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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