Bear Market RSI Strategy
Timeframe
1h
Direction
Long Only
Stoploss
-3.0%
Trailing Stop
Yes
ROI
0m: 5.0%, 30m: 3.0%, 60m: 2.0%, 120m: 1.0%
Interface Version
N/A
Startup Candles
N/A
Indicators
3
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
# flake8: noqa: F401
# --- Do not remove these libs ---
import numpy as np # noqa
import pandas as pd # noqa
from pandas import DataFrame
from datetime import datetime
from freqtrade.strategy import (BooleanParameter, CategoricalParameter, DecimalParameter,
IStrategy, IntParameter)
# --------------------------------
# Add your lib to import here
import talib
import talib.abstract as ta
import pandas_ta as pta
import freqtrade.vendor.qtpylib.indicators as qtpylib
from technical.util import resample_to_interval, resampled_merge
class BearMarketRSI(IStrategy):
"""
Bear Market RSI Strategy
This strategy only trades during bear markets (price below moving average)
and uses RSI oversold signals to identify buying opportunities.
Key Features:
- Only trades when price is below 100-day moving average (bear market)
- Uses RSI oversold signals (< 30) for entries
- Exits when RSI becomes overbought (> 70) or market turns bullish
- Includes volume confirmation for stronger signals
Expected Performance:
- Should avoid bull market losses
- Should capture bear market bounces
- Lower frequency but higher quality trades
"""
INTERFACE_VERSION: int = 3
# Buy hyperspace params:
buy_params = {
"rsi_period": 14,
"rsi_oversold": 30,
"rsi_overbought": 70,
"sma_period": 100,
"volume_multiplier": 1.5,
}
# ROI table - designed for bear market bounces
minimal_roi = {
"0": 0.05, # 5% profit target
"30": 0.03, # 3% after 30 minutes
"60": 0.02, # 2% after 1 hour
"120": 0.01, # 1% after 2 hours
"240": 0.005, # 0.5% after 4 hours
}
# Stoploss - tighter for bear market conditions
stoploss = -0.03 # 3% stop loss
# Trailing stop - protect profits in volatile conditions
trailing_stop = True
trailing_stop_positive = 0.01 # 1% trailing stop
trailing_stop_positive_offset = 0.02 # Start trailing after 2% profit
trailing_only_offset_is_reached = True
# Optimal timeframe for the strategy
timeframe = '1h' # 1-hour timeframe for more opportunities
# Strategy parameters
rsi_period = IntParameter(10, 20, default=14, space="buy")
rsi_oversold = IntParameter(20, 40, default=30, space="buy")
rsi_overbought = IntParameter(60, 80, default=70, space="buy")
sma_period = IntParameter(50, 200, default=100, space="buy")
volume_multiplier = DecimalParameter(1.0, 3.0, default=1.5, space="buy")
# Run "populate_indicators" only for new candle.
process_only_new_candles = True
# These values can be overridden in the config.
use_exit_signal = True
exit_profit_only = False
ignore_roi_if_entry_signal = False
# Number of candles the strategy requires before producing valid signals
startup_candle_count: int = 100
def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
"""
Adds several different TA indicators to the given DataFrame
"""
# RSI
dataframe['rsi'] = ta.RSI(dataframe, timeperiod=self.rsi_period.value)
# Moving Averages
dataframe['sma'] = ta.SMA(dataframe, timeperiod=self.sma_period.value)
dataframe['sma_20'] = ta.SMA(dataframe, timeperiod=20)
# Volume indicators
dataframe['volume_sma'] = ta.SMA(dataframe['volume'], timeperiod=20)
dataframe['volume_high'] = dataframe['volume'] > (dataframe['volume_sma'] * self.volume_multiplier.value)
# Bear market detection
dataframe['bear_market'] = dataframe['close'] < dataframe['sma']
# RSI conditions
dataframe['rsi_oversold'] = dataframe['rsi'] < self.rsi_oversold.value
dataframe['rsi_overbought'] = dataframe['rsi'] > self.rsi_overbought.value
# Price momentum
dataframe['price_change'] = dataframe['close'].pct_change()
dataframe['negative_momentum'] = dataframe['price_change'] < 0
# Bollinger Bands for additional context
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_percent'] = (dataframe['close'] - dataframe['bb_lowerband']) / (dataframe['bb_upperband'] - dataframe['bb_lowerband'])
# Near support levels
dataframe['near_support'] = dataframe['bb_percent'] < 0.2
return dataframe
def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
"""
Based on TA indicators, populates the entry signal for the given dataframe
"""
dataframe.loc[
(
# Bear market condition - price below moving average
(dataframe['bear_market'] == True) &
# RSI oversold signal
(dataframe['rsi_oversold'] == True) &
# Volume confirmation
(dataframe['volume_high'] == True) &
# Near support levels (optional additional filter)
(dataframe['near_support'] == True) &
# Ensure we have volume
(dataframe['volume'] > 0)
),
'enter_long'] = 1
return dataframe
def populate_exit_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
"""
Based on TA indicators, populates the exit signal for the given dataframe
"""
dataframe.loc[
(
# Exit if RSI becomes overbought
(dataframe['rsi_overbought'] == True) |
# Exit if market turns bullish (price above MA)
(dataframe['bear_market'] == False) |
# Exit if price moves too far from support
(dataframe['bb_percent'] > 0.8)
),
'exit_long'] = 1
return dataframe
def custom_stoploss(self, pair: str, trade: 'Trade', current_time: datetime,
current_rate: float, current_profit: float, **kwargs) -> float:
"""
Custom stoploss logic, returning the new distance relative to current_rate
"""
# If we're in a strong bear market, use tighter stops
dataframe, _ = self.dp.get_analyzed_dataframe(pair, self.timeframe)
last_candle = dataframe.iloc[-1].squeeze()
# Tighter stop in strong bear markets
if last_candle['rsi'] < 25:
return -0.02 # 2% stop in very oversold conditions
# Normal stop
return self.stoploss