Datadog Premium Research

Posted on January 14, 2020June 30, 2026 by io-fund

509d3f81-5ab0-47d8-b044-88c293b4f7b5_Datadog-Premium-Research.pdf

Datadog Premium Research

Fundamentals

Following its IPO in September, Datadog reported revenue that was up 87.8% year-over-year at $95.9 million compared to analyst expectations of $87.73 million. The revenue growth was higher than the 79.5% reported earlier in the year.

Company guidance for Q4 revenue is between $101 million and $103 million. Full year revenue is expected to be between $350 million and $352 million. Forward 1-year revenue is expected to be around the $500 million mark.

This places Datadog second to Crowdstrike for estimated forward revenue growth in the cloud software category.

However, it bears mentioning, Datadog is closer to profitability than any other cloud software company (among those currently reporting negative EPS). Datadog hovers near profitability with non-GAAP operating margin of 0.7% and non-GAAP EBIT of $0.6 million. GAAP operating margin is negative -4.4%. Free cash flow is negative $3.7 million.

Full year non-GAAP EPS is estimated at negative -$0.12 to -$0.11.

Datadog went public in September and is trading at 34 times the midpoint on its full-year guidance of $351 million. If other IPOs in 2019 are any indication, the expiration of the lock-up period on March 17th will likely see some level of adjustment in valuation.

Addressable Market

The broad infrastructure monitoring market is quite nuanced with many players specializing in various aspects of cloud and IT. The broad addressable market will be worth $34 billion by 2024. It requires further effort, however, to break this down into the areas that Datadog directly serves.

According to IDC, the global APM market reached $4.3 billion in 2018, posting 13.4% growth from 2017. The fastest growing companies during 2018 was AppDynamics at 42% market share and New Relic at 35% market share. The market is expected to grow annually by 11.84% over the next several years.

Datadog is expanding into network performance monitoring with a current addressable market of $2 billion. The log management market (as a standalone) is worth $1 billion.

Conservatively, Datadog’s addressable market is around $8 billion to $10 billion. For optimists, (such as the Jefferies’ analyst who stated Elastic’s market was around $40 billion), you could look at the $34 billion IT infrastructure monitoring market especially since Datadog does help monitor on-premise.

The $8-$10 billion market is sufficient enough for Datadog to continue its 65% growth with current revenue of $350 million. The company would only have to claim 5-10% of the market to be a breakout stock.

Product Overview

Datadog is a cloud-based monitoring and analytics company that offers infrastructure monitoring and has expanded into application performance monitoring (APM). The company aggregates metrics and events across the full infrastructure and application stack for a single point of view.

Datadog began as an infrastructure monitoring tool in 2010 and expanded into APM in 2016 with the public release in February of 2017 for full stack observability.

Application performance monitoring assures applications and websites run as expected with optimal speeds across mobile platforms, cloud-native infrastructures, virtualized and containerized servers, etcetera. APM also assures that the application is performing as it should, backend processes are executing as they should, including transaction processing, and detects bug or errors in the application code, in the application server, website front end, a slow query, or a slow network.

Distributed application environments can cause numerous bottlenecks and it can be challenging to figure where the bottleneck is coming from. Meanwhile, slow speeds can cause customer drop-off.

APM performs the following functions:

• Digital user experience monitoring: determines if there is slowness, errors or downtime that could lead to a loss of revenue

• Transaction profiling: analyzes the transaction flow to isolate the cause

• Code-level diagnostics: According to DZone, 43% of application performance issues come from code. Diagnostics help to identify the line of code or query causing the issue.

• Deep-dive analysis: Looks beyond code at the server and application infrastructure for problems such as insufficient memory or long wait times

• Infrastructure monitoring: similar to deep drive analysis, ideally infrastructure monitoring is part of the APM package to monitor slow network connections or virtualization bottlenecks.

Competitors

There are a few 800-lb gorillas in the space, such as New Relic, AppDynamics (Cisco) and CA Technologies owned by Broadcom. Dynatrace is also considered a leader in APM and is a private company.

Datadog lists New Relic, AppDynamics and Dynatrace as their main competitors.

Source: IDC APM Market

New Relic

New Relic was founded in 2008 and entered the market with a SaaS-only APM solution. The products have expanded since then to include Infrastructure, Synthetics, Browser, Mobile and Insights for analytics.

The company has expanded into monitoring Kubernetes containers and microservices monitoring (important for automation and machine learning) and now has a presence in Europe although global geographic coverage is a weakness for New Relic. Another weakness is the lack of on-premise.

New Relic’s most recent acquisition was SignifiAI for incident management, which occurred in February 2019. The company is also focused on root cause analysis including predictive anamoly detection, topology-enhanced operational event correlation, and automatic deployment tracking.

Annual revenue of $479 million is expected to grow to $591 million revenue in the current fiscal year ending in March. Forward 1-year revenue is estimated at $693 million. New Relic is distinguished by its profitability, with EPS of $0.24 in the last quarter and current fiscal EPS of $0.64.

App Dynamics

AppDynamics offers both on-premise and SaaS-based APM. Cisco bought AppDynamics for $3.7 billion. According to Gartner, App Dynamics revenue is in the $500 million range+ from sales of APM suites in 2018 (this matches IDC’s data). In an effort to improve its machine learning capabilities, Cisco acquired Perspica in 2017 for a purchase price of $3.7 billion. Perspica helps to surface issues by applying machine learning to large amounts of operations data. Instead of analyzing data after it’s in the database, this helps to analyze the data real-time as it’s being ingested.

AppDynamics weakness is also its strength: Cisco. The revenue is likely reflective of Cisco’s market dominance in networking, yet many APM-specific customers are more apt to go with a smaller, specialized company that is solely focused on APM.

Dynatrace

Dynatrace’s analytics are sold as a package rather than as separate modules with analytics offering real-time topology and AI algorithms to detect anomalies, business impact and root cause across users, applications and infrastructure. The product roadmap includes expanding into multi-cloud and hybrid cloud and using purposebuilt AI to perform root cause analysis faster.

Dynatrace is a premium solution and is priced higher than other APM products.

Catalysts

Hybrid Cloud

I’ve covered the strengths of hybrid cloud in-depth with my Microsoft coverage over the past 1-2 years. Essentially, hybrid cloud allows companies to keep their most sensitive data on-premise while sending less sensitive workloads to the cloud for real-time data processing. Microsoft’s lead in hybrid cloud is what caused me to predict the Pentagon would choose Microsoft over Amazon, as well as Azure’s ongoing growth despite AWSdominance, as Amazon has been focused on cloud-only while ignoring the needs of Fortune 500 companies and others who are more cautious with intellectual property and first-party data.

Read my analysis on Microsoft being a hybrid clod leader here and why this is an important cloud market.

Datadog serves hybrid cloud customers and allows for monitoring of both environments. New Relic, on other hand, is SaaS-only (or cloud only). From my perspective, the most growth will come from hybrid over the next few years as the majority of companies today have resisted sending data to another company’s servers and must eventually choose a solution to remain competitive on AI and ML.

In my opinion, the future growth of hybrid is an important catalyst and market opportunity for Datadog. You can read more about Datadog’s hybrid offering here.

Network Performance Monitoring

Network performance monitoring is a potential catalyst for Datadog in 2020. Although the addressable market is quite small at around $2.1 billion, the cross-selling with customers could strengthen Datadog’s revenue growth.

The company launched network performance monitoring in November of 2019 to expand on infrastructure monitoring and application monitoring. By monitoring virtualized networks, the product helps to increase performance optimization and reduce costs by looking for more optimized network patterns and to quickly find the source for network issues.

For instance, if a cluster is saturating the network capacity, this monitoring tool helps to pinpoint the root cause. There are also topology and traffic flow tools to visualize network connections.

Competitors in network performance monitoring include Netscout, Riverbed, Viavi and Extra Hop. Again, it’s about the cross-selling with the other products that Datadog provides rather than competing in network performance monitoring as a standalone product.

Technical Analysis

By Knox Ridley

Like many new IPOs, Datadog (DDOG), with just 4 months of price action to analyze, is showing a series of overlapping, corrective patterns, signaling uncertainty by the market.

In November of 2019, Datadog hit an all-time high of $44, just before a near 25% drawdown took it down to $33.15. Since bottoming, it’s been in an overlapping uptrend, attempting to repair the damage done.

Using basic Technical Analysis, we have a clear picture of where DDOG is, and what hurdles it needs to clear to regain higher prices. Starting with the upward trend lines that are highlighted in the blue, we can see a clear trend in price that is supported by a rising MACD and RSI. In other words, the momentum is building with price, which indicates a healthy trend. If we see a break of these trend lines in unison, expect Datadog’s price to test the recent low around $33.

If we see these trendlines broken, the red retrace levels offer likely targets, based on basic rules of symmetry and Fibonacci levels. My main target will be around the $31 region if this scenario unfolds, which coincides with the

78.6% retrace level and the 100% extension of the initial move down. However, we could see it bottom at the 61.6% retrace level around $33, or to hit the 127.2% extension around $29-$28. This is my justification of the yellow target box, which I will watch closely if DDOG fails to breakout.

Just above Datadog’s current price is a heavy clusters of Fibonacci prices that coincide with the $40-$42 region. With momentum fading, shown in the declining RSI and MACD, as well as negative divergence between price and the RSI, the probability of Datadog breaking this region is low.

However, if this resistance is broken on heavy volume, expect an acceleration in price due to the high level of short interest in the current float, sitting around 25%. As price moves against shorts, especially breaking a region as significant as the $42 price level, they will cover their positions, creating a rush of buying pressure, which in turn, pushes the price higher at a rapid pace.

Elliott Wave – Scenario 1

The above chart offers my primary Elliott Wave count, which suggests another leg down. This would put us in the early stages of the final (C) wave down. The evidence I use to support leaning towards this count is as follows:

– The (B) wave up is slightly overlapping, which is what we see in corrective moves.

– The declining internals in RSI and the MACD recently discussed are fading, and showing divergences as well.

– The heavy concentration of Fibonacci levels around $40-$42.

– The general fact that the market is heavily stretched and due for a correction.

Expect the (C) wave to retest the last low around $33. If this scenario unfolds, my likely target will be around the $31 region, which is a concentration of significant Fibonacci levels and basic symmetry. I will likely be a buyer around the levels outlined in the yellow target box between $33-$28.

Elliott Wave – Scenario 2

This is my alternative game plan, if DDOG can break through the $42 region with heavy volume. If this happens, we will have a clear 5-waves up, which would tilt the probability that we are in a 3rd wave higher. If that is the case, I would likely go long, with tight stops, which I will update if this scenario unfolds.

Please note, Datadog’s lock-up expires March 17th, 2020. We expect to see some price volatility following this date.

Thank you to all of our subscribers in 2019. What to Expect in 2020.

Posted on January 9, 2020June 30, 2026 by io-fund

We want to extend a warm appreciation to our subscribers this past year. Since launching in July, the site has completely exceeded our expectations. We are keenly aware of the contributions all of you have made in helping this site become possible.

When we first set out, our goal was to take a stock picker who has analyzed technology and products for nearly a decade and combine this skillset with a calculated, technical trader. We think we’ve done a decent job of delivering bullish ideas this year with a wide range of analysis.

Here’s a snapshot of our coverage this past year:

You can check out Knox’s 2019 track record trading off my analysis here.

What to Expect in 2020

We are both dedicated to improving the site in 2020. For starters, we have been working on algorithmic safety nets to keep us in long positions longer and to help exit losing positions faster. We’ve been working on this system for months and it will become a premium benefit for subscribers that Knox will help to spearhead. Once we have this in place, we will begin to launch portfolios on various categories.

Please keep in mind that we write for both styles: buy and hold, as well as some active trading. When we recommend stops, we are simply making sure to provide the exit plans we use ourselves if there is an unforeseen market event. This ensures we lose no more than 10-30% while letting our winners run.

Stock Lists

Recently, we published a 2020 cloud software list followed by a few PDFs. We think cloud software has more room in 2020 than the market currently has priced in, and this is one reason we’ve been focused on this category.

We plan to publish similar lists for future technologies, such as 5G, artificial intelligence, and blockchain. We will also publish more lists for common categories, such as semiconductors, finance tech (fintech), and a high conviction list (a conviction ranking has been requested a few times by our readers). We will start with the lists we think are most time sensitive.

These lists achieve a few things:

Creates a master index of stocks in a category
Organizes companies the market is most likely to reward based on financial strength
Organizes which companies are most likely to surprise the market based on product strength
Helps to track which companies are breaking out
Short-lists the best companies if a trend breaks out
Help to keep companies on our radar even if we haven’t been able to cover them with a full-length analysis just yet

We Appreciate Your Referrals

Paraphrasing on forums or social media about any wins you’ve had from our service is encouraged and much appreciated. We greatly appreciate all of the referrals you’ve sent us. We couldn’t do this without you.

Analysis can be a competitive field. We only provide original analysis, which requires domain technology experience and is incredibly time consuming. For this reason, please do not share the links to the lists or PDFs (i.e. the verbatim analysis).

We honor the premium site far and above our free analysis. In fact, I’d say my free analysis has suffered a bit since launching the premium site as I am giving my best ideas to the premium members. You will see me publishing more on Forbes this year, as well as MarketWatch, but my best work goes to the premium site.

Thanks Again …

Our mission is to help you make as much money as possible in the tech sector – and to provide value that goes above and beyond your subscription.

Our plan is to far exceed what we did in 2019 with in-depth tech analysis on companies you won’t find elsewhere and to combine this with smarter and sharper entries.

Thank you again for your vote of confidence and subscribership.

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Silicon Valley is Losing its Entrepreneurial Spirit

Posted on January 9, 2020June 30, 2026 by io-fund

This past week, I wrote about how Silicon Valley is losing some of its entrepreneurial spirit as venture capitalists shifted their attention to later stage deals with higher valuations. In the analysis, I pointed out that 2019 was the most lucrative year for exits in more than a decade, with $200 billion in exits generated from venture-backed IPOs.

For context, I went back to the golden years of Silicon Valley – 2006 to 2014. During this period, venture capital that was invested in deals below $5 million grew by 290%.

However, things changed in 2015, when early stage deals from below $1 million to under $100 million began to decline at a rate of 20% to 36% per year. Early stage software companies suffered most from the reallocation during this period, while early stage deals declined from 388 in 2018 to around 279 in 2019.

So how did this happen?

I identified two culprits behind the trend – Silicon Valley’s declining entrepreneurial culture and the increasing attractiveness of late stage investments.

Startup pitches and dynamic innovation have been replaced by a relatively closed circle of investors who are only targeting high valuations. In fact, we are seeing an aggregate all-time high for 180 private companies with $1 billion-plus valuations, and they have undermined the attractiveness of seed and Series A round companies.

Moreover, the IPO window is shifting from a range of six to eight years to ten to twelve years, which drove several startups to go public at valuations of over $10 billion this year. Consequently, this has made late stage investments more attractive due to their longer duration and higher valuations. The downside is that it has suppressed early stage investments (defined as deals below $5 million), which only further hampered Silicon Valley’s entrepreneurial culture.

Exciting early stage entrepreneurial stories have become rarer over the past few years, and many of the start-up tech events that I go to have either a noticeable lull or have moved overseas. The sad reality is that Silicon Valley’s entrepreneurial culture has faded, and entrepreneurs have a better chance at attracting capital from strangers on Kickstarter than from Silicon Valley angels and VCs.

Read the full article in MarketWatch here.

Image by Patrick Nouhailler

Elastic Premium Analysis

Posted on January 7, 2020June 30, 2026 by io-fund

c95f5842-95ad-4186-ba97-be2e10dcc7ab_Elastic-Premium-Analysis.pdf

Elastic Premium Analysis

SECTION 1: Product Overview

The Elastic stack includes Elasticsearch, Logstash, Beats and Kibana.

Elastic has partnered with cloud infrastructure companies, such as Microsoft Azure, Amazon AWS, Google Cloud and Alibaba Cloud. The Elastic Stack works well with Kubernetes, which is the machine learning powerhouse for containerized applications.

Elasticsearch:

Elasticsearch is the core product and search engine that allows for storing, searching and analyzing data.

Google search is built for users to query hundreds of thousands of terabytes of HTML data (or about 20 petabytes per day). In contrast, Elasticsearch is built for developers who need to design more complex application searches. For instance, Elasticsearch can forecast data center storage capacity with queries or can search customer sentiment that is determined by natural language processing.

To further illustrate, here are some examples of how Elasticsearch is used today:

Business-to-consumer:

• Pairing a passenger with an Uber driver with search

• Recommending grocery items on Instacart

• Matching online dating profiles for Tinder dates

• Processing billions of log events for Sprint to monitor outages

• Processing billions of log events for Fitbit, which has a rate of 250,000 logs per second, to enhance data discovery and validate failures

Business-to-business:

• E-trade uses Elasticsearch to identify trading anomalies across 115 terabytes of data

• Adobe uses Elasticsearch to search across both textual and non-textual formats, such as images, videos, 3D templates. This helps assist computer vision, which trains computers with ML to have a high-level understanding of non-textual items. This is done at a rate of 600 queries per second and an ingestion rate of 25,000 per second.

• Blizzard uses Elasticsearch to make sure their games are running at peak performance

• Cox Communications and TV2 use Elasticsearch to analyze billions of content delivery logs

• John Deere uses Elasticsearch to handle 18 billion documents and 11 terabytes of data storage, running 20,000 events every second. This helps to support remote management, variable rate application and field and water management.

Products such as Elastic Cloud Enterprise allows companies to run the entire stack in the cloud with a SaaS offering. The Elastic Stack is also available on-premise, or a hybrid of both. Elastic Cloud Kubernetes extends the Elastic Stack for use on cloud native technologies and containerized architectures.

Logstash and Beats:

Logstash and Beats are ingestion tools that can be put on thousands of applications to query external systems. From the examples above, John Deere is ingesting data from thousands of external sensors in the field, for instance. This helps complete the stack for the optimal use of Elasticsearch.

Ingestion can become quite technical. For instance, eBay uses Beats to break down the silos from containerized machine learning platforms, such as Docker and Kubernetes. This helps to automate application deployment and keep up with fast-evolving application lifecycles. The Beat product also has an auto-discovery feature to ingest newly discovered workloads, to collect and enrich data, and to send to the internal monitoring system.

In eBay’s case, the company built a way to tag the metadata from Beats auto-discovery so that users could access the information with familiar labels. A real-life example of this might be analyzing data from website logs, call centers and competitor website scans with the end result being a system that can the difference between skis named SALOMON QST 92 17/18, Salomon QST 92 2017-18 and Salomon QST 92 Skis 2018 – and also measure rising popularity perhaps with social media.

Kibana:

Kibana is a free, open source tool that integrates tightly with Elasticsearch.

The visualization and exploration tools include interactive charts, mapping support, pre-built aggregations and filters, plus easily accessible dashboards. Elastic allows enterprises to pull more data in for visualization purposes with products such as Elastic Cloud Enterprise or Elastic Cloud on Kubernetes.

SECTION 2: Elastic Fundamentals

A major fundamental risk for Elastic is that revenue growth may not be rewarded in 2020 as we’ve seen plenty of evidence that investors’ appetites are turning more towards profitability, which Elastic is far from reaching.

The market was not kind to Elastic in 2019. Tech growth companies with negative earnings that did not have a perfect earnings report were penalized last year. Elastic was no exception.

Analysts are projecting EPS estimate of negative -$1.35 in fiscal year 2021 compared to negative -$1.22 in fiscal year 2020 ending in April. Revenue in fiscal year 2020 ending in April is estimated at $416 million and fiscal year 2021 at $567 million. The company has $307 million in cash and $42 million in debt.

The stock has a 52-week high of $104 in July and hit a new 52-week low of $61 a month ago on December 5th. The most recent earnings report revealed a slowdown in annual billings from 53% in the year-ago quarter to 45% in the most recent quarter. The company beat on all other estimates, including revenue, EPS and net expansion rate – which is phenomenal at 130. Despite this, the billing slowdown was enough to cause a 20% drawdown on stock price following the earnings report, from $78 to $61.

The recent drawdown has resulted in Elastic trading at one of its lowest valuations yet as a public company. The stock is currently trading at 12 Forward EV/Revenue with an enterprise value of $5.03 billion. When considering revenue growth, this is an attractive valuation as Elastic is reporting 59% YoY revenue growth. Notably, revenue has decelerated from 79% YoY in early 2018 — yet has remained stable at 59% for the past three quarters.

So, the question that remains, will Elastic have an earnings surprise, which is the purpose of this report. There is an important catalyst to consider, which I’ve outlined below. I believe it’s highly probable that Elastic’s fundamentals improve in the coming year due to its entry into endpoint security.

Market Size & Competitors

Determining the market size for Elastic is challenging for a few reasons. The first is that the company does not neatly fit into a specific category. In fact, the company is absent from Gartner’s magic quadrant for Insight Engines, although Elastic is mentioned as the brains behind two products on the quadrant: Intrafind and Lucidworks. Here is what Gartner says about Elastic:

“Of the vendors most often shortlisted by the reference customers we surveyed, Elastic (Elasticsearch) appears in the top five, and Apache (Solr) in the top 10. Neither Elasticsearch nor Solr are considered insight engines — extensive development is required for them to meet the market definition. However, they do provide highly effective search engines for those seeking only search capability or wishing to undertake development. Consequently, they form a foundational layer in the stacks of a number of commercial insight engines, including two in this Magic Quadrant: Lucidworks and IntraFind.” Of the vendors most often shortlisted by the reference customers we surveyed, Elastic (Elasticsearch) appears in the top five, and Apache (Solr) in the top 10. Neither Elasticsearch nor Solr are considered insight engines — extensive development is required for them to meet the market definition. However, they do provide highly effective search engines for those seeking only search capability or wishing to undertake development. Consequently, they form a foundational layer in the stacks of a number of commercial insight engines, including two in this Magic Quadrant: Lucidworks and IntraFind.”

According to Jefferies’ analyst John DiFucci, Elastic’s addressable market is around $40 billion and could double to $71 billion by 2022. He cites the technology is only limited by the “creativity of the customers.” I agree that the limitations for Elastic’s growth will eventually lift as we are in the early stages of machine learning (ML), computer vision and natural language processing (NLP).

ML and NLP are two markets that will grow rapidly over the next decade, with Elastic a recipient. NLP will grow from $3 billion in 2017 to $43 billion in 2025 (source: Statista). Research on Global Markets estimates ML will reach $19.4 billion by 2023, at a CAGR of 48.3%. Big data analytics is quite large at $168 billion in 2018 and forecast to grow at a CAGR of 13.2% to $274 billion by 2022. Elastic sits somewhere between these three markets.

The companies listed on the Insight Engines magic quadrant are not direct competitors. Rather, I would consider Splunk the biggest competitor as their customer value proposition of providing log analytics most closely overlaps. Elastic Stack and Splunk frequently compete for customers. According to developers who work with the products, Splunk is more mature while Elastic is more flexible due to its roots in open source. Other competitors include Sumo Logic on the enterprise side and Greylog on the open source side. The Elasticsearch open source tools are also available through Amazon without the need for enterprises to upgrade through Elastic (although the more optimal experience is with Elastic Stack).

SECTION 3: Endpoint Security

The log management and analytics dashboard offered by both Splunk and Elastic has evolved into an important secondary offering for Security Information and Events Management (SIEM). In other words, because these companies monitor so many endpoints for data ingestion, the platforms are also useful for security monitoring.

By using Search Processing Language (SPL), log management systems are able to perform high-order security analysis and assessments regarding the collective state of these systems from a single interface. For instance, Elastic has been used for threat protection by organizations such as the University of Indiana to monitor hundreds of thousands of devices across students, faculty, and staff.

In October of 2019, Elastic closed the acquisition for Endgame, a leader in endpoint security for $234 million. This followed Elastic launching a Security Information and Events Management (SIEM) product in June. Endgame has enough credibility to be used by the U.S. Navy and the U.S. Air Force.

To raise the stakes, Elastic will be the first to charge for endpoint security by the amount of the data stored rather than by machine. This will allow companies to scale endpoint security more efficiently and will help Elastic compete with Splunk.

Splunk was ranked number one in SIEM by market share in 2018. The SEIM market was worth $5.3 billion in 2018 and is expected to grow at a 19.7% annual rate to reach $12.9 billion by 2023. Elastic should be able to compete very closely with Splunk due to Elastic Stack having similar capabilities of ingesting data from endpoints. Even claiming a small portion of this market should help boost Elastic’s $500 million in annual revenue.

We believe Elastic’s market entry into endpoint security along with competitive pricing will be an important catalyst for the company in the near future.

SECTION 4: Basic Technical Analysis and Closing the Gap

By Knox Ridley

Elastic (ESTC) is in a clear downtrend. After testing the all-time lows at $60.10, Elastic is bouncing back towards an important resistance price region at $67.50, highlighted with a red dotted line in the chart. If Elastic can close above this price, we will likely see a closing of the gap around $77.50, which is highlighted in yellow. This move would be about a 14% move from current prices (and about 24% from its recent bottom).

Furthermore, it’s worth noting that Elastic has taken back its 10-day Exponential Moving Average (EMA) and closed just below the 21-day EMA. The volume of this bounce is on very light volume, which is descending as the price is ascending. This means that the market is not buying this beyond just a bear market bounce, and should be factored into any buying plan.

Internals (MACD, RSI, MFI)

Looking at the internal strength of Elastic (ESTC), the MACD, Relative Strength Index (RSI), and Money Flow Index (MFI) are all telling an interesting story. First off, they are all in well-defined downtrends, which is highlighted by the dashed red lines.

The RSI and MACD are also bouncing against support while trending down, and doing so at lower levels, which looks to be coiling for a move up. We will want to see these indicators break this downtrend in unison with the price of ESTC breaking out as well to indicate a possible change in trend.

The MFI, which is basically the RSI with volume factored in, is showing a positive divergence. In other words, as the price is making lower lows, the MFI is making higher lows. This is always a great indication of fading selling, and usually indicates a shift in sentiment.

Closing above the start of this gap with high volume, and internals that are breaking out would be a key pivot that would further identify that Elastic is in a new uptrend, and not just a bear market correction.

Price Symmetry

Symmetry is a very powerful force in technical analysis. When a move in a certain direction reaches the length of prior moves, we usually see this resistance zone terminate the short-term, corrective move, and signal the continuation of the bigger trend. On the other hand, if the price can break through, it’s a strong indication that the larger trend has possibly changed.

The above chart shows the length of the last correction in the current downtrend. The price increased by about 24% before continuing the larger trend to new lows. After bottoming recently, a 24% move up from the low would coincide with closing the gap just discussed. There is a lot of overhead resistance above Elastic, and until we see ESTC break above this region with heavy volume, we can only assume that this move is a correction in a larger downtrend trend.

Elliott Wave Count

Elastic’s price structure, since its IPO, has been a series of overlapping structures. An overlapping structure, where the price retreats most of the previous move, indicates the uncertainty in the market.

The above count shows Elastic is completing a corrective, 3-wave move up, which is highlighted by the red (A), (B), (C), and then began a new, corrective 3-wave move back down. In both 3-wave trends, the C wave unfolded in a 5-wave move, which is highlighted in blue and also common in 3-wave corrections. My best count has us in the final 4th wave of the C wave, which would suggest newer lows ahead.

The alternative count has us as completing the 5th wave down, which would suggest we are in the very early stages of a renewed uptrend. I have a problem with this count due to Elliott Wave rules; however, if Elastic can close the above gap on heavy volume and close above the 100% extension, I will take that as evidence that this alternative count is in play.

How to Trade

Elastic is a high conviction idea, that may take some time to play out. The market obviously hasn’t seen the value in Elastic over the past month, hence the current trend. We do not think this will last, but how early we are is the real question. Depending on your investment style, we have 3 suggestions:

Buy now and Forget: If you want to buy your position today and hold it, a good stop would be just under $53. Below here and we are entering uncharted territory with all-time lows.

Layering in: If you prefer to layer in, the same stop above would hold. Then, if Elastic hits lower, you can layer in more in the green target region on the chart, between $58.50-$53.50. if ESTC decides to continue higher from current prices, if it closes above $74.50 on heavy volume, with the internals breaking the current downtrend, then you can put the remainder of your position in with a 25% trailing stop.

We plan to trade with the layering in approach.

Wait and See: If you prefer to wait and see, I’d target the green box in the chart between $58.50-$53.50