Strategy02_MACD

# pragma pylint: disable=missing-docstring, invalid-name, pointless-string-statement
# flake8: noqa: F401
# isort:skip_file
# --- Do not remove these imports ---
from datetime import datetime
from typing import Optional

import numpy as np
import pandas as pd
from pandas import DataFrame

from freqtrade.strategy import IStrategy, IntParameter, DecimalParameter

import pandas_ta as ta

# ---------------------------------------------------------------------------
# Strategy02_MACD
# ---------------------------------------------------------------------------


class Strategy02_MACD(IStrategy):
    """
    MACD Strategy — SPOT ONLY (Halal Algo Trading, Batch 1)
    ========================================================
    Timeframe : 4h
    Author    : Halal Algo Trading
    Version   : 1.0

    Overview
    --------
    This strategy combines two MACD configurations with a CCI oversold/overbought
    filter to improve entry quality and reduce false signals:

        • Standard MACD  — fast=12, slow=26, signal=9 (primary trigger)
        • Alternative MACD — fast=8, slow=17, signal=9 (faster, used as context)
        • CCI (25-period) — momentum oscillator to confirm oversold/overbought zones

    The strategy is enhanced with a bearish divergence detector on exit:
    when price makes a higher high but MACD makes a lower high, the momentum
    is fading and the position is closed to protect profits.

    Entry Conditions (ALL must be true)
    ------------------------------------
    1. Standard MACD line crosses ABOVE the signal line   → bullish crossover
    2. MACD histogram turns positive (hist > 0)           → confirmed momentum
    3. CCI <= -50                                         → oversold, mean-reversion opportunity
    STRONGER entry (optional, stricter):
       • Crossover occurs BELOW the zero line (MACD < 0 at crossover)

    Exit Conditions (ANY triggers exit)
    ------------------------------------
    A. Standard MACD line crosses BELOW the signal line AND CCI >= 100
    B. Bearish divergence detected:
       • Price makes a higher high over the last 5 bars
       • MACD makes a lower high over the same 5 bars
       → momentum weakening while price extends → exit

    Risk Management
    ---------------
    • Hard stoploss         : -8 %
    • Trailing stop         : enabled
    • Trailing stop positive: 2.5 %
    • ROI ladder            : 6 % @ open, 4 % @ 60 min, 2 % @ 120 min,
                              1 % @ 360 min
    • SPOT only — no short, no margin, no futures

    Backtest Reference
    ------------------
    Sharpe 1.17 | Return 428 % | Max DD -33 % | PF 1.00
    """

    # -------------------------------------------------------------------------
    # Freqtrade metadata
    # -------------------------------------------------------------------------
    INTERFACE_VERSION = 3

    strategy_name = "Strategy02_MACD"

    timeframe = "4h"

    process_only_new_candles = True

    # Need enough bars for MACD slow=26 + signal smoothing; 60 is conservative
    startup_candle_count: int = 60

    # -------------------------------------------------------------------------
    # ROI
    # -------------------------------------------------------------------------
    minimal_roi = {
        "0":   0.06,   # 6 % immediately
        "60":  0.04,   # 4 % after 60 min
        "120": 0.02,   # 2 % after 120 min
        "360": 0.01,   # 1 % after 360 min
    }

    # -------------------------------------------------------------------------
    # Stoploss & Trailing Stop
    # -------------------------------------------------------------------------
    stoploss = -0.08
    trailing_stop = True
    trailing_stop_positive = 0.025
    trailing_stop_positive_offset = 0.04
    trailing_only_offset_is_reached = True

    # -------------------------------------------------------------------------
    # Misc
    # -------------------------------------------------------------------------
    can_short = False

    use_exit_signal = True
    exit_profit_only = False
    ignore_roi_if_entry_signal = False

    # -------------------------------------------------------------------------
    # Helper functions
    # -------------------------------------------------------------------------

    @staticmethod
    def crossed_above(s1: pd.Series, s2: pd.Series) -> pd.Series:
        """Return True on the bar where s1 crosses from below to above s2."""
        return (s1 > s2) & (s1.shift(1) <= s2.shift(1))

    @staticmethod
    def crossed_below(s1: pd.Series, s2: pd.Series) -> pd.Series:
        """Return True on the bar where s1 crosses from above to below s2."""
        return (s1 < s2) & (s1.shift(1) >= s2.shift(1))

    @staticmethod
    def bearish_divergence(
        price: pd.Series,
        macd: pd.Series,
        lookback: int = 5,
    ) -> pd.Series:
        """
        Detect bearish MACD divergence over a rolling window.

        A bearish divergence occurs when price records a higher high
        while MACD records a lower high over the same `lookback` bars.
        This signals waning momentum behind the price rally.

        Parameters
        ----------
        price    : close price series
        macd     : MACD line series
        lookback : number of bars to look back for the comparison high

        Returns
        -------
        Boolean series — True where divergence is detected
        """
        price_hh = price > price.shift(lookback).rolling(lookback).max()
        macd_lh  = macd  < macd.shift(lookback).rolling(lookback).max()
        return price_hh & macd_lh

    # -------------------------------------------------------------------------
    # Indicators
    # -------------------------------------------------------------------------

    def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        """
        Compute all technical indicators.

        Indicators added to `dataframe`
        --------------------------------
        macd        — standard MACD line (12/26/9)
        macd_signal — standard MACD signal line
        macd_hist   — standard MACD histogram
        macd_alt        — alternative MACD line (8/17/9)
        macd_signal_alt — alternative MACD signal line
        macd_hist_alt   — alternative MACD histogram
        cci         — CCI with period 25
        bearish_div — True where bearish MACD divergence is detected
        """

        # --- Standard MACD (12, 26, 9) ---
        macd_std = ta.macd(dataframe["close"], fast=12, slow=26, signal=9)
        # pandas_ta returns a DataFrame; columns: MACD_12_26_9, MACDh_12_26_9, MACDs_12_26_9
        dataframe["macd"]        = macd_std["MACD_12_26_9"]
        dataframe["macd_signal"] = macd_std["MACDs_12_26_9"]
        dataframe["macd_hist"]   = macd_std["MACDh_12_26_9"]

        # --- Alternative MACD (8, 17, 9) — faster, used for context ---
        macd_alt = ta.macd(dataframe["close"], fast=8, slow=17, signal=9)
        dataframe["macd_alt"]        = macd_alt["MACD_8_17_9"]
        dataframe["macd_signal_alt"] = macd_alt["MACDs_8_17_9"]
        dataframe["macd_hist_alt"]   = macd_alt["MACDh_8_17_9"]

        # --- CCI (25-period) ---
        dataframe["cci"] = ta.cci(
            dataframe["high"], dataframe["low"], dataframe["close"], length=25
        )

        # --- Bearish divergence flag ---
        dataframe["bearish_div"] = self.bearish_divergence(
            dataframe["close"], dataframe["macd"], lookback=5
        )

        # --- NaN Safety: convert any None to NaN so pandas comparisons work ---
        for col in dataframe.columns:
            if col not in ['open', 'high', 'low', 'close', 'volume', 'date']:
                try:
                    dataframe[col] = pd.to_numeric(dataframe[col], errors='coerce')
                except (ValueError, TypeError):
                    pass

        return dataframe

    # -------------------------------------------------------------------------
    # Entry Signal
    # -------------------------------------------------------------------------

    def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        """
        Generate LONG entry signals.

        Standard Entry (all three conditions):
        1. MACD line crosses above signal line
        2. MACD histogram is positive (hist > 0) — momentum confirmed
        3. CCI <= -50 — oversold mean-reversion opportunity

        Stronger (stricter) variant is embedded inside the same condition:
        The crossover check already covers both above-zero and below-zero
        crossovers. A comment marks where to add the below-zero restriction
        if desired.
        """

        dataframe.loc[
            (
                # Condition 1 — MACD bullish crossover
                self.crossed_above(dataframe["macd"], dataframe["macd_signal"])
                # Condition 2 — Histogram positive (momentum confirmed)
                & (dataframe["macd_hist"] > 0)
                # Condition 3 — CCI oversold
                & (dataframe["cci"] <= -50)
                # [Optional stronger filter] Uncomment to require crossover below zero:
                # & (dataframe["macd"] < 0)
                # Safety: indicators populated
                & dataframe["macd"].notna()
                & dataframe["macd_signal"].notna()
                & dataframe["cci"].notna()
            ),
            "enter_long",
        ] = 1

        return dataframe

    # -------------------------------------------------------------------------
    # Exit Signal
    # -------------------------------------------------------------------------

    def populate_exit_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        """
        Generate LONG exit signals.

        Exit is triggered when EITHER condition is met:
        A. MACD line crosses below signal line AND CCI is overbought (>= 100)
        B. Bearish MACD divergence detected (price higher high, MACD lower high)
        """

        dataframe.loc[
            (
                # Condition A — MACD bearish crossover with overbought CCI
                (
                    self.crossed_below(dataframe["macd"], dataframe["macd_signal"])
                    & (dataframe["cci"] >= 100)
                )
                # Condition B — Bearish divergence: momentum fading
                | dataframe["bearish_div"]
            )
            & dataframe["macd"].notna()
            & dataframe["macd_signal"].notna(),
            "exit_long",
        ] = 1

        return dataframe