import numpy as np

from pandas import DataFrame, Series

def calculate_moving_average(data: DataFrame, window: int = 200) -> Series:
    """
    Calculate the 200-period moving average and return it as a Series without modifying the original DataFrame.
    """
    return data['Close'].rolling(window = window).mean()

def calculate_rsi(data: DataFrame, period: int = 2) -> Series:
    """
    Calculate the 2-period 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:
    """
    Calculate signals based on the Time, Price, Scale-in (TPS) strategy.
    Returns a Series with 'Long' for signals and 'None' otherwise, without modifying the original DataFrame.
    """
    ma_200 = calculate_moving_average(data)
    rsi_2 = calculate_rsi(data)

    above_ma_200 = data['Close'] > ma_200

    rsi_below_25 = (rsi_2 < 25)
    rsi_below_25_for_two_days = rsi_below_25 & rsi_below_25.shift(1, fill_value = False)

    conditions = above_ma_200 & rsi_below_25_for_two_days
    return Series(np.where(conditions, 'L', 'N'), index = data.index)