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

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moshferatu 2025-01-10 08:42:25 -08:00
parent a879c0cf3e
commit c777259d71
1 changed files with 5 additions and 25 deletions
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30
strategies/tps.py
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@ -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:
"""
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))
from indicators import rsi, sma
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)
rsi_2 = calculate_rsi(data)
ma_200 = sma(data, period = 200)
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)