Add technical indicators module for OHLCV data analysis
- Introduced `indicators.py` containing implementations for SMA, EMA, RSI, MACD, and Bollinger Bands, optimized for handling sparse OHLCV data. - Added `IndicatorResult` dataclass to encapsulate results of indicator calculations. - Implemented methods for calculating multiple indicators efficiently with JSON configuration support and validation. - Updated `__init__.py` to include new indicators in the module's exports. - Enhanced documentation to cover the new technical indicators module, including usage examples and integration details. - Added comprehensive unit tests to ensure accuracy and robustness of the indicators module.
This commit is contained in:
Vasily.onl
@@ -29,6 +29,13 @@ from .validation import (
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ValidationResult
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)
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from .indicators import (
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TechnicalIndicators,
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IndicatorResult,
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create_default_indicators_config,
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validate_indicator_config
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)
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__all__ = [
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# Data types
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'StandardizedTrade',
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@@ -48,5 +55,11 @@ __all__ = [
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# Validation
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'BaseDataValidator',
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'ValidationResult'
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'ValidationResult',
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# Technical Indicators
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'TechnicalIndicators',
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'IndicatorResult',
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'create_default_indicators_config',
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'validate_indicator_config'
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]
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468
data/common/indicators.py
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468
data/common/indicators.py
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@@ -0,0 +1,468 @@
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"""
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Technical Indicators Module for OHLCV Data
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This module provides technical indicator calculations optimized for sparse OHLCV data
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as produced by the TCP Trading Platform's aggregation strategy.
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IMPORTANT: Handles Sparse Data
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- Missing candles (time gaps) are normal in this system
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- Indicators properly handle gaps without interpolation
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- Uses pandas for efficient vectorized calculations
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- Follows right-aligned timestamp convention
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Supported Indicators:
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- Simple Moving Average (SMA)
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- Exponential Moving Average (EMA)
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- Relative Strength Index (RSI)
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- Moving Average Convergence Divergence (MACD)
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- Bollinger Bands
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"""
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from datetime import datetime, timedelta
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from decimal import Decimal
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from typing import Dict, List, Optional, Any, Union, Tuple
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import pandas as pd
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import numpy as np
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from dataclasses import dataclass
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from .data_types import OHLCVCandle
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@dataclass
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class IndicatorResult:
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"""
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Container for technical indicator 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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values: Dictionary of indicator values
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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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values: Dict[str, float]
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metadata: Optional[Dict[str, Any]] = None
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class TechnicalIndicators:
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"""
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Technical indicator calculator for OHLCV candle data.
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This class provides vectorized technical indicator calculations
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designed to handle sparse data efficiently. All calculations use
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pandas for performance and handle missing data appropriately.
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SPARSE DATA HANDLING:
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- Gaps in timestamps are preserved (no interpolation)
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- Indicators calculate only on available data points
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- Periods with insufficient data return NaN
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- Results maintain original timestamp alignment
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"""
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def __init__(self, logger=None):
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"""
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Initialize technical indicators calculator.
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Args:
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logger: Optional logger instance
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"""
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self.logger = logger
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if self.logger:
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self.logger.info("TechnicalIndicators: Initialized indicator calculator")
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def prepare_dataframe(self, candles: List[OHLCVCandle]) -> pd.DataFrame:
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"""
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Convert OHLCV candles to pandas DataFrame for efficient calculations.
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Args:
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candles: List of OHLCV candles (can be sparse)
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Returns:
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DataFrame with OHLCV data, sorted by timestamp
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"""
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if not candles:
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return pd.DataFrame()
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# Convert to DataFrame
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data = []
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for candle in candles:
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data.append({
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'timestamp': candle.end_time, # Right-aligned timestamp
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'symbol': candle.symbol,
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'timeframe': candle.timeframe,
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'open': float(candle.open),
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'high': float(candle.high),
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'low': float(candle.low),
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'close': float(candle.close),
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'volume': float(candle.volume),
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'trade_count': candle.trade_count
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})
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df = pd.DataFrame(data)
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# Sort by timestamp to ensure proper order
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df = df.sort_values('timestamp').reset_index(drop=True)
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# Set timestamp as index for time-series operations
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df.set_index('timestamp', inplace=True)
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return df
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def sma(self, candles: List[OHLCVCandle], period: int,
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price_column: str = 'close') -> List[IndicatorResult]:
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"""
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Calculate Simple Moving Average (SMA).
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Args:
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candles: List of OHLCV candles
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period: Number of periods for moving average
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price_column: Price column to use ('open', 'high', 'low', 'close')
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Returns:
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List of indicator results with SMA values
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"""
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df = self.prepare_dataframe(candles)
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if df.empty or len(df) < period:
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return []
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# Calculate SMA using pandas rolling window
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df['sma'] = df[price_column].rolling(window=period, min_periods=period).mean()
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# Convert results back to IndicatorResult objects
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results = []
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for timestamp, row in df.iterrows():
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if not pd.isna(row['sma']):
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result = IndicatorResult(
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timestamp=timestamp,
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symbol=row['symbol'],
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timeframe=row['timeframe'],
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values={'sma': row['sma']},
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metadata={'period': period, 'price_column': price_column}
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)
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results.append(result)
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return results
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def ema(self, candles: List[OHLCVCandle], period: int,
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price_column: str = 'close') -> List[IndicatorResult]:
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"""
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Calculate Exponential Moving Average (EMA).
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Args:
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candles: List of OHLCV candles
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period: Number of periods for moving average
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price_column: Price column to use ('open', 'high', 'low', 'close')
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Returns:
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List of indicator results with EMA values
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"""
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df = self.prepare_dataframe(candles)
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if df.empty or len(df) < period:
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return []
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# Calculate EMA using pandas exponential weighted moving average
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df['ema'] = df[price_column].ewm(span=period, adjust=False).mean()
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# Convert results back to IndicatorResult objects
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results = []
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for i, (timestamp, row) in enumerate(df.iterrows()):
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# Only return results after minimum period
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if i >= period - 1 and not pd.isna(row['ema']):
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result = IndicatorResult(
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timestamp=timestamp,
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symbol=row['symbol'],
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timeframe=row['timeframe'],
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values={'ema': row['ema']},
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metadata={'period': period, 'price_column': price_column}
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)
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results.append(result)
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return results
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def rsi(self, candles: List[OHLCVCandle], period: int = 14,
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price_column: str = 'close') -> List[IndicatorResult]:
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"""
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Calculate Relative Strength Index (RSI).
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Args:
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candles: List of OHLCV candles
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period: Number of periods for RSI calculation (default 14)
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price_column: Price column to use ('open', 'high', 'low', 'close')
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Returns:
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List of indicator results with RSI values
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"""
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df = self.prepare_dataframe(candles)
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if df.empty or len(df) < period + 1:
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return []
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# Calculate price changes
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df['price_change'] = df[price_column].diff()
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# Separate gains and losses
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df['gain'] = df['price_change'].where(df['price_change'] > 0, 0)
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df['loss'] = (-df['price_change']).where(df['price_change'] < 0, 0)
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# Calculate average gain and loss using EMA
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df['avg_gain'] = df['gain'].ewm(span=period, adjust=False).mean()
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df['avg_loss'] = df['loss'].ewm(span=period, adjust=False).mean()
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# Calculate RS and RSI
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df['rs'] = df['avg_gain'] / df['avg_loss']
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df['rsi'] = 100 - (100 / (1 + df['rs']))
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# Handle division by zero
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df['rsi'] = df['rsi'].fillna(50) # Neutral RSI when no losses
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# Convert results back to IndicatorResult objects
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results = []
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for i, (timestamp, row) in enumerate(df.iterrows()):
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# Only return results after minimum period
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if i >= period and not pd.isna(row['rsi']):
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result = IndicatorResult(
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timestamp=timestamp,
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symbol=row['symbol'],
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timeframe=row['timeframe'],
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values={'rsi': row['rsi']},
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metadata={'period': period, 'price_column': price_column}
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)
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results.append(result)
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return results
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def macd(self, candles: List[OHLCVCandle],
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fast_period: int = 12, slow_period: int = 26, signal_period: int = 9,
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price_column: str = 'close') -> List[IndicatorResult]:
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"""
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Calculate Moving Average Convergence Divergence (MACD).
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Args:
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candles: List of OHLCV candles
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fast_period: Fast EMA period (default 12)
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slow_period: Slow EMA period (default 26)
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signal_period: Signal line EMA period (default 9)
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price_column: Price column to use ('open', 'high', 'low', 'close')
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Returns:
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List of indicator results with MACD, signal, and histogram values
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"""
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df = self.prepare_dataframe(candles)
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if df.empty or len(df) < slow_period + signal_period:
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return []
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# Calculate fast and slow EMAs
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df['ema_fast'] = df[price_column].ewm(span=fast_period, adjust=False).mean()
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df['ema_slow'] = df[price_column].ewm(span=slow_period, adjust=False).mean()
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# Calculate MACD line
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df['macd'] = df['ema_fast'] - df['ema_slow']
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# Calculate signal line (EMA of MACD)
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df['signal'] = df['macd'].ewm(span=signal_period, adjust=False).mean()
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# Calculate histogram
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df['histogram'] = df['macd'] - df['signal']
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# Convert results back to IndicatorResult objects
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results = []
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for i, (timestamp, row) in enumerate(df.iterrows()):
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# Only return results after minimum period
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if i >= slow_period + signal_period - 1:
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if not (pd.isna(row['macd']) or pd.isna(row['signal']) or pd.isna(row['histogram'])):
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result = IndicatorResult(
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timestamp=timestamp,
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symbol=row['symbol'],
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timeframe=row['timeframe'],
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values={
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'macd': row['macd'],
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'signal': row['signal'],
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'histogram': row['histogram']
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},
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metadata={
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'fast_period': fast_period,
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'slow_period': slow_period,
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'signal_period': signal_period,
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'price_column': price_column
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}
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)
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results.append(result)
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return results
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def bollinger_bands(self, candles: List[OHLCVCandle], period: int = 20,
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std_dev: float = 2.0, price_column: str = 'close') -> List[IndicatorResult]:
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"""
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Calculate Bollinger Bands.
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Args:
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candles: List of OHLCV candles
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period: Number of periods for moving average (default 20)
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std_dev: Number of standard deviations for bands (default 2.0)
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price_column: Price column to use ('open', 'high', 'low', 'close')
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Returns:
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List of indicator results with upper band, middle band (SMA), and lower band
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"""
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df = self.prepare_dataframe(candles)
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if df.empty or len(df) < period:
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return []
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# Calculate middle band (SMA)
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df['middle_band'] = df[price_column].rolling(window=period, min_periods=period).mean()
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# Calculate standard deviation
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df['std'] = df[price_column].rolling(window=period, min_periods=period).std()
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# Calculate upper and lower bands
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df['upper_band'] = df['middle_band'] + (std_dev * df['std'])
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df['lower_band'] = df['middle_band'] - (std_dev * df['std'])
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# Calculate bandwidth and %B
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df['bandwidth'] = (df['upper_band'] - df['lower_band']) / df['middle_band']
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df['percent_b'] = (df[price_column] - df['lower_band']) / (df['upper_band'] - df['lower_band'])
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# Convert results back to IndicatorResult objects
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results = []
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for timestamp, row in df.iterrows():
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if not pd.isna(row['middle_band']):
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result = IndicatorResult(
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timestamp=timestamp,
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symbol=row['symbol'],
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timeframe=row['timeframe'],
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values={
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'upper_band': row['upper_band'],
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'middle_band': row['middle_band'],
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'lower_band': row['lower_band'],
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'bandwidth': row['bandwidth'],
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'percent_b': row['percent_b']
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},
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metadata={
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'period': period,
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'std_dev': std_dev,
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'price_column': price_column
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}
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)
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results.append(result)
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return results
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def calculate_multiple_indicators(self, candles: List[OHLCVCandle],
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indicators_config: Dict[str, Dict[str, Any]]) -> Dict[str, List[IndicatorResult]]:
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"""
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Calculate multiple indicators at once for efficiency.
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Args:
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candles: List of OHLCV candles
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indicators_config: Configuration for indicators to calculate
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Example: {
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'sma_20': {'type': 'sma', 'period': 20},
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'ema_12': {'type': 'ema', 'period': 12},
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'rsi_14': {'type': 'rsi', 'period': 14},
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'macd': {'type': 'macd'},
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'bb_20': {'type': 'bollinger_bands', 'period': 20}
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}
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Returns:
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Dictionary mapping indicator names to their results
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"""
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results = {}
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for indicator_name, config in indicators_config.items():
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indicator_type = config.get('type')
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try:
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if indicator_type == 'sma':
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period = config.get('period', 20)
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price_column = config.get('price_column', 'close')
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results[indicator_name] = self.sma(candles, period, price_column)
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elif indicator_type == 'ema':
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period = config.get('period', 20)
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price_column = config.get('price_column', 'close')
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results[indicator_name] = self.ema(candles, period, price_column)
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elif indicator_type == 'rsi':
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period = config.get('period', 14)
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price_column = config.get('price_column', 'close')
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results[indicator_name] = self.rsi(candles, period, price_column)
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elif indicator_type == 'macd':
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fast_period = config.get('fast_period', 12)
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slow_period = config.get('slow_period', 26)
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signal_period = config.get('signal_period', 9)
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price_column = config.get('price_column', 'close')
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results[indicator_name] = self.macd(candles, fast_period, slow_period, signal_period, price_column)
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elif indicator_type == 'bollinger_bands':
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period = config.get('period', 20)
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std_dev = config.get('std_dev', 2.0)
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price_column = config.get('price_column', 'close')
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results[indicator_name] = self.bollinger_bands(candles, period, std_dev, price_column)
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else:
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if self.logger:
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self.logger.warning(f"TechnicalIndicators: Unknown indicator type: {indicator_type}")
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results[indicator_name] = []
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except Exception as e:
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if self.logger:
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self.logger.error(f"TechnicalIndicators: Error calculating {indicator_name}: {e}")
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results[indicator_name] = []
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return results
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def create_default_indicators_config() -> Dict[str, Dict[str, Any]]:
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"""
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Create default configuration for common technical indicators.
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Returns:
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Dictionary with default indicator configurations
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"""
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return {
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'sma_20': {'type': 'sma', 'period': 20},
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'sma_50': {'type': 'sma', 'period': 50},
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'ema_12': {'type': 'ema', 'period': 12},
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'ema_26': {'type': 'ema', 'period': 26},
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'rsi_14': {'type': 'rsi', 'period': 14},
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'macd_default': {'type': 'macd'},
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'bollinger_bands_20': {'type': 'bollinger_bands', 'period': 20}
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}
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def validate_indicator_config(config: Dict[str, Any]) -> bool:
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"""
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Validate technical indicator configuration.
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Args:
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config: Indicator configuration dictionary
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Returns:
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True if configuration is valid, False otherwise
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"""
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required_fields = ['type']
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# Check required fields
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for field in required_fields:
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if field not in config:
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return False
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# Validate indicator type
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valid_types = ['sma', 'ema', 'rsi', 'macd', 'bollinger_bands']
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if config['type'] not in valid_types:
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return False
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# Validate period fields
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if 'period' in config and (not isinstance(config['period'], int) or config['period'] <= 0):
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return False
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||||
# Validate standard deviation for Bollinger Bands
|
||||
if config['type'] == 'bollinger_bands' and 'std_dev' in config:
|
||||
if not isinstance(config['std_dev'], (int, float)) or config['std_dev'] <= 0:
|
||||
return False
|
||||
|
||||
return True
|
||||
Reference in New Issue
Block a user