Concretum Research Lugano

University of St.Gallen and Swiss Finance Institute

Peak Capital Trading and Bear Bull Traders, Canada

 A Profitable Day Trading Strategy
For The U.S. Equity Market
Carlo Zarattini1 , Andrea Barbon2 , Andrew Aziz3,4

1 Concretum Research, Viale Carlo Cattaneo 1, 6900 Lugano, Switzerland

2 Universityof St.Gallen, Dufourstrasse 50, 9000 St. Gallen, Switzerland
3 Peak Capital Trading, 744 West Hastings Street, Vancouver, BC, Canada V6C 1A5
4 Bear Bull Traders, 744 West Hastings Street, Vancouver, BC, Canada V6C 1A5

Q 1 carlo@concretumgroup.com, 2 andrea.barbon@unisg.ch, 4 andrew@peakcapitaltrading.com

X: 1 @ConcretumR,2 @Andrea Barbon,3 @BearBullTraders ,

First Version: February 16, 2024

Abstract

The validity of day trading as a long-term consistent and uncorrelated source of

income for traders and investors is a matter of debate. In this paper, we endeav-
ored to answer this question by conducting a thorough analysis of the profitability
of Opening Range Breakout (ORB) strategies, with a particular focus on the 5-
minute ORB. Using a large dataset that covered more than 7,000 US stocks traded
from 2016 to 2023, the research aimed to assess how effective this strategy was in
producing consistent and uncorrelated returns. A new aspect of our study was the
focus on Stocks in Play, which are stocks that show higher than normal trading
activity on a specific day, mostly because of fundamental news about the company.
Our results showed a significant benefit in limiting day trading only to those Stocks
in Play (even after considering transaction costs). A portfolio that consisted of
the top 20 Stocks in Play achieved a total net performance of over 1,600%, with a
Sharpe ratio of 2.81, and an annualized alpha of 36%. Passive exposure in the S&P
500 would have achieved a total return of 198% during the same period. Further-
more, this paper expanded the analysis to compare the return profile of the ORB
strategy applied to different time frames, such as 15, 30, and 60 minutes. In the
last part of the paper, we presented detailed stock-specific statistics for the 25 best
and worst performers of an ORB strategy over all the time frames. To the best
of our knowledge, this is the first public paper with such intraday granularity and
comprehensive stock-level database.

Keywords: Day Trading, Day Trading Systems, Opening Range Breakout, Algo Trading, Stock in Play,
News Trading

2
1 Introduction
Until the mid-2000s, the financial markets were largely the playground of Wall Street pro-
fessionals. With the advent of advanced trading technologies, a flood of information on
trading strategies, and the rise of affordable, even free, brokerage services, the landscape
shifted dramatically. Now, the majority of Main Street are actively engaging with finan-
cial markets. In 2023, 61% of adults in the United States invested in the stock market
directly [1]. The 2020 pandemic served as a catalyst in this transformation. Lockdowns
and market volatility sparked a surge in retail trading, especially in the stock market
[17, 9, 14]. A landmark event was the 2021 GameStop (GME) short squeeze, an extraor-
dinary David vs. Goliath tale where retail traders triumphed over Wall Street giants,
leading to the dissolution of some professional trading firms [3]. This event symbolized
the unleashed power of retail trading, a genie that is definitely not going back in the bottle.

Most retail traders gravitate toward day trading or short-term swing trades. But there
is a daunting challenge for retail traders who are competing with high-frequency trading
(HFT) algorithms. They need access to proven strategies that can give them an edge and
consistent advantage in the markets, often with technical analysis. Our previous research
had been focused on informing retail traders [22] and developing, testing, and refining
day trading strategies that are useful for retail traders without access to the lowest com-
mission tiers or HFT capabilities [24, 23].

Over the years, technical traders have developed and documented hundreds of trading
systems. Among them, one of the most important and well-studied strategy that has
gained considerable attention is the n-minute ORB, with popular variants including 5-
minute, 15-minute, 30-minute and 60-minute time frames. As shown conceptually in
Figure 1, the ORB strategy typically focuses on identifying the highest and lowest prices
during the first n-minutes of trading, and then buying or selling when the stock breaks
out of this range only in the direction of the opening range. For instance, a positive open
suggests a long position upon breaking the high, while a negative open indicates a short
position upon breaking the low.

3
Figure 1: Conceptual illustrations of where a trader would enter into a trade using the ORB strategy
for going long (as shown on the left-hand side) and for going short (as shown on the right-hand side).

Toby Crabel is often credited with first introducing the ORB (back in 1990)[11]. In his
book, Day Trading with Short Term Price Patterns and Opening Range Breakout, which
was one of the earliest and most detailed descriptions of how to trade short-term price
patterns across markets, he extensively analyzed the profitability of volatility breakout
strategies across various futures markets. In his model, the range was calibrated combin-
ing previous days’ volatility with current opening prices. The strategy did not use any
intraday data information to define the opening range. In the book, Crabel detailed his
core trading philosophy, that any robust trading strategy should capture explainable mar-
ket participant behavior. Traders must understand the mass psychology of the traders
behind the price action. Essentially, traders are social psychologists behind a computer
program [12].

Over the years, the ORB strategy has been studied and documented significantly both
by market participants [23, 4, 2, 10, 15] and academia [8, 18, 13, 21, 19, 16]. In 1995,
Raschke and Connors [10] introduced the Momentum Pinball strategy, a model that im-
proves the ORB strategy by combining a 60-minute ORB with a 3-day Relative Strength
Index (RSI) of a 1-day rate of change. Their study was tested on different futures mar-
kets, but the authors suggested that the same methodology could have been applied to
common equity markets.

4
In his seminal work in 1992, Brock et al. [8] demonstrated the effectiveness of simple
technical trading rules, specifically the moving average and ORBs, applied on almost
100 years of Dow Jones Index data. His findings were pivotal in challenging the random
walk hypothesis, a financial theory stating that stock market prices evolve according to
a random walk and thus cannot be predicted, and emphasized the potential of techni-
cal analysis strategies in market prediction. In 2009, Schulmeister [18] focused on the
frequency of data in technical trading. By analyzing S&P 500 data from 1960 to 2000,
and the futures market from 1983 to 2000, Schulmeister demonstrated that models using
30-minute interval data were more profitable than those relying on daily data, suggesting
a shift toward higher frequency trading in technical analysis.

In 2012, Holmberg et al. [13] studied the profitability of volatility ORBs on US crude
oil futures prices from 1983 until 2011. Their results showed that a volatility ORB was
significantly profitable, but the profitability was mostly generated in the last decade
(2000-2011). In 2018, Tsai et al. [19] further expanded the research on ORB strategies
by assessing their performance across multiple indices (Dow Jones Industrial Average,
S&P 500, and Nasdaq) and for different opening range time frames. The results showed
that the most significant profits occur when the opening range length is within 5 minutes
from the open. Their research did not consider any profit target or stop loss mechanisms.

In 2017, Lundström [16] examined ORB returns in different volatility states based on
long-term crude oil data and S&P 500 futures contracts, and he found that the prof-
itability of ORB grows with the volatility of the underlyng asset.

To improve the system and generate larger returns compared to the overall market, Wu et
al. in 2020 introduced an evolutionary approach by utilizing a genetic algorithm in their
ORB-based model [21]. Their method optimized thresholds and protective closing strate-
gies, significantly enhancing profitability and reducing risks. This study was conducted
on Taiwan Index Futures between 2007 and 2018 and showed that the introduction of
a profit target is detrimental for overall profitability. The study showed enhanced prof-
itability once a stop loss mechanism was introduced.

5
Recently, we provided significant evidence of profitability for a 5-minute ORB applied on
QQQ and TQQQ ETFs [23]. We introduced the use of stop losses and large profit targets
and found that a 5-minute ORB on TQQQ would have earned an outstanding return of
1,484% between 2016 and 2023, while a passive investment in the QQQ ETF would have
earned only 169%.

In this paper, our goal was to apply the 5-minute ORB framework developed in our pre-
vious work [23], but instead of utilizing only QQQ or TQQQ, we expanded the study to
all US stocks traded between 2016 and 2023, and assessed if trading volume and other
parameters have any statistically meaningful forecasting power on day trading profitabil-
ity.

2 Strategy Definition
As previously referenced, a 5-minute ORB strategy is a trading strategy that focuses
on catching a breakout from the initial range in the first 5 minutes of the trading day
[23, 4, 5]. In this current work, we introduced a crucial parameter to this ORB strategy.
If the first 5-minute candlestick was bearish (meaning it closed below its opening price),
our system would only take a short position. We would not go for a long position even
if the price broke above the 5-minute opening range candlestick. Similarly, if the first
5-minute candlestick was bullish (meaning it closed above the opening price), we would
stick to taking only a long position. We would avoid taking a short position, even if the
price dropped below the 5-minute opening range candlestick [23].

The opening range is often thought to provide some useful insight about the institutional
supply and demand imbalance that will prevail throughout the day. Day traders typically
employ tight stop losses to maximize exposure to intraday trends, letting profits run as
long as the trend persists. Setting the right stop loss is crucial and can really make or
break a trading strategy. If you set your stop loss too close to your entry point, you might
get stopped out too soon or too often. This can lead to missing out on big moves, rack-

6
ing up small losses, and paying higher commissions and fees. On the other hand, a stop
loss that is too wide could lead to bigger losses and unsatisfactory levels of reward to risk.

Traders have different ways of setting stop losses. Some use technical tools like mov-
ing averages, VWAP (Volume Weighted Average Price) or other mathematical indicators
[24]. Others prefer to set them based on judgment calls, using key levels like the low or
high of the day. A common approach [24, 4] is to set the stop loss at a percentage of the
14-day average true range (ATR)1 .

For example, as shown in Figure 2, the daily ATR of a company called BLDR on January
22, 2024 was $5. The following day the stock dipped in the first 5 minutes, prompting
a sell stop order at the opening range’s low ($174.44). Half an hour later, the stock
breached this level, executing the order. The stop loss was set at 10% of the daily ATR
(10% x $5 = $0.50) from the entry point. We have previously looked into how using a
stop loss based on ATR affects the ORB strategy and found a link between the expected
value of the return and the stop loss [23].

To analyze the result, Profit & Loss (PnL) is often quantified in units of risk (R) rather
than in dollar value. Figure 2 shows an example of howR unit is utilized in trade manage-
ment and analysis. In this case, a short trade was triggered, and the R was the potential
maximum loss per share, which was $0.50. The profit target was set at the end of the
day (EoD). The trade’s per share movement was $6.81 ($174.44 - $167.63), translating
to a PnL of 13.62 times the R, or 13.62R ($6.81/0.50).

The US stocks analyzed in this study encompassed all equities listed on US exchanges
(both NYSE and Nasdaq) from January 1, 2016 to December 31, 2023 (we called them
the universe. This universe comprised approximately 7,000 stocks and was free from
1
The ATR is a technical analysis indicator used to measure market volatility. It was introduced by
J. Welles Wilder Jr. in his 1978 book, New Concepts in Technical Trading Systems [20]. The ATR
calculates the average range between the highest and lowest prices over a given number of past trading
sessions, typically 14 days. This range includes the comparison of the current high to the previous
close, the current low to the previous close, and the current high to the current low. The ATR does not
indicate price direction but rather the degree of price volatility. High ATR values indicate high volatility,
suggesting wider price ranges and potentially greater risk or opportunity for traders. Conversely, low
ATR values suggest low market volatility, indicating tighter price ranges.

7
Figure 2: A hypothetical example of the 5-minute ORB strategy discussed in this paper on Builders
FirstSource, Inc. (BLDR) on 23 Jan 2024, detailing entry, exit, and stop loss points. The first 5 min
candlestick is red therefore no long position is allowed to be triggered. Only a short position is allowed
to trigger. The entry is triggered at 10am and a stop is added at 10%ATR from the entry. A profit
target is set at the EOD. Gray areas are pre- and post-market hours trading.

survivorship bias2 . The data for these stocks were sourced from the Center for Research
in Security Prices (CRSP). Intraday data for all stocks were obtained from IQFeed. No-
tably, this intraday data remained unadjusted for stock splits or dividends, ensuring that
the database was not influenced by any retrospective price adjustments. All backtests
and statistical analyses were performed using MATLAB R2023a.

2
A database is considered free from survivorship bias if it includes stocks that have been delisted due
to bankruptcy, mergers, takeovers, or other corporate actions. For instance, Twitter, which was delisted
on October 27, 2022, is included in our database.

8
2.1 Base Strategy

We implemented some rules for choosing the stocks we studied. Not all stocks in the US
markets are suitable for day trading due to varying levels of liquidity or trading volume.
Our best approach was to exclude penny stocks and other low-liquidity stocks. To avoid
making decisions based on hindsight, we used set criteria to narrow down our list of stocks
on any given day. The stocks we considered had to meet the following requirements:

1. The opening price had to be above $5.

2. The average trading volume over the previous 14 days had to be at least 1,000,000
shares per day.

3. The ATR over the previous 14 days had to be more than $0.50.

These criteria ensured that the stocks we analyzed had sufficient liquidity and volatility
as well as favorable conditions for day trading [4, 2].

Entry Conditions

With each eligible stock, we placed a stop order (not to be confused with a stop loss
order) at a level equal to the high/low of the 5-minute range, in the direction of the
opening range. For example, if a stock had a bullish move within the first 5 minutes of
trading (from 9:30 am until 9:35 am ET), we placed a stop order at the highest value of
the 5-minute opening range (known as the 5-minute high). Conversely, if a stock had a
bearish move within the first 5 minutes of trading, we placed a stop order at the lowest
value of the 5-minute opening range (known as the 5-minute low ). In the case of a doji
(open = close) forming in the first 5 minutes, no order was placed.

Stop Loss and Profit Target

In case the order was triggered, we placed a stop loss order at a 10% ATR distance from
the executed entry price. If the stop loss was not reached intraday, we closed the position
at the end of the trading session (i.e., 4:00 pm ET).

9
Position Sizing

Each stock was traded such that in case of the stop loss being hit, the resulting loss
incurred on the capital deployed for that position would be 1%. We also set a maximum
leverage constraint at 4x, in accordance with the majority of US FINRA-regulated bro-
kers3 .

The resulting long-short portfolio to be traded on any given day was thus composed of
all the stocks that satisfied the filters (as defined above) and whose opening range was
either positive or negative.

The backtest was conducted from January 1, 2016 to December 31, 2023. We assumed a
starting capital of $25,000 and incorporated a commission cost of $0.0035 per share (this
figure represented the entry-level commission fee charged by Interactive Brokers Pro –
Tiered as of December 31, 2023).

Figure 3 displays the equity curve for the portfolio generated by the diversified 5-minute
ORB strategy across all US stocks. With an initial investment of $25,000, the portfolio
appreciated by 30%, resulting in a net profit of only $7,500 after accounting for commis-
sion fees. In contrast, during the same period, a passive long position in the S&P 500
would have seen an increase of nearly 200%, equating to a profit of about $50,000.

As detailed in Table 1, the active 5-minute ORB strategy underperformed, yielding a

modest annual return of only 3.2% and experiencing an annual volatility of approxi-
mately 6.6%. This resulted in a Sharpe Ratio of 0.48, which was significantly lower than
the 0.78 Sharpe ratio for the S&P 500.

Despite this overall underperformance when compared to the benchmark, the 5-minute
ORB strategy showed some encouraging results. Specifically, its maximum drawdown
(MDD) was only 13%, compared to the S&P 500’s MDD of 34% within the same time
frame. Moreover, in terms of the worst single day returns, the 5-minute ORB strategy’s
3
The leverage constraint may imply that in some trades, the maximum loss per trade is less than 1%.
The effect of leverage on position sizing was well-documented and studied in our previous paper [23].

10
Figure 3: Equity curve comparison of a S&P500 buy-and-hold portfolio (red line) and a portfolio
engaged in day trading both long and short positions on all stocks using the 5-minute ORB strategy
described in Section 2.1 (black line). The analysis covers the period from January 1, 2016 to December
31, 2023, with an initial net asset value of $25,000 and a commission rate of $0.0035 per share.

performance was notably better, with a maximum loss of only -0.8% in a single day, com-
pared to the S&P 500’s maximum loss (on March 16, 2020, during the COVID pandemic)
of -10.9%.

Since the validity of day trading as a long-term consistent and uncorrelated source of
income for traders and investors is a matter of debate, we decided to run a simple linear
regression analysis to see if the 5-minute ORB returns were correlated with S&P 500
returns and to see if there was any abnormal return in excess of what can be extracted
by a passive market exposure. Further insights were also gained from this simple linear
regression analysis, where the daily returns of the 5-minute ORB strategy were regressed
against the daily returns of the S&P 500. This method, commonly employed by academia

11
Table 1: Performance comparison of a S&P500 buy-and-hold portfolio and a portfolio engaged in
day trading both long and short positions on all stocks using the 5-minute ORB strategy described in
Section 2.1. The analysis covers the period from January 1, 2016 to December 31, 2023, with an initial
net asset value of $25,000 and a commission rate of $0.0035 per share.

Total Sharpe Hit Worst

Strategy IRR Volatility MDD Alpha Beta
Return Ratio Ratio Day
ORB Base 29% 3.2% 6.6% 0.48 41.4% 13% -0.8% 3.3% 0.01
S&P500 198% 14.2% 18.3% 0.78 54.9% 34% -10.9% 0.00% 1.00

and institutional investors, assesses the dependency between two strategies or assets. The
regression equation utilized was4 :

RetORB Base = α + β × RetS&P500 .

The beta coefficient of the ORB strategy, being close to 0, indicated a negligible correla-
tion with the S&P 500. Additionally, an alpha of 3.26% per annum represented the profit
generated by the ORB strategy that was not attributable to simple market exposure.

What factors, therefore, contributed to the basic 5-minute ORB strategy’s lackluster per-
formance? To understand the underlying causes, we must revisit the core concept of the
ORB strategy. This strategy aims to identify assets that exhibit an abnormal imbalance
between demand and supply in the first few minutes of the trading session. The hypothe-
sis is that this imbalance will persist throughout the session, creating exploitable intraday
trends. While the direction of the demand-supply imbalance can be inferred from the
opening range, its abnormality can be assessed by comparing its current opening range
volume to its recent average.

In the following section, we introduce a straightforward metric to measure the abnormal-

ity of the opening range volume. We will also examine whether this metric effectively
predicts the subsequent realized PnL of the ORB strategy.

4
For the purposes of simplicity, the risk-free rate was not included in the regression analysis. Nev-
ertheless, throughout the backtesting period, the risk-free rate in the US was negligible and, therefore,
should not have a significant impact on the results.

12
3 Not All Opening Range Are Created Equally
You are only as good as the stocks that you trade
– Mike Bellafiore [6]

A prevalent strategy among experienced day traders is to focus their intraday trading
activities on Stocks in Play [6, 7]. A stock is considered in play when it shows unusual
trading activity throughout the day, which often results in an expansion of its daily price
range and a distinct trend in its intraday price movements. A stock is typically expected
to be in play in response to a major fundamental catalyst that prompts institutional
investors to re-evaluate their financial positions in it. Common catalysts include:

• Earnings reports

• Earnings warnings or pre-announcements

• Earnings surprises

• FDA approvals or disapprovals

• Mergers/acquisitions

• Alliances, partnerships, or major product releases

• Major contract wins/losses

• Restructuring, layoffs, or management changes

• Stock splits, buybacks, or debt offerings

• Break of key technical levels.

While a fundamental catalyst is often necessary to trigger abnormal trading activity in a

stock, it is not always sufficient to classify it as a Stock in Play. If the market has already
priced in the catalyst, institutional investors may not react significantly, resulting in
minimal trading activity. An effective method for traders to determine if a catalyst is
indeed causing unusually high trading activity is through the use of Relative Volume. This
metric is a statistical comparison of the day’s trading volume against the average volume
from previous days. For real-time analysis, traders can calculate the Relative Volume

13
Figure 4: Average PnL (in R) of 5-minute ORB grouped by the Relative Volume in the first 5 minutes
of the trading session.

continuosly throughout the day. In our study, we focused on the Relative Volume during
the opening range. Specifically, we calculated the Relative Volume for each stock j after
the first 5 minutes of each trading day t using this formula:

ORV olumet,j
RelativeV olumet,j = 1
P14 ,
14 i=1 ORV olumet−1,j

where ORV olumet,j represents the volume traded in stock j during the first 5-minutes
of trading in day t.

Building upon the basic filters used in the Base Strategy (Price > $5, Average Volume
14 Days > 1,000,000 shares, ATR 14 Days > $0.50), we further analyzed the relation-
ship between Relative Volume and average PnL in R. Figure 4 distinctly demonstrates
a strong positive correlation between Relative Volume and subsequent realized PnL (net
of commissions).

As shown in Figure 4, when the Relative Volume was below 100%, the average PnL of
5-minute ORB trades was notably low at -0.02R. However, this figure improved signifi-
cantly to 0.08R per trade when the Relative Volume exceeded 100%. Remarkably, at a

14
Relative Volume of over 30x (or more than 3,000%), the average profitability per trade
soared to 0.38R.

While focusing exclusively on stocks with a 30x trading activity may seem attractive in
terms of PnL per trade, this approach might limit the total number of trades available per
year, potentially impacting the ability to meet a predefined annual target. There is only
a limited number of Stocks in Play every week that can reach such high trading volume.
In the upcoming section, we will explain how we leveraged these insights to enhance the
efficiency of trading the 5-minute ORB strategy in the US market.

4 Opening Range Breakout on Stocks in Play

To enhance the effectiveness of the Base Strategy, we proposed an additional constraint:
the strategy should not trade stocks that exhibit below-average trading activity during
the opening range (9:30 am to 9:35 am ET). This means we would exclusively focus
on those stocks whose Relative Volume was at least 100%. Furthermore, to ensure we
were trading the most in play stocks of the day, our strategy would only take positions
in the top 20 stocks experiencing the highest Relative Volume. The revised strategy
incorporated the following filters:

1. The opening price had to be above $5.

2. The average trading volume over the previous 14 days had to be at least 1,000,000
shares per day.

3. The ATR over the previous 14 days had to be more than $0.50.

4. The Relative Volume had to be at least 100%.

5. Trade the stocks with the top 20 Relative Volume.

As for the base strategy, the direction of each trade (long or short) was determined by
the initial movement of the opening range. A positive opening range prompted a stop
buy order, whereas a negative one led to a stop sell order. For every position we opened,

15
Figure 5: Equity curve comparison of a S&P500 buy-and-hold portfolio (red line), a portfolio engaged
in day trading both long and short positions on all stocks using the 5-minute ORB strategy described
in Section 2.1 (black line) and the 5-minute ORB strategy with Relative Volume described in Section 4
(blue line). The analysis covers the period from January 1, 2016 to December 31, 2023, with an initial
net asset value of $25,000 and a commission rate of $0.0035 per share.

we set a stop loss at 10% of the ATR. If a position was not stopped during the day, it
was unwound at the end of the trading day.

Consistent with the Base Strategy, each stock was traded in such a way that should the
stop loss be triggered, the loss on the capital allocated to that position would not exceed
1%. Additionally, we imposed a maximum leverage constraint of 4x, in line with the reg-
ulations of most US FINRA-regulated brokers. The starting assets under management
(AUM) and the commission per share remained the same as in the base case.

Figure 5 displays in blue the equity trajectory for this new version of the 5-minute ORB
strategy. The improvement compared to the base strategy is substantial, with a remark-

16
Table 2: Performance comparison of a S&P500 buy-and-hold portfolio, a portfolio engaged in day trad-
ing both long and short positions on all stocks using the 5-minute ORB strategy described in Section 2.1
and the 5-minute ORB strategy with Relative Volume described in Section 4. The analysis covers the
period from January 1, 2016 to December 31, 2023, with an initial net asset value of $25,000 and a
commission rate of $0.0035 per share.

Total Sharpe Hit Worst

Strategy IRR Volatility MDD Alpha Beta
Return Ratio Ratio Day
ORB Base 29% 3.2% 6.6% 0.48 41.4% 13% -0.8% 3.3% 0.01
ORB + Rel Vol 1,637% 41.6% 14.8% 2.81 48.4% 12% -1.61% 35.8% 0.00
S&P500 198% 14.2% 18.3% 0.78 54.9% 34% -10.9% 0.00% 1.00

able outperformance against the passive buy-and-hold approach. From January 1, 2016
to December 31, 2023, an initial investment of $25,000 in this strategy would have grown
to approximately $435,000, equating to an extraordinary total net return of 1,637%. In
contrast, a passive investment in the S&P 500 during the same period would have seen
growth from $25,000 to about $75,000, which is roughly a 200% increase.

Table 2 presents the performance statistics for this newly refined 5-minute ORB strategy,
highlighting significant improvements over the Base Strategy. The annual rate of return
soared from 3.2% in the ORB Base to an impressive 41.6%. Equally noteworthy was the
increase in the Sharpe Ratio, which rose more than 5-fold, from 0.48 to an extraordinary
2.81.

While the MDD showed a modest improvement, the worst day loss slightly deteriorated,
likely due to the more concentrated nature of the portfolio (in fact, in the ORB + Rel
Vol portfolio we traded only the top 20 stocks). The potential for a greater worst day
loss in a more concentrated portfolio arises from the increased exposure to specific stock
movements, which can lead to more pronounced losses on days when those stocks perform
poorly. In line with the previous analysis, we conducted a regression of the strategy’s daily
returns against those of the S&P 500, with remarkable findings. The beta coefficient re-
mained close to zero, indicating minimal dependency on overall market movements. Most
strikingly, the alpha surged to an impressive 36% per annum.

Considering that these returns were net of commission and the strategy’s parameters

17
Figure 6: Equity curve comparison of a S&P500 buy-and-hold portfolio (red line) and a portfolio
engaged in day trading both long and short positions using the 5-minute, 15-minute, 30-minute, and
60-minute ORB system described in Section 4. The COMBO (black line) represents an equally weighted
portfolio of ORB portfolios across various time frames (5-minute, 15-minute, 30-minute, and 60-minute).
The analysis covers the period from January 1, 2016 to December 31, 2023, with an initial net asset
value of $25,000 and a commission rate of $0.0035 per share.

were minimal and based on economic rationale rather than retrospective optimization,
we are confident that these results could maintain their robustness and significance in
future applications.

5 Opening Range Breakout on Other-Time Frames

The ORB strategy can be applied across various time frames during the first trading
hour from the open (9:30am to around 10:30am ET), where market volatility and liq-
uidity are at their peak. Although the 5-minute frame is standard, we thought it would

18
Table 3: Performance comparison of a S&P500 buy-and-hold portfolio and a portfolio engaged in day
trading both long and short positions using the 5-minute, 15-minute, 30-minute, and 60-minute ORB
system described in Section 4. The COMBO represents an equally weighted portfolio of ORB portfolios
across various time frames (5-minute, 15-minute, 30-minute, and 60-minute). The analysis covers the
period from January 1, 2016 to December 31, 2023, with an initial net asset value of $25,000 and a
commission rate of $0.0035 per share.

Total Sharpe Hit

Strategy IRR Volatility MDD Alpha Beta
Return Ratio Ratio
5m-ORB + Rel Vol 1,637% 41.6% 14.8% 2.81 48.4% 12% 35.8% 0.00
15m-ORB + Rel Vol 272% 17.4% 12.2% 1.43 44.7% 11% 16.9% -0.01
30m-ORB + Rel Vol 21% 2.3% 11.1% 0.21 42.4% 35% 2.8% 0.01
60m-ORB + Rel Vol 39% 4.1% 10.2% 0.40 42.3% 21% 4.4% 0.01
COMBO 234% 15.8% 7.9% 1.99 47.3% 7% 15.0% 0.00
S&P500 198% 14.2% 18.3% 0.78 54.9% 34% 0.00% 1.00

be worthwhile to also explore other popular time frames such as 15, 30, or 60 minutes.
For this purpose, we investigated our ORB strategy across various time frames, limiting
our study to Stocks in Play with at least 100% Relative Volume in their respective first
n-minute time frame.

For example, the Relative Volume for a 15-minute ORB is measured in the first 15 min-
utes, and for a 30-minute ORB, it is compared with the first 30-minute average over the
previous 14 days. The results are presented in Figure 6 and Table 3, comparing them
to a simple S&P 500 buy-and-hold portfolio. As can be seen, the 5-minute ORB sig-
nificantly outperformed the other time frames as well as a passive exposure in the S&P
500. The reason for the 5-minute ORB’s superior performance is unclear and warrants
further investigation. A plausible explanation might be that the shorter the time-frame
that define the opening range, the greater the portion of the move captured by the ORB
on trend days.

6 Best/Worst Performers on 5-minute ORB

In this section, we will delve into the specifics of the 25 best and 25 worst performing
stocks in the US stock market based on the results from the n-minute ORB strategies
with Relative Volume above 1. It is fascinating to observe which stocks emerged as the

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Table 4: Best performing stocks for the n-minute ORB strategies with Relative Volume of at least 100%
based on cumulative R.

5m-ORB 15m-ORB 30m-ORB 60m-ORB

PnL Win PnL Win PnL Win PnL Win
Ticker Ticker Ticker Ticker
(R) Ratio (%) (R) Ratio (%) (R) Ratio (%) (R) Ratio (%)
DDD 385 21% CAR 233 21% MXIM 214 26% DDD 185 24%
FSLR 370 20% NVDA 200 20% SAVE 213 23% THC 183 26%
NVDA 309 19% AMD 189 19% ACAD 201 21% TKAT 177 40%
SWBI 272 24% LITE 187 22% CDNS 196 24% DISH 169 25%
RCL 271 20% FOSL 187 20% WOLF 185 25% EXEL 162 25%
W 252 21% WW 180 22% FLR 171 23% CDNS 161 26%
VIR 244 20% WOLF 170 21% TKAT 171 30% FLR 154 22%
EXAS 229 19% MTCH 167 20% CSGP 163 28% BA 151 25%
ALK 207 18% ASML 161 24% FOSL 161 21% IONS 145 28%
FOSL 205 23% OMF 159 27% HRB 156 25% VLO 144 25%
WW 190 19% SWBI 152 23% NET 149 23% AMD 140 25%
OKTA 188 19% BWA 150 22% DDD 142 21% INTU 137 26%
PBF 186 19% NFLX 147 20% DISH 139 20% FOSL 136 23%
AMD 184 17% FSLR 146 18% GDDY 135 21% FIS 133 24%
TSLA 183 18% NKTR 141 21% MTCH 134 22% KA 131 26%
ADBE 182 17% CDNS 141 20% NTNX 130 18% SAVE 128 25%
ACAD 176 17% LRCX 140 19% MA 129 20% W 126 24%
ELV 174 20% TER 138 22% ALK 128 18% TRU 126 25%
TWLO 172 19% FLR 137 19% OMF 128 25% STZ 126 26%
TDOC 170 17% CSGP 136 23% TMX 126 27% AXDX 125 22%
SPLK 165 19% LIN 134 20% LIN 122 20% CSGP 125 26%
PARA 164 17% QQQ 134 18% OLN 122 23% BAX 125 22%
WDC 163 17% BCRX 134 24% AAOI 122 21% VRTX 124 23%
NWL 159 18% YELP 133 18% TT 121 19% MAR 122 25%
SQ 158 18% AZTA 132 27% NFLX 119 21% NFLX 119 21%

top performers and which fell behind. As illustrated in Table 4, familiar names such
as Tesla (TSLA), NVIDIA (NVDA), and Advanced Micro Devices (AMD) were among
the top performing stocks. These tickers are popular among retail traders and typically
exhibit significant trading volume, surpassing many other stocks. In contrast, Table 5
lists the worst performing stocks based on the 5-minute ORB strategy.

These findings highlight the importance of selecting stocks with high Relative Volume for
day trading strategies like the n-minute ORB. The higher trading volume in these stocks
may contribute to their pronounced intraday price movements, offering traders greater
opportunities for profit. Additionally, the popularity of these stocks among retail traders
might also increase the likelihood of price movements that are conducive to the ORB
strategy, as these stocks are more susceptible to rapid shifts in sentiment and momentum.

The success of stocks like TSLA, NVDA, and AMD within the 5-minute ORB framework
underscores the strategy’s potential when applied to high volume, volatile stocks. These

20
Table 5: Worst performing stocks for the n-minute ORB strategies with Relative Volume of at least
100% based on cumulative R.

5m-ORB 15m-ORB 30m-ORB 60m-ORB

PnL Win PnL Win PnL Win PnL Win
Ticker Ticker Ticker Ticker
(R) Ratio (%) (R) Ratio (%) (R) Ratio (%) (R) Ratio (%)
CMC -154 12% CLR -155 13% BIIB -266 16% BIIB -216 21%
TRGP -132 14% TRGP -131 15% AEO -165 14% DINO -124 17%
CSX -128 12% TSCO -130 13% KMX -161 14% DBI -113 20%
CNP -127 13% IVZ -119 16% CLR -145 14% GM -104 19%
BJ -120 10% HOG -119 13% VST -141 12% ADI -102 19%
PSTG -120 13% TPR -105 15% CNK -136 15% BKR -99 15%
WMB -113 13% RES -105 11% HAL -136 16% GILD -96 15%
TT -112 12% INCY -104 13% EA -125 16% KDNY -96 10%
HP -112 13% YUMC -101 16% GM -115 17% AEO -94 16%
ALLY -110 13% TFFP -98 8% BG -114 15% DBX -91 16%
FL -109 11% GM -98 16% REG -110 12% FCX -89 19%
PSX -107 13% REG -96 13% XYL -110 13% UBX -89 9%
WYNN -105 14% FCX -93 14% ANF -108 14% CMCSA -89 17%
DOW -103 11% MET -92 15% SEDG -105 13% TDOC -89 15%
URBN -101 14% EQT -92 16% MCK -105 15% EBAY -88 16%
APC -100 11% KNX -91 13% GPS -105 15% LBRT -88 13%
ROST -99 12% EXPE -89 13% NKE -99 17% EVLO -85 10%
JBL -95 12% URBN -88 16% DAL -96 16% OGE -84 16%
DD -92 10% BG -88 12% INTC -93 15% CSX -84 17%
MARA -91 13% ROK -88 12% XEC -92 16% NTRS -83 16%
VOYA -89 14% MU -87 16% SM -91 17% WMB -82 17%
BLMN -87 13% IOVA -87 12% FCX -90 15% EWBC -82 17%
BRO -84 11% MGY -84 13% PHM -87 15% MRO -80 17%
HOG -84 15% SEDG -82 12% SGEN -87 13% CTSH -78 18%
SKX -83 15% TSN -81 15% PSTG -85 15% TMUS -78 17%

results offer valuable insights for traders looking to optimize their day trading approaches,
suggesting that focusing on stocks with substantial trading activity and widespread in-
terest among the trading community can enhance the performance of the ORB strategy.

7 Conclusion
In conclusion, our comprehensive analysis of the ORB strategy within the US equity
market offers significant insights into its profitability and viability as a day trading ap-
proach. By examining a vast dataset covering over 7,000 US stocks traded from 2016
to 2023, we have highlighted the substantial potential of the ORB strategy, especially
when applied to Stocks in Play exhibiting high Relative Volume of at least 100%. Our
findings underscore the strategy’s effectiveness in generating consistent and uncorrelated
returns, thereby addressing the long-standing debate about the feasibility of day trading

21
as a sustainable income source.

The remarkable performance of the 5-minute ORB strategy, in particular, stands out,
demonstrating a notable advantage over both longer time frames within the ORB strat-
egy and a passive buy-and-hold approach. This strategy achieved a net performance of
over 1,600%, with a Sharpe ratio of 2.81, and an annualized alpha of 36%, significantly
outperforming the passive S&P 500 return of 198% over the same period. Such results
not only provide empirical support for the ORB strategy’s efficacy but also emphasize
the critical role of selecting stocks with substantial trading activity, driven by underlying
fundamental news, to capitalize on intraday volatility and liquidity.

Furthermore, our exploration into varying time frames for the ORB strategy enriches the
discourse on day trading methodologies, offering traders nuanced perspectives on optimiz-
ing their strategies to enhance profitability and manage risk. The superior performance
of the 5-minute ORB suggests a unique dynamic at play in the earliest phases of the
trading day, highlighting an area ripe for further exploration.

This paper contributes to the body of financial literature by providing a detailed, stock-
specific analysis of ORB performance across different time frames, a first of its kind with
such intraday granularity. Our rigorous statistical analysis, grounded in economic ratio-
nale rather than retrospective optimization, suggests that the findings presented herein
could maintain their robustness and significance in future applications.

As the landscape of retail trading continues to evolve, our study reaffirms the importance
of informed strategy selection, emphasizing the potential of technical analysis and specif-
ically the ORB strategy, to level the playing field for individual traders against more
sophisticated market participants. However, while the ORB strategy presents a promis-
ing avenue for day traders, it is crucial to approach it with thorough research, disciplined
risk management, and a clear understanding of market dynamics.

In future research, further investigation into the reasons behind the 5-minute ORB’s
exceptional performance, as well as the exploration of additional variables that may

22
influence the strategy’s success, will be invaluable. This could include the impact of
market conditions, the role of news and earnings announcements, and the integration of
other technical indicators to refine entry and exit points. Through ongoing analysis and
adaptation, traders can continue to hone their strategies to navigate the complexities of
the financial markets effectively.

23
Author Biography

Andrew Aziz is a Canadian trader, investor, and official

Forbes Council member. He has ranked as one of the top
100 best-selling authors in ”Business and Finance” for 7 con-
secutive years from 2016 to 2023. Aziz’s book on finance has
been published in 13 different languages. Originally from Iran,
Andrew moved to Canada in 2008 to pursue a PhD in chem-
ical engineering, initiating a distinguished career in academia
and industry. As a research scientist, Andrew made significant
contributions to the field, authoring 13 papers and securing 3
US patents. Following a successful stint in research in chemical
engineering and clean technology, he transitioned to the world
of trading. Currently Andrew is a trader and proprietary fund
manager at Peak Capital Trading in Vancouver, BC Canada.

Carlo Zarattini, originally from Italy, currently resides in

Lugano, Switzerland. After completing his mathematics de-
gree in Padova, he pursued a dual master’s in quantitative
finance at Imperial College London and USI Lugano. He for-
merly served as a quantitative analyst at BlackRock, where
he developed volatility and trend-following trading strategies.
Carlo later established Concretum Research, assisting institu-
tional clients with both high and medium-frequency quantita-
tive strategies in stocks, futures, and options. Additionally, he
founded R-Candles.com, the first backtester for discretionary
technical traders.

Andrea Barbon, born in Venice, currently resides in Zurich,

Switzerland. He holds a Master degree in pure mathemat-
ics from the University of Amsterdam and a PhD in finance
from the University of Lugano. He is Assistant Professor of
Financial Technology at the FSI Center of the University of
St.Gallen, Switzerland and at the Swiss Finance Institute. His
research interests include asset pricing, monetary policy, fin-
tech, blockchain, and machine learning. He is also head of AI
at Concretum Research, and lead developer for the R-Candles
web application.

24
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