import logging

import pandas as pd
import ta

import config

logger = logging.getLogger(__name__)


def calculate_indicators(candles_df: pd.DataFrame) -> pd.DataFrame:
    """Add technical indicators to a candles DataFrame.

    Expected input columns: timestamp, open, close, high, low, volume.
    Returns the DataFrame with additional indicator columns.
    """
    df = candles_df.copy()
    if df.empty or len(df) < 26:
        logger.warning("Not enough candle data to compute indicators (got %d rows)", len(df))
        return df

    close = df["close"]
    high = df["high"]
    low = df["low"]
    volume = df["volume"]

    # RSI(14)
    df["rsi"] = ta.momentum.rsi(close, window=14)

    # MACD(12, 26, 9)
    macd = ta.trend.MACD(close, window_slow=26, window_fast=12, window_sign=9)
    df["macd"] = macd.macd()
    df["macd_signal"] = macd.macd_signal()
    df["macd_hist"] = macd.macd_diff()

    # Bollinger Bands(20, 2)
    bb = ta.volatility.BollingerBands(close, window=20, window_dev=2)
    df["bb_upper"] = bb.bollinger_hband()
    df["bb_middle"] = bb.bollinger_mavg()
    df["bb_lower"] = bb.bollinger_lband()

    # EMA(9) and EMA(21)
    df["ema9"] = ta.trend.ema_indicator(close, window=9)
    df["ema21"] = ta.trend.ema_indicator(close, window=21)

    # VWAP (cumulative for the session)
    tp = (high + low + close) / 3
    cumvol = volume.cumsum()
    df["vwap"] = (tp * volume).cumsum() / cumvol.replace(0, float("nan"))

    # ATR(14)
    df["atr"] = ta.volatility.average_true_range(high, low, close, window=14)

    # Volume moving average (20)
    df["vol_ma20"] = volume.rolling(window=20).mean()

    return df


def summarize_indicators(df: pd.DataFrame, symbol: str) -> str:
    """Produce a concise text summary of the latest indicator values for LLM consumption."""
    if df.empty or len(df) < 2:
        return f"{symbol}: insufficient data\n"

    last = df.iloc[-1]
    prev = df.iloc[-2]
    name = config.SYMBOL_NAMES.get(symbol, symbol)

    lines = [f"### {name} ({symbol})"]
    lines.append(f"Price: {last['close']:.6g} | High: {last['high']:.6g} | Low: {last['low']:.6g}")
    lines.append(f"Volume: {last['volume']:.2f} | Vol MA20: {last.get('vol_ma20', 0):.2f}")

    if pd.notna(last.get("rsi")):
        lines.append(f"RSI(14): {last['rsi']:.1f}")
    if pd.notna(last.get("macd")):
        hist_dir = "↑" if last["macd_hist"] > prev.get("macd_hist", 0) else "↓"
        lines.append(
            f"MACD: {last['macd']:.6g} | Signal: {last['macd_signal']:.6g} | "
            f"Hist: {last['macd_hist']:.6g} ({hist_dir})"
        )
    if pd.notna(last.get("bb_upper")):
        lines.append(
            f"BB: Upper={last['bb_upper']:.6g} Mid={last['bb_middle']:.6g} Lower={last['bb_lower']:.6g}"
        )
    if pd.notna(last.get("ema9")):
        trend = "bullish" if last["ema9"] > last["ema21"] else "bearish"
        lines.append(f"EMA9: {last['ema9']:.6g} | EMA21: {last['ema21']:.6g} → {trend}")
    if pd.notna(last.get("atr")):
        lines.append(f"ATR(14): {last['atr']:.6g}")
    if pd.notna(last.get("vwap")):
        lines.append(f"VWAP: {last['vwap']:.6g}")

    return "\n".join(lines) + "\n"


def summarize_all(indicators_by_symbol: dict[str, pd.DataFrame]) -> str:
    """Build a combined indicator summary for all symbols."""
    parts = []
    for sym, df in sorted(indicators_by_symbol.items()):
        parts.append(summarize_indicators(df, sym))
    return "\n".join(parts)