rolling Method¶

The rolling() method applies operations over a fixed-size sliding window, fundamental for time series analysis.

Basic Rolling¶

Create rolling window calculations.

1. Moving Average¶

import pandas as pd
import yfinance as yf

aapl = yf.download('AAPL', start='2023-01-01', end='2024-01-01')
aapl['5D_MA'] = aapl['Close'].rolling(window=5).mean()
print(aapl[['Close', '5D_MA']].head(10))

2. Rolling Standard Deviation¶

aapl['5D_STD'] = aapl['Close'].rolling(window=5).std()

3. Rolling Sum¶

aapl['5D_SUM'] = aapl['Volume'].rolling(window=5).sum()

Key Parameters¶

Configure rolling window behavior.

1. window¶

# Size of the moving window
s.rolling(window=5).mean()    # 5-period window
s.rolling(window=20).mean()   # 20-period window

2. min_periods¶

# Minimum observations required
s.rolling(window=5, min_periods=1).mean()
# Returns value even with fewer than 5 observations

3. center¶

# Label at window center vs. right edge
s.rolling(window=5, center=True).mean()
# Value labeled at middle of window

Rolling Statistics¶

Common rolling calculations.

1. Central Tendency¶

s.rolling(20).mean()    # Moving average
s.rolling(20).median()  # Moving median

2. Dispersion¶

s.rolling(20).std()     # Moving standard deviation
s.rolling(20).var()     # Moving variance

3. Extremes¶

s.rolling(20).min()     # Rolling minimum
s.rolling(20).max()     # Rolling maximum

Custom Functions¶

Apply custom functions to rolling windows.

1. Custom Aggregation¶

# Range within window
aapl['5D_Range'] = aapl['Close'].rolling(window=5).apply(
    lambda x: x.max() - x.min()
)

2. With NumPy¶

import numpy as np

aapl['5D_Skew'] = aapl['Close'].rolling(window=20).apply(
    lambda x: pd.Series(x).skew()
)

3. Multiple Outputs¶

def custom_stats(x):
    return x.mean() / x.std()  # Sharpe-like ratio

aapl['Custom'] = aapl['Returns'].rolling(20).apply(custom_stats)

Financial Example¶

Volatility estimation.

1. Rolling Volatility¶

# 30-day rolling standard deviation
aapl['30D_Volatility'] = aapl['Close'].rolling(window=30).std()

2. Annualized Volatility¶

returns = aapl['Close'].pct_change()
aapl['Annualized_Vol'] = returns.rolling(20).std() * np.sqrt(252)

3. Bollinger Bands¶

aapl['MA20'] = aapl['Close'].rolling(20).mean()
aapl['Upper'] = aapl['MA20'] + 2 * aapl['Close'].rolling(20).std()
aapl['Lower'] = aapl['MA20'] - 2 * aapl['Close'].rolling(20).std()

NaN Handling¶

Rolling calculations produce NaN at the start.

1. Default Behavior¶

# First (window-1) values are NaN
s.rolling(5).mean()  # First 4 values are NaN

2. With min_periods¶

# Start computing earlier
s.rolling(5, min_periods=1).mean()

3. Drop NaN¶

result = s.rolling(5).mean().dropna()