c9ae507bb7
- Introduced a comprehensive framework for incremental trading strategies, including modules for strategy execution, backtesting, and data processing. - Added key components such as `IncTrader`, `IncBacktester`, and various trading strategies (e.g., `MetaTrendStrategy`, `BBRSStrategy`, `RandomStrategy`) to facilitate real-time trading and backtesting. - Implemented a robust backtesting framework with configuration management, parallel execution, and result analysis capabilities. - Developed an incremental indicators framework to support real-time data processing with constant memory usage. - Enhanced documentation to provide clear usage examples and architecture overview, ensuring maintainability and ease of understanding for future development. - Ensured compatibility with existing strategies and maintained a focus on performance and scalability throughout the implementation.
254 lines
8.1 KiB
Python
254 lines
8.1 KiB
Python
"""
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Average True Range (ATR) Indicator State
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This module implements incremental ATR calculation that maintains constant memory usage
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and provides identical results to traditional batch calculations. ATR is used by
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Supertrend and other volatility-based indicators.
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"""
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from typing import Dict, Union, Optional
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from .base import OHLCIndicatorState
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from .moving_average import ExponentialMovingAverageState
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class ATRState(OHLCIndicatorState):
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"""
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Incremental Average True Range calculation state.
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ATR measures market volatility by calculating the average of true ranges over
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a specified period. True Range is the maximum of:
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1. Current High - Current Low
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2. |Current High - Previous Close|
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3. |Current Low - Previous Close|
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This implementation uses exponential moving average for smoothing, which is
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more responsive than simple moving average and requires less memory.
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Attributes:
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period (int): The ATR period
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ema_state (ExponentialMovingAverageState): EMA state for smoothing true ranges
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previous_close (float): Previous period's close price
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Example:
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atr = ATRState(period=14)
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# Add OHLC data incrementally
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ohlc = {'open': 100, 'high': 105, 'low': 98, 'close': 103}
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atr_value = atr.update(ohlc) # Returns current ATR value
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# Check if warmed up
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if atr.is_warmed_up():
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current_atr = atr.get_current_value()
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"""
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def __init__(self, period: int = 14):
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"""
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Initialize ATR state.
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Args:
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period: Number of periods for ATR calculation (default: 14)
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Raises:
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ValueError: If period is not a positive integer
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"""
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super().__init__(period)
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self.ema_state = ExponentialMovingAverageState(period)
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self.previous_close = None
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self.is_initialized = True
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def update(self, ohlc_data: Dict[str, float]) -> float:
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"""
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Update ATR with new OHLC data.
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Args:
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ohlc_data: Dictionary with 'open', 'high', 'low', 'close' keys
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Returns:
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Current ATR value
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Raises:
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ValueError: If OHLC data is invalid
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TypeError: If ohlc_data is not a dictionary
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"""
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# Validate input
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if not isinstance(ohlc_data, dict):
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raise TypeError(f"ohlc_data must be a dictionary, got {type(ohlc_data)}")
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self.validate_input(ohlc_data)
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high = float(ohlc_data['high'])
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low = float(ohlc_data['low'])
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close = float(ohlc_data['close'])
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# Calculate True Range
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if self.previous_close is None:
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# First period - True Range is just High - Low
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true_range = high - low
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else:
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# True Range is the maximum of:
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# 1. Current High - Current Low
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# 2. |Current High - Previous Close|
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# 3. |Current Low - Previous Close|
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tr1 = high - low
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tr2 = abs(high - self.previous_close)
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tr3 = abs(low - self.previous_close)
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true_range = max(tr1, tr2, tr3)
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# Update EMA with the true range
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atr_value = self.ema_state.update(true_range)
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# Store current close as previous close for next calculation
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self.previous_close = close
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self.values_received += 1
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# Store current ATR value
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self._current_values = {'atr': atr_value}
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return atr_value
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def is_warmed_up(self) -> bool:
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"""
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Check if ATR has enough data for reliable values.
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Returns:
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True if EMA state is warmed up (has enough true range values)
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"""
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return self.ema_state.is_warmed_up()
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def reset(self) -> None:
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"""Reset ATR state to initial conditions."""
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self.ema_state.reset()
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self.previous_close = None
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self.values_received = 0
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self._current_values = {}
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def get_current_value(self) -> Optional[float]:
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"""
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Get current ATR value without updating.
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Returns:
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Current ATR value, or None if not warmed up
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"""
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if not self.is_warmed_up():
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return None
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return self.ema_state.get_current_value()
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def get_state_summary(self) -> dict:
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"""Get detailed state summary for debugging."""
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base_summary = super().get_state_summary()
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base_summary.update({
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'previous_close': self.previous_close,
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'ema_state': self.ema_state.get_state_summary(),
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'current_atr': self.get_current_value()
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})
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return base_summary
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class SimpleATRState(OHLCIndicatorState):
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"""
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Simple ATR implementation using simple moving average instead of EMA.
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This version uses a simple moving average for smoothing true ranges,
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which matches some traditional ATR implementations but requires more memory.
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"""
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def __init__(self, period: int = 14):
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"""
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Initialize simple ATR state.
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Args:
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period: Number of periods for ATR calculation (default: 14)
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"""
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super().__init__(period)
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from collections import deque
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self.true_ranges = deque(maxlen=period)
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self.tr_sum = 0.0
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self.previous_close = None
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self.is_initialized = True
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def update(self, ohlc_data: Dict[str, float]) -> float:
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"""
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Update simple ATR with new OHLC data.
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Args:
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ohlc_data: Dictionary with 'open', 'high', 'low', 'close' keys
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Returns:
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Current ATR value
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"""
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# Validate input
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if not isinstance(ohlc_data, dict):
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raise TypeError(f"ohlc_data must be a dictionary, got {type(ohlc_data)}")
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self.validate_input(ohlc_data)
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high = float(ohlc_data['high'])
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low = float(ohlc_data['low'])
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close = float(ohlc_data['close'])
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# Calculate True Range
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if self.previous_close is None:
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true_range = high - low
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else:
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tr1 = high - low
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tr2 = abs(high - self.previous_close)
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tr3 = abs(low - self.previous_close)
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true_range = max(tr1, tr2, tr3)
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# Update rolling sum
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if len(self.true_ranges) == self.period:
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self.tr_sum -= self.true_ranges[0] # Remove oldest value
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self.true_ranges.append(true_range)
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self.tr_sum += true_range
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# Calculate ATR
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atr_value = self.tr_sum / len(self.true_ranges)
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# Store current close as previous close for next calculation
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self.previous_close = close
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self.values_received += 1
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# Store current ATR value
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self._current_values = {'atr': atr_value}
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return atr_value
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def is_warmed_up(self) -> bool:
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"""
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Check if simple ATR has enough data for reliable values.
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Returns:
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True if we have at least 'period' number of true range values
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"""
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return len(self.true_ranges) >= self.period
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def reset(self) -> None:
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"""Reset simple ATR state to initial conditions."""
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self.true_ranges.clear()
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self.tr_sum = 0.0
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self.previous_close = None
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self.values_received = 0
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self._current_values = {}
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def get_current_value(self) -> Optional[float]:
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"""
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Get current simple ATR value without updating.
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Returns:
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Current ATR value, or None if not warmed up
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"""
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if not self.is_warmed_up():
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return None
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return self.tr_sum / len(self.true_ranges)
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def get_state_summary(self) -> dict:
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"""Get detailed state summary for debugging."""
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base_summary = super().get_state_summary()
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base_summary.update({
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'previous_close': self.previous_close,
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'tr_sum': self.tr_sum,
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'true_ranges_count': len(self.true_ranges),
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'current_atr': self.get_current_value()
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})
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return base_summary |