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TrendFollowingStrategy

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hamidreza07/freqtrade/freqtrade/templates/futures/TrendFollowingStrategy.py · ★6 · first seen 2026-07-16 · repo updated 2024-09-09 · ⬇ 1 download

Basics mode: spot timeframe: 5m interface version: 3
Settings stoploss: -0.265 has minimal roi trailing
Indicators OBV talib
Concepts trailing trend_following
15 related strategies (⧉ identical code, ≈ similar name)

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from functools import reduce
from pandas import DataFrame
from freqtrade.strategy import IStrategy

import talib.abstract as ta

from freqtrade.strategy.interface import IStrategy

class TrendFollowingStrategy(IStrategy):

    INTERFACE_VERSION: int = 3
    # ROI table:
    minimal_roi = {"0": 0.15, "30": 0.1, "60": 0.05}
    # minimal_roi = {"0": 1}

    # Stoploss:
    stoploss = -0.265

    # Trailing stop:
    trailing_stop = True
    trailing_stop_positive = 0.05
    trailing_stop_positive_offset = 0.1
    trailing_only_offset_is_reached = False

    timeframe = "5m"

    def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        # Calculate OBV
        dataframe['obv'] = ta.OBV(dataframe['close'], dataframe['volume'])
        
        # Add your trend following indicators here
        dataframe['trend'] = dataframe['close'].ewm(span=20, adjust=False).mean()
        
        return dataframe
    
    def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        # Add your trend following buy signals here
        dataframe.loc[
            (dataframe['close'] > dataframe['trend']) & 
            (dataframe['close'].shift(1) <= dataframe['trend'].shift(1)) &
            (dataframe['obv'] > dataframe['obv'].shift(1)), 
            'enter_long'] = 1
        
        # Add your trend following sell signals here
        dataframe.loc[
            (dataframe['close'] < dataframe['trend']) & 
            (dataframe['close'].shift(1) >= dataframe['trend'].shift(1)) &
            (dataframe['obv'] < dataframe['obv'].shift(1)), 
            'enter_short'] = -1
        
        return dataframe

    def populate_exit_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        # Add your trend following exit signals for long positions here
        dataframe.loc[
            (dataframe['close'] < dataframe['trend']) & 
            (dataframe['close'].shift(1) >= dataframe['trend'].shift(1)) &
            (dataframe['obv'] > dataframe['obv'].shift(1)), 
            'exit_long'] = 1
        
        # Add your trend following exit signals for short positions here
        dataframe.loc[
            (dataframe['close'] > dataframe['trend']) & 
            (dataframe['close'].shift(1) <= dataframe['trend'].shift(1)) &
            (dataframe['obv'] < dataframe['obv'].shift(1)), 
            'exit_short'] = 1
        
        return dataframe