fix 1s 1m chart less candles ;
fix vertical zoom
This commit is contained in:
Dobromir Popov
@@ -2430,13 +2430,14 @@ class RealTrainingAdapter:
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if not hasattr(self, 'inference_sessions'):
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self.inference_sessions = {}
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# Create inference session
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# Create inference session with position tracking
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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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'signals': [], # All signals (including rejected ones)
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'executed_trades': [], # Only executed trades (open/close positions)
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'stop_flag': False,
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'live_training_enabled': enable_live_training,
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'train_every_candle': train_every_candle,
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@@ -2447,7 +2448,13 @@ class RealTrainingAdapter:
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'loss': 0.0,
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'steps': 0
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},
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'last_candle_time': None
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'last_candle_time': None,
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# Position tracking
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'position': None, # {'type': 'long/short', 'entry_price': float, 'entry_time': str, 'entry_id': str}
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'total_pnl': 0.0,
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'win_count': 0,
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'loss_count': 0,
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'total_trades': 0
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}
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training_mode = "per-candle" if train_every_candle else ("pivot-based" if enable_live_training else "inference-only")
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@@ -3211,13 +3218,39 @@ class RealTrainingAdapter:
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'predicted_candle': prediction.get('predicted_candle')
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}
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# Store signal (all signals, including rejected ones)
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session['signals'].append(signal)
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# Keep only last 100 signals
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if len(session['signals']) > 100:
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session['signals'] = session['signals'][-100:]
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logger.info(f"Live Signal: {signal['action']} @ {signal['price']:.2f} (conf: {signal['confidence']:.2f})")
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# Execute trade logic (only if confidence is high enough and position logic allows)
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executed_trade = self._execute_realtime_trade(session, signal, current_price)
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if executed_trade:
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logger.info(f"Live Trade EXECUTED: {executed_trade['action']} @ {executed_trade['price']:.2f} (conf: {signal['confidence']:.2f})")
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# Send executed trade to frontend via WebSocket
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if hasattr(self, 'socketio') and self.socketio:
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self.socketio.emit('executed_trade', {
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'trade': executed_trade,
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'position_state': {
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'has_position': session['position'] is not None,
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'position_type': session['position']['type'] if session['position'] else None,
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'entry_price': session['position']['entry_price'] if session['position'] else None,
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'unrealized_pnl': self._calculate_unrealized_pnl(session, current_price) if session['position'] else 0.0
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},
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'session_metrics': {
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'total_pnl': session['total_pnl'],
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'total_trades': session['total_trades'],
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'win_count': session['win_count'],
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'loss_count': session['loss_count'],
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'win_rate': (session['win_count'] / session['total_trades'] * 100) if session['total_trades'] > 0 else 0
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}
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})
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else:
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logger.info(f"Live Signal (NOT executed): {signal['action']} @ {signal['price']:.2f} (conf: {signal['confidence']:.2f}) - {self._get_rejection_reason(session, signal)}")
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# Store prediction for visualization
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if self.orchestrator and hasattr(self.orchestrator, 'store_transformer_prediction'):
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@@ -3250,3 +3283,173 @@ class RealTrainingAdapter:
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logger.error(f"Fatal error in inference loop: {e}")
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session['status'] = 'error'
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session['error'] = str(e)
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def _execute_realtime_trade(self, session: Dict, signal: Dict, current_price: float) -> Optional[Dict]:
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"""
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Execute trade based on signal, respecting position management rules
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Rules:
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1. Only execute if confidence >= 0.6
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2. Only open new position if no position is currently open
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3. Close position on opposite signal
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4. Track all executed trades for visualization
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Returns:
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Dict with executed trade info, or None if signal was rejected
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"""
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action = signal['action']
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confidence = signal['confidence']
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timestamp = signal['timestamp']
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# Rule 1: Confidence threshold
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if confidence < 0.6:
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return None # Rejected: low confidence
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# Rule 2 & 3: Position management
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position = session.get('position')
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if action == 'BUY':
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if position is None:
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# Open long position
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trade_id = str(uuid.uuid4())[:8]
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session['position'] = {
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'type': 'long',
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'entry_price': current_price,
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'entry_time': timestamp,
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'entry_id': trade_id,
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'signal_confidence': confidence
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}
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executed_trade = {
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'trade_id': trade_id,
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'action': 'OPEN_LONG',
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'price': current_price,
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'timestamp': timestamp,
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'confidence': confidence
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}
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session['executed_trades'].append(executed_trade)
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return executed_trade
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elif position['type'] == 'short':
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# Close short position
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entry_price = position['entry_price']
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pnl = entry_price - current_price # Short profit
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pnl_pct = (pnl / entry_price) * 100
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executed_trade = {
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'trade_id': position['entry_id'],
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'action': 'CLOSE_SHORT',
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'price': current_price,
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'timestamp': timestamp,
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'confidence': confidence,
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'entry_price': entry_price,
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'entry_time': position['entry_time'],
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'pnl': pnl,
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'pnl_pct': pnl_pct
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}
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# Update session metrics
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session['total_pnl'] += pnl
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session['total_trades'] += 1
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if pnl > 0:
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session['win_count'] += 1
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else:
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session['loss_count'] += 1
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session['position'] = None
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session['executed_trades'].append(executed_trade)
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logger.info(f"Position CLOSED: SHORT @ {current_price:.2f}, PnL=${pnl:.2f} ({pnl_pct:+.2f}%)")
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return executed_trade
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elif action == 'SELL':
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if position is None:
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# Open short position
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trade_id = str(uuid.uuid4())[:8]
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session['position'] = {
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'type': 'short',
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'entry_price': current_price,
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'entry_time': timestamp,
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'entry_id': trade_id,
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'signal_confidence': confidence
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}
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executed_trade = {
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'trade_id': trade_id,
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'action': 'OPEN_SHORT',
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'price': current_price,
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'timestamp': timestamp,
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'confidence': confidence
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}
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session['executed_trades'].append(executed_trade)
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return executed_trade
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elif position['type'] == 'long':
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# Close long position
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entry_price = position['entry_price']
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pnl = current_price - entry_price # Long profit
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pnl_pct = (pnl / entry_price) * 100
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executed_trade = {
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'trade_id': position['entry_id'],
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'action': 'CLOSE_LONG',
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'price': current_price,
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'timestamp': timestamp,
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'confidence': confidence,
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'entry_price': entry_price,
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'entry_time': position['entry_time'],
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'pnl': pnl,
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'pnl_pct': pnl_pct
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}
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# Update session metrics
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session['total_pnl'] += pnl
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session['total_trades'] += 1
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if pnl > 0:
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session['win_count'] += 1
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else:
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session['loss_count'] += 1
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session['position'] = None
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session['executed_trades'].append(executed_trade)
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logger.info(f"Position CLOSED: LONG @ {current_price:.2f}, PnL=${pnl:.2f} ({pnl_pct:+.2f}%)")
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return executed_trade
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# HOLD or position already open in same direction
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return None
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def _get_rejection_reason(self, session: Dict, signal: Dict) -> str:
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"""Get reason why a signal was not executed"""
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action = signal['action']
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confidence = signal['confidence']
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position = session.get('position')
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if confidence < 0.6:
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return f"Low confidence ({confidence:.2f} < 0.6)"
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if action == 'HOLD':
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return "HOLD signal (no trade)"
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if position:
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if action == 'BUY' and position['type'] == 'long':
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return "Already in LONG position"
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elif action == 'SELL' and position['type'] == 'short':
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return "Already in SHORT position"
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return "Unknown reason"
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def _calculate_unrealized_pnl(self, session: Dict, current_price: float) -> float:
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"""Calculate unrealized PnL for open position"""
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position = session.get('position')
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if not position or not current_price:
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return 0.0
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entry_price = position['entry_price']
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if position['type'] == 'long':
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return ((current_price - entry_price) / entry_price) * 100 # Percentage
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else: # short
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return ((entry_price - current_price) / entry_price) * 100 # Percentage
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@@ -538,6 +538,9 @@ class AnnotationDashboard:
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engineio_logger=False
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)
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self.has_socketio = True
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# Pass socketio to training adapter for live trade updates
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if self.training_adapter:
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self.training_adapter.socketio = self.socketio
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logger.info("SocketIO initialized for real-time updates")
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except ImportError:
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self.socketio = None
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@@ -586,6 +589,8 @@ class AnnotationDashboard:
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self.annotation_manager = AnnotationManager()
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# Use REAL training adapter - NO SIMULATION!
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self.training_adapter = RealTrainingAdapter(None, self.data_provider)
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# Pass socketio to training adapter for live trade updates
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self.training_adapter.socketio = None # Will be set after socketio initialization
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# Backtest runner for replaying visible chart with predictions
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self.backtest_runner = BacktestRunner()
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@@ -17,6 +17,7 @@ class ChartManager {
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this.lastPredictionHash = null; // Track if predictions actually changed
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this.ghostCandleHistory = {}; // Store ghost candles per timeframe (max 50 each)
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this.maxGhostCandles = 150; // Maximum number of ghost candles to keep
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this.modelAccuracyMetrics = {}; // Track overall model accuracy per timeframe
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// Helper to ensure all timestamps are in UTC
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this.normalizeTimestamp = (timestamp) => {
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@@ -81,7 +82,8 @@ class ChartManager {
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*/
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async updateChart(timeframe) {
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try {
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const response = await fetch(`/api/chart-data?timeframe=${timeframe}&limit=1000`);
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// Use consistent candle count across all timeframes (2500 for sufficient training context)
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const response = await fetch(`/api/chart-data?timeframe=${timeframe}&limit=2500`);
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if (!response.ok) {
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throw new Error(`HTTP ${response.status}`);
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}
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@@ -109,7 +111,7 @@ class ChartManager {
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Plotly.restyle(plotId, candlestickUpdate, [0]);
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Plotly.restyle(plotId, volumeUpdate, [1]);
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console.log(`Updated ${timeframe} chart at ${new Date().toLocaleTimeString()}`);
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console.log(`Updated ${timeframe} chart with ${chartData.timestamps.length} candles at ${new Date().toLocaleTimeString()}`);
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}
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} catch (error) {
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console.error(`Error updating ${timeframe} chart:`, error);
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@@ -546,9 +548,9 @@ class ChartManager {
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plot_bgcolor: '#1f2937',
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paper_bgcolor: '#1f2937',
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font: { color: '#f8f9fa', size: 11 },
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margin: { l: 60, r: 20, t: 10, b: 40 },
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margin: { l: 80, r: 20, t: 10, b: 40 }, // Increased left margin for better Y-axis drag area
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hovermode: 'x unified',
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dragmode: 'pan',
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dragmode: 'pan', // Pan mode for main chart area (horizontal panning)
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// Performance optimizations
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autosize: true,
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staticPlot: false
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@@ -562,7 +564,7 @@ class ChartManager {
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scrollZoom: true,
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// Performance optimizations
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doubleClick: 'reset', // Enable double-click reset
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showAxisDragHandles: true, // Enable axis dragging
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showAxisDragHandles: true, // Enable axis dragging - allows Y-axis vertical zoom when dragging on Y-axis area
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showAxisRangeEntryBoxes: false
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};
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@@ -711,6 +713,10 @@ class ChartManager {
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Plotly.newPlot(plotId, chartData, layout, config).then(() => {
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// Optimize rendering after initial plot
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plotElement._fullLayout._replotting = false;
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// Add custom handler for Y-axis vertical zoom
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// When user drags on Y-axis area (left side), enable vertical zoom
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this._setupYAxisZoom(plotElement, plotId, timeframe);
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});
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// Store chart reference
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@@ -778,6 +784,134 @@ class ChartManager {
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console.log(`Chart created for ${timeframe} with ${data.timestamps.length} candles`);
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}
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/**
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* Setup Y-axis vertical zoom handler
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* Allows vertical zoom when dragging on the Y-axis area (left side of chart)
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*/
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_setupYAxisZoom(plotElement, plotId, timeframe) {
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let isDraggingYAxis = false;
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let dragStartY = null;
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let dragStartRange = null;
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const Y_AXIS_MARGIN = 80; // Left margin width in pixels
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// Mouse down handler - check if on Y-axis area
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const handleMouseDown = (event) => {
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const rect = plotElement.getBoundingClientRect();
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const x = event.clientX - rect.left;
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// Check if click is in Y-axis area (left margin)
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if (x < Y_AXIS_MARGIN) {
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isDraggingYAxis = true;
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dragStartY = event.clientY;
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// Get current Y-axis range
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const layout = plotElement._fullLayout;
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if (layout && layout.yaxis && layout.yaxis.range) {
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dragStartRange = {
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min: layout.yaxis.range[0],
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max: layout.yaxis.range[1],
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range: layout.yaxis.range[1] - layout.yaxis.range[0]
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};
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}
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// Change cursor to indicate vertical zoom
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plotElement.style.cursor = 'ns-resize';
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event.preventDefault();
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event.stopPropagation();
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}
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};
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// Mouse move handler - handle vertical zoom and cursor update
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const handleMouseMove = (event) => {
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const rect = plotElement.getBoundingClientRect();
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const x = event.clientX - rect.left;
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// Update cursor when hovering over Y-axis area (only if not dragging)
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if (!isDraggingYAxis) {
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if (x < Y_AXIS_MARGIN) {
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plotElement.style.cursor = 'ns-resize';
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} else {
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plotElement.style.cursor = 'default';
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}
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}
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// Handle vertical zoom drag
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if (isDraggingYAxis && dragStartY !== null && dragStartRange !== null) {
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const deltaY = dragStartY - event.clientY; // Negative = zoom in (drag up), Positive = zoom out (drag down)
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const zoomFactor = 1 + (deltaY / 200); // Adjust sensitivity (200px = 2x zoom)
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// Clamp zoom factor to reasonable limits
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const clampedZoom = Math.max(0.1, Math.min(10, zoomFactor));
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// Calculate new range centered on current view
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const center = (dragStartRange.min + dragStartRange.max) / 2;
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const newRange = dragStartRange.range * clampedZoom;
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const newMin = center - newRange / 2;
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const newMax = center + newRange / 2;
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// Update Y-axis range
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Plotly.relayout(plotId, {
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'yaxis.range': [newMin, newMax]
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});
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event.preventDefault();
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event.stopPropagation();
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}
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};
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// Mouse up handler - end drag (use document level to catch even if mouse leaves element)
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const handleMouseUp = () => {
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if (isDraggingYAxis) {
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isDraggingYAxis = false;
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dragStartY = null;
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dragStartRange = null;
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plotElement.style.cursor = 'default';
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}
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};
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// Mouse leave handler - reset cursor but keep dragging state
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const handleMouseLeave = () => {
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if (!isDraggingYAxis) {
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plotElement.style.cursor = 'default';
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}
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};
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// Attach event listeners
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// Use element-level for mousedown and mouseleave (hover detection)
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plotElement.addEventListener('mousedown', handleMouseDown);
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plotElement.addEventListener('mouseleave', handleMouseLeave);
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plotElement.addEventListener('mousemove', handleMouseMove);
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// Use document-level for mousemove and mouseup during drag (works even if mouse leaves element)
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const handleDocumentMouseMove = (event) => {
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if (isDraggingYAxis) {
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handleMouseMove(event);
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}
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};
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const handleDocumentMouseUp = () => {
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if (isDraggingYAxis) {
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handleMouseUp();
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}
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};
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document.addEventListener('mousemove', handleDocumentMouseMove);
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document.addEventListener('mouseup', handleDocumentMouseUp);
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// Store handlers for cleanup if needed
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if (!plotElement._yAxisZoomHandlers) {
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plotElement._yAxisZoomHandlers = {
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mousedown: handleMouseDown,
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mousemove: handleMouseMove,
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mouseleave: handleMouseLeave,
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documentMousemove: handleDocumentMouseMove,
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documentMouseup: handleDocumentMouseUp
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};
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}
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console.log(`[${timeframe}] Y-axis vertical zoom enabled - drag on left side (Y-axis area) to zoom vertically`);
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}
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/**
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* Handle chart click for annotation
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*/
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@@ -2081,6 +2215,12 @@ class ChartManager {
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};
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validatedCount++;
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// Calculate prediction range vs actual range to diagnose "wide" predictions
|
||||
const predRange = predCandle[1] - predCandle[2]; // High - Low
|
||||
const actualRange = actualCandle[1] - actualCandle[2];
|
||||
const rangeRatio = predRange / actualRange; // >1 means prediction is wider
|
||||
|
||||
console.log(`[${timeframe}] Prediction validated (#${validatedCount}):`, {
|
||||
timestamp: prediction.timestamp,
|
||||
matchedTo: timestamps[matchIdx],
|
||||
@@ -2090,34 +2230,144 @@ class ChartManager {
|
||||
volumeError: pctErrors.volume.toFixed(2) + '%',
|
||||
direction: directionCorrect ? '✓' : '✗',
|
||||
timeDiff: Math.abs(predTime - new Date(timestamps[matchIdx]).getTime()) + 'ms',
|
||||
rangeAnalysis: {
|
||||
predictedRange: predRange.toFixed(2),
|
||||
actualRange: actualRange.toFixed(2),
|
||||
rangeRatio: rangeRatio.toFixed(2) + 'x', // Shows if prediction is wider
|
||||
isWider: rangeRatio > 1.2 ? 'YES (too wide)' : rangeRatio < 0.8 ? 'NO (too narrow)' : 'OK'
|
||||
},
|
||||
predicted: {
|
||||
O: predCandle[0].toFixed(2),
|
||||
H: predCandle[1].toFixed(2),
|
||||
L: predCandle[2].toFixed(2),
|
||||
C: predCandle[3].toFixed(2),
|
||||
V: predCandle[4].toFixed(2)
|
||||
V: predCandle[4].toFixed(2),
|
||||
Range: predRange.toFixed(2)
|
||||
},
|
||||
actual: {
|
||||
O: actualCandle[0].toFixed(2),
|
||||
H: actualCandle[1].toFixed(2),
|
||||
L: actualCandle[2].toFixed(2),
|
||||
C: actualCandle[3].toFixed(2),
|
||||
V: actualCandle[4].toFixed(2)
|
||||
V: actualCandle[4].toFixed(2),
|
||||
Range: actualRange.toFixed(2)
|
||||
}
|
||||
});
|
||||
|
||||
// Send metrics to backend for training feedback
|
||||
this._sendPredictionMetrics(timeframe, prediction);
|
||||
|
||||
// Update overall model accuracy metrics
|
||||
this._updateModelAccuracyMetrics(timeframe, accuracy, directionCorrect);
|
||||
}
|
||||
});
|
||||
|
||||
// Summary log
|
||||
if (validatedCount > 0) {
|
||||
const totalPending = predictions.filter(p => !p.accuracy).length;
|
||||
const avgAccuracy = this.modelAccuracyMetrics[timeframe]?.avgAccuracy || 0;
|
||||
const directionAccuracy = this.modelAccuracyMetrics[timeframe]?.directionAccuracy || 0;
|
||||
console.log(`[${timeframe}] Validated ${validatedCount} predictions, ${totalPending} still pending`);
|
||||
console.log(`[${timeframe}] Model Accuracy: ${avgAccuracy.toFixed(1)}% avg, ${directionAccuracy.toFixed(1)}% direction`);
|
||||
|
||||
// CRITICAL: Re-render predictions to show updated accuracy in tooltips
|
||||
// Trigger a refresh of prediction display
|
||||
this._refreshPredictionDisplay(timeframe);
|
||||
}
|
||||
}
|
||||
|
||||
/**
|
||||
* Update overall model accuracy metrics
|
||||
*/
|
||||
_updateModelAccuracyMetrics(timeframe, accuracy, directionCorrect) {
|
||||
if (!this.modelAccuracyMetrics[timeframe]) {
|
||||
this.modelAccuracyMetrics[timeframe] = {
|
||||
accuracies: [],
|
||||
directionCorrect: [],
|
||||
totalValidated: 0
|
||||
};
|
||||
}
|
||||
|
||||
const metrics = this.modelAccuracyMetrics[timeframe];
|
||||
metrics.accuracies.push(accuracy);
|
||||
metrics.directionCorrect.push(directionCorrect);
|
||||
metrics.totalValidated++;
|
||||
|
||||
// Calculate averages
|
||||
metrics.avgAccuracy = metrics.accuracies.reduce((a, b) => a + b, 0) / metrics.accuracies.length;
|
||||
metrics.directionAccuracy = (metrics.directionCorrect.filter(c => c).length / metrics.directionCorrect.length) * 100;
|
||||
|
||||
// Keep only last 100 validations for rolling average
|
||||
if (metrics.accuracies.length > 100) {
|
||||
metrics.accuracies = metrics.accuracies.slice(-100);
|
||||
metrics.directionCorrect = metrics.directionCorrect.slice(-100);
|
||||
}
|
||||
}
|
||||
|
||||
/**
|
||||
* Refresh prediction display to show updated accuracy
|
||||
*/
|
||||
_refreshPredictionDisplay(timeframe) {
|
||||
const chart = this.charts[timeframe];
|
||||
if (!chart) return;
|
||||
|
||||
const plotId = chart.plotId;
|
||||
const plotElement = document.getElementById(plotId);
|
||||
if (!plotElement) return;
|
||||
|
||||
// Get current predictions from history
|
||||
if (!this.ghostCandleHistory[timeframe] || this.ghostCandleHistory[timeframe].length === 0) {
|
||||
return;
|
||||
}
|
||||
|
||||
// Rebuild prediction traces with updated accuracy
|
||||
const predictionTraces = [];
|
||||
for (const ghost of this.ghostCandleHistory[timeframe]) {
|
||||
this._addGhostCandlePrediction(ghost.candle, timeframe, predictionTraces, ghost.targetTime, ghost.accuracy);
|
||||
}
|
||||
|
||||
// Remove old prediction traces
|
||||
const currentTraces = plotElement.data.length;
|
||||
const indicesToRemove = [];
|
||||
for (let i = currentTraces - 1; i >= 0; i--) {
|
||||
const name = plotElement.data[i].name;
|
||||
if (name === 'Ghost Prediction' || name === 'Shadow Prediction') {
|
||||
indicesToRemove.push(i);
|
||||
}
|
||||
}
|
||||
if (indicesToRemove.length > 0) {
|
||||
Plotly.deleteTraces(plotId, indicesToRemove);
|
||||
}
|
||||
|
||||
// Add updated traces
|
||||
if (predictionTraces.length > 0) {
|
||||
Plotly.addTraces(plotId, predictionTraces);
|
||||
console.log(`[${timeframe}] Refreshed ${predictionTraces.length} prediction candles with updated accuracy`);
|
||||
}
|
||||
}
|
||||
|
||||
/**
|
||||
* Get overall model accuracy metrics for a timeframe
|
||||
*/
|
||||
getModelAccuracyMetrics(timeframe) {
|
||||
if (!this.modelAccuracyMetrics[timeframe]) {
|
||||
return {
|
||||
avgAccuracy: 0,
|
||||
directionAccuracy: 0,
|
||||
totalValidated: 0,
|
||||
recentAccuracies: []
|
||||
};
|
||||
}
|
||||
|
||||
const metrics = this.modelAccuracyMetrics[timeframe];
|
||||
return {
|
||||
avgAccuracy: metrics.avgAccuracy || 0,
|
||||
directionAccuracy: metrics.directionAccuracy || 0,
|
||||
totalValidated: metrics.totalValidated || 0,
|
||||
recentAccuracies: metrics.accuracies.slice(-10) || [] // Last 10 accuracies
|
||||
};
|
||||
}
|
||||
|
||||
/**
|
||||
* Send prediction accuracy metrics to backend for training feedback
|
||||
*/
|
||||
@@ -2814,6 +3064,169 @@ class ChartManager {
|
||||
}
|
||||
}
|
||||
|
||||
/**
|
||||
* Add executed trade marker to chart
|
||||
* Shows entry/exit points, PnL, and position lines
|
||||
*/
|
||||
addExecutedTradeMarker(trade, positionState) {
|
||||
try {
|
||||
if (!trade || !trade.timestamp) return;
|
||||
|
||||
// Find which timeframe to display on (prefer 1m, fallback to 1s)
|
||||
const timeframe = this.timeframes.includes('1m') ? '1m' : (this.timeframes.includes('1s') ? '1s' : null);
|
||||
if (!timeframe) return;
|
||||
|
||||
const chart = this.charts[timeframe];
|
||||
if (!chart) return;
|
||||
|
||||
const plotId = chart.plotId;
|
||||
const plotElement = document.getElementById(plotId);
|
||||
if (!plotElement) return;
|
||||
|
||||
// Parse timestamp
|
||||
const timestamp = new Date(trade.timestamp);
|
||||
const year = timestamp.getUTCFullYear();
|
||||
const month = String(timestamp.getUTCMonth() + 1).padStart(2, '0');
|
||||
const day = String(timestamp.getUTCDate()).padStart(2, '0');
|
||||
const hours = String(timestamp.getUTCHours()).padStart(2, '0');
|
||||
const minutes = String(timestamp.getUTCMinutes()).padStart(2, '0');
|
||||
const seconds = String(timestamp.getUTCSeconds()).padStart(2, '0');
|
||||
const formattedTimestamp = `${year}-${month}-${day} ${hours}:${minutes}:${seconds}`;
|
||||
|
||||
// Determine action type and styling
|
||||
let shape, annotation;
|
||||
|
||||
if (trade.action === 'OPEN_LONG') {
|
||||
// Green upward arrow for long entry
|
||||
shape = {
|
||||
type: 'line',
|
||||
x0: formattedTimestamp,
|
||||
x1: formattedTimestamp,
|
||||
y0: trade.price * 0.997,
|
||||
y1: trade.price * 0.993,
|
||||
line: { color: '#10b981', width: 3 },
|
||||
name: `trade_${trade.trade_id}`
|
||||
};
|
||||
annotation = {
|
||||
x: formattedTimestamp,
|
||||
y: trade.price * 0.992,
|
||||
text: `LONG<br>$${trade.price.toFixed(2)}`,
|
||||
showarrow: true,
|
||||
arrowhead: 2,
|
||||
arrowcolor: '#10b981',
|
||||
ax: 0,
|
||||
ay: 30,
|
||||
font: { size: 10, color: '#10b981', weight: 'bold' },
|
||||
bgcolor: 'rgba(16, 185, 129, 0.2)'
|
||||
};
|
||||
} else if (trade.action === 'OPEN_SHORT') {
|
||||
// Red downward arrow for short entry
|
||||
shape = {
|
||||
type: 'line',
|
||||
x0: formattedTimestamp,
|
||||
x1: formattedTimestamp,
|
||||
y0: trade.price * 1.003,
|
||||
y1: trade.price * 1.007,
|
||||
line: { color: '#ef4444', width: 3 },
|
||||
name: `trade_${trade.trade_id}`
|
||||
};
|
||||
annotation = {
|
||||
x: formattedTimestamp,
|
||||
y: trade.price * 1.008,
|
||||
text: `SHORT<br>$${trade.price.toFixed(2)}`,
|
||||
showarrow: true,
|
||||
arrowhead: 2,
|
||||
arrowcolor: '#ef4444',
|
||||
ax: 0,
|
||||
ay: -30,
|
||||
font: { size: 10, color: '#ef4444', weight: 'bold' },
|
||||
bgcolor: 'rgba(239, 68, 68, 0.2)'
|
||||
};
|
||||
} else if (trade.action === 'CLOSE_LONG' || trade.action === 'CLOSE_SHORT') {
|
||||
// Exit marker with PnL
|
||||
const isProfit = trade.pnl > 0;
|
||||
const color = isProfit ? '#10b981' : '#ef4444';
|
||||
const positionType = trade.action === 'CLOSE_LONG' ? 'LONG' : 'SHORT';
|
||||
|
||||
shape = {
|
||||
type: 'line',
|
||||
x0: formattedTimestamp,
|
||||
x1: formattedTimestamp,
|
||||
y0: trade.price,
|
||||
y1: trade.price,
|
||||
line: { color: color, width: 4, dash: 'dot' },
|
||||
name: `trade_${trade.trade_id}_exit`
|
||||
};
|
||||
annotation = {
|
||||
x: formattedTimestamp,
|
||||
y: trade.price,
|
||||
text: `EXIT ${positionType}<br>$${trade.price.toFixed(2)}<br>PnL: ${isProfit ? '+' : ''}$${trade.pnl.toFixed(2)} (${trade.pnl_pct >= 0 ? '+' : ''}${trade.pnl_pct.toFixed(2)}%)`,
|
||||
showarrow: true,
|
||||
arrowhead: 1,
|
||||
arrowcolor: color,
|
||||
ax: 0,
|
||||
ay: isProfit ? -40 : 40,
|
||||
font: { size: 10, color: color, weight: 'bold' },
|
||||
bgcolor: isProfit ? 'rgba(16, 185, 129, 0.3)' : 'rgba(239, 68, 68, 0.3)'
|
||||
};
|
||||
|
||||
// Add position line connecting entry to exit if entry time available
|
||||
if (trade.entry_time) {
|
||||
const entryTimestamp = new Date(trade.entry_time);
|
||||
const entryYear = entryTimestamp.getUTCFullYear();
|
||||
const entryMonth = String(entryTimestamp.getUTCMonth() + 1).padStart(2, '0');
|
||||
const entryDay = String(entryTimestamp.getUTCDate()).padStart(2, '0');
|
||||
const entryHours = String(entryTimestamp.getUTCHours()).padStart(2, '0');
|
||||
const entryMinutes = String(entryTimestamp.getUTCMinutes()).padStart(2, '0');
|
||||
const entrySeconds = String(entryTimestamp.getUTCSeconds()).padStart(2, '0');
|
||||
const formattedEntryTime = `${entryYear}-${entryMonth}-${entryDay} ${entryHours}:${entryMinutes}:${entrySeconds}`;
|
||||
|
||||
const positionLine = {
|
||||
type: 'rect',
|
||||
x0: formattedEntryTime,
|
||||
x1: formattedTimestamp,
|
||||
y0: trade.entry_price,
|
||||
y1: trade.price,
|
||||
fillcolor: isProfit ? 'rgba(16, 185, 129, 0.1)' : 'rgba(239, 68, 68, 0.1)',
|
||||
line: { color: color, width: 2, dash: isProfit ? 'solid' : 'dash' },
|
||||
name: `position_${trade.trade_id}`
|
||||
};
|
||||
|
||||
// Add both position rectangle and exit marker
|
||||
const currentShapes = plotElement.layout.shapes || [];
|
||||
Plotly.relayout(plotId, {
|
||||
shapes: [...currentShapes, positionLine, shape]
|
||||
});
|
||||
} else {
|
||||
// Just add exit marker
|
||||
const currentShapes = plotElement.layout.shapes || [];
|
||||
Plotly.relayout(plotId, {
|
||||
shapes: [...currentShapes, shape]
|
||||
});
|
||||
}
|
||||
} else {
|
||||
// Entry marker only (no position line yet)
|
||||
const currentShapes = plotElement.layout.shapes || [];
|
||||
Plotly.relayout(plotId, {
|
||||
shapes: [...currentShapes, shape]
|
||||
});
|
||||
}
|
||||
|
||||
// Add annotation
|
||||
if (annotation) {
|
||||
const currentAnnotations = plotElement.layout.annotations || [];
|
||||
Plotly.relayout(plotId, {
|
||||
annotations: [...currentAnnotations, annotation]
|
||||
});
|
||||
}
|
||||
|
||||
console.log(`Added executed trade marker: ${trade.action} @ ${trade.price.toFixed(2)}`);
|
||||
|
||||
} catch (error) {
|
||||
console.error('Error adding executed trade marker:', error);
|
||||
}
|
||||
}
|
||||
|
||||
/**
|
||||
* Remove live metrics overlay
|
||||
*/
|
||||
|
||||
@@ -99,6 +99,18 @@ class LiveUpdatesWebSocket {
|
||||
console.error('Prediction error:', data);
|
||||
});
|
||||
|
||||
this.socket.on('executed_trade', (data) => {
|
||||
console.log('Executed trade received:', data);
|
||||
if (this.onExecutedTrade) {
|
||||
this.onExecutedTrade(data);
|
||||
}
|
||||
});
|
||||
|
||||
this.socket.on('training_update', (data) => {
|
||||
console.log('Training update received:', data);
|
||||
// Training feedback from incremental learning
|
||||
});
|
||||
|
||||
// Error events
|
||||
this.socket.on('connect_error', (error) => {
|
||||
console.error('WebSocket connection error:', error);
|
||||
@@ -230,6 +242,26 @@ document.addEventListener('DOMContentLoaded', function() {
|
||||
}
|
||||
};
|
||||
|
||||
window.liveUpdatesWS.onExecutedTrade = function(data) {
|
||||
// Visualize executed trade on chart
|
||||
if (window.appState && window.appState.chartManager) {
|
||||
window.appState.chartManager.addExecutedTradeMarker(data.trade, data.position_state);
|
||||
}
|
||||
|
||||
// Update position state display
|
||||
if (typeof updatePositionStateDisplay === 'function') {
|
||||
updatePositionStateDisplay(data.position_state, data.session_metrics);
|
||||
}
|
||||
|
||||
// Log trade details
|
||||
console.log('Executed Trade:', {
|
||||
action: data.trade.action,
|
||||
price: data.trade.price,
|
||||
pnl: data.trade.pnl ? `$${data.trade.pnl.toFixed(2)} (${data.trade.pnl_pct.toFixed(2)}%)` : 'N/A',
|
||||
position: data.position_state.has_position ? `${data.position_state.position_type.toUpperCase()} @ $${data.position_state.entry_price}` : 'CLOSED'
|
||||
});
|
||||
};
|
||||
|
||||
// Auto-connect
|
||||
console.log('Auto-connecting to WebSocket...');
|
||||
window.liveUpdatesWS.connect();
|
||||
|
||||
Reference in New Issue
Block a user