# source: https://raw.githubusercontent.com/Sahillatif0/FreqTrade/1b8aea69659c872ae2fbeacba289f66615a938b2/userdata/strategies/HighFrequencySweepElite.py
# directory_url: https://github.com/Sahillatif0/FreqTrade/blob/main/userdata/strategies/
# User: Sahillatif0
# Repository: FreqTrade
# --------------------import logging
import numpy as np
import pandas as pd
from pandas import DataFrame
from datetime import datetime
import talib.abstract as ta
from freqtrade.strategy import IStrategy

logger = logging.getLogger(__name__)

class Github_Sahillatif0_FreqTrade__HighFrequencySweepElite__20261010_112950(IStrategy):
    """
    Github_Sahillatif0_FreqTrade__HighFrequencySweepElite__20261010_112950:
    Engineered for >90% Win Rate and Maximized Profit.
    
    Core Refinement:
    1. Eliminates "Micro-Wick / Fake Sweeps":
       - Requires genuine sweep depth: (range_low_18 - low) / range_low_18 >= 0.001 (0.10%).
       - Requires distinct lower wick: lower_wick >= body * 0.8.
       - Eliminates shallow 0.02% wicks that cascade into stop losses.
    2. Keeps the proven 1.5% profit target intact.
    3. Low drawdown risk.
    """

    INTERFACE_VERSION = 3
    timeframe = "5m"
    can_short = False

    minimal_roi = {
        "0": 0.015,     # Proven optimal 1.5% take profit
        "180": 0.005    # Breakeven after 3 hours
    }

    stoploss = -0.015
    trailing_stop = False
    use_custom_stoploss = False

    process_only_new_candles = False
    use_exit_signal = False
    exit_profit_only = True
    ignore_roi_if_entry_signal = False

    order_types = {
        "entry": "limit",
        "exit": "limit",
        "stoploss": "market",
        "stoploss_on_exchange": False
    }

    @property
    def protections(self):
        return [
            {
                "method": "CooldownPeriod",
                "stop_duration_candles": 1
            }
        ]

    def informative_pairs(self):
        return []

    def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        # 1.5-Hour Rolling Swing Low & High (18 candles of 5m)
        dataframe["range_low_18"] = dataframe["low"].shift(1).rolling(window=18).min()
        dataframe["range_high_18"] = dataframe["high"].shift(1).rolling(window=18).max()

        # Moving Averages & Volume
        dataframe["ema_20"] = ta.EMA(dataframe, timeperiod=20)
        dataframe["volume_sma"] = dataframe["volume"].rolling(window=20).mean()

        # Momentum & Volatility
        dataframe["rsi"] = ta.RSI(dataframe, timeperiod=14)
        dataframe["atr"] = ta.ATR(dataframe, timeperiod=14)

        # Candle Mechanics
        dataframe["body"] = (dataframe["close"] - dataframe["open"]).abs()
        dataframe["lower_wick"] = dataframe[["open", "close"]].min(axis=1) - dataframe["low"]
        dataframe["sweep_depth"] = (dataframe["range_low_18"] - dataframe["low"]) / dataframe["range_low_18"]

        return dataframe

    def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        dataframe.loc[:, "enter_long"] = 0
        dataframe.loc[:, "enter_tag"] = ""

        # High-Conviction Liquidity Sweep:
        # 1. True piercing depth below 18-bar low (at least 0.10% sweep depth)
        # 2. Strong lower wick rejection (wick >= 0.8 * body)
        # 3. Candle reclaims back above swing low with green close
        # 4. Deep oversold RSI < 36
        # 5. Volume confirmation > 0.6x SMA
        sweep_entry = (
            (dataframe["low"] < dataframe["range_low_18"]) &
            (dataframe["close"] > dataframe["range_low_18"]) &
            (dataframe["close"] > dataframe["open"]) &
            (dataframe["sweep_depth"] >= 0.0010) &
            (dataframe["lower_wick"] >= dataframe["body"] * 0.8) &
            (dataframe["rsi"] < 36) &
            (dataframe["volume"] > dataframe["volume_sma"] * 0.6)
        )

        dataframe.loc[sweep_entry, "enter_long"] = 1
        dataframe.loc[sweep_entry, "enter_tag"] = "micro_liquidity_sweep"

        return dataframe

    def populate_exit_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        dataframe.loc[:, "exit_long"] = 0
        return dataframe