kiro steering, live training wip
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Dobromir Popov
288
ANNOTATE/core/live_pivot_trainer.py
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288
ANNOTATE/core/live_pivot_trainer.py
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"""
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Live Pivot Trainer - Automatic Training on L2 Pivot Points
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This module monitors live 1s and 1m charts for L2 pivot points (peaks/troughs)
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and automatically creates training samples when they occur.
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Integrates with:
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- Williams Market Structure for pivot detection
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- Real Training Adapter for model training
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- Data Provider for live market data
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"""
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import logging
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import threading
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import time
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from typing import Dict, List, Optional, Tuple
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from datetime import datetime, timezone
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from collections import deque
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logger = logging.getLogger(__name__)
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class LivePivotTrainer:
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"""
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Monitors live charts for L2 pivots and automatically trains models
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Features:
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- Detects L2 pivot points on 1s and 1m timeframes
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- Creates training samples automatically
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- Trains models in background without blocking inference
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- Tracks training history to avoid duplicate training
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"""
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def __init__(self, orchestrator, data_provider, training_adapter):
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"""
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Initialize Live Pivot Trainer
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Args:
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orchestrator: TradingOrchestrator instance
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data_provider: DataProvider for market data
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training_adapter: RealTrainingAdapter for training
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"""
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self.orchestrator = orchestrator
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self.data_provider = data_provider
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self.training_adapter = training_adapter
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# Tracking
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self.running = False
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self.trained_pivots = deque(maxlen=1000) # Track last 1000 trained pivots
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self.pivot_history = {
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'1s': deque(maxlen=100),
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'1m': deque(maxlen=100)
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}
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# Configuration
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self.check_interval = 5 # Check for new pivots every 5 seconds
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self.min_pivot_spacing = 60 # Minimum 60 seconds between training on same timeframe
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self.last_training_time = {
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'1s': 0,
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'1m': 0
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}
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# Williams Market Structure for pivot detection
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try:
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from core.williams_market_structure import WilliamsMarketStructure
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self.williams_1s = WilliamsMarketStructure(num_levels=5)
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self.williams_1m = WilliamsMarketStructure(num_levels=5)
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logger.info("Williams Market Structure initialized for pivot detection")
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except Exception as e:
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logger.error(f"Failed to initialize Williams Market Structure: {e}")
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self.williams_1s = None
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self.williams_1m = None
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logger.info("LivePivotTrainer initialized")
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def start(self, symbol: str = 'ETH/USDT'):
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"""Start monitoring for L2 pivots"""
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if self.running:
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logger.warning("LivePivotTrainer already running")
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return
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self.running = True
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self.symbol = symbol
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# Start monitoring thread
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thread = threading.Thread(
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target=self._monitoring_loop,
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args=(symbol,),
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daemon=True
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)
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thread.start()
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logger.info(f"LivePivotTrainer started for {symbol}")
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def stop(self):
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"""Stop monitoring"""
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self.running = False
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logger.info("LivePivotTrainer stopped")
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def _monitoring_loop(self, symbol: str):
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"""Main monitoring loop - checks for new L2 pivots"""
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logger.info(f"LivePivotTrainer monitoring loop started for {symbol}")
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while self.running:
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try:
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# Check 1s timeframe
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self._check_timeframe_for_pivots(symbol, '1s')
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# Check 1m timeframe
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self._check_timeframe_for_pivots(symbol, '1m')
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# Sleep before next check
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time.sleep(self.check_interval)
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except Exception as e:
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logger.error(f"Error in LivePivotTrainer monitoring loop: {e}")
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time.sleep(10) # Wait longer on error
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def _check_timeframe_for_pivots(self, symbol: str, timeframe: str):
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"""
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Check a specific timeframe for new L2 pivots
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Args:
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symbol: Trading symbol
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timeframe: '1s' or '1m'
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"""
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try:
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# Rate limiting - don't train too frequently on same timeframe
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current_time = time.time()
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if current_time - self.last_training_time[timeframe] < self.min_pivot_spacing:
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return
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# Get recent candles
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candles = self.data_provider.get_historical_data(
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symbol=symbol,
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timeframe=timeframe,
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limit=200 # Need enough candles to detect pivots
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)
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if candles is None or candles.empty:
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logger.debug(f"No candles available for {symbol} {timeframe}")
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return
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# Detect pivots using Williams Market Structure
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williams = self.williams_1s if timeframe == '1s' else self.williams_1m
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if williams is None:
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return
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pivots = williams.calculate_pivots(candles)
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if not pivots or 'L2' not in pivots:
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return
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l2_pivots = pivots['L2']
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# Check for new L2 pivots (not in history)
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new_pivots = []
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for pivot in l2_pivots:
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pivot_id = f"{symbol}_{timeframe}_{pivot['timestamp']}_{pivot['type']}"
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if pivot_id not in self.trained_pivots:
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new_pivots.append(pivot)
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self.trained_pivots.append(pivot_id)
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if new_pivots:
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logger.info(f"Found {len(new_pivots)} new L2 pivots on {symbol} {timeframe}")
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# Train on the most recent pivot
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latest_pivot = new_pivots[-1]
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self._train_on_pivot(symbol, timeframe, latest_pivot, candles)
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self.last_training_time[timeframe] = current_time
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except Exception as e:
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logger.error(f"Error checking {timeframe} for pivots: {e}")
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def _train_on_pivot(self, symbol: str, timeframe: str, pivot: Dict, candles):
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"""
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Create training sample from pivot and train model
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Args:
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symbol: Trading symbol
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timeframe: Timeframe of pivot
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pivot: Pivot point data
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candles: DataFrame with OHLCV data
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"""
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try:
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logger.info(f"Training on L2 {pivot['type']} pivot @ {pivot['price']} on {symbol} {timeframe}")
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# Determine trade direction based on pivot type
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if pivot['type'] == 'high':
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# High pivot = potential SHORT entry
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direction = 'SHORT'
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action = 'SELL'
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else:
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# Low pivot = potential LONG entry
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direction = 'LONG'
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action = 'BUY'
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# Create training sample
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training_sample = {
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'test_case_id': f"live_pivot_{symbol}_{timeframe}_{pivot['timestamp']}",
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'symbol': symbol,
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'timestamp': pivot['timestamp'],
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'action': action,
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'expected_outcome': {
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'direction': direction,
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'entry_price': pivot['price'],
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'exit_price': None, # Will be determined by model
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'profit_loss_pct': 0.0, # Unknown yet
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'holding_period_seconds': 300 # 5 minutes default
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},
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'training_config': {
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'timeframes': ['1s', '1m', '1h', '1d'],
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'candles_per_timeframe': 200
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},
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'annotation_metadata': {
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'source': 'live_pivot_detection',
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'pivot_level': 'L2',
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'pivot_type': pivot['type'],
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'confidence': pivot.get('strength', 1.0)
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}
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}
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# Train model in background (non-blocking)
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thread = threading.Thread(
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target=self._background_training,
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args=(training_sample,),
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daemon=True
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)
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thread.start()
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logger.info(f"Started background training on L2 pivot")
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except Exception as e:
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logger.error(f"Error training on pivot: {e}")
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def _background_training(self, training_sample: Dict):
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"""
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Execute training in background thread
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Args:
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training_sample: Training sample data
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"""
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try:
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# Use Transformer model for live pivot training
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model_name = 'Transformer'
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logger.info(f"Background training started for {training_sample['test_case_id']}")
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# Start training session
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training_id = self.training_adapter.start_training(
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model_name=model_name,
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test_cases=[training_sample]
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)
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logger.info(f"Live pivot training session started: {training_id}")
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# Monitor training (optional - could poll status)
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# For now, just fire and forget
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except Exception as e:
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logger.error(f"Error in background training: {e}")
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def get_stats(self) -> Dict:
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"""Get training statistics"""
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return {
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'running': self.running,
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'total_trained_pivots': len(self.trained_pivots),
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'last_training_1s': self.last_training_time.get('1s', 0),
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'last_training_1m': self.last_training_time.get('1m', 0),
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'pivot_history_1s': len(self.pivot_history['1s']),
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'pivot_history_1m': len(self.pivot_history['1m'])
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}
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# Global instance
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_live_pivot_trainer = None
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def get_live_pivot_trainer(orchestrator=None, data_provider=None, training_adapter=None):
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"""Get or create global LivePivotTrainer instance"""
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global _live_pivot_trainer
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if _live_pivot_trainer is None and all([orchestrator, data_provider, training_adapter]):
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_live_pivot_trainer = LivePivotTrainer(orchestrator, data_provider, training_adapter)
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return _live_pivot_trainer
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