diff --git a/financial/Relative Strength Index (RSI).py b/financial/relative_strength_index.py
similarity index 54%
rename from financial/Relative Strength Index (RSI).py
rename to financial/relative_strength_index.py
index 01696d53c361..dff6c2995a05 100644
--- a/financial/Relative Strength Index (RSI).py	
+++ b/financial/relative_strength_index.py
@@ -1,7 +1,4 @@
-from typing import List
-
-
-def calculate_rsi(prices: List[float], period: int = 14) -> List[float]:
+def calculate_rsi(prices: list[float], period: int = 14) -> list[float]:
     """
     Calculate the Relative Strength Index (RSI) for a given list of prices.
 
@@ -9,11 +6,11 @@ def calculate_rsi(prices: List[float], period: int = 14) -> List[float]:
     It is typically used to identify overbought or oversold conditions in a market.
 
     Args:
-        prices (List[float]): A list of prices for a financial asset.
+        prices (list[float]): A list of prices for a financial asset.
         period (int): The number of periods to use in the calculation (default is 14).
 
     Returns:
-        List[float]: A list of RSI values corresponding to the input price data.
+        list[float]: A list of RSI values corresponding to the input price data.
 
     Example:
         >>> calculate_rsi([44.0, 44.15, 44.09, 44.20, 44.30, 44.25, 44.40], 14)
@@ -22,82 +19,55 @@ def calculate_rsi(prices: List[float], period: int = 14) -> List[float]:
     Reference:
         https://en.wikipedia.org/wiki/Relative_strength_index
     """
-
     # Validate that there are enough prices to calculate RSI
     if len(prices) < period:
         raise ValueError("Not enough price data to calculate RSI.")
 
-    # Initialize lists to store gains and losses
     gains = []
     losses = []
 
     # Calculate price changes between consecutive days
     for i in range(1, len(prices)):
         delta = prices[i] - prices[i - 1]
-
-        # Gain if delta is positive, otherwise it's a loss
         gains.append(max(0, delta))
         losses.append(max(0, -delta))
 
-    # Calculate the initial average gain and average loss (Simple Moving Average)
-    avg_gain = sum(gains[:period]) / period
-    avg_loss = sum(losses[:period]) / period
+    # Initial averages for gain and loss
+    avg_gain = float(sum(gains[:period]) / period)
+    avg_loss = float(sum(losses[:period]) / period)
 
-    # Initialize the RSI list with the first calculated RSI value
-    rsi_values = []
+    # Initialize RSI list and first RSI value
+    rsi_values: list[float] = []
 
-    # First RSI value calculation
     if avg_loss == 0:
-        rsi = 100  # No loss means RSI is 100 (overbought)
+        rsi = 100.0
     else:
-        rs = avg_gain / avg_loss  # Relative Strength
-        rsi = 100 - (100 / (1 + rs))  # RSI formula
+        rs = avg_gain / avg_loss
+        rsi = 100.0 - (100.0 / (1.0 + rs))
 
     rsi_values.append(rsi)
 
-    # Calculate the rest of the RSI values using smoothed moving averages
+    # Calculate subsequent RSI values
     for i in range(period, len(prices)):
         delta = prices[i] - prices[i - 1]
-
-        # Update gains and losses
         gain = max(0, delta)
         loss = max(0, -delta)
 
-        # Calculate smoothed averages
         avg_gain = (avg_gain * (period - 1) + gain) / period
         avg_loss = (avg_loss * (period - 1) + loss) / period
 
-        # Compute RSI
         if avg_loss == 0:
-            rsi = 100
+            rsi = 100.0
         else:
             rs = avg_gain / avg_loss
-            rsi = 100 - (100 / (1 + rs))
+            rsi = 100.0 - (100.0 / (1.0 + rs))
 
-        # Append RSI value to list
         rsi_values.append(rsi)
 
     return rsi_values
 
 
-# Example usage:
 if __name__ == "__main__":
-    prices = [
-        44.0,
-        44.15,
-        44.09,
-        44.20,
-        44.30,
-        44.25,
-        44.40,
-        44.35,
-        44.50,
-        44.60,
-        44.55,
-        44.75,
-        44.80,
-        44.70,
-        44.85,
-    ]
+    prices = [44.0, 44.15, 44.09, 44.20, 44.30, 44.25, 44.40, 44.35, 44.50, 44.60, 44.55, 44.75, 44.80, 44.70, 44.85]
     rsi = calculate_rsi(prices, 14)
     print("RSI Values:", rsi)