40 lines
1.4 KiB
Python
40 lines
1.4 KiB
Python
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import numpy as np
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from pandas import DataFrame, Series
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def calculate_moving_average(data: DataFrame, window: int = 200) -> Series:
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"""
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Calculate the 200-period moving average and return it as a Series without modifying the original DataFrame.
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"""
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return data['Close'].rolling(window = window).mean()
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def calculate_rsi(data: DataFrame, period: int = 2) -> Series:
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"""
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Calculate the 2-period RSI and return it as a Series without modifying the original DataFrame.
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"""
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delta = data['Close'].diff()
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gain = np.where(delta > 0, delta, 0)
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loss = np.where(delta < 0, -delta, 0)
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alpha = 1 / period
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avg_gain = Series(gain).ewm(alpha = alpha, adjust = False).mean()
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avg_loss = Series(loss).ewm(alpha = alpha, adjust = False).mean()
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rs = avg_gain / avg_loss
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return 100 - (100 / (1 + rs))
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def signals(data: DataFrame) -> Series:
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"""
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Calculate signals based on the Time, Price, Scale-in (TPS) strategy.
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Returns a Series with 'Long' for signals and 'None' otherwise, without modifying the original DataFrame.
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"""
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ma_200 = calculate_moving_average(data)
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rsi_2 = calculate_rsi(data)
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above_ma_200 = data['Close'] > ma_200
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rsi_below_25 = (rsi_2 < 25)
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rsi_below_25_for_two_days = rsi_below_25 & rsi_below_25.shift(1, fill_value = False)
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conditions = above_ma_200 & rsi_below_25_for_two_days
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return Series(np.where(conditions, 'L', 'N'), index = data.index)
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