✨ AI Search 🔒
💬 Forum

BreakoutTrendStrategy

🏆 League #805 / 1936
♡

wentixiaogege/binance_trade/user_data/strategies/trend/BreakoutTrendStrategy.py · first seen 2026-07-16 · repo updated 2026-06-21

Basics mode: spot timeframe: 5m
Settings stoploss: -0.08 has minimal roi custom stoploss process only new candles: false startup candle count: 200 hyperopt
Indicators ADX ATR EMA RSI talib
Concepts breakout
Methods custom_stake_amount custom_stoploss
15 related strategies (⧉ identical code, ≈ similar name)

Each tile is a different kind of check — from an instant code lint to full sandboxed backtests and forward tests on recent data. Not sure what a check actually proves? See the FAQ →

Source

Download Raw 
  1
  2
  3
  4
  5
  6
  7
  8
  9
 10
 11
 12
 13
 14
 15
 16
 17
 18
 19
 20
 21
 22
 23
 24
 25
 26
 27
 28
 29
 30
 31
 32
 33
 34
 35
 36
 37
 38
 39
 40
 41
 42
 43
 44
 45
 46
 47
 48
 49
 50
 51
 52
 53
 54
 55
 56
 57
 58
 59
 60
 61
 62
 63
 64
 65
 66
 67
 68
 69
 70
 71
 72
 73
 74
 75
 76
 77
 78
 79
 80
 81
 82
 83
 84
 85
 86
 87
 88
 89
 90
 91
 92
 93
 94
 95
 96
 97
 98
 99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
# strategy_breakout_trend_optimized.py
# Breakout Trend Strategy v2 — 突破趋势 + 1h ATR动态止损

from freqtrade.strategy.interface import IStrategy
from pandas import DataFrame
import talib.abstract as ta
import pandas as pd
pd.options.mode.chained_assignment = None
from functools import reduce
from datetime import datetime
import numpy as np
from freqtrade.strategy import merge_informative_pair

class BreakoutTrendStrategy(IStrategy):
    """
    Breakout Trend Strategy v2
    价格突破 + EMA趋势 + ADX强度 + 1h ATR动态止损
    """

    WHITELIST = ['BTC/USDT', 'ETH/USDT', 'BNB/USDT', 'SOL/USDT', 'XRP/USDT',
                 'DOGE/USDT', 'ADA/USDT', 'TRX/USDT', 'AVAX/USDT', 'LINK/USDT']

    buy_params = {
        "ema_short": 20,
        "ema_long": 50,
        "adx_threshold": 22,
        "rsi_floor": 40,
        "rsi_ceiling": 75,
        "atr_period": 14,
        "atr_sl_multiplier": 3.0,
        "informative_timeframe": "1h",
    }

    sell_params = {}

    minimal_roi = {
        "0": 0.10,
        "60": 0.06,
        "120": 0.04,
        "240": 0.02,
        "480": 0
    }

    stoploss = -0.08

    timeframe = '5m'
    startup_candle_count = 200
    process_only_new_candles = False
    trailing_stop = False
    use_exit_signal = False
    exit_profit_only = False
    ignore_roi_if_entry_signal = False
    use_custom_stoploss = True

    def informative_pairs(self):
        pairs = self.dp.current_whitelist()
        return [(pair, self.buy_params['informative_timeframe']) for pair in pairs]

    def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        informative = self.dp.get_pair_dataframe(pair=metadata['pair'], timeframe=self.buy_params['informative_timeframe'])
        informative['atr_1h'] = ta.ATR(informative, timeperiod=self.buy_params['atr_period'])

        dataframe = merge_informative_pair(dataframe, informative, self.timeframe, self.buy_params['informative_timeframe'], ffill=True)

        dataframe['ema_short'] = ta.EMA(dataframe, timeperiod=self.buy_params['ema_short'])
        dataframe['ema_long'] = ta.EMA(dataframe, timeperiod=self.buy_params['ema_long'])
        dataframe['adx'] = ta.ADX(dataframe, timeperiod=14)
        dataframe['plus_di'] = ta.PLUS_DI(dataframe, timeperiod=14)
        dataframe['minus_di'] = ta.MINUS_DI(dataframe, timeperiod=14)
        dataframe['rsi'] = ta.RSI(dataframe, timeperiod=14)

        # 突破信号：价格突破N周期高点
        dataframe['highest_20'] = dataframe['high'].rolling(window=20).max()
        dataframe['breakout'] = dataframe['close'] > dataframe['highest_20'].shift(1)

        return dataframe

    def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        conditions = [
            dataframe['ema_short'] > dataframe['ema_long'],
            dataframe['adx'] > self.buy_params['adx_threshold'],
            dataframe['plus_di'] > dataframe['minus_di'],
            dataframe['breakout'],
            dataframe['rsi'] > self.buy_params['rsi_floor'],
            dataframe['rsi'] < self.buy_params['rsi_ceiling'],
        ]
        dataframe.loc[reduce(lambda x, y: x & y, conditions), 'enter_long'] = 1
        return dataframe

    def populate_exit_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        return dataframe

    def custom_stoploss(self, pair: str, trade: 'Trade', current_time: datetime,
                        current_rate: float, current_profit: float, **kwargs) -> float:
        dataframe, _ = self.dp.get_analyzed_dataframe(pair, self.timeframe)
        if len(dataframe) == 0:
            return self.stoploss

        last_candle = dataframe.iloc[-1].squeeze()
        atr = last_candle.get('atr_1h', 0)
        if atr <= 0:
            return self.stoploss

        sl_mult = self.buy_params['atr_sl_multiplier']

        if current_profit > 0.10:
            trail_stop = (current_rate - atr * 1.0 - trade.open_rate) / trade.open_rate
            return max(trail_stop, self.stoploss)
        elif current_profit > 0.05:
            trail_stop = (current_rate - atr * 2.0 - trade.open_rate) / trade.open_rate
            return max(trail_stop, self.stoploss)
        elif current_profit > 0.02:
            return max(0.005, self.stoploss)
        else:
            base_stop = (current_rate - atr * sl_mult - trade.open_rate) / trade.open_rate
            return max(base_stop, self.stoploss)

    def custom_stake_amount(self, pair: str, current_time: datetime, current_rate: float,
                            proposed_stake: float, min_stake: float, max_stake: float,
                            entry_tag: str, **kwargs) -> float:
        dataframe, _ = self.dp.get_analyzed_dataframe(pair, self.timeframe)
        if len(dataframe) == 0:
            return proposed_stake

        last_candle = dataframe.iloc[-1].squeeze()
        risk_factor = 1.0

        adx = last_candle.get('adx', 20)
        if adx > 30:
            risk_factor *= 1.3
        elif adx < 18:
            risk_factor *= 0.7

        risk_factor = max(0.3, min(1.5, risk_factor))
        adjusted_stake = proposed_stake * risk_factor
        return max(min_stake, min(adjusted_stake, max_stake))