diff --git a/strategies/tps.py b/strategies/tps.py
new file mode 100644
index 0000000..1123f96
--- /dev/null
+++ b/strategies/tps.py
@@ -0,0 +1,40 @@
+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)
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