random strategy

cycles/IncStrategies/random_strategy.py

@@ -0,0 +1,360 @@
+"""
+Incremental Random Strategy for Testing
+
+This strategy generates random entry and exit signals for testing the incremental strategy system.
+It's useful for verifying that the incremental strategy framework is working correctly.
+"""
+
+import random
+import logging
+import time
+from typing import Dict, Optional
+import pandas as pd
+
+from .base import IncStrategyBase, IncStrategySignal
+
+logger = logging.getLogger(__name__)
+
+
+class IncRandomStrategy(IncStrategyBase):
+    """
+    Incremental random signal generator strategy for testing.
+    
+    This strategy generates random entry and exit signals with configurable
+    probability and confidence levels. It's designed to test the incremental
+    strategy framework and signal processing system.
+    
+    The incremental version maintains minimal state and processes each new
+    data point independently, making it ideal for testing real-time performance.
+    
+    Parameters:
+        entry_probability: Probability of generating an entry signal (0.0-1.0)
+        exit_probability: Probability of generating an exit signal (0.0-1.0)
+        min_confidence: Minimum confidence level for signals
+        max_confidence: Maximum confidence level for signals
+        timeframe: Timeframe to operate on (default: "1min")
+        signal_frequency: How often to generate signals (every N bars)
+        random_seed: Optional seed for reproducible random signals
+    
+    Example:
+        strategy = IncRandomStrategy(
+            weight=1.0,
+            params={
+                "entry_probability": 0.1,
+                "exit_probability": 0.15,
+                "min_confidence": 0.7,
+                "max_confidence": 0.9,
+                "signal_frequency": 5,
+                "random_seed": 42  # For reproducible testing
+            }
+        )
+    """
+    
+    def __init__(self, weight: float = 1.0, params: Optional[Dict] = None):
+        """Initialize the incremental random strategy."""
+        super().__init__("inc_random", weight, params)
+        
+        # Strategy parameters with defaults
+        self.entry_probability = self.params.get("entry_probability", 0.05)  # 5% chance per bar
+        self.exit_probability = self.params.get("exit_probability", 0.1)     # 10% chance per bar
+        self.min_confidence = self.params.get("min_confidence", 0.6)
+        self.max_confidence = self.params.get("max_confidence", 0.9)
+        self.timeframe = self.params.get("timeframe", "1min")
+        self.signal_frequency = self.params.get("signal_frequency", 1)       # Every bar
+        
+        # Create separate random instance for this strategy
+        self._random = random.Random()
+        random_seed = self.params.get("random_seed")
+        if random_seed is not None:
+            self._random.seed(random_seed)
+            logger.info(f"IncRandomStrategy: Set random seed to {random_seed}")
+        
+        # Internal state (minimal for random strategy)
+        self._bar_count = 0
+        self._last_signal_bar = -1
+        self._current_price = None
+        self._last_timestamp = None
+        
+        logger.info(f"IncRandomStrategy initialized with entry_prob={self.entry_probability}, "
+                   f"exit_prob={self.exit_probability}, timeframe={self.timeframe}")
+    
+    def get_minimum_buffer_size(self) -> Dict[str, int]:
+        """
+        Return minimum data points needed for each timeframe.
+        
+        Random strategy doesn't need any historical data for calculations,
+        so we only need 1 data point to start generating signals.
+        
+        Returns:
+            Dict[str, int]: Minimal buffer requirements
+        """
+        return {"1min": 1}  # Only need current data point
+    
+    def supports_incremental_calculation(self) -> bool:
+        """
+        Whether strategy supports incremental calculation.
+        
+        Random strategy is ideal for incremental mode since it doesn't
+        depend on historical calculations.
+        
+        Returns:
+            bool: Always True for random strategy
+        """
+        return True
+    
+    def calculate_on_data(self, new_data_point: Dict[str, float], timestamp: pd.Timestamp) -> None:
+        """
+        Process a single new data point incrementally.
+        
+        For random strategy, we just update our internal state with the
+        current price and increment the bar counter.
+        
+        Args:
+            new_data_point: OHLCV data point {open, high, low, close, volume}
+            timestamp: Timestamp of the data point
+        """
+        start_time = time.perf_counter()
+        
+        try:
+            # Update timeframe buffers (handled by base class)
+            self._update_timeframe_buffers(new_data_point, timestamp)
+            
+            # Update internal state
+            self._current_price = new_data_point['close']
+            self._last_timestamp = timestamp
+            self._data_points_received += 1
+            
+            # Check if we should update bar count based on timeframe
+            if self._should_update_bar_count(timestamp):
+                self._bar_count += 1
+                
+                # Debug logging every 10 bars
+                if self._bar_count % 10 == 0:
+                    logger.debug(f"IncRandomStrategy: Processing bar {self._bar_count}, "
+                               f"price=${self._current_price:.2f}, timestamp={timestamp}")
+            
+            # Update warm-up status
+            if not self._is_warmed_up and self._data_points_received >= 1:
+                self._is_warmed_up = True
+                self._calculation_mode = "incremental"
+                logger.info(f"IncRandomStrategy: Warmed up after {self._data_points_received} data points")
+            
+            # Record performance metrics
+            update_time = time.perf_counter() - start_time
+            self._performance_metrics['update_times'].append(update_time)
+            
+        except Exception as e:
+            logger.error(f"IncRandomStrategy: Error in calculate_on_data: {e}")
+            self._performance_metrics['state_validation_failures'] += 1
+            raise
+    
+    def _should_update_bar_count(self, timestamp: pd.Timestamp) -> bool:
+        """
+        Check if we should increment bar count based on timeframe.
+        
+        For 1min timeframe, increment every data point.
+        For other timeframes, increment when timeframe period has passed.
+        
+        Args:
+            timestamp: Current timestamp
+            
+        Returns:
+            bool: Whether to increment bar count
+        """
+        if self.timeframe == "1min":
+            return True  # Every data point is a new bar
+        
+        if self._last_timestamp is None:
+            return True  # First data point
+        
+        # Calculate timeframe interval
+        if self.timeframe.endswith("min"):
+            minutes = int(self.timeframe[:-3])
+            interval = pd.Timedelta(minutes=minutes)
+        elif self.timeframe.endswith("h"):
+            hours = int(self.timeframe[:-1])
+            interval = pd.Timedelta(hours=hours)
+        else:
+            return True  # Unknown timeframe, update anyway
+        
+        # Check if enough time has passed
+        return timestamp >= self._last_timestamp + interval
+    
+    def get_entry_signal(self) -> IncStrategySignal:
+        """
+        Generate random entry signals based on current state.
+        
+        Returns:
+            IncStrategySignal: Entry signal with confidence level
+        """
+        if not self._is_warmed_up:
+            return IncStrategySignal("HOLD", 0.0)
+        
+        start_time = time.perf_counter()
+        
+        try:
+            # Check if we should generate a signal based on frequency
+            if (self._bar_count - self._last_signal_bar) < self.signal_frequency:
+                return IncStrategySignal("HOLD", 0.0)
+            
+            # Generate random entry signal using strategy's random instance
+            random_value = self._random.random()
+            if random_value < self.entry_probability:
+                confidence = self._random.uniform(self.min_confidence, self.max_confidence)
+                self._last_signal_bar = self._bar_count
+                
+                logger.info(f"IncRandomStrategy: Generated ENTRY signal at bar {self._bar_count}, "
+                           f"price=${self._current_price:.2f}, confidence={confidence:.2f}, "
+                           f"random_value={random_value:.3f}")
+                
+                signal = IncStrategySignal(
+                    "ENTRY", 
+                    confidence=confidence,
+                    price=self._current_price,
+                    metadata={
+                        "strategy": "inc_random",
+                        "bar_count": self._bar_count,
+                        "timeframe": self.timeframe,
+                        "random_value": random_value,
+                        "timestamp": self._last_timestamp
+                    }
+                )
+                
+                # Record performance metrics
+                signal_time = time.perf_counter() - start_time
+                self._performance_metrics['signal_generation_times'].append(signal_time)
+                
+                return signal
+            
+            return IncStrategySignal("HOLD", 0.0)
+            
+        except Exception as e:
+            logger.error(f"IncRandomStrategy: Error in get_entry_signal: {e}")
+            return IncStrategySignal("HOLD", 0.0)
+    
+    def get_exit_signal(self) -> IncStrategySignal:
+        """
+        Generate random exit signals based on current state.
+        
+        Returns:
+            IncStrategySignal: Exit signal with confidence level
+        """
+        if not self._is_warmed_up:
+            return IncStrategySignal("HOLD", 0.0)
+        
+        start_time = time.perf_counter()
+        
+        try:
+            # Generate random exit signal using strategy's random instance
+            random_value = self._random.random()
+            if random_value < self.exit_probability:
+                confidence = self._random.uniform(self.min_confidence, self.max_confidence)
+                
+                # Randomly choose exit type
+                exit_types = ["SELL_SIGNAL", "TAKE_PROFIT", "STOP_LOSS"]
+                exit_type = self._random.choice(exit_types)
+                
+                logger.info(f"IncRandomStrategy: Generated EXIT signal at bar {self._bar_count}, "
+                           f"price=${self._current_price:.2f}, confidence={confidence:.2f}, "
+                           f"type={exit_type}, random_value={random_value:.3f}")
+                
+                signal = IncStrategySignal(
+                    "EXIT",
+                    confidence=confidence,
+                    price=self._current_price,
+                    metadata={
+                        "type": exit_type,
+                        "strategy": "inc_random",
+                        "bar_count": self._bar_count,
+                        "timeframe": self.timeframe,
+                        "random_value": random_value,
+                        "timestamp": self._last_timestamp
+                    }
+                )
+                
+                # Record performance metrics
+                signal_time = time.perf_counter() - start_time
+                self._performance_metrics['signal_generation_times'].append(signal_time)
+                
+                return signal
+            
+            return IncStrategySignal("HOLD", 0.0)
+            
+        except Exception as e:
+            logger.error(f"IncRandomStrategy: Error in get_exit_signal: {e}")
+            return IncStrategySignal("HOLD", 0.0)
+    
+    def get_confidence(self) -> float:
+        """
+        Return random confidence level for current market state.
+        
+        Returns:
+            float: Random confidence level between min and max confidence
+        """
+        if not self._is_warmed_up:
+            return 0.0
+        
+        return self._random.uniform(self.min_confidence, self.max_confidence)
+    
+    def reset_calculation_state(self) -> None:
+        """Reset internal calculation state for reinitialization."""
+        super().reset_calculation_state()
+        
+        # Reset random strategy specific state
+        self._bar_count = 0
+        self._last_signal_bar = -1
+        self._current_price = None
+        self._last_timestamp = None
+        
+        # Reset random state if seed was provided
+        random_seed = self.params.get("random_seed")
+        if random_seed is not None:
+            self._random.seed(random_seed)
+        
+        logger.info("IncRandomStrategy: Calculation state reset")
+    
+    def _reinitialize_from_buffers(self) -> None:
+        """
+        Reinitialize indicators from available buffer data.
+        
+        For random strategy, we just need to restore the current price
+        from the latest data point in the buffer.
+        """
+        try:
+            # Get the latest data point from 1min buffer
+            buffer_1min = self._timeframe_buffers.get("1min")
+            if buffer_1min and len(buffer_1min) > 0:
+                latest_data = buffer_1min[-1]
+                self._current_price = latest_data['close']
+                self._last_timestamp = latest_data.get('timestamp')
+                self._bar_count = len(buffer_1min)
+                
+                logger.info(f"IncRandomStrategy: Reinitialized from buffer with {self._bar_count} bars")
+            else:
+                logger.warning("IncRandomStrategy: No buffer data available for reinitialization")
+                
+        except Exception as e:
+            logger.error(f"IncRandomStrategy: Error reinitializing from buffers: {e}")
+            raise
+    
+    def get_current_state_summary(self) -> Dict[str, any]:
+        """Get summary of current calculation state for debugging."""
+        base_summary = super().get_current_state_summary()
+        base_summary.update({
+            'entry_probability': self.entry_probability,
+            'exit_probability': self.exit_probability,
+            'bar_count': self._bar_count,
+            'last_signal_bar': self._last_signal_bar,
+            'current_price': self._current_price,
+            'last_timestamp': self._last_timestamp,
+            'signal_frequency': self.signal_frequency,
+            'timeframe': self.timeframe
+        })
+        return base_summary
+    
+    def __repr__(self) -> str:
+        """String representation of the strategy."""
+        return (f"IncRandomStrategy(entry_prob={self.entry_probability}, "
+                f"exit_prob={self.exit_probability}, timeframe={self.timeframe}, "
+                f"mode={self._calculation_mode}, warmed_up={self._is_warmed_up}, "
+                f"bars={self._bar_count})") 

cycles/IncStrategies/test_random_strategy.py

@@ -0,0 +1,249 @@
+"""
+Test script for IncRandomStrategy
+
+This script tests the incremental random strategy to verify it works correctly
+and can generate signals incrementally with proper performance characteristics.
+"""
+
+import pandas as pd
+import numpy as np
+import time
+import logging
+from typing import List, Dict
+
+from .random_strategy import IncRandomStrategy
+
+# Configure logging
+logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(name)s - %(levelname)s - %(message)s')
+logger = logging.getLogger(__name__)
+
+
+def generate_test_data(num_points: int = 100) -> List[Dict[str, float]]:
+    """
+    Generate synthetic OHLCV data for testing.
+    
+    Args:
+        num_points: Number of data points to generate
+        
+    Returns:
+        List of OHLCV data dictionaries
+    """
+    np.random.seed(42)  # For reproducible test data
+    
+    data_points = []
+    base_price = 50000.0
+    
+    for i in range(num_points):
+        # Generate realistic OHLCV data with some volatility
+        price_change = np.random.normal(0, 100)  # Random walk with volatility
+        base_price += price_change
+        
+        # Ensure realistic OHLC relationships
+        open_price = base_price
+        high_price = open_price + abs(np.random.normal(0, 50))
+        low_price = open_price - abs(np.random.normal(0, 50))
+        close_price = open_price + np.random.normal(0, 30)
+        
+        # Ensure OHLC constraints
+        high_price = max(high_price, open_price, close_price)
+        low_price = min(low_price, open_price, close_price)
+        
+        volume = np.random.uniform(1000, 10000)
+        
+        data_points.append({
+            'open': open_price,
+            'high': high_price,
+            'low': low_price,
+            'close': close_price,
+            'volume': volume
+        })
+    
+    return data_points
+
+
+def test_inc_random_strategy():
+    """Test the IncRandomStrategy with synthetic data."""
+    logger.info("Starting IncRandomStrategy test...")
+    
+    # Create strategy with test parameters
+    strategy_params = {
+        "entry_probability": 0.2,  # Higher probability for testing
+        "exit_probability": 0.3,
+        "min_confidence": 0.7,
+        "max_confidence": 0.9,
+        "signal_frequency": 3,  # Generate signal every 3 bars
+        "random_seed": 42  # For reproducible results
+    }
+    
+    strategy = IncRandomStrategy(weight=1.0, params=strategy_params)
+    
+    # Generate test data
+    test_data = generate_test_data(50)
+    timestamps = pd.date_range(start='2024-01-01 09:00:00', periods=len(test_data), freq='1min')
+    
+    logger.info(f"Generated {len(test_data)} test data points")
+    logger.info(f"Strategy minimum buffer size: {strategy.get_minimum_buffer_size()}")
+    logger.info(f"Strategy supports incremental: {strategy.supports_incremental_calculation()}")
+    
+    # Track signals and performance
+    entry_signals = []
+    exit_signals = []
+    update_times = []
+    signal_times = []
+    
+    # Process data incrementally
+    for i, (data_point, timestamp) in enumerate(zip(test_data, timestamps)):
+        # Measure update time
+        start_time = time.perf_counter()
+        strategy.calculate_on_data(data_point, timestamp)
+        update_time = time.perf_counter() - start_time
+        update_times.append(update_time)
+        
+        # Generate signals
+        start_time = time.perf_counter()
+        entry_signal = strategy.get_entry_signal()
+        exit_signal = strategy.get_exit_signal()
+        signal_time = time.perf_counter() - start_time
+        signal_times.append(signal_time)
+        
+        # Track signals
+        if entry_signal.signal_type == "ENTRY":
+            entry_signals.append((i, entry_signal))
+            logger.info(f"Entry signal at index {i}: confidence={entry_signal.confidence:.2f}, "
+                       f"price=${entry_signal.price:.2f}")
+        
+        if exit_signal.signal_type == "EXIT":
+            exit_signals.append((i, exit_signal))
+            logger.info(f"Exit signal at index {i}: confidence={exit_signal.confidence:.2f}, "
+                       f"price=${exit_signal.price:.2f}, type={exit_signal.metadata.get('type')}")
+        
+        # Log progress every 10 points
+        if (i + 1) % 10 == 0:
+            logger.info(f"Processed {i + 1}/{len(test_data)} data points, "
+                       f"warmed_up={strategy.is_warmed_up}")
+    
+    # Performance analysis
+    avg_update_time = np.mean(update_times) * 1000  # Convert to milliseconds
+    max_update_time = np.max(update_times) * 1000
+    avg_signal_time = np.mean(signal_times) * 1000
+    max_signal_time = np.max(signal_times) * 1000
+    
+    logger.info("\n" + "="*50)
+    logger.info("TEST RESULTS")
+    logger.info("="*50)
+    logger.info(f"Total data points processed: {len(test_data)}")
+    logger.info(f"Entry signals generated: {len(entry_signals)}")
+    logger.info(f"Exit signals generated: {len(exit_signals)}")
+    logger.info(f"Strategy warmed up: {strategy.is_warmed_up}")
+    logger.info(f"Final calculation mode: {strategy.calculation_mode}")
+    
+    logger.info("\nPERFORMANCE METRICS:")
+    logger.info(f"Average update time: {avg_update_time:.3f} ms")
+    logger.info(f"Maximum update time: {max_update_time:.3f} ms")
+    logger.info(f"Average signal time: {avg_signal_time:.3f} ms")
+    logger.info(f"Maximum signal time: {max_signal_time:.3f} ms")
+    
+    # Performance targets check
+    target_update_time = 1.0  # 1ms target
+    target_signal_time = 10.0  # 10ms target
+    
+    logger.info("\nPERFORMANCE TARGET CHECK:")
+    logger.info(f"Update time target (<{target_update_time}ms): {'✅ PASS' if avg_update_time < target_update_time else '❌ FAIL'}")
+    logger.info(f"Signal time target (<{target_signal_time}ms): {'✅ PASS' if avg_signal_time < target_signal_time else '❌ FAIL'}")
+    
+    # State summary
+    state_summary = strategy.get_current_state_summary()
+    logger.info(f"\nFINAL STATE SUMMARY:")
+    for key, value in state_summary.items():
+        if key != 'performance_metrics':  # Skip detailed performance metrics
+            logger.info(f"  {key}: {value}")
+    
+    # Test state reset
+    logger.info("\nTesting state reset...")
+    strategy.reset_calculation_state()
+    logger.info(f"After reset - warmed_up: {strategy.is_warmed_up}, mode: {strategy.calculation_mode}")
+    
+    logger.info("\n✅ IncRandomStrategy test completed successfully!")
+    
+    return {
+        'entry_signals': len(entry_signals),
+        'exit_signals': len(exit_signals),
+        'avg_update_time_ms': avg_update_time,
+        'avg_signal_time_ms': avg_signal_time,
+        'performance_targets_met': avg_update_time < target_update_time and avg_signal_time < target_signal_time
+    }
+
+
+def test_strategy_comparison():
+    """Test that incremental strategy produces consistent results with same random seed."""
+    logger.info("\nTesting strategy consistency with same random seed...")
+    
+    # Create two strategies with same parameters and seed
+    params = {
+        "entry_probability": 0.15,
+        "exit_probability": 0.2,
+        "random_seed": 123
+    }
+    
+    strategy1 = IncRandomStrategy(weight=1.0, params=params)
+    strategy2 = IncRandomStrategy(weight=1.0, params=params)
+    
+    # Generate test data
+    test_data = generate_test_data(20)
+    timestamps = pd.date_range(start='2024-01-01 10:00:00', periods=len(test_data), freq='1min')
+    
+    signals1 = []
+    signals2 = []
+    
+    # Process same data with both strategies
+    for data_point, timestamp in zip(test_data, timestamps):
+        strategy1.calculate_on_data(data_point, timestamp)
+        strategy2.calculate_on_data(data_point, timestamp)
+        
+        entry1 = strategy1.get_entry_signal()
+        entry2 = strategy2.get_entry_signal()
+        
+        signals1.append(entry1.signal_type)
+        signals2.append(entry2.signal_type)
+    
+    # Check if signals are identical
+    signals_match = signals1 == signals2
+    logger.info(f"Signals consistency test: {'✅ PASS' if signals_match else '❌ FAIL'}")
+    
+    if not signals_match:
+        logger.warning("Signal mismatch detected:")
+        for i, (s1, s2) in enumerate(zip(signals1, signals2)):
+            if s1 != s2:
+                logger.warning(f"  Index {i}: Strategy1={s1}, Strategy2={s2}")
+    
+    return signals_match
+
+
+if __name__ == "__main__":
+    try:
+        # Run main test
+        test_results = test_inc_random_strategy()
+        
+        # Run consistency test
+        consistency_result = test_strategy_comparison()
+        
+        # Summary
+        logger.info("\n" + "="*60)
+        logger.info("OVERALL TEST SUMMARY")
+        logger.info("="*60)
+        logger.info(f"Main test completed: ✅")
+        logger.info(f"Performance targets met: {'✅' if test_results['performance_targets_met'] else '❌'}")
+        logger.info(f"Consistency test passed: {'✅' if consistency_result else '❌'}")
+        logger.info(f"Entry signals generated: {test_results['entry_signals']}")
+        logger.info(f"Exit signals generated: {test_results['exit_signals']}")
+        logger.info(f"Average update time: {test_results['avg_update_time_ms']:.3f} ms")
+        logger.info(f"Average signal time: {test_results['avg_signal_time_ms']:.3f} ms")
+        
+        if test_results['performance_targets_met'] and consistency_result:
+            logger.info("\n🎉 ALL TESTS PASSED! IncRandomStrategy is ready for use.")
+        else:
+            logger.warning("\n⚠️  Some tests failed. Review the results above.")
+            
+    except Exception as e:
+        logger.error(f"Test failed with error: {e}")
+        raise