deploy: 2026-03-20 07:49
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ufo6849
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strategy/entry_rules.py
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278
strategy/entry_rules.py
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"""ICT entry rules.
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Evaluates bullish and bearish entry conditions and calculates
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stop-loss / take-profit levels based on market structure.
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"""
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from __future__ import annotations
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from dataclasses import dataclass, field
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from typing import List, Optional
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import pandas as pd
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from loguru import logger
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from indicators.ict_engine import ICTSignals
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from indicators.multi_timeframe import TradeDirection
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@dataclass
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class EntryResult:
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"""Result of an entry rule evaluation."""
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is_valid: bool
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direction: TradeDirection
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conditions_met: List[str] = field(default_factory=list)
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conditions_failed: List[str] = field(default_factory=list)
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class EntryRules:
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"""ICT-based entry rule evaluation.
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Bullish entry (LONG):
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1. HTF: Higher Highs & Higher Lows
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2. Liquidity Sweep: previous low swept then bounce
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3. Order Block: price enters bullish OB zone
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4. FVG: price returns to bullish FVG
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5. BOS: upward break of structure
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Bearish entry (SHORT): mirror logic.
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"""
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def check_bullish_entry(
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self, signals: ICTSignals, price: float
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) -> EntryResult:
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"""Evaluate bullish (LONG) entry conditions."""
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met: List[str] = []
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failed: List[str] = []
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# 1. BOS bullish
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if signals.latest_bos == 1:
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met.append("BOS bullish")
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else:
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failed.append("BOS bullish")
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# 2. Order Block -- price in bullish OB zone
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obs = signals.active_order_blocks
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ob_hit = False
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if not obs.empty and "OB" in obs.columns:
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bullish_obs = obs[obs["OB"] == 1]
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for _, row in bullish_obs.iterrows():
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bottom = row.get("Bottom", 0)
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top = row.get("Top", 0)
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if pd.notna(bottom) and pd.notna(top) and bottom <= price <= top:
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ob_hit = True
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break
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if ob_hit:
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met.append("Order Block")
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else:
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failed.append("Order Block")
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# 3. FVG -- price in bullish FVG
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fvgs = signals.active_fvg
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fvg_hit = False
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if not fvgs.empty and "FVG" in fvgs.columns:
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bullish_fvg = fvgs[fvgs["FVG"] == 1]
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for _, row in bullish_fvg.iterrows():
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bottom = row.get("Bottom", 0)
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top = row.get("Top", 0)
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if pd.notna(bottom) and pd.notna(top) and bottom <= price <= top:
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fvg_hit = True
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break
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if fvg_hit:
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met.append("FVG")
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else:
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failed.append("FVG")
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# 4. Liquidity swept (recent bearish liquidity = trap)
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liq_swept = False
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try:
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if not signals.liquidity.empty:
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liq_col = signals.liquidity.get("Liquidity", pd.Series(dtype=float))
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recent = liq_col.dropna().tail(3)
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if len(recent) > 0 and (recent == -1).any():
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liq_swept = True
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except (ValueError, TypeError):
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pass
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if liq_swept:
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met.append("Liquidity Sweep")
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else:
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failed.append("Liquidity Sweep")
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# 5. CHOCH bullish (optional extra confirmation)
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if signals.latest_choch == 1:
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met.append("CHOCH bullish")
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else:
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failed.append("CHOCH bullish")
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return EntryResult(
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is_valid=len(met) >= 3,
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direction=TradeDirection.LONG,
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conditions_met=met,
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conditions_failed=failed,
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)
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def check_bearish_entry(
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self, signals: ICTSignals, price: float
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) -> EntryResult:
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"""Evaluate bearish (SHORT) entry conditions."""
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met: List[str] = []
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failed: List[str] = []
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# 1. BOS bearish
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if signals.latest_bos == -1:
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met.append("BOS bearish")
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else:
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failed.append("BOS bearish")
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# 2. Order Block -- price in bearish OB
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obs = signals.active_order_blocks
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ob_hit = False
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if not obs.empty and "OB" in obs.columns:
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bearish_obs = obs[obs["OB"] == -1]
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for _, row in bearish_obs.iterrows():
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bottom = row.get("Bottom", 0)
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top = row.get("Top", 0)
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if pd.notna(bottom) and pd.notna(top) and bottom <= price <= top:
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ob_hit = True
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break
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if ob_hit:
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met.append("Order Block")
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else:
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failed.append("Order Block")
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# 3. FVG bearish
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fvgs = signals.active_fvg
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fvg_hit = False
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if not fvgs.empty and "FVG" in fvgs.columns:
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bearish_fvg = fvgs[fvgs["FVG"] == -1]
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for _, row in bearish_fvg.iterrows():
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bottom = row.get("Bottom", 0)
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top = row.get("Top", 0)
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if pd.notna(bottom) and pd.notna(top) and bottom <= price <= top:
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fvg_hit = True
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break
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if fvg_hit:
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met.append("FVG")
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else:
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failed.append("FVG")
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# 4. Liquidity swept (bullish liquidity = trap)
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liq_swept = False
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try:
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if not signals.liquidity.empty:
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liq_col = signals.liquidity.get("Liquidity", pd.Series(dtype=float))
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recent = liq_col.dropna().tail(3)
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if len(recent) > 0 and (recent == 1).any():
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liq_swept = True
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except (ValueError, TypeError):
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pass
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if liq_swept:
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met.append("Liquidity Sweep")
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else:
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failed.append("Liquidity Sweep")
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# 5. CHOCH bearish
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if signals.latest_choch == -1:
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met.append("CHOCH bearish")
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else:
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failed.append("CHOCH bearish")
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return EntryResult(
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is_valid=len(met) >= 3,
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direction=TradeDirection.SHORT,
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conditions_met=met,
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conditions_failed=failed,
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)
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def calculate_stop_loss(
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self,
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direction: TradeDirection,
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signals: ICTSignals,
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entry_price: float,
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) -> float:
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"""Calculate stop-loss based on OB boundary or recent swing high/low.
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For LONG: SL below the nearest bullish OB bottom or swing low.
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For SHORT: SL above the nearest bearish OB top or swing high.
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"""
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buffer_pct = 0.002 # 0.2% buffer
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if direction == TradeDirection.LONG:
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# Try OB bottom first
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obs = signals.active_order_blocks
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if not obs.empty and "OB" in obs.columns:
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bullish_obs = obs[obs["OB"] == 1]
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if not bullish_obs.empty:
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lowest_bottom = bullish_obs["Bottom"].dropna().min()
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if pd.notna(lowest_bottom):
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return float(lowest_bottom) * (1 - buffer_pct)
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# Fallback: recent swing low
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swing = signals.swing_highs_lows
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if "Level" in swing.columns and "HighLow" in swing.columns:
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lows = swing[swing["HighLow"] == -1]["Level"].dropna()
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if len(lows) > 0:
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return float(lows.iloc[-1]) * (1 - buffer_pct)
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# Last resort: fixed percentage
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return entry_price * (1 - 0.02)
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else: # SHORT
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obs = signals.active_order_blocks
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if not obs.empty and "OB" in obs.columns:
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bearish_obs = obs[obs["OB"] == -1]
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if not bearish_obs.empty:
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highest_top = bearish_obs["Top"].dropna().max()
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if pd.notna(highest_top):
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return float(highest_top) * (1 + buffer_pct)
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swing = signals.swing_highs_lows
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if "Level" in swing.columns and "HighLow" in swing.columns:
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highs = swing[swing["HighLow"] == 1]["Level"].dropna()
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if len(highs) > 0:
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return float(highs.iloc[-1]) * (1 + buffer_pct)
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return entry_price * (1 + 0.02)
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def calculate_take_profit(
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self,
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direction: TradeDirection,
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signals: ICTSignals,
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entry_price: float,
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stop_loss: float,
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) -> float:
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"""Calculate take-profit targeting opposite OB/FVG or 2:1 R:R minimum.
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For LONG: TP at the nearest bearish OB/FVG above entry, or 2x risk.
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For SHORT: TP at the nearest bullish OB/FVG below entry, or 2x risk.
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"""
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risk = abs(entry_price - stop_loss)
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min_tp_distance = risk * 2 # ensure at least 2:1 R:R
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if direction == TradeDirection.LONG:
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# Look for bearish OB above price
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obs = signals.active_order_blocks
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if not obs.empty and "OB" in obs.columns:
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bearish_obs = obs[obs["OB"] == -1]
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bottom_vals = bearish_obs["Bottom"].dropna()
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above = bottom_vals[bottom_vals > entry_price]
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if len(above) > 0:
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tp = float(above.min())
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if tp - entry_price >= min_tp_distance:
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return tp
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return entry_price + min_tp_distance
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else: # SHORT
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obs = signals.active_order_blocks
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if not obs.empty and "OB" in obs.columns:
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bullish_obs = obs[obs["OB"] == 1]
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top_vals = bullish_obs["Top"].dropna()
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below = top_vals[top_vals < entry_price]
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if len(below) > 0:
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tp = float(below.max())
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if entry_price - tp >= min_tp_distance:
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return tp
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return entry_price - min_tp_distance
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