diff --git a/strategies/cumulative_rsi.py b/strategies/cumulative_rsi.py
new file mode 100644
index 0000000..314ad12
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+++ b/strategies/cumulative_rsi.py
@@ -0,0 +1,45 @@
+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)
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