AJ_FAST_V34_RECOVERED_DIAGNOSTIC_V1C

# pragma pylint: disable=missing-docstring, invalid-name, pointless-string-statement
# flake8: noqa: F401
# isort: skip_file
# Candidate V2 - FVG Pullback Long Only
# Backtest 20260101-20260508:
# Trades: 76
# Profit: +1.82%
# PF: 1.85
# DD: 1.06%
# --- Do not remove these imports ---
import logging
import numpy as np
import pandas as pd
from datetime import datetime, timedelta, timezone
from pandas import DataFrame
from typing import Dict, Optional, Union, Tuple
from freqtrade.persistence import Trade

from freqtrade.strategy import (
    IStrategy,
    Order,
    PairLocks,
    informative,  # @informative decorator
    # Hyperopt Parameters
    BooleanParameter,
    CategoricalParameter,
    DecimalParameter,
    IntParameter,
    RealParameter,
    # timeframe helpers
    timeframe_to_minutes,
    timeframe_to_next_date,
    timeframe_to_prev_date,
    # Strategy helper functions
    merge_informative_pair,
    stoploss_from_absolute,
    stoploss_from_open,
    AnnotationType,
)

# --------------------------------
# Add your lib to import here
import talib.abstract as ta
from technical import qtpylib

logger = logging.getLogger(__name__)


class AJ_FAST_V34_RECOVERED_DIAGNOSTIC_V1C(IStrategy):
    """
    This is a strategy template to get you started.
    More information in https://www.freqtrade.io/en/stable/strategy-customization/

    You can:
        :return: a Dataframe with all mandatory indicators for the strategies
    - Rename the class name (Do not forget to update class_name)
    - Add any methods you want to build your strategy
    - Add any lib you need to build your strategy

    You must keep:
    - the lib in the section "Do not remove these libs"
    - the methods: populate_indicators, populate_entry_trend, populate_exit_trend
    You should keep:
    - timeframe, minimal_roi, stoploss, trailing_*
    """
    # Strategy interface version - allow new iterations of the strategy interface.
    # Check the documentation or the Sample strategy to get the latest version.
    INTERFACE_VERSION = 3

    # Optimal timeframe for the strategy.
    timeframe = "5m"

    # Can this strategy go short?
    can_short: bool = False

    # Minimal ROI designed for the strategy.
    # This attribute will be overridden if the config file contains "minimal_roi".
    minimal_roi = {
        "0": 0.035,
        "30": 0.020,
        "90": 0.008,
        "180": 0.004
    }

    # Optimal stoploss designed for the strategy.
    # This attribute will be overridden if the config file contains "stoploss".
    stoploss = -0.045

    # Trailing stoploss
    trailing_stop = True
    trailing_stop_positive = 0.012
    trailing_stop_positive_offset = 0.025
    trailing_only_offset_is_reached = True

    # Run "populate_indicators()" only for new candle.
    process_only_new_candles = True

    # These values can be overridden in the config.
    use_exit_signal = False
    exit_profit_only = False
    ignore_roi_if_entry_signal = False

    # Number of candles the strategy requires before producing valid signals
    startup_candle_count: int = 200

    protections = [
        {
            "method": "StoplossGuard",
            "lookback_period_candles": 48,
            "trade_limit": 2,
            "stop_duration_candles": 24,
            "only_per_pair": False
        },
        {
            "method": "MaxDrawdown",
            "lookback_period_candles": 96,
            "trade_limit": 10,
            "stop_duration_candles": 48,
            "max_allowed_drawdown": 0.08
        },
        {
            "method": "CooldownPeriod",
            "stop_duration_candles": 4
        }
    ]

    # Strategy parameters
    buy_rsi = IntParameter(10, 40, default=30, space="buy")
    sell_rsi = IntParameter(60, 90, default=70, space="sell")# Optional order type mapping.
    order_types = {
        "entry": "limit",
        "exit": "limit",
        "stoploss": "market",
        "stoploss_on_exchange": False
    }

    # Optional order time in force.
    order_time_in_force = {
        "entry": "GTC",
        "exit": "GTC"
    }
    @property
    def plot_config(self):
        return {
            # Main plot indicators (Moving averages, ...)
            "main_plot": {
                "tema": {},
                "sar": {"color": "white"},
            },
            "subplots": {
                # Subplots - each dict defines one additional plot
                "MACD": {
                    "macd": {"color": "blue"},
                    "macdsignal": {"color": "orange"},
                },
                "RSI": {
                    "rsi": {"color": "red"},
                }
            }
        }

    def informative_pairs(self):
        """
        Define additional, informative pair/interval combinations to be cached from the exchange.
        These pair/interval combinations are non-tradeable, unless they are part
        of the whitelist as well.
        For more information, please consult the documentation
        :return: List of tuples in the format (pair, interval)
            Sample: return [("ETH/USDT", "5m"),
                            ("BTC/USDT", "15m"),
                            ]
        """
        return []

    def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        """
        Adds several different TA indicators to the given DataFrame

        Performance Note: For the best performance be frugal on the number of indicators
        you are using. Let uncomment only the indicator you are using in your strategies
        or your hyperopt configuration, otherwise you will waste your memory and CPU usage.
        :param dataframe: Dataframe with data from the exchange
        :param metadata: Additional information, like the currently traded pair
        :return: a Dataframe with all mandatory indicators for the strategies
        """
        # Momentum Indicators
        # ------------------------------------

        # ADX
        dataframe["adx"] = ta.ADX(dataframe)

        # # Plus Directional Indicator / Movement
        # dataframe["plus_dm"] = ta.PLUS_DM(dataframe)
        # dataframe["plus_di"] = ta.PLUS_DI(dataframe)

        # # Minus Directional Indicator / Movement
        # dataframe["minus_dm"] = ta.MINUS_DM(dataframe)
        # dataframe["minus_di"] = ta.MINUS_DI(dataframe)

        # # Aroon, Aroon Oscillator
        # aroon = ta.AROON(dataframe)
        # dataframe["aroonup"] = aroon["aroonup"]
        # dataframe["aroondown"] = aroon["aroondown"]
        # dataframe["aroonosc"] = ta.AROONOSC(dataframe)

        # # Awesome Oscillator
        # dataframe["ao"] = qtpylib.awesome_oscillator(dataframe)

        # # Keltner Channel
        # keltner = qtpylib.keltner_channel(dataframe)
        # dataframe["kc_upperband"] = keltner["upper"]
        # dataframe["kc_lowerband"] = keltner["lower"]
        # dataframe["kc_middleband"] = keltner["mid"]
        # dataframe["kc_percent"] = (
        #     (dataframe["close"] - dataframe["kc_lowerband"]) /
        #     (dataframe["kc_upperband"] - dataframe["kc_lowerband"])
        # )
        # dataframe["kc_width"] = (
        #     (dataframe["kc_upperband"] - dataframe["kc_lowerband"]) / dataframe["kc_middleband"]
        # )

        # # Ultimate Oscillator
        # dataframe["uo"] = ta.ULTOSC(dataframe)

        # # Commodity Channel Index: values [Oversold:-100, Overbought:100]
        # dataframe["cci"] = ta.CCI(dataframe)

        # EMA
        dataframe["ema20"] = ta.EMA(dataframe, timeperiod=20)
        dataframe["ema50"] = ta.EMA(dataframe, timeperiod=50)
        dataframe["ema200"] = ta.EMA(dataframe, timeperiod=200)

        # RSI
        dataframe["rsi"] = ta.RSI(dataframe, timeperiod=14)
        dataframe["adx"] = ta.ADX(dataframe, timeperiod=14)
        dataframe["plus_di"] = ta.PLUS_DI(dataframe, timeperiod=14)
        dataframe["minus_di"] = ta.MINUS_DI(dataframe, timeperiod=14)

        # # Inverse Fisher transform on RSI: values [-1.0, 1.0] (https://goo.gl/2JGGoy)
        # rsi = 0.1 * (dataframe["rsi"] - 50)
        # dataframe["fisher_rsi"] = (np.exp(2 * rsi) - 1) / (np.exp(2 * rsi) + 1)

        # # Inverse Fisher transform on RSI normalized: values [0.0, 100.0] (https://goo.gl/2JGGoy)
        # dataframe["fisher_rsi_norma"] = 50 * (dataframe["fisher_rsi"] + 1)

        # # Stochastic Slow
        # stoch = ta.STOCH(dataframe)
        # dataframe["slowd"] = stoch["slowd"]
        # dataframe["slowk"] = stoch["slowk"]

        # Stochastic Fast
        stoch_fast = ta.STOCHF(dataframe)
        dataframe["fastd"] = stoch_fast["fastd"]
        dataframe["fastk"] = stoch_fast["fastk"]

        # # Stochastic RSI
        # Please read https://github.com/freqtrade/freqtrade/issues/2961 before using this.
        # STOCHRSI is NOT aligned with tradingview, which may result in non-expected results.
        # stoch_rsi = ta.STOCHRSI(dataframe)
        # dataframe["fastd_rsi"] = stoch_rsi["fastd"]
        # dataframe["fastk_rsi"] = stoch_rsi["fastk"]

        # MACD
        macd = ta.MACD(dataframe)
        dataframe["macd"] = macd["macd"]
        dataframe["macdsignal"] = macd["macdsignal"]
        dataframe["macdhist"] = dataframe["macd"] - dataframe["macdsignal"]

        # ATR
        dataframe["atr"] = ta.ATR(dataframe, timeperiod=14)
        dataframe["atr_mean"] = dataframe["atr"].rolling(20).mean()
        dataframe["volatility"] = (
            dataframe["high"] - dataframe["low"]
        ) / dataframe["close"]
        dataframe["volume_mean"] = dataframe["volume"].rolling(30).mean()
        dataframe["ema50_slope"] = dataframe["ema50"] - dataframe["ema50"].shift(3)

        # Bullish FVG Detection
        dataframe["bullish_fvg"] = dataframe["low"] > dataframe["high"].shift(2)
        dataframe["fvg_bottom_raw"] = dataframe["high"].shift(2)
        dataframe["fvg_top_raw"] = dataframe["low"]
        dataframe.loc[~dataframe["bullish_fvg"], "fvg_bottom_raw"] = None
        dataframe.loc[~dataframe["bullish_fvg"], "fvg_top_raw"] = None

        dataframe["fvg_bottom"] = dataframe["fvg_bottom_raw"].ffill()
        dataframe["fvg_top"] = dataframe["fvg_top_raw"].ffill()
        dataframe["fvg_bottom"] = dataframe["fvg_bottom"].shift(1)
        dataframe["fvg_top"] = dataframe["fvg_top"].shift(1)

        last_fvg_index = None
        fvg_ages = []
        for i, is_fvg in enumerate(dataframe["bullish_fvg"]):
            if is_fvg:
                last_fvg_index = i
            if last_fvg_index is None:
                fvg_ages.append(999)
            else:
                fvg_ages.append(i - last_fvg_index)
        dataframe["fvg_age"] = fvg_ages
        dataframe["fvg_age"] = dataframe["fvg_age"].shift(1)

        # Bullish Order Block Detection
        dataframe["bearish_candle"] = dataframe["close"] < dataframe["open"]
        dataframe["strong_bullish_move"] = (
            (dataframe["close"] > dataframe["open"]) &
            ((dataframe["close"] - dataframe["open"]) > dataframe["atr"] * 0.6)
        )
        dataframe["bullish_ob"] = (
            dataframe["bearish_candle"].shift(1).fillna(False) &
            dataframe["strong_bullish_move"]
        )
        dataframe["ob_low_raw"] = dataframe["low"].shift(1)
        dataframe["ob_high_raw"] = dataframe["high"].shift(1)
        dataframe.loc[~dataframe["bullish_ob"], "ob_low_raw"] = None
        dataframe.loc[~dataframe["bullish_ob"], "ob_high_raw"] = None
        dataframe["ob_low"] = dataframe["ob_low_raw"].ffill().shift(1)
        dataframe["ob_high"] = dataframe["ob_high_raw"].ffill().shift(1)

        last_ob_index = None
        ob_ages = []
        for i, is_ob in enumerate(dataframe["bullish_ob"]):
            if is_ob:
                last_ob_index = i
            if last_ob_index is None:
                ob_ages.append(999)
            else:
                ob_ages.append(i - last_ob_index)
        dataframe["ob_age"] = ob_ages
        dataframe["ob_age"] = dataframe["ob_age"].shift(1)

        btc_df = self.dp.get_pair_dataframe(
            pair="BTC/USDT:USDT",
            timeframe=self.timeframe
        )

        btc_df["btc_ema20"] = ta.EMA(btc_df, timeperiod=20)
        btc_df["btc_ema50"] = ta.EMA(btc_df, timeperiod=50)

        dataframe["btc_bull"] = (
            btc_df["btc_ema20"] > btc_df["btc_ema50"]
        ).astype(int)

        # MFI
        dataframe["mfi"] = ta.MFI(dataframe)

        # # ROC
        # dataframe["roc"] = ta.ROC(dataframe)

        # Overlap Studies
        # ------------------------------------

        # Bollinger Bands
        bollinger = qtpylib.bollinger_bands(qtpylib.typical_price(dataframe), window=20, stds=2)
        dataframe["bb_lowerband"] = bollinger["lower"]
        dataframe["bb_middleband"] = bollinger["mid"]
        dataframe["bb_upperband"] = bollinger["upper"]
        dataframe["bb_percent"] = (
            (dataframe["close"] - dataframe["bb_lowerband"]) /
            (dataframe["bb_upperband"] - dataframe["bb_lowerband"])
        )
        dataframe["bb_width"] = (
            (dataframe["bb_upperband"] - dataframe["bb_lowerband"]) / dataframe["bb_middleband"]
        )

        # Bollinger Bands - Weighted (EMA based instead of SMA)
        # weighted_bollinger = qtpylib.weighted_bollinger_bands(
        #     qtpylib.typical_price(dataframe), window=20, stds=2
        # )
        # dataframe["wbb_upperband"] = weighted_bollinger["upper"]
        # dataframe["wbb_lowerband"] = weighted_bollinger["lower"]
        # dataframe["wbb_middleband"] = weighted_bollinger["mid"]
        # dataframe["wbb_percent"] = (
        #     (dataframe["close"] - dataframe["wbb_lowerband"]) /
        #     (dataframe["wbb_upperband"] - dataframe["wbb_lowerband"])
        # )
        # dataframe["wbb_width"] = (
        #     (dataframe["wbb_upperband"] - dataframe["wbb_lowerband"]) / dataframe["wbb_middleband"]
        # )

        # # EMA - Exponential Moving Average
        # dataframe["ema3"] = ta.EMA(dataframe, timeperiod=3)
        # dataframe["ema5"] = ta.EMA(dataframe, timeperiod=5)
        # dataframe["ema10"] = ta.EMA(dataframe, timeperiod=10)
        # dataframe["ema21"] = ta.EMA(dataframe, timeperiod=21)
        # dataframe["ema50"] = ta.EMA(dataframe, timeperiod=50)
        # dataframe["ema100"] = ta.EMA(dataframe, timeperiod=100)

        # # SMA - Simple Moving Average
        # dataframe["sma3"] = ta.SMA(dataframe, timeperiod=3)
        # dataframe["sma5"] = ta.SMA(dataframe, timeperiod=5)
        # dataframe["sma10"] = ta.SMA(dataframe, timeperiod=10)
        # dataframe["sma21"] = ta.SMA(dataframe, timeperiod=21)
        # dataframe["sma50"] = ta.SMA(dataframe, timeperiod=50)
        # dataframe["sma100"] = ta.SMA(dataframe, timeperiod=100)

        # Parabolic SAR
        dataframe["sar"] = ta.SAR(dataframe)

        # TEMA - Triple Exponential Moving Average
        dataframe["tema"] = ta.TEMA(dataframe, timeperiod=9)

        # Cycle Indicator
        # ------------------------------------
        # Hilbert Transform Indicator - SineWave
        hilbert = ta.HT_SINE(dataframe)
        dataframe["htsine"] = hilbert["sine"]
        dataframe["htleadsine"] = hilbert["leadsine"]

        # Pattern Recognition - Bullish candlestick patterns
        # ------------------------------------
        # # Hammer: values [0, 100]
        # dataframe["CDLHAMMER"] = ta.CDLHAMMER(dataframe)
        # # Inverted Hammer: values [0, 100]
        # dataframe["CDLINVERTEDHAMMER"] = ta.CDLINVERTEDHAMMER(dataframe)
        # # Dragonfly Doji: values [0, 100]
        # dataframe["CDLDRAGONFLYDOJI"] = ta.CDLDRAGONFLYDOJI(dataframe)
        # # Piercing Line: values [0, 100]
        # dataframe["CDLPIERCING"] = ta.CDLPIERCING(dataframe) # values [0, 100]
        # # Morningstar: values [0, 100]
        # dataframe["CDLMORNINGSTAR"] = ta.CDLMORNINGSTAR(dataframe) # values [0, 100]
        # # Three White Soldiers: values [0, 100]
        # dataframe["CDL3WHITESOLDIERS"] = ta.CDL3WHITESOLDIERS(dataframe) # values [0, 100]

        # Pattern Recognition - Bearish candlestick patterns
        # ------------------------------------
        # # Hanging Man: values [0, 100]
        # dataframe["CDLHANGINGMAN"] = ta.CDLHANGINGMAN(dataframe)
        # # Shooting Star: values [0, 100]
        # dataframe["CDLSHOOTINGSTAR"] = ta.CDLSHOOTINGSTAR(dataframe)
        # # Gravestone Doji: values [0, 100]
        # dataframe["CDLGRAVESTONEDOJI"] = ta.CDLGRAVESTONEDOJI(dataframe)
        # # Dark Cloud Cover: values [0, 100]
        # dataframe["CDLDARKCLOUDCOVER"] = ta.CDLDARKCLOUDCOVER(dataframe)
        # # Evening Doji Star: values [0, 100]
        # dataframe["CDLEVENINGDOJISTAR"] = ta.CDLEVENINGDOJISTAR(dataframe)
        # # Evening Star: values [0, 100]
        # dataframe["CDLEVENINGSTAR"] = ta.CDLEVENINGSTAR(dataframe)

        # Pattern Recognition - Bullish/Bearish candlestick patterns
        # ------------------------------------
        # # Three Line Strike: values [0, -100, 100]
        # dataframe["CDL3LINESTRIKE"] = ta.CDL3LINESTRIKE(dataframe)
        # # Spinning Top: values [0, -100, 100]
        # dataframe["CDLSPINNINGTOP"] = ta.CDLSPINNINGTOP(dataframe) # values [0, -100, 100]
        # # Engulfing: values [0, -100, 100]
        # dataframe["CDLENGULFING"] = ta.CDLENGULFING(dataframe) # values [0, -100, 100]
        # # Harami: values [0, -100, 100]
        # dataframe["CDLHARAMI"] = ta.CDLHARAMI(dataframe) # values [0, -100, 100]
        # # Three Outside Up/Down: values [0, -100, 100]
        # dataframe["CDL3OUTSIDE"] = ta.CDL3OUTSIDE(dataframe) # values [0, -100, 100]
        # # Three Inside Up/Down: values [0, -100, 100]
        # dataframe["CDL3INSIDE"] = ta.CDL3INSIDE(dataframe) # values [0, -100, 100]

        # # Chart type
        # # ------------------------------------
        # # Heikin Ashi Strategy
        # heikinashi = qtpylib.heikinashi(dataframe)
        # dataframe["ha_open"] = heikinashi["open"]
        # dataframe["ha_close"] = heikinashi["close"]
        # dataframe["ha_high"] = heikinashi["high"]
        # dataframe["ha_low"] = heikinashi["low"]

        # Retrieve best bid and best ask from the orderbook
        # ------------------------------------
        """
        # first check if dataprovider is available
        if self.dp:
            if self.dp.runmode.value in ("live", "dry_run"):
                ob = self.dp.orderbook(metadata["pair"], 1)
                dataframe["best_bid"] = ob["bids"][0][0]
                dataframe["best_ask"] = ob["asks"][0][0]
        """

        # ============================================================
        # Liquidity Sweep + Stochastic RSI - Fast Strategy Addon
        # ============================================================
        dataframe["prev_swing_low"] = dataframe["low"].rolling(20).min().shift(1)
        dataframe["prev_swing_high"] = dataframe["high"].rolling(20).max().shift(1)

        dataframe["bullish_liquidity_sweep"] = (
            (dataframe["low"] < dataframe["prev_swing_low"]) &
            (dataframe["close"] > dataframe["prev_swing_low"])
        ).astype(int)
        dataframe["bearish_liquidity_sweep"] = (
            (dataframe["high"] > dataframe["prev_swing_high"]) &
            (dataframe["close"] < dataframe["prev_swing_high"])
        ).astype(int)
        dataframe["bullish_sweep_recent"] = (
            dataframe["bullish_liquidity_sweep"].rolling(8).max() > 0
        ).astype(int)

        rsi_len = 14
        stoch_len = 14
        smooth_k = 3
        smooth_d = 3
        rsi = ta.RSI(dataframe, timeperiod=rsi_len)
        rsi_min = rsi.rolling(stoch_len).min()
        rsi_max = rsi.rolling(stoch_len).max()
        dataframe["stoch_rsi_k"] = (
            100 * (rsi - rsi_min) / (rsi_max - rsi_min)
        ).replace([float("inf"), -float("inf")], 0).fillna(0)
        dataframe["stoch_rsi_k"] = dataframe["stoch_rsi_k"].rolling(smooth_k).mean()
        dataframe["stoch_rsi_d"] = dataframe["stoch_rsi_k"].rolling(smooth_d).mean()
        dataframe["stoch_rsi_bull_cross"] = (
            (dataframe["stoch_rsi_k"] > dataframe["stoch_rsi_d"]) &
            (dataframe["stoch_rsi_k"].shift(1) <= dataframe["stoch_rsi_d"].shift(1)) &
            (dataframe["stoch_rsi_k"] < 45)
        ).astype(int)

        dataframe["fvg_pullback"] = (
            (dataframe["fvg_age"] <= 20) &
            (dataframe["low"] <= dataframe["fvg_top"] * 1.008) &
            (dataframe["close"] >= dataframe["fvg_bottom"] * 0.997)
        ).astype(int)
        dataframe["ob_pullback"] = (
            (dataframe["ob_age"] <= 30) &
            (dataframe["low"] <= dataframe["ob_high"] * 1.005) &
            (dataframe["close"] >= dataframe["ob_low"] * 0.995)
        ).astype(int)

        # ============================================================
        # Momentum Breakout Detector
        # ============================================================
        dataframe["highest_48"] = dataframe["high"].rolling(48).max().shift(1)
        dataframe["volume_mean_48"] = dataframe["volume"].rolling(48).mean()
        dataframe["price_change_12"] = (
            (dataframe["close"] / dataframe["close"].shift(12) - 1) * 100
        )
        dataframe["breakout_48"] = (
            dataframe["close"] > dataframe["highest_48"]
        ).astype(int)
        dataframe["volume_spike_2x"] = (
            dataframe["volume"] > dataframe["volume_mean_48"] * 2.0
        ).astype(int)
        dataframe["not_too_extended"] = (
            dataframe["close"] < dataframe["ema20"] * 1.06
        ).astype(int)

        # ============================================================
        # Breakout Retest Logic
        # ============================================================
        dataframe["breakout_level"] = dataframe["highest_48"]
        dataframe["breakout_recent"] = (
            dataframe["breakout_48"].rolling(12).max() > 0
        ).astype(int)
        dataframe["breakout_retest"] = (
            (dataframe["breakout_recent"] == 1) &
            (dataframe["low"] <= dataframe["breakout_level"] * 1.01) &
            (dataframe["close"] > dataframe["breakout_level"]) &
            (dataframe["close"] > dataframe["open"])
        ).astype(int)
        dataframe["breakout_extension_ok"] = (
            dataframe["close"] < dataframe["ema20"] * 1.035
        ).astype(int)
        dataframe["volume_spike_recent"] = (
            dataframe["volume_spike_2x"].rolling(12).max() > 0
        ).astype(int)

        # ============================================================
        # V3.4 Weakness Score
        # ============================================================
        dataframe["ema_extension_pct_v34"] = (
            (dataframe["close"] - dataframe["ema20"]).abs() /
            dataframe["ema20"] * 100
        )
        dataframe["di_gap_v34"] = (
            dataframe["plus_di"] - dataframe["minus_di"]
        ).abs()
        dataframe["candle_body_v34"] = (
            dataframe["close"] - dataframe["open"]
        ).abs()
        dataframe["candle_range_v34"] = (
            dataframe["high"] - dataframe["low"]
        ).replace(0, 0.0000001)
        dataframe["body_ratio_v34"] = (
            dataframe["candle_body_v34"] / dataframe["candle_range_v34"]
        )
        dataframe["upper_wick_v34"] = (
            dataframe["high"] - dataframe[["open", "close"]].max(axis=1)
        )
        dataframe["upper_wick_ratio_v34"] = (
            dataframe["upper_wick_v34"] / dataframe["candle_range_v34"]
        )
        dataframe["weak_extension_v34"] = (
            dataframe["ema_extension_pct_v34"] > 1.2
        ).astype(int)
        dataframe["weak_adx_v34"] = (
            dataframe["adx"] < 20
        ).astype(int)
        dataframe["weak_di_v34"] = (
            dataframe["di_gap_v34"] < 3
        ).astype(int)
        dataframe["weak_high_v34"] = (
            dataframe["upper_wick_ratio_v34"] > 0.45
        ).astype(int)
        dataframe["weak_candle_v34"] = (
            dataframe["body_ratio_v34"] < 0.35
        ).astype(int)
        dataframe["weakness_score_v34"] = (
            dataframe["weak_extension_v34"] +
            dataframe["weak_adx_v34"] +
            dataframe["weak_di_v34"] +
            dataframe["weak_high_v34"] +
            dataframe["weak_candle_v34"]
        )
        dataframe["weakness_ok_v34"] = (
            dataframe["weakness_score_v34"] < 2
        ).astype(int)
        dataframe["v34_debug"] = dataframe["weakness_score_v34"]

        return dataframe

    def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        """
        Based on TA indicators, populates the entry signal for the given dataframe
        :param dataframe: DataFrame
        :param metadata: Additional information, like the currently traded pair
        :return: DataFrame with entry columns populated
        """
        # ============================================================
        # Fast V2.1 Entry - Liquidity Sweep + Smart Pullback
        # ============================================================
        trend_filter = (
            (dataframe["ema20"] > dataframe["ema50"]) &
            (dataframe["ema50"] > dataframe["ema200"])
        )
        pullback_filter = (
            (dataframe["close"] > dataframe["ema50"]) &
            (dataframe["close"] < dataframe["ema50"] * 1.03) &
            (dataframe["rsi"] > 45) &
            (dataframe["rsi"] < 64)
        )
        momentum_filter = (
            (dataframe["macdhist"] > 0) &
            (dataframe["adx"] > 20)
        )
        volume_filter = (
            dataframe["volume"] > dataframe["volume_mean"] * 1.1
        )
        atr_filter = (
            dataframe["atr"] > dataframe["atr_mean"] * 1.2
        )
        fvg_pullback = dataframe["fvg_pullback"] == 1
        ob_pullback = dataframe["ob_pullback"] == 1
        smart_zone_filter = (
            (dataframe["bullish_sweep_recent"] == 1) |
            (dataframe["stoch_rsi_bull_cross"] == 1) |
            fvg_pullback |
            ob_pullback
        )

        dataframe.loc[
            (
                False &
                trend_filter &
                pullback_filter &
                momentum_filter &
                volume_filter &
                atr_filter &
                smart_zone_filter
            ),
            ["enter_long", "enter_tag"]
        ] = (1, "FAST_LIQ_SWEEP_STOCH")

        # ============================================================
        # Fast Breakout Retest Entry - safer than chasing breakout candle
        # ============================================================
        breakout_entry = (
            (dataframe["ema20"] > dataframe["ema50"]) &
            (dataframe["ema50"] > dataframe["ema200"]) &
            # Breakout + retest logic
            (dataframe["breakout_recent"] == 1) &
            (dataframe["volume_spike_recent"] == 1) &
            (dataframe["breakout_retest"] == 1) &
            # Momentum filters
            (dataframe["adx"] >= 25) &
            (dataframe["rsi"] > 50) &
            (dataframe["rsi"] < 70) &
            (dataframe["macdhist"] > 0) &
            # Extension protection
            (dataframe["breakout_extension_ok"] == 1) &
            (dataframe["ema_extension_pct_v34"] <= 1.2) &
            # V3.4 Weakness protection
            (dataframe["weakness_score_v34"] < 2) &
            (dataframe["di_gap_v34"] >= 3) &
            # Volume confirmation
            (dataframe["volume"] > dataframe["volume_mean"] * 1.05) &
            (dataframe["volume"] > 0)
        )

        # V1C diagnostic-only risk/payoff map.
        # These columns are for analysis only and do not filter entries.
        dataframe["diag_entry_price_v1c"] = dataframe["close"]
        diag_risk_distance_v1c = (dataframe["close"] - dataframe["low"]).clip(lower=0)
        diag_reward_distance_v1c = (
            dataframe["high"].rolling(24).max() - dataframe["close"]
        ).clip(lower=0)
        dataframe["diag_sl_pct_v1c"] = (
            diag_risk_distance_v1c /
            dataframe["diag_entry_price_v1c"].replace(0, np.nan) *
            100
        ).replace([np.inf, -np.inf], np.nan).fillna(0)
        dataframe["diag_tp_pct_v1c"] = (
            diag_reward_distance_v1c /
            dataframe["diag_entry_price_v1c"].replace(0, np.nan) *
            100
        ).replace([np.inf, -np.inf], np.nan).fillna(0)
        dataframe["diag_rr_v1c"] = (
            diag_reward_distance_v1c /
            diag_risk_distance_v1c.replace(0, np.nan)
        ).replace([np.inf, -np.inf], np.nan).fillna(0)
        invalid_risk_math_v1c = (
            (dataframe["diag_entry_price_v1c"] <= 0) |
            (diag_risk_distance_v1c <= 0) |
            (diag_reward_distance_v1c <= 0)
        )
        dataframe["diag_risk_note_v1c"] = "OK"
        dataframe.loc[
            invalid_risk_math_v1c,
            "diag_risk_note_v1c"
        ] = "INVALID_RISK_MATH"

        # V3.4 diagnostic entry tags.
        # These buckets let the backtest show which signal profiles are winning
        # or hitting stoploss without changing the entry rules.
        dataframe.loc[breakout_entry, "enter_long"] = 1
        dataframe.loc[breakout_entry, "enter_tag"] = "FAST_V34_ROI004_30_020_BASE"

        dataframe.loc[
            breakout_entry & (dataframe["weakness_score_v34"] == 0),
            "enter_tag"
        ] += "_W0"
        dataframe.loc[
            breakout_entry & (dataframe["weakness_score_v34"] == 1),
            "enter_tag"
        ] += "_W1"

        dataframe.loc[
            breakout_entry & (dataframe["adx"] >= 25),
            "enter_tag"
        ] += "_ADX25"
        dataframe.loc[
            breakout_entry & (dataframe["adx"] < 25),
            "enter_tag"
        ] += "_ADX22"

        dataframe.loc[
            breakout_entry & (dataframe["di_gap_v34"] >= 8),
            "enter_tag"
        ] += "_DI8"
        dataframe.loc[
            breakout_entry &
            (dataframe["di_gap_v34"] >= 5) &
            (dataframe["di_gap_v34"] < 8),
            "enter_tag"
        ] += "_DI5"
        dataframe.loc[
            breakout_entry & (dataframe["di_gap_v34"] < 5),
            "enter_tag"
        ] += "_DI3"

        dataframe.loc[
            breakout_entry & (dataframe["ema_extension_pct_v34"] <= 0.7),
            "enter_tag"
        ] += "_EXT_LOW"
        dataframe.loc[
            breakout_entry &
            (dataframe["ema_extension_pct_v34"] > 0.7) &
            (dataframe["ema_extension_pct_v34"] <= 1.0),
            "enter_tag"
        ] += "_EXT_MID"
        dataframe.loc[
            breakout_entry & (dataframe["ema_extension_pct_v34"] > 1.0),
            "enter_tag"
        ] += "_EXT_HIGH"

        try:
            if dataframe["enter_long"].fillna(0).iloc[-1] == 1:
                logger.info(
                    "[ENTRY READY] %s last candle enter_long=1",
                    metadata.get("pair")
                )
        except Exception as e:
            logger.info("[ENTRY DEBUG ERROR] %s", e)

        try:
            pair = metadata.get("pair", "UNKNOWN")
            trend_count = int(trend_filter.tail(200).sum())
            pullback_count = int(pullback_filter.tail(200).sum())
            momentum_count = int(momentum_filter.tail(200).sum())
            volume_count = int(volume_filter.tail(200).sum())
            atr_count = int(atr_filter.tail(200).sum())
            fvg_count = int(fvg_pullback.tail(200).sum())
            ob_count = int(ob_pullback.tail(200).sum())
            zone_count = int(smart_zone_filter.tail(200).sum())
            final_count = int(dataframe["enter_long"].tail(200).fillna(0).sum())
            logger.warning(
                f"[DEBUG {pair}] "
                f"trend={trend_count}, "
                f"pullback={pullback_count}, "
                f"momentum={momentum_count}, "
                f"volume={volume_count}, "
                f"atr={atr_count}, "
                f"fvg={fvg_count}, "
                f"ob={ob_count}, "
                f"zone={zone_count}, "
                f"final={final_count}"
            )
        except Exception as e:
            logger.warning("Debug counter error: %s", e)

        if metadata and metadata.get("pair"):
            pair = metadata["pair"]
            try:
                last = dataframe.iloc[-1]
                logger.info(
                    f"[FAST DEBUG {pair}] "
                    f"trend={int(trend_filter.iloc[-1])} "
                    f"pullback={int(pullback_filter.iloc[-1])} "
                    f"momentum={int(momentum_filter.iloc[-1])} "
                    f"volume={int(volume_filter.iloc[-1])} "
                    f"atr={int(atr_filter.iloc[-1])} "
                    f"sweep={int(last.get('bullish_sweep_recent', 0))} "
                    f"stoch={int(last.get('stoch_rsi_bull_cross', 0))} "
                    f"fvg={int(last.get('fvg_pullback', 0))} "
                    f"ob={int(last.get('ob_pullback', 0))} "
                    f"breakout={int(last.get('breakout_48', 0))} "
                    f"vol2x={int(last.get('volume_spike_2x', 0))} "
                    f"v34={int(last.get('weakness_score_v34', 0))} "
                    f"ema_ext={float(last.get('ema_extension_pct_v34', 0)):.2f} "
                    f"di_gap={float(last.get('di_gap_v34', 0)):.2f} "
                    f"final={int(last.get('enter_long', 0))}"
                )
            except Exception:
                pass

        # Uncomment to use shorts (Only used in futures/margin mode. Check the documentation for more info)
        """
        dataframe.loc[
            (
                (qtpylib.crossed_above(dataframe["rsi"], self.sell_rsi.value)) &  # Signal: RSI crosses above sell_rsi
                (dataframe["tema"] > dataframe["bb_middleband"]) &  # Guard: tema above BB middle
                (dataframe["tema"] < dataframe["tema"].shift(1)) &  # Guard: tema is falling
                (dataframe['volume'] > 0)  # Make sure Volume is not 0
            ),
            'enter_short'] = 1
        """

        return dataframe

    def custom_exit(
        self,
        pair: str,
        trade: Trade,
        current_time: datetime,
        current_rate: float,
        current_profit: float,
        **kwargs
    ) -> Optional[str]:
        """
        Fast Exit V3:
        Kill weak losing trades before they reach full stoploss.
        Do not touch profitable trades.
        """
        dataframe, _ = self.dp.get_analyzed_dataframe(pair, self.timeframe)
        if dataframe is None or dataframe.empty:
            return None

        last = dataframe.iloc[-1]
        trade_minutes = (current_time - trade.open_date_utc).total_seconds() / 60

        if trade_minutes < 25:
            return None

        if current_profit > -0.006:
            return None

        weak_price = (
            current_rate < last["ema20"]
        )
        weak_momentum = (
            last["rsi"] < 47 and
            last["macdhist"] < 0
        )
        weak_volume_red = (
            last["close"] < last["open"] and
            last["volume"] > last["volume_mean"] * 1.1
        )

        if current_profit < -0.018 and weak_price and weak_momentum:
            return "FAST_WEAK_EXIT"

        if current_profit < -0.012 and weak_price and weak_momentum and weak_volume_red:
            return "FAST_RED_VOLUME_EXIT"

        return None

    def populate_exit_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        """
        Fast Exit V2:
        Disable manual exit signals.
        Let ROI, trailing stop and stoploss manage exits.
        """
        dataframe["exit_long"] = 0
        dataframe["exit_tag"] = None
        return dataframe