training fixes
This commit is contained in:
Dobromir Popov
@ -40,6 +40,7 @@ from threading import Lock
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import warnings
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from dataclasses import asdict
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import math
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import subprocess
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# Setup logger
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logger = logging.getLogger(__name__)
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@ -258,18 +259,19 @@ class CleanTradingDashboard:
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def _setup_callbacks(self):
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"""Setup dashboard callbacks"""
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# Callbacks setup - no process killing needed
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@self.app.callback(
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[Output('current-price', 'children'),
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Output('session-pnl', 'children'),
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Output('current-position', 'children'),
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# Output('leverage-info', 'children'),
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Output('trade-count', 'children'),
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Output('portfolio-value', 'children'),
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Output('mexc-status', 'children')],
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[Input('interval-component', 'n_intervals')]
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)
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def update_metrics(n):
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"""Update key metrics"""
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"""Update key metrics - FIXED callback mismatch"""
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try:
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# Sync position from trading executor first
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symbol = 'ETH/USDT'
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@ -712,27 +714,44 @@ class CleanTradingDashboard:
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buy_trades = []
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sell_trades = []
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for signal in executed_signals[-20:]: # Last 20 executed trades
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signal_time = self._get_signal_attribute(signal, 'timestamp')
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for signal in executed_signals[-50:]: # Last 50 executed trades (increased from 20)
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# Try to get full timestamp first, fall back to string timestamp
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signal_time = self._get_signal_attribute(signal, 'full_timestamp')
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if not signal_time:
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signal_time = self._get_signal_attribute(signal, 'timestamp')
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signal_price = self._get_signal_attribute(signal, 'price', 0)
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signal_action = self._get_signal_attribute(signal, 'action', 'HOLD')
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signal_confidence = self._get_signal_attribute(signal, 'confidence', 0)
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if signal_time and signal_price and signal_confidence > 0:
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# Convert timestamp if needed
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# FIXED: Better timestamp conversion to prevent race conditions
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if isinstance(signal_time, str):
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try:
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# Handle time-only format
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# Handle time-only format with current date
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if ':' in signal_time and len(signal_time.split(':')) == 3:
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signal_time = datetime.now().replace(
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hour=int(signal_time.split(':')[0]),
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minute=int(signal_time.split(':')[1]),
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second=int(signal_time.split(':')[2]),
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now = datetime.now()
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time_parts = signal_time.split(':')
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signal_time = now.replace(
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hour=int(time_parts[0]),
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minute=int(time_parts[1]),
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second=int(time_parts[2]),
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microsecond=0
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)
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# Handle day boundary issues - if signal seems from future, subtract a day
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if signal_time > now + timedelta(minutes=5):
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signal_time -= timedelta(days=1)
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else:
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signal_time = pd.to_datetime(signal_time)
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except:
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except Exception as e:
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logger.debug(f"Error parsing timestamp {signal_time}: {e}")
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continue
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elif not isinstance(signal_time, datetime):
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# Convert other timestamp formats to datetime
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try:
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signal_time = pd.to_datetime(signal_time)
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except Exception as e:
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logger.debug(f"Error converting timestamp to datetime: {e}")
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continue
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if signal_action == 'BUY':
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@ -797,34 +816,51 @@ class CleanTradingDashboard:
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if not self.recent_decisions:
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return
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# Show ALL signals on the mini chart
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all_signals = self.recent_decisions[-50:] # Last 50 signals
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# Show ALL signals on the mini chart - MORE SIGNALS for better visibility
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all_signals = self.recent_decisions[-100:] # Last 100 signals (increased from 50)
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buy_signals = []
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sell_signals = []
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for signal in all_signals:
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signal_time = self._get_signal_attribute(signal, 'timestamp')
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# Try to get full timestamp first, fall back to string timestamp
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signal_time = self._get_signal_attribute(signal, 'full_timestamp')
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if not signal_time:
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signal_time = self._get_signal_attribute(signal, 'timestamp')
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signal_price = self._get_signal_attribute(signal, 'price', 0)
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signal_action = self._get_signal_attribute(signal, 'action', 'HOLD')
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signal_confidence = self._get_signal_attribute(signal, 'confidence', 0)
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is_executed = self._get_signal_attribute(signal, 'executed', False)
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if signal_time and signal_price and signal_confidence and signal_confidence > 0:
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# Convert timestamp if needed
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# FIXED: Same timestamp conversion as main chart
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if isinstance(signal_time, str):
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try:
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# Handle time-only format
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# Handle time-only format with current date
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if ':' in signal_time and len(signal_time.split(':')) == 3:
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signal_time = datetime.now().replace(
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hour=int(signal_time.split(':')[0]),
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minute=int(signal_time.split(':')[1]),
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second=int(signal_time.split(':')[2]),
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now = datetime.now()
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time_parts = signal_time.split(':')
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signal_time = now.replace(
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hour=int(time_parts[0]),
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minute=int(time_parts[1]),
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second=int(time_parts[2]),
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microsecond=0
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)
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# Handle day boundary issues
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if signal_time > now + timedelta(minutes=5):
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signal_time -= timedelta(days=1)
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else:
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signal_time = pd.to_datetime(signal_time)
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except:
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except Exception as e:
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logger.debug(f"Error parsing mini chart timestamp {signal_time}: {e}")
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continue
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elif not isinstance(signal_time, datetime):
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# Convert other timestamp formats to datetime
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try:
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signal_time = pd.to_datetime(signal_time)
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except Exception as e:
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logger.debug(f"Error converting mini chart timestamp to datetime: {e}")
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continue
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signal_data = {
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@ -1543,7 +1579,7 @@ class CleanTradingDashboard:
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next_pivot_price = recent_low + (price_range * 0.5) # Mid-range target
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confidence = base_confidence + cob_confidence_boost
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# Calculate time prediction (in minutes)
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# Calculate time prediction (in minutes)
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try:
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recent_closes = [float(x) for x in closes[-20:]]
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if len(recent_closes) > 1:
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@ -1659,12 +1695,14 @@ class CleanTradingDashboard:
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# Don't generate HOLD signals - return None instead
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return None
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now = datetime.now()
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return {
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'action': action,
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'symbol': symbol,
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'price': current_price,
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'confidence': confidence,
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'timestamp': datetime.now().strftime('%H:%M:%S'),
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'timestamp': now.strftime('%H:%M:%S'),
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'full_timestamp': now, # Add full timestamp for chart persistence
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'size': 0.005,
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'reason': f'Momentum signal (s={short_momentum:.4f}, m={medium_momentum:.4f})',
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'model': 'Momentum'
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@ -1937,9 +1975,11 @@ class CleanTradingDashboard:
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logger.warning(f"Failed to capture model inputs with COB data: {e}")
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model_inputs = {}
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# Create manual trading decision
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# Create manual trading decision with FULL TIMESTAMP for chart persistence
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now = datetime.now()
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decision = {
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'timestamp': datetime.now().strftime('%H:%M:%S'),
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'timestamp': now.strftime('%H:%M:%S'),
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'full_timestamp': now, # Store full datetime for better chart positioning
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'action': action,
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'confidence': 1.0, # Manual trades have 100% confidence
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'price': current_price,
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@ -3124,12 +3164,14 @@ class CleanTradingDashboard:
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logger.debug(f"Ignoring BTC signal: {symbol}")
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return
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# Convert orchestrator decision to dashboard format
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# Convert orchestrator decision to dashboard format with FULL TIMESTAMP
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# Handle both TradingDecision objects and dictionary formats
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now = datetime.now()
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if hasattr(decision, 'action'):
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# This is a TradingDecision object (dataclass)
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dashboard_decision = {
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'timestamp': datetime.now().strftime('%H:%M:%S'),
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'timestamp': now.strftime('%H:%M:%S'),
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'full_timestamp': now, # Add full timestamp for chart persistence
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'action': decision.action,
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'confidence': decision.confidence,
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'price': decision.price,
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@ -3141,7 +3183,8 @@ class CleanTradingDashboard:
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else:
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# This is a dictionary format
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dashboard_decision = {
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'timestamp': datetime.now().strftime('%H:%M:%S'),
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'timestamp': now.strftime('%H:%M:%S'),
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'full_timestamp': now, # Add full timestamp for chart persistence
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'action': decision.get('action', 'UNKNOWN'),
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'confidence': decision.get('confidence', 0),
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'price': decision.get('price', 0),
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@ -3359,7 +3402,7 @@ class CleanTradingDashboard:
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if not self.orchestrator:
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logger.warning("No orchestrator available for training")
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return
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# Check if DQN needs training
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dqn_status = self._is_model_actually_training('dqn')
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if not dqn_status['is_training'] and hasattr(self.orchestrator, 'rl_agent') and self.orchestrator.rl_agent:
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