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

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moshferatu 2025-01-08 11:05:39 -08:00
parent e25e53eb70
commit c7e434778f
1 changed files with 9 additions and 37 deletions
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strategies/cumulative_rsi.py
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@ -1,45 +1,17 @@
import pandas as pd from numpy import where
import numpy as np
from pandas import DataFrame, Series from pandas import DataFrame, Series
def calculate_moving_average(data: DataFrame, window: int = 200) -> Series: from indicators import rsi, sma
"""
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()
def cumulative_rsi(data: DataFrame, rsi_period: int = 2, cumulative_period: int = 2) -> Series: 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. Calculate signals for the Cumulative RSI strategy.
Returns a Series with 'L' for entry signals and 'N' otherwise without modifying the original DataFrame.
Returns a Series with 'L' for long signals and 'N' otherwise.
Entry Condition: 2-period cumulative RSI below 35 and above the 200-day moving average.
""" """
ma_200 = calculate_moving_average(data) ma_200 = sma(data, period = 200)
rsi_2 = calculate_rsi(data, period = rsi_period) rsi_2 = rsi(data, period = rsi_period)
cumulative_rsi_2 = calculate_cumulative_rsi(rsi_2, window = cumulative_period) cumulative_rsi_2 = rsi_2.rolling(window = cumulative_period).sum()
long_condition = (data['Close'] > ma_200) & (cumulative_rsi_2 < 35) 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)