import pandas as pd
import numpy as np

from pandas import DataFrame, Series

def calculate_moving_average(data: DataFrame, window: int = 200) -> Series:
    """
    Calculate the 200-day 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 = pd.Series(gain).ewm(alpha = alpha, adjust = False).mean()
    avg_loss = pd.Series(loss).ewm(alpha = alpha, adjust = False).mean()

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

def calculate_cumulative_rsi(rsi: Series, window: int = 2) -> Series:
    """
    Calculate the cumulative RSI over a specified window period and return it as a Series.
    """
    return rsi.rolling(window = window).sum()

def signals(data: DataFrame, rsi_period: int = 2, cumulative_period: int = 2) -> Series:
    """
    Generate 'L'ong entry signals based on the Cumulative RSI strategy.
    Returns a Series with 'L' for entry signals and 'N' otherwise without modifying the original DataFrame.
    
    Entry Condition: 2-period cumulative RSI below 35 and above the 200-day moving average.
    """
    ma_200 = calculate_moving_average(data)
    rsi_2 = calculate_rsi(data, period = rsi_period)
    cumulative_rsi_2 = calculate_cumulative_rsi(rsi_2, window = cumulative_period)

    long_condition = (data['Close'] > ma_200) & (cumulative_rsi_2 < 35)
    return Series(np.where(long_condition, 'L', 'N'), index = data.index)