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Market Regime Detection Module
Identifies market conditions to adapt trading strategy:
- TRENDING_UP: Strong uptrend (ADX > 25, +DI > -DI)
- TRENDING_DOWN: Strong downtrend (ADX > 25, -DI > +DI)
- RANGING: No clear trend (ADX < 20)
- HIGH_VOLATILITY: Large price swings (ATR > 1.5x average)
- LOW_VOLATILITY: Quiet market (ATR < 0.5x average)
"""
import numpy as np
import pandas as pd
from typing import Dict, Tuple, Optional
from dataclasses import dataclass
from enum import Enum
import logging
logger = logging.getLogger(__name__)
class MarketRegime(Enum):
TRENDING_UP = "trending_up"
TRENDING_DOWN = "trending_down"
RANGING = "ranging"
HIGH_VOLATILITY = "high_volatility"
LOW_VOLATILITY = "low_volatility"
UNKNOWN = "unknown"
@dataclass
class RegimeInfo:
"""Container for regime analysis results."""
regime: MarketRegime
trend_strength: float # ADX value (0-100)
trend_direction: float # +1 bullish, -1 bearish, 0 neutral
volatility_ratio: float # Current ATR / Average ATR
confidence: float # 0-1 confidence in the regime
recommendation: str # Trading recommendation
class MarketRegimeDetector:
"""
Detect market regime using ADX, ATR, and price action.
Adapts trading behavior:
- Trending: Follow trend, wider stops
- Ranging: Mean reversion, tight stops
- High Vol: Reduce size, use momentum
- Low Vol: Skip or use breakout
"""
def __init__(
self,
adx_period: int = 14,
atr_period: int = 14,
trend_threshold: float = 25.0,
range_threshold: float = 20.0,
vol_lookback: int = 50
):
"""
Initialize regime detector.
Args:
adx_period: Period for ADX calculation
atr_period: Period for ATR calculation
trend_threshold: ADX above this = trending
range_threshold: ADX below this = ranging
vol_lookback: Bars to compare current volatility
"""
self.adx_period = adx_period
self.atr_period = atr_period
self.trend_threshold = trend_threshold
self.range_threshold = range_threshold
self.vol_lookback = vol_lookback
logger.info(f"📊 MarketRegimeDetector initialized (ADX>{trend_threshold}=trend, ADX<{range_threshold}=range)")
def calculate_adx(self, df: pd.DataFrame) -> Tuple[pd.Series, pd.Series, pd.Series]:
"""
Calculate ADX, +DI, and -DI.
Returns:
Tuple of (ADX, +DI, -DI) series
"""
high = df['high']
low = df['low']
close = df['close']
# True Range
tr1 = high - low
tr2 = abs(high - close.shift(1))
tr3 = abs(low - close.shift(1))
tr = pd.concat([tr1, tr2, tr3], axis=1).max(axis=1)
atr = tr.rolling(self.adx_period).mean()
# Directional Movement
up_move = high - high.shift(1)
down_move = low.shift(1) - low
plus_dm = np.where((up_move > down_move) & (up_move > 0), up_move, 0)
minus_dm = np.where((down_move > up_move) & (down_move > 0), down_move, 0)
plus_dm = pd.Series(plus_dm, index=df.index)
minus_dm = pd.Series(minus_dm, index=df.index)
# Smoothed DM
plus_dm_smooth = plus_dm.rolling(self.adx_period).mean()
minus_dm_smooth = minus_dm.rolling(self.adx_period).mean()
# DI calculations
plus_di = 100 * (plus_dm_smooth / atr)
minus_di = 100 * (minus_dm_smooth / atr)
# DX and ADX
dx = 100 * abs(plus_di - minus_di) / (plus_di + minus_di + 1e-10)
adx = dx.rolling(self.adx_period).mean()
return adx, plus_di, minus_di
def calculate_atr(self, df: pd.DataFrame) -> pd.Series:
"""Calculate Average True Range."""
high = df['high']
low = df['low']
close = df['close']
tr1 = high - low
tr2 = abs(high - close.shift(1))
tr3 = abs(low - close.shift(1))
tr = pd.concat([tr1, tr2, tr3], axis=1).max(axis=1)
return tr.rolling(self.atr_period).mean()
def detect_regime(self, df: pd.DataFrame) -> RegimeInfo:
"""
Detect current market regime.
Args:
df: DataFrame with OHLCV data
Returns:
RegimeInfo with current regime and metrics
"""
if len(df) < self.vol_lookback + self.adx_period:
return RegimeInfo(
regime=MarketRegime.UNKNOWN,
trend_strength=0,
trend_direction=0,
volatility_ratio=1.0,
confidence=0,
recommendation="Insufficient data"
)
# Calculate indicators
adx, plus_di, minus_di = self.calculate_adx(df)
atr = self.calculate_atr(df)
# Get current values
current_adx = adx.iloc[-1]
current_plus_di = plus_di.iloc[-1]
current_minus_di = minus_di.iloc[-1]
current_atr = atr.iloc[-1]
avg_atr = atr.iloc[-self.vol_lookback:].mean()
# Volatility ratio
vol_ratio = current_atr / avg_atr if avg_atr > 0 else 1.0
# Trend direction: +1 bullish, -1 bearish
trend_direction = 0.0
if current_plus_di > current_minus_di:
trend_direction = min((current_plus_di - current_minus_di) / 20, 1.0)
else:
trend_direction = max((current_plus_di - current_minus_di) / 20, -1.0)
# Determine regime
regime = MarketRegime.UNKNOWN
confidence = 0.0
recommendation = ""
# Check volatility first
if vol_ratio > 1.5:
regime = MarketRegime.HIGH_VOLATILITY
confidence = min((vol_ratio - 1.5) / 0.5 + 0.5, 1.0)
recommendation = "High volatility - reduce position size, use momentum"
elif vol_ratio < 0.5:
regime = MarketRegime.LOW_VOLATILITY
confidence = min((0.5 - vol_ratio) / 0.25 + 0.5, 1.0)
recommendation = "Low volatility - wait for breakout or skip"
# Then check trend
elif current_adx >= self.trend_threshold:
if trend_direction > 0:
regime = MarketRegime.TRENDING_UP
confidence = min(current_adx / 40, 1.0)
recommendation = "Uptrend - follow trend, buy dips"
else:
regime = MarketRegime.TRENDING_DOWN
confidence = min(current_adx / 40, 1.0)
recommendation = "Downtrend - follow trend, sell rallies"
elif current_adx <= self.range_threshold:
regime = MarketRegime.RANGING
confidence = min((self.range_threshold - current_adx) / 10 + 0.5, 1.0)
recommendation = "Ranging - mean reversion, tight stops"
else:
# Between range and trend thresholds
regime = MarketRegime.RANGING
confidence = 0.3
recommendation = "Weak trend - be cautious"
result = RegimeInfo(
regime=regime,
trend_strength=current_adx,
trend_direction=trend_direction,
volatility_ratio=vol_ratio,
confidence=confidence,
recommendation=recommendation
)
logger.info(
f"📊 Regime: {regime.value.upper()} | "
f"ADX: {current_adx:.1f} | "
f"Direction: {'↑' if trend_direction > 0 else '↓' if trend_direction < 0 else '→'} | "
f"Vol: {vol_ratio:.2f}x"
)
return result
def should_trade(self, df: pd.DataFrame, trade_type: str = "any") -> Tuple[bool, str, float]:
"""
Check if trading is advisable given current regime.
Args:
df: DataFrame with OHLCV data
trade_type: "long", "short", or "any"
Returns:
Tuple of (should_trade, reason, position_size_multiplier)
"""
regime_info = self.detect_regime(df)
# Default position size multiplier
size_mult = 1.0
if regime_info.regime == MarketRegime.UNKNOWN:
return True, "Unknown regime - using default", 1.0
# High volatility: reduce size
if regime_info.regime == MarketRegime.HIGH_VOLATILITY:
size_mult = 0.5 # Half position size
return True, f"High vol ({regime_info.volatility_ratio:.1f}x) - reduced size", size_mult
# Low volatility: skip or small size
if regime_info.regime == MarketRegime.LOW_VOLATILITY:
if regime_info.confidence > 0.7:
return False, "Very low volatility - skipping", 0.0
size_mult = 0.75
return True, "Low vol - smaller size", size_mult
# Trending up
if regime_info.regime == MarketRegime.TRENDING_UP:
if trade_type == "short":
return False, f"Uptrend (ADX={regime_info.trend_strength:.0f}) - blocking SHORT", 0.0
size_mult = 1.2 # Larger size in trend
return True, f"Uptrend - favor LONG", size_mult
# Trending down
if regime_info.regime == MarketRegime.TRENDING_DOWN:
if trade_type == "long":
return False, f"Downtrend (ADX={regime_info.trend_strength:.0f}) - blocking LONG", 0.0
size_mult = 1.2
return True, f"Downtrend - favor SHORT", size_mult
# Ranging
if regime_info.regime == MarketRegime.RANGING:
size_mult = 0.8 # Smaller size in range
return True, f"Ranging market - use mean reversion", size_mult
return True, "Default regime rules", 1.0
def get_regime_summary(self, df: pd.DataFrame) -> Dict:
"""Get a summary dict of current regime for UI/logging."""
regime_info = self.detect_regime(df)
return {
'regime': regime_info.regime.value,
'trend_strength': regime_info.trend_strength,
'trend_direction': regime_info.trend_direction,
'volatility_ratio': regime_info.volatility_ratio,
'confidence': regime_info.confidence,
'recommendation': regime_info.recommendation,
'emoji': self._get_regime_emoji(regime_info.regime)
}
def _get_regime_emoji(self, regime: MarketRegime) -> str:
"""Get emoji for regime."""
emojis = {
MarketRegime.TRENDING_UP: "📈",
MarketRegime.TRENDING_DOWN: "📉",
MarketRegime.RANGING: "↔️",
MarketRegime.HIGH_VOLATILITY: "🌊",
MarketRegime.LOW_VOLATILITY: "😴",
MarketRegime.UNKNOWN: "❓"
}
return emojis.get(regime, "❓")
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