Algorithmic Trading & Indicators Intermediate

Calculate Garman-Klass Volatility Estimator in Python Pandas

Compute high-efficiency Garman-Klass historical volatility in Python using Open, High, Low, and Close prices to capture intraday price range swings.

#pandas#numpy

Problem Statement

Standard close-to-close volatility ignores intraday high-low swings, missing up to 80% of true price volatility. Garman-Klass incorporates OHLC to deliver 8x more efficient volatility estimates.

Formula / Core Logic

GK = 0.5 * ln(H/L)^2 - (2*ln(2) - 1) * ln(C/O)^2

Installation

pip install pandas numpy

garman-klass-volatility-estimator-python.py

import pandas as pd
import numpy as np

def calculate_garman_klass_vol(df, window=20):
    df = df.copy()
    hl = np.log(df['High'] / df['Low']) ** 2
    co = np.log(df['Close'] / df['Open']) ** 2
    gk_daily = 0.5 * hl - (2 * np.log(2) - 1) * co
    df['GK_Volatility'] = np.sqrt(gk_daily.rolling(window).mean() * 252) * 100
    return df