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ICT_SMC_Strategy

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win-boom/BTCquant/user_data/strategies/ict_smc_strategy.py · first seen 2026-07-16 · repo updated 2026-06-20 · ⬇ 1 download

Basics mode: futures timeframe: 15m interface version: 3
Settings stoploss: -0.02 has minimal roi trailing process only new candles startup candle count: 300
Indicators ATR EMA RSI SMA talib
Concepts trailing
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"""
ICT/SMC Strategy — Smart Money Concepts for Freqtrade
Efficient implementation using vectorized operations
15m timeframe, Binance Futures

Core ICT Concepts Implemented:
1. Market Structure (HH/HL/BOS/CHoCH)
2. Liquidity Sweeps
3. Order Blocks (simplified)
4. Fair Value Gaps (simplified)
5. Displacement / Momentum
6. Session Killzones
"""
from pandas import DataFrame
import pandas as pd
import talib.abstract as ta
import numpy as np
from freqtrade.strategy import IStrategy


class ICT_SMC_Strategy(IStrategy):
    INTERFACE_VERSION = 3
    timeframe = '15m'
    can_short = True

    stoploss = -0.02
    trailing_stop = True
    trailing_stop_positive = 0.005
    trailing_stop_positive_offset = 0.012
    trailing_only_offset_is_reached = True

    minimal_roi = {"0": 0.06, "480": 0.04, "1440": 0.02, "4320": 0}

    max_open_trades = 6
    startup_candle_count = 300
    process_only_new_candles = True
    use_exit_signal = False

    def populate_indicators(self, d: DataFrame, m: dict) -> DataFrame:
        # ============================================================
        # 1. SWING HIGHS/LOWS (vectorized)
        # ============================================================
        # Swing high: candle high > 3 before and 3 after
        d['swing_high'] = (
            (d['high'] > d['high'].shift(1)) &
            (d['high'] > d['high'].shift(2)) &
            (d['high'] > d['high'].shift(3)) &
            (d['high'] >= d['high'].shift(-1)) &
            (d['high'] >= d['high'].shift(-2)) &
            (d['high'] >= d['high'].shift(-3))
        )
        # Swing low: candle low < 3 before and 3 after
        d['swing_low'] = (
            (d['low'] < d['low'].shift(1)) &
            (d['low'] < d['low'].shift(2)) &
            (d['low'] < d['low'].shift(3)) &
            (d['low'] <= d['low'].shift(-1)) &
            (d['low'] <= d['low'].shift(-2)) &
            (d['low'] <= d['low'].shift(-3))
        )

        # Forward-fill last swing high/low values
        d['last_sh'] = d['high'].where(d['swing_high']).ffill()
        d['last_sl'] = d['low'].where(d['swing_low']).ffill()

        # ============================================================
        # 2. BREAK OF STRUCTURE (BOS)
        # ============================================================
        # Bull BOS: close breaks above last swing high
        d['bos_bull'] = d['close'] > d['last_sh'].shift(1)
        # Bear BOS: close breaks below last swing low
        d['bos_bear'] = d['close'] < d['last_sl'].shift(1)

        # CHoCH: BOS after opposite trend (simplified: 2+ BOS in one direction, then opposite BOS)
        d['bull_count'] = d['bos_bull'].rolling(24).sum()   # ~6 hours
        d['bear_count'] = d['bos_bear'].rolling(24).sum()
        d['choch_bear'] = d['bos_bear'] & (d['bull_count'].shift(1) >= 2)  # bear BOS after bull run
        d['choch_bull'] = d['bos_bull'] & (d['bear_count'].shift(1) >= 2)  # bull BOS after bear run

        # ============================================================
        # 3. LIQUIDITY SWEEPS
        # ============================================================
        # Sweep of 20-bar highs/lows
        d['hh_20'] = d['high'].rolling(20).max()
        d['ll_20'] = d['low'].rolling(20).min()
        # High sweep: price hits 20-bar high then closes back below
        d['sweep_high'] = (d['high'] >= d['hh_20'].shift(1) * 0.998) & (d['close'] < d['hh_20'].shift(1) * 0.997)
        # Low sweep: price hits 20-bar low then closes back above
        d['sweep_low'] = (d['low'] <= d['ll_20'].shift(1) * 1.002) & (d['close'] > d['ll_20'].shift(1) * 1.003)

        # Sweep of previous candle extremes
        d['sweep_prev_high'] = (d['high'] > d['high'].shift(1)) & (d['close'] < d['high'].shift(1))
        d['sweep_prev_low'] = (d['low'] < d['low'].shift(1)) & (d['close'] > d['low'].shift(1))

        # ============================================================
        # 4. ORDER BLOCKS (simplified: last opposite candle before displacement)
        # ============================================================
        # Displacement: candle body > 2x 20-period average body
        d['body'] = (d['close'] - d['open']).abs()
        d['avg_body'] = d['body'].rolling(20).mean()
        d['displacement_up'] = (d['close'] > d['open']) & (d['body'] > d['avg_body'] * 1.8)
        d['displacement_down'] = (d['close'] < d['open']) & (d['body'] > d['avg_body'] * 1.8)
        d['displacement'] = d['displacement_up'] | d['displacement_down']

        # Bull OB: bear candle before displacement up = reversal zone
        d['ob_bull'] = d['displacement_up'] & (d['close'].shift(1) < d['open'].shift(1))
        # Bear OB: bull candle before displacement down
        d['ob_bear'] = d['displacement_down'] & (d['close'].shift(1) > d['open'].shift(1))

        # OB candle range becomes potential entry zone
        d['ob_bull_high'] = np.where(d['ob_bull'], d['high'].shift(1), np.nan)
        d['ob_bull_low'] = np.where(d['ob_bull'], d['low'].shift(1), np.nan)
        d['ob_bear_high'] = np.where(d['ob_bear'], d['high'].shift(1), np.nan)
        d['ob_bear_low'] = np.where(d['ob_bear'], d['low'].shift(1), np.nan)

        # OB retest: price returns to OB zone within 12 candles of OB formation
        d['ob_bull_retest'] = False
        d['ob_bear_retest'] = False
        for lookback in [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12]:
            d['ob_bull_retest'] = d['ob_bull_retest'] | (
                d['ob_bull'].shift(lookback) &
                (d['low'] <= d['ob_bull_high'].shift(lookback)) &
                (d['close'] >= d['ob_bull_low'].shift(lookback))
            )
            d['ob_bear_retest'] = d['ob_bear_retest'] | (
                d['ob_bear'].shift(lookback) &
                (d['high'] >= d['ob_bear_low'].shift(lookback)) &
                (d['close'] <= d['ob_bear_high'].shift(lookback))
            )

        # ============================================================
        # 5. FAIR VALUE GAP (FVG) — simplified vectorized
        # ============================================================
        # Bull FVG: C[2] low > C[0] high → unfilled gap
        d['fvg_bull'] = d['low'].shift(2) > d['high'].shift(0) * 1.002
        d['fvg_bear'] = d['high'].shift(2) < d['low'].shift(0) * 0.998

        # FVG retest within 12 candles
        d['fvg_bull_retest'] = False
        d['fvg_bear_retest'] = False
        for lb in [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12]:
            d['fvg_bull_retest'] = d['fvg_bull_retest'] | (
                d['fvg_bull'].shift(lb) & (d['low'] <= d['high'].shift(lb - 2))
            )
            d['fvg_bear_retest'] = d['fvg_bear_retest'] | (
                d['fvg_bear'].shift(lb) & (d['high'] >= d['low'].shift(lb - 2))
            )

        # ============================================================
        # 6. FIBONACCI / OTE ZONE
        # ============================================================
        # Use rolling 96-period (1 day) swing high/low for fib levels
        d['swing_high_96'] = d['high'].rolling(96).max()
        d['swing_low_96'] = d['low'].rolling(96).min()
        swing_range = d['swing_high_96'] - d['swing_low_96']

        d['fib_062'] = d['swing_high_96'] - swing_range * 0.618  # OTE entry
        d['fib_079'] = d['swing_high_96'] - swing_range * 0.786  # OTE boundary
        d['fib_062_s'] = d['swing_low_96'] + swing_range * 0.618  # Short OTE
        d['fib_079_s'] = d['swing_low_96'] + swing_range * 0.786

        # In OTE zone for long: price between 0.62 and 0.79 retrace
        d['in_ote_long'] = (d['close'] >= d['fib_079']) & (d['close'] <= d['fib_062'])
        d['in_ote_short'] = (d['close'] >= d['fib_062_s']) & (d['close'] <= d['fib_079_s'])

        # ============================================================
        # 7. TRADITIONAL INDICATORS
        # ============================================================
        d['e20'] = ta.EMA(d, timeperiod=20)
        d['e50'] = ta.EMA(d, timeperiod=50)
        d['rsi'] = ta.RSI(d, timeperiod=14)
        d['atr'] = ta.ATR(d, timeperiod=14)
        d['vr'] = d['volume'] / ta.SMA(d['volume'], timeperiod=20)

        # ============================================================
        # 8. KILLZONE — London/NY session
        # ============================================================
        d['hour'] = pd.to_datetime(d['date']).dt.hour
        d['in_killzone'] = d['hour'].between(7, 9) | d['hour'].between(12, 14)

        return d

    def populate_entry_trend(self, d: DataFrame, m: dict) -> DataFrame:
        # ===== ICT LONG ENTRY =====
        # Structure: channel_h/bos_bull/choch_bull confirming uptrend
        structure_bull = d['bos_bull'] | d['close'] > d['e50']

        # Setup: liquidity sweep below recent low (stop hunt) + OB/FVG entry
        entry_setup_long = (
            d['sweep_low'] | d['sweep_prev_low']
        ) & (
            d['ob_bull_retest'] | d['fvg_bull_retest'] | d['in_ote_long']
        )

        # Confirmation: displacement + RSI
        confirm_long = (
            d['displacement_up'] &
            d['rsi'].between(30, 70) &
            d['vr'] > 0.6
        )

        d.loc[
            structure_bull & entry_setup_long & confirm_long & (d['volume'] > 0),
            ['enter_long', 'enter_tag']
        ] = (1, 'ICT_L')

        # ===== ICT SHORT ENTRY =====
        structure_bear = d['bos_bear'] | d['close'] < d['e50']

        entry_setup_short = (
            d['sweep_high'] | d['sweep_prev_high']
        ) & (
            d['ob_bear_retest'] | d['fvg_bear_retest'] | d['in_ote_short']
        )

        confirm_short = (
            d['displacement_down'] &
            d['rsi'].between(30, 70) &
            d['vr'] > 0.6
        )

        d.loc[
            structure_bear & entry_setup_short & confirm_short & (d['volume'] > 0),
            ['enter_short', 'enter_tag']
        ] = (1, 'ICT_S')

        return d

    def populate_exit_trend(self, d: DataFrame, m: dict) -> DataFrame:
        return d