Breakout Strategy Engine

Breakout Strategy Engine

Pre-Built Breakout Strategies with Confirmation Filters

import pandas as pd
import numpy as np
from dataclasses import dataclass
from typing import Optional

@dataclass
class BreakoutSignal:
    symbol: str
    direction: str  # "long" or "short"
    entry: float
    stop_loss: float
    target: float
    strategy: str
    confirmation: list[str]
    strength: float  # 0-1

class BreakoutEngine:

    # ═══════════════════════════════════════
    # 1. BOLLINGER SQUEEZE BREAKOUT
    # ═══════════════════════════════════════
    @staticmethod
    def bollinger_squeeze(df: pd.DataFrame, bb_period: int = 20, kc_period: int = 20,
                          kc_mult: float = 1.5) -> dict:
        """Bollinger inside Keltner Channel = squeeze. Breakout when squeeze releases."""
        close = df["close"]
        bb_mid = close.rolling(bb_period).mean()
        bb_std = close.rolling(bb_period).std()
        bb_upper = bb_mid + 2 * bb_std
        bb_lower = bb_mid - 2 * bb_std

        atr = ((df["high"] - df["low"]).rolling(kc_period).mean())
        kc_upper = bb_mid + kc_mult * atr
        kc_lower = bb_mid - kc_mult * atr

        squeeze_on = (bb_lower > kc_lower) & (bb_upper < kc_upper)
        squeeze_off = ~squeeze_on
        # Squeeze just released
        squeeze_fire = squeeze_off & squeeze_on.shift(1)

        # Direction from momentum
        momentum = close - close.rolling(bb_period).mean()
        direction = np.where(momentum > 0, "long", "short")

        df_out = df.copy()
        df_out["squeeze_on"] = squeeze_on
        df_out["squeeze_fire"] = squeeze_fire
        df_out["direction"] = direction
        df_out["bb_width"] = (bb_upper - bb_lower) / bb_mid * 100

        current = df_out.iloc[-1]
        return {
            "strategy": "bollinger_squeeze",
            "squeeze_active": bool(current["squeeze_on"]),
            "squeeze_firing": bool(current["squeeze_fire"]),
            "direction": current["direction"],
            "bb_width": round(current["bb_width"], 3),
            "bars_in_squeeze": int(squeeze_on.iloc[-20:].sum()),
            "signal": "BREAKOUT FIRING" if current["squeeze_fire"] else
                     "SQUEEZE BUILDING" if current["squeeze_on"] else "NO SQUEEZE",
        }

    # ═══════════════════════════════════════
    # 2. RANGE BREAKOUT (Donchian)
    # ═══════════════════════════════════════
    @staticmethod
    def donchian_breakout(df: pd.DataFrame, period: int = 20, atr_mult: float = 1.5) -> dict:
        """Break above/below N-period high/low with ATR confirmation."""
        high_n = df["high"].rolling(period).max().shift(1)
        low_n = df["low"].rolling(period).min().shift(1)
        atr_val = ((df["high"] - df["low"]).rolling(14).mean())
        close = df["close"]

        long_break = close > high_n
        short_break = close < low_n
        # Volume confirmation
        vol_confirm = df["volume"] > df["volume"].rolling(20).mean() * 1.5

        current = df.iloc[-1]
        return {
            "strategy": "donchian_breakout",
            "upper_channel": round(high_n.iloc[-1], 5),
            "lower_channel": round(low_n.iloc[-1], 5),
            "current_price": round(current["close"], 5),
            "long_breakout": bool(long_break.iloc[-1]),
            "short_breakout": bool(short_break.iloc[-1]),
            "volume_confirmed": bool(vol_confirm.iloc[-1]),
            "atr": round(atr_val.iloc[-1], 5),
            "stop_long": round(high_n.iloc[-1] - atr_mult * atr_val.iloc[-1], 5),
            "stop_short": round(low_n.iloc[-1] + atr_mult * atr_val.iloc[-1], 5),
        }

    # ═══════════════════════════════════════
    # 3. MOMENTUM BREAKOUT
    # ═══════════════════════════════════════
    @staticmethod
    def momentum_breakout(df: pd.DataFrame) -> dict:
        """Multi-filter momentum breakout: ADX + volume + close above/below structure."""
        close = df["close"]
        atr = (df["high"] - df["low"]).rolling(14).mean()
        # ADX proxy
        plus_dm = df["high"].diff().clip(lower=0).rolling(14).mean()
        minus_dm = (-df["low"].diff()).clip(lower=0).rolling(14).mean()
        dx = abs(plus_dm - minus_dm) / (plus_dm + minus_dm + 1e-10) * 100
        adx = dx.rolling(14).mean()
        # Momentum
        mom_10 = close.pct_change(10)
        vol_ratio = df["volume"] / df["volume"].rolling(20).mean()
        # Structure break
        high_20 = df["high"].rolling(20).max()
        low_20 = df["low"].rolling(20).min()

        current = df.iloc[-1]
        filters = []
        if adx.iloc[-1] > 25: filters.append("ADX>25 (trending)")
        if vol_ratio.iloc[-1] > 1.5: filters.append("Volume 1.5x avg")
        if current["close"] > high_20.iloc[-2]: filters.append("New 20-bar high")
        if current["close"] < low_20.iloc[-2]: filters.append("New 20-bar low")
        if abs(mom_10.iloc[-1]) > 0.01: filters.append("Strong 10-bar momentum")

        direction = "long" if mom_10.iloc[-1] > 0 else "short"
        return {
            "strategy": "momentum_breakout",
            "direction": direction,
            "adx": round(adx.iloc[-1], 1),
            "momentum_10": round(mom_10.iloc[-1] * 100, 2),
            "volume_ratio": round(vol_ratio.iloc[-1], 2),
            "confirmations": filters,
            "n_confirmations": len(filters),
            "signal_quality": "A+" if len(filters) >= 4 else "A" if len(filters) >= 3 else "B" if len(filters) >= 2 else "C",
            "atr_stop": round(atr.iloc[-1] * 2, 5),
        }

    # ═══════════════════════════════════════
    # FALSE BREAKOUT FILTER
    # ═══════════════════════════════════════
    @staticmethod
    def false_breakout_probability(df: pd.DataFrame, lookback: int = 100) -> dict:
        """Historical false breakout rate for current pair to calibrate expectations."""
        high_n = df["high"].rolling(20).max().shift(1)
        low_n = df["low"].rolling(20).min().shift(1)
        breakouts = (df["close"] > high_n) | (df["close"] < low_n)
        # A breakout is false if price returns inside range within 5 bars
        false_count = 0
        total = 0
        for i in range(20, len(df) - 5):
            if breakouts.iloc[i]:
                total += 1
                future = df.iloc[i+1:i+6]
                mid = (high_n.iloc[i] + low_n.iloc[i]) / 2
                if (future["close"] < high_n.iloc[i]).any() and (future["close"] > low_n.iloc[i]).any():
                    false_count += 1
        rate = false_count / max(total, 1)
        return {
            "false_breakout_rate": round(rate * 100, 1),
            "total_breakouts": total,
            "recommendation": "Wait for retest" if rate > 0.5 else "Trade breakout with confirmation",
        }

    @staticmethod
    def scan_all(df: pd.DataFrame, symbol: str = "") -> dict:
        return {
            "symbol": symbol,
            "squeeze": BreakoutEngine.bollinger_squeeze(df),
            "donchian": BreakoutEngine.donchian_breakout(df),
            "momentum": BreakoutEngine.momentum_breakout(df),
            "false_breakout_rate": BreakoutEngine.false_breakout_probability(df),
        }

Automated Breakout Detection with Liquidity Sweep (CodeTrading Python)

Source: "Automated Break Out Detection in Python" by CodeTrading (Nov 2025)

Why Simple Breakouts Fail

A candle closing above a recent high is NOT enough — price often reverts back (fake breakout). A stronger breakout pattern is preceded by a liquidity sweep.

The Liquidity-Confirmed Breakout Pattern

Bullish breakout with sweep:

1. Identify pivot highs and pivot lows within a lookback window (e.g., 40 candles)
2. Find the most recent pivot high (resistance level)
3. Check if a pivot low formed AFTER that high AND is lower than all previous pivot lows in the window = liquidity sweep
4. Current candle closes above the pivot high AND previous candle was below it = breakout confirmed
5. Filter: only take bullish breakouts when last 15 candles are ALL above EMA (trend confirmation)

Bearish breakout with sweep: Mirror logic — pivot low → higher high sweep → breakdown below pivot low

Python Implementation Details

Pivot Detection (no look-ahead bias):

def mark_pivots(df, window=7, high_col='high', low_col='low'):
    # Compare candle high to W neighbors left AND right
    # Pivot high: higher than all W neighbors
    # Pivot low: lower than all W neighbors
    # CRITICAL: stop at current_candle - window + 1 to avoid future data

Breakout Detection Function:

def detect_breakout(candle_idx, back_candles=40, window=5):
    # 1. Find pivots in [candle_idx - back_candles : candle_idx - window + 1]
    # 2. Find last pivot high value
    # 3. Check if close[candle_idx] > pivot_high AND close[candle_idx-1] < pivot_high
    # 4. Find pivot low between last_pivot_high and breakout candle
    # 5. Confirm pivot_low < min(all previous pivot lows) = SWEEP
    # Return: 2 = bullish breakout, 1 = bearish breakout, 0 = no signal

Trend Filter (EMA-based):

• Check if last N candles (e.g., 15) are ALL above EMA → uptrend → only bullish breakouts
• ALL below EMA → downtrend → only bearish breakouts
• Adjustable selectivity: 10, 15, 20, 25 candles

Backtest Results (EURUSD, 20 years hourly data, 2003-2023)

Optimization surface (TP/SL ratio vs ATR multiplier):

• Best zone: TP/SL ratio 4-5x, ATR multiplier 4+
• Returns: 5-52% depending on parameters (no leverage, no commission)
• Drawdown minimized at ATR mult ~1, TP/SL ~1 (but returns limited to 5-8%)

Key Parameters

back_candles = 40     # Lookback window for pattern detection
pivot_window = 5-7    # Neighbors for pivot detection
ema_candles = 15      # Candles for trend confirmation
atr_mult = 1-5        # Stop loss = ATR * multiplier
tp_sl_ratio = 1-5     # Take profit / stop loss ratio

Usage Recommendation

• Best as an alert indicator — bot detects pattern, human manages trade
• Run simultaneously on multiple assets to multiply signal count without losing precision
• Trade management (TP/SL optimization) is asset-specific — needs tuning per instrument

See also: smart-money-trap-detector (fake breakout detection), liquidity-order-flow-mapper, chart-pattern-scanner