Introduction

In this research post, we examine a popular momentum strategy for intraday traders, the opening range breakout. To diversify our portfolio and reduce risk, we apply the strategy to a universe of liquid US Equities that are experiencing abnormally large trading volumes. The results show that the strategy outperforms buy and hold, achieving a 2.4 Sharpe ratio and a beta close to zero. The algorithm we implement in this post is a recreation of the research done by Zarattini, Barbon, & Aziz (2024).

Background

The opening range breakout strategy is a momentum strategy where we examine the asset's price action during the first n minutes of the day. If the price increases at the start of the day, we enter a long position when the price breaks above the highest price of the opening range. Conversely, if the price decreases during the start of the day, we enter a short position when the price breaks below the lowest price of the opening range. In this strategy, we use an opening range duration of 5 minutes.

Stocks in Play

We apply this strategy to a universe of assets to increase diversification and reduce risk. The universe consists of the 1,000 most liquid US Equities that are trading above $5/share and have an Average True Range (ATR) > $0.50. We then trade the 20 stocks that are most “in play,” meaning they have abnormally high trading volume, probably from some positive or negative catalyst. To quantify the stocks that are the most “in play,” we divide the asset’s volume during the first 5 minutes of trading activity in the current day by the average trading volume during the first 5 minutes of trading activity in the previous 14 days.

Risk Management

After one of the stocks in play breaks out of its opening range and we enter a position, there are two cases for exiting the position. In the first case, the momentum continues throughout the rest of the day, and we exit the position at close with a profit. In the second case, the stock reverts, hits our stop loss, and we exit the position before the market closes with a loss.

Stop Loss Placement

There are many techniques for placing a stop loss. In this strategy, we place the stop loss as a function of the entry price and the 14-day ATR. This technique means we apply wider stop losses to assets with greater volatility.

Position Sizing

The trade quantity is set so that if the stop loss is hit, we lose 1% of the portfolio value allocated to the asset. To reduce concentration risk, we limit the position size of each trade so that the weight of each asset doesn’t exceed the weight we would give the asset in an equal-weighted portfolio.

Implementation

To implement this strategy, we start by adding the universe of US Equities in the Initialize method.

  1. _universe = AddUniverse(fundamentals => fundamentals
  2.     .Where(f => f.Price > 5 && f.Symbol != spy)
  3.     .OrderByDescending(f => f.DollarVolume)
  4.     .Take(_universeSize)
  5.     .Select(f => f.Symbol)
  6.     .ToList()
  7. );

For each asset that enters the universe, we create a SymbolData object. At 9:35 AM, in the OnData method, we select the stocks in play and look for entries.

  1. var filtered = ActiveSecurities.Values
  2.     .Where(s => s.Price != 0 && _universe.Selected.Contains(s.Symbol)).Select(s => _symbolDataBySymbol[s.Symbol]).Where(s => s.RelativeVolume > 1 && s.ATR > _atrThreshold)
  3.     .OrderByDescending(s => s.RelativeVolume).Take(MaxPositions);
  4. foreach (var symbolData in filtered)
  5. {
  6.     symbolData.Scan();
  7. }

The Scan method uses the opening range bar to determine the stop price for the entry and exit orders.

  1. if (OpeningBar.Close > OpeningBar.Open)
  2. {
  3.     PlaceTrade(OpeningBar.High, OpeningBar.High - _stopLossAtrDistance * ATR);
  4. }
  5. else if (OpeningBar.Close < OpeningBar.Open)
  6. {
  7.     PlaceTrade(OpeningBar.Low, OpeningBar.Low + _stopLossAtrDistance * ATR);
  8. }

The PlaceTrade method determines the trade size, places the entry order, and records the stop loss price.

  1. var quantity = (int)((_stopLossRiskSize * _algorithm.Portfolio.TotalPortfolioValue / _algorithm.MaxPositions) / (entryPrice - stopPrice));
  2. var quantityLimit = _algorithm.CalculateOrderQuantity(_security.Symbol, 1m/_algorithm.MaxPositions);
  3. quantity = (int)(Math.Min(Math.Abs(quantity), quantityLimit) * Math.Sign(quantity));
  4. if (quantity != 0)
  5. {
  6.     StopLossPrice = stopPrice;
  7.     EntryTicket = _algorithm.StopMarketOrder(_security.Symbol, quantity, entryPrice, $"Entry");
  8. }

Results

We backtested the algorithm during 2016, the first year of the paper's backtest period. The benchmark is buy-and-hold with the SPY, which produced a 0.836 Sharpe ratio. In contrast, the opening range breakout strategy generated a 2.396 Sharpe ratio and a -0.042 beta. Therefore, the strategy outperformed buy-and-hold.

To test the sensitivity of the parameters chosen, we ran a parameter optimization job. We tested opening range durations of 5 to 25 minutes in steps of 5 minutes, and we tested universe sizes of 500 to 1500 US Equities in steps of 250. Of the 25 parameter combinations, 17 (68%) produced a greater Sharpe ratio than the benchmark. The following image shows the heatmap of Sharpe ratios for the parameter combinations: 

AD_4nXeWx9EYT0rJEbpqyI34ffD1AYhI-dWpEwKPnRWbOCGOUrbP2_my-IWD1YWoh4WYh0EkNFB9oLyp0AkUcDIHiw9iPn0cyV811neU0GzDxGLEpUqbBgqCwtNE1RiuGLMGJQj6dgwI9A?key=9wSjT6l6cwaapocnbhM1M7lc

The red circle in the preceding image identifies the parameters we chose as the default parameter for the strategy. We chose an opening range duration of 5 minutes because it was the best-performing duration of all the intervals tested by Zarattini et al. (2024). We chose a universe size of 1,000 because it was a round number that was large enough to diversify the strategy across many assets yet small enough that the backtest could run in under 10 minutes.

All of the parameters in this implementation match the parameters selected by the original authors. The only exception is the size of the universe. Zarattini et al. (2024) use a universe of 7,000 US Equities. However, the preceding optimization result shows that any universe size we selected produces a Sharpe ratio above 2, outperforming the benchmark.

It seemed odd to filter the ATR according to an arbitrary absolute value when the price of the stocks in the portfolio can be at greatly different scales.  To ensure this parameter ($0.50) was not a cherry-picked value, we ran an optimization to test the sensitivity of the strategy to the ATR value. We tested $0 ATR to $2 ATR in steps of $0.25. We discovered all of these ATR values outperformed buy-and-hold, with Sharpe ratios ranging from 1.5 to 2.7.

derek-melchin_1733851600.jpg

In addition to testing the sensitivity of ATR dollar values, we adjusted the filter to select stocks that had an ATR above 1% of the asset's price, effectively making the filter unit-less. With this adjustment, the algorithm still produced a 2.237 Sharpe ratio and a 97% Probabilistic Sharpe Ratio.

References

  • Zarattini, Carlo and Barbon, Andrea and Aziz, Andrew, A Profitable Day Trading Strategy For The U.S. Equity Market (February 16, 2024). Swiss Finance Institute Research Paper No. 24-98, Available at SSRN: https://ssrn.com/abstract=4729284 or http://dx.doi.org/10.2139/ssrn.4729284

Author

Derek Melchin

December 2024