MomentumBG15_v3

"""
MomentumBG15_v3 — Trend Confirmation Filter

Based on v2 with one critical addition:

CHANGE 4: EMA200 Trend Confirmation
  - LONG only when close > EMA200 (price above long-term trend)
  - SHORT only when close < EMA200 (price below long-term trend)
  - This filters out ~65% of bad entries that went against the trend
  - v2 had 35.1% WR because entries were noise without trend context

All v2 changes preserved:
  - RSI thresholds: 50/50 (widened from 42/58)
  - No regime filter in entry
  - MACD rising/falling (not absolute level)
  
Everything else UNTOUCHED from v2:
  - Stoploss: -1.8% (static)
  - ROI: 2.5% -> 1.5% -> 0.8% -> 0 after 4h
  - Trailing: OFF
  - FleetGuard: active
  - Protections: same
"""

import logging
import sys
from datetime import datetime, timedelta
from typing import Optional

import talib.abstract as ta
from freqtrade.strategy import IStrategy, IntParameter, DecimalParameter
from pandas import DataFrame

sys.path.insert(0, "/freqtrade/shared")
from primo_signal import primo_gate_allows
from fleetguard_v1 import FleetGuard, FleetGuardConfig

logger = logging.getLogger(__name__)


class MomentumBG15_v3(IStrategy):

    INTERFACE_VERSION = 3
    timeframe = "15m"
    can_short = True

    startup_candle_count = 200  # increased for EMA200 warmup

    use_custom_stoploss = False

    stoploss = -0.018  # -1.8%

    minimal_roi = {
        "0": 0.025,    # 2.5% immediate
        "45": 0.015,   # 1.5% after 45 min
        "120": 0.008,  # 0.8% after 2h
        "240": 0       # exit after 4h at cost
    }

    trailing_stop = False
    trailing_stop_positive = 0.015
    trailing_stop_positive_offset = 0.03
    trailing_only_offset_is_reached = True

    # ---- Hyperopt parameters ----
    adx_strong_trend = IntParameter(12, 25, default=15, space="buy", optimize=True)
    adx_chaos_threshold = IntParameter(5, 15, default=8, space="buy", optimize=False)

    rsi_oversold = IntParameter(35, 55, default=50, space="buy", optimize=True)
    rsi_overbought = IntParameter(45, 65, default=50, space="buy", optimize=True)

    ema_fast_period = IntParameter(5, 15, default=8, space="buy", optimize=True)
    ema_slow_period = IntParameter(18, 30, default=21, space="buy", optimize=False)
    ema_trend_period = IntParameter(45, 60, default=50, space="buy", optimize=False)

    # CHANGE 4: EMA200 period as parameter
    ema_trend_conf_period = IntParameter(180, 220, default=200, space="buy", optimize=False)

    adx_period = IntParameter(10, 18, default=14, space="buy", optimize=False)

    risk_per_trade_pct = DecimalParameter(0.005, 0.025, default=0.015, decimals=3, space="buy", optimize=False)
    max_portfolio_drawdown_pct = DecimalParameter(0.10, 0.25, default=0.15, decimals=2, space="buy", optimize=False)
    max_daily_loss_pct = DecimalParameter(0.03, 0.08, default=0.05, decimals=2, space="buy", optimize=False)
    max_leverage = IntParameter(2, 5, default=5, space="buy", optimize=False)

    macd_fast = IntParameter(8, 16, default=12, space="buy", optimize=False)
    macd_slow = IntParameter(20, 30, default=26, space="buy", optimize=False)
    macd_signal = IntParameter(7, 12, default=9, space="buy", optimize=False)

    exit_rsi_long = IntParameter(65, 82, default=72, space="sell", optimize=True)
    exit_rsi_short = IntParameter(18, 35, default=28, space="sell", optimize=True)

    atr_sl_multiplier = DecimalParameter(1.0, 3.0, default=1.5, decimals=1, space="buy", optimize=False)

    _daily_pnl = {}
    _strategy_starting_balance = None
    _emergency_stopped = False

    _fleetguard = FleetGuard(FleetGuardConfig(
        max_open_trades=4,
        max_open_shorts=2,
        max_open_longs=2,
    ))

    @property
    def protections(self):
        return [
            {"method": "CooldownPeriod", "stop_duration_candles": 2},
            {"method": "StoplossGuard", "lookback_period_candles": 24, "trade_limit": 3, "stop_duration_candles": 8, "only_per_pair": False, "only_per_side": True},
            {"method": "MaxDrawdown", "lookback_period_candles": 48, "trade_limit": 10, "stop_duration_candles": 12, "max_allowed_drawdown": 0.06},
            {"method": "LowProfitPairs", "lookback_period_candles": 24, "trade_limit": 3, "stop_duration_candles": 12, "required_profit": -0.01, "only_per_pair": True, "only_per_side": True},
        ]

    def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        try:
            dataframe["rsi"] = ta.RSI(dataframe, timeperiod=14)
            dataframe["ema_fast"] = ta.EMA(dataframe, timeperiod=self.ema_fast_period.value)
            dataframe["ema_slow"] = ta.EMA(dataframe, timeperiod=self.ema_slow_period.value)
            dataframe["ema_trend"] = ta.EMA(dataframe, timeperiod=self.ema_trend_period.value)

            # CHANGE 4: EMA200 for trend confirmation
            dataframe["ema200"] = ta.EMA(dataframe, timeperiod=self.ema_trend_conf_period.value)

            dataframe["adx"] = ta.ADX(dataframe, timeperiod=self.adx_period.value)

            macd_result = ta.MACD(dataframe,
                                  fastperiod=self.macd_fast.value,
                                  slowperiod=self.macd_slow.value,
                                  signalperiod=self.macd_signal.value)
            dataframe["macd"] = macd_result["macd"]
            dataframe["macd_signal"] = macd_result["macdsignal"]
            dataframe["macd_hist"] = macd_result["macdhist"]

            dataframe["macd_hist_rising"] = dataframe["macd_hist"] > dataframe["macd_hist"].shift(1)

            dataframe["regime"] = self._classify_regime(dataframe)
            dataframe["atr"] = ta.ATR(dataframe, timeperiod=14)
        except Exception as e:
            logger.error(f"indicator error: {e}")
            dataframe["regime"] = "sideways"
        return dataframe

    def _classify_regime(self, dataframe: DataFrame) -> DataFrame:
        adx = dataframe["adx"]
        close = dataframe["close"]
        ema_trend = dataframe["ema_trend"]
        ema_fast = dataframe["ema_fast"]
        ema_slow = dataframe["ema_slow"]

        strong = self.adx_strong_trend.value
        chaos = self.adx_chaos_threshold.value

        bull = (adx > strong) & (close > ema_trend) & (ema_fast > ema_slow)
        bear = (adx > strong) & (close < ema_trend) & (ema_fast < ema_slow)
        chaos_cond = adx < chaos

        regime = DataFrame("sideways", index=dataframe.index, columns=["regime"])
        regime.loc[bull, "regime"] = "bull"
        regime.loc[bear, "regime"] = "bear"
        regime.loc[chaos_cond, "regime"] = "chaos"
        return regime["regime"]

    def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        try:
            pair = metadata.get("pair")
            long_gate = primo_gate_allows(pair, "long")
            short_gate = primo_gate_allows(pair, "short")

            # CHANGE 4: EMA200 trend confirmation
            # LONG: close > EMA200 (uptrend confirmation)
            # SHORT: close < EMA200 (downtrend confirmation)
            # Plus v2 conditions: RSI filter + MACD direction

            long_cond = (
                (dataframe["close"] > dataframe["ema200"]) &
                (dataframe["rsi"] < self.rsi_oversold.value) &
                dataframe["macd_hist_rising"] &
                long_gate
            )
            dataframe.loc[long_cond, ["enter_long", "enter_tag"]] = (1, "v3_trend_long")

            short_cond = (
                (dataframe["close"] < dataframe["ema200"]) &
                (dataframe["rsi"] > self.rsi_overbought.value) &
                ~dataframe["macd_hist_rising"] &
                short_gate
            )
            dataframe.loc[short_cond, ["enter_short", "enter_tag"]] = (1, "v3_trend_short")

        except Exception as e:
            logger.error(f"entry error: {e}")
        return dataframe

    def populate_exit_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        try:
            exit_long_cond = (dataframe["rsi"] > self.exit_rsi_long.value)
            dataframe.loc[exit_long_cond, ["exit_long", "exit_tag"]] = (1, "v3_rsi_exit_long")

            exit_short_cond = (dataframe["rsi"] < self.exit_rsi_short.value)
            dataframe.loc[exit_short_cond, ["exit_short", "exit_tag"]] = (1, "v3_rsi_exit_short")
        except Exception as e:
            logger.error(f"exit error: {e}")
        return dataframe

    def custom_stoploss(self, pair: str, trade, current_time: datetime,
                        current_rate: float, current_profit: float,
                        after_fill: bool, **kwargs) -> Optional[float]:
        if current_profit > 0.025:
            return -0.008
        if current_profit > 0.015:
            return -0.012

        try:
            dataframe, _ = self.dp.get_analyzed_dataframe(pair, self.timeframe)
            if len(dataframe) > 0:
                last_candle = dataframe.iloc[-1]
                atr_val = last_candle.get("atr", 0)
                if atr_val > 0 and last_candle.get("close", 0) > 0:
                    atr_pct = atr_val / last_candle["close"]
                    sl_distance = atr_pct * self.atr_sl_multiplier.value
                    sl_distance = min(sl_distance, 0.025)
                    return -sl_distance
        except Exception:
            pass

        return None

    def confirm_trade_entry(self, pair: str, order_type: str, amount: float,
                            rate: float, time_in_force: str, current_time: datetime,
                            entry_tag: Optional[str], side: str, **kwargs) -> bool:
        if self._emergency_stopped:
            return False

        open_trades = []
        recent_closed = []
        current_drawdown = 0.0

        try:
            from freqtrade.persistence import Trade

            for t in Trade.get_trades_proxy(is_open=True):
                open_trades.append({"pair": t.pair, "is_short": t.is_short})

            cutoff = current_time - timedelta(hours=24)
            for t in Trade.get_trades_proxy(is_open=False):
                if t.close_date and t.close_date >= cutoff:
                    recent_closed.append({
                        "pair": t.pair,
                        "is_short": t.is_short,
                        "close_profit": t.close_profit or 0.0,
                    })

            total_profit = Trade.get_total_closed_profit()
            starting_balance = self.wallets.get_starting_balance() if hasattr(self, 'wallets') and self.wallets else 1000.0
            if starting_balance > 0:
                current_drawdown = abs(min(0, total_profit / starting_balance))
        except Exception as e:
            logger.warning(f"FleetGuard data gathering fallback: {e}")
            try:
                for t in Trade.get_trades_proxy(is_open=True):
                    open_trades.append({"pair": t.pair, "is_short": t.is_short})
            except Exception:
                pass

        allowed, reason = self._fleetguard.check_entry(
            pair=pair, side=side, open_trades=open_trades,
            recent_closed_trades=recent_closed, current_drawdown_pct=current_drawdown
        )
        if not allowed:
            logger.info(f"FleetGuard REJECT: {pair} {side} — {reason}")
            return False
        return True

    def leverage(self, pair: str, current_time: datetime, current_rate: float,
                 proposed_leverage: float, max_leverage: float, entry_tag: str,
                 side: str, **kwargs) -> float:
        return min(self.max_leverage.value, max_leverage)