Utilize RSI and SMA indicators provided by indicators module in TPS strategy

strategies/tps.py

@@ -1,35 +1,15 @@
-import numpy as np
+from numpy import where
-
 from pandas import DataFrame, Series
 
-def calculate_moving_average(data: DataFrame, window: int = 200) -> Series:
+from indicators import rsi, sma
-    """
-    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 tps(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)
+    ma_200 = sma(data, period = 200)
-    rsi_2 = calculate_rsi(data)
+    rsi_2 = rsi(data, period = 2)
 
     above_ma_200 = data['Close'] > ma_200
 
@@ -37,4 +17,4 @@ def tps(data: DataFrame) -> Series:
     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)
+    return Series(where(conditions, 'L', 'N'), index = data.index)