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WIP: feat(nautilus): initial integration #1

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hjnormey wants to merge 14 commits from feat/nautilus-dolphin-integration into main
pull from: feat/nautilus-dolphin-integration
merge into: hjnormey:main
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nautilus_actor_test_implementation_5x.py
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@@ -38,6 +38,10 @@ SYMBOLS_DISCOVERED_TOPIC = "SILOQY.SYMBOLS.DISCOVERED"
CANDLES_INITIAL_TOPIC = "SILOQY.CANDLES.INITIAL"
# ADDED LINE 18:
TICK_SIZES_TOPIC = "SILOQY.TICK.SIZES"
# NEW: Enhanced indicator topics for data bus publishing
REGIME_INDICATORS_TOPIC = "DOLPHIN.REGIME.INDICATORS"
BB_METRICS_TOPIC = "DOLPHIN.BB.METRICS"
TEMPORAL_PATTERNS_TOPIC = "DOLPHIN.TEMPORAL.PATTERNS"
# Rate limiting constant
MIN_INTERVAL = 2.5 # seconds between API batches
@@ -920,6 +924,13 @@ class DOLPHINRegimeActor(Actor):
self.processed_ticks = 0
self.regime_calculations = 0
# NEW: Enhanced indicator tracking for BB and temporal patterns
self.signal_history = deque(maxlen=100) # For BB calculations
self.bb_period = 20 # BB calculation period
self.bb_std_dev = 2.0 # BB standard deviation multiplier
self.velocity_history = deque(maxlen=10) # For regime velocity tracking
self.confidence_history = deque(maxlen=20) # For confidence trend analysis
self.log.info(f"DOLPHINRegimeActor initialized with Nautilus ActorExecutor - max_symbols: {self.max_symbols}, "
f"ticks_per_analysis: {self.ticks_per_analysis}")
@@ -1041,6 +1052,101 @@ class DOLPHINRegimeActor(Actor):
except Exception as e:
self.log.error(f"Nautilus ActorExecutor: Regime detection error: {e}")
def _calculate_enhanced_indicators(self, bull_ratio, bear_ratio, confidence, analyzed, total_symbols):
"""NEW: Calculate enhanced indicators including BB metrics and temporal patterns"""
# Calculate regime momentum signal
base_momentum = (bull_ratio - bear_ratio) * 100 # -100 to +100
sample_quality = min(analyzed / total_symbols, 1.0) if total_symbols > 0 else 0.0
signal = base_momentum * confidence * sample_quality
# Add to signal history
self.signal_history.append(signal)
# Calculate velocity (rate of change in signal)
velocity = 0.0
if len(self.signal_history) >= 2:
velocity = self.signal_history[-1] - self.signal_history[-2]
self.velocity_history.append(velocity)
# Store confidence for trending
self.confidence_history.append(confidence)
# Calculate Bollinger Bands if enough history
bb_metrics = {}
if len(self.signal_history) >= self.bb_period:
recent_signals = list(self.signal_history)[-self.bb_period:]
sma = sum(recent_signals) / len(recent_signals)
# Calculate standard deviation
variance = sum((x - sma) ** 2 for x in recent_signals) / len(recent_signals)
std_dev = variance ** 0.5
upper_band = sma + (self.bb_std_dev * std_dev)
lower_band = sma - (self.bb_std_dev * std_dev)
# Position within BBs (mean reversion interpretation)
if signal > upper_band:
bb_position = 'ABOVE_UPPER'
elif signal < lower_band:
bb_position = 'BELOW_LOWER'
elif signal >= sma:
bb_position = 'UPPER_HALF'
else:
bb_position = 'LOWER_HALF'
# Momentum persistence interpretation
if signal > upper_band:
momentum_signal = 'STRONG_BULL_BREAKOUT'
elif signal < lower_band:
momentum_signal = 'STRONG_BEAR_BREAKOUT'
elif signal > sma:
momentum_signal = 'MILD_BULLISH'
else:
momentum_signal = 'MILD_BEARISH'
bb_metrics = {
'signal': signal,
'sma': sma,
'upper_band': upper_band,
'lower_band': lower_band,
'bb_position': bb_position,
'momentum_signal': momentum_signal,
'bb_ready': True
}
else:
bb_metrics = {
'signal': signal,
'sma': None,
'upper_band': None,
'lower_band': None,
'bb_position': 'INSUFFICIENT_DATA',
'momentum_signal': 'INSUFFICIENT_DATA',
'bb_ready': False
}
# Calculate temporal patterns
temporal_metrics = {}
if len(self.velocity_history) >= 3:
avg_velocity = sum(self.velocity_history) / len(self.velocity_history)
velocity_trend = 'ACCELERATING' if velocity > avg_velocity else 'DECELERATING'
else:
avg_velocity = velocity
velocity_trend = 'BUILDING_HISTORY'
if len(self.confidence_history) >= 5:
confidence_trend = 'RISING' if confidence > sum(self.confidence_history[-5:-1])/4 else 'FALLING'
else:
confidence_trend = 'BUILDING_HISTORY'
temporal_metrics = {
'velocity': velocity,
'avg_velocity': avg_velocity,
'velocity_trend': velocity_trend,
'confidence_trend': confidence_trend
}
return bb_metrics, temporal_metrics
def _run_regime_detection(self):
self.regime_calculations += 1
@@ -1106,6 +1212,11 @@ class DOLPHINRegimeActor(Actor):
# PRESERVED: Original confidence calculation
confidence = self._calculate_confidence(bull_ratio, bear_ratio, analyzed, total_symbols)
# NEW: Calculate enhanced indicators
bb_metrics, temporal_metrics = self._calculate_enhanced_indicators(
bull_ratio, bear_ratio, confidence, analyzed, total_symbols
)
self.previous_bull_ratio = bull_ratio
# Publish regime result using Nautilus message bus
@@ -1124,6 +1235,41 @@ class DOLPHINRegimeActor(Actor):
self.msgbus.publish(REGIME_TOPIC, regime_tuple)
# NEW: Publish enhanced indicators to data bus
indicator_data = {
'timestamp': int(time.time() * 1000),
'regime_momentum_signal': bb_metrics['signal'],
'bb_ready': bb_metrics['bb_ready'],
'velocity': temporal_metrics['velocity'],
'velocity_trend': temporal_metrics['velocity_trend'],
'confidence_trend': temporal_metrics['confidence_trend']
}
self.msgbus.publish(REGIME_INDICATORS_TOPIC, indicator_data)
# Publish BB metrics separately for specialized consumers
if bb_metrics['bb_ready']:
bb_data = {
'timestamp': int(time.time() * 1000),
'signal': bb_metrics['signal'],
'sma': bb_metrics['sma'],
'upper_band': bb_metrics['upper_band'],
'lower_band': bb_metrics['lower_band'],
'bb_position': bb_metrics['bb_position'],
'momentum_signal': bb_metrics['momentum_signal']
}
self.msgbus.publish(BB_METRICS_TOPIC, bb_data)
# Publish temporal patterns
temporal_data = {
'timestamp': int(time.time() * 1000),
'velocity': temporal_metrics['velocity'],
'avg_velocity': temporal_metrics['avg_velocity'],
'velocity_trend': temporal_metrics['velocity_trend'],
'confidence_trend': temporal_metrics['confidence_trend'],
'signal_history_length': len(self.signal_history)
}
self.msgbus.publish(TEMPORAL_PATTERNS_TOPIC, temporal_data)
except Exception as e:
self.log.error(f"Nautilus ActorExecutor: Failed to publish regime result: {e}")
@@ -1150,6 +1296,19 @@ class DOLPHINRegimeActor(Actor):
self.log.info(f"{color_code}REGIME STATUS: {regime.value} | Bull: {bull_ratio:.2%} "
f"Bear: {bear_ratio:.2%} ({bullish}/{bearish}) | Processed: {self.processed_ticks} ticks{reset_code}")
# NEW: Enhanced indicator line after regime status
if bb_metrics['bb_ready']:
self.log.info(f"{color_code}INDICATORS: Signal:{bb_metrics['signal']:.1f} | "
f"SMA:{bb_metrics['sma']:.1f} | Upper:{bb_metrics['upper_band']:.1f} | "
f"Lower:{bb_metrics['lower_band']:.1f} | Pos:{bb_metrics['bb_position']} | "
f"Mom:{bb_metrics['momentum_signal']} | Vel:{temporal_metrics['velocity']:.1f} | "
f"VelTrend:{temporal_metrics['velocity_trend']} | ConfTrend:{temporal_metrics['confidence_trend']}{reset_code}")
else:
self.log.info(f"{color_code}INDICATORS: Signal:{bb_metrics['signal']:.1f} | "
f"Status:BUILDING_BB_HISTORY ({len(self.signal_history)}/{self.bb_period}) | "
f"Vel:{temporal_metrics['velocity']:.1f} | VelTrend:{temporal_metrics['velocity_trend']} | "
f"ConfTrend:{temporal_metrics['confidence_trend']}{reset_code}")
# NEW: Log symbol pattern and counts
if symbol_pattern: # Only if we have symbols to show
pattern_str = " ".join(symbol_pattern) + " " # Create pattern with spaces