Merge branch 'main' of https://dep.sokaris.link/vasily/TCPDashboard

CONTEXT.md

@@ -18,7 +18,9 @@ The platform is a **monolithic application** built with Python, designed for pro
 
 - **Real-time Messaging Infrastructure**: Redis-based pub/sub system for component communication, with organized channel structures and both synchronous and asynchronous support.
 
-- **Strategy Engine & Bot Manager**: (⚠️ **NOT YET IMPLEMENTED**) Core trading logic components for strategy execution, bot lifecycle management, and virtual portfolio tracking.
+- **Strategy Engine**: (✅ **IMPLEMENTED**) Core trading logic components for strategy execution, including base strategy interface, dynamic loading, signal generation, batch processing, and real-time execution.
+
+- **Bot Manager**: (⚠️ **NOT YET IMPLEMENTED**) Core trading logic components for bot lifecycle management, and virtual portfolio tracking.
 
 - **Backtesting Engine**: (⚠️ **NOT YET IMPLEMENTED**) Historical strategy testing capabilities.
 
@@ -29,7 +31,7 @@ The platform is a **monolithic application** built with Python, designed for pro
 4. **Storage**: Both raw trade data and processed OHLCV stored in PostgreSQL
 5. **Distribution**: Real-time data distributed via Redis pub/sub channels
 6. **Visualization**: Dashboard reads from database for charts and system monitoring
-7. **Strategy Processing**: (Future) Strategy engine will consume OHLCV data for signal generation
+7. **Strategy Processing**: Strategy engine now consumes OHLCV data for signal generation and execution.
 
 ### System Health & Monitoring
 The platform includes comprehensive system health monitoring with:
@@ -55,7 +57,7 @@ The platform includes comprehensive system health monitoring with:
 
 ## 3. Coding Conventions
 
-- **Modular Design**: Code is organized into modules with a clear purpose (e.g., `data`, `database`, `dashboard`). See `architecture.md` for more details.
+- **Modular Design**: Code is organized into modules with a clear purpose (e.g., `data`, `database`, `dashboard`, `strategies`). See `architecture.md` for more details.
 - **Naming Conventions**:
     - **Classes**: `PascalCase` (e.g., `MarketData`, `BaseDataCollector`, `CollectorManager`)
     - **Functions & Methods**: `snake_case` (e.g., `get_system_health_layout`, `connect`)
@@ -66,7 +68,7 @@ The platform includes comprehensive system health monitoring with:
 
 - **File Organization & Code Structure**:
     - **Repository Pattern**: Database operations centralized through repository classes
-    - **Factory Pattern**: Used for collector creation and management
+    - **Factory Pattern**: Used for collector creation, strategy loading and management
     - **Abstract Base Classes**: Well-defined interfaces for extensibility  
     - **Dependency Injection**: Configuration and dependencies injected rather than hardcoded
     - **Error Handling**: Custom exception hierarchies with proper error context
@@ -121,6 +123,14 @@ The platform includes comprehensive system health monitoring with:
 - **Chart Integration**: Sophisticated indicator overlay system with configuration options
 - **Strategy Configurations**: Advanced chart strategy templates and examples
 
+**🤖 Strategy Engine (95% Complete)**
+- **BaseStrategy Interface**: Implemented and serving as a foundation for strategies
+- **Strategy Implementations**: EMA Crossover, MACD, and RSI strategies implemented
+- **Strategy Factory**: Dynamic strategy loading and registration system in place
+- **Signal Generation**: Core trading signal logic implemented with vectorized operations
+- **Batch Processing**: Capabilities for backtesting large datasets
+- **Real-time Execution**: Event-driven real-time signal generation and broadcasting
+
 **🔧 Supporting Systems (90% Complete)**
 - **Logging System**: Unified, component-specific logging with automatic cleanup
 - **Configuration Management**: Type-safe settings with environment variable support
@@ -129,12 +139,6 @@ The platform includes comprehensive system health monitoring with:
 
 ### ⚠️ **Critical Gaps - Core Business Logic Missing**
 
-**🤖 Strategy Engine (0% Complete)**
-- **BaseStrategy Interface**: Not implemented
-- **Strategy Implementations**: No EMA crossover, MACD, or RSI strategies exist
-- **Strategy Factory**: No dynamic strategy loading system
-- **Signal Generation**: No actual trading signal logic
-
 **🎮 Bot Management System (10% Complete)**  
 - **Bot Database Models**: Exist but no business logic implementation
 - **BotManager Class**: Does not exist (`bot_manager.py` missing)
@@ -154,22 +158,19 @@ The platform includes comprehensive system health monitoring with:
 ### 🔧 **Technical Debt & Issues**
 
 **📋 Testing Issues**
-- Import errors in test suite due to file structure changes
-- Some tests reference non-existent modules
+- Import errors in test suite due to file structure changes (mostly resolved with recent updates)
 - Test coverage needs update for new components
 
 **📄 Documentation Gaps**  
-- Context documentation was significantly outdated
-- Some complex functions lack comprehensive documentation
 - API documentation needs expansion
 
 ### 🎯 **Next Phase Priority**
 
-The project has **excellent infrastructure** but needs **core business logic implementation**:
+The project has **excellent infrastructure** and a nearly complete **Strategy Engine**. The next phases will focus on:
 
-1. **Strategy Engine Foundation** (Task Group 4.0) - Implement base strategy classes and signal generation
+1. **Chart Integration and Visualization** (Task Group 5.0) - Implement live strategy signal visualization layers and performance metrics display.
 2. **Bot Management System** (Task Group 6.0) - Create bot lifecycle management and virtual portfolios  
-3. **Backtesting Engine** (Task Group 5.0) - Build historical strategy testing capabilities
-4. **Real-Time Simulation** (Task Group 7.0) - Implement live strategy execution with virtual trading
+3. **Backtesting Engine** (Task Group 7.0) - Build historical strategy testing capabilities
+4. **Real-Time Simulation** (Task Group 8.0) - Implement live strategy execution with virtual trading
 
 The sophisticated infrastructure provides a solid foundation for rapid development of the trading logic components. 

dashboard/callbacks/__init__.py

@@ -6,10 +6,12 @@ from .navigation import register_navigation_callbacks
 from .charts import register_chart_callbacks
 from .indicators import register_indicator_callbacks
 from .system_health import register_system_health_callbacks
+from .realtime_strategies import register_realtime_strategy_callbacks
 
 __all__ = [
     'register_navigation_callbacks',
     'register_chart_callbacks',
     'register_indicator_callbacks',
-    'register_system_health_callbacks'
+    'register_system_health_callbacks',
+    'register_realtime_strategy_callbacks'
 ] 

dashboard/callbacks/realtime_strategies.py

@@ -0,0 +1,291 @@
+"""
+Real-time Strategy Callbacks
+
+This module provides callbacks for integrating real-time strategy execution
+with the dashboard chart refresh cycle and user interactions.
+"""
+
+import json
+from dash import Output, Input, State, Patch, ctx, html, no_update, dcc, callback
+import dash_bootstrap_components as dbc
+from datetime import datetime, timedelta
+from typing import Dict, Any, List, Optional
+
+from utils.logger import get_logger
+from strategies.realtime_execution import (
+    get_realtime_strategy_processor,
+    initialize_realtime_strategy_system,
+    RealTimeConfig,
+    RealTimeSignal
+)
+from strategies.manager import StrategyManager
+from config.strategies.config_utils import StrategyConfigurationManager
+
+logger = get_logger()
+
+# Global processor instance
+_processor = None
+
+
+def get_processor():
+    """Get or initialize the real-time strategy processor."""
+    global _processor
+    if _processor is None:
+        config = RealTimeConfig(
+            refresh_interval_seconds=30,
+            max_strategies_concurrent=3,
+            incremental_calculation=True,
+            signal_batch_size=50,
+            enable_signal_broadcasting=True
+        )
+        _processor = initialize_realtime_strategy_system(config)
+    return _processor
+
+
+def register_realtime_strategy_callbacks(app):
+    """Register real-time strategy callbacks."""
+    
+    @app.callback(
+        Output('realtime-strategies-store', 'data'),
+        [Input('realtime-strategy-toggle', 'value'),
+         Input('symbol-dropdown', 'value'),
+         Input('timeframe-dropdown', 'value'),
+         Input('strategy-dropdown', 'value')],
+        [State('realtime-strategies-store', 'data')],
+        prevent_initial_call=True
+    )
+    def manage_realtime_strategies(enable_realtime, symbol, timeframe, strategy_name, current_data):
+        """
+        Manage real-time strategy registration based on user selections.
+        
+        This callback handles enabling/disabling real-time strategy execution
+        and registers strategies based on current chart selections.
+        """
+        try:
+            current_data = current_data or {'active_strategies': [], 'enabled': False}
+            processor = get_processor()
+            
+            if not enable_realtime:
+                # Disable all strategies
+                for context_id in current_data.get('active_strategies', []):
+                    processor.unregister_strategy(context_id)
+                    logger.info(f"Unregistered real-time strategy: {context_id}")
+                
+                return {'active_strategies': [], 'enabled': False}
+            
+            # Enable real-time strategies
+            if symbol and timeframe and strategy_name and strategy_name != 'basic':
+                # Load strategy configuration
+                try:
+                    config_manager = StrategyConfigurationManager()
+                    strategy_config = config_manager.load_user_strategy_config(strategy_name)
+                    
+                    if not strategy_config:
+                        # Load from templates if user config doesn't exist
+                        strategy_config = config_manager.load_strategy_template(strategy_name)
+                    
+                    if strategy_config:
+                        # Register strategy for real-time execution
+                        context_id = processor.register_strategy(
+                            strategy_name=strategy_name,
+                            strategy_config=strategy_config,
+                            symbol=symbol,
+                            timeframe=timeframe
+                        )
+                        
+                        active_strategies = [context_id]
+                        logger.info(f"Registered real-time strategy: {context_id}")
+                        
+                        return {
+                            'active_strategies': active_strategies,
+                            'enabled': True,
+                            'current_symbol': symbol,
+                            'current_timeframe': timeframe,
+                            'current_strategy': strategy_name
+                        }
+                
+                except Exception as e:
+                    logger.error(f"Error loading strategy configuration for {strategy_name}: {e}")
+                    return current_data
+            
+            return current_data
+            
+        except Exception as e:
+            logger.error(f"Error managing real-time strategies: {e}")
+            return current_data or {'active_strategies': [], 'enabled': False}
+    
+    @app.callback(
+        Output('realtime-strategy-status', 'children'),
+        [Input('realtime-strategies-store', 'data'),
+         Input('interval-component', 'n_intervals')],
+        prevent_initial_call=True
+    )
+    def update_realtime_status(strategy_data, n_intervals):
+        """
+        Update real-time strategy status display.
+        
+        Shows current status of real-time strategy execution including
+        active strategies and performance metrics.
+        """
+        try:
+            if not strategy_data or not strategy_data.get('enabled'):
+                return dbc.Alert("Real-time strategy execution is disabled", color="secondary", className="mb-2")
+            
+            processor = get_processor()
+            active_strategies = processor.get_active_strategies()
+            perf_stats = processor.get_performance_stats()
+            
+            if not active_strategies:
+                return dbc.Alert("No active real-time strategies", color="warning", className="mb-2")
+            
+            # Build status display
+            status_items = []
+            
+            # Active strategies
+            for context_id, context in active_strategies.items():
+                status_items.append(
+                    html.Li([
+                        html.Strong(f"{context.strategy_name}: "),
+                        f"{context.symbol} {context.timeframe}",
+                        html.Span(
+                            " ✓" if context.is_active else " ⚠️",
+                            style={'color': 'green' if context.is_active else 'orange'}
+                        )
+                    ])
+                )
+            
+            # Performance metrics
+            success_rate = 0
+            if perf_stats['total_calculations'] > 0:
+                success_rate = (perf_stats['successful_calculations'] / perf_stats['total_calculations']) * 100
+            
+            metrics_text = f"Calculations: {perf_stats['total_calculations']} | " \
+                          f"Success Rate: {success_rate:.1f}% | " \
+                          f"Signals Generated: {perf_stats['signals_generated']}"
+            
+            return dbc.Card([
+                dbc.CardHeader("Real-time Strategy Status"),
+                dbc.CardBody([
+                    html.H6("Active Strategies:", className="mb-2"),
+                    html.Ul(status_items, className="mb-3"),
+                    html.P(metrics_text, className="small mb-0")
+                ])
+            ], className="mb-2")
+            
+        except Exception as e:
+            logger.error(f"Error updating real-time status: {e}")
+            return dbc.Alert(f"Error updating status: {str(e)}", color="danger", className="mb-2")
+    
+    # Integration with chart refresh cycle
+    @app.callback(
+        Output('realtime-execution-trigger', 'data'),
+        [Input('interval-component', 'n_intervals')],
+        [State('symbol-dropdown', 'value'),
+         State('timeframe-dropdown', 'value'),
+         State('realtime-strategies-store', 'data'),
+         State('analysis-mode-toggle', 'value')],
+        prevent_initial_call=True
+    )
+    def trigger_realtime_execution(n_intervals, symbol, timeframe, strategy_data, analysis_mode):
+        """
+        Trigger real-time strategy execution when new data is available.
+        
+        This callback integrates with the existing chart refresh cycle to
+        execute real-time strategies when new candle data arrives.
+        """
+        try:
+            # Only execute in live mode
+            if analysis_mode == 'locked':
+                return no_update
+            
+            # Only execute if real-time strategies are enabled
+            if not strategy_data or not strategy_data.get('enabled'):
+                return no_update
+            
+            # Only execute if we have symbol and timeframe
+            if not symbol or not timeframe:
+                return no_update
+            
+            processor = get_processor()
+            
+            # Execute real-time strategy update
+            signals = processor.execute_realtime_update(
+                symbol=symbol,
+                timeframe=timeframe,
+                exchange="okx"
+            )
+            
+            if signals:
+                logger.info(f"Real-time execution generated {len(signals)} signals for {symbol} {timeframe}")
+                return {
+                    'timestamp': datetime.now().isoformat(),
+                    'signals_generated': len(signals),
+                    'symbol': symbol,
+                    'timeframe': timeframe
+                }
+            
+            return no_update
+            
+        except Exception as e:
+            logger.error(f"Error in real-time strategy execution: {e}")
+            return no_update
+
+
+def add_realtime_strategy_components():
+    """
+    Add real-time strategy components to the dashboard layout.
+    
+    Returns:
+        List of Dash components for real-time strategy controls
+    """
+    return [
+        # Real-time strategy toggle
+        dbc.Row([
+            dbc.Col([
+                dbc.Label("Real-time Strategy Execution", className="fw-bold"),
+                dbc.Switch(
+                    id="realtime-strategy-toggle",
+                    label="Enable Real-time Execution",
+                    value=False,
+                    className="mb-2"
+                ),
+            ], width=12)
+        ], className="mb-3"),
+        
+        # Status display
+        html.Div(id="realtime-strategy-status"),
+        
+        # Hidden stores for state management
+        dcc.Store(id="realtime-strategies-store", data={'active_strategies': [], 'enabled': False}),
+        dcc.Store(id="realtime-execution-trigger", data={}),
+    ]
+
+
+def setup_chart_update_callback():
+    """
+    Setup chart update callback for real-time signals.
+    
+    This function configures the real-time processor to trigger
+    chart updates when new signals are generated.
+    """
+    def chart_update_callback(signal: RealTimeSignal):
+        """Handle chart updates for real-time signals."""
+        try:
+            # This would trigger chart refresh for the specific symbol/timeframe
+            # For now, we'll log the signal and let the regular refresh cycle handle it
+            logger.debug(
+                f"Chart update requested for signal: {signal.context.strategy_name} "
+                f"on {signal.context.symbol} {signal.context.timeframe}"
+            )
+            
+            # Future enhancement: Could trigger specific chart layer updates here
+            
+        except Exception as e:
+            logger.error(f"Error in chart update callback: {e}")
+    
+    processor = get_processor()
+    processor.set_chart_update_callback(chart_update_callback)
+
+
+# Initialize the chart update callback when module is imported
+setup_chart_update_callback() 

data/common/data_types.py

@@ -201,6 +201,67 @@ class ProcessingStats:
         }
 
 
+class SignalType(str, Enum):
+    """
+    Types of trading signals that strategies can generate.
+    """
+    BUY = "buy"
+    SELL = "sell"
+    HOLD = "hold"
+    ENTRY_LONG = "entry_long"
+    EXIT_LONG = "exit_long"
+    ENTRY_SHORT = "entry_short"
+    EXIT_SHORT = "exit_short"
+    STOP_LOSS = "stop_loss"
+    TAKE_PROFIT = "take_profit"
+
+
+@dataclass
+class StrategySignal:
+    """
+    Container for individual strategy signals.
+    
+    Attributes:
+        timestamp: Signal timestamp (right-aligned with candle)
+        symbol: Trading symbol
+        timeframe: Candle timeframe
+        signal_type: Type of signal (buy/sell/hold etc.)
+        price: Price at which signal was generated
+        confidence: Signal confidence score (0.0 to 1.0)
+        metadata: Additional signal metadata (e.g., indicator values)
+    """
+    timestamp: datetime
+    symbol: str
+    timeframe: str
+    signal_type: SignalType
+    price: float
+    confidence: float = 1.0
+    metadata: Optional[Dict[str, Any]] = None
+
+
+@dataclass
+class StrategyResult:
+    """
+    Container for strategy calculation results.
+    
+    Attributes:
+        timestamp: Candle timestamp (right-aligned)
+        symbol: Trading symbol
+        timeframe: Candle timeframe
+        strategy_name: Name of the strategy that generated this result
+        signals: List[StrategySignal]
+        indicators_used: Dictionary of indicator values used in calculation
+        metadata: Additional calculation metadata
+    """
+    timestamp: datetime
+    symbol: str
+    timeframe: str
+    strategy_name: str
+    signals: List[StrategySignal]
+    indicators_used: Dict[str, float]
+    metadata: Optional[Dict[str, Any]] = None
+
+
 # Re-export from base_collector for convenience
 __all__ = [
     'DataType',

database/repositories/strategy_repository.py

@@ -8,7 +8,7 @@ from sqlalchemy import desc, and_, func
 from sqlalchemy.orm import joinedload
 
 from ..models import StrategySignal, StrategyRun
-from strategies.data_types import StrategySignal as StrategySignalDataType, StrategyResult
+from data.common.data_types import StrategySignal as StrategySignalDataType, StrategyResult
 from .base_repository import BaseRepository, DatabaseOperationError
 
 
@@ -95,23 +95,25 @@ class StrategyRepository(BaseRepository):
             with self.get_session() as session:
                 for result in strategy_results:
                     for signal in result.signals:
-                        strategy_signal = StrategySignal(
-                            run_id=run_id,
-                            strategy_name=result.strategy_name,
-                            strategy_config=None,  # Could be populated from StrategyRun.config
-                            symbol=signal.symbol,
-                            timeframe=signal.timeframe,
-                            timestamp=signal.timestamp,
-                            signal_type=signal.signal_type.value,
-                            price=Decimal(str(signal.price)),
-                            confidence=Decimal(str(signal.confidence)),
-                            signal_metadata={
-                                'indicators_used': result.indicators_used,
-                                'metadata': signal.metadata or {}
-                            }
-                        )
-                        session.add(strategy_signal)
-                        signals_stored += 1
+                        # Only store BUY or SELL signals, not HOLD
+                        if signal.signal_type.value in ["BUY", "SELL"]:
+                            strategy_signal = StrategySignal(
+                                run_id=run_id,
+                                strategy_name=result.strategy_name,
+                                strategy_config=None,  # Could be populated from StrategyRun.config
+                                symbol=signal.symbol,
+                                timeframe=signal.timeframe,
+                                timestamp=signal.timestamp,
+                                signal_type=signal.signal_type.value,
+                                price=Decimal(str(signal.price)),
+                                confidence=Decimal(str(signal.confidence)),
+                                signal_metadata={
+                                    'indicators_used': result.indicators_used,
+                                    'metadata': signal.metadata or {}
+                                }
+                            )
+                            session.add(strategy_signal)
+                            signals_stored += 1
                 
                 session.commit()
                 self.log_info(f"Stored {signals_stored} strategy signals for run {run_id}")
@@ -181,6 +183,43 @@ class StrategyRepository(BaseRepository):
             self.log_error(f"Error retrieving strategy signals: {e}")
             raise DatabaseOperationError(f"Failed to retrieve strategy signals: {e}")
     
+    def store_signals_batch(self, signal_data_list: List[Dict[str, Any]]) -> int:
+        """
+        Store a batch of real-time strategy signals.
+        
+        Args:
+            signal_data_list: List of signal data dictionaries
+            
+        Returns:
+            Number of signals stored
+        """
+        try:
+            signals_stored = 0
+            with self.get_session() as session:
+                for signal_data in signal_data_list:
+                    strategy_signal = StrategySignal(
+                        run_id=None,  # Real-time signals don't have a run_id
+                        strategy_name=signal_data.get('strategy_name'),
+                        strategy_config=signal_data.get('strategy_config'),
+                        symbol=signal_data.get('symbol'),
+                        timeframe=signal_data.get('timeframe'),
+                        timestamp=signal_data.get('timestamp'),
+                        signal_type=signal_data.get('signal_type', 'HOLD'),
+                        price=Decimal(str(signal_data.get('price'))) if signal_data.get('price') else None,
+                        confidence=Decimal(str(signal_data.get('confidence', 0.0))),
+                        signal_metadata=signal_data.get('signal_metadata', {})
+                    )
+                    session.add(strategy_signal)
+                    signals_stored += 1
+                
+                session.commit()
+                self.log_info(f"Stored batch of {signals_stored} real-time strategy signals")
+                return signals_stored
+                
+        except Exception as e:
+            self.log_error(f"Error storing signals batch: {e}")
+            raise DatabaseOperationError(f"Failed to store signals batch: {e}")
+
     def get_strategy_signal_stats(self, run_id: Optional[int] = None) -> Dict[str, Any]:
         """Get statistics about strategy signals."""
         try:

docs/modules/strategies.md

@@ -0,0 +1,150 @@
+# Module: Strategies
+
+## Purpose
+This module encompasses the core strategy engine for the TCP Trading Platform. It provides a flexible and extensible framework for defining, managing, and executing trading strategies. It includes components for real-time signal generation, batch backtesting, and data integration with market data and technical indicators.
+
+## Public Interface
+
+### Classes
+- `strategies.base.BaseStrategy`: Abstract base class for all trading strategies, defining the common interface for `calculate()` and `get_required_indicators()`.
+- `strategies.factory.StrategyFactory`: Manages dynamic loading and registration of strategy implementations based on configuration.
+- `strategies.manager.StrategyManager`: Handles user-defined strategy configurations, enabling loading, saving, and validation of strategy parameters.
+- `strategies.data_integration.StrategyDataIntegrator`: Orchestrates the integration of market data and pre-calculated technical indicators for strategy execution. It handles data fetching, caching, and dependency resolution.
+- `strategies.batch_processing.BacktestingBatchProcessor`: Provides capabilities for efficient batch execution of multiple strategies across large historical datasets, including memory management and performance optimization.
+- `strategies.realtime_execution.RealTimeStrategyProcessor`: Manages real-time strategy execution, generating signals incrementally as new market data becomes available. It integrates with the dashboard's chart refresh cycle and handles signal broadcasting.
+- `strategies.realtime_execution.StrategySignalBroadcaster`: A component of the real-time execution pipeline responsible for distributing generated signals, storing them in the database, and triggering chart updates.
+
+### Functions
+- `strategies.realtime_execution.get_realtime_strategy_processor()`: Returns the singleton instance of the `RealTimeStrategyProcessor`.
+- `strategies.realtime_execution.initialize_realtime_strategy_system()`: Initializes and starts the real-time strategy execution system.
+- `strategies.realtime_execution.shutdown_realtime_strategy_system()`: Shuts down the real-time strategy execution system.
+
+## Usage Examples
+
+### 1. Registering and Executing a Real-time Strategy
+```python
+from strategies.realtime_execution import initialize_realtime_strategy_system
+from config.strategies.config_utils import StrategyConfigurationManager
+
+# Initialize the real-time system (usually done once at application startup)
+processor = initialize_realtime_strategy_system()
+
+# Load a strategy configuration (e.g., from a user-defined config or template)
+config_manager = StrategyConfigurationManager()
+strategy_name = "ema_crossover"
+strategy_config = config_manager.load_user_strategy_config(strategy_name) or \
+                  config_manager.load_strategy_template(strategy_name)
+
+if strategy_config:
+    # Register the strategy for real-time execution on a specific symbol and timeframe
+    context_id = processor.register_strategy(
+        strategy_name=strategy_name,
+        strategy_config=strategy_config,
+        symbol="BTC-USDT",
+        timeframe="1h"
+    )
+    print(f"Strategy '{strategy_name}' registered for real-time updates with ID: {context_id}")
+
+    # In a real application, new data would arrive (e.g., via a WebSocket or API poll)
+    # and `processor.execute_realtime_update()` would be called from a data refresh callback.
+    # Example of manual trigger (for illustration):
+    # signals = processor.execute_realtime_update(symbol="BTC-USDT", timeframe="1h")
+    # print(f"Generated {len(signals)} signals for {strategy_name}")
+
+# To stop the system (usually done at application shutdown)
+# shutdown_realtime_strategy_system()
+```
+
+### 2. Running a Backtesting Batch Process
+```python
+from strategies.batch_processing import BacktestingBatchProcessor, BatchProcessingConfig
+from config.strategies.config_utils import StrategyConfigurationManager
+import pandas as pd
+from datetime import datetime, timedelta, timezone
+
+# Example historical data (in a real scenario, this would come from a database)
+# This is a placeholder for demonstration purposes
+# Ensure you have actual OHLCVCandle data or a DataFrame that mimics it
+example_data = [
+    # ... populate with enough OHLCVCandle objects for indicator warm-up
+    # For instance, 100 OHLCVCandle objects
+]
+
+# Create a mock DataFrame for demonstration, ensure it has 'open', 'high', 'low', 'close', 'volume', 'timestamp'
+# For testing, you'd load actual data or generate a synthetic one.
+ohlcv_data = []
+start_ts = datetime.now(timezone.utc) - timedelta(days=30)
+for i in range(300):
+    ohlcv_data.append({
+        "timestamp": (start_ts + timedelta(hours=i)).isoformat(),
+        "open": 100.0 + i * 0.1,
+        "high": 101.0 + i * 0.1,
+        "low": 99.0 + i * 0.1,
+        "close": 100.5 + i * 0.1,
+        "volume": 1000.0 + i * 10
+    })
+market_df = pd.DataFrame(ohlcv_data).set_index("timestamp")
+market_df.index = pd.to_datetime(market_df.index).to_pydatetime() # Convert to datetime objects if needed
+
+# Initialize batch processor config
+batch_config = BatchProcessingConfig(
+    chunk_size=100, # Process in chunks for memory efficiency
+    max_concurrent_tasks=2
+)
+batch_processor = BacktestingBatchProcessor(batch_config)
+
+# Load strategies to backtest
+config_manager = StrategyConfigurationManager()
+strategies_to_backtest = {
+    "ema_crossover": config_manager.load_strategy_template("ema_crossover"),
+    "macd": config_manager.load_strategy_template("macd")
+}
+
+# Run batch processing
+if all(strategies_to_backtest.values()): # Ensure all configs are loaded
+    print("Starting batch backtesting...")
+    results = batch_processor.process_strategies_batch(
+        market_df=market_df,
+        strategies_config=strategies_to_backtest,
+        symbols=["BTC-USDT", "ETH-USDT"],
+        timeframe="1h"
+    )
+    print("Batch backtesting complete. Results:")
+    for strategy_name, symbol_results in results.items():
+        for symbol, strategy_results_list in symbol_results.items():
+            print(f"  Strategy: {strategy_name}, Symbol: {symbol}, Signals: {len(strategy_results_list)}")
+else:
+    print("Failed to load all strategy configurations. Skipping batch processing.")
+```
+
+## Dependencies
+
+### Internal Dependencies
+- `database.operations`: For database interactions (storing signals).
+- `data.common.data_types`: Defines common data structures like `OHLCVCandle`.
+- `data.common.indicators.TechnicalIndicators`: Provides access to pre-calculated technical indicators.
+- `components.charts.data_integration.MarketDataIntegrator`: Used by `StrategyDataIntegrator` to fetch raw market data.
+- `utils.logger`: For logging within the module.
+- `config.strategies.config_utils`: For loading and managing strategy configurations.
+
+### External Dependencies
+- `pandas`: For data manipulation and vectorized operations.
+- `datetime`: For handling timestamps and time-related calculations.
+- `typing`: For type hints.
+- `dataclasses`: For defining data classes like `RealTimeConfig`, `StrategyExecutionContext`, etc.
+- `threading`, `queue`, `concurrent.futures`: For managing concurrency and background processing in real-time execution.
+
+## Testing
+
+To run tests for the `strategies` module, navigate to the project root and execute:
+
+```bash
+python -m pytest tests/strategies/test_realtime_execution.py -v
+python -m pytest tests/strategies/test_batch_processing.py -v
+# Add other relevant strategy tests here, e.g., for data_integration, manager, factory
+```
+
+## Known Issues
+
+- **Incremental Calculation Optimization**: The `_calculate_incremental_signals` method in `RealTimeStrategyProcessor` currently falls back to full recalculation. True incremental calculation (only processing new candles and updating existing indicator series) is a future optimization.
+- **Chart Layer Updates**: While the `StrategySignalBroadcaster` triggers a chart update callback, the actual chart layer integration (`components/charts/layers/strategy_signals.py`) and dynamic signal display on the chart are part of Task 5.0 and not fully implemented within this module. 

strategies/__init__.py

@@ -14,8 +14,12 @@ IMPORTANT: Mirrors Indicator Patterns
 
 from .base import BaseStrategy
 from .factory import StrategyFactory
-from .data_types import StrategySignal, SignalType, StrategyResult
+from data.common.data_types import StrategySignal, SignalType, StrategyResult
 from .manager import StrategyManager, StrategyConfig, StrategyType, StrategyCategory, get_strategy_manager
+from .data_integration import StrategyDataIntegrator, StrategyDataIntegrationConfig, get_strategy_data_integrator
+from .validation import StrategySignalValidator, ValidationConfig
+from .batch_processing import BacktestingBatchProcessor, BatchProcessingConfig
+from .realtime_execution import RealTimeStrategyProcessor, RealTimeConfig, get_realtime_strategy_processor
 
 __all__ = [
     'BaseStrategy',
@@ -27,5 +31,15 @@ __all__ = [
     'StrategyConfig',
     'StrategyType', 
     'StrategyCategory',
-    'get_strategy_manager'
+    'get_strategy_manager',
+    'StrategyDataIntegrator',
+    'StrategyDataIntegrationConfig',
+    'get_strategy_data_integrator',
+    'StrategySignalValidator',
+    'ValidationConfig',
+    'BacktestingBatchProcessor',
+    'BatchProcessingConfig',
+    'RealTimeStrategyProcessor',
+    'RealTimeConfig',
+    'get_realtime_strategy_processor'
 ] 

strategies/data_types.py

@@ -1,72 +0,0 @@
-"""
-Strategy Data Types and Signal Definitions
-
-This module provides data types for strategy calculations, signals,
-and results in a standardized format.
-"""
-
-from dataclasses import dataclass
-from datetime import datetime
-from typing import Dict, Optional, Any, List
-from enum import Enum
-
-
-class SignalType(str, Enum):
-    """
-    Types of trading signals that strategies can generate.
-    """
-    BUY = "buy"
-    SELL = "sell"
-    HOLD = "hold"
-    ENTRY_LONG = "entry_long"
-    EXIT_LONG = "exit_long"
-    ENTRY_SHORT = "entry_short"
-    EXIT_SHORT = "exit_short"
-    STOP_LOSS = "stop_loss"
-    TAKE_PROFIT = "take_profit"
-
-
-@dataclass
-class StrategySignal:
-    """
-    Container for individual strategy signals.
-    
-    Attributes:
-        timestamp: Signal timestamp (right-aligned with candle)
-        symbol: Trading symbol
-        timeframe: Candle timeframe
-        signal_type: Type of signal (buy/sell/hold etc.)
-        price: Price at which signal was generated
-        confidence: Signal confidence score (0.0 to 1.0)
-        metadata: Additional signal metadata (e.g., indicator values)
-    """
-    timestamp: datetime
-    symbol: str
-    timeframe: str
-    signal_type: SignalType
-    price: float
-    confidence: float = 1.0
-    metadata: Optional[Dict[str, Any]] = None
-
-
-@dataclass
-class StrategyResult:
-    """
-    Container for strategy calculation results.
-    
-    Attributes:
-        timestamp: Candle timestamp (right-aligned)
-        symbol: Trading symbol
-        timeframe: Candle timeframe
-        strategy_name: Name of the strategy that generated this result
-        signals: List of signals generated for this timestamp
-        indicators_used: Dictionary of indicator values used in calculation
-        metadata: Additional calculation metadata
-    """
-    timestamp: datetime
-    symbol: str
-    timeframe: str
-    strategy_name: str
-    signals: List[StrategySignal]
-    indicators_used: Dict[str, float]
-    metadata: Optional[Dict[str, Any]] = None 

strategies/realtime_execution.py

@@ -0,0 +1,655 @@
+"""
+Real-time Strategy Execution Pipeline
+
+This module provides real-time strategy execution capabilities that integrate
+with the existing chart data refresh cycle. It handles incremental strategy
+calculations, real-time signal generation, and live chart updates.
+"""
+
+import pandas as pd
+from datetime import datetime, timezone, timedelta
+from typing import List, Dict, Any, Optional, Callable, Set, Tuple
+from dataclasses import dataclass, field
+from threading import Thread, Event, Lock
+from queue import Queue, Empty
+import asyncio
+from concurrent.futures import ThreadPoolExecutor
+import time
+
+from database.operations import get_database_operations, DatabaseOperationError
+from data.common.data_types import OHLCVCandle
+from components.charts.data_integration import MarketDataIntegrator
+from .data_integration import StrategyDataIntegrator, StrategyDataIntegrationConfig
+from .factory import StrategyFactory
+from .data_types import StrategyResult, StrategySignal
+from utils.logger import get_logger
+
+# Initialize logger
+logger = get_logger()
+
+
+@dataclass
+class RealTimeConfig:
+    """Configuration for real-time strategy execution"""
+    refresh_interval_seconds: int = 30  # How often to check for new data
+    max_strategies_concurrent: int = 5   # Maximum concurrent strategy calculations
+    incremental_calculation: bool = True  # Use incremental vs full recalculation
+    signal_batch_size: int = 100         # Batch size for signal storage
+    enable_signal_broadcasting: bool = True  # Enable real-time signal broadcasting
+    max_signal_queue_size: int = 1000    # Maximum signals in queue before dropping
+    chart_update_throttle_ms: int = 1000 # Minimum time between chart updates
+    error_retry_attempts: int = 3        # Number of retries on calculation errors
+    error_retry_delay_seconds: int = 5   # Delay between retry attempts
+
+
+@dataclass
+class StrategyExecutionContext:
+    """Context for strategy execution"""
+    strategy_name: str
+    strategy_config: Dict[str, Any]
+    symbol: str
+    timeframe: str
+    exchange: str = "okx"
+    last_calculation_time: Optional[datetime] = None
+    consecutive_errors: int = 0
+    is_active: bool = True
+
+
+@dataclass
+class RealTimeSignal:
+    """Real-time signal with metadata"""
+    strategy_result: StrategyResult
+    context: StrategyExecutionContext
+    generation_time: datetime = field(default_factory=lambda: datetime.now(timezone.utc))
+    chart_update_required: bool = True
+
+
+class StrategySignalBroadcaster:
+    """
+    Handles real-time signal broadcasting and distribution.
+    
+    Manages signal queues, chart updates, and database storage
+    for real-time strategy signals.
+    """
+    
+    def __init__(self, config: RealTimeConfig):
+        """Initialize signal broadcaster."""
+        self.config = config
+        self.logger = logger
+        self.db_ops = get_database_operations(self.logger)
+        
+        # Signal queues
+        self._signal_queue: Queue[RealTimeSignal] = Queue(maxsize=self.config.max_signal_queue_size)
+        self._chart_update_queue: Queue[RealTimeSignal] = Queue()
+        
+        # Chart update throttling
+        self._last_chart_update = {}  # symbol_timeframe -> timestamp
+        self._chart_update_lock = Lock()
+        
+        # Background processing
+        self._processing_thread: Optional[Thread] = None
+        self._stop_event = Event()
+        self._is_running = False
+        
+        # Callback for chart updates
+        self._chart_update_callback: Optional[Callable] = None
+        
+        if self.logger:
+            self.logger.info("StrategySignalBroadcaster: Initialized")
+    
+    def start(self) -> None:
+        """Start the signal broadcasting service."""
+        if self._is_running:
+            return
+        
+        self._is_running = True
+        self._stop_event.clear()
+        
+        # Start background processing thread
+        self._processing_thread = Thread(
+            target=self._process_signals_loop,
+            name="StrategySignalProcessor",
+            daemon=True
+        )
+        self._processing_thread.start()
+        
+        if self.logger:
+            self.logger.info("StrategySignalBroadcaster: Started signal processing")
+    
+    def stop(self) -> None:
+        """Stop the signal broadcasting service."""
+        if not self._is_running:
+            return
+        
+        self._is_running = False
+        self._stop_event.set()
+        
+        if self._processing_thread and self._processing_thread.is_alive():
+            self._processing_thread.join(timeout=5.0)
+        
+        if self.logger:
+            self.logger.info("StrategySignalBroadcaster: Stopped signal processing")
+    
+    def broadcast_signal(self, signal: RealTimeSignal) -> bool:
+        """
+        Broadcast a real-time signal.
+        
+        Args:
+            signal: Real-time signal to broadcast
+            
+        Returns:
+            True if signal was queued successfully, False if queue is full
+        """
+        try:
+            self._signal_queue.put_nowait(signal)
+            return True
+        except:
+            # Queue is full, drop the signal
+            if self.logger:
+                self.logger.warning(f"Signal queue full, dropping signal for {signal.context.symbol}")
+            return False
+    
+    def set_chart_update_callback(self, callback: Callable[[RealTimeSignal], None]) -> None:
+        """Set callback for chart updates."""
+        self._chart_update_callback = callback
+    
+    def _process_signals_loop(self) -> None:
+        """Main signal processing loop."""
+        batch_signals = []
+        
+        while not self._stop_event.is_set():
+            try:
+                # Collect signals in batches
+                try:
+                    signal = self._signal_queue.get(timeout=1.0)
+                    batch_signals.append(signal)
+                    
+                    # Collect more signals if available (up to batch size)
+                    while len(batch_signals) < self.config.signal_batch_size:
+                        try:
+                            signal = self._signal_queue.get_nowait()
+                            batch_signals.append(signal)
+                        except Empty:
+                            break
+                    
+                    # Process the batch
+                    if batch_signals:
+                        self._process_signal_batch(batch_signals)
+                        batch_signals.clear()
+                
+                except Empty:
+                    # No signals to process, continue
+                    continue
+                    
+            except Exception as e:
+                if self.logger:
+                    self.logger.error(f"Error in signal processing loop: {e}")
+                time.sleep(1.0)  # Brief pause on error
+    
+    def _process_signal_batch(self, signals: List[RealTimeSignal]) -> None:
+        """Process a batch of signals."""
+        try:
+            # Store signals in database
+            self._store_signals_batch(signals)
+            
+            # Process chart updates
+            self._process_chart_updates(signals)
+            
+        except Exception as e:
+            if self.logger:
+                self.logger.error(f"Error processing signal batch: {e}")
+    
+    def _store_signals_batch(self, signals: List[RealTimeSignal]) -> None:
+        """Store signals in database."""
+        try:
+            signal_data = []
+            for signal in signals:
+                result = signal.strategy_result
+                context = signal.context
+                
+                # Only store BUY or SELL signals, not HOLD
+                if result.signal and result.signal.signal_type in [SignalType.BUY, SignalType.SELL]:
+                    signal_data.append({
+                        'strategy_name': context.strategy_name,
+                        'strategy_config': context.strategy_config,
+                        'symbol': context.symbol,
+                        'timeframe': context.timeframe,
+                        'exchange': context.exchange,
+                        'timestamp': result.timestamp,
+                        'signal_type': result.signal.signal_type.value,
+                        'price': float(result.price) if result.price else None,
+                        'confidence': result.confidence,
+                        'signal_metadata': result.metadata or {},
+                        'generation_time': signal.generation_time
+                    })
+            
+            if signal_data: # Only call store_signals_batch if there are signals to store
+                # Batch insert into database
+                self.db_ops.strategy.store_signals_batch(signal_data)
+                
+                if self.logger:
+                    self.logger.debug(f"Stored batch of {len(signal_data)} real-time signals")
+            else:
+                if self.logger:
+                    self.logger.debug("No BUY/SELL signals to store in this batch.")
+                
+        except Exception as e:
+            if self.logger:
+                self.logger.error(f"Error storing signal batch: {e}")
+    
+    def _process_chart_updates(self, signals: List[RealTimeSignal]) -> None:
+        """Process chart updates for signals."""
+        if not self._chart_update_callback:
+            return
+        
+        # Group signals by symbol/timeframe for throttling
+        signal_groups = {}
+        for signal in signals:
+            if not signal.chart_update_required:
+                continue
+                
+            key = f"{signal.context.symbol}_{signal.context.timeframe}"
+            if key not in signal_groups:
+                signal_groups[key] = []
+            signal_groups[key].append(signal)
+        
+        # Process chart updates with throttling
+        current_time = time.time() * 1000  # milliseconds
+        
+        with self._chart_update_lock:
+            for key, group_signals in signal_groups.items():
+                last_update = self._last_chart_update.get(key, 0)
+                
+                if current_time - last_update >= self.config.chart_update_throttle_ms:
+                    # Update chart with latest signal from group
+                    latest_signal = max(group_signals, key=lambda s: s.generation_time)
+                    
+                    try:
+                        self._chart_update_callback(latest_signal)
+                        self._last_chart_update[key] = current_time
+                    except Exception as e:
+                        if self.logger:
+                            self.logger.error(f"Error in chart update callback: {e}")
+    
+    def get_signal_stats(self) -> Dict[str, Any]:
+        """Get signal broadcasting statistics."""
+        return {
+            'queue_size': self._signal_queue.qsize(),
+            'chart_queue_size': self._chart_update_queue.qsize(),
+            'is_running': self._is_running,
+            'last_chart_updates': dict(self._last_chart_update)
+        }
+
+
+class RealTimeStrategyProcessor:
+    """
+    Real-time strategy execution processor.
+    
+    Integrates with existing chart data refresh cycle to provide
+    real-time strategy signal generation and broadcasting.
+    """
+    
+    def __init__(self, config: RealTimeConfig = None):
+        """Initialize real-time strategy processor."""
+        self.config = config or RealTimeConfig()
+        self.logger = logger
+        
+        # Core components
+        self.data_integrator = StrategyDataIntegrator(
+            StrategyDataIntegrationConfig(
+                cache_timeout_minutes=1,  # Shorter cache for real-time
+                enable_indicator_caching=True
+            )
+        )
+        self.market_integrator = MarketDataIntegrator()
+        self.strategy_factory = StrategyFactory(self.logger)
+        self.signal_broadcaster = StrategySignalBroadcaster(self.config)
+        
+        # Strategy execution contexts
+        self._execution_contexts: Dict[str, StrategyExecutionContext] = {}
+        self._context_lock = Lock()
+        
+        # Performance tracking
+        self._performance_stats = {
+            'total_calculations': 0,
+            'successful_calculations': 0,
+            'failed_calculations': 0,
+            'average_calculation_time_ms': 0.0,
+            'signals_generated': 0,
+            'last_update_time': None
+        }
+        
+        # Thread pool for concurrent strategy execution
+        self._executor = ThreadPoolExecutor(max_workers=self.config.max_strategies_concurrent)
+        
+        if self.logger:
+            self.logger.info("RealTimeStrategyProcessor: Initialized")
+    
+    def start(self) -> None:
+        """Start the real-time strategy processor."""
+        self.signal_broadcaster.start()
+        if self.logger:
+            self.logger.info("RealTimeStrategyProcessor: Started")
+    
+    def stop(self) -> None:
+        """Stop the real-time strategy processor."""
+        self.signal_broadcaster.stop()
+        self._executor.shutdown(wait=True)
+        if self.logger:
+            self.logger.info("RealTimeStrategyProcessor: Stopped")
+    
+    def register_strategy(
+        self,
+        strategy_name: str,
+        strategy_config: Dict[str, Any],
+        symbol: str,
+        timeframe: str,
+        exchange: str = "okx"
+    ) -> str:
+        """
+        Register a strategy for real-time execution.
+        
+        Args:
+            strategy_name: Name of the strategy
+            strategy_config: Strategy configuration
+            symbol: Trading symbol
+            timeframe: Timeframe
+            exchange: Exchange name
+            
+        Returns:
+            Context ID for the registered strategy
+        """
+        context_id = f"{strategy_name}_{symbol}_{timeframe}_{exchange}"
+        
+        context = StrategyExecutionContext(
+            strategy_name=strategy_name,
+            strategy_config=strategy_config,
+            symbol=symbol,
+            timeframe=timeframe,
+            exchange=exchange
+        )
+        
+        with self._context_lock:
+            self._execution_contexts[context_id] = context
+        
+        if self.logger:
+            self.logger.info(f"Registered strategy for real-time execution: {context_id}")
+        
+        return context_id
+    
+    def unregister_strategy(self, context_id: str) -> bool:
+        """
+        Unregister a strategy from real-time execution.
+        
+        Args:
+            context_id: Context ID to unregister
+            
+        Returns:
+            True if strategy was unregistered, False if not found
+        """
+        with self._context_lock:
+            if context_id in self._execution_contexts:
+                del self._execution_contexts[context_id]
+                if self.logger:
+                    self.logger.info(f"Unregistered strategy: {context_id}")
+                return True
+        return False
+    
+    def execute_realtime_update(
+        self,
+        symbol: str,
+        timeframe: str,
+        exchange: str = "okx"
+    ) -> List[RealTimeSignal]:
+        """
+        Execute real-time strategy update for new market data.
+        
+        This method should be called when new candle data is available,
+        typically triggered by the chart refresh cycle.
+        
+        Args:
+            symbol: Trading symbol that was updated
+            timeframe: Timeframe that was updated
+            exchange: Exchange name
+            
+        Returns:
+            List of generated real-time signals
+        """
+        start_time = time.time()
+        generated_signals = []
+        
+        try:
+            # Find all strategies for this symbol/timeframe
+            matching_contexts = []
+            with self._context_lock:
+                for context_id, context in self._execution_contexts.items():
+                    if (context.symbol == symbol and 
+                        context.timeframe == timeframe and 
+                        context.exchange == exchange and 
+                        context.is_active):
+                        matching_contexts.append((context_id, context))
+            
+            if not matching_contexts:
+                return generated_signals
+            
+            # Execute strategies concurrently
+            futures = []
+            for context_id, context in matching_contexts:
+                future = self._executor.submit(
+                    self._execute_strategy_context,
+                    context_id,
+                    context
+                )
+                futures.append((context_id, future))
+            
+            # Collect results
+            for context_id, future in futures:
+                try:
+                    signals = future.result(timeout=10.0)  # 10 second timeout
+                    generated_signals.extend(signals)
+                except Exception as e:
+                    if self.logger:
+                        self.logger.error(f"Error executing strategy {context_id}: {e}")
+                    self._handle_strategy_error(context_id, e)
+            
+            # Update performance stats
+            calculation_time = (time.time() - start_time) * 1000
+            self._update_performance_stats(len(generated_signals), calculation_time, True)
+            
+            if self.logger and generated_signals:
+                self.logger.debug(f"Generated {len(generated_signals)} real-time signals for {symbol} {timeframe}")
+            
+            return generated_signals
+            
+        except Exception as e:
+            if self.logger:
+                self.logger.error(f"Error in real-time strategy execution: {e}")
+            calculation_time = (time.time() - start_time) * 1000
+            self._update_performance_stats(0, calculation_time, False)
+            return generated_signals
+    
+    def _execute_strategy_context(
+        self,
+        context_id: str,
+        context: StrategyExecutionContext
+    ) -> List[RealTimeSignal]:
+        """Execute a single strategy context."""
+        try:
+            # Calculate strategy signals
+            if self.config.incremental_calculation and context.last_calculation_time:
+                # Use incremental calculation for better performance
+                results = self._calculate_incremental_signals(context)
+            else:
+                # Full recalculation
+                results = self._calculate_full_signals(context)
+            
+            # Convert to real-time signals
+            real_time_signals = []
+            for result in results:
+                signal = RealTimeSignal(
+                    strategy_result=result,
+                    context=context
+                )
+                real_time_signals.append(signal)
+                
+                # Broadcast signal
+                self.signal_broadcaster.broadcast_signal(signal)
+            
+            # Update context
+            with self._context_lock:
+                context.last_calculation_time = datetime.now(timezone.utc)
+                context.consecutive_errors = 0
+            
+            return real_time_signals
+            
+        except Exception as e:
+            if self.logger:
+                self.logger.error(f"Error executing strategy context {context_id}: {e}")
+            self._handle_strategy_error(context_id, e)
+            return []
+    
+    def _calculate_incremental_signals(
+        self,
+        context: StrategyExecutionContext
+    ) -> List[StrategyResult]:
+        """Calculate signals incrementally (only for new data)."""
+        # For this initial implementation, fall back to full calculation
+        # Incremental calculation optimization can be added later
+        return self._calculate_full_signals(context)
+    
+    def _calculate_full_signals(
+        self,
+        context: StrategyExecutionContext
+    ) -> List[StrategyResult]:
+        """Calculate signals with full recalculation."""
+        return self.data_integrator.calculate_strategy_signals(
+            strategy_name=context.strategy_name,
+            strategy_config=context.strategy_config,
+            symbol=context.symbol,
+            timeframe=context.timeframe,
+            days_back=7,  # Use shorter history for real-time
+            exchange=context.exchange,
+            enable_caching=True
+        )
+    
+    def _handle_strategy_error(self, context_id: str, error: Exception) -> None:
+        """Handle strategy execution error."""
+        with self._context_lock:
+            if context_id in self._execution_contexts:
+                context = self._execution_contexts[context_id]
+                context.consecutive_errors += 1
+                
+                # Disable strategy if too many consecutive errors
+                if context.consecutive_errors >= self.config.error_retry_attempts:
+                    context.is_active = False
+                    if self.logger:
+                        self.logger.warning(
+                            f"Disabling strategy {context_id} due to consecutive errors: {context.consecutive_errors}"
+                        )
+    
+    def _update_performance_stats(
+        self,
+        signals_generated: int,
+        calculation_time_ms: float,
+        success: bool
+    ) -> None:
+        """Update performance statistics."""
+        self._performance_stats['total_calculations'] += 1
+        if success:
+            self._performance_stats['successful_calculations'] += 1
+        else:
+            self._performance_stats['failed_calculations'] += 1
+        
+        self._performance_stats['signals_generated'] += signals_generated
+        
+        # Update average calculation time
+        total_calcs = self._performance_stats['total_calculations']
+        current_avg = self._performance_stats['average_calculation_time_ms']
+        self._performance_stats['average_calculation_time_ms'] = (
+            (current_avg * (total_calcs - 1) + calculation_time_ms) / total_calcs
+        )
+        
+        self._performance_stats['last_update_time'] = datetime.now(timezone.utc)
+    
+    def set_chart_update_callback(self, callback: Callable[[RealTimeSignal], None]) -> None:
+        """Set callback for chart updates."""
+        self.signal_broadcaster.set_chart_update_callback(callback)
+    
+    def get_active_strategies(self) -> Dict[str, StrategyExecutionContext]:
+        """Get all active strategy contexts."""
+        with self._context_lock:
+            return {
+                context_id: context 
+                for context_id, context in self._execution_contexts.items()
+                if context.is_active
+            }
+    
+    def get_performance_stats(self) -> Dict[str, Any]:
+        """Get real-time execution performance statistics."""
+        stats = dict(self._performance_stats)
+        stats.update(self.signal_broadcaster.get_signal_stats())
+        return stats
+    
+    def pause_strategy(self, context_id: str) -> bool:
+        """Pause a strategy (set as inactive)."""
+        with self._context_lock:
+            if context_id in self._execution_contexts:
+                self._execution_contexts[context_id].is_active = False
+                return True
+        return False
+    
+    def resume_strategy(self, context_id: str) -> bool:
+        """Resume a strategy (set as active)."""
+        with self._context_lock:
+            if context_id in self._execution_contexts:
+                context = self._execution_contexts[context_id]
+                context.is_active = True
+                context.consecutive_errors = 0  # Reset error count
+                return True
+        return False
+
+
+# Singleton instance for global access
+_realtime_processor: Optional[RealTimeStrategyProcessor] = None
+
+
+def get_realtime_strategy_processor(config: RealTimeConfig = None) -> RealTimeStrategyProcessor:
+    """
+    Get the singleton real-time strategy processor instance.
+    
+    Args:
+        config: Configuration for the processor (only used on first call)
+        
+    Returns:
+        RealTimeStrategyProcessor instance
+    """
+    global _realtime_processor
+    
+    if _realtime_processor is None:
+        _realtime_processor = RealTimeStrategyProcessor(config)
+    
+    return _realtime_processor
+
+
+def initialize_realtime_strategy_system(config: RealTimeConfig = None) -> RealTimeStrategyProcessor:
+    """
+    Initialize the real-time strategy system.
+    
+    Args:
+        config: Configuration for the system
+        
+    Returns:
+        Initialized RealTimeStrategyProcessor
+    """
+    processor = get_realtime_strategy_processor(config)
+    processor.start()
+    return processor
+
+
+def shutdown_realtime_strategy_system() -> None:
+    """Shutdown the real-time strategy system."""
+    global _realtime_processor
+    
+    if _realtime_processor is not None:
+        _realtime_processor.stop()
+        _realtime_processor = None

strategies/validation.py

@@ -0,0 +1,375 @@
+"""
+Strategy Signal Validation Pipeline
+
+This module provides validation, filtering, and quality assessment
+for strategy-generated signals to ensure reliability and consistency.
+"""
+
+from typing import List, Dict, Any, Optional, Tuple
+from datetime import datetime, timezone
+from dataclasses import dataclass
+
+from .data_types import StrategySignal, SignalType, StrategyResult
+from utils.logger import get_logger
+
+
+@dataclass
+class ValidationConfig:
+    """Configuration for signal validation."""
+    min_confidence: float = 0.0
+    max_confidence: float = 1.0
+    required_metadata_fields: List[str] = None
+    allowed_signal_types: List[SignalType] = None
+    price_tolerance_percent: float = 5.0  # Max price deviation from market
+    
+    def __post_init__(self):
+        if self.required_metadata_fields is None:
+            self.required_metadata_fields = []
+        if self.allowed_signal_types is None:
+            self.allowed_signal_types = list(SignalType)
+
+
+class StrategySignalValidator:
+    """
+    Validates strategy signals for quality, consistency, and compliance.
+    
+    Provides comprehensive validation including confidence checks,
+    signal type validation, price reasonableness, and metadata validation.
+    """
+    
+    def __init__(self, config: ValidationConfig = None):
+        """
+        Initialize signal validator.
+        
+        Args:
+            config: Validation configuration
+        """
+        self.config = config or ValidationConfig()
+        self.logger = get_logger()
+        
+        # Validation statistics
+        self._validation_stats = {
+            'total_signals_validated': 0,
+            'valid_signals': 0,
+            'invalid_signals': 0,
+            'validation_errors': {}
+        }
+    
+    def validate_signal(self, signal: StrategySignal) -> Tuple[bool, List[str]]:
+        """
+        Validate a single strategy signal.
+        
+        Args:
+            signal: Signal to validate
+            
+        Returns:
+            Tuple of (is_valid, list_of_errors)
+        """
+        errors = []
+        self._validation_stats['total_signals_validated'] += 1
+        
+        # Validate confidence
+        if not (self.config.min_confidence <= signal.confidence <= self.config.max_confidence):
+            errors.append(f"Invalid confidence {signal.confidence}, must be between {self.config.min_confidence} and {self.config.max_confidence}")
+        
+        # Validate signal type
+        if signal.signal_type not in self.config.allowed_signal_types:
+            errors.append(f"Signal type {signal.signal_type} not in allowed types")
+        
+        # Validate price
+        if signal.price <= 0:
+            errors.append(f"Invalid price {signal.price}, must be positive")
+        
+        # Validate required metadata
+        if self.config.required_metadata_fields:
+            if not signal.metadata:
+                errors.append(f"Missing required metadata fields: {self.config.required_metadata_fields}")
+            else:
+                missing_fields = [field for field in self.config.required_metadata_fields 
+                                if field not in signal.metadata]
+                if missing_fields:
+                    errors.append(f"Missing required metadata fields: {missing_fields}")
+        
+        # Update statistics
+        is_valid = len(errors) == 0
+        if is_valid:
+            self._validation_stats['valid_signals'] += 1
+        else:
+            self._validation_stats['invalid_signals'] += 1
+            for error in errors:
+                error_type = error.split(':')[0] if ':' in error else error
+                self._validation_stats['validation_errors'][error_type] = \
+                    self._validation_stats['validation_errors'].get(error_type, 0) + 1
+        
+        return is_valid, errors
+    
+    def validate_signals_batch(self, signals: List[StrategySignal]) -> Tuple[List[StrategySignal], List[StrategySignal]]:
+        """
+        Validate multiple signals and return valid and invalid lists.
+        
+        Args:
+            signals: List of signals to validate
+            
+        Returns:
+            Tuple of (valid_signals, invalid_signals)
+        """
+        valid_signals = []
+        invalid_signals = []
+        
+        for signal in signals:
+            is_valid, errors = self.validate_signal(signal)
+            if is_valid:
+                valid_signals.append(signal)
+            else:
+                invalid_signals.append(signal)
+                self.logger.debug(f"Invalid signal filtered out: {errors}")
+        
+        return valid_signals, invalid_signals
+    
+    def filter_signals_by_confidence(
+        self, 
+        signals: List[StrategySignal], 
+        min_confidence: float = None
+    ) -> List[StrategySignal]:
+        """
+        Filter signals by minimum confidence threshold.
+        
+        Args:
+            signals: List of signals to filter
+            min_confidence: Minimum confidence threshold (uses config if None)
+            
+        Returns:
+            Filtered list of signals
+        """
+        threshold = min_confidence if min_confidence is not None else self.config.min_confidence
+        
+        filtered_signals = [signal for signal in signals if signal.confidence >= threshold]
+        
+        self.logger.debug(f"Filtered {len(signals) - len(filtered_signals)} signals below confidence {threshold}")
+        
+        return filtered_signals
+    
+    def filter_signals_by_type(
+        self, 
+        signals: List[StrategySignal], 
+        allowed_types: List[SignalType] = None
+    ) -> List[StrategySignal]:
+        """
+        Filter signals by allowed signal types.
+        
+        Args:
+            signals: List of signals to filter
+            allowed_types: Allowed signal types (uses config if None)
+            
+        Returns:
+            Filtered list of signals
+        """
+        types = allowed_types if allowed_types is not None else self.config.allowed_signal_types
+        
+        filtered_signals = [signal for signal in signals if signal.signal_type in types]
+        
+        self.logger.debug(f"Filtered {len(signals) - len(filtered_signals)} signals by type")
+        
+        return filtered_signals
+    
+    def get_validation_statistics(self) -> Dict[str, Any]:
+        """Get comprehensive validation statistics."""
+        stats = self._validation_stats.copy()
+        
+        if stats['total_signals_validated'] > 0:
+            stats['validation_success_rate'] = stats['valid_signals'] / stats['total_signals_validated']
+            stats['validation_failure_rate'] = stats['invalid_signals'] / stats['total_signals_validated']
+        else:
+            stats['validation_success_rate'] = 0.0
+            stats['validation_failure_rate'] = 0.0
+        
+        return stats
+    
+    def transform_signal_confidence(
+        self, 
+        signal: StrategySignal, 
+        confidence_multiplier: float = 1.0,
+        max_confidence: float = None
+    ) -> StrategySignal:
+        """
+        Transform signal confidence with multiplier and cap.
+        
+        Args:
+            signal: Signal to transform
+            confidence_multiplier: Multiplier for confidence
+            max_confidence: Maximum confidence cap (uses config if None)
+            
+        Returns:
+            Transformed signal with updated confidence
+        """
+        max_conf = max_confidence if max_confidence is not None else self.config.max_confidence
+        
+        # Create new signal with transformed confidence
+        new_confidence = min(signal.confidence * confidence_multiplier, max_conf)
+        
+        transformed_signal = StrategySignal(
+            timestamp=signal.timestamp,
+            symbol=signal.symbol,
+            timeframe=signal.timeframe,
+            signal_type=signal.signal_type,
+            price=signal.price,
+            confidence=new_confidence,
+            metadata=signal.metadata.copy() if signal.metadata else None
+        )
+        
+        return transformed_signal
+    
+    def enrich_signal_metadata(
+        self, 
+        signal: StrategySignal, 
+        additional_metadata: Dict[str, Any]
+    ) -> StrategySignal:
+        """
+        Enrich signal with additional metadata.
+        
+        Args:
+            signal: Signal to enrich
+            additional_metadata: Additional metadata to add
+            
+        Returns:
+            Signal with enriched metadata
+        """
+        # Merge metadata
+        enriched_metadata = signal.metadata.copy() if signal.metadata else {}
+        enriched_metadata.update(additional_metadata)
+        
+        enriched_signal = StrategySignal(
+            timestamp=signal.timestamp,
+            symbol=signal.symbol,
+            timeframe=signal.timeframe,
+            signal_type=signal.signal_type,
+            price=signal.price,
+            confidence=signal.confidence,
+            metadata=enriched_metadata
+        )
+        
+        return enriched_signal
+    
+    def transform_signals_batch(
+        self, 
+        signals: List[StrategySignal], 
+        confidence_multiplier: float = 1.0,
+        additional_metadata: Dict[str, Any] = None
+    ) -> List[StrategySignal]:
+        """
+        Apply transformations to multiple signals.
+        
+        Args:
+            signals: List of signals to transform
+            confidence_multiplier: Confidence multiplier
+            additional_metadata: Additional metadata to add
+            
+        Returns:
+            List of transformed signals
+        """
+        transformed_signals = []
+        
+        for signal in signals:
+            # Apply confidence transformation
+            transformed_signal = self.transform_signal_confidence(signal, confidence_multiplier)
+            
+            # Apply metadata enrichment if provided
+            if additional_metadata:
+                transformed_signal = self.enrich_signal_metadata(transformed_signal, additional_metadata)
+            
+            transformed_signals.append(transformed_signal)
+        
+        self.logger.debug(f"Transformed {len(signals)} signals")
+        
+        return transformed_signals
+    
+    def calculate_signal_quality_metrics(self, signals: List[StrategySignal]) -> Dict[str, Any]:
+        """
+        Calculate comprehensive quality metrics for signals.
+        
+        Args:
+            signals: List of signals to analyze
+            
+        Returns:
+            Dictionary containing quality metrics
+        """
+        if not signals:
+            return {'error': 'No signals provided for quality analysis'}
+        
+        # Basic metrics
+        total_signals = len(signals)
+        confidence_values = [signal.confidence for signal in signals]
+        
+        # Signal type distribution
+        signal_type_counts = {}
+        for signal in signals:
+            signal_type_counts[signal.signal_type.value] = signal_type_counts.get(signal.signal_type.value, 0) + 1
+        
+        # Confidence metrics
+        avg_confidence = sum(confidence_values) / total_signals
+        min_confidence = min(confidence_values)
+        max_confidence = max(confidence_values)
+        
+        # Quality scoring (0-100)
+        high_confidence_signals = sum(1 for conf in confidence_values if conf >= 0.7)
+        quality_score = (high_confidence_signals / total_signals) * 100
+        
+        # Metadata completeness
+        signals_with_metadata = sum(1 for signal in signals if signal.metadata)
+        metadata_completeness = (signals_with_metadata / total_signals) * 100
+        
+        return {
+            'total_signals': total_signals,
+            'signal_type_distribution': signal_type_counts,
+            'confidence_metrics': {
+                'average': round(avg_confidence, 3),
+                'minimum': round(min_confidence, 3),
+                'maximum': round(max_confidence, 3),
+                'high_confidence_count': high_confidence_signals,
+                'high_confidence_percentage': round((high_confidence_signals / total_signals) * 100, 1)
+            },
+            'quality_score': round(quality_score, 1),
+            'metadata_completeness_percentage': round(metadata_completeness, 1),
+            'recommendations': self._generate_quality_recommendations(signals)
+        }
+    
+    def _generate_quality_recommendations(self, signals: List[StrategySignal]) -> List[str]:
+        """Generate quality improvement recommendations."""
+        recommendations = []
+        
+        confidence_values = [signal.confidence for signal in signals]
+        avg_confidence = sum(confidence_values) / len(confidence_values)
+        
+        if avg_confidence < 0.5:
+            recommendations.append("Consider increasing confidence thresholds or improving signal generation logic")
+        
+        signals_with_metadata = sum(1 for signal in signals if signal.metadata)
+        if signals_with_metadata / len(signals) < 0.8:
+            recommendations.append("Enhance metadata collection to improve signal traceability")
+        
+        signal_types = set(signal.signal_type for signal in signals)
+        if len(signal_types) == 1:
+            recommendations.append("Consider diversifying signal types for better strategy coverage")
+        
+        return recommendations if recommendations else ["Signal quality appears good - no specific recommendations"]
+    
+    def generate_validation_report(self) -> Dict[str, Any]:
+        """Generate comprehensive validation report."""
+        stats = self.get_validation_statistics()
+        
+        return {
+            'report_timestamp': datetime.now(timezone.utc).isoformat(),
+            'validation_summary': {
+                'total_validated': stats['total_signals_validated'],
+                'success_rate': f"{stats.get('validation_success_rate', 0) * 100:.1f}%",
+                'failure_rate': f"{stats.get('validation_failure_rate', 0) * 100:.1f}%"
+            },
+            'error_analysis': stats.get('validation_errors', {}),
+            'configuration': {
+                'min_confidence': self.config.min_confidence,
+                'max_confidence': self.config.max_confidence,
+                'allowed_signal_types': [st.value for st in self.config.allowed_signal_types],
+                'required_metadata_fields': self.config.required_metadata_fields
+            },
+            'health_status': 'good' if stats.get('validation_success_rate', 0) >= 0.8 else 'needs_attention'
+        } 

tasks/4.0-strategy-engine-foundation.md

@@ -22,12 +22,21 @@
 - `database/migrations/versions/add_strategy_signals_table.py` - Alembic migration for strategy signals table
 - `components/charts/layers/strategy_signals.py` - Strategy signal chart layer for visualization
 - `components/charts/data_integration.py` - Updated to include strategy data integration
+- `strategies/data_integration.py` - Strategy data integration with indicator orchestration and caching
+- `strategies/validation.py` - Strategy signal validation and quality assurance
+- `strategies/batch_processing.py` - Batch processing engine for backtesting multiple strategies across large datasets
+- `strategies/realtime_execution.py` - Real-time strategy execution pipeline for live signal generation
+- `dashboard/callbacks/realtime_strategies.py` - Dashboard callbacks for real-time strategy integration
 - `tests/strategies/test_base_strategy.py` - Unit tests for BaseStrategy abstract class
 - `tests/strategies/test_strategy_factory.py` - Unit tests for strategy factory system
 - `tests/strategies/test_strategy_manager.py` - Unit tests for StrategyManager class
 - `tests/strategies/implementations/test_ema_crossover.py` - Unit tests for EMA Crossover strategy
 - `tests/strategies/implementations/test_rsi.py` - Unit tests for RSI strategy
 - `tests/strategies/implementations/test_macd.py` - Unit tests for MACD strategy
+- `tests/strategies/test_data_integration.py` - Unit tests for strategy data integration
+- `tests/strategies/test_validation.py` - Unit tests for strategy signal validation
+- `tests/strategies/test_batch_processing.py` - Unit tests for batch processing capabilities
+- `tests/strategies/test_realtime_execution.py` - Unit tests for real-time execution pipeline
 - `tests/database/test_strategy_repository.py` - Unit tests for strategy repository
 
 ### Notes
@@ -73,6 +82,31 @@
 - **Reasoning**: Maintains consistency with existing database access patterns, ensures proper session management, and provides a clean API for strategy data operations.
 - **Impact**: All strategy database operations follow the same patterns as other modules, with proper error handling, logging, and transaction management.
 
+### 7. Vectorized Data Integration
+- **Decision**: Implement vectorized approaches in `StrategyDataIntegrator` for DataFrame construction, indicator batching, and multi-strategy processing while maintaining iterative interfaces for backward compatibility.
+- **Reasoning**: Significant performance improvements for backtesting and bulk analysis scenarios, better memory efficiency with pandas operations, and preparation for multi-strategy batch processing capabilities.
+- **Impact**: Enhanced performance for large datasets while maintaining existing single-strategy interfaces. Sets foundation for efficient multi-strategy and multi-timeframe processing in future phases.
+
+### 8. Single-Strategy Orchestration Focus
+- **Decision**: Implement strategy calculation orchestration focused on single-strategy optimization with indicator dependency resolution, avoiding premature multi-strategy complexity.
+- **Reasoning**: Multi-strategy coordination is better handled at the backtesting layer or through parallelization. Single-strategy optimization provides immediate benefits while keeping code maintainable and focused.
+- **Impact**: Cleaner, more maintainable code with optimized single-strategy performance. Provides foundation for future backtester-level parallelization without architectural complexity.
+
+### 9. Indicator Warm-up Handling for Streaming Batch Processing
+- **Decision**: Implemented dynamic warm-up period calculation and overlapping windows with result trimming for streaming batch processing.
+- **Reasoning**: To ensure accurate indicator calculations and prevent false signals when processing large datasets in chunks, as indicators require a certain amount of historical data to 'warm up'.
+- **Impact**: Guarantees correct backtest results for strategies relying on indicators with warm-up periods, even when using memory-efficient streaming. Automatically adjusts chunk processing to include necessary historical context and removes duplicate/invalid initial signals.
+
+### 10. Real-time Strategy Execution Architecture
+- **Decision**: Implemented event-driven real-time strategy execution pipeline with signal broadcasting, chart integration, and concurrent processing capabilities.
+- **Reasoning**: Real-time strategy execution requires different architecture than batch processing - event-driven triggers, background signal processing, throttled chart updates, and integration with existing dashboard refresh cycles.
+- **Impact**: Enables live strategy signal generation that integrates seamlessly with the existing chart system. Provides concurrent strategy execution, real-time signal storage, error handling with automatic strategy disabling, and performance monitoring for production use.
+
+### 11. Exclusion of "HOLD" Signals from All Signal Storage
+- **Decision**: Modified signal storage mechanisms across both real-time and batch processing to exclude "HOLD" signals, only persisting explicit "BUY" or "SELL" signals in the database.
+- **Reasoning**: To significantly reduce data volume and improve storage/query performance across all execution types, as the absence of a BUY/SELL signal implicitly means "HOLD" and can be inferred during analysis or visualization.
+- **Impact**: Leads to more efficient database usage and faster data retrieval for all stored signals. Requires careful handling in visualization and backtesting components to correctly interpret gaps as "HOLD" periods.
+
 ## Tasks
 
 - [x] 1.0 Core Strategy Foundation Setup
@@ -109,16 +143,16 @@
   - [x] 3.8 Add data retention policies for strategy signals (configurable cleanup of old analysis data)
   - [x] 3.9 Implement strategy signal aggregation queries for performance analysis
 
-- [ ] 4.0 Strategy Data Integration
-  - [ ] 4.1 Create `StrategyDataIntegrator` class in new `strategies/data_integration.py` module
-  - [ ] 4.2 Implement data loading interface that leverages existing `TechnicalIndicators` class for indicator dependencies
+- [x] 4.0 Strategy Data Integration
+  - [x] 4.1 Create `StrategyDataIntegrator` class in new `strategies/data_integration.py` module
+  - [x] 4.2 Implement data loading interface that leverages existing `TechnicalIndicators` class for indicator dependencies
   - [x] 4.3 Add multi-timeframe data handling for strategies that require indicators from different timeframes
-  - [ ] 4.4 Implement strategy calculation orchestration with proper indicator dependency resolution
-  - [ ] 4.5 Create caching layer for computed indicator results to avoid recalculation across strategies
-  - [ ] 4.6 Add strategy signal generation and validation pipeline
-  - [ ] 4.7 Implement batch processing capabilities for backtesting large datasets
-  - [ ] 4.8 Create real-time strategy execution pipeline that integrates with existing chart data refresh
-  - [ ] 4.9 Add error handling and recovery mechanisms for strategy calculation failures
+  - [x] 4.4 Implement strategy calculation orchestration with proper indicator dependency resolution
+  - [x] 4.5 Create caching layer for computed indicator results to avoid recalculation across strategies
+  - [x] 4.6 Add strategy signal generation and validation pipeline
+  - [x] 4.7 Implement batch processing capabilities for backtesting large datasets
+  - [x] 4.8 Create real-time strategy execution pipeline that integrates with existing chart data refresh
+  - [x] 4.9 Add error handling and recovery mechanisms for strategy calculation failures
 
 - [ ] 5.0 Chart Integration and Visualization
   - [ ] 5.1 Create `StrategySignalLayer` class in `components/charts/layers/strategy_signals.py`

tests/strategies/test_batch_processing.py

@@ -0,0 +1,798 @@
+"""
+Tests for Strategy Batch Processing
+
+This module tests batch processing capabilities for strategy backtesting
+including memory management, parallel processing, and performance monitoring.
+"""
+
+import pytest
+from unittest.mock import patch, MagicMock
+from datetime import datetime, timezone
+import pandas as pd
+
+from strategies.batch_processing import BacktestingBatchProcessor, BatchProcessingConfig
+from strategies.data_types import StrategyResult, StrategySignal, SignalType
+
+
+class TestBatchProcessingConfig:
+    """Tests for BatchProcessingConfig dataclass."""
+    
+    def test_default_config(self):
+        """Test default batch processing configuration."""
+        config = BatchProcessingConfig()
+        
+        assert config.max_concurrent_strategies == 4
+        assert config.max_memory_usage_percent == 80.0
+        assert config.chunk_size_days == 30
+        assert config.enable_memory_monitoring is True
+        assert config.enable_result_validation is True
+        assert config.result_cache_size == 1000
+        assert config.progress_reporting_interval == 10
+    
+    def test_custom_config(self):
+        """Test custom batch processing configuration."""
+        config = BatchProcessingConfig(
+            max_concurrent_strategies=8,
+            max_memory_usage_percent=90.0,
+            chunk_size_days=60,
+            enable_memory_monitoring=False,
+            enable_result_validation=False,
+            result_cache_size=500,
+            progress_reporting_interval=5
+        )
+        
+        assert config.max_concurrent_strategies == 8
+        assert config.max_memory_usage_percent == 90.0
+        assert config.chunk_size_days == 60
+        assert config.enable_memory_monitoring is False
+        assert config.enable_result_validation is False
+        assert config.result_cache_size == 500
+        assert config.progress_reporting_interval == 5
+
+
+class TestBacktestingBatchProcessor:
+    """Tests for BacktestingBatchProcessor class."""
+    
+    @pytest.fixture
+    def processor(self):
+        """Create batch processor with default configuration."""
+        config = BatchProcessingConfig(
+            enable_memory_monitoring=False,  # Disable for testing
+            progress_reporting_interval=1,    # Report every strategy for testing
+            enable_result_validation=False   # Disable validation for basic tests
+        )
+        with patch('strategies.batch_processing.StrategyDataIntegrator'):
+            return BacktestingBatchProcessor(config)
+    
+    @pytest.fixture
+    def sample_strategy_configs(self):
+        """Create sample strategy configurations for testing."""
+        return [
+            {
+                'name': 'ema_crossover',
+                'type': 'trend_following',
+                'parameters': {'fast_ema': 12, 'slow_ema': 26}
+            },
+            {
+                'name': 'rsi_momentum',
+                'type': 'momentum',
+                'parameters': {'rsi_period': 14, 'oversold': 30, 'overbought': 70}
+            },
+            {
+                'name': 'macd_trend',
+                'type': 'trend_following',
+                'parameters': {'fast_ema': 12, 'slow_ema': 26, 'signal': 9}
+            }
+        ]
+    
+    @pytest.fixture
+    def sample_strategy_results(self):
+        """Create sample strategy results for testing."""
+        return [
+            StrategyResult(
+                timestamp=datetime.now(timezone.utc),
+                symbol='BTC-USDT',
+                timeframe='1h',
+                strategy_name='test_strategy',
+                signals=[
+                    StrategySignal(
+                        timestamp=datetime.now(timezone.utc),
+                        symbol='BTC-USDT',
+                        timeframe='1h',
+                        signal_type=SignalType.BUY,
+                        price=50000.0,
+                        confidence=0.8,
+                        metadata={'rsi': 30}
+                    )
+                ],
+                indicators_used={'rsi': 30, 'ema': 49000},
+                metadata={'execution_time': 0.5}
+            )
+        ]
+    
+    def test_initialization(self, processor):
+        """Test batch processor initialization."""
+        assert processor.config is not None
+        assert processor.logger is not None
+        assert processor.data_integrator is not None
+        assert processor._processing_stats['strategies_processed'] == 0
+        assert processor._processing_stats['total_signals_generated'] == 0
+        assert processor._processing_stats['errors_count'] == 0
+    
+    def test_initialization_with_validation_disabled(self):
+        """Test initialization with validation disabled."""
+        config = BatchProcessingConfig(enable_result_validation=False)
+        with patch('strategies.batch_processing.StrategyDataIntegrator'):
+            processor = BacktestingBatchProcessor(config)
+            assert processor.signal_validator is None
+    
+    @patch('strategies.batch_processing.StrategyDataIntegrator')
+    def test_process_strategies_batch(self, mock_integrator_class, processor, sample_strategy_configs, sample_strategy_results):
+        """Test batch processing of multiple strategies."""
+        # Setup mock data integrator
+        mock_integrator = MagicMock()
+        mock_integrator.calculate_strategy_signals_orchestrated.return_value = sample_strategy_results
+        processor.data_integrator = mock_integrator
+        
+        symbols = ['BTC-USDT', 'ETH-USDT']
+        timeframe = '1h'
+        days_back = 30
+        
+        results = processor.process_strategies_batch(
+            strategy_configs=sample_strategy_configs,
+            symbols=symbols,
+            timeframe=timeframe,
+            days_back=days_back
+        )
+        
+        # Verify results structure
+        assert len(results) == len(sample_strategy_configs)
+        assert 'ema_crossover' in results
+        assert 'rsi_momentum' in results
+        assert 'macd_trend' in results
+        
+        # Verify statistics
+        stats = processor.get_processing_statistics()
+        assert stats['strategies_processed'] == 3
+        assert stats['total_signals_generated'] == 6  # 3 strategies × 2 symbols × 1 signal each
+        assert stats['errors_count'] == 0
+    
+    def test_process_single_strategy_batch(self, processor, sample_strategy_results):
+        """Test processing a single strategy across multiple symbols."""
+        # Setup mock data integrator
+        mock_integrator = MagicMock()
+        mock_integrator.calculate_strategy_signals_orchestrated.return_value = sample_strategy_results
+        processor.data_integrator = mock_integrator
+        
+        strategy_config = {'name': 'test_strategy', 'type': 'test'}
+        symbols = ['BTC-USDT', 'ETH-USDT']
+        
+        results = processor._process_single_strategy_batch(
+            strategy_config, symbols, '1h', 30, 'okx'
+        )
+        
+        assert len(results) == 2  # Results for 2 symbols
+        assert processor._processing_stats['total_signals_generated'] == 2
+    
+    def test_validate_strategy_results(self, processor, sample_strategy_results):
+        """Test strategy result validation."""
+        # Setup mock signal validator
+        mock_validator = MagicMock()
+        mock_validator.validate_signals_batch.return_value = (
+            sample_strategy_results[0].signals,  # valid signals
+            []  # no invalid signals
+        )
+        processor.signal_validator = mock_validator
+        
+        validated_results = processor._validate_strategy_results(sample_strategy_results)
+        
+        assert len(validated_results) == 1
+        assert len(validated_results[0].signals) == 1
+        mock_validator.validate_signals_batch.assert_called_once()
+    
+    @patch('strategies.batch_processing.psutil')
+    def test_check_memory_usage_normal(self, mock_psutil, processor):
+        """Test memory usage monitoring under normal conditions."""
+        # Mock memory usage below threshold
+        mock_process = MagicMock()
+        mock_process.memory_percent.return_value = 60.0  # Below 80% threshold
+        mock_process.memory_info.return_value.rss = 500 * 1024 * 1024  # 500 MB
+        mock_psutil.Process.return_value = mock_process
+        
+        processor._check_memory_usage()
+        
+        assert processor._processing_stats['memory_peak_mb'] == 500.0
+    
+    @patch('strategies.batch_processing.psutil')
+    def test_check_memory_usage_high(self, mock_psutil, processor):
+        """Test memory usage monitoring with high usage."""
+        # Mock memory usage above threshold
+        mock_process = MagicMock()
+        mock_process.memory_percent.return_value = 85.0  # Above 80% threshold
+        mock_process.memory_info.return_value.rss = 1000 * 1024 * 1024  # 1000 MB
+        mock_psutil.Process.return_value = mock_process
+        
+        with patch.object(processor, '_cleanup_memory') as mock_cleanup:
+            processor._check_memory_usage()
+            mock_cleanup.assert_called_once()
+    
+    def test_cleanup_memory(self, processor):
+        """Test memory cleanup operations."""
+        # Fill result cache beyond limit
+        for i in range(1500):  # Above 1000 limit
+            processor._result_cache[f'key_{i}'] = f'result_{i}'
+        
+        initial_cache_size = len(processor._result_cache)
+        
+        with patch.object(processor.data_integrator, 'clear_cache') as mock_clear, \
+             patch('strategies.batch_processing.gc.collect') as mock_gc:
+            
+            processor._cleanup_memory()
+            
+            # Verify cache was reduced
+            assert len(processor._result_cache) < initial_cache_size
+            assert len(processor._result_cache) == 500  # Half of cache size limit
+            
+            # Verify other cleanup operations
+            mock_clear.assert_called_once()
+            mock_gc.assert_called_once()
+    
+    def test_get_processing_statistics(self, processor):
+        """Test processing statistics calculation."""
+        # Set some test statistics
+        processor._processing_stats.update({
+            'strategies_processed': 5,
+            'total_signals_generated': 25,
+            'processing_time_seconds': 10.0,
+            'errors_count': 1,
+            'validation_failures': 2
+        })
+        
+        stats = processor.get_processing_statistics()
+        
+        assert stats['strategies_processed'] == 5
+        assert stats['total_signals_generated'] == 25
+        assert stats['average_signals_per_strategy'] == 5.0
+        assert stats['average_processing_time_per_strategy'] == 2.0
+        assert stats['error_rate'] == 20.0  # 1/5 * 100
+        assert stats['validation_failure_rate'] == 8.0  # 2/25 * 100
+    
+    def test_get_processing_statistics_zero_division(self, processor):
+        """Test statistics calculation with zero values."""
+        stats = processor.get_processing_statistics()
+        
+        assert stats['average_signals_per_strategy'] == 0
+        assert stats['average_processing_time_per_strategy'] == 0
+        assert stats['error_rate'] == 0.0
+        assert stats['validation_failure_rate'] == 0.0
+    
+    def test_process_strategies_batch_with_error(self, processor, sample_strategy_configs):
+        """Test batch processing with errors."""
+        # Setup mock to raise an exception
+        mock_integrator = MagicMock()
+        mock_integrator.calculate_strategy_signals_orchestrated.side_effect = Exception("Test error")
+        processor.data_integrator = mock_integrator
+        
+        results = processor.process_strategies_batch(
+            strategy_configs=sample_strategy_configs,
+            symbols=['BTC-USDT'],
+            timeframe='1h',
+            days_back=30
+        )
+        
+        # Should handle errors gracefully
+        assert isinstance(results, dict)
+        assert processor._processing_stats['errors_count'] > 0
+    
+    @patch('strategies.batch_processing.StrategyDataIntegrator')
+    def test_process_strategies_parallel(self, mock_integrator_class, processor, sample_strategy_configs, sample_strategy_results):
+        """Test parallel processing of multiple strategies."""
+        # Setup mock data integrator
+        mock_integrator = MagicMock()
+        mock_integrator.calculate_strategy_signals_orchestrated.return_value = sample_strategy_results
+        processor.data_integrator = mock_integrator
+        
+        symbols = ['BTC-USDT', 'ETH-USDT']
+        timeframe = '1h'
+        days_back = 30
+        
+        results = processor.process_strategies_parallel(
+            strategy_configs=sample_strategy_configs,
+            symbols=symbols,
+            timeframe=timeframe,
+            days_back=days_back
+        )
+        
+        # Verify results structure (same as sequential processing)
+        assert len(results) == len(sample_strategy_configs)
+        assert 'ema_crossover' in results
+        assert 'rsi_momentum' in results
+        assert 'macd_trend' in results
+        
+        # Verify statistics
+        stats = processor.get_processing_statistics()
+        assert stats['strategies_processed'] == 3
+        assert stats['total_signals_generated'] == 6  # 3 strategies × 2 symbols × 1 signal each
+        assert stats['errors_count'] == 0
+    
+    def test_process_symbols_parallel(self, processor, sample_strategy_results):
+        """Test parallel processing of single strategy across multiple symbols."""
+        # Setup mock data integrator
+        mock_integrator = MagicMock()
+        mock_integrator.calculate_strategy_signals_orchestrated.return_value = sample_strategy_results
+        processor.data_integrator = mock_integrator
+        
+        strategy_config = {'name': 'test_strategy', 'type': 'test'}
+        symbols = ['BTC-USDT', 'ETH-USDT', 'BNB-USDT']
+        
+        results = processor.process_symbols_parallel(
+            strategy_config=strategy_config,
+            symbols=symbols,
+            timeframe='1h',
+            days_back=30
+        )
+        
+        # Should have results for all symbols
+        assert len(results) == 3  # Results for 3 symbols
+        assert processor._processing_stats['total_signals_generated'] == 3
+    
+    def test_process_strategy_for_symbol(self, processor, sample_strategy_results):
+        """Test processing a single strategy for a single symbol."""
+        # Setup mock data integrator
+        mock_integrator = MagicMock()
+        mock_integrator.calculate_strategy_signals_orchestrated.return_value = sample_strategy_results
+        processor.data_integrator = mock_integrator
+        
+        strategy_config = {'name': 'test_strategy', 'type': 'test'}
+        
+        results = processor._process_strategy_for_symbol(
+            strategy_config=strategy_config,
+            symbol='BTC-USDT',
+            timeframe='1h',
+            days_back=30,
+            exchange='okx'
+        )
+        
+        assert len(results) == 1
+        assert results[0].strategy_name == 'test_strategy'
+        assert results[0].symbol == 'BTC-USDT'
+    
+    def test_process_strategy_for_symbol_with_error(self, processor):
+        """Test symbol processing with error handling."""
+        # Setup mock to raise an exception
+        mock_integrator = MagicMock()
+        mock_integrator.calculate_strategy_signals_orchestrated.side_effect = Exception("Test error")
+        processor.data_integrator = mock_integrator
+        
+        strategy_config = {'name': 'test_strategy', 'type': 'test'}
+        
+        results = processor._process_strategy_for_symbol(
+            strategy_config=strategy_config,
+            symbol='BTC-USDT',
+            timeframe='1h',
+            days_back=30,
+            exchange='okx'
+        )
+        
+        # Should return empty list on error
+        assert results == []
+    
+    def test_process_large_dataset_streaming(self, processor, sample_strategy_configs, sample_strategy_results):
+        """Test streaming processing for large datasets."""
+        # Setup mock data integrator
+        mock_integrator = MagicMock()
+        mock_integrator.calculate_strategy_signals_orchestrated.return_value = sample_strategy_results
+        processor.data_integrator = mock_integrator
+        
+        # Mock the parallel processing method to avoid actual parallel execution
+        with patch.object(processor, 'process_strategies_parallel') as mock_parallel:
+            mock_parallel.return_value = {
+                'test_strategy': sample_strategy_results
+            }
+            
+            # Test streaming with 90 days split into 30-day chunks
+            stream = processor.process_large_dataset_streaming(
+                strategy_configs=sample_strategy_configs,
+                symbols=['BTC-USDT'],
+                timeframe='1h',
+                total_days_back=90  # Should create 3 chunks
+            )
+            
+            # Collect all chunks
+            chunks = list(stream)
+            
+            assert len(chunks) == 3  # 90 days / 30 days per chunk
+            
+            # Each chunk should have results for all strategies
+            for chunk in chunks:
+                assert 'test_strategy' in chunk
+    
+    def test_aggregate_streaming_results(self, processor, sample_strategy_results):
+        """Test aggregation of streaming results."""
+        # Create mock streaming results
+        chunk1 = {'strategy1': sample_strategy_results[:1], 'strategy2': []}
+        chunk2 = {'strategy1': [], 'strategy2': sample_strategy_results[:1]}
+        chunk3 = {'strategy1': sample_strategy_results[:1], 'strategy2': sample_strategy_results[:1]}
+        
+        stream = iter([chunk1, chunk2, chunk3])
+        
+        aggregated = processor.aggregate_streaming_results(stream)
+        
+        assert len(aggregated) == 2
+        assert 'strategy1' in aggregated
+        assert 'strategy2' in aggregated
+        assert len(aggregated['strategy1']) == 2  # From chunk1 and chunk3
+        assert len(aggregated['strategy2']) == 2  # From chunk2 and chunk3
+    
+    @patch('strategies.batch_processing.psutil')
+    def test_process_with_memory_constraints_sufficient_memory(self, mock_psutil, processor, sample_strategy_configs):
+        """Test memory-constrained processing with sufficient memory."""
+        # Mock low memory usage
+        mock_process = MagicMock()
+        mock_process.memory_info.return_value.rss = 100 * 1024 * 1024  # 100 MB
+        mock_psutil.Process.return_value = mock_process
+        
+        with patch.object(processor, 'process_strategies_parallel') as mock_parallel:
+            mock_parallel.return_value = {}
+            
+            processor.process_with_memory_constraints(
+                strategy_configs=sample_strategy_configs,
+                symbols=['BTC-USDT'],
+                timeframe='1h',
+                days_back=30,
+                max_memory_mb=1000.0  # High limit
+            )
+            
+            # Should use parallel processing for sufficient memory
+            mock_parallel.assert_called_once()
+    
+    @patch('strategies.batch_processing.psutil')
+    def test_process_with_memory_constraints_moderate_constraint(self, mock_psutil, processor, sample_strategy_configs):
+        """Test memory-constrained processing with moderate constraint."""
+        # Mock moderate memory usage
+        mock_process = MagicMock()
+        mock_process.memory_info.return_value.rss = 400 * 1024 * 1024  # 400 MB
+        mock_psutil.Process.return_value = mock_process
+        
+        with patch.object(processor, 'process_strategies_batch') as mock_batch:
+            mock_batch.return_value = {}
+            
+            processor.process_with_memory_constraints(
+                strategy_configs=sample_strategy_configs,
+                symbols=['BTC-USDT'],
+                timeframe='1h',
+                days_back=30,
+                max_memory_mb=500.0  # Moderate limit
+            )
+            
+            # Should use sequential batch processing
+            mock_batch.assert_called_once()
+    
+    @patch('strategies.batch_processing.psutil')
+    def test_process_with_memory_constraints_severe_constraint(self, mock_psutil, processor, sample_strategy_configs):
+        """Test memory-constrained processing with severe constraint."""
+        # Mock high memory usage
+        mock_process = MagicMock()
+        mock_process.memory_info.return_value.rss = 450 * 1024 * 1024  # 450 MB
+        mock_psutil.Process.return_value = mock_process
+        
+        with patch.object(processor, 'process_large_dataset_streaming_with_warmup') as mock_streaming, \
+             patch.object(processor, 'aggregate_streaming_results') as mock_aggregate:
+            
+            mock_streaming.return_value = iter([{}])
+            mock_aggregate.return_value = {}
+            
+            processor.process_with_memory_constraints(
+                strategy_configs=sample_strategy_configs,
+                symbols=['BTC-USDT'],
+                timeframe='1h',
+                days_back=30,
+                max_memory_mb=500.0  # Low limit with high current usage
+            )
+            
+            # Should use streaming processing with warm-up
+            mock_streaming.assert_called_once()
+            mock_aggregate.assert_called_once()
+    
+    def test_get_performance_metrics(self, processor):
+        """Test comprehensive performance metrics calculation."""
+        # Set some test statistics
+        processor._processing_stats.update({
+            'strategies_processed': 5,
+            'total_signals_generated': 25,
+            'processing_time_seconds': 10.0,
+            'memory_peak_mb': 500.0,
+            'errors_count': 1,
+            'validation_failures': 2
+        })
+        
+        with patch.object(processor.data_integrator, 'get_cache_stats') as mock_cache_stats:
+            mock_cache_stats.return_value = {'cache_hits': 80, 'cache_misses': 20}
+            
+            metrics = processor.get_performance_metrics()
+            
+            assert 'cache_hit_rate' in metrics
+            assert 'memory_efficiency' in metrics
+            assert 'throughput_signals_per_second' in metrics
+            assert 'parallel_efficiency' in metrics
+            assert 'optimization_recommendations' in metrics
+            
+            assert metrics['cache_hit_rate'] == 80.0  # 80/(80+20) * 100
+            assert metrics['throughput_signals_per_second'] == 2.5  # 25/10
+    
+    def test_calculate_cache_hit_rate(self, processor):
+        """Test cache hit rate calculation."""
+        with patch.object(processor.data_integrator, 'get_cache_stats') as mock_cache_stats:
+            mock_cache_stats.return_value = {'cache_hits': 70, 'cache_misses': 30}
+            
+            hit_rate = processor._calculate_cache_hit_rate()
+            assert hit_rate == 70.0  # 70/(70+30) * 100
+    
+    def test_calculate_memory_efficiency(self, processor):
+        """Test memory efficiency calculation."""
+        processor._processing_stats.update({
+            'memory_peak_mb': 200.0,
+            'strategies_processed': 2
+        })
+        
+        efficiency = processor._calculate_memory_efficiency()
+        # 200MB / 2 strategies = 100MB per strategy
+        # Baseline is 100MB, so efficiency should be 50%
+        assert efficiency == 50.0
+    
+    def test_generate_optimization_recommendations(self, processor):
+        """Test optimization recommendations generation."""
+        # Set up poor performance metrics
+        processor._processing_stats.update({
+            'strategies_processed': 1,
+            'total_signals_generated': 1,
+            'processing_time_seconds': 10.0,
+            'memory_peak_mb': 1000.0,  # High memory usage
+            'errors_count': 2,  # High error rate
+            'validation_failures': 0
+        })
+        
+        with patch.object(processor.data_integrator, 'get_cache_stats') as mock_cache_stats:
+            mock_cache_stats.return_value = {'cache_hits': 1, 'cache_misses': 9}  # Low cache hit rate
+            
+            recommendations = processor._generate_optimization_recommendations()
+            
+            assert isinstance(recommendations, list)
+            assert len(recommendations) > 0
+            # Should recommend memory efficiency improvement
+            assert any('memory efficiency' in rec.lower() for rec in recommendations)
+    
+    def test_optimize_configuration(self, processor):
+        """Test automatic configuration optimization."""
+        # Set up metrics that indicate poor memory efficiency
+        processor._processing_stats.update({
+            'strategies_processed': 4,
+            'total_signals_generated': 20,
+            'processing_time_seconds': 8.0,
+            'memory_peak_mb': 2000.0,  # Very high memory usage
+            'errors_count': 0,
+            'validation_failures': 0
+        })
+        
+        with patch.object(processor.data_integrator, 'get_cache_stats') as mock_cache_stats:
+            mock_cache_stats.return_value = {'cache_hits': 10, 'cache_misses': 90}
+            
+            original_workers = processor.config.max_concurrent_strategies
+            original_chunk_size = processor.config.chunk_size_days
+            
+            optimized_config = processor.optimize_configuration()
+            
+            # Should reduce workers and chunk size due to poor memory efficiency
+            assert optimized_config.max_concurrent_strategies <= original_workers
+            assert optimized_config.chunk_size_days <= original_chunk_size
+    
+    def test_benchmark_processing_methods(self, processor, sample_strategy_configs):
+        """Test processing method benchmarking."""
+        with patch.object(processor, 'process_strategies_batch') as mock_batch, \
+             patch.object(processor, 'process_strategies_parallel') as mock_parallel:
+            
+            # Mock batch processing results
+            mock_batch.return_value = {'strategy1': []}
+            
+            # Mock parallel processing results  
+            mock_parallel.return_value = {'strategy1': []}
+            
+            benchmark_results = processor.benchmark_processing_methods(
+                strategy_configs=sample_strategy_configs,
+                symbols=['BTC-USDT'],
+                timeframe='1h',
+                days_back=7
+            )
+            
+            assert 'sequential' in benchmark_results
+            assert 'parallel' in benchmark_results
+            assert 'recommendation' in benchmark_results
+            
+            # Verify both methods were called
+            mock_batch.assert_called_once()
+            mock_parallel.assert_called_once()
+    
+    def test_reset_stats(self, processor):
+        """Test statistics reset functionality."""
+        # Set some statistics
+        processor._processing_stats.update({
+            'strategies_processed': 5,
+            'total_signals_generated': 25,
+            'processing_time_seconds': 10.0
+        })
+        processor._result_cache['test'] = 'data'
+        
+        processor._reset_stats()
+        
+        # Verify all stats are reset
+        assert processor._processing_stats['strategies_processed'] == 0
+        assert processor._processing_stats['total_signals_generated'] == 0
+        assert processor._processing_stats['processing_time_seconds'] == 0.0
+        assert len(processor._result_cache) == 0
+    
+    def test_calculate_warmup_period_ema_strategy(self, processor):
+        """Test warm-up period calculation for EMA strategy."""
+        strategy_configs = [
+            {
+                'name': 'ema_crossover',
+                'fast_period': 12,
+                'slow_period': 26
+            }
+        ]
+        
+        warmup = processor._calculate_warmup_period(strategy_configs)
+        
+        # Should be max(12, 26) + 10 safety buffer = 36
+        assert warmup == 36
+    
+    def test_calculate_warmup_period_macd_strategy(self, processor):
+        """Test warm-up period calculation for MACD strategy."""
+        strategy_configs = [
+            {
+                'name': 'macd_trend',
+                'slow_period': 26,
+                'signal_period': 9
+            }
+        ]
+        
+        warmup = processor._calculate_warmup_period(strategy_configs)
+        
+        # Should be max(26, 9) + 10 MACD buffer + 10 safety buffer = 46
+        assert warmup == 46
+    
+    def test_calculate_warmup_period_rsi_strategy(self, processor):
+        """Test warm-up period calculation for RSI strategy."""
+        strategy_configs = [
+            {
+                'name': 'rsi_momentum',
+                'period': 14
+            }
+        ]
+        
+        warmup = processor._calculate_warmup_period(strategy_configs)
+        
+        # Should be 14 + 5 RSI buffer + 10 safety buffer = 29
+        assert warmup == 29
+    
+    def test_calculate_warmup_period_multiple_strategies(self, processor):
+        """Test warm-up period calculation with multiple strategies."""
+        strategy_configs = [
+            {'name': 'ema_crossover', 'slow_period': 26},
+            {'name': 'rsi_momentum', 'period': 14},
+            {'name': 'macd_trend', 'slow_period': 26, 'signal_period': 9}
+        ]
+        
+        warmup = processor._calculate_warmup_period(strategy_configs)
+        
+        # Should be max of all strategies: 46 (from MACD)
+        assert warmup == 46
+    
+    def test_calculate_warmup_period_unknown_strategy(self, processor):
+        """Test warm-up period calculation for unknown strategy type."""
+        strategy_configs = [
+            {
+                'name': 'custom_strategy',
+                'some_param': 100
+            }
+        ]
+        
+        warmup = processor._calculate_warmup_period(strategy_configs)
+        
+        # Should be 30 default + 10 safety buffer = 40
+        assert warmup == 40
+    
+    def test_process_large_dataset_streaming_with_warmup(self, processor, sample_strategy_configs, sample_strategy_results):
+        """Test streaming processing with warm-up period handling."""
+        # Mock the warm-up calculation
+        with patch.object(processor, '_calculate_warmup_period') as mock_warmup:
+            mock_warmup.return_value = 10  # 10 days warm-up
+            
+            # Mock the parallel processing method
+            with patch.object(processor, 'process_strategies_parallel') as mock_parallel:
+                mock_parallel.return_value = {
+                    'test_strategy': sample_strategy_results
+                }
+                
+                # Mock the trimming method
+                with patch.object(processor, '_trim_warmup_from_results') as mock_trim:
+                    mock_trim.return_value = {'test_strategy': sample_strategy_results}
+                    
+                    # Test streaming with 60 days split into 30-day chunks
+                    stream = processor.process_large_dataset_streaming_with_warmup(
+                        strategy_configs=sample_strategy_configs,
+                        symbols=['BTC-USDT'],
+                        timeframe='1h',
+                        total_days_back=60  # Should create 2 chunks
+                    )
+                    
+                    # Collect all chunks
+                    chunks = list(stream)
+                    
+                    assert len(chunks) == 2  # 60 days / 30 days per chunk
+                    
+                    # Verify parallel processing was called with correct parameters
+                    assert mock_parallel.call_count == 2
+                    
+                    # First chunk should not have warm-up, second should
+                    first_call_args = mock_parallel.call_args_list[0]
+                    second_call_args = mock_parallel.call_args_list[1]
+                    
+                    # First chunk: 30 days (no warm-up)
+                    assert first_call_args[1]['days_back'] == 30
+                    
+                    # Second chunk: 30 + 10 warm-up = 40 days
+                    assert second_call_args[1]['days_back'] == 40
+                    
+                    # Trimming should only be called for second chunk
+                    assert mock_trim.call_count == 1
+    
+    def test_trim_warmup_from_results(self, processor, sample_strategy_results):
+        """Test trimming warm-up period from results."""
+        # Create test results with multiple signals
+        extended_results = sample_strategy_results * 10  # 10 results total
+        chunk_results = {
+            'strategy1': extended_results,
+            'strategy2': sample_strategy_results * 5  # 5 results
+        }
+        
+        trimmed = processor._trim_warmup_from_results(
+            chunk_results=chunk_results,
+            warmup_days=10,
+            target_start_days=30,
+            target_end_days=60
+        )
+        
+        # Verify trimming occurred
+        assert len(trimmed['strategy1']) <= len(extended_results)
+        assert len(trimmed['strategy2']) <= len(sample_strategy_results * 5)
+        
+        # Results should be sorted by timestamp
+        for strategy_name, results in trimmed.items():
+            if len(results) > 1:
+                timestamps = [r.timestamp for r in results]
+                assert timestamps == sorted(timestamps)
+    
+    def test_streaming_with_warmup_chunk_size_adjustment(self, processor, sample_strategy_configs):
+        """Test automatic chunk size adjustment when too small for warm-up."""
+        # Set up small chunk size relative to warm-up
+        processor.config.chunk_size_days = 15  # Small chunk size
+        
+        with patch.object(processor, '_calculate_warmup_period') as mock_warmup:
+            mock_warmup.return_value = 30  # Large warm-up period
+            
+            with patch.object(processor, 'process_strategies_parallel') as mock_parallel:
+                mock_parallel.return_value = {}
+                
+                # This should trigger chunk size adjustment
+                stream = processor.process_large_dataset_streaming_with_warmup(
+                    strategy_configs=sample_strategy_configs,
+                    symbols=['BTC-USDT'],
+                    timeframe='1h',
+                    total_days_back=90
+                )
+                
+                # Consume the stream to trigger processing
+                list(stream)
+                
+                # Verify warning was logged about chunk size adjustment
+                # (In a real implementation, you might want to capture log messages) 

tests/strategies/test_realtime_execution.py

@@ -0,0 +1,558 @@
+"""
+Tests for real-time strategy execution pipeline.
+"""
+
+import pytest
+import pandas as pd
+from datetime import datetime, timezone, timedelta
+from unittest.mock import Mock, patch, MagicMock
+import time
+from queue import Queue, Empty
+import threading
+
+from strategies.realtime_execution import (
+    RealTimeStrategyProcessor,
+    StrategySignalBroadcaster,
+    RealTimeConfig,
+    StrategyExecutionContext,
+    RealTimeSignal,
+    get_realtime_strategy_processor,
+    initialize_realtime_strategy_system,
+    shutdown_realtime_strategy_system
+)
+from strategies.data_types import StrategyResult, StrategySignal, SignalType
+from data.common.data_types import OHLCVCandle
+
+
+class TestRealTimeConfig:
+    """Test RealTimeConfig dataclass."""
+    
+    def test_default_config(self):
+        """Test default configuration values."""
+        config = RealTimeConfig()
+        
+        assert config.refresh_interval_seconds == 30
+        assert config.max_strategies_concurrent == 5
+        assert config.incremental_calculation == True
+        assert config.signal_batch_size == 100
+        assert config.enable_signal_broadcasting == True
+        assert config.max_signal_queue_size == 1000
+        assert config.chart_update_throttle_ms == 1000
+        assert config.error_retry_attempts == 3
+        assert config.error_retry_delay_seconds == 5
+    
+    def test_custom_config(self):
+        """Test custom configuration values."""
+        config = RealTimeConfig(
+            refresh_interval_seconds=15,
+            max_strategies_concurrent=3,
+            incremental_calculation=False,
+            signal_batch_size=50
+        )
+        
+        assert config.refresh_interval_seconds == 15
+        assert config.max_strategies_concurrent == 3
+        assert config.incremental_calculation == False
+        assert config.signal_batch_size == 50
+
+
+class TestStrategyExecutionContext:
+    """Test StrategyExecutionContext dataclass."""
+    
+    def test_context_creation(self):
+        """Test strategy execution context creation."""
+        context = StrategyExecutionContext(
+            strategy_name="ema_crossover",
+            strategy_config={"short_period": 12, "long_period": 26},
+            symbol="BTC-USDT",
+            timeframe="1h"
+        )
+        
+        assert context.strategy_name == "ema_crossover"
+        assert context.strategy_config == {"short_period": 12, "long_period": 26}
+        assert context.symbol == "BTC-USDT"
+        assert context.timeframe == "1h"
+        assert context.exchange == "okx"
+        assert context.last_calculation_time is None
+        assert context.consecutive_errors == 0
+        assert context.is_active == True
+    
+    def test_context_with_custom_exchange(self):
+        """Test context with custom exchange."""
+        context = StrategyExecutionContext(
+            strategy_name="rsi",
+            strategy_config={"period": 14},
+            symbol="ETH-USDT",
+            timeframe="4h",
+            exchange="binance"
+        )
+        
+        assert context.exchange == "binance"
+
+
+class TestRealTimeSignal:
+    """Test RealTimeSignal dataclass."""
+    
+    def test_signal_creation(self):
+        """Test real-time signal creation."""
+        # Create mock strategy result
+        strategy_result = Mock(spec=StrategyResult)
+        strategy_result.timestamp = datetime.now(timezone.utc)
+        strategy_result.confidence = 0.8
+        
+        # Create context
+        context = StrategyExecutionContext(
+            strategy_name="macd",
+            strategy_config={"fast_period": 12},
+            symbol="BTC-USDT",
+            timeframe="1d"
+        )
+        
+        # Create signal
+        signal = RealTimeSignal(
+            strategy_result=strategy_result,
+            context=context
+        )
+        
+        assert signal.strategy_result == strategy_result
+        assert signal.context == context
+        assert signal.chart_update_required == True
+        assert isinstance(signal.generation_time, datetime)
+
+
+class TestStrategySignalBroadcaster:
+    """Test StrategySignalBroadcaster class."""
+    
+    @pytest.fixture
+    def config(self):
+        """Test configuration."""
+        return RealTimeConfig(
+            signal_batch_size=5,
+            max_signal_queue_size=10,
+            chart_update_throttle_ms=100
+        )
+    
+    @pytest.fixture
+    def mock_db_ops(self):
+        """Mock database operations."""
+        with patch('strategies.realtime_execution.get_database_operations') as mock:
+            db_ops = Mock()
+            db_ops.strategy = Mock()
+            db_ops.strategy.store_signals_batch = Mock(return_value=5)
+            mock.return_value = db_ops
+            yield db_ops
+    
+    @pytest.fixture
+    def broadcaster(self, config, mock_db_ops):
+        """Create broadcaster instance."""
+        return StrategySignalBroadcaster(config)
+    
+    def test_broadcaster_initialization(self, broadcaster, config):
+        """Test broadcaster initialization."""
+        assert broadcaster.config == config
+        assert broadcaster._is_running == False
+        assert broadcaster._chart_update_callback is None
+    
+    def test_start_stop_broadcaster(self, broadcaster):
+        """Test starting and stopping broadcaster."""
+        assert not broadcaster._is_running
+        
+        broadcaster.start()
+        assert broadcaster._is_running
+        assert broadcaster._processing_thread is not None
+        
+        broadcaster.stop()
+        assert not broadcaster._is_running
+    
+    def test_broadcast_signal(self, broadcaster):
+        """Test broadcasting signals."""
+        # Create test signal
+        strategy_result = Mock(spec=StrategyResult)
+        context = StrategyExecutionContext(
+            strategy_name="test",
+            strategy_config={},
+            symbol="BTC-USDT",
+            timeframe="1h"
+        )
+        signal = RealTimeSignal(strategy_result=strategy_result, context=context)
+        
+        # Broadcast signal
+        success = broadcaster.broadcast_signal(signal)
+        assert success == True
+        
+        # Check queue has signal
+        assert broadcaster._signal_queue.qsize() == 1
+    
+    def test_broadcast_signal_queue_full(self, config, mock_db_ops):
+        """Test broadcasting when queue is full."""
+        # Create broadcaster with very small queue
+        small_config = RealTimeConfig(max_signal_queue_size=1)
+        broadcaster = StrategySignalBroadcaster(small_config)
+        
+        # Create test signals
+        strategy_result = Mock(spec=StrategyResult)
+        context = StrategyExecutionContext(
+            strategy_name="test",
+            strategy_config={},
+            symbol="BTC-USDT",
+            timeframe="1h"
+        )
+        signal1 = RealTimeSignal(strategy_result=strategy_result, context=context)
+        signal2 = RealTimeSignal(strategy_result=strategy_result, context=context)
+        
+        # Fill queue
+        success1 = broadcaster.broadcast_signal(signal1)
+        assert success1 == True
+        
+        # Try to overfill queue
+        success2 = broadcaster.broadcast_signal(signal2)
+        assert success2 == False  # Should fail due to full queue
+    
+    def test_set_chart_update_callback(self, broadcaster):
+        """Test setting chart update callback."""
+        callback = Mock()
+        broadcaster.set_chart_update_callback(callback)
+        assert broadcaster._chart_update_callback == callback
+    
+    def test_get_signal_stats(self, broadcaster):
+        """Test getting signal statistics."""
+        stats = broadcaster.get_signal_stats()
+        
+        assert 'queue_size' in stats
+        assert 'chart_queue_size' in stats
+        assert 'is_running' in stats
+        assert 'last_chart_updates' in stats
+        assert stats['is_running'] == False
+
+
+class TestRealTimeStrategyProcessor:
+    """Test RealTimeStrategyProcessor class."""
+    
+    @pytest.fixture
+    def config(self):
+        """Test configuration."""
+        return RealTimeConfig(
+            max_strategies_concurrent=2,
+            error_retry_attempts=2
+        )
+    
+    @pytest.fixture
+    def mock_dependencies(self):
+        """Mock all external dependencies."""
+        mocks = {}
+        
+        with patch('strategies.realtime_execution.StrategyDataIntegrator') as mock_integrator:
+            mocks['data_integrator'] = Mock()
+            mock_integrator.return_value = mocks['data_integrator']
+            
+            with patch('strategies.realtime_execution.MarketDataIntegrator') as mock_market:
+                mocks['market_integrator'] = Mock()
+                mock_market.return_value = mocks['market_integrator']
+                
+                with patch('strategies.realtime_execution.StrategyFactory') as mock_factory:
+                    mocks['strategy_factory'] = Mock()
+                    mock_factory.return_value = mocks['strategy_factory']
+                    
+                    yield mocks
+    
+    @pytest.fixture
+    def processor(self, config, mock_dependencies):
+        """Create processor instance."""
+        return RealTimeStrategyProcessor(config)
+    
+    def test_processor_initialization(self, processor, config):
+        """Test processor initialization."""
+        assert processor.config == config
+        assert processor._execution_contexts == {}
+        assert processor._performance_stats['total_calculations'] == 0
+    
+    def test_start_stop_processor(self, processor):
+        """Test starting and stopping processor."""
+        processor.start()
+        assert processor.signal_broadcaster._is_running == True
+        
+        processor.stop()
+        assert processor.signal_broadcaster._is_running == False
+    
+    def test_register_strategy(self, processor):
+        """Test registering strategy for real-time execution."""
+        context_id = processor.register_strategy(
+            strategy_name="ema_crossover",
+            strategy_config={"short_period": 12, "long_period": 26},
+            symbol="BTC-USDT",
+            timeframe="1h"
+        )
+        
+        expected_id = "ema_crossover_BTC-USDT_1h_okx"
+        assert context_id == expected_id
+        assert context_id in processor._execution_contexts
+        
+        context = processor._execution_contexts[context_id]
+        assert context.strategy_name == "ema_crossover"
+        assert context.symbol == "BTC-USDT"
+        assert context.timeframe == "1h"
+        assert context.is_active == True
+    
+    def test_unregister_strategy(self, processor):
+        """Test unregistering strategy."""
+        # Register first
+        context_id = processor.register_strategy(
+            strategy_name="rsi",
+            strategy_config={"period": 14},
+            symbol="ETH-USDT",
+            timeframe="4h"
+        )
+        
+        assert context_id in processor._execution_contexts
+        
+        # Unregister
+        success = processor.unregister_strategy(context_id)
+        assert success == True
+        assert context_id not in processor._execution_contexts
+        
+        # Try to unregister again
+        success2 = processor.unregister_strategy(context_id)
+        assert success2 == False
+    
+    def test_execute_realtime_update_no_strategies(self, processor):
+        """Test real-time update with no registered strategies."""
+        signals = processor.execute_realtime_update("BTC-USDT", "1h")
+        assert signals == []
+    
+    def test_execute_realtime_update_with_strategies(self, processor, mock_dependencies):
+        """Test real-time update with registered strategies."""
+        # Mock strategy calculation results
+        mock_result = Mock(spec=StrategyResult)
+        mock_result.timestamp = datetime.now(timezone.utc)
+        mock_result.confidence = 0.8
+        
+        mock_dependencies['data_integrator'].calculate_strategy_signals.return_value = [mock_result]
+        
+        # Register strategy
+        processor.register_strategy(
+            strategy_name="ema_crossover",
+            strategy_config={"short_period": 12, "long_period": 26},
+            symbol="BTC-USDT",
+            timeframe="1h"
+        )
+        
+        # Execute update
+        signals = processor.execute_realtime_update("BTC-USDT", "1h")
+        
+        assert len(signals) == 1
+        assert isinstance(signals[0], RealTimeSignal)
+        assert signals[0].strategy_result == mock_result
+    
+    def test_get_active_strategies(self, processor):
+        """Test getting active strategies."""
+        # Register some strategies
+        processor.register_strategy("ema", {}, "BTC-USDT", "1h")
+        processor.register_strategy("rsi", {}, "ETH-USDT", "4h")
+        
+        active = processor.get_active_strategies()
+        assert len(active) == 2
+        
+        # Pause one strategy
+        context_id = list(active.keys())[0]
+        processor.pause_strategy(context_id)
+        
+        active_after_pause = processor.get_active_strategies()
+        assert len(active_after_pause) == 1
+    
+    def test_pause_resume_strategy(self, processor):
+        """Test pausing and resuming strategies."""
+        context_id = processor.register_strategy("macd", {}, "BTC-USDT", "1d")
+        
+        # Pause strategy
+        success = processor.pause_strategy(context_id)
+        assert success == True
+        assert not processor._execution_contexts[context_id].is_active
+        
+        # Resume strategy
+        success = processor.resume_strategy(context_id)
+        assert success == True
+        assert processor._execution_contexts[context_id].is_active
+        
+        # Test with invalid context_id
+        invalid_success = processor.pause_strategy("invalid_id")
+        assert invalid_success == False
+    
+    def test_get_performance_stats(self, processor):
+        """Test getting performance statistics."""
+        stats = processor.get_performance_stats()
+        
+        assert 'total_calculations' in stats
+        assert 'successful_calculations' in stats
+        assert 'failed_calculations' in stats
+        assert 'average_calculation_time_ms' in stats
+        assert 'signals_generated' in stats
+        assert 'queue_size' in stats  # From signal broadcaster
+
+
+class TestSingletonAndInitialization:
+    """Test singleton pattern and system initialization."""
+    
+    def test_get_realtime_strategy_processor_singleton(self):
+        """Test that processor is singleton."""
+        # Clean up any existing processor
+        shutdown_realtime_strategy_system()
+        
+        processor1 = get_realtime_strategy_processor()
+        processor2 = get_realtime_strategy_processor()
+        
+        assert processor1 is processor2
+        
+        # Clean up
+        shutdown_realtime_strategy_system()
+    
+    def test_initialize_realtime_strategy_system(self):
+        """Test system initialization."""
+        # Clean up any existing processor
+        shutdown_realtime_strategy_system()
+        
+        config = RealTimeConfig(max_strategies_concurrent=2)
+        processor = initialize_realtime_strategy_system(config)
+        
+        assert processor is not None
+        assert processor.signal_broadcaster._is_running == True
+        
+        # Clean up
+        shutdown_realtime_strategy_system()
+    
+    def test_shutdown_realtime_strategy_system(self):
+        """Test system shutdown."""
+        # Initialize system
+        processor = initialize_realtime_strategy_system()
+        assert processor.signal_broadcaster._is_running == True
+        
+        # Shutdown
+        shutdown_realtime_strategy_system()
+        
+        # Verify shutdown
+        # Note: After shutdown, the global processor is set to None
+        # So we can't check the processor state, but we can verify
+        # a new processor is created on next call
+        new_processor = get_realtime_strategy_processor()
+        assert new_processor is not None
+
+
+class TestIntegration:
+    """Integration tests for real-time execution pipeline."""
+    
+    @pytest.fixture
+    def integration_config(self):
+        """Configuration for integration tests."""
+        return RealTimeConfig(
+            signal_batch_size=2,
+            max_signal_queue_size=5,
+            chart_update_throttle_ms=50
+        )
+    
+    def test_end_to_end_signal_flow(self, integration_config):
+        """Test complete signal flow from strategy to storage."""
+        with patch('strategies.realtime_execution.get_database_operations') as mock_db:
+            # Setup mocks
+            db_ops = Mock()
+            db_ops.strategy = Mock()
+            db_ops.strategy.store_signals_batch = Mock(return_value=2)
+            mock_db.return_value = db_ops
+            
+            # Create processor
+            processor = RealTimeStrategyProcessor(integration_config)
+            processor.start()
+            
+            try:
+                # Mock strategy calculation
+                mock_result = Mock(spec=StrategyResult)
+                mock_result.timestamp = datetime.now(timezone.utc)
+                mock_result.confidence = 0.8
+                mock_result.signal = Mock()
+                mock_result.signal.signal_type = SignalType.BUY
+                mock_result.price = 50000.0
+                mock_result.metadata = {"test": True}
+                
+                with patch.object(processor.data_integrator, 'calculate_strategy_signals') as mock_calc:
+                    mock_calc.return_value = [mock_result]
+                    
+                    # Register strategy
+                    processor.register_strategy(
+                        strategy_name="test_strategy",
+                        strategy_config={"param": "value"},
+                        symbol="BTC-USDT",
+                        timeframe="1h"
+                    )
+                    
+                    # Execute real-time update
+                    signals = processor.execute_realtime_update("BTC-USDT", "1h")
+                    
+                    assert len(signals) == 1
+                    
+                    # Wait for signal processing
+                    time.sleep(0.2)  # Allow background processing
+                    
+                    # Verify calculation was called
+                    mock_calc.assert_called_once()
+                    
+            finally:
+                processor.stop()
+    
+    def test_error_handling_and_retry(self, integration_config):
+        """Test error handling and retry mechanisms."""
+        processor = RealTimeStrategyProcessor(integration_config)
+        processor.start()
+        
+        try:
+            # Mock strategy calculation to raise error
+            with patch.object(processor.data_integrator, 'calculate_strategy_signals') as mock_calc:
+                mock_calc.side_effect = Exception("Test error")
+                
+                # Register strategy
+                context_id = processor.register_strategy(
+                    strategy_name="error_strategy",
+                    strategy_config={},
+                    symbol="BTC-USDT",
+                    timeframe="1h"
+                )
+                
+                # Execute multiple times to trigger error handling
+                for _ in range(integration_config.error_retry_attempts + 1):
+                    processor.execute_realtime_update("BTC-USDT", "1h")
+                
+                # Strategy should be disabled after max errors
+                context = processor._execution_contexts[context_id]
+                assert not context.is_active
+                assert context.consecutive_errors >= integration_config.error_retry_attempts
+                
+        finally:
+            processor.stop()
+    
+    def test_concurrent_strategy_execution(self, integration_config):
+        """Test concurrent execution of multiple strategies."""
+        processor = RealTimeStrategyProcessor(integration_config)
+        processor.start()
+        
+        try:
+            # Mock strategy calculations
+            mock_result1 = Mock(spec=StrategyResult)
+            mock_result1.timestamp = datetime.now(timezone.utc)
+            mock_result1.confidence = 0.7
+            
+            mock_result2 = Mock(spec=StrategyResult)
+            mock_result2.timestamp = datetime.now(timezone.utc)
+            mock_result2.confidence = 0.9
+            
+            with patch.object(processor.data_integrator, 'calculate_strategy_signals') as mock_calc:
+                mock_calc.side_effect = [[mock_result1], [mock_result2]]
+                
+                # Register multiple strategies for same symbol/timeframe
+                processor.register_strategy("strategy1", {}, "BTC-USDT", "1h")
+                processor.register_strategy("strategy2", {}, "BTC-USDT", "1h")
+                
+                # Execute update
+                signals = processor.execute_realtime_update("BTC-USDT", "1h")
+                
+                # Should get signals from both strategies
+                assert len(signals) == 2
+                
+        finally:
+            processor.stop()

tests/strategies/test_validation.py

@@ -0,0 +1,478 @@
+"""
+Tests for Strategy Signal Validation Pipeline
+
+This module tests signal validation, filtering, and quality assessment
+functionality for strategy-generated signals.
+"""
+
+import pytest
+from datetime import datetime, timezone
+from unittest.mock import patch
+
+from strategies.validation import StrategySignalValidator, ValidationConfig
+from strategies.data_types import StrategySignal, SignalType
+
+
+class TestValidationConfig:
+    """Tests for ValidationConfig dataclass."""
+    
+    def test_default_config(self):
+        """Test default validation configuration."""
+        config = ValidationConfig()
+        
+        assert config.min_confidence == 0.0
+        assert config.max_confidence == 1.0
+        assert config.required_metadata_fields == []
+        assert config.allowed_signal_types == list(SignalType)
+        assert config.price_tolerance_percent == 5.0
+    
+    def test_custom_config(self):
+        """Test custom validation configuration."""
+        config = ValidationConfig(
+            min_confidence=0.3,
+            max_confidence=0.9,
+            required_metadata_fields=['indicator1', 'indicator2'],
+            allowed_signal_types=[SignalType.BUY, SignalType.SELL],
+            price_tolerance_percent=2.0
+        )
+        
+        assert config.min_confidence == 0.3
+        assert config.max_confidence == 0.9
+        assert config.required_metadata_fields == ['indicator1', 'indicator2']
+        assert config.allowed_signal_types == [SignalType.BUY, SignalType.SELL]
+        assert config.price_tolerance_percent == 2.0
+
+
+class TestStrategySignalValidator:
+    """Tests for StrategySignalValidator class."""
+    
+    @pytest.fixture
+    def validator(self):
+        """Create validator with default configuration."""
+        return StrategySignalValidator()
+    
+    @pytest.fixture
+    def strict_validator(self):
+        """Create validator with strict configuration."""
+        config = ValidationConfig(
+            min_confidence=0.5,
+            max_confidence=1.0,
+            required_metadata_fields=['rsi', 'macd'],
+            allowed_signal_types=[SignalType.BUY, SignalType.SELL]
+        )
+        return StrategySignalValidator(config)
+    
+    @pytest.fixture
+    def valid_signal(self):
+        """Create a valid strategy signal for testing."""
+        return StrategySignal(
+            timestamp=datetime.now(timezone.utc),
+            symbol='BTC-USDT',
+            timeframe='1h',
+            signal_type=SignalType.BUY,
+            price=50000.0,
+            confidence=0.8,
+            metadata={'rsi': 30, 'macd': 0.05}
+        )
+    
+    def test_initialization(self, validator):
+        """Test validator initialization."""
+        assert validator.config is not None
+        assert validator.logger is not None
+        assert validator._validation_stats['total_signals_validated'] == 0
+        assert validator._validation_stats['valid_signals'] == 0
+        assert validator._validation_stats['invalid_signals'] == 0
+    
+    def test_validate_valid_signal(self, validator, valid_signal):
+        """Test validation of a completely valid signal."""
+        is_valid, errors = validator.validate_signal(valid_signal)
+        
+        assert is_valid is True
+        assert errors == []
+        assert validator._validation_stats['total_signals_validated'] == 1
+        assert validator._validation_stats['valid_signals'] == 1
+        assert validator._validation_stats['invalid_signals'] == 0
+    
+    def test_validate_invalid_confidence_low(self, validator, valid_signal):
+        """Test validation with confidence too low."""
+        valid_signal.confidence = -0.1
+        
+        is_valid, errors = validator.validate_signal(valid_signal)
+        
+        assert is_valid is False
+        assert len(errors) == 1
+        assert "Invalid confidence" in errors[0]
+        assert validator._validation_stats['invalid_signals'] == 1
+    
+    def test_validate_invalid_confidence_high(self, validator, valid_signal):
+        """Test validation with confidence too high."""
+        valid_signal.confidence = 1.5
+        
+        is_valid, errors = validator.validate_signal(valid_signal)
+        
+        assert is_valid is False
+        assert len(errors) == 1
+        assert "Invalid confidence" in errors[0]
+    
+    def test_validate_invalid_signal_type(self, strict_validator, valid_signal):
+        """Test validation with disallowed signal type."""
+        valid_signal.signal_type = SignalType.HOLD
+        
+        is_valid, errors = strict_validator.validate_signal(valid_signal)
+        
+        assert is_valid is False
+        assert len(errors) == 1
+        assert "Signal type" in errors[0] and "not in allowed types" in errors[0]
+    
+    def test_validate_invalid_price(self, validator, valid_signal):
+        """Test validation with invalid price."""
+        valid_signal.price = -100.0
+        
+        is_valid, errors = validator.validate_signal(valid_signal)
+        
+        assert is_valid is False
+        assert len(errors) == 1
+        assert "Invalid price" in errors[0]
+    
+    def test_validate_missing_required_metadata(self, strict_validator, valid_signal):
+        """Test validation with missing required metadata."""
+        valid_signal.metadata = {'rsi': 30}  # Missing 'macd'
+        
+        is_valid, errors = strict_validator.validate_signal(valid_signal)
+        
+        assert is_valid is False
+        assert len(errors) == 1
+        assert "Missing required metadata fields" in errors[0]
+        assert "macd" in errors[0]
+    
+    def test_validate_multiple_errors(self, strict_validator, valid_signal):
+        """Test validation with multiple errors."""
+        valid_signal.confidence = 1.5  # Too high
+        valid_signal.price = -100.0    # Invalid
+        valid_signal.signal_type = SignalType.HOLD  # Not allowed
+        valid_signal.metadata = {}     # Missing required fields
+        
+        is_valid, errors = strict_validator.validate_signal(valid_signal)
+        
+        assert is_valid is False
+        assert len(errors) == 4
+        assert any("confidence" in error for error in errors)
+        assert any("price" in error for error in errors)
+        assert any("Signal type" in error for error in errors)
+        assert any("Missing required metadata" in error for error in errors)
+    
+    def test_validation_statistics_tracking(self, validator, valid_signal):
+        """Test that validation statistics are properly tracked."""
+        # Validate multiple signals
+        validator.validate_signal(valid_signal)  # Valid
+        
+        invalid_signal = valid_signal
+        invalid_signal.confidence = 1.5  # Invalid
+        validator.validate_signal(invalid_signal)  # Invalid
+        
+        stats = validator._validation_stats
+        assert stats['total_signals_validated'] == 2
+        assert stats['valid_signals'] == 1
+        assert stats['invalid_signals'] == 1
+        assert len(stats['validation_errors']) > 0
+    
+    def test_validate_signals_batch(self, validator, valid_signal):
+        """Test batch validation of multiple signals."""
+        # Create a mix of valid and invalid signals
+        signals = [
+            valid_signal,  # Valid
+            StrategySignal(  # Invalid confidence
+                timestamp=datetime.now(timezone.utc),
+                symbol='ETH-USDT',
+                timeframe='1h',
+                signal_type=SignalType.SELL,
+                price=3000.0,
+                confidence=1.5,  # Invalid
+                metadata={}
+            ),
+            StrategySignal(  # Valid
+                timestamp=datetime.now(timezone.utc),
+                symbol='BNB-USDT',
+                timeframe='1h',
+                signal_type=SignalType.BUY,
+                price=300.0,
+                confidence=0.7,
+                metadata={}
+            )
+        ]
+        
+        valid_signals, invalid_signals = validator.validate_signals_batch(signals)
+        
+        assert len(valid_signals) == 2
+        assert len(invalid_signals) == 1
+        assert invalid_signals[0].confidence == 1.5
+    
+    def test_filter_signals_by_confidence(self, validator, valid_signal):
+        """Test filtering signals by confidence threshold."""
+        signals = [
+            valid_signal,  # confidence 0.8
+            StrategySignal(
+                timestamp=datetime.now(timezone.utc),
+                symbol='ETH-USDT',
+                timeframe='1h',
+                signal_type=SignalType.SELL,
+                price=3000.0,
+                confidence=0.3,  # Low confidence
+                metadata={}
+            ),
+            StrategySignal(
+                timestamp=datetime.now(timezone.utc),
+                symbol='BNB-USDT',
+                timeframe='1h',
+                signal_type=SignalType.BUY,
+                price=300.0,
+                confidence=0.9,  # High confidence
+                metadata={}
+            )
+        ]
+        
+        # Filter with threshold 0.5
+        filtered_signals = validator.filter_signals_by_confidence(signals, min_confidence=0.5)
+        
+        assert len(filtered_signals) == 2
+        assert all(signal.confidence >= 0.5 for signal in filtered_signals)
+        assert filtered_signals[0].confidence == 0.8
+        assert filtered_signals[1].confidence == 0.9
+    
+    def test_filter_signals_by_type(self, validator, valid_signal):
+        """Test filtering signals by allowed types."""
+        signals = [
+            valid_signal,  # BUY
+            StrategySignal(
+                timestamp=datetime.now(timezone.utc),
+                symbol='ETH-USDT',
+                timeframe='1h',
+                signal_type=SignalType.SELL,
+                price=3000.0,
+                confidence=0.8,
+                metadata={}
+            ),
+            StrategySignal(
+                timestamp=datetime.now(timezone.utc),
+                symbol='BNB-USDT',
+                timeframe='1h',
+                signal_type=SignalType.HOLD,
+                price=300.0,
+                confidence=0.7,
+                metadata={}
+            )
+        ]
+        
+        # Filter to only allow BUY and SELL
+        filtered_signals = validator.filter_signals_by_type(
+            signals, 
+            allowed_types=[SignalType.BUY, SignalType.SELL]
+        )
+        
+        assert len(filtered_signals) == 2
+        assert filtered_signals[0].signal_type == SignalType.BUY
+        assert filtered_signals[1].signal_type == SignalType.SELL
+    
+    def test_get_validation_statistics(self, validator, valid_signal):
+        """Test comprehensive validation statistics."""
+        # Validate some signals to generate statistics
+        validator.validate_signal(valid_signal)  # Valid
+        
+        invalid_signal = valid_signal
+        invalid_signal.confidence = -0.1  # Invalid
+        validator.validate_signal(invalid_signal)  # Invalid
+        
+        stats = validator.get_validation_statistics()
+        
+        assert stats['total_signals_validated'] == 2
+        assert stats['valid_signals'] == 1
+        assert stats['invalid_signals'] == 1
+        assert stats['validation_success_rate'] == 0.5
+        assert stats['validation_failure_rate'] == 0.5
+        assert 'validation_errors' in stats
+    
+    def test_transform_signal_confidence(self, validator, valid_signal):
+        """Test signal confidence transformation."""
+        original_confidence = valid_signal.confidence  # 0.8
+        
+        # Test confidence multiplier
+        transformed_signal = validator.transform_signal_confidence(
+            valid_signal, 
+            confidence_multiplier=1.2
+        )
+        
+        assert transformed_signal.confidence == original_confidence * 1.2
+        assert transformed_signal.symbol == valid_signal.symbol
+        assert transformed_signal.signal_type == valid_signal.signal_type
+        assert transformed_signal.price == valid_signal.price
+        
+        # Test confidence cap
+        capped_signal = validator.transform_signal_confidence(
+            valid_signal,
+            confidence_multiplier=2.0,  # Would exceed 1.0
+            max_confidence=1.0
+        )
+        
+        assert capped_signal.confidence == 1.0  # Capped at max
+    
+    def test_enrich_signal_metadata(self, validator, valid_signal):
+        """Test signal metadata enrichment."""
+        additional_metadata = {
+            'validation_timestamp': datetime.now(timezone.utc).isoformat(),
+            'validation_status': 'approved',
+            'risk_score': 0.2
+        }
+        
+        enriched_signal = validator.enrich_signal_metadata(valid_signal, additional_metadata)
+        
+        # Original metadata should be preserved
+        assert enriched_signal.metadata['rsi'] == 30
+        assert enriched_signal.metadata['macd'] == 0.05
+        
+        # New metadata should be added
+        assert enriched_signal.metadata['validation_status'] == 'approved'
+        assert enriched_signal.metadata['risk_score'] == 0.2
+        assert 'validation_timestamp' in enriched_signal.metadata
+        
+        # Other properties should remain unchanged
+        assert enriched_signal.confidence == valid_signal.confidence
+        assert enriched_signal.signal_type == valid_signal.signal_type
+    
+    def test_transform_signals_batch(self, validator, valid_signal):
+        """Test batch signal transformation."""
+        signals = [
+            valid_signal,
+            StrategySignal(
+                timestamp=datetime.now(timezone.utc),
+                symbol='ETH-USDT',
+                timeframe='1h',
+                signal_type=SignalType.SELL,
+                price=3000.0,
+                confidence=0.6,
+                metadata={'ema': 2950}
+            )
+        ]
+        
+        additional_metadata = {'batch_id': 'test_batch_001'}
+        
+        transformed_signals = validator.transform_signals_batch(
+            signals,
+            confidence_multiplier=1.1,
+            additional_metadata=additional_metadata
+        )
+        
+        assert len(transformed_signals) == 2
+        
+        # Check confidence transformation
+        assert transformed_signals[0].confidence == 0.8 * 1.1
+        assert transformed_signals[1].confidence == 0.6 * 1.1
+        
+        # Check metadata enrichment
+        assert transformed_signals[0].metadata['batch_id'] == 'test_batch_001'
+        assert transformed_signals[1].metadata['batch_id'] == 'test_batch_001'
+        
+        # Verify original metadata preserved
+        assert transformed_signals[0].metadata['rsi'] == 30
+        assert transformed_signals[1].metadata['ema'] == 2950
+    
+    def test_calculate_signal_quality_metrics(self, validator, valid_signal):
+        """Test signal quality metrics calculation."""
+        signals = [
+            valid_signal,  # confidence 0.8, has metadata
+            StrategySignal(
+                timestamp=datetime.now(timezone.utc),
+                symbol='ETH-USDT',
+                timeframe='1h',
+                signal_type=SignalType.SELL,
+                price=3000.0,
+                confidence=0.9,  # High confidence
+                metadata={'volume_spike': True}
+            ),
+            StrategySignal(
+                timestamp=datetime.now(timezone.utc),
+                symbol='BNB-USDT',
+                timeframe='1h',
+                signal_type=SignalType.HOLD,
+                price=300.0,
+                confidence=0.4,  # Low confidence
+                metadata=None  # No metadata
+            )
+        ]
+        
+        metrics = validator.calculate_signal_quality_metrics(signals)
+        
+        assert metrics['total_signals'] == 3
+        assert metrics['confidence_metrics']['average'] == round((0.8 + 0.9 + 0.4) / 3, 3)
+        assert metrics['confidence_metrics']['minimum'] == 0.4
+        assert metrics['confidence_metrics']['maximum'] == 0.9
+        assert metrics['confidence_metrics']['high_confidence_count'] == 2  # >= 0.7
+        assert metrics['quality_score'] == round((2/3) * 100, 1)  # 66.7%
+        assert metrics['metadata_completeness_percentage'] == round((2/3) * 100, 1)
+        
+        # Check signal type distribution
+        assert metrics['signal_type_distribution']['buy'] == 1
+        assert metrics['signal_type_distribution']['sell'] == 1
+        assert metrics['signal_type_distribution']['hold'] == 1
+        
+        # Check recommendations
+        assert isinstance(metrics['recommendations'], list)
+        assert len(metrics['recommendations']) > 0
+    
+    def test_calculate_signal_quality_metrics_empty(self, validator):
+        """Test quality metrics with empty signal list."""
+        metrics = validator.calculate_signal_quality_metrics([])
+        
+        assert 'error' in metrics
+        assert metrics['error'] == 'No signals provided for quality analysis'
+    
+    def test_generate_quality_recommendations(self, validator):
+        """Test quality recommendation generation."""
+        # Test low confidence signals
+        low_confidence_signals = [
+            StrategySignal(
+                timestamp=datetime.now(timezone.utc),
+                symbol='BTC-USDT',
+                timeframe='1h',
+                signal_type=SignalType.BUY,
+                price=50000.0,
+                confidence=0.3,  # Low confidence
+                metadata=None  # No metadata
+            )
+        ]
+        
+        recommendations = validator._generate_quality_recommendations(low_confidence_signals)
+        
+        assert any("confidence" in rec.lower() for rec in recommendations)
+        assert any("metadata" in rec.lower() for rec in recommendations)
+    
+    def test_generate_validation_report(self, validator, valid_signal):
+        """Test comprehensive validation report generation."""
+        # Generate some validation activity
+        validator.validate_signal(valid_signal)  # Valid
+        
+        invalid_signal = valid_signal
+        invalid_signal.confidence = -0.1  # Invalid
+        validator.validate_signal(invalid_signal)  # Invalid
+        
+        report = validator.generate_validation_report()
+        
+        assert 'report_timestamp' in report
+        assert 'validation_summary' in report
+        assert 'error_analysis' in report
+        assert 'configuration' in report
+        assert 'health_status' in report
+        
+        # Check validation summary
+        summary = report['validation_summary']
+        assert summary['total_validated'] == 2
+        assert '50.0%' in summary['success_rate']
+        assert '50.0%' in summary['failure_rate']
+        
+        # Check configuration
+        config = report['configuration']
+        assert config['min_confidence'] == 0.0
+        assert config['max_confidence'] == 1.0
+        assert isinstance(config['allowed_signal_types'], list)
+        
+        # Check health status
+        assert report['health_status'] in ['good', 'needs_attention']