version: "1.0.0" name: breakout description: Use when writing a swing/intraday breakout strategy on Superior Trade — anything described as breakout, momentum, trend following, 12-hour high, range expansion, riding new highs, Donchian breakout. Note this template was unprofitable in our reference backtest (long-only in a -13% market); explain regime sensitivity to the user. metadata: version: 0.1.0 updated: 2026-05-07

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

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

Config requirements

{
  "exchange": { "name": "hyperliquid", "pair_whitelist": ["BTC/USDC:USDC"] },
  "stake_currency": "USDC",
  "stake_amount": 100,
  "timeframe": "1h",
  "max_open_trades": 1,
  "stoploss": -0.05,
  "minimal_roi": { "0": 100.0 },
  "trading_mode": "futures",
  "margin_mode": "cross",
  "trailing_stop": true,
  "trailing_stop_positive": 0.015,
  "trailing_stop_positive_offset": 0.025,
  "trailing_only_offset_is_reached": true,
  "entry_pricing": { "price_side": "same" },
  "exit_pricing": { "price_side": "same" },
  "pairlists": [{ "method": "StaticPairList" }]
}

The trailing-stop block is what makes this template worth keeping — it locks in profits once a breakout extends past +2.5%, then trails 1.5% behind.

Tunable parameters

Variants worth testing

• Higher-timeframe regime filter: only enter when 1d close > 1d ema_50. Removes trades in clear downtrends (would have killed most of the −0.95% in the reference).
• Donchian channel proper: rolling 20-bar high (instead of 12) is the textbook breakout. Test with longer rolling window.
• Multi-pair (top 30 perps): replace StaticPairList with VolumePairList filtered to top 30 by 24h volume. Diversifies regime risk.
• Add ATR-scaled position sizing: smaller stake when ATR is high (more risk per trade) keeps risk-per-trade flat.

Common pitfalls

1. Long-only in downtrends. As shown by the reference. Add a regime filter or accept the strategy will lose money in bear markets.
2. `process_only_new_candles = False`. Default True is correct here; setting it false fires on every tick during backtest dry-run and triple-counts entries.
3. Conflict between `minimal_roi` and trailing stop. Setting minimal_roi: { "0": 0.05 } exits at +5% before the trailing stop activates at +2.5% offset. Use {"0": 100.0} and let the trailing stop run.
4. `startup_candle_count` too small for ATR-14. ATR needs 14 bars of warmup; the default 30 is fine. If you switch to ATR-100, bump startup to 100+.

Sources

• Internal audit — docs/standard-strategies-audit.md, backtest 01kqypw5bqsaezpgm8pxcrpvyb
• Freqtrade trailing stop — https://www.freqtrade.io/en/stable/stoploss/#trailing-stop-loss