# 5. Lagged Feature df['Return_Lag_1'] = df['Log_Return'].shift(1) Scorpio Nights 3 Lk21 Free Install
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# 2. Volatility df['Volatility_20'] = df['Log_Return'].rolling(window=20).std()
# 1. Log Returns df['Log_Return'] = np.log(df['Close'] / df['Close'].shift(1))
If "GBCE" refers to something else (like a specific dataset, the French stock exchange, or a typo), please clarify!
import pandas as pd import numpy as np
However, assuming "GBCE" refers to the or a similar commodities exchange entity, or if you meant a specific data source you are analyzing, I can generate high-quality features typically used for algorithmic trading or analysis on such platforms.
# 4. Moving Averages df['SMA_50'] = df['Close'].rolling(window=50).mean() df['Price_vs_SMA50'] = df['Close'] / df['SMA_50'] # Relative strength