"""
ForexBot2 — Data Provider (Twelve Data SDK, USD/JPY only)
==========================================================
Fetches and validates OHLCV via the official `twelvedata` Python SDK.
No synthetic fallback — if the API fails the caller learns about it.
Adds a parquet on-disk cache so the backtest worker pool reuses one
download instead of hammering Twelve Data during a parameter sweep.
"""

import logging
import os
import time
from collections import deque
from typing import Dict, Optional

import pandas as pd
from twelvedata import TDClient

from config import PlatformConfig, Timeframe, USDJPY, DISPLAY, PIP_VALUE

logger = logging.getLogger(__name__)


class DataIntegrityError(Exception):
    """Raised when data fails validation checks."""
    pass


class ForexDataProvider:
    """Twelve Data SDK client + on-disk parquet cache."""

    # Local MetaTrader CSV exports — tab-delimited, 7 columns (includes Spread)
    LOCAL_CSV_MAP = {
        Timeframe.M15: "USDJPY_M15.csv",
        Timeframe.H1:  "USDJPY_H1.csv",
        Timeframe.H4:  "USDJPY_H4.csv",
        Timeframe.D1:  "USDJPY_D1.csv",
    }

    TD_INTERVALS = {
        Timeframe.M1:  "1min",
        Timeframe.M5:  "5min",
        Timeframe.M15: "15min",
        Timeframe.M30: "30min",
        Timeframe.H1:  "1h",
        Timeframe.H4:  "4h",
        Timeframe.D1:  "1day",
        Timeframe.W1:  "1week",
    }

    BARS_PER_DAY = {
        Timeframe.M1:  1440,
        Timeframe.M5:  288,
        Timeframe.M15: 96,
        Timeframe.M30: 48,
        Timeframe.H1:  24,
        Timeframe.H4:  6,
        Timeframe.D1:  1,
        Timeframe.W1:  0.143,
    }

    # Free plan: 8 credits / minute
    RATE_LIMIT = 8
    RATE_WINDOW = 60.0

    def __init__(self, config: PlatformConfig):
        self.config = config
        self._api_key = (
            os.environ.get("TWELVE_DATA_API_KEY")
            or config.twelve_data_api_key
        )
        self._cache: Dict[str, pd.DataFrame] = {}
        self._cache_timestamps: Dict[str, float] = {}
        self._request_times: deque = deque()
        self._td: Optional[TDClient] = (
            TDClient(apikey=self._api_key) if self._api_key else None
        )

    @staticmethod
    def _to_td_symbol(symbol: str) -> str:
        clean = symbol.replace("=X", "")
        return f"{clean[:3]}/{clean[3:]}"

    # ── Public API ─────────────────────────────────────────────────

    def fetch_ohlcv(
        self,
        symbol: str = USDJPY,
        timeframe: Timeframe = Timeframe.H1,
        lookback_days: Optional[int] = None,
    ) -> pd.DataFrame:
        """Fetch OHLCV with in-memory caching for live mode."""
        if lookback_days is None:
            lookback_days = self.config.data_lookback_days

        cache_key = f"{symbol}_{timeframe.value}"
        now = time.time()

        if (
            cache_key in self._cache_timestamps
            and (now - self._cache_timestamps[cache_key]) < self.config.refresh_interval
            and cache_key in self._cache
        ):
            cached = self._cache.get(cache_key)
            if cached is not None and not cached.empty:
                return cached

        outputsize = max(
            300,
            min(int(lookback_days * self.BARS_PER_DAY[timeframe]) + 20, 5000),
        )
        df = self._fetch_twelve_data(symbol, timeframe, outputsize)
        df = self._validate_and_clean(df, symbol, timeframe)

        self._cache[cache_key] = df
        self._cache_timestamps[cache_key] = now
        return df

    def fetch_full_history(
        self,
        symbol: str = USDJPY,
        timeframe: Timeframe = Timeframe.H1,
        max_bars: int = 5000,
        use_disk_cache: bool = True,
        start_date: Optional[str] = None,
        end_date: Optional[str] = None,
    ) -> pd.DataFrame:
        """
        Pull the largest window Twelve Data allows in one call.
        On-disk parquet cache prevents re-download across worker subprocesses
        during an optimizer sweep.
        """
        cache_path = self._disk_cache_path(symbol, timeframe, start_date, end_date)

        if use_disk_cache and os.path.exists(cache_path):
            try:
                df = pd.read_parquet(cache_path)
                if df.index.tz is None:
                    df.index = df.index.tz_localize("UTC")
                logger.info(
                    f"Loaded {len(df)} bars from disk cache: {cache_path}"
                )
                return df
            except Exception as e:
                logger.warning(f"Disk cache read failed ({e}); refetching.")

        outputsize = min(max_bars, 5000)
        df = self._fetch_twelve_data(
            symbol, timeframe, outputsize,
            start_date=start_date, end_date=end_date,
        )
        df = self._validate_and_clean(df, symbol, timeframe)

        if use_disk_cache:
            os.makedirs(os.path.dirname(cache_path), exist_ok=True)
            try:
                df.to_parquet(cache_path)
                logger.info(f"Cached {len(df)} bars → {cache_path}")
            except Exception as e:
                logger.warning(f"Disk cache write failed: {e}")

        return df

    def load_local_csv(
        self,
        timeframe: Timeframe,
        data_dir: str = "market_data",
        start_date: Optional[str] = None,
        end_date: Optional[str] = None,
    ) -> Optional[pd.DataFrame]:
        """
        Load OHLCV from a local MetaTrader CSV export.
        Format: tab-separated, columns Time/Open/High/Low/Close/Volume/Spread.
        Returns None silently if the file doesn't exist for this timeframe.
        """
        fname = self.LOCAL_CSV_MAP.get(timeframe)
        if fname is None:
            logger.info(f"No local CSV mapping for {timeframe.value} — skipping")
            return None

        path = os.path.join(data_dir, fname)
        if not os.path.exists(path):
            logger.warning(f"Local CSV not found: {path}")
            return None

        df = pd.read_csv(
            path, sep="\t",
            names=["Time", "Open", "High", "Low", "Close", "Volume", "Spread"],
            header=0,
            parse_dates=["Time"],
        )
        df = df.set_index("Time")
        df = df.drop(columns=["Spread"], errors="ignore")
        for col in ("Open", "High", "Low", "Close", "Volume"):
            if col in df.columns:
                df[col] = pd.to_numeric(df[col], errors="coerce")
        if "Volume" not in df.columns:
            df["Volume"] = 0.0

        if df.index.tz is None:
            df.index = df.index.tz_localize("UTC")
        df.index.name = "Datetime"

        if start_date:
            df = df[df.index >= pd.Timestamp(start_date, tz="UTC")]
        if end_date:
            df = df[df.index <= pd.Timestamp(end_date, tz="UTC")]

        df = self._validate_and_clean(df, USDJPY, timeframe)
        logger.info(f"Loaded {len(df):,} bars from local CSV: {path}")
        return df

    def get_latest_price(self, symbol: str = USDJPY) -> Optional[float]:
        try:
            df = self.fetch_ohlcv(symbol, Timeframe.M5, lookback_days=1)
            if df is not None and not df.empty:
                return float(df["Close"].iloc[-1])
        except Exception:
            pass
        return None

    def get_spread_estimate(self, symbol: str = USDJPY) -> float:
        """Estimate spread from recent M5 H-L median; fallback to 1 pip on JPY."""
        try:
            df = self.fetch_ohlcv(symbol, Timeframe.M5, lookback_days=1)
            if df is not None and not df.empty:
                return float((df["High"] - df["Low"]).median())
        except Exception:
            pass
        # Fallback: 1 pip = 0.01 for JPY pairs
        return PIP_VALUE

    def get_pair_info(self, symbol: str = USDJPY) -> Dict:
        return {
            "symbol": symbol,
            "display": DISPLAY,
            "pip_value": PIP_VALUE,
            "is_jpy_pair": True,
        }

    # ── Private ────────────────────────────────────────────────────

    def _disk_cache_path(
        self,
        symbol: str,
        timeframe: Timeframe,
        start_date: Optional[str] = None,
        end_date: Optional[str] = None,
    ) -> str:
        clean = symbol.replace("=X", "").lower()
        suffix = ""
        if start_date or end_date:
            suffix = f"_{start_date or 'min'}_{end_date or 'max'}"
        return os.path.join(
            self.config.backtest.cache_dir,
            f"{clean}_{timeframe.value}{suffix}.parquet",
        )

    def _throttle(self, credits: int = 1) -> None:
        """Free tier: 8 credits / minute. Block until budget allows."""
        while True:
            now = time.time()
            while self._request_times and now - self._request_times[0] >= self.RATE_WINDOW:
                self._request_times.popleft()

            available = self.RATE_LIMIT - len(self._request_times)
            if available >= credits:
                for _ in range(credits):
                    self._request_times.append(time.time())
                return

            wait = (self._request_times[0] + self.RATE_WINDOW) - now + 0.2
            logger.info(
                f"Rate limit: {len(self._request_times)}/{self.RATE_LIMIT} credits used "
                f"— waiting {wait:.1f}s..."
            )
            time.sleep(wait)

    def _fetch_twelve_data(
        self,
        symbol: str,
        timeframe: Timeframe,
        outputsize: int,
        start_date: Optional[str] = None,
        end_date: Optional[str] = None,
    ) -> pd.DataFrame:
        if self._td is None:
            raise DataIntegrityError(
                "No Twelve Data API key. Set TWELVE_DATA_API_KEY env var "
                "or config.twelve_data_api_key."
            )

        td_symbol = self._to_td_symbol(symbol)
        interval = self.TD_INTERVALS[timeframe]

        self._throttle(credits=1)

        kwargs = {
            "symbol": td_symbol,
            "interval": interval,
            "outputsize": min(outputsize, 5000),
            "timezone": "UTC",
        }
        if start_date:
            kwargs["start_date"] = start_date
        if end_date:
            kwargs["end_date"] = end_date

        try:
            df = self._td.time_series(**kwargs).as_pandas()
        except Exception as e:
            raise DataIntegrityError(
                f"Twelve Data fetch failed for {td_symbol}: {e}"
            ) from e

        if df is None or df.empty:
            raise DataIntegrityError(f"Empty response from Twelve Data for {td_symbol}")

        # SDK returns lowercase columns; engine expects capitalised.
        df = df.rename(columns={
            "open": "Open", "high": "High", "low": "Low",
            "close": "Close", "volume": "Volume",
        })
        for col in ("Open", "High", "Low", "Close", "Volume"):
            if col in df.columns:
                df[col] = pd.to_numeric(df[col], errors="coerce")
        if "Volume" not in df.columns:
            df["Volume"] = 0.0

        # SDK returns newest-first; engine + indicators want oldest-first.
        df = df.sort_index()
        if df.index.tz is None:
            df.index = df.index.tz_localize("UTC")
        df.index.name = "Datetime"

        logger.debug(f"Fetched {len(df)} bars for {td_symbol} @ {interval}")
        return df

    def _validate_and_clean(
        self, df: pd.DataFrame, symbol: str, timeframe: Timeframe
    ) -> pd.DataFrame:
        if df is None or df.empty:
            raise DataIntegrityError(f"Empty dataset for {symbol}")

        original_len = len(df)

        df = df.dropna(subset=["Open", "High", "Low", "Close"], how="all")
        df = df.ffill(limit=3)
        df = df.dropna(subset=["Close"])

        now = pd.Timestamp.utcnow()
        future_mask = df.index > now + pd.Timedelta(minutes=5)
        if future_mask.any():
            logger.warning(f"{symbol}: Dropping {future_mask.sum()} future timestamps")
            df = df[~future_mask]

        invalid_ohlc = (
            (df["High"] < df["Low"]) |
            (df["High"] < df["Open"]) |
            (df["High"] < df["Close"]) |
            (df["Low"] > df["Open"]) |
            (df["Low"] > df["Close"])
        )
        if invalid_ohlc.any():
            logger.warning(f"{symbol}: Fixing {invalid_ohlc.sum()} OHLC-inconsistent bars")
            for idx in df[invalid_ohlc].index:
                vals = [df.loc[idx, "Open"], df.loc[idx, "Close"]]
                df.loc[idx, "High"] = max(vals + [df.loc[idx, "High"]])
                df.loc[idx, "Low"]  = min(vals + [df.loc[idx, "Low"]])

        returns = df["Close"].pct_change()
        std_ret = returns.std()
        if std_ret > 0:
            outlier_mask = (returns - returns.mean()).abs() > 10 * std_ret
            if outlier_mask.any():
                logger.warning(f"{symbol}: Removing {outlier_mask.sum()} extreme outlier bars")
                df = df[~outlier_mask]

        min_required = max(
            self.config.technical.ema_trend,
            self.config.technical.ichimoku_senkou_b,
            200,
        ) + 10

        if len(df) < min_required:
            logger.warning(
                f"{symbol} {timeframe.value}: Only {len(df)} bars "
                f"(need {min_required}). Some indicators will be partial."
            )

        df = df.sort_index()
        df = df[~df.index.duplicated(keep="last")]

        cleaned = original_len - len(df)
        if cleaned > 0:
            logger.info(f"{symbol}: Cleaned {cleaned}/{original_len} bars")

        return df