breakout

Strategy: Momentum · Breakout

When to use

A user asks for "breakout", "momentum", "trend following", "buy new highs", "Donchian breakout", "range expansion". Single or multi-pair, hour-scale, with a trailing stop.

Honest framing

The reference backtest was unprofitable (36% WR, −0.95% PnL) on BTC/USDC:USDC 1h Jan-May 2026 — but BTC fell −13% in that window. Long-only breakouts in a downtrend are structurally a losing setup. The strategy is correct; the regime was wrong.

Two practical paths to make this work:

• Add a regime filter (e.g. only enter when close > ema_200 on the higher timeframe).
• Run on a wider, multi-pair scan so trending alts contribute even when BTC is weak.

Backtest reference

Window	BTC/USDC:USDC 1h, 2026-01-01 → 2026-05-01 (BTC −13%)
Trades	64
Win rate	36%
Wallet PnL	−0.95%
Backtest ID	01kqypw5bqsaezpgm8pxcrpvyb

Trailing stop kept losses small per trade, but the entry signal fired into too many failed breakouts in a downtrend. Re-run on Q4 2025 or a trending alt to see the strategy in its native regime.

Reference implementation

from freqtrade.strategy import IStrategy
import pandas as pd
import talib.abstract as ta


class MomentumBreakoutStrategy(IStrategy):
    minimal_roi = {"0": 100.0}   # let trailing stop manage exits
    stoploss = -0.05
    trailing_stop = True
    trailing_stop_positive = 0.015
    trailing_stop_positive_offset = 0.025
    trailing_only_offset_is_reached = True
    timeframe = "1h"
    process_only_new_candles = True
    startup_candle_count = 30
    can_short = False

    def populate_indicators(self, dataframe: pd.DataFrame, metadata: dict) -> pd.DataFrame:
        dataframe["high_12h"] = dataframe["high"].rolling(12).max().shift(1)
        dataframe["low_6h"] = dataframe["low"].rolling(6).min().shift(1)
        dataframe["vol_avg20"] = dataframe["volume"].rolling(20).mean()
        dataframe["atr_14"] = ta.ATR(dataframe, timeperiod=14)
        return dataframe

    def populate_entry_trend(self, dataframe: pd.DataFrame, metadata: dict) -> pd.DataFrame:
        # Break the prior 12h high on above-average volume.
        dataframe.loc[
            (dataframe["close"] > dataframe["high_12h"])
            & (dataframe["volume"] > dataframe["vol_avg20"]),
            "enter_long",
        ] = 1
        return dataframe

    def populate_exit_trend(self, dataframe: pd.DataFrame, metadata: dict) -> pd.DataFrame:
        # Break the prior 6h low → exit (momentum failed).
        dataframe.loc[(dataframe["close"] < dataframe["low_6h"]), "exit_long"] = 1
        return dataframe