Opening Range Breakout (ORB) Backtester

A Python backtesting implementation of the 5-minute Opening Range Breakout (ORB) strategy for U.S. equities.

This project is inspired by the research paper:

A Profitable Day Trading Strategy For The U.S. Equity Market Carlo Zarattini, Andrea Barbon, Andrew Aziz SSRN 4729284

The backtester filters liquid and volatile U.S. stocks, ranks them using relative opening volume, and evaluates a 5-minute ORB strategy.

Strategy Overview

1. Stock Filtering

Stocks must satisfy the following conditions:

• Stock price ≥ $5
• 14-day average daily volume ≥ 1,000,000 shares
• 14-day ATR ≥ $0.50

2. Relative Volume Ranking

For each stock, the relative opening volume is calculated as:

Relative Volume =
Current day's first 5-minute volume
-----------------------------------
Average first 5-minute volume over the previous 14 trading days

Stocks are ranked by this score, and the top candidates are selected for the ORB strategy.

3. Opening Range Breakout

The first 5-minute candle defines the opening range.

Long

If the first 5-minute candle is bullish:

Entry: Break above the first 5-minute high
Stop Loss: Entry Price - 10% of 14-day ATR
Exit: Market close

Short

If the first 5-minute candle is bearish:

Entry: Break below the first 5-minute low
Stop Loss: Entry Price + 10% of 14-day ATR
Exit: Market close

The daily portfolio return is calculated using the top 20 valid candidates.

Project Structure

obr-strategy/
├── backtest.py
├── filters/
│   ├── basic_filter.py
│   └── relative_volume_score.py
├── returncalc/
│   └── daily_return.py
├── tests/
└── gen_data/

Installation

Clone the repository:

git clone https://github.com/pderer/obr-strategy.git
cd obr-strategy

Clone the U.S. stock symbol repository:

git clone https://github.com/rreichel3/US-Stock-Symbols.git

Install dependencies:

pip install pandas numpy yfinance pytz

Usage

Display available options:

python backtest.py -h

Run a backtest:

python backtest.py \
    --start_date <START_DATE> \
    --end_date <END_DATE>

For example:

python backtest.py \
    --start_date 2026-08-18 \
    --end_date 2026-08-18

Dates must use the YYYY-MM-DD format.

Because the backtester uses 5-minute historical data from Yahoo Finance, the current implementation is intended for relatively recent dates.

Output

Backtest results are stored under:

gen_data/
└── YYYY-MM-DD/
    ├── filtered_stocks.csv
    ├── volume_ratio_score.csv
    ├── daily_return.csv
    └── result.csv

The console also prints summary statistics such as:

Individual Stock Hit Ratio Mean
Daily Return Mean
Positive Daily Return Ratio
Cumulative Daily Return

Disclaimer

This project is intended for research and educational purposes only.

Backtest results do not guarantee future performance and should not be considered financial advice.