import pandas as pd
import numpy as np
from config import settings


def check_long_entry(
    prev_k: float,
    prev_d: float,
    curr_k: float,
    curr_d: float,
    volume: float,
    volume_sma: float,
    close: float,
    ema_200: float
) -> bool:
    """
    Check if all LONG entry conditions are met.

    Conditions:
    1. Previous candle: K < D and K <= 20 (oversold)
    2. Current candle: K crosses above D and K <= 30
    3. Volume > 1.5 * Volume SMA(20)
    4. Close > EMA(200) (trend filter)
    """
    if pd.isna(prev_k) or pd.isna(curr_k) or pd.isna(ema_200):
        return False

    # StochRSI conditions
    was_oversold = prev_k <= settings.STOCH_OVERSOLD
    was_below_d = prev_k < prev_d
    crossed_above = curr_k > curr_d
    still_low = curr_k <= settings.STOCH_ENTRY_MAX

    stoch_signal = was_oversold and was_below_d and crossed_above and still_low

    # Volume condition
    volume_ok = volume > settings.VOLUME_MULTIPLIER * volume_sma

    # Trend condition
    trend_ok = close > ema_200

    return stoch_signal and volume_ok and trend_ok


def check_exit_signal(curr_k: float, curr_d: float) -> bool:
    """
    Check if early exit condition is met.
    Exit when StochRSI K crosses below D above 70 (overbought zone).
    """
    if pd.isna(curr_k) or pd.isna(curr_d):
        return False

    return curr_k < curr_d and curr_k > settings.STOCH_OVERBOUGHT - 10


def generate_signals(df: pd.DataFrame) -> pd.DataFrame:
    """
    Generate trading signals for the entire dataframe.

    Adds columns:
    - signal: 1 for LONG entry, 0 for no signal
    - exit_signal: 1 for early exit, 0 otherwise

    Returns:
        DataFrame with signal columns added
    """
    df = df.copy()
    df["signal"] = 0
    df["exit_signal"] = 0

    for i in range(1, len(df)):
        prev = df.iloc[i - 1]
        curr = df.iloc[i]

        # Check entry signal
        if check_long_entry(
            prev_k=prev["stoch_k"],
            prev_d=prev["stoch_d"],
            curr_k=curr["stoch_k"],
            curr_d=curr["stoch_d"],
            volume=curr["volume"],
            volume_sma=curr["volume_sma"],
            close=curr["close"],
            ema_200=curr["ema_200"]
        ):
            df.iloc[i, df.columns.get_loc("signal")] = 1

        # Check exit signal
        if check_exit_signal(curr["stoch_k"], curr["stoch_d"]):
            df.iloc[i, df.columns.get_loc("exit_signal")] = 1

    return df