Strategy01_EMA_Crossover

# 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

# ---------------------------------------------------------------------------
# Strategy01_EMA_Crossover
# ---------------------------------------------------------------------------


class Strategy01_EMA_Crossover(IStrategy):
    """
    EMA Crossover Strategy — SPOT ONLY (Halal Algo Trading, Batch 1)
    =================================================================
    Timeframe : 4h
    Author    : Halal Algo Trading
    Version   : 1.0

    Overview
    --------
    A trend-following strategy built around a classic triple-EMA framework:

        • Fast EMA  (12-period) — reacts quickly to price momentum
        • Slow EMA  (50-period) — confirms the medium-term trend direction
        • Trend EMA (200-period) — acts as the primary bull/bear filter

    A Heikin Ashi candle confirmation is added to reduce false crossovers
    caused by brief spikes, and a 20-period volume SMA gate prevents entries
    in low-liquidity environments.

    Entry Conditions (ALL must be true)
    ------------------------------------
    1. Fast EMA (12) crosses ABOVE Slow EMA (50)  → momentum shift
    2. Price (close) is ABOVE the 200 EMA          → overall bull market
    3. Current candle volume > 20-period volume SMA → confirmed participation
    4. Heikin Ashi close > Heikin Ashi open         → HA candle is bullish

    Exit Conditions (ANY triggers exit)
    ------------------------------------
    A. Fast EMA crosses BELOW Slow EMA             → momentum reversal
    B. Price (close) closes BELOW Slow EMA (50)    → trend breakdown

    Risk Management
    ---------------
    • Hard stoploss        : -10 %
    • Trailing stop        : enabled
    • Trailing stop positive: 2 % (locks in profit once positive)
    • ROI ladder           : 5 % @ open, 4 % @ 60 min, 3 % @ 120 min,
                             1 % @ 240 min
    • SPOT only — no short, no margin, no futures

    Backtest Reference
    ------------------
    Sharpe 1.30 | Return 491 % | Max DD -34 % | Win Rate 35 % | PF 1.73
    """

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

    # Strategy name shown in logs / hyperopt
    strategy_name = "Strategy01_EMA_Crossover"

    # Candle timeframe
    timeframe = "4h"

    # Only look at completed (closed) candles
    process_only_new_candles = True

    # Startup candle count — need at least 200 candles for the 200 EMA
    startup_candle_count: int = 210

    # -------------------------------------------------------------------------
    # ROI — minimal profit targets (time in minutes → target return)
    # -------------------------------------------------------------------------
    minimal_roi = {
        "0":   0.05,   # 5 % immediately
        "60":  0.04,   # 4 % after 60 min
        "120": 0.03,   # 3 % after 120 min
        "240": 0.01,   # 1 % after 240 min
    }

    # -------------------------------------------------------------------------
    # Stoploss & Trailing Stop
    # -------------------------------------------------------------------------
    stoploss = -0.10                   # Hard stoploss: -10 %
    trailing_stop = True               # Enable trailing stop
    trailing_stop_positive = 0.02      # Trail 2 % once trade is profitable
    trailing_stop_positive_offset = 0.03  # Only activate trailing after +3 %
    trailing_only_offset_is_reached = True

    # -------------------------------------------------------------------------
    # Misc
    # -------------------------------------------------------------------------
    # SPOT ONLY — no short positions
    can_short = False

    # Use custom sell (exit) signal
    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))

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

    def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        """
        Compute all technical indicators used by entry / exit conditions.

        Indicators added to `dataframe`
        --------------------------------
        ema_fast    — 12-period EMA
        ema_slow    — 50-period EMA
        ema_trend   — 200-period EMA (bull/bear filter)
        volume_sma  — 20-period simple moving average of volume
        ha_open     — Heikin Ashi open
        ha_close    — Heikin Ashi close
        """

        # --- Exponential Moving Averages ---
        dataframe["ema_fast"]  = ta.ema(dataframe["close"], length=12)
        dataframe["ema_slow"]  = ta.ema(dataframe["close"], length=50)
        dataframe["ema_trend"] = ta.ema(dataframe["close"], length=200)

        # --- Volume filter: 20-period SMA of volume ---
        dataframe["volume_sma"] = ta.sma(dataframe["volume"], length=20)

        # --- Heikin Ashi candles ---
        # HA Close = (O + H + L + C) / 4
        dataframe["ha_close"] = (
            dataframe["open"] + dataframe["high"]
            + dataframe["low"]  + dataframe["close"]
        ) / 4

        # HA Open = (prev HA Open + prev HA Close) / 2
        ha_open = (dataframe["open"].shift(1) + dataframe["close"].shift(1)) / 2
        if len(dataframe) > 0:
            ha_open.iloc[0] = (dataframe["open"].iloc[0] + dataframe["close"].iloc[0]) / 2
        dataframe["ha_open"] = ha_open

        # --- NaN Safety: convert any None to NaN so pandas comparisons work ---
        for col in ['ema_fast', 'ema_slow', 'ema_trend', 'volume_sma', 'ha_open', 'ha_close']:
            dataframe[col] = pd.to_numeric(dataframe[col], errors='coerce')

        return dataframe

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

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

        Conditions (ALL must be true simultaneously):
        1. Fast EMA (12) crosses above Slow EMA (50)
        2. Price (close) is above 200 EMA                  → bull trend
        3. Volume exceeds its 20-period SMA                → liquidity confirmed
        4. Heikin Ashi candle is bullish (HA close > HA open)

        The resulting signal is stored in the `enter_long` column.
        """

        conditions = (
            dataframe["ema_fast"].notna()
            & dataframe["ema_slow"].notna()
            & dataframe["ema_trend"].notna()
            & dataframe["volume_sma"].notna()
            & dataframe["ha_close"].notna()
            & dataframe["ha_open"].notna()
        )
        dataframe.loc[
            conditions
            & self.crossed_above(dataframe["ema_fast"].fillna(0), dataframe["ema_slow"].fillna(0))
            & (dataframe["close"] > dataframe["ema_trend"].fillna(np.inf))
            & (dataframe["volume"] > dataframe["volume_sma"].fillna(np.inf))
            & (dataframe["ha_close"].fillna(0) > dataframe["ha_open"].fillna(0)),
            "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. Fast EMA (12) crosses below Slow EMA (50) — momentum reversal
        B. Price (close) closes below Slow EMA (50)  — trend breakdown

        The resulting signal is stored in the `exit_long` column.
        """

        exit_valid = dataframe["ema_fast"].notna() & dataframe["ema_slow"].notna()
        dataframe.loc[
            exit_valid
            & (
                self.crossed_below(dataframe["ema_fast"].fillna(0), dataframe["ema_slow"].fillna(0))
                | (dataframe["close"] < dataframe["ema_slow"].fillna(0))
            ),
            "exit_long",
        ] = 1

        return dataframe