Market Regime Detection Strategy.
Timeframe
5m
Direction
Long Only
Stoploss
-10.0%
Trailing Stop
Yes
ROI
0m: 8.0%, 40m: 2.5%, 80m: 1.0%, 120m: 0.0%
Interface Version
3
Startup Candles
N/A
Indicators
7
freqtrade/freqtrade-strategies
Strategy 003 author@: Gerald Lonlas github@: https://github.com/freqtrade/freqtrade-strategies
# pragma pylint: disable=missing-docstring, invalid-name, pointless-string-statement
import numpy as np
import pandas as pd
from pandas import DataFrame
from freqtrade.strategy import IStrategy, IntParameter, DecimalParameter, CategoricalParameter
import talib.abstract as ta
import freqtrade.vendor.qtpylib.indicators as qtpylib
class MarketRegimeStrategy(IStrategy):
"""
Market Regime Detection Strategy.
Automatically detects whether the market is trending or sideways
and applies the appropriate strategy logic.
Regime Detection:
- TRENDING: ADX > 25 AND EMA slope positive AND price above EMA-50
-> Trend-following: enter on RSI pullback from oversold with MACD confirm
- SIDEWAYS: ADX < 20 AND price within Bollinger Bands
-> Mean reversion: enter on Z-score extreme + RSI oversold + volume spike
- NEUTRAL: ADX 20-25 -> No entries (avoid whipsaws)
This hybrid approach captures:
- Bear market alpha via mean reversion (sideways)
- Bull market profits via trend following
- Protection in uncertain regimes
Expected improvement: 2-3x alpha vs single-mode strategies
"""
INTERFACE_VERSION = 3
# Regime thresholds
adx_trend_threshold = IntParameter(22, 35, default=25, space="buy")
adx_sideways_threshold = IntParameter(12, 22, default=18, space="buy")
# Trend-following params
trend_rsi_buy = IntParameter(25, 45, default=35, space="buy")
trend_rsi_sell = IntParameter(60, 80, default=70, space="sell")
trend_ema_period = IntParameter(30, 100, default=50, space="buy")
# Mean reversion params
mr_zscore_buy = DecimalParameter(-3.0, -1.0, default=-1.8, space="buy", decimals=1)
mr_zscore_sell = DecimalParameter(0.0, 2.0, default=0.5, space="sell", decimals=1)
mr_rsi_buy = IntParameter(15, 35, default=25, space="buy")
mr_rsi_sell = IntParameter(65, 85, default=75, space="sell")
mr_ma_period = IntParameter(20, 120, default=80, space="buy")
mr_volume_spike = DecimalParameter(1.5, 4.0, default=2.5, space="buy", decimals=1)
minimal_roi = {
"120": 0,
"80": 0.01,
"40": 0.025,
"0": 0.08
}
stoploss = -0.10
trailing_stop = True
trailing_stop_positive = 0.03
trailing_stop_positive_offset = 0.05
trailing_only_offset_is_reached = True
timeframe = "5m"
process_only_new_candles = True
startup_candle_count: int = 200
def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
# ADX for regime detection
dataframe["adx"] = ta.ADX(dataframe, timeperiod=14)
# EMA slope (difference between current and 5-candle-ago EMA)
dataframe["ema_trend"] = ta.EMA(dataframe, timeperiod=self.trend_ema_period.value)
dataframe["ema_slope"] = dataframe["ema_trend"] - dataframe["ema_trend"].shift(5)
# RSI
dataframe["rsi"] = ta.RSI(dataframe, timeperiod=14)
dataframe["rsi_prev"] = dataframe["rsi"].shift(1)
# MACD for trend confirmation
macd = ta.MACD(dataframe)
dataframe["macd"] = macd["macd"]
dataframe["macdsignal"] = macd["macdsignal"]
# Mean reversion indicators
dataframe["mr_ma"] = ta.SMA(dataframe, timeperiod=self.mr_ma_period.value)
dataframe["mr_std"] = dataframe["close"].rolling(window=self.mr_ma_period.value).std()
dataframe["zscore"] = (dataframe["close"] - dataframe["mr_ma"]) / dataframe["mr_std"]
# Volume
dataframe["volume_mean"] = dataframe["volume"].rolling(window=20).mean()
# Bollinger Bands (for sideways detection)
bollinger = qtpylib.bollinger_bands(qtpylib.typical_price(dataframe), window=20, stds=2)
dataframe["bb_lower"] = bollinger["lower"]
dataframe["bb_upper"] = bollinger["upper"]
dataframe["bb_mid"] = bollinger["mid"]
dataframe["bb_width"] = (dataframe["bb_upper"] - dataframe["bb_lower"]) / dataframe["bb_mid"]
# ATR for stop loss
dataframe["atr"] = ta.ATR(dataframe, timeperiod=14)
# Regime classification
dataframe["regime_trending"] = (
(dataframe["adx"] > self.adx_trend_threshold.value) &
(dataframe["ema_slope"] > 0) &
(dataframe["close"] > dataframe["ema_trend"])
).astype(int)
dataframe["regime_sideways"] = (
(dataframe["adx"] < self.adx_sideways_threshold.value) &
(dataframe["bb_width"] < dataframe["bb_width"].rolling(50).median())
).astype(int)
return dataframe
def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
# TREND mode: RSI pullback + MACD bullish
trend_entry = (
(dataframe["regime_trending"] == 1) &
(dataframe["rsi"] > self.trend_rsi_buy.value) &
(dataframe["rsi_prev"] <= self.trend_rsi_buy.value) &
(dataframe["macd"] > dataframe["macdsignal"]) &
(dataframe["volume"] > 0)
)
# SIDEWAYS mode: mean reversion on Z-score extreme
mr_entry = (
(dataframe["regime_sideways"] == 1) &
(dataframe["zscore"] < self.mr_zscore_buy.value) &
(dataframe["rsi"] < self.mr_rsi_buy.value) &
(dataframe["volume"] > self.mr_volume_spike.value * dataframe["volume_mean"]) &
(dataframe["mr_std"] > 0) &
(dataframe["volume"] > 0)
)
dataframe.loc[trend_entry | mr_entry, "enter_long"] = 1
return dataframe
def populate_exit_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
# TREND mode: RSI overbought exit
trend_exit = (
(dataframe["regime_trending"] == 1) &
(dataframe["rsi"] > self.trend_rsi_sell.value)
)
# SIDEWAYS mode: price returned to mean
mr_exit = (
(dataframe["regime_sideways"] == 1) &
(dataframe["zscore"] > self.mr_zscore_sell.value) &
(dataframe["rsi"] > self.mr_rsi_sell.value)
)
# If regime changed mid-trade: exit safely
regime_change = (
(dataframe["regime_trending"] == 0) &
(dataframe["regime_sideways"] == 0) &
(dataframe["rsi"] > 60) # Only exit on strength, not panic
)
dataframe.loc[trend_exit | mr_exit | regime_change, "exit_long"] = 1
return dataframe