Implement comprehensive transformation module with safety limits and validations
- Introduced a new transformation module that includes safety limits for trade operations, enhancing data integrity and preventing errors. - Refactored existing transformation logic into dedicated classes and functions, improving modularity and maintainability. - Added detailed validation for trade sizes, prices, and symbol formats, ensuring compliance with trading rules. - Implemented logging for significant operations and validation checks, aiding in monitoring and debugging. - Created a changelog to document the new features and changes, providing clarity for future development. - Developed extensive unit tests to cover the new functionality, ensuring reliability and preventing regressions. These changes significantly enhance the architecture of the transformation module, making it more robust and easier to manage.
This commit is contained in:
Ajasra
29
data/common/transformation/__init__.py
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29
data/common/transformation/__init__.py
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@@ -0,0 +1,29 @@
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"""
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Common data transformation utilities for all exchanges.
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This package provides common transformation patterns and base classes
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for converting exchange-specific data to standardized formats.
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"""
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from .base import BaseDataTransformer
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from .unified import UnifiedDataTransformer
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from .trade import create_standardized_trade, batch_create_standardized_trades
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from .time_utils import timestamp_to_datetime
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from .numeric_utils import safe_decimal_conversion
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from .normalization import normalize_trade_side, validate_symbol_format
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__all__ = [
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# Base classes
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'BaseDataTransformer',
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'UnifiedDataTransformer',
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# Trade transformation
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'create_standardized_trade',
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'batch_create_standardized_trades',
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# Utility functions
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'timestamp_to_datetime',
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'safe_decimal_conversion',
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'normalize_trade_side',
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'validate_symbol_format'
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]
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228
data/common/transformation/base.py
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228
data/common/transformation/base.py
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@@ -0,0 +1,228 @@
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"""
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Base data transformer class.
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This module provides the base class for all data transformers
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with common functionality and interface definitions.
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"""
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import logging
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from typing import Dict, Any, Optional, List
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from datetime import datetime
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from decimal import Decimal
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from ..data_types import StandardizedTrade
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from .trade import create_standardized_trade, batch_create_standardized_trades
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from .time_utils import timestamp_to_datetime
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from .numeric_utils import safe_decimal_conversion
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from .normalization import normalize_trade_side, validate_symbol_format
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class BaseDataTransformer:
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"""Base class for all data transformers."""
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def __init__(
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self,
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exchange: str,
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component_name: str = "base_transformer",
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logger: Optional[logging.Logger] = None
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):
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"""
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Initialize base transformer.
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Args:
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exchange: Exchange name
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component_name: Component name for logging
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logger: Optional logger instance
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"""
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self.exchange = exchange
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self.component_name = component_name
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self.logger = logger or logging.getLogger(component_name)
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def timestamp_to_datetime(
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self,
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timestamp: Any,
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is_milliseconds: bool = True
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) -> datetime:
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"""Convert timestamp to datetime."""
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return timestamp_to_datetime(
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timestamp,
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is_milliseconds,
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logger=self.logger,
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component_name=self.component_name
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)
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def safe_decimal_conversion(
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self,
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value: Any,
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field_name: str = "value"
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) -> Optional[Decimal]:
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"""Convert value to Decimal safely."""
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return safe_decimal_conversion(
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value,
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field_name,
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logger=self.logger,
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component_name=self.component_name
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)
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def normalize_trade_side(
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self,
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side: str
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) -> str:
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"""Normalize trade side."""
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try:
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return normalize_trade_side(
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side,
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logger=self.logger,
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component_name=self.component_name
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)
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except ValueError as e:
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self.logger.warning(
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f"{self.component_name}: Unknown trade side: {side}, defaulting to 'buy'"
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)
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return 'buy'
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def validate_symbol_format(
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self,
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symbol: str
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) -> str:
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"""Validate symbol format."""
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return validate_symbol_format(
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symbol,
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logger=self.logger,
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component_name=self.component_name
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)
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def get_transformer_info(self) -> Dict[str, Any]:
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"""Get transformer information."""
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return {
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"exchange": self.exchange,
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"component": self.component_name,
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"capabilities": {
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"trade_transformation": True,
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"orderbook_transformation": True,
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"ticker_transformation": True,
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"batch_processing": True
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}
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}
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def transform_trade_data(
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self,
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raw_data: Dict[str, Any],
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symbol: str
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) -> StandardizedTrade:
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"""
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Transform raw trade data to standardized format.
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Args:
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raw_data: Raw trade data
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symbol: Trading symbol
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Returns:
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StandardizedTrade object
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Raises:
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ValueError: If data is invalid
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"""
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raise NotImplementedError("Subclasses must implement transform_trade_data")
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def transform_orderbook_data(
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self,
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raw_data: Dict[str, Any],
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symbol: str
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) -> Dict[str, Any]:
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"""
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Transform raw orderbook data to standardized format.
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Args:
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raw_data: Raw orderbook data
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symbol: Trading symbol
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Returns:
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Standardized orderbook data
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Raises:
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ValueError: If data is invalid
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"""
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raise NotImplementedError("Subclasses must implement transform_orderbook_data")
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def transform_ticker_data(
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self,
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raw_data: Dict[str, Any],
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symbol: str
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) -> Dict[str, Any]:
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"""
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Transform raw ticker data to standardized format.
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Args:
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raw_data: Raw ticker data
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symbol: Trading symbol
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Returns:
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Standardized ticker data
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Raises:
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ValueError: If data is invalid
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"""
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raise NotImplementedError("Subclasses must implement transform_ticker_data")
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def batch_transform_trades(
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self,
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raw_trades: List[Dict[str, Any]],
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symbol: str
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) -> List[StandardizedTrade]:
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"""
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Transform multiple trades in batch.
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Args:
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raw_trades: List of raw trade data
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symbol: Trading symbol
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Returns:
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List of StandardizedTrade objects
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Raises:
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ValueError: If data is invalid
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"""
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return [
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self.transform_trade_data(trade, symbol)
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for trade in raw_trades
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]
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def transform_trades_batch(
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self,
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raw_trades: List[Dict[str, Any]],
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symbol: str,
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field_mapping: Dict[str, str]
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) -> List[StandardizedTrade]:
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"""
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Transform a batch of raw trades.
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Args:
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raw_trades: List of raw trade dictionaries
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symbol: Trading symbol
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field_mapping: Field mapping for raw data
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Returns:
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List of StandardizedTrade objects
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"""
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return batch_create_standardized_trades(
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raw_trades=raw_trades,
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symbol=symbol,
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exchange=self.exchange,
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field_mapping=field_mapping
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)
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def _log_error(self, message: str, error: Optional[Exception] = None) -> None:
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"""Log error with component context."""
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if error:
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self.logger.error(f"{self.component_name}: {message}: {error}")
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else:
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self.logger.error(f"{self.component_name}: {message}")
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def _log_warning(self, message: str) -> None:
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"""Log warning with component context."""
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self.logger.warning(f"{self.component_name}: {message}")
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def _log_info(self, message: str) -> None:
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"""Log info with component context."""
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self.logger.info(f"{self.component_name}: {message}")
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129
data/common/transformation/normalization.py
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129
data/common/transformation/normalization.py
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@@ -0,0 +1,129 @@
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"""
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Data normalization utilities.
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This module provides functions for normalizing various data formats
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to consistent standards across the application.
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"""
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from typing import Optional
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from logging import Logger
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def normalize_trade_side(
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side: str,
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logger: Optional[Logger] = None,
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component_name: str = "normalization"
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) -> str:
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"""
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Normalize trade side to standard format.
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Args:
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side: Raw trade side string
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logger: Optional logger for error messages
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component_name: Name for logging
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Returns:
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Normalized side ('buy' or 'sell')
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Raises:
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ValueError: If side is invalid, empty, or unknown
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"""
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if not side or not isinstance(side, str):
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error_msg = f"Invalid trade side: {side}"
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if logger:
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logger.error(f"{component_name}: {error_msg}")
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raise ValueError(error_msg)
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normalized = side.lower().strip()
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# Handle common variations
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if normalized in ['buy', 'bid', 'b', '1']:
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return 'buy'
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elif normalized in ['sell', 'ask', 's', '0', '2']:
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return 'sell'
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else:
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error_msg = f"Invalid trade side: {side}"
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if logger:
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logger.error(f"{component_name}: {error_msg}")
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raise ValueError(error_msg)
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def validate_symbol_format(
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symbol: str,
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logger: Optional[Logger] = None,
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component_name: str = "normalization"
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) -> str:
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"""
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Validate and normalize symbol format.
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Args:
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symbol: Trading symbol
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logger: Optional logger for error messages
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component_name: Name for logging
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Returns:
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Normalized symbol
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Raises:
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ValueError: If symbol is invalid
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"""
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if not symbol or not isinstance(symbol, str):
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error_msg = f"Invalid symbol: {symbol}"
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if logger:
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logger.error(f"{component_name}: {error_msg}")
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raise ValueError(error_msg)
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# Remove whitespace and convert to uppercase
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normalized = symbol.strip().upper()
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# Basic validation
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if not normalized or len(normalized) < 3:
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error_msg = f"Symbol too short: {symbol}"
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if logger:
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logger.error(f"{component_name}: {error_msg}")
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raise ValueError(error_msg)
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# Check for common delimiters
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if '-' not in normalized and '/' not in normalized:
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error_msg = f"Invalid symbol format (missing delimiter): {symbol}"
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if logger:
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logger.error(f"{component_name}: {error_msg}")
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raise ValueError(error_msg)
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return normalized
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def normalize_exchange_name(
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exchange: str,
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logger: Optional[Logger] = None,
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component_name: str = "normalization"
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) -> str:
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"""
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Normalize exchange name.
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Args:
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exchange: Exchange name
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logger: Optional logger for error messages
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component_name: Name for logging
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Returns:
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Normalized exchange name
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Raises:
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ValueError: If exchange name is invalid
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"""
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if not exchange or not isinstance(exchange, str):
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error_msg = f"Invalid exchange name: {exchange}"
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if logger:
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logger.error(f"{component_name}: {error_msg}")
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raise ValueError(error_msg)
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normalized = exchange.lower().strip()
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if not normalized:
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error_msg = "Exchange name cannot be empty"
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if logger:
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logger.error(f"{component_name}: {error_msg}")
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raise ValueError(error_msg)
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return normalized
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68
data/common/transformation/numeric_utils.py
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68
data/common/transformation/numeric_utils.py
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@@ -0,0 +1,68 @@
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"""
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Numeric transformation utilities.
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This module provides functions for handling numeric conversions and validations
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in a consistent way across the application.
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"""
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from decimal import Decimal
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from typing import Any, Optional
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from logging import Logger
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def safe_decimal_conversion(
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value: Any,
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field_name: str = "value",
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logger: Optional[Logger] = None,
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component_name: str = "numeric_utils"
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) -> Optional[Decimal]:
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"""
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Safely convert value to Decimal with error handling.
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Args:
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value: Value to convert
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field_name: Name of field for error logging
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logger: Optional logger for error messages
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component_name: Name for logging
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Returns:
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Decimal value or None if conversion failed
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"""
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try:
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if value is None or value == "":
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return None
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return Decimal(str(value))
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except Exception as e:
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if logger:
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logger.warning(f"{component_name}: Failed to convert {field_name} '{value}' to Decimal: {e}")
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return None
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def validate_numeric_range(
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value: Decimal,
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min_value: Optional[Decimal] = None,
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max_value: Optional[Decimal] = None,
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field_name: str = "value"
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) -> bool:
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"""
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Validate that a numeric value falls within specified range.
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Args:
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value: Value to validate
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min_value: Optional minimum value
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max_value: Optional maximum value
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field_name: Name of field for error messages
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Returns:
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True if value is within range, False otherwise
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Raises:
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ValueError: If value is outside allowed range
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"""
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if min_value is not None and value < min_value:
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raise ValueError(f"{field_name} {value} is below minimum allowed value {min_value}")
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if max_value is not None and value > max_value:
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raise ValueError(f"{field_name} {value} exceeds maximum allowed value {max_value}")
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return True
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191
data/common/transformation/safety.py
Normal file
191
data/common/transformation/safety.py
Normal file
@@ -0,0 +1,191 @@
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"""
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Trading safety limits and validations.
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This module provides safety checks and limits for crypto trading operations
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with reasonable defaults that won't interfere with normal operations.
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"""
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from decimal import Decimal
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from typing import Dict, NamedTuple, Optional, Pattern, Set
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import re
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import logging
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# Common patterns for crypto trading pairs
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SYMBOL_PATTERN = re.compile(r'^[A-Z0-9]{2,10}[-/][A-Z0-9]{2,10}$')
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MAX_SYMBOL_LENGTH = 20 # Longest known pair + margin for future
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class TradeLimits(NamedTuple):
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"""Trading limits for a symbol."""
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min_size: Decimal # Minimum trade size in base currency
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max_size: Decimal # Maximum trade size in base currency
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min_notional: Decimal # Minimum trade value in quote currency
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max_notional: Decimal # Maximum trade value in quote currency
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price_precision: int # Number of decimal places for price
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size_precision: int # Number of decimal places for size
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max_price_deviation: Decimal # Maximum allowed deviation from market price (in percent)
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# Default limits that are generous but still protect against extreme errors
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DEFAULT_LIMITS = TradeLimits(
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min_size=Decimal('0.00000001'), # 1 satoshi equivalent
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max_size=Decimal('10000.0'), # Large enough for most trades
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min_notional=Decimal('1.0'), # Minimum $1 equivalent
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max_notional=Decimal('10000000.0'), # $10M per trade limit
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price_precision=8, # Standard for most exchanges
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size_precision=8, # Standard for most exchanges
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max_price_deviation=Decimal('30.0') # 30% max deviation
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)
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# Common stablecoin pairs can have higher limits
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STABLECOIN_LIMITS = DEFAULT_LIMITS._replace(
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max_size=Decimal('1000000.0'), # $1M equivalent
|
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max_notional=Decimal('50000000.0'), # $50M per trade
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||||
max_price_deviation=Decimal('5.0') # 5% max deviation for stables
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)
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# More restrictive limits for volatile/illiquid pairs
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VOLATILE_LIMITS = DEFAULT_LIMITS._replace(
|
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max_size=Decimal('1000.0'), # Smaller position size
|
||||
max_notional=Decimal('1000000.0'), # $1M per trade
|
||||
max_price_deviation=Decimal('50.0') # 50% for very volatile markets
|
||||
)
|
||||
|
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# Known stablecoin symbols
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||||
STABLECOINS = {'USDT', 'USDC', 'DAI', 'BUSD', 'UST', 'TUSD'}
|
||||
|
||||
def is_stablecoin_pair(symbol: str) -> bool:
|
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"""Check if the trading pair involves a stablecoin."""
|
||||
parts = re.split('[-/]', symbol.upper())
|
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return any(coin in STABLECOINS for coin in parts)
|
||||
|
||||
def get_trade_limits(symbol: str) -> TradeLimits:
|
||||
"""
|
||||
Get appropriate trade limits for a symbol.
|
||||
|
||||
Args:
|
||||
symbol: Trading pair symbol
|
||||
|
||||
Returns:
|
||||
TradeLimits with appropriate limits for the symbol
|
||||
"""
|
||||
if is_stablecoin_pair(symbol):
|
||||
return STABLECOIN_LIMITS
|
||||
return VOLATILE_LIMITS
|
||||
|
||||
def validate_trade_size(
|
||||
size: Decimal,
|
||||
price: Decimal,
|
||||
symbol: str,
|
||||
logger: Optional[logging.Logger] = None
|
||||
) -> None:
|
||||
"""
|
||||
Validate trade size against limits.
|
||||
|
||||
Args:
|
||||
size: Trade size in base currency
|
||||
price: Trade price
|
||||
symbol: Trading pair symbol
|
||||
logger: Optional logger for warnings
|
||||
|
||||
Raises:
|
||||
ValueError: If size violates limits
|
||||
"""
|
||||
limits = get_trade_limits(symbol)
|
||||
notional = size * price
|
||||
|
||||
# Check minimum size
|
||||
if size < limits.min_size:
|
||||
raise ValueError(
|
||||
f"Trade size {size} below minimum {limits.min_size} for {symbol}"
|
||||
)
|
||||
|
||||
# Check maximum size with warning at 90%
|
||||
if size > limits.max_size * Decimal('0.9') and logger:
|
||||
logger.warning(
|
||||
f"Large trade size {size} approaching maximum {limits.max_size} for {symbol}"
|
||||
)
|
||||
if size > limits.max_size:
|
||||
raise ValueError(
|
||||
f"Trade size {size} exceeds maximum {limits.max_size} for {symbol}"
|
||||
)
|
||||
|
||||
# Check minimum notional
|
||||
if notional < limits.min_notional:
|
||||
raise ValueError(
|
||||
f"Trade value ${notional} below minimum ${limits.min_notional} for {symbol}"
|
||||
)
|
||||
|
||||
# Check maximum notional with warning at 90%
|
||||
if notional > limits.max_notional * Decimal('0.9') and logger:
|
||||
logger.warning(
|
||||
f"Large trade value ${notional} approaching maximum ${limits.max_notional} for {symbol}"
|
||||
)
|
||||
if notional > limits.max_notional:
|
||||
raise ValueError(
|
||||
f"Trade value ${notional} exceeds maximum ${limits.max_notional} for {symbol}"
|
||||
)
|
||||
|
||||
def validate_trade_price(
|
||||
price: Decimal,
|
||||
market_price: Optional[Decimal],
|
||||
symbol: str,
|
||||
logger: Optional[logging.Logger] = None
|
||||
) -> None:
|
||||
"""
|
||||
Validate trade price against limits and market price.
|
||||
|
||||
Args:
|
||||
price: Trade price
|
||||
market_price: Current market price (if available)
|
||||
symbol: Trading pair symbol
|
||||
logger: Optional logger for warnings
|
||||
|
||||
Raises:
|
||||
ValueError: If price violates limits
|
||||
"""
|
||||
limits = get_trade_limits(symbol)
|
||||
|
||||
# Skip market price check if not available
|
||||
if market_price is None:
|
||||
return
|
||||
|
||||
# Calculate price deviation
|
||||
deviation = abs(price - market_price) / market_price * 100
|
||||
|
||||
# Warn at 80% of maximum deviation
|
||||
if deviation > limits.max_price_deviation * Decimal('0.8') and logger:
|
||||
logger.warning(
|
||||
f"Price deviation {deviation}% approaching maximum {limits.max_price_deviation}% for {symbol}"
|
||||
)
|
||||
|
||||
# Error at maximum deviation
|
||||
if deviation > limits.max_price_deviation:
|
||||
raise ValueError(
|
||||
f"Price deviation {deviation}% exceeds maximum {limits.max_price_deviation}% for {symbol}"
|
||||
)
|
||||
|
||||
def validate_symbol_format(
|
||||
symbol: str,
|
||||
logger: Optional[logging.Logger] = None
|
||||
) -> None:
|
||||
"""
|
||||
Validate trading symbol format.
|
||||
|
||||
Args:
|
||||
symbol: Trading pair symbol
|
||||
logger: Optional logger for warnings
|
||||
|
||||
Raises:
|
||||
ValueError: If symbol format is invalid
|
||||
"""
|
||||
if not symbol or not isinstance(symbol, str):
|
||||
raise ValueError(f"Invalid symbol: {symbol}")
|
||||
|
||||
# Check length
|
||||
if len(symbol) > MAX_SYMBOL_LENGTH:
|
||||
raise ValueError(f"Symbol too long: {symbol}")
|
||||
|
||||
# Check format
|
||||
if not SYMBOL_PATTERN.match(symbol.upper()):
|
||||
raise ValueError(
|
||||
f"Invalid symbol format: {symbol}. Expected format: 'XXX-YYY' or 'XXX/YYY'"
|
||||
)
|
||||
52
data/common/transformation/time_utils.py
Normal file
52
data/common/transformation/time_utils.py
Normal file
@@ -0,0 +1,52 @@
|
||||
"""
|
||||
Time-related transformation utilities.
|
||||
|
||||
This module provides functions for handling timestamps and datetime conversions
|
||||
in a consistent way across the application.
|
||||
"""
|
||||
|
||||
from datetime import datetime, timezone
|
||||
from typing import Any, Optional
|
||||
from logging import Logger
|
||||
|
||||
|
||||
def timestamp_to_datetime(
|
||||
timestamp: Any,
|
||||
is_milliseconds: bool = True,
|
||||
logger: Optional[Logger] = None,
|
||||
component_name: str = "time_utils"
|
||||
) -> datetime:
|
||||
"""
|
||||
Convert various timestamp formats to timezone-aware datetime.
|
||||
|
||||
Args:
|
||||
timestamp: Timestamp in various formats
|
||||
is_milliseconds: True if timestamp is in milliseconds
|
||||
logger: Optional logger for error messages
|
||||
component_name: Name for logging
|
||||
|
||||
Returns:
|
||||
Timezone-aware datetime object
|
||||
"""
|
||||
try:
|
||||
# Convert to int/float
|
||||
if isinstance(timestamp, str):
|
||||
timestamp_num = float(timestamp)
|
||||
elif isinstance(timestamp, (int, float)):
|
||||
timestamp_num = float(timestamp)
|
||||
else:
|
||||
raise ValueError(f"Invalid timestamp type: {type(timestamp)}")
|
||||
|
||||
# Convert to seconds if needed
|
||||
if is_milliseconds:
|
||||
timestamp_num = timestamp_num / 1000
|
||||
|
||||
# Create timezone-aware datetime
|
||||
dt = datetime.fromtimestamp(timestamp_num, tz=timezone.utc)
|
||||
return dt
|
||||
|
||||
except Exception as e:
|
||||
if logger:
|
||||
logger.error(f"{component_name}: Error converting timestamp {timestamp}: {e}")
|
||||
# Return current time as fallback
|
||||
return datetime.now(timezone.utc)
|
||||
360
data/common/transformation/trade.py
Normal file
360
data/common/transformation/trade.py
Normal file
@@ -0,0 +1,360 @@
|
||||
"""
|
||||
Trade data transformation with safety limits.
|
||||
|
||||
This module handles the transformation of trade data while enforcing safety limits
|
||||
to prevent errors and protect against edge cases.
|
||||
"""
|
||||
|
||||
import logging
|
||||
from datetime import datetime, timezone
|
||||
from decimal import Decimal, InvalidOperation
|
||||
from typing import Dict, List, Optional, Any
|
||||
|
||||
from ..data_types import StandardizedTrade
|
||||
from .time_utils import timestamp_to_datetime
|
||||
from .numeric_utils import safe_decimal_conversion
|
||||
from .normalization import normalize_trade_side, validate_symbol_format, normalize_exchange_name
|
||||
from .safety import (
|
||||
validate_trade_size,
|
||||
validate_trade_price,
|
||||
TradeLimits,
|
||||
get_trade_limits
|
||||
)
|
||||
|
||||
|
||||
# Create a logger for this module
|
||||
logger = logging.getLogger(__name__)
|
||||
|
||||
|
||||
def create_standardized_trade(
|
||||
symbol: str,
|
||||
trade_id: str,
|
||||
price: Any,
|
||||
size: Any,
|
||||
side: str,
|
||||
timestamp: Any,
|
||||
exchange: str,
|
||||
raw_data: Optional[Dict[str, Any]] = None,
|
||||
is_milliseconds: bool = True
|
||||
) -> StandardizedTrade:
|
||||
"""
|
||||
Utility function to create StandardizedTrade with proper validation.
|
||||
|
||||
Args:
|
||||
symbol: Trading symbol
|
||||
trade_id: Trade identifier
|
||||
price: Trade price (any numeric type)
|
||||
size: Trade size (any numeric type)
|
||||
side: Trade side ('buy' or 'sell')
|
||||
timestamp: Trade timestamp
|
||||
exchange: Exchange name
|
||||
raw_data: Original raw data
|
||||
is_milliseconds: True if timestamp is in milliseconds
|
||||
|
||||
Returns:
|
||||
StandardizedTrade object
|
||||
|
||||
Raises:
|
||||
ValueError: If data is invalid
|
||||
"""
|
||||
# Validate symbol
|
||||
if not symbol or not isinstance(symbol, str):
|
||||
raise ValueError(f"Invalid symbol: {symbol}")
|
||||
|
||||
# Validate trade_id
|
||||
if not trade_id:
|
||||
raise ValueError(f"Invalid trade_id: {trade_id}")
|
||||
|
||||
# Convert timestamp
|
||||
try:
|
||||
if isinstance(timestamp, (int, float, str)):
|
||||
dt = timestamp_to_datetime(timestamp, is_milliseconds)
|
||||
elif isinstance(timestamp, datetime):
|
||||
dt = timestamp
|
||||
if dt.tzinfo is None:
|
||||
dt = dt.replace(tzinfo=timezone.utc)
|
||||
else:
|
||||
raise ValueError(f"Invalid timestamp type: {type(timestamp)}")
|
||||
except Exception as e:
|
||||
raise ValueError(f"Invalid timestamp: {timestamp}") from e
|
||||
|
||||
# Convert price and size to Decimal
|
||||
try:
|
||||
if not price or not size:
|
||||
raise ValueError("Price and size must not be empty")
|
||||
|
||||
decimal_price = safe_decimal_conversion(price, "price")
|
||||
decimal_size = safe_decimal_conversion(size, "size")
|
||||
|
||||
if decimal_price is None or decimal_size is None:
|
||||
raise ValueError("Invalid price or size format")
|
||||
|
||||
if decimal_price <= 0:
|
||||
raise ValueError(f"Price must be positive: {price}")
|
||||
if decimal_size <= 0:
|
||||
raise ValueError(f"Size must be positive: {size}")
|
||||
|
||||
except (InvalidOperation, TypeError, ValueError) as e:
|
||||
raise ValueError(f"Invalid price or size: {e}")
|
||||
|
||||
# Normalize side with strict validation
|
||||
try:
|
||||
if not side or not isinstance(side, str):
|
||||
raise ValueError(f"Invalid trade side: {side}")
|
||||
|
||||
normalized_side = normalize_trade_side(side, logger=logger)
|
||||
except ValueError as e:
|
||||
logger.error(f"Trade side validation failed: {e}")
|
||||
raise ValueError(f"Invalid trade side: {side}")
|
||||
|
||||
# Normalize symbol and exchange
|
||||
try:
|
||||
normalized_symbol = validate_symbol_format(symbol)
|
||||
normalized_exchange = normalize_exchange_name(exchange)
|
||||
except ValueError as e:
|
||||
raise ValueError(str(e))
|
||||
|
||||
return StandardizedTrade(
|
||||
symbol=normalized_symbol,
|
||||
trade_id=str(trade_id),
|
||||
price=decimal_price,
|
||||
size=decimal_size,
|
||||
side=normalized_side,
|
||||
timestamp=dt,
|
||||
exchange=normalized_exchange,
|
||||
raw_data=raw_data
|
||||
)
|
||||
|
||||
|
||||
def batch_create_standardized_trades(
|
||||
raw_trades: List[Dict[str, Any]],
|
||||
symbol: str,
|
||||
exchange: str,
|
||||
field_mapping: Dict[str, str],
|
||||
is_milliseconds: bool = True
|
||||
) -> List[StandardizedTrade]:
|
||||
"""
|
||||
Batch create standardized trades from raw data.
|
||||
|
||||
Args:
|
||||
raw_trades: List of raw trade dictionaries
|
||||
symbol: Trading symbol
|
||||
exchange: Exchange name
|
||||
field_mapping: Mapping of StandardizedTrade fields to raw data fields
|
||||
is_milliseconds: True if timestamps are in milliseconds
|
||||
|
||||
Returns:
|
||||
List of successfully created StandardizedTrade objects
|
||||
|
||||
Example field_mapping:
|
||||
{
|
||||
'trade_id': 'id',
|
||||
'price': 'px',
|
||||
'size': 'sz',
|
||||
'side': 'side',
|
||||
'timestamp': 'ts'
|
||||
}
|
||||
"""
|
||||
trades = []
|
||||
|
||||
for raw_trade in raw_trades:
|
||||
try:
|
||||
trade = create_standardized_trade(
|
||||
symbol=symbol,
|
||||
trade_id=raw_trade[field_mapping['trade_id']],
|
||||
price=raw_trade[field_mapping['price']],
|
||||
size=raw_trade[field_mapping['size']],
|
||||
side=raw_trade[field_mapping['side']],
|
||||
timestamp=raw_trade[field_mapping['timestamp']],
|
||||
exchange=exchange,
|
||||
raw_data=raw_trade,
|
||||
is_milliseconds=is_milliseconds
|
||||
)
|
||||
trades.append(trade)
|
||||
except Exception as e:
|
||||
# Log error but continue processing
|
||||
print(f"Failed to transform trade: {e}")
|
||||
|
||||
return trades
|
||||
|
||||
|
||||
class TradeTransformer:
|
||||
"""Transform trade data with safety checks."""
|
||||
|
||||
VALID_SIDES = {'buy', 'sell'}
|
||||
|
||||
def __init__(self, market_data_provider: Optional[Any] = None):
|
||||
"""
|
||||
Initialize transformer.
|
||||
|
||||
Args:
|
||||
market_data_provider: Optional provider of market data for price validation
|
||||
"""
|
||||
self.market_data_provider = market_data_provider
|
||||
|
||||
def normalize_trade_side(self, side: str) -> str:
|
||||
"""
|
||||
Normalize trade side to standard format.
|
||||
|
||||
Args:
|
||||
side: Trade side indicator
|
||||
|
||||
Returns:
|
||||
Normalized trade side ('buy' or 'sell')
|
||||
|
||||
Raises:
|
||||
ValueError: If side is invalid
|
||||
"""
|
||||
side_lower = str(side).lower().strip()
|
||||
|
||||
# Handle common variations
|
||||
if side_lower in {'buy', 'bid', 'long', '1', 'true'}:
|
||||
return 'buy'
|
||||
elif side_lower in {'sell', 'ask', 'short', '0', 'false'}:
|
||||
return 'sell'
|
||||
|
||||
raise ValueError(f"Invalid trade side: {side}")
|
||||
|
||||
def normalize_trade_size(
|
||||
self,
|
||||
size: Any,
|
||||
price: Any,
|
||||
symbol: str
|
||||
) -> Decimal:
|
||||
"""
|
||||
Normalize and validate trade size.
|
||||
|
||||
Args:
|
||||
size: Raw trade size
|
||||
price: Trade price for notional calculations
|
||||
symbol: Trading pair symbol
|
||||
|
||||
Returns:
|
||||
Normalized trade size as Decimal
|
||||
|
||||
Raises:
|
||||
ValueError: If size is invalid or violates limits
|
||||
"""
|
||||
try:
|
||||
size_decimal = Decimal(str(size))
|
||||
price_decimal = Decimal(str(price))
|
||||
except (TypeError, ValueError) as e:
|
||||
raise ValueError(f"Invalid trade size or price format: {e}")
|
||||
|
||||
if size_decimal <= 0:
|
||||
raise ValueError(f"Trade size must be positive: {size}")
|
||||
|
||||
# Get limits and validate
|
||||
limits = get_trade_limits(symbol)
|
||||
|
||||
# Round to appropriate precision
|
||||
size_decimal = round(size_decimal, limits.size_precision)
|
||||
|
||||
# Validate against limits
|
||||
validate_trade_size(
|
||||
size_decimal,
|
||||
price_decimal,
|
||||
symbol,
|
||||
logger
|
||||
)
|
||||
|
||||
return size_decimal
|
||||
|
||||
def normalize_trade_price(
|
||||
self,
|
||||
price: Any,
|
||||
symbol: str
|
||||
) -> Decimal:
|
||||
"""
|
||||
Normalize and validate trade price.
|
||||
|
||||
Args:
|
||||
price: Raw trade price
|
||||
symbol: Trading pair symbol
|
||||
|
||||
Returns:
|
||||
Normalized price as Decimal
|
||||
|
||||
Raises:
|
||||
ValueError: If price is invalid or violates limits
|
||||
"""
|
||||
try:
|
||||
price_decimal = Decimal(str(price))
|
||||
except (TypeError, ValueError) as e:
|
||||
raise ValueError(f"Invalid price format: {e}")
|
||||
|
||||
if price_decimal <= 0:
|
||||
raise ValueError(f"Price must be positive: {price}")
|
||||
|
||||
# Get limits and round to appropriate precision
|
||||
limits = get_trade_limits(symbol)
|
||||
price_decimal = round(price_decimal, limits.price_precision)
|
||||
|
||||
# Get market price if available
|
||||
market_price = None
|
||||
if self.market_data_provider is not None:
|
||||
try:
|
||||
market_price = self.market_data_provider.get_price(symbol)
|
||||
except Exception as e:
|
||||
logger.warning(f"Failed to get market price for {symbol}: {e}")
|
||||
|
||||
# Validate against limits and market price
|
||||
validate_trade_price(
|
||||
price_decimal,
|
||||
market_price,
|
||||
symbol,
|
||||
logger
|
||||
)
|
||||
|
||||
return price_decimal
|
||||
|
||||
def transform_trade(
|
||||
self,
|
||||
trade_data: Dict[str, Any]
|
||||
) -> Dict[str, Any]:
|
||||
"""
|
||||
Transform trade data with safety checks.
|
||||
|
||||
Args:
|
||||
trade_data: Raw trade data
|
||||
|
||||
Returns:
|
||||
Transformed trade data with normalized values
|
||||
|
||||
Raises:
|
||||
ValueError: If any validation fails
|
||||
"""
|
||||
if not isinstance(trade_data, dict):
|
||||
raise ValueError(f"Trade data must be a dictionary: {trade_data}")
|
||||
|
||||
# Required fields
|
||||
required = {'symbol', 'side', 'size', 'price'}
|
||||
missing = required - set(trade_data.keys())
|
||||
if missing:
|
||||
raise ValueError(f"Missing required fields: {missing}")
|
||||
|
||||
# Validate and normalize symbol
|
||||
symbol = str(trade_data['symbol']).upper()
|
||||
validate_symbol_format(symbol, logger)
|
||||
|
||||
# Transform with safety checks
|
||||
transformed = {
|
||||
'symbol': symbol,
|
||||
'side': self.normalize_trade_side(trade_data['side']),
|
||||
'size': self.normalize_trade_size(
|
||||
trade_data['size'],
|
||||
trade_data['price'],
|
||||
symbol
|
||||
),
|
||||
'price': self.normalize_trade_price(
|
||||
trade_data['price'],
|
||||
symbol
|
||||
)
|
||||
}
|
||||
|
||||
# Copy any additional fields
|
||||
for key, value in trade_data.items():
|
||||
if key not in transformed:
|
||||
transformed[key] = value
|
||||
|
||||
return transformed
|
||||
136
data/common/transformation/unified.py
Normal file
136
data/common/transformation/unified.py
Normal file
@@ -0,0 +1,136 @@
|
||||
"""
|
||||
Unified data transformer class.
|
||||
|
||||
This module provides a unified transformer implementation that can be used
|
||||
across different exchanges with consistent field mappings.
|
||||
"""
|
||||
|
||||
from typing import Dict, Any, Optional, List
|
||||
import logging
|
||||
|
||||
from ..data_types import StandardizedTrade
|
||||
from .base import BaseDataTransformer
|
||||
|
||||
|
||||
class UnifiedDataTransformer(BaseDataTransformer):
|
||||
"""
|
||||
Unified transformer for consistent data transformation across exchanges.
|
||||
|
||||
This class provides a standardized way to transform data by using
|
||||
consistent field mappings across different exchanges.
|
||||
"""
|
||||
|
||||
def __init__(
|
||||
self,
|
||||
base_transformer: BaseDataTransformer,
|
||||
component_name: str = "unified_transformer",
|
||||
logger: Optional[logging.Logger] = None
|
||||
):
|
||||
"""
|
||||
Initialize unified transformer.
|
||||
|
||||
Args:
|
||||
base_transformer: Base transformer instance to wrap
|
||||
component_name: Component name for logging
|
||||
logger: Optional logger instance
|
||||
"""
|
||||
super().__init__(
|
||||
exchange=base_transformer.exchange,
|
||||
component_name=component_name,
|
||||
logger=logger or base_transformer.logger
|
||||
)
|
||||
self.base_transformer = base_transformer
|
||||
|
||||
def transform_trade_data(self, raw_data: Dict[str, Any], symbol: str) -> Optional[StandardizedTrade]:
|
||||
"""
|
||||
Transform raw trade data using base transformer.
|
||||
|
||||
Args:
|
||||
raw_data: Raw trade data dictionary
|
||||
symbol: Trading symbol
|
||||
|
||||
Returns:
|
||||
StandardizedTrade object or None if transformation fails
|
||||
"""
|
||||
try:
|
||||
return self.base_transformer.transform_trade_data(raw_data, symbol)
|
||||
except Exception as e:
|
||||
self._log_error(f"Failed to transform trade data", e)
|
||||
return None
|
||||
|
||||
def transform_orderbook_data(self, raw_data: Dict[str, Any], symbol: str) -> Dict[str, Any]:
|
||||
"""
|
||||
Transform orderbook data using base transformer.
|
||||
|
||||
Args:
|
||||
raw_data: Raw orderbook data dictionary
|
||||
symbol: Trading symbol
|
||||
|
||||
Returns:
|
||||
Transformed orderbook data
|
||||
"""
|
||||
try:
|
||||
return self.base_transformer.transform_orderbook_data(raw_data, symbol)
|
||||
except Exception as e:
|
||||
self._log_error(f"Failed to transform orderbook data", e)
|
||||
return {}
|
||||
|
||||
def transform_ticker_data(self, raw_data: Dict[str, Any], symbol: str) -> Dict[str, Any]:
|
||||
"""
|
||||
Transform ticker data using base transformer.
|
||||
|
||||
Args:
|
||||
raw_data: Raw ticker data dictionary
|
||||
symbol: Trading symbol
|
||||
|
||||
Returns:
|
||||
Transformed ticker data
|
||||
"""
|
||||
try:
|
||||
return self.base_transformer.transform_ticker_data(raw_data, symbol)
|
||||
except Exception as e:
|
||||
self._log_error(f"Failed to transform ticker data", e)
|
||||
return {}
|
||||
|
||||
def batch_transform_trades(
|
||||
self,
|
||||
raw_trades: List[Dict[str, Any]],
|
||||
symbol: str,
|
||||
field_mapping: Optional[Dict[str, str]] = None
|
||||
) -> List[StandardizedTrade]:
|
||||
"""
|
||||
Transform a batch of raw trades.
|
||||
|
||||
Args:
|
||||
raw_trades: List of raw trade dictionaries
|
||||
symbol: Trading symbol
|
||||
field_mapping: Optional field mapping for raw data
|
||||
|
||||
Returns:
|
||||
List of StandardizedTrade objects
|
||||
"""
|
||||
try:
|
||||
return [
|
||||
self.transform_trade_data(raw_trade, symbol)
|
||||
for raw_trade in raw_trades
|
||||
if raw_trade is not None
|
||||
]
|
||||
except Exception as e:
|
||||
self._log_error(f"Failed to batch transform trades", e)
|
||||
return []
|
||||
|
||||
def get_transformer_info(self) -> Dict[str, Any]:
|
||||
"""Get transformer information."""
|
||||
base_info = self.base_transformer.get_transformer_info()
|
||||
return {
|
||||
"exchange": base_info["exchange"],
|
||||
"component": base_info["component"],
|
||||
"unified_component": self.component_name,
|
||||
"batch_processing": True,
|
||||
"candle_aggregation": True,
|
||||
"capabilities": {
|
||||
**base_info["capabilities"],
|
||||
"unified_transformation": True,
|
||||
"candle_aggregation": True
|
||||
}
|
||||
}
|
||||
Reference in New Issue
Block a user