import numpy as np

from pandas import DataFrame, Series

from daily_data import get_daily_data

def calculate_rsi(data: DataFrame, period: int = 2) -> Series:
    """
    Calculate the RSI and return it as a Series without modifying the original DataFrame.
    """
    delta = data['Close'].diff()
    gain = np.where(delta > 0, delta, 0)
    loss = np.where(delta < 0, -delta, 0)

    alpha = 1 / period
    avg_gain = Series(gain).ewm(alpha = alpha, adjust = False).mean()
    avg_loss = Series(loss).ewm(alpha = alpha, adjust = False).mean()

    rs = avg_gain / avg_loss
    return 100 - (100 / (1 + rs))

def signals(data: DataFrame) -> Series:
    """
    Generate long signals based on the VIX RSI strategy.
    Returns a Series with 'L' for long signals and 'N' for no signal.
    """
    ma_200 = data['Close'].rolling(window = 200).mean()
    rsi_2 = calculate_rsi(data, period = 2)

    start_date = data['Date'].min()
    end_date = data['Date'].max()
    vix_data = get_daily_data(symbol = 'VIX.XO', start_date = start_date, end_date = end_date)

    vix_rsi_2 = calculate_rsi(vix_data, period = 2)

    above_200_ma = data['Close'] > ma_200
    vix_rsi_above_90 = vix_rsi_2 > 90
    vix_open_greater_than_prev_close = vix_data['Open'] > vix_data['Close'].shift(1)
    rsi_below_30 = rsi_2 < 30

    signals = Series('N', index = data.index)
    signals[above_200_ma & vix_rsi_above_90 & vix_open_greater_than_prev_close & rsi_below_30] = 'L'

    return signals