t perd viz - wip
This commit is contained in:
Dobromir Popov
@@ -107,7 +107,7 @@ class BacktestRunner:
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self.lock = threading.Lock()
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def start_backtest(self, backtest_id: str, model, data_provider, symbol: str, timeframe: str,
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start_time: Optional[str] = None, end_time: Optional[str] = None):
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orchestrator=None, start_time: Optional[str] = None, end_time: Optional[str] = None):
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"""Start backtest in background thread"""
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# Initialize backtest state
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@@ -122,22 +122,34 @@ class BacktestRunner:
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'new_predictions': [],
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'position': None, # {'type': 'long/short', 'entry_price': float, 'entry_time': str}
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'error': None,
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'stop_requested': False
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'stop_requested': False,
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'orchestrator': orchestrator,
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'symbol': symbol
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}
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# Clear previous predictions from orchestrator
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if orchestrator and hasattr(orchestrator, 'recent_transformer_predictions'):
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if symbol in orchestrator.recent_transformer_predictions:
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orchestrator.recent_transformer_predictions[symbol].clear()
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if symbol in orchestrator.recent_cnn_predictions:
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orchestrator.recent_cnn_predictions[symbol].clear()
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if symbol in orchestrator.recent_dqn_predictions:
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orchestrator.recent_dqn_predictions[symbol].clear()
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logger.info(f"Cleared previous predictions for backtest on {symbol}")
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with self.lock:
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self.active_backtests[backtest_id] = state
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# Run backtest in background thread
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thread = threading.Thread(
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target=self._run_backtest,
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args=(backtest_id, model, data_provider, symbol, timeframe, start_time, end_time)
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args=(backtest_id, model, data_provider, symbol, timeframe, orchestrator, start_time, end_time)
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)
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thread.daemon = True
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thread.start()
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def _run_backtest(self, backtest_id: str, model, data_provider, symbol: str, timeframe: str,
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start_time: Optional[str] = None, end_time: Optional[str] = None):
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orchestrator=None, start_time: Optional[str] = None, end_time: Optional[str] = None):
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"""Execute backtest candle-by-candle"""
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try:
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state = self.active_backtests[backtest_id]
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@@ -203,10 +215,51 @@ class BacktestRunner:
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'price': current_price,
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'action': prediction['action'],
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'confidence': prediction['confidence'],
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'timeframe': timeframe
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'timeframe': timeframe,
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'current_price': current_price
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}
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state['new_predictions'].append(pred_data)
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# Store in orchestrator for visualization
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if orchestrator and hasattr(orchestrator, 'store_transformer_prediction'):
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# Determine model type from model class name
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model_type = model.__class__.__name__.lower()
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# Store in appropriate prediction collection
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if 'transformer' in model_type:
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orchestrator.store_transformer_prediction(symbol, {
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'timestamp': current_time,
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'current_price': current_price,
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'predicted_price': current_price * (1.01 if prediction['action'] == 'BUY' else 0.99),
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'price_change': 1.0 if prediction['action'] == 'BUY' else -1.0,
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'confidence': prediction['confidence'],
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'action': prediction['action'],
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'horizon_minutes': 10
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})
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elif 'cnn' in model_type:
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if hasattr(orchestrator, 'recent_cnn_predictions'):
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if symbol not in orchestrator.recent_cnn_predictions:
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from collections import deque
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orchestrator.recent_cnn_predictions[symbol] = deque(maxlen=50)
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orchestrator.recent_cnn_predictions[symbol].append({
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'timestamp': current_time,
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'current_price': current_price,
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'predicted_price': current_price * (1.01 if prediction['action'] == 'BUY' else 0.99),
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'confidence': prediction['confidence'],
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'direction': 2 if prediction['action'] == 'BUY' else 0
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})
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elif 'dqn' in model_type or 'rl' in model_type:
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if hasattr(orchestrator, 'recent_dqn_predictions'):
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if symbol not in orchestrator.recent_dqn_predictions:
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from collections import deque
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orchestrator.recent_dqn_predictions[symbol] = deque(maxlen=100)
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orchestrator.recent_dqn_predictions[symbol].append({
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'timestamp': current_time,
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'current_price': current_price,
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'action': prediction['action'],
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'confidence': prediction['confidence']
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})
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# Execute trade logic
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self._execute_trade_logic(state, prediction, current_price, current_time)
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@@ -1678,6 +1731,7 @@ class AnnotationDashboard:
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data_provider=self.data_provider,
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symbol=symbol,
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timeframe=timeframe,
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orchestrator=self.orchestrator,
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start_time=start_time,
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end_time=end_time
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)
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@@ -2024,6 +2078,81 @@ class AnnotationDashboard:
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}
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})
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@self.server.route('/api/live-updates', methods=['POST'])
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def get_live_updates():
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"""Get live chart and prediction updates (polling endpoint)"""
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try:
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data = request.get_json()
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symbol = data.get('symbol', 'ETH/USDT')
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timeframe = data.get('timeframe', '1m')
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response = {
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'success': True,
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'chart_update': None,
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'prediction': None
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}
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# Get latest candle for the requested timeframe
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if self.orchestrator and self.orchestrator.data_provider:
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try:
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# Get latest candle
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ohlcv_data = self.orchestrator.data_provider.get_ohlcv_data(symbol, timeframe, limit=1)
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if ohlcv_data and len(ohlcv_data) > 0:
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latest_candle = ohlcv_data[-1]
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response['chart_update'] = {
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'symbol': symbol,
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'timeframe': timeframe,
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'candle': {
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'timestamp': latest_candle[0],
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'open': float(latest_candle[1]),
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'high': float(latest_candle[2]),
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'low': float(latest_candle[3]),
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'close': float(latest_candle[4]),
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'volume': float(latest_candle[5])
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}
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}
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except Exception as e:
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logger.debug(f"Error getting latest candle: {e}")
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# Get latest model predictions
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if self.orchestrator:
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try:
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# Get latest predictions from orchestrator
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predictions = {}
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# DQN predictions
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if hasattr(self.orchestrator, 'recent_dqn_predictions') and symbol in self.orchestrator.recent_dqn_predictions:
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dqn_preds = list(self.orchestrator.recent_dqn_predictions[symbol])
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if dqn_preds:
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predictions['dqn'] = dqn_preds[-1]
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# CNN predictions
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if hasattr(self.orchestrator, 'recent_cnn_predictions') and symbol in self.orchestrator.recent_cnn_predictions:
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cnn_preds = list(self.orchestrator.recent_cnn_predictions[symbol])
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if cnn_preds:
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predictions['cnn'] = cnn_preds[-1]
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# Transformer predictions
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if hasattr(self.orchestrator, 'recent_transformer_predictions') and symbol in self.orchestrator.recent_transformer_predictions:
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transformer_preds = list(self.orchestrator.recent_transformer_predictions[symbol])
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if transformer_preds:
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predictions['transformer'] = transformer_preds[-1]
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if predictions:
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response['prediction'] = predictions
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except Exception as e:
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logger.debug(f"Error getting predictions: {e}")
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return jsonify(response)
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except Exception as e:
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logger.error(f"Error in live updates: {e}")
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return jsonify({
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'success': False,
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'error': str(e)
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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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