2024-10-28 18:27:03 +00:00
|
|
|
import numpy as np
|
|
|
|
|
|
|
|
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))
|
|
|
|
|
|
|
|
def signals(data: DataFrame) -> Series:
|
|
|
|
"""
|
|
|
|
Calculate signals based on the 200-period MA and 2-period RSI.
|
|
|
|
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)
|
|
|
|
|
2024-11-21 20:30:42 +00:00
|
|
|
conditions = (data['Close'] > ma_200) & (rsi_2 < 15)
|
2024-10-28 18:33:46 +00:00
|
|
|
return Series(np.where(conditions, 'L', 'N'), index = data.index)
|