April 2nd, 2023
The primary objective of this report is to present a systematic approach to studying and analyzing stock market movements, rather than providing a definitive guide on how to trade breakouts. By examining historical market data, we aim to develop a framework for understanding the various factors that influence trade performance and provide insights to inform future research.
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The study is structured as follows: we first outline the methodology employed, detailing the data sources, entry and exit strategies, and other parameters used to analyze the market. Next, we delve into the analysis section, where we explore different dimensions such as seasonality, price, volume, consolidation, and Average Daily Range percentage (ADR%). In each subsection, we present the data and offer in-depth interpretations to uncover trends and relationships.
Following the analysis, we present the key findings, summarizing the most significant insights gleaned from the study. We then discuss areas for further research, highlighting unanswered questions and potential avenues for future exploration.
Through this report, we hope to provide a valuable framework for examining and analyzing past moves of the stock market, offering insights that can inform future research and decision-making processes. It is important to note that this report is not intended to provide financial advice or specific trading recommendations. Rather, it aims to contribute to the broader knowledge base around stock market dynamics and foster a deeper understanding of the factors that drive market performance.
In this section, the Average Daily Range (ADR) plays a crucial role in determining the entry and exit criteria for the backtesting of systematic breakout setups on the daily timeframe. ADR is the measure of the average price movement of a security over a specified period, often used to gauge volatility and potential price movements. By using the ADR percentage of the past 20 trading days as a key factor in the entry and exit criteria, the strategy aims to capture breakout setups with substantial price movements and to avoid securities with low volatility. The ADR not only helps to establish the consolidation range, but also serves as the basis for setting partial targets and identifying breakouts with higher than average price movement. By incorporating ADR into multiple aspects of the methodology, the strategy is designed to identify and capitalize on significant price moves while managing risk and mitigating potential losses.
Historical daily prices, up to and including March 21, 2023, were collected from tickers listed on base.report. The data, originally sourced from Financial Modeling Prep, includes tickers from the NASDAQ, NYSE, and AMEX exchanges in the U.S. equity market.
Though a small number of recently delisted stocks, such as TWTR, may be present, the majority of historically delisted stocks are not included. Thus, be aware of potential survivorship bias.
Backtesting begins on the first trading day for each ticker and iterates until the most recent trading day (2023-03-21), identifying trades that fit the entry and exit criteria. The following fields are collected for each trade:
For more information on these fields, refer to the following sections.
Each recorded trade must meet the following requirements:
A trade starts when these conditions are met, with the following hard stops (full position sell) added:
Figure 1. A stock chart with satisfactory entry parameters.
The partial target, set as the entry price plus the ADR% of the past 20 trading days prior to the entry day, aims to identify a continuation of strength.
Partial Sell Window
During the partial sell window, which spans from day 3 to day 5 (with day 1 being the entry day), one of three outcomes can occur:
In cases 2 or 3, when a partial sell is made, the hard stop is moved up to the entry price to minimize loss and reduce trade risk. A field is also recorded for each trade to indicate whether the partial target was reached.
Figure 2. A stock chart with a breakout entry, a partial sell window, and an exit on SMA cross.
A trade is exited under any of the following conditions:
For cases 2 and 3, if a gap down occurs (when the price opens significantly lower than the previous close), the exit sell price is set to the opening price. Otherwise, the exit sell price is set to the entry LOD or the entry price, respectively.
Along with the exit date, sell price, and reason, the number of days held (trading days from entry to exit) is also recorded.
Figure 3. A stock chart showing a losing trade with exit on LOD cross.
This concludes the trade process.
With the methodology for backtesting systematic breakout setups on the daily timeframe now established, the next section delves into the analysis of the gathered data. Various aspects such as sector performance, yearly trends, seasonality, liquidity, consolidation, and ADR will be examined to identify patterns and derive insights.
Before diving deep into the data from various perspectives, a general overview of the data was conducted. One of the initial comparisons made was to assess the average performance of full sells versus partial sells, where 50% of the position was sold during the partial sell window. This comparison was crucial in understanding the potential benefits of each approach and determining the most effective strategy for maximizing gains in the systematic breakout setups.
The backtesting results reveal that making full sells at the exit provides significantly better performance compared to selling 50% of the position during the partial sell window. This observation can be attributed to the fact that selling the entire position at the exit allows the trade to capitalize on the full extent of the price movement, maximizing the potential gains from the momentum. On the other hand, selling 50% during the partial sell window reduces the exposure to the remaining upward price movement and could lead to missed opportunities for larger profits.
In both cases, if a partial sell is made, the hard stop is moved up from the low of the entry day (LOD) to breakeven (the entry price). This adjustment effectively reduces the risk and minimizes potential losses for the trade. However, the superior performance of full sells at the exit suggests that the strategy benefits more from allowing the position to capture the entirety of the momentum, rather than attempting to secure partial gains during the specified sell window.
It is important to consider that the partial taking strategy might work better for variations of the core strategy being tested. For instance, if the entry was made earlier in the day instead of always using the closing price, it could lead to different outcomes for the partial selling approach. The effectiveness of partial selling in such scenarios might be significantly different and could result in better overall performance. Therefore, it is essential to explore various alternatives and nuances of the strategy to identify the most optimal approach for different trading scenarios and market conditions.
A few outliers were removed as their excessive gains skewed the data. Given the small number of cases, their exclusion from the final study seemed reasonable. For transparency, the outliers are as follows:
The dataset can be broken down into different gain categories, which reveals the following insights:
This data highlights that more than half of the trades resulted in a loss, while nearly 30% experienced a modest gain of up to 5%. A smaller percentage of trades, around 6% each, fell into the 5-10% gain and 20%+ gain categories. Meanwhile, 5.73% of trades experienced gains between 10% and 20%.
In this subsection, we will analyze the data from a historical perspective, focusing on the performance of the strategies on a year-by-year basis. This approach helps to uncover potential trends and patterns that may have emerged over time. Please note that the number of trades prior to 1990 is relatively sparse. Therefore, to provide a more accurate analysis, we have consolidated the data from those years into a single category. Let's examine the yearly performance of the breakout strategies, taking into consideration both the no partial sells and partial sells approaches.
The data provides a year-by-year analysis of average gains with and without partial sells. Some key observations from the data are:
Overall, the data suggests that the effectiveness of the systematic breakout strategy varies over time, with some years exhibiting better performance than others. Moreover, the trend of higher average gains without partial sells seems to persist across the years.
Given the clear indication that the strategy performs better without partial sells, we will analyze the data in the following sections without partial sells. However, please keep in mind that the results may vary depending on the approach used.
In this subsection, we will explore the seasonality of the systematic breakout strategy by analyzing the average gains and number of trades per month. Understanding the seasonal patterns can provide insights into how the strategy performs throughout the year and help identify potential opportunities or challenges associated with certain months.
The data reveals some interesting trends regarding the performance of the systematic breakout strategy across different months:
In summary, the seasonality analysis highlights that the performance of the systematic breakout strategy can vary across different months.
This subsection presents a detailed examination of the strategy, focusing on its application across various market sectors. The Healthcare sector had the highest number of trades at 6,340, with an average gain of 2.10%. Technology followed with 5,293 trades and an average gain of 1.34%. Consumer Cyclical had 3,491 trades, yielding an average gain of 2.26%, while Industrials had 3,047 trades with a 2.01% average gain. Basic Materials saw 1,925 trades and an average gain of 2.18%, whereas Energy had 1,862 trades with a 1.27% average gain. Financial Services recorded 1,848 trades with a 0.97% average gain, and Communication Services had 1,500 trades with the highest average gain of 2.68%. Consumer Defensive had 751 trades and a 1.14% average gain, while the Other category (for tickers without a category such as ETFs) had 704 trades with a 0.79% average gain. Real Estate had the lowest average gain at -0.03% with 546 trades, and Utilities had 193 trades with a 0.98% average gain.
The data reveals interesting patterns in the performance of different sectors. Communication Services, despite having a relatively lower number of trades (1,500), outperformed all other sectors in terms of average gains at 2.68%. This indicates that the sector might have more successful breakout setups, or the underlying stocks in this sector tend to have stronger price movements during breakouts.
On the other hand, Real Estate had the lowest average gain at -0.03%, with 546 trades, suggesting that breakout strategies might not be as effective in this sector. The negative average gain could be attributed to factors such as lower volatility or different market dynamics in the Real Estate sector compared to other sectors.
The Healthcare sector had the highest number of trades (6,340), which may point to more frequent breakout opportunities or higher volatility in this sector. The average gain of 2.10% is also among the top performers, indicating that Healthcare could be an area worth exploring for systematic breakout strategies.
Sectors with fewer trades, such as Utilities (193 trades) and Consumer Defensive (751 trades), might not provide as many opportunities for breakout trading, but they still show positive average gains of 0.98% and 1.14%, respectively.
The analysis reveals a potential inclination towards sectors with higher average gains or more frequent breakout opportunities, such as Communication Services, Healthcare, and Consumer Cyclical. At the same time, it suggests a cautious approach to sectors like Real Estate, where the strategy appears to underperform.
Taking a closer look at the performance distributions, some trends emerge across various sectors:
This data analysis suggests that the performance of systematic breakout strategies varies across sectors, with different sectors showing distinctive trends in their gain categories.
In this subsection, we analyze the performance of breakout strategies based on different price groups. It's important to note that the data used for this analysis is split-adjusted, which means that historical prices have been retroactively altered to account for stock splits. As a result, the prices in these groups may not accurately represent the actual trading prices at the time of the trades. Despite this limitation, examining price groups can still provide valuable insights into the influence of stock prices on the performance of breakout strategies.
Here's a breakdown of the average gains for each price group:
From this data, it appears that lower-priced stocks (below $3) had the highest average gains, while the gains generally decreased as the stock price increased. This trend could be a result of various factors, such as higher volatility or lower liquidity in lower-priced stocks, which might lead to larger price movements. However, it's crucial to keep in mind the potential limitations of using split-adjusted data when interpreting these findings, as it may not accurately represent the trading prices at the time of the trades.
In this section, we analyze the performance of breakout strategies based on different volume groups. The volume of a stock can provide insights into the level of interest, liquidity, and potential volatility of a security. Examining volume groups allows us to understand how these factors may impact the performance of breakout strategies.
Here's a breakdown of the average gains for each volume group:
The data indicates that the highest average gains were observed in stocks with a volume between 100K - 150K and 2.25M - 5M. However, the differences in average gains between the volume groups are relatively small. This suggests that the relationship between volume and the performance of breakout strategies might not be as strong as other factors such as stock price or sector.
In this section, we analyze the performance of breakout strategies based on the length of consolidation periods. Consolidation refers to a period where a stock's price is moving within a relatively tight range before a potential breakout. Understanding the relationship between the duration of consolidation and the performance of breakout strategies can help in identifying the optimal consolidation periods for executing trades. Please note that the consolidation calculation used in this study is relatively simple, and a more sophisticated determination of consolidation may provide better results.
Here's a breakdown of the average gains for each consolidation group:
The data suggests that longer consolidation periods generally result in higher average gains, with the highest gains observed in the 10-day consolidation group. However, please note that the number of trades tends to decrease as the consolidation period increases.
Last but certainly not least, we delve into the impact of ADR% on the performance of breakout strategies. ADR% represents the average daily range percentage of a stock, calculated as the average difference between the high and low prices over a specified period, divided by the stock's price. This metric can provide insight into a stock's volatility and the potential risk associated with trading it. ADR% proved to be instrumental in the data collection phase, as it allowed for the identification and categorization of stocks based on their volatility, providing valuable insights into the relationship between volatility and the performance of breakout strategies. In this section, we examine how different ADR% groups affect the average gains of breakout strategies.
Here's an overview of the average gains for each ADR% group:
The data indicates that higher ADR% values generally correspond to higher average gains. This suggests that breakout strategies may perform better when trading more volatile stocks, as the increased volatility provides more room for price movements.
Upon closer examination, it becomes apparent that the relative gain seems to map well to the ADR%. By dividing the ADR% by the average gain in each group, it can be observed that the numbers are relatively consistent, suggesting a strong correlation between the two and emphasizing the importance of considering ADR% when evaluating breakout strategies. However, it is essential to remember that higher volatility also carries higher risks.
Throughout our analysis of breakout strategies, we have uncovered several critical insights that can help provide better insight into the underlying dynamics of these trading approaches. The key findings of our study include:
By examining these factors, we were able to gain a deeper understanding of the underlying mechanisms driving breakout strategies and their effectiveness across different market conditions.
This is certainly one of the studies of all time. Bad puns aside, it is important to emphasize that the primary objective of this research is to present a framework for examining and analyzing historical market movements.
The numerous entry and exit parameters at our disposal allow for virtually endless variations in the study, which can lead to vastly different outcomes. There are several intriguing questions that remain unanswered, such as:
This study relies solely on historical daily data. Access to intra-day data would offer greater flexibility for testing various entry strategies, such as using a 5-minute candle for breakout confirmation.
The consolidation and breakout calculations could also be adjusted. For example, the consolidation rules might be made more lenient by permitting minor deviations outside the range as long as the overall consolidation remains intact. Alternatively, the rules could be made more stringent by incorporating requirements for "higher lows" and other criteria.
Another promising avenue for exploration involves the use of machine learning (ML) techniques to better understand the data. For instance, stock charts prior to the entry day for each trade can be compiled and analyzed using ML algorithms. By clustering these charts into similar groups based on various features, such as price patterns or technical indicators, we may be able to identify correlations between these groupings and trade performance. This approach could lead to the discovery of new insights and potentially unveil previously unrecognized patterns in the market data. Combining traditional analysis methods with advanced ML techniques has the potential to significantly enhance our understanding of the factors that influence stock market performance and refine our trading strategies.
In summary, this research provides a foundation for further exploration of the stock market's dynamics and the factors influencing trading outcomes. By considering various entry and exit strategies and adjusting consolidation and breakout calculations, we can continue to refine our understanding of market behavior and the performance of different trading approaches. Future research can build on this study's framework to address the remaining questions and dive deeper into the art of studying breakout setups.
If you have any thoughts, feedback, or ideas for collaboration regarding this study, we would love to hear what you have to say. We invite you to join our communities on discord and reddit and contribute to the ongoing discussion and exploration of this approach.
For those who are extra ambitious, don't forget that you can access the full dataset (as well as the PDF of this study) here.
As a student of Qullamaggie , we strongly believe in openness and collaboration. We'd like to give a shoutout to Kristjan and encourage you to join the Qullamaggies discord server. It's an open, yet strictly moderated, community of traders who aim to learn Kristjan's methodology and help each other improve. As the author of this study, I ( e0 ) have learned a great deal from this server, not only about trading but also about the importance of persistence and discipline.
Finally, if you enjoyed this study and would like to directly support us, please consider paying an amount that you are comfortable with for the PDF + Dataset. Alternatively, if you're not already a subscriber of base.report Pro, please take a look at our offerings, including a stock screener with unique filters that works excellently for scanning for Qullamaggie style breakouts . Our aim with this study is to inspire further exploration and refinement of systematic approaches and frameworks in market analysis, empowering traders with valuable insights. Thank you for your support!
2023-04-02 - Initial release.
2023-04-18 - In the “Exit Criteria” section, the exit reasons for 2 and 3 were mixed up. This has now been fixed. Please note that the exit reasons were recorded correctly during data collection and only the text in the article was mixed up.