TrendFollowV1

import json
import logging
import sys
from datetime import datetime, timedelta
from pathlib import Path

import freqtrade.vendor.qtpylib.indicators as qtpylib
import pandas_ta as ta
from pandas import DataFrame

# Resolve project root from this file's location (works on any machine)
_BASE = Path(__file__).resolve().parent.parent.parent
if str(_BASE) not in sys.path:
    sys.path.insert(0, str(_BASE))

from freqtrade.persistence import Trade
from freqtrade.strategy import IntParameter, IStrategy

from indicators.macro_merge import merge_macro_data
from qnt.oracle.hmm_regime import detect_regime, get_regime_for_strategy
from qnt.thesis.thesis_reader import read_thesis
from risk.correlation_guard import is_blocked as corr_blocked
from risk.risk_manager import run_all_checks
from risk.stake_sizer import get_stake_multiplier
from sentiment.reader import get_current_sentiment, get_funding_rate, get_sentiment_signal

logger = logging.getLogger(__name__)

_partial_exits_done: set = set()


class TrendFollowV1(IStrategy):
    INTERFACE_VERSION = 3
    timeframe = "4h"

    # Parameters
    buy_rsi_min = IntParameter(30, 50, default=40, space="buy")
    buy_rsi_max = IntParameter(60, 80, default=80, space="buy")
    sell_rsi_limit = IntParameter(70, 90, default=75, space="sell")

    minimal_roi = {"0": 0.03}
    stoploss = -0.06

    def load_dynamic_params(self):
        self.buy_rsi_min_val = (
            self.buy_rsi_min.value if hasattr(self.buy_rsi_min, "value") else self.buy_rsi_min
        )
        self.buy_rsi_max_val = (
            self.buy_rsi_max.value if hasattr(self.buy_rsi_max, "value") else self.buy_rsi_max
        )
        self.sell_rsi_limit_val = (
            self.sell_rsi_limit.value
            if hasattr(self.sell_rsi_limit, "value")
            else self.sell_rsi_limit
        )
        try:
            import json

            p = _BASE / "config/dynamic_params.json"
            if p.exists():
                sp = json.loads(p.read_text()).get("TrendFollowV1", {})
                if "buy_rsi_min" in sp:
                    self.buy_rsi_min_val = int(sp["buy_rsi_min"])
                if "buy_rsi_max" in sp:
                    self.buy_rsi_max_val = int(sp["buy_rsi_max"])
                if "sell_rsi_limit" in sp:
                    self.sell_rsi_limit_val = int(sp["sell_rsi_limit"])
        except Exception:
            pass

    stoploss_on_exchange = True
    stoploss_on_exchange_interval = 60

    # Startup candles
    startup_candle_count: int = 50

    def feature_engineering_expand_all(
        self, dataframe: DataFrame, period: int, metadata: dict, **kwargs
    ) -> DataFrame:
        # Standard indicators for the model
        dataframe["%-rsi-period"] = ta.rsi(dataframe["close"], length=period) / 100
        dataframe["%-ema_9-period"] = ta.ema(dataframe["close"], length=9) / dataframe["close"]

        # External signal features
        import json

        try:
            CIPHER_PATH = str(_BASE)
            with open(f"{CIPHER_PATH}/sentiment/scores/current_score.json") as f:
                sentiment_data = json.load(f)

            dataframe["sentiment_score"] = sentiment_data.get("score", 0.0)
            dataframe["fear_greed_raw"] = sentiment_data.get("component_scores", {}).get(
                "feargreed", 0.0
            )
            dataframe["funding_rate_raw"] = sentiment_data.get("component_scores", {}).get(
                "funding", 0.0
            )

            # Calendar risk as numeric feature
            from qnt.oracle.oracle_calendar import check_calendar_risk_today

            risk_map = {"LOW": 0, "MEDIUM": 1, "HIGH": 2, "EXTREME": 3}
            dataframe["calendar_risk"] = risk_map.get(check_calendar_risk_today(), 1)
        except Exception:
            dataframe["sentiment_score"] = 0.0
            dataframe["fear_greed_raw"] = 0.0
            dataframe["funding_rate_raw"] = 0.0
            dataframe["calendar_risk"] = 1

        return dataframe

    def feature_engineering_expand_basic(
        self, dataframe: DataFrame, metadata: dict, **kwargs
    ) -> DataFrame:
        return dataframe

    def feature_engineering_standard(
        self, dataframe: DataFrame, metadata: dict, **kwargs
    ) -> DataFrame:
        return dataframe

    def set_freqai_targets(self, dataframe: DataFrame, metadata: dict, **kwargs) -> DataFrame:
        dataframe["&-s_close"] = (
            dataframe["close"].shift(
                -self.freqai_info["feature_parameters"]["label_period_candles"]
            )
            / dataframe["close"]
            - 1
        )
        return dataframe

    def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        from qnt.polars_indicators import add_ema, add_macd, add_rsi
        from qnt.polars_ohlcv import ohlcv_to_pandas, pandas_to_polars

        if "ema_fast" not in dataframe.columns:
            df_pl = pandas_to_polars(dataframe)

            df_pl = add_ema(df_pl, period=20, alias="ema_fast")
            df_pl = add_ema(df_pl, period=50, alias="ema_slow")
            df_pl = add_macd(df_pl, fast=12, slow=26, signal=9, prefix="macd")
            df_pl = add_rsi(df_pl, period=14, alias="rsi")

            dataframe = ohlcv_to_pandas(df_pl)

        dataframe = merge_macro_data(dataframe)
        return dataframe

    def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        self.load_dynamic_params()
        regime = detect_regime(dataframe, metadata["pair"])
        regime_ok = get_regime_for_strategy("TrendFollowV1", regime)

        dataframe.loc[
            (
                (qtpylib.crossed_above(dataframe["ema_fast"], dataframe["ema_slow"]))
                & (dataframe["macd_hist"] > 0)
                & (regime_ok)
                & (dataframe["rsi"] > self.buy_rsi_min_val)
                & (dataframe["rsi"] < self.buy_rsi_max_val)
            ),
            "enter_long",
        ] = 1
        return dataframe

    def populate_exit_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        self.load_dynamic_params()
        dataframe.loc[
            (
                (qtpylib.crossed_below(dataframe["ema_fast"], dataframe["ema_slow"]))
                | (dataframe["rsi"] > self.sell_rsi_limit_val)
            ),
            "exit_long",
        ] = 1
        return dataframe

    def adjust_trade_position(
        self,
        trade,
        current_time: datetime,
        current_rate: float,
        current_profit: float,
        min_stake,
        max_stake,
        current_entry_rate: float,
        current_exit_rate: float,
        current_entry_profit: float,
        current_exit_profit: float,
        **kwargs,
    ):
        if trade.id in _partial_exits_done:
            return None
        if current_profit >= 0.015:
            _partial_exits_done.add(trade.id)
            logger.info(
                f"[PARTIAL EXIT] TrendFollowV1 {trade.pair} profit={current_profit:.2%} — exiting 50%"
            )
            return -(trade.stake_amount * 0.5)
        return None

    def custom_stake_amount(
        self,
        current_time,
        current_rate,
        proposed_stake,
        min_stake,
        max_stake,
        leverage,
        entry_tag,
        side,
        **kwargs,
    ):
        multiplier = get_stake_multiplier("TrendFollowV1")
        stake = proposed_stake * multiplier
        if min_stake is not None:
            stake = max(stake, min_stake)
        return min(stake, max_stake)

    def confirm_trade_entry(
        self,
        pair: str,
        order_type: str,
        amount: float,
        rate: float,
        time_in_force: str,
        current_time: datetime,
        entry_tag: str,
        side: str,
        **kwargs,
    ) -> bool:
        if self.config.get("runmode", {}).value not in ("live", "dry_run"):
            return True

        # --- LAYER 0: THESIS GATE ---
        thesis = read_thesis(pair)
        if thesis["bias"] == "SELL":
            logger.info(
                f"[THESIS BLOCK] {pair} bias=SELL confidence={thesis['confidence']:.2f} — {thesis['reasoning']}"
            )
            return False
        stake_modifier = thesis.get("stake_modifier", 1.0)

        # --- LAYER 1: RISK & SENTIMENT CHECKS ---
        try:
            total_balance = self.wallets.get_total("USDT")

            # Fetch recent trades
            recent_trades = [
                {
                    "profit_ratio": float(
                        getattr(t, "close_profit", None) or getattr(t, "profit_ratio", None) or 0.0
                    ),
                    "close_date": getattr(t, "close_date", None),
                }
                for t in Trade.get_trades_proxy(is_open=False)
            ][:10]

            # Count trades in the last hour
            one_hour_ago = current_time - timedelta(hours=1)
            trades_last_hour = len(
                [
                    t
                    for t in Trade.get_trades_proxy(is_open=False)
                    if t.close_date and t.close_date >= one_hour_ago
                ]
            )

            # Load balance state
            state_file = _BASE / "risk/balance_state.json"
            if state_file.exists():
                with open(state_file) as f:
                    state = json.load(f)
                start_of_day = state.get("start_of_day", total_balance)
                start_of_week = state.get("start_of_week", total_balance)
            else:
                start_of_day = total_balance
                start_of_week = total_balance

            risk_result = run_all_checks(
                current_balance=total_balance,
                start_of_day_balance=start_of_day,
                start_of_week_balance=start_of_week,
                trade_amount_usdt=amount * rate * stake_modifier,
                trades_last_hour=trades_last_hour,
                recent_trades=recent_trades,
                min_sentiment="NEUTRAL",
            )

            if not risk_result["safe_to_trade"]:
                logger.info(
                    f"[RISK/SENTIMENT BLOCK] TrendFollow blocked for {pair}. Reasons: {risk_result['blocking_reasons']}"
                )
                return False

            # Log sentiment for visibility
            sentiment = get_current_sentiment()
            signal = get_sentiment_signal()
            logger.info(
                f"[Sentiment Check] {pair} | Score: {sentiment['score']:.3f} | Signal: {signal}"
            )

        except Exception as e:
            logger.error(f"[RISK WARNING] Risk check error: {e}")

        base = pair.split("/")[0]
        if corr_blocked(base, side):
            logger.info(f"[CORR BLOCK] TrendFollowV1 {pair} — too many concurrent longs on {base}")
            return False

        if side == "long":
            funding = get_funding_rate()
            if funding < -0.5:
                logger.info(
                    f"[FUNDING BLOCK] TrendFollowV1 {pair} funding={funding:.2f} — extreme negative funding"
                )
                return False

        return True