Refactor Cumulative RSI strategy to utilize RSI and SMA calculations from indicators module

strategies/cumulative_rsi.py

@@ -1,45 +1,17 @@
-import pandas as pd
-import numpy as np
-
+from numpy import where
 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()
+from indicators import rsi, sma
 
 def cumulative_rsi(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.
+    Calculate signals for the Cumulative RSI strategy.
+
+    Returns a Series with 'L' for long signals and 'N' otherwise.
     """
-    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)
+    ma_200 = sma(data, period = 200)
+    rsi_2 = rsi(data, period = rsi_period)
+    cumulative_rsi_2 = rsi_2.rolling(window = cumulative_period).sum()
 
     long_condition = (data['Close'] > ma_200) & (cumulative_rsi_2 < 35)
-    return Series(np.where(long_condition, 'L', 'N'), index = data.index)
+    return Series(where(long_condition, 'L', 'N'), index = data.index)