Algorithmic Trading & Indicators Intermediate

15-Minute Opening Range Breakout (ORB) Intraday Strategy in Python Pandas

Automate the classic 15-minute Opening Range Breakout (9:15 to 9:30 High/Low) intraday strategy in Python with volume confirmation and EOD square-off.

#pandas#numpy

Problem Statement

The first 15 minutes of market open establishes institutional high/low boundaries. Coding ORB across multi-day 5-minute datasets requires mapping each day's 9:15–9:30 high and low across the rest of the session.

Formula / Core Logic

ORB_High = Max(High) between 09:15 and 09:30 ORB_Low = Min(Low) between 09:15 and 09:30 Long when Close > ORB_High after 09:30.

Installation

pip install pandas numpy

opening-range-breakout-orb-15min-strategy-python.py

import pandas as pd
import numpy as np

def intraday_orb_signals(df, orb_bars=3):
    # Assumes 5-min DatetimeIndex; first 3 bars = 15-min Opening Range
    df = df.copy()
    df['Date'] = df.index.date
    df['Bar_Num'] = df.groupby('Date').cumcount()
    
    orb_slice = df[df['Bar_Num'] < orb_bars]
    orb_high = orb_slice.groupby('Date')['High'].max()
    orb_low = orb_slice.groupby('Date')['Low'].min()
    
    df['ORB_High'] = df['Date'].map(orb_high)
    df['ORB_Low'] = df['Date'].map(orb_low)
    df['ORB_Signal'] = np.where((df['Bar_Num'] >= orb_bars) & (df['Close'] > df['ORB_High']), 1,
                       np.where((df['Bar_Num'] >= orb_bars) & (df['Close'] < df['ORB_Low']), -1, 0))
    return df.drop(['Date', 'Bar_Num'], axis=1)