Timeframe
15m
Direction
Long Only
Stoploss
-2.6%
Trailing Stop
Yes
ROI
0m: 6.0%, 60m: 3.0%, 120m: 1.0%, 240m: 0.0%
Interface Version
3
Startup Candles
500
Indicators
4
freqtrade/freqtrade-strategies
Strategy 003 author@: Gerald Lonlas github@: https://github.com/freqtrade/freqtrade-strategies
"""
RegimeSwitchingHybrid_v3_Final
A hybrid strategy that switches between Trend Following (ADX/EMA)
and Mean Reversion (BB/RSI) based on market regime detection.
Timeframe: 15m
HTF Informative: 1h
Optimized via Phase 16 - Fixed ROI + Recursive Optimization
"""
import logging
from datetime import datetime
from typing import Optional
import talib.abstract as ta
from freqtrade.strategy import IStrategy, IntParameter, DecimalParameter, BooleanParameter, merge_informative_pair
import freqtrade.vendor.qtpylib.indicators as qtpylib
from pandas import DataFrame
logger = logging.getLogger(__name__)
class RegimeSwitchingHybrid_v3_Final(IStrategy):
INTERFACE_VERSION = 3
timeframe = "15m"
informative_timeframe = "1h"
# Strategy settings
can_short = False
# Base ROI (Fixed for Phase 16 as per instruction)
minimal_roi = {
"0": 0.06,
"60": 0.03,
"120": 0.01,
"240": 0
}
# Best parameters from Main Hyperopt (400 epochs)
stoploss = -0.026
trailing_stop = True
trailing_stop_positive = 0.248
trailing_stop_positive_offset = 0.33
trailing_only_offset_is_reached = True
rsi_overbought = 66
startup_candle_count = 500
@property
def protections(self):
return [
{"method": "CooldownPeriod", "stop_duration_candles": 5},
{"method": "StoplossGuard", "lookback_period_candles": 60, "trade_limit": 3, "stop_duration_candles": 60, "only_per_pair": False},
{"method": "MaxDrawdown", "lookback_period_candles": 480, "trade_limit": 20, "stop_duration_candles": 96, "max_allowed_drawdown": 0.10},
{"method": "LowProfitPairs", "lookback_period_candles": 1440, "trade_limit": 2, "stop_duration_candles": 60, "required_profit": 0.00}
]
# Hyperoptable parameters (Buy space)
# Using defaults from the best Main Hyperopt run
adx_threshold = IntParameter(20, 35, default=29, space="buy")
rsi_oversold = IntParameter(20, 40, default=20, space="buy")
def informative_pairs(self):
pairs = self.dp.current_whitelist()
return [(pair, self.informative_timeframe) for pair in pairs]
def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
if not self.dp: return dataframe
informative = self.dp.get_pair_dataframe(pair=metadata['pair'], timeframe=self.informative_timeframe)
informative['ema200'] = ta.EMA(informative, timeperiod=200)
informative['adx'] = ta.ADX(informative)
informative['rsi'] = ta.RSI(informative)
dataframe = merge_informative_pair(dataframe, informative, self.timeframe, self.informative_timeframe, ffill=True)
# Local indicators
dataframe['adx'] = ta.ADX(dataframe)
dataframe['rsi'] = ta.RSI(dataframe)
dataframe['ema50'] = ta.EMA(dataframe, timeperiod=50)
dataframe['ema200'] = ta.EMA(dataframe, timeperiod=200)
bollinger = qtpylib.bollinger_bands(qtpylib.typical_price(dataframe), window=20, stds=2)
dataframe['bb_lowerband'] = bollinger['lower']
dataframe['bb_middleband'] = bollinger['mid']
dataframe['bb_upperband'] = bollinger['upper']
dataframe['bb_width'] = (dataframe['bb_upperband'] - dataframe['bb_lowerband']) / dataframe['bb_middleband']
dataframe['volume_mean'] = dataframe['volume'].rolling(window=30).mean()
return dataframe
def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
adx_htf = dataframe[f'adx_{self.informative_timeframe}']
ema200_htf = dataframe[f'ema200_{self.informative_timeframe}']
# Trend Regime Pullback
trend_long = (
(adx_htf > self.adx_threshold.value) &
(dataframe['close'] > ema200_htf) &
(dataframe['close'] > dataframe['ema200']) &
(dataframe['close'] < dataframe['ema50']) &
(dataframe['rsi'] < 50) &
(dataframe['volume'] > 0)
)
# Range Regime Reversion
range_long = (
(adx_htf <= self.adx_threshold.value) &
(dataframe['rsi'] < self.rsi_oversold.value) &
(dataframe['close'] < dataframe['bb_lowerband']) &
(dataframe['volume'] > 0)
)
dataframe.loc[trend_long, 'enter_long'] = 1
dataframe.loc[trend_long, 'enter_tag'] = 'trend_pullback'
dataframe.loc[range_long, 'enter_long'] = 1
dataframe.loc[range_long, 'enter_tag'] = 'range_reversion'
return dataframe
def populate_exit_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
dataframe.loc[
(dataframe['rsi'] > self.rsi_overbought) |
(dataframe['close'] > dataframe['bb_upperband']),
'exit_long'
] = 1
return dataframe