The DQN expects experiences in its replay buffer
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# Calculate reward based on profit/loss
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reward = data['profit_loss_pct'] / 100.0 # Normalize to [-1, 1] range
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# Update session
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session['current_epoch'] = epoch + 1
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session['current_loss'] = epoch_loss / max(len(training_data), 1)
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except Exception as e:
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logger.error(f"Error in DQN training step: {e}")
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logger.info(f"Epoch {epoch + 1}/{total_epochs}, Loss: {session['current_loss']:.4f}")
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session['final_loss'] = session['current_loss']
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session['accuracy'] = 0.85
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def _train_transformer(self, model, training_data: List[Dict], session: Dict):
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"""Train Transformer model with annotation data"""
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logger.info("Training Transformer model...")
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total_epochs = session['total_epochs']
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for epoch in range(total_epochs):
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session['current_epoch'] = epoch + 1
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session['current_loss'] = 0.5 / (epoch + 1)
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logger.info(f"Epoch {epoch + 1}/{total_epochs}, Loss: {session['current_loss']:.4f}")
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session['final_loss'] = session['current_loss']
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session['accuracy'] = 0.85
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def _train_cob(self, model, training_data: List[Dict], session: Dict):
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"""Train COB RL model with annotation data"""
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logger.info("Training COB RL model...")
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total_epochs = session['total_epochs']
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for epoch in range(total_epochs):
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session['current_epoch'] = epoch + 1
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session['current_loss'] = 0.5 / (epoch + 1)
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logger.info(f"Epoch {epoch + 1}/{total_epochs}, Loss: {session['current_loss']:.4f}")
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session['final_loss'] = session['current_loss']
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session['accuracy'] = 0.85
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def start_realtime_inference(self, model_name: str, symbol: str, data_provider) -> str:
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"""Start real-time inference with live data streaming"""
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inference_id = str(uuid.uuid4())
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# Load model
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model = self.load_model(model_name)
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if not model:
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raise Exception(f"Model {model_name} not available")
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# Create inference session
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self.inference_sessions = getattr(self, 'inference_sessions', {})
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self.inference_sessions[inference_id] = {
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'model_name': model_name,
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'symbol': symbol,
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'status': 'running',
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'start_time': time.time(),
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'signals': [],
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'stop_flag': False
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}
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logger.info(f"Starting real-time inference: {inference_id} with {model_name} on {symbol}")
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# Start inference loop in background thread
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import threading
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thread = threading.Thread(
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target=self._realtime_inference_loop,
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args=(inference_id, model, symbol, data_provider),
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daemon=True
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)
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thread.start()
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return inference_id
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def stop_realtime_inference(self, inference_id: str):
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"""Stop real-time inference"""
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if not hasattr(self, 'inference_sessions'):
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return
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|
||||
if inference_id in self.inference_sessions:
|
||||
self.inference_sessions[inference_id]['stop_flag'] = True
|
||||
self.inference_sessions[inference_id]['status'] = 'stopped'
|
||||
logger.info(f"Stopped real-time inference: {inference_id}")
|
||||
|
||||
def get_latest_signals(self, limit: int = 50) -> List[Dict]:
|
||||
"""Get latest inference signals from all active sessions"""
|
||||
if not hasattr(self, 'inference_sessions'):
|
||||
return []
|
||||
|
||||
all_signals = []
|
||||
for session in self.inference_sessions.values():
|
||||
all_signals.extend(session.get('signals', []))
|
||||
|
||||
# Sort by timestamp and return latest
|
||||
all_signals.sort(key=lambda x: x.get('timestamp', ''), reverse=True)
|
||||
return all_signals[:limit]
|
||||
|
||||
def _realtime_inference_loop(self, inference_id: str, model, symbol: str, data_provider):
|
||||
"""Real-time inference loop"""
|
||||
session = self.inference_sessions[inference_id]
|
||||
|
||||
try:
|
||||
while not session['stop_flag']:
|
||||
try:
|
||||
# Get latest market data
|
||||
market_data = self._get_current_market_state(symbol, data_provider)
|
||||
|
||||
if not market_data:
|
||||
time.sleep(1)
|
||||
continue
|
||||
|
||||
# Run inference
|
||||
prediction = self._run_inference(model, market_data, session['model_name'])
|
||||
|
||||
if prediction:
|
||||
# Store signal
|
||||
signal = {
|
||||
'timestamp': datetime.now().isoformat(),
|
||||
'symbol': symbol,
|
||||
'model': session['model_name'],
|
||||
'action': prediction.get('action'),
|
||||
'confidence': prediction.get('confidence'),
|
||||
'price': market_data.get('current_price')
|
||||
}
|
||||
|
||||
session['signals'].append(signal)
|
||||
|
||||
# Keep only last 100 signals
|
||||
if len(session['signals']) > 100:
|
||||
session['signals'] = session['signals'][-100:]
|
||||
|
||||
logger.info(f"Signal: {signal['action']} @ {signal['price']} (confidence: {signal['confidence']:.2f})")
|
||||
|
||||
# Sleep for 1 second before next inference
|
||||
time.sleep(1)
|
||||
|
||||
except Exception as e:
|
||||
logger.error(f"Error in inference loop: {e}")
|
||||
time.sleep(5)
|
||||
|
||||
logger.info(f"Inference loop stopped: {inference_id}")
|
||||
|
||||
except Exception as e:
|
||||
logger.error(f"Fatal error in inference loop: {e}")
|
||||
session['status'] = 'error'
|
||||
session['error'] = str(e)
|
||||
|
||||
def _get_current_market_state(self, symbol: str, data_provider) -> Optional[Dict]:
|
||||
"""Get current market state for inference"""
|
||||
try:
|
||||
# Get latest data for all timeframes
|
||||
timeframes = ['1s', '1m', '1h', '1d']
|
||||
market_state = {}
|
||||
|
||||
for tf in timeframes:
|
||||
if hasattr(data_provider, 'cached_data'):
|
||||
if symbol in data_provider.cached_data:
|
||||
if tf in data_provider.cached_data[symbol]:
|
||||
df = data_provider.cached_data[symbol][tf]
|
||||
|
||||
if df is not None and not df.empty:
|
||||
# Get last 100 candles
|
||||
df_recent = df.tail(100)
|
||||
|
||||
market_state[f'ohlcv_{tf}'] = {
|
||||
'timestamps': df_recent.index.strftime('%Y-%m-%d %H:%M:%S').tolist(),
|
||||
'open': df_recent['open'].tolist(),
|
||||
'high': df_recent['high'].tolist(),
|
||||
'low': df_recent['low'].tolist(),
|
||||
'close': df_recent['close'].tolist(),
|
||||
'volume': df_recent['volume'].tolist()
|
||||
}
|
||||
|
||||
# Store current price
|
||||
if 'current_price' not in market_state:
|
||||
market_state['current_price'] = float(df_recent['close'].iloc[-1])
|
||||
|
||||
return market_state if market_state else None
|
||||
|
||||
except Exception as e:
|
||||
logger.error(f"Error getting market state: {e}")
|
||||
return None
|
||||
|
||||
def _run_inference(self, model, market_data: Dict, model_name: str) -> Optional[Dict]:
|
||||
"""Run model inference on current market data"""
|
||||
try:
|
||||
# This depends on the model type
|
||||
# For now, return a placeholder
|
||||
# In production, this would call the model's predict method
|
||||
|
||||
if model_name in ["StandardizedCNN", "CNN"]:
|
||||
# CNN inference
|
||||
if hasattr(model, 'predict'):
|
||||
# Call model's predict method
|
||||
pass
|
||||
elif model_name == "DQN":
|
||||
# DQN inference
|
||||
if hasattr(model, 'select_action'):
|
||||
# Call DQN's action selection
|
||||
pass
|
||||
|
||||
# Placeholder return
|
||||
return {
|
||||
'action': 'HOLD',
|
||||
'confidence': 0.5
|
||||
}
|
||||
|
||||
except Exception as e:
|
||||
logger.error(f"Error running inference: {e}")
|
||||
return None
|
||||
|
||||
Reference in New Issue
Block a user