import pandas as pd
from deepcoin.api.bithumb import HTS
from dateutil.relativedelta import relativedelta
from datetime import datetime
import sqlite3
import time

try:
    import telegram
except ImportError:
    telegram = None  # type: ignore
import requests
import json
import asyncio
from multiprocessing import Pool
import numpy as np
import os

from config import *
from deepcoin.data.candle_intervals import (
    candle_api_segment,
    interval_display_label,
    pagination_step,
)

class Monitor(HTS):
    """WLD 코인 데이터·지표·시장 상태 출력."""

    last_signal = None
    cooldown_file = None

    def __init__(self, cooldown_file: str | None = None) -> None:
        HTS.__init__(self)
        # 최근 매수 신호 저장용(파일은 [신규] 포맷으로 저장)
        self.last_signal: dict[str, str] = {}
        if cooldown_file is not None:
            self.cooldown_file = cooldown_file
            self.buy_cooldown = self._load_buy_cooldown()
        else:
            self.cooldown_file = None
            self.buy_cooldown = {}

    # ------------- Persistence -------------
    def _load_buy_cooldown(self) -> dict:
        """load trade record file into nested dict {symbol:{'buy':{'datetime':dt,'signal':s},'sell':{...}}}"""
        if not os.path.exists(self.cooldown_file):
            return {}

        try:
            with open(self.cooldown_file, 'r', encoding='utf-8') as f:
                raw = json.load(f)
        except Exception as e:
            print(f"Error loading cooldown data: {e}")
            return {}

        record: dict[str, dict] = {}
        for symbol, value in raw.items():
            # 신규 포맷: value has 'buy'/'sell'
            if isinstance(value, dict) and ('buy' in value or 'sell' in value):
                record[symbol] = {}
                for side in ['buy', 'sell']:
                    side_val = value.get(side)
                    if isinstance(side_val, dict):
                        dt_iso = side_val.get('datetime')
                        sig = side_val.get('signal', '')
                        if dt_iso:
                            try:
                                dt_obj = datetime.fromisoformat(dt_iso)
                            except Exception:
                                dt_obj = None
                        else:
                            dt_obj = None
                        record[symbol][side] = {'datetime': dt_obj, 'signal': sig}
            else:
                # 구 포맷 처리 (매수만 기록)
                try:
                    dt_obj = None
                    sig = ''
                    if isinstance(value, str):
                        dt_obj = datetime.fromisoformat(value)
                    elif isinstance(value, dict):
                        dt_iso = value.get('datetime')
                        sig = value.get('signal', '')
                        if dt_iso:
                            dt_obj = datetime.fromisoformat(dt_iso)
                    record.setdefault(symbol, {})['buy'] = {'datetime': dt_obj, 'signal': sig}
                except Exception:
                    continue

        # last_signal 채우기 (buy 기준)
        for sym, sides in record.items():
            if 'buy' in sides and sides['buy'].get('signal'):
                self.last_signal[sym] = sides['buy']['signal']
        return record

    def _save_buy_cooldown(self) -> None:
        """save nested trade record structure"""
        try:
            data: dict[str, dict] = {}
            for symbol, sides in self.buy_cooldown.items():
                data[symbol] = {}
                for side in ['buy', 'sell']:
                    info = sides.get(side)
                    if not info:
                        continue
                    dt_obj = info.get('datetime')
                    sig = info.get('signal', '')
                    data[symbol][side] = {
                        'datetime': dt_obj.isoformat() if isinstance(dt_obj, datetime) else '',
                        'signal': sig,
                    }
            with open(self.cooldown_file, 'w', encoding='utf-8') as f:
                json.dump(data, f, ensure_ascii=False, indent=2)
        except Exception as e:
            print(f"Error saving cooldown data: {e}")

    # ------------- Telegram -------------
    def _send_coin_msg(self, text: str) -> None:
        if telegram is None:
            print(f"[telegram skip] {text}")
            return
        coin_client = telegram.Bot(token=COIN_TELEGRAM_BOT_TOKEN)
        asyncio.run(coin_client.send_message(chat_id=COIN_TELEGRAM_CHAT_ID, text=text))

    def sendMsg(self, msg):
        try:
            pool = Pool(12)
            pool.map(self._send_coin_msg, [msg])
        except Exception as e:
            print(f"Error sending Telegram message: {str(e)}")
        return

    def send_coin_telegram_message(self, message_list: list[str], header: str) -> None:
        payload = header + "\n"
        for i, message in enumerate(message_list):
            payload += message
            if i + 1 % MONITOR_TELEGRAM_BATCH_SIZE == 0:
                pool = Pool(MONITOR_POOL_WORKERS)
                pool.map(self._send_coin_msg, [payload])
                payload = ''
        if len(message_list) % MONITOR_TELEGRAM_BATCH_SIZE != 0:
            pool = Pool(MONITOR_POOL_WORKERS)
            pool.map(self._send_coin_msg, [payload])

    # ------------- Indicators -------------
    def normalize_data(self, data: pd.DataFrame) -> pd.DataFrame:
        columns_to_normalize = ['Open', 'High', 'Low', 'Close', 'Volume']
        normalized_data = data.copy()
        for column in columns_to_normalize:
            min_val = data[column].rolling(window=MONITOR_NORM_WINDOW).min()
            max_val = data[column].rolling(window=MONITOR_NORM_WINDOW).max()
            denominator = max_val - min_val
            normalized_data[f'{column}_Norm'] = np.where(
                denominator != 0,
                (data[column] - min_val) / denominator,
                0.5,
            )
        return normalized_data

    def inverse_data(self, data: pd.DataFrame) -> pd.DataFrame:
        """원본 data 가격 시계를 상하 대칭(글로벌 min/max 기준)으로 반전하여 하락↔상승 트렌드를 뒤집는다."""
        price_cols = ['Open', 'High', 'Low', 'Close']
        inv = data.copy()
        global_min = data[price_cols].min().min()
        global_max = data[price_cols].max().max()
        # 축 기준은 global_mid = (max+min), so transformed = max+min - price
        for col in price_cols:
            inv[col] = global_max + global_min - data[col]
        # Volume은 그대로 유지
        inv['Volume'] = data['Volume']
        # 지표 다시 계산
        inv = self.normalize_data(inv)

        for w in MONITOR_MA_WINDOWS:
            inv[f"MA{w}"] = inv["Close"].rolling(window=w).mean()
            inv[f"Deviation{w}"] = (inv["Close"] / inv[f"MA{w}"]) * 100
        if len(MONITOR_MA_WINDOWS) >= 2:
            w_fast, w_slow = MONITOR_MA_WINDOWS[0], MONITOR_MA_WINDOWS[1]
            inv["golden_cross"] = (inv[f"MA{w_fast}"] > inv[f"MA{w_slow}"]) & (
                inv[f"MA{w_fast}"].shift(1) <= inv[f"MA{w_slow}"].shift(1)
            )
        inv["MA"] = inv["Close"].rolling(window=BB_PERIOD).mean()
        inv["STD"] = inv["Close"].rolling(window=BB_PERIOD).std()
        inv["Upper"] = inv["MA"] + (BB_STD * inv["STD"])
        inv["Lower"] = inv["MA"] - (BB_STD * inv["STD"])
        return inv

    def calculate_technical_indicators(self, data: pd.DataFrame) -> pd.DataFrame:
        data = self.normalize_data(data)

        for w in MONITOR_MA_WINDOWS:
            data[f"MA{w}"] = data["Close"].rolling(window=w).mean()
            data[f"Deviation{w}"] = (data["Close"] / data[f"MA{w}"]) * 100
        if len(MONITOR_MA_WINDOWS) >= 2:
            w_fast, w_slow = MONITOR_MA_WINDOWS[0], MONITOR_MA_WINDOWS[1]
            data["golden_cross"] = (data[f"MA{w_fast}"] > data[f"MA{w_slow}"]) & (
                data[f"MA{w_fast}"].shift(1) <= data[f"MA{w_slow}"].shift(1)
            )
        data["MA"] = data["Close"].rolling(window=BB_PERIOD).mean()
        data["STD"] = data["Close"].rolling(window=BB_PERIOD).std()
        data["Upper"] = data["MA"] + (BB_STD * data["STD"])
        data["Lower"] = data["MA"] - (BB_STD * data["STD"])

        from deepcoin.common.indicators import add_macd, add_stochastic

        data = add_macd(data)
        data = add_stochastic(data)
        return data

    def process_wld_market_status(self, symbol: str) -> None:
        """
        WLD: 전 봉 BB·일목 위치·추세만 출력 (자동 매매 없음).
        """
        from deepcoin.common.candle_features import describe_latest_position
        from deepcoin.common.indicators import get_trend
        from deepcoin.data.mtf_bb import load_frames_from_db

        try:
            frames = load_frames_from_db(self, symbol)
            if not frames:
                print(f"Data for {symbol}: 로드된 봉 없음.")
                return

            df_1d = frames.get(TREND_INTERVAL_1D)
            df_1h = frames.get(TREND_INTERVAL_1H)
            if df_1d is None or df_1d.empty:
                df_1d = frames.get(ENTRY_INTERVAL)
            if df_1h is None or df_1h.empty:
                df_1h = frames.get(ENTRY_INTERVAL)

            trend = get_trend(df_1d, df_1h)
            print(f"{symbol} 추세(참고): {trend}")
            print("--- 봉별 BB·일목 위치 ---")
            for iv in sorted(frames.keys()):
                pos = describe_latest_position(frames[iv], iv)
                macd_s = ""
                if pos.get("macd_hist") is not None:
                    macd_s = f" | MACD {pos.get('macd_state', '-')} h={pos['macd_hist']}"
                stoch_s = ""
                if pos.get("stoch_k") is not None:
                    stoch_s = (
                        f" | Stoch K={pos['stoch_k']} D={pos.get('stoch_d')} "
                        f"{pos.get('stoch_zone', '')}"
                    )
                disp_s = ""
                if pos.get("disparity"):
                    parts = [f"{p}={v:.1f}" for p, v in sorted(pos["disparity"].items())]
                    disp_s = " | D.I. " + " ".join(parts)
                print(
                    f"  {pos['label']:>6} | BB {pos['bb_zone']} {pos['bb_state']:>16} | "
                    f"일목 {pos['ichi_position']} TK={pos['ichi_tk']}"
                    f"{macd_s}{stoch_s}{disp_s}"
                )
        except Exception as e:
            print(f"Error processing {symbol}: {str(e)}")

    def process_symbol(
        self,
        symbol: str,
        interval: int | None = None,
        balances: dict | None = None,
        use_inverse: bool = False,
    ) -> None:
        """하위 호환: 시장 상태 출력으로 위임."""
        self.process_wld_market_status(symbol)

    def load_balances_dict(self) -> dict:
        """getBalances() 결과를 currency 키 dict로 변환."""
        tmps = self.getBalances()
        balances = {}
        for tmp in tmps:
            balances[tmp["currency"]] = {
                "balance": float(tmp["balance"]),
                "avg_buy_price": float(tmp["avg_buy_price"]),
            }
        return balances

    # ------------- Formatting -------------
    def format_message(
        self, symbol: str, symbol_name: str, close: float, signal: str, buy_amount: float
    ) -> str:
        message = f"[매수] {symbol_name} ({symbol}) [{signal}]: "

        if int(close) >= 100:
            message += f"₩{close}"
            message += f" (₩{buy_amount})"
        elif int(close) >= 10:
            message += f"₩{close:.2f}"
            message += f" (₩{buy_amount:.2f})"
        elif int(close) >= 1:
            message += f"₩{close:.3f}"
            message += f" (₩{buy_amount:.3f})"
        else:
            message += f"₩{close:.4f}"
            message += f" (₩{buy_amount:.4f})"

        if signal != '':
            message += f"[{signal}]"
        return message

    # ------------- Data fetch -------------
    def get_coin_data(
        self,
        symbol: str,
        interval: int = MONITOR_DEFAULT_INTERVAL,
        to: str | None = None,
        retries: int = MONITOR_API_RETRIES,
    ) -> pd.DataFrame | None:
        base = BITHUMB_API_URL.rstrip("/")
        count = BITHUMB_API_CANDLE_COUNT
        segment = candle_api_segment(interval)
        for attempt in range(retries):
            try:
                path = f"/v1/candles/{segment}"
                if to is None:
                    url = f"{base}{path}?market=KRW-{symbol}&count={count}"
                else:
                    url = f"{base}{path}?market=KRW-{symbol}&count={count}&to={to}"
                headers = {"accept": "application/json"}
                response = requests.get(url, headers=headers)
                json_data = json.loads(response.text)
                df_temp = pd.DataFrame(json_data)
                df_temp = df_temp.sort_index(ascending=False)
                if 'candle_date_time_kst' not in df_temp:
                    return None
                data = pd.DataFrame()
                data['datetime'] = pd.to_datetime(df_temp['candle_date_time_kst'], format='%Y-%m-%dT%H:%M:%S')
                data['Open'] = df_temp['opening_price']
                data['Close'] = df_temp['trade_price']
                data['High'] = df_temp['high_price']
                data['Low'] = df_temp['low_price']
                data['Volume'] = df_temp['candle_acc_trade_volume']
                data = data.set_index('datetime')
                data = data.astype(float)
                data["datetime"] = data.index
                if not data.empty:
                    return data
                print(f"No data received for {symbol}, attempt {attempt + 1}")
                time.sleep(MONITOR_SLEEP_AFTER_REQUEST_SEC)
            except Exception as e:
                print(f"Attempt {attempt + 1} failed for {symbol}: {str(e)}")
                if attempt < retries - 1:
                    time.sleep(MONITOR_SLEEP_RATE_LIMIT_SEC)
                continue
        return None

    def get_coin_more_data(
        self,
        symbol: str,
        interval: int,
        bong_count: int = MONITOR_API_BONG_COUNT,
        verbose: bool = False,
    ) -> pd.DataFrame:
        """
        빗썸 API를 반복 호출해 bong_count개까지 과거 봉을 수집합니다.

        Args:
            verbose: True면 수집 진행 상황을 출력합니다.
        """
        to = datetime.now()
        data: pd.DataFrame | None = None
        step = 0
        while data is None or len(data) < bong_count:
            step += 1
            if data is None:
                chunk = self.get_coin_data(symbol, interval, to.strftime("%Y-%m-%d %H:%M:%S"))
                data = chunk
            else:
                previous_count = len(data)
                df = self.get_coin_data(symbol, interval, to.strftime("%Y-%m-%d %H:%M:%S"))
                if df is not None and not df.empty:
                    data = pd.concat([data, df], ignore_index=True)
                if df is None or df.empty or previous_count == len(data):
                    if verbose:
                        print(f"    API 추가 데이터 없음 (수집 {len(data)}봉)")
                    break
            if verbose and (step == 1 or step % 5 == 0 or len(data) >= bong_count):
                label = interval_display_label(interval)
                print(f"    [{label}] 요청 {step}회 — 누적 {len(data)}/{bong_count}봉")
            time.sleep(MONITOR_SLEEP_BETWEEN_CHUNKS_SEC)
            to = to - pagination_step(interval, MONITOR_API_CHUNK_BARS)
        if data is None or data.empty:
            return pd.DataFrame()
        data = data.set_index("datetime")
        data = data.sort_index()
        data = data.drop_duplicates(keep="first")
        data["datetime"] = data.index
        return data

    @staticmethod
    def db_row_limit_for_interval(interval: int, lookback_days: int) -> int:
        """
        lookback_days 구간 + 지표 워밍업을 담을 SQLite LIMIT(봉 개수)을 계산합니다.

        Args:
            interval: 봉 간격(분). 1440이면 일봉.
            lookback_days: 과거 조회 일수.

        Returns:
            LIMIT에 넣을 최대 행 수.
        """
        from config import MONTH_INTERVAL_MIN, WEEK_INTERVAL_MIN

        if interval == WEEK_INTERVAL_MIN:
            return max(lookback_days // 7 + 10, DB_ROW_MIN_DAILY_BARS)
        if interval == MONTH_INTERVAL_MIN:
            return max(lookback_days // 30 + 6, DB_ROW_MIN_DAILY_BARS)
        if interval >= DAILY_INTERVAL_MIN:
            return max(
                lookback_days + DB_ROW_DAILY_PADDING_DAYS,
                DB_ROW_MIN_DAILY_BARS,
            )
        bars_per_day = max((24 * 60) // max(interval, 1), 1)
        return bars_per_day * lookback_days + DB_ROW_WARMUP_BARS

    def persist_api_candles_to_db(
        self,
        symbol: str,
        interval: int,
        data: pd.DataFrame,
        db_path: str = DB_PATH,
    ) -> tuple[int, int]:
        """
        API로 받은 봉을 coins.db에 증분 INSERT합니다 (01_download.append_data와 동일).

        05·06·live_eval이 load_frames_from_db 할 때마다 최신 봉이 쌓입니다.

        Returns:
            (추가 행 수, 스킵 행 수)
        """
        if not MONITOR_PERSIST_CANDLES or data is None or data.empty:
            return 0, 0

        from deepcoin.data.downloader import (
            append_data,
            get_last_timestamp,
            months_for_interval,
            prune_before_cutoff,
        )

        if not isinstance(data.index, pd.DatetimeIndex):
            data = data.copy()
            data.index = pd.to_datetime(data.index)
        data = data.sort_index()

        last_ts = get_last_timestamp(symbol, interval, db_path=db_path)
        inserted, skipped = append_data(
            symbol, interval, data, last_ts=last_ts, db_path=db_path
        )
        if inserted > 0:
            months = months_for_interval(interval, DOWNLOAD_MONTHS)
            prune_before_cutoff(symbol, interval, months, db_path=db_path)
        return inserted, skipped

    def read_candles_from_db(
        self,
        symbol: str,
        interval: int,
        db_path: str = DB_PATH,
        max_rows: int = DB_READ_LIMIT_DEFAULT,
    ) -> pd.DataFrame:
        """
        coins.db에서 저장된 봉을 읽습니다.

        scripts/01_download.py 또는 persist_api_candles_to_db로 적재된 데이터.
        """
        conn = sqlite3.connect(db_path)
        cursor = conn.cursor()
        table_name = f"{symbol}_{interval}"
        cursor.execute(
            f"CREATE TABLE IF NOT EXISTS {table_name} "
            "(CODE text, NAME text, ymdhms datetime, ymd text, hms text, "
            "Close REAL, Open REAL, High REAL, Low REAL, Volume REAL)"
        )
        cursor.execute(
            f"CREATE INDEX IF NOT EXISTS {table_name}_idx ON {table_name}(CODE, ymdhms)"
        )

        cursor.execute(
            f"SELECT Open, Close, High, Low, Volume, ymdhms AS datetime "
            f"FROM (SELECT Open, Close, High, Low, Volume, ymdhms "
            f"FROM {table_name} ORDER BY ymdhms DESC LIMIT {int(max_rows)}) "
            f"ORDER BY datetime"
        )
        result = cursor.fetchall()
        conn.commit()
        cursor.close()
        conn.close()

        if not result:
            return pd.DataFrame(
                columns=["Open", "Close", "High", "Low", "Volume", "datetime"]
            )

        df = pd.DataFrame(
            result, columns=["Open", "Close", "High", "Low", "Volume", "datetime"]
        )
        df = df.set_index("datetime")
        df = df.sort_index()
        df["datetime"] = df.index
        return df

    def get_coin_saved_data(
        self,
        symbol: str,
        interval: int,
        data: pd.DataFrame,
        db_path: str = DB_PATH,
        max_rows: int = DB_READ_LIMIT_DEFAULT,
    ) -> pd.DataFrame:
        """하위 호환: API 봉 저장 후 DB에서 읽기."""
        self.persist_api_candles_to_db(symbol, interval, data, db_path=db_path)
        return self.read_candles_from_db(
            symbol, interval, db_path=db_path, max_rows=max_rows
        )

    def get_coin_some_data(
        self, symbol: str, interval: int, db_max_rows: int | None = None
    ) -> pd.DataFrame:
        """
        WLD 시세: API 최신 봉 + coins.db 과거 봉을 합칩니다.

        MONITOR_PERSIST_CANDLES=1 이면 API 청크를 즉시 coins.db에 INSERT합니다.
        1분봉은 다운로드·병합하지 않습니다.
        """
        data = self.get_coin_data(symbol, interval)
        if data is None or data.empty:
            return pd.DataFrame()

        self.persist_api_candles_to_db(symbol, interval, data)

        row_limit = DB_READ_LIMIT_DEFAULT if db_max_rows is None else int(db_max_rows)
        saved_data = self.read_candles_from_db(
            symbol, interval, max_rows=row_limit
        )
        parts = [data]
        if saved_data is not None and not saved_data.empty:
            parts.append(saved_data)

        merged = pd.concat(parts, ignore_index=True)
        merged["datetime"] = pd.to_datetime(merged["datetime"], format="%Y-%m-%d %H:%M:%S")
        merged = merged.set_index("datetime")
        merged = merged.sort_index()
        merged = merged.drop_duplicates(keep="first")
        merged["datetime"] = merged.index
        return merged