--- 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 | 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 ```python 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 ```json { "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 | Knob | Effect | |---|---| | `12` (rolling high length) | Shorter (`6`) → more entries, lower-quality breakouts. Longer (`24`) → fewer, higher-conviction. | | `volume > vol_avg20` | Stricter (`> vol_avg20 × 1.5`) → only volume-confirmed breakouts. | | `trailing_stop_positive_offset` (0.025) | Higher → trailing stop activates later, gives more room. Lower → locks in earlier, exits more often. | | `trailing_stop_positive` (0.015) | Tighter trail → exits closer to highs, more stops out. | | `low_6h` exit | Shorter window → faster invalidation. Longer → patience but bigger giveback. | ## 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