4.0
- fixing data-type circular import - removing HOLD signal from saving to database to reduce data size
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Vasily.onl
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0d6bfea48a
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061b467d43
@ -201,6 +201,67 @@ class ProcessingStats:
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}
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class SignalType(str, Enum):
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"""
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Types of trading signals that strategies can generate.
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"""
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BUY = "buy"
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SELL = "sell"
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HOLD = "hold"
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ENTRY_LONG = "entry_long"
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EXIT_LONG = "exit_long"
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ENTRY_SHORT = "entry_short"
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EXIT_SHORT = "exit_short"
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STOP_LOSS = "stop_loss"
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TAKE_PROFIT = "take_profit"
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@dataclass
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class StrategySignal:
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"""
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Container for individual strategy signals.
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Attributes:
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timestamp: Signal timestamp (right-aligned with candle)
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symbol: Trading symbol
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timeframe: Candle timeframe
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signal_type: Type of signal (buy/sell/hold etc.)
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price: Price at which signal was generated
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confidence: Signal confidence score (0.0 to 1.0)
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metadata: Additional signal metadata (e.g., indicator values)
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"""
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timestamp: datetime
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symbol: str
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timeframe: str
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signal_type: SignalType
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price: float
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confidence: float = 1.0
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metadata: Optional[Dict[str, Any]] = None
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@dataclass
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class StrategyResult:
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"""
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Container for strategy calculation results.
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Attributes:
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timestamp: Candle timestamp (right-aligned)
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symbol: Trading symbol
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timeframe: Candle timeframe
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strategy_name: Name of the strategy that generated this result
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signals: List[StrategySignal]
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indicators_used: Dictionary of indicator values used in calculation
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metadata: Additional calculation metadata
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"""
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timestamp: datetime
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symbol: str
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timeframe: str
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strategy_name: str
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signals: List[StrategySignal]
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indicators_used: Dict[str, float]
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metadata: Optional[Dict[str, Any]] = None
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# Re-export from base_collector for convenience
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__all__ = [
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'DataType',
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@ -8,7 +8,7 @@ from sqlalchemy import desc, and_, func
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from sqlalchemy.orm import joinedload
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from ..models import StrategySignal, StrategyRun
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from strategies.data_types import StrategySignal as StrategySignalDataType, StrategyResult
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from data.common.data_types import StrategySignal as StrategySignalDataType, StrategyResult
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from .base_repository import BaseRepository, DatabaseOperationError
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@ -95,23 +95,25 @@ class StrategyRepository(BaseRepository):
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with self.get_session() as session:
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for result in strategy_results:
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for signal in result.signals:
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strategy_signal = StrategySignal(
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run_id=run_id,
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strategy_name=result.strategy_name,
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strategy_config=None, # Could be populated from StrategyRun.config
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symbol=signal.symbol,
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timeframe=signal.timeframe,
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timestamp=signal.timestamp,
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signal_type=signal.signal_type.value,
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price=Decimal(str(signal.price)),
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confidence=Decimal(str(signal.confidence)),
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signal_metadata={
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'indicators_used': result.indicators_used,
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'metadata': signal.metadata or {}
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}
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)
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session.add(strategy_signal)
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signals_stored += 1
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# Only store BUY or SELL signals, not HOLD
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if signal.signal_type.value in ["BUY", "SELL"]:
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strategy_signal = StrategySignal(
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run_id=run_id,
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strategy_name=result.strategy_name,
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strategy_config=None, # Could be populated from StrategyRun.config
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symbol=signal.symbol,
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timeframe=signal.timeframe,
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timestamp=signal.timestamp,
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signal_type=signal.signal_type.value,
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price=Decimal(str(signal.price)),
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confidence=Decimal(str(signal.confidence)),
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signal_metadata={
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'indicators_used': result.indicators_used,
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'metadata': signal.metadata or {}
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}
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)
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session.add(strategy_signal)
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signals_stored += 1
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session.commit()
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self.log_info(f"Stored {signals_stored} strategy signals for run {run_id}")
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@ -14,7 +14,7 @@ IMPORTANT: Mirrors Indicator Patterns
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from .base import BaseStrategy
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from .factory import StrategyFactory
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from .data_types import StrategySignal, SignalType, StrategyResult
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from data.common.data_types import StrategySignal, SignalType, StrategyResult
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from .manager import StrategyManager, StrategyConfig, StrategyType, StrategyCategory, get_strategy_manager
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from .data_integration import StrategyDataIntegrator, StrategyDataIntegrationConfig, get_strategy_data_integrator
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from .validation import StrategySignalValidator, ValidationConfig
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@ -20,7 +20,7 @@ from data.common.data_types import OHLCVCandle
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from data.common.indicators import TechnicalIndicators
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from components.charts.config.indicator_defs import convert_database_candles_to_ohlcv
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from .factory import StrategyFactory
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from .data_types import StrategyResult
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from data.common.data_types import StrategyResult
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from utils.logger import get_logger
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# Initialize logger
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@ -1,72 +0,0 @@
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"""
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Strategy Data Types and Signal Definitions
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This module provides data types for strategy calculations, signals,
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and results in a standardized format.
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"""
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from dataclasses import dataclass
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from datetime import datetime
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from typing import Dict, Optional, Any, List
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from enum import Enum
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class SignalType(str, Enum):
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"""
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Types of trading signals that strategies can generate.
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"""
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BUY = "buy"
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SELL = "sell"
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HOLD = "hold"
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ENTRY_LONG = "entry_long"
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EXIT_LONG = "exit_long"
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ENTRY_SHORT = "entry_short"
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EXIT_SHORT = "exit_short"
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STOP_LOSS = "stop_loss"
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TAKE_PROFIT = "take_profit"
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@dataclass
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class StrategySignal:
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"""
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Container for individual strategy signals.
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Attributes:
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timestamp: Signal timestamp (right-aligned with candle)
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symbol: Trading symbol
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timeframe: Candle timeframe
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signal_type: Type of signal (buy/sell/hold etc.)
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price: Price at which signal was generated
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confidence: Signal confidence score (0.0 to 1.0)
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metadata: Additional signal metadata (e.g., indicator values)
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"""
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timestamp: datetime
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symbol: str
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timeframe: str
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signal_type: SignalType
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price: float
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confidence: float = 1.0
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metadata: Optional[Dict[str, Any]] = None
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@dataclass
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class StrategyResult:
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"""
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Container for strategy calculation results.
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Attributes:
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timestamp: Candle timestamp (right-aligned)
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symbol: Trading symbol
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timeframe: Candle timeframe
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strategy_name: Name of the strategy that generated this result
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signals: List of signals generated for this timestamp
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indicators_used: Dictionary of indicator values used in calculation
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metadata: Additional calculation metadata
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"""
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timestamp: datetime
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symbol: str
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timeframe: str
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strategy_name: str
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signals: List[StrategySignal]
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indicators_used: Dict[str, float]
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metadata: Optional[Dict[str, Any]] = None
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@ -207,25 +207,31 @@ class StrategySignalBroadcaster:
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result = signal.strategy_result
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context = signal.context
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signal_data.append({
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'strategy_name': context.strategy_name,
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'strategy_config': context.strategy_config,
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'symbol': context.symbol,
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'timeframe': context.timeframe,
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'exchange': context.exchange,
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'timestamp': result.timestamp,
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'signal_type': result.signal.signal_type.value if result.signal else 'HOLD',
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'price': float(result.price) if result.price else None,
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'confidence': result.confidence,
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'signal_metadata': result.metadata or {},
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'generation_time': signal.generation_time
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})
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# Only store BUY or SELL signals, not HOLD
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if result.signal and result.signal.signal_type in [SignalType.BUY, SignalType.SELL]:
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signal_data.append({
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'strategy_name': context.strategy_name,
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'strategy_config': context.strategy_config,
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'symbol': context.symbol,
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'timeframe': context.timeframe,
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'exchange': context.exchange,
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'timestamp': result.timestamp,
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'signal_type': result.signal.signal_type.value,
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'price': float(result.price) if result.price else None,
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'confidence': result.confidence,
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'signal_metadata': result.metadata or {},
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'generation_time': signal.generation_time
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})
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# Batch insert into database
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self.db_ops.strategy.store_signals_batch(signal_data)
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if self.logger:
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self.logger.debug(f"Stored batch of {len(signals)} real-time signals")
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if signal_data: # Only call store_signals_batch if there are signals to store
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# Batch insert into database
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self.db_ops.strategy.store_signals_batch(signal_data)
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if self.logger:
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self.logger.debug(f"Stored batch of {len(signal_data)} real-time signals")
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else:
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if self.logger:
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self.logger.debug("No BUY/SELL signals to store in this batch.")
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except Exception as e:
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if self.logger:
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@ -102,6 +102,11 @@
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- **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.
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- **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.
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### 11. Exclusion of "HOLD" Signals from All Signal Storage
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- **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.
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- **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.
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- **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.
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## Tasks
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- [x] 1.0 Core Strategy Foundation Setup
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