rl cob subscription model
This commit is contained in:
Dobromir Popov
@ -23,7 +23,7 @@ import torch.optim as optim
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from datetime import datetime, timedelta
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from typing import Dict, List, Optional, Any, Callable, Tuple
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from collections import deque, defaultdict
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from dataclasses import dataclass
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from dataclasses import dataclass, asdict
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import json
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import time
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import threading
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@ -66,6 +66,30 @@ class SignalAccumulator:
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if self.last_reset_time is None:
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self.last_reset_time = datetime.now()
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@dataclass
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class TrainingUpdate:
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"""Training update event data"""
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timestamp: datetime
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symbol: str
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epoch: int
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loss: float
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batch_size: int
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learning_rate: float
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accuracy: float
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avg_confidence: float
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@dataclass
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class TradeSignal:
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"""Trade signal event data"""
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timestamp: datetime
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symbol: str
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action: str # 'BUY', 'SELL', 'HOLD'
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confidence: float
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quantity: float
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price: float
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signals_count: int
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reason: str
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class MassiveRLNetwork(nn.Module):
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"""
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Massive 1B+ parameter RL network optimized for real-time COB trading
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@ -193,7 +217,7 @@ class MassiveRLNetwork(nn.Module):
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class RealtimeRLCOBTrader:
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"""
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Real-time RL trader using COB data
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Real-time RL trader using COB data with comprehensive subscriber system
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"""
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def __init__(self,
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@ -231,9 +255,17 @@ class RealtimeRLCOBTrader:
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)
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self.scalers[symbol] = torch.cuda.amp.GradScaler()
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# Subscriber system for real-time events
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self.prediction_subscribers: List[Callable[[PredictionResult], None]] = []
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self.training_subscribers: List[Callable[[TrainingUpdate], None]] = []
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self.signal_subscribers: List[Callable[[TradeSignal], None]] = []
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self.async_prediction_subscribers: List[Callable[[PredictionResult], Any]] = []
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self.async_training_subscribers: List[Callable[[TrainingUpdate], Any]] = []
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self.async_signal_subscribers: List[Callable[[TradeSignal], Any]] = []
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# COB integration
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self.cob_integration = COBIntegration(symbols=self.symbols)
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self.cob_integration.add_dqn_callback(self._on_cob_update)
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self.cob_integration.add_dqn_callback(self._on_cob_update_sync)
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# Data storage for real-time training
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self.prediction_history: Dict[str, deque] = {}
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@ -280,6 +312,111 @@ class RealtimeRLCOBTrader:
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logger.info(f"RealtimeRLCOBTrader initialized for symbols: {self.symbols}")
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logger.info(f"Inference interval: {self.inference_interval_ms}ms")
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logger.info(f"Required confident predictions: {self.required_confident_predictions}")
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# Subscriber system methods
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def add_prediction_subscriber(self, callback: Callable[[PredictionResult], None]):
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"""Add a subscriber for prediction events"""
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self.prediction_subscribers.append(callback)
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logger.info(f"Added prediction subscriber, total: {len(self.prediction_subscribers)}")
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def add_training_subscriber(self, callback: Callable[[TrainingUpdate], None]):
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"""Add a subscriber for training events"""
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self.training_subscribers.append(callback)
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logger.info(f"Added training subscriber, total: {len(self.training_subscribers)}")
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def add_signal_subscriber(self, callback: Callable[[TradeSignal], None]):
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"""Add a subscriber for trade signal events"""
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self.signal_subscribers.append(callback)
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logger.info(f"Added signal subscriber, total: {len(self.signal_subscribers)}")
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def add_async_prediction_subscriber(self, callback: Callable[[PredictionResult], Any]):
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"""Add an async subscriber for prediction events"""
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self.async_prediction_subscribers.append(callback)
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logger.info(f"Added async prediction subscriber, total: {len(self.async_prediction_subscribers)}")
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def add_async_training_subscriber(self, callback: Callable[[TrainingUpdate], Any]):
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"""Add an async subscriber for training events"""
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self.async_training_subscribers.append(callback)
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logger.info(f"Added async training subscriber, total: {len(self.async_training_subscribers)}")
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def add_async_signal_subscriber(self, callback: Callable[[TradeSignal], Any]):
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"""Add an async subscriber for trade signal events"""
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self.async_signal_subscribers.append(callback)
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logger.info(f"Added async signal subscriber, total: {len(self.async_signal_subscribers)}")
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async def _emit_prediction(self, prediction: PredictionResult):
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"""Emit prediction to all subscribers"""
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try:
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# Sync subscribers
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for callback in self.prediction_subscribers:
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try:
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callback(prediction)
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except Exception as e:
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logger.warning(f"Error in prediction subscriber: {e}")
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# Async subscribers
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for callback in self.async_prediction_subscribers:
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try:
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if asyncio.iscoroutinefunction(callback):
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asyncio.create_task(callback(prediction))
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else:
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callback(prediction)
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except Exception as e:
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logger.warning(f"Error in async prediction subscriber: {e}")
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except Exception as e:
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logger.error(f"Error emitting prediction: {e}")
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async def _emit_training_update(self, update: TrainingUpdate):
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"""Emit training update to all subscribers"""
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try:
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# Sync subscribers
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for callback in self.training_subscribers:
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try:
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callback(update)
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except Exception as e:
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logger.warning(f"Error in training subscriber: {e}")
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# Async subscribers
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for callback in self.async_training_subscribers:
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try:
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if asyncio.iscoroutinefunction(callback):
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asyncio.create_task(callback(update))
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else:
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callback(update)
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except Exception as e:
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logger.warning(f"Error in async training subscriber: {e}")
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except Exception as e:
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logger.error(f"Error emitting training update: {e}")
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async def _emit_trade_signal(self, signal: TradeSignal):
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"""Emit trade signal to all subscribers"""
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try:
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# Sync subscribers
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for callback in self.signal_subscribers:
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try:
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callback(signal)
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except Exception as e:
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logger.warning(f"Error in signal subscriber: {e}")
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# Async subscribers
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for callback in self.async_signal_subscribers:
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try:
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if asyncio.iscoroutinefunction(callback):
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asyncio.create_task(callback(signal))
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else:
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callback(signal)
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except Exception as e:
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logger.warning(f"Error in async signal subscriber: {e}")
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except Exception as e:
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logger.error(f"Error emitting trade signal: {e}")
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def _on_cob_update_sync(self, symbol: str, data: Dict):
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"""Sync wrapper for async COB update handler"""
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try:
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# Schedule the async method
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asyncio.create_task(self._on_cob_update(symbol, data))
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except Exception as e:
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logger.error(f"Error scheduling COB update for {symbol}: {e}")
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async def start(self):
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"""Start the real-time RL trader"""
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@ -484,6 +621,9 @@ class RealtimeRLCOBTrader:
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# Store prediction for later training
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self.prediction_history[symbol].append(result)
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# Emit prediction to subscribers
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await self._emit_prediction(result)
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# Add to signal accumulator if confident enough
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if prediction['confidence'] >= self.min_confidence_threshold:
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self._add_signal(symbol, result)
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@ -606,7 +746,7 @@ class RealtimeRLCOBTrader:
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return # No action for sideways
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# Execute trade signal
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await self._execute_trade_signal(symbol, action, avg_confidence, recent_signals)
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await self._execute_trade_signal(symbol, action, float(avg_confidence), recent_signals)
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# Reset accumulator after trade signal
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self._reset_accumulator(symbol)
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@ -624,6 +764,21 @@ class RealtimeRLCOBTrader:
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if self.price_history[symbol]:
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current_price = self.price_history[symbol][-1]['price']
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# Create trade signal for emission
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trade_signal = TradeSignal(
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timestamp=datetime.now(),
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symbol=symbol,
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action=action,
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confidence=confidence,
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quantity=1.0, # Default quantity
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price=current_price,
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signals_count=len(signals),
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reason=f"Consensus of {len(signals)} predictions"
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)
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# Emit trade signal to subscribers
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await self._emit_trade_signal(trade_signal)
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# Execute through trading executor if available
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if self.trading_executor and current_price > 0:
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success = self.trading_executor.execute_signal(
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@ -707,6 +862,25 @@ class RealtimeRLCOBTrader:
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)
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stats['last_training_time'] = datetime.now()
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# Calculate accuracy and confidence
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accuracy = stats['successful_predictions'] / max(1, stats['total_predictions']) * 100
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avg_confidence = sum(p.confidence for p in batch_predictions) / len(batch_predictions)
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# Create training update for emission
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training_update = TrainingUpdate(
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timestamp=datetime.now(),
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symbol=symbol,
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epoch=stats['total_training_steps'],
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loss=loss,
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batch_size=batch_size,
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learning_rate=self.optimizers[symbol].param_groups[0]['lr'],
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accuracy=accuracy,
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avg_confidence=avg_confidence
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)
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# Emit training update to subscribers
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await self._emit_training_update(training_update)
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logger.debug(f"Training {symbol}: loss={loss:.6f}, batch_size={batch_size}")
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except Exception as e:
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