Trading in financial markets today is dominated by automated trading across most asset classes, but current programs are implemented using structured programming approaches which are static and represent a snapshot of the authors ideas, biases, and shortcomings at the time of implementation. Building automated trading bots that can learn from experience and can adapt to changing market conditions is changing the landscape and will deeply change trading as we know it.
In this presentation we will explore the history of automated trading, the environment in which these programs operate, current state, and challenges of the current approach. We will explore how a machine learning approach can be applied to automated trading and the forces driving this transformation. Analysis, which used to take hours or days, can now be done in seconds, back-testing over a larger length of time with fuller data now possible, and more data sources are available that can be used to build richer more accurate models.
Speaker
Diego Baez, GM Financial Services, Hortonworks
We, Bitdeal - Best Cryptocurrency Exchange and Blockchain Development Company offers the best crypto trading development services with complete customer satisfaction.
This presentation provide a general overview on Algorithmic trading. It has basic definitions and some details on general aspect of the environment in which algo trading is used.
Managing an Option Portfolio and how Automated Trading makes it easierQuantInsti
This presentation is a part of the series of webinars conducted by QI every month. This webinar was conducted on 3rd August, 2013. The topic was 'Managing an Option Portfolio and how Automated Trading makes it easier'.
The session was be taken by Mr Rajib Ranjan Borah who is a leading expert in Options Market Making. The talk focused on (i) fundamentals of options trading, (ii) ways to Managing Options Positions, and (iii) Building Sophisticated Algorithmic Options Trading Strategies.
To view a recording of the webinar, please email contact@quantinsti.com
Quant trading with artificial intelligenceRoger Lee, CFA
Foster discussion on the practical approach of applying artificial intelligence (AI) in quant trading and avoiding the common pitfalls and the opportunities and future of quant trading.
Financial trading is the space in which people have to make decisions under uncertain uncertainty and requires decisions to be explained. However, most AI algorithm (e.g. neural network) are black-box and serves well under only constrained environment (e.g. Go / Atari). Therefore, it is important to gather domain knowledge such that AI (e.g. PGM) could have more cognitive ability to provide a white-box explanation for their trading decisions.
If AI could provide a clearer visibility on the reality & present, it can assist human to provide faster, more-accurate and more-informed investment decisions. With a better understanding of the world, we can allocate resources more efficiently and ultimately create a better world to live.
Video for the slides:
https://www.youtube.com/watch?v=sideoQYAVDM&t=351s
This months speaker is Quantitative Researcher Yann-Shin Aaron Chen. Chen grew up in Taipei and moved to Southern California when he was a teenager. He participated in numerous math and physics competitions in high school and was ranked in the top 24 students in the US Physics Olympiad. He obtained a B.A. in mathematics at U.C. Berkeley, and got his PhD also at Berkeley in 2012. During his graduate studies, he did a summer internship at Morgan Stanley. After graduation, he joined Citadel, one of the largest hedge funds in US, as a quantitative researcher and worked there for 5 years. He left Citadel a few months ago, and he is now looking forward to his next venture.
Quantitative trading is a relatively new field in the world of finance. With the advances of information technology and data science, quantitative trading has generated significant interest in the past decade. In his talk, Aaron will cover the basic facts about quantitative trading and open the floor for questions. This short presentation is intended for people that are not in this industry and want to learn more about it.
We, Bitdeal - Best Cryptocurrency Exchange and Blockchain Development Company offers the best crypto trading development services with complete customer satisfaction.
This presentation provide a general overview on Algorithmic trading. It has basic definitions and some details on general aspect of the environment in which algo trading is used.
Managing an Option Portfolio and how Automated Trading makes it easierQuantInsti
This presentation is a part of the series of webinars conducted by QI every month. This webinar was conducted on 3rd August, 2013. The topic was 'Managing an Option Portfolio and how Automated Trading makes it easier'.
The session was be taken by Mr Rajib Ranjan Borah who is a leading expert in Options Market Making. The talk focused on (i) fundamentals of options trading, (ii) ways to Managing Options Positions, and (iii) Building Sophisticated Algorithmic Options Trading Strategies.
To view a recording of the webinar, please email contact@quantinsti.com
Quant trading with artificial intelligenceRoger Lee, CFA
Foster discussion on the practical approach of applying artificial intelligence (AI) in quant trading and avoiding the common pitfalls and the opportunities and future of quant trading.
Financial trading is the space in which people have to make decisions under uncertain uncertainty and requires decisions to be explained. However, most AI algorithm (e.g. neural network) are black-box and serves well under only constrained environment (e.g. Go / Atari). Therefore, it is important to gather domain knowledge such that AI (e.g. PGM) could have more cognitive ability to provide a white-box explanation for their trading decisions.
If AI could provide a clearer visibility on the reality & present, it can assist human to provide faster, more-accurate and more-informed investment decisions. With a better understanding of the world, we can allocate resources more efficiently and ultimately create a better world to live.
Video for the slides:
https://www.youtube.com/watch?v=sideoQYAVDM&t=351s
This months speaker is Quantitative Researcher Yann-Shin Aaron Chen. Chen grew up in Taipei and moved to Southern California when he was a teenager. He participated in numerous math and physics competitions in high school and was ranked in the top 24 students in the US Physics Olympiad. He obtained a B.A. in mathematics at U.C. Berkeley, and got his PhD also at Berkeley in 2012. During his graduate studies, he did a summer internship at Morgan Stanley. After graduation, he joined Citadel, one of the largest hedge funds in US, as a quantitative researcher and worked there for 5 years. He left Citadel a few months ago, and he is now looking forward to his next venture.
Quantitative trading is a relatively new field in the world of finance. With the advances of information technology and data science, quantitative trading has generated significant interest in the past decade. In his talk, Aaron will cover the basic facts about quantitative trading and open the floor for questions. This short presentation is intended for people that are not in this industry and want to learn more about it.
Classification of quantitative trading strategies webinar pptQuantInsti
There exist thousands of academic research papers written on trading strategies. Learn what these academics found out and how we can use their knowledge in the trading world.
The webinar covers:
- Overview of research in a field of quantitative trading
- Taxonomy of quantitative trading strategies
- Where to look for unique alpha
- Examples of lesser-known trading strategies
- Common issues in quant research
Learn more about our EPAT™ course here: https://www.quantinsti.com/epat/
Most Useful links
Join EPAT – Executive Programme in Algorithmic Trading: https://goo.gl/3Oyf2B
Visit us at: https://www.quantinsti.com/
Like us on Facebook: https://www.facebook.com/quantinsti/
Follow us on Twitter: https://twitter.com/QuantInsti
Access the webinar recording here: http://ow.ly/1YwO30dz5FD
Know more about EPAT™ by QuantInsti™ at http://www.quantinsti.com/epat/
Support resistance trading strategies - a comparisonHimanshu Patil
Support/Resistance is one of the key techniques in Technical Analysis that performs very well if done properly. This webinar will focus on all the strategies based on Support/Resistance and give the pros and cons of each one with examples so the attendee can decide which one to use.
- Pivot Levels
- New High/New Low
- Fibonacci Retracements
- Support/Resistance (Manually drawn or Automatic like Auto-SR)
- Brief intro to factors identifying strong support/resistance.
- Best ways to use Auto-Support/Resistance
- Using the Risk/Reward Ratio
- Using Support/Resistance Zones for more accuracy
- Brief intro to Volume Breakout strategy
The focus of the webinar will be for all types of traders, intraday, short-term and long-term
"Deep Q-Learning for Trading" by Dr. Tucker Balch, Professor of Interactive C...Quantopian
Reinforcement Learning (RL) has been around for a long time, but it has not attracted much attention over the last decade. Until, that is, a group of Google researchers showed how RL can be used to train a computer to play video games at far above human capabilities.
Besides video games, the RL problem is also well aligned to solve trading problems as well (e.g., work by Dr. Michael Kearns). In this talk, Tucker will provide a gentle introduction to Q-Learning, one of the leading RL methods.
He will also show how Q-Learning can be integrated with artificial neural network learners and how such a system can be used to learn and execute a trading strategy. This is joint work with David Byrd at Georgia Tech.
EXANTE's lecture at Stockholm School of Economics in Riga.
– Objectives of algorithmic trading
– Various types of algorithms
– The process of creating one
– Testing and evaluation
– Understanding the possible pitfalls (and solutions)
By www.ProfitableTradingTips.com
Scalping in Day Trading
Traders who engage in rapid momentum trades are often scalping in day trading. These traders make their profit from the difference between bid and ask prices. Even in a flat market traders can profit from scalping in day trading. In order to successfully make a business out of scalping in day trading the trader needs to pay close attention to the market, always be aware of market fundamentals, and keep abreast of technical analysis. Despite the theoretical possibility of trading in an absolutely flat market the price of a stock constantly moves to some degree throughout the trading day. Thus when scalping in day trading one acts as a mini trend trader as well.
In and Out of Positions in a Hurry
There is a rhythm to scalping in day trading and it is fast. Traders seek to profit from the actions of traders to simply take the bid and ask prices of a stock. This strategy guarantees a profit if the trader acts quickly. It can result in losses if the stock price moves too quickly. As an example, Xyz Corporation has a bid price of $10.10 and ask price of $10.15. If the scalper can buy at the bid price and sell at the ask price he gains $0.05 per share, a small amount but a lot if repeated many times throughout the day. However, the market might move lower before he can complete his trade. Let’s say that the stock moves so that the bid price is now $9.90 and the ask price is $9.95. The trader who purchased for $10.10 now needs to sell at $9.95 if he wants to quickly exit his trade. The other choice is to continue the trade in hopes that the market will turn upward and not fall farther. This later course is anathema to scalping in day trading. When scalping a trader is never trying to outguess the market but simply helping to make the market and make repetitive small profits.
The Nature of Bid and Ask Prices
Bid and ask prices are available on markets across the world. By using this price system traders are able to execute trades immediately, so long as there are enough bid prices to match ask prices. The difference between bid and ask prices is called the spread. Gaining the spread on every trade is the goal when scalping in day trading. The ideal scalping trade would be instantaneous. Buy at the low price and sell at the high. Getting in and out in an instant would seem to be the ideal situation if dealing with absolutely static bid and ask prices. However, the market is never static so traders must look to market direction even when scalping in day trading. A successful scalper also engages in trend following in day trading.
Think of the Spread as a Bonus
Scalping in day trading takes advantage of market movement as well as the bid to ask spread. While trend traders use technical analysis to read market sentiment they attempt to ride out a trade to gain the maximum profit.
This presentation from FXstreet.com will help you design your own trading system from scratch with a proven and practical example.
Creating a trading system is the best way to manage risk, increase profitability and avoid emotions and subjective elements from affecting your judgement when trading forex.
"Trading Strategies That Are Designed Not Fitted" by Robert Carver, Independe...Quantopian
Engineers design stuff. Why do Quants prefer to fit? In this talk, Robert will explain what designing a trading system actually involves, explore why designing might be better than fitting, and introduce some of the tools you could use. He will also take you through the design process for an example trading strategy.
Finally, he will discuss how we can have the best of both worlds: strategies that are well designed and also fitted to the data.
This is part of the Education Series prepared by StockStream Financial Services. This session looks at developing trading strategies using Pivot Points.
Support/Resistance is one of the techniques that performs very well if done properly. This webinar will focus on all the strategies based on Support/Resistance and show the attendee some tips on using each one and which one gives the best results and the reasons why. It also introduces a new feature in Investar, namely, Risk/Reward Ratio.
- Pivot Levels
- Fibonacci Retracements
- Gap Up/Gap Down
- New High/New Low
- Support/Resistance (Manually drawn or Automatic like Auto-SR)
- Brief intro to factors identifying strong support/resistance.
- Best settings for Auto-Support/Resistance
- Using the Risk/Reward Ratio
- Using Support/Resistance Zones
Algorithmic trading, also called automated trading, black-box trading, or algo trading, is the use of electronic platforms for entering trading orders with an algorithm which executes pre-programmed trading instructions accounting for a variety of variables such as timing, price, and volume.
Statistics - The Missing Link Between Technical Analysis and Algorithmic Trad...Quantopian
Trading leveraged derivatives using only technical analysis or speculative analysis can lead to windfall losses for even the most disciplined trader and investor. Statistics are often an ignored area of work when it comes to derivatives trading. Our talk shall focus upon how volatility can be used for dynamically adjusting the stop losses. It will talk about how correlation is an essential method to diversify the class of derivatives being traded or hedged. It will focus on co-integration as a key method to distinguish a mean reverting time series to a non-mean reverting time series. It will touch upon other essential time series econometrics like OU process, VRT as well as statistical tools like PCA, ARCH, GARCH etc. which are essential for derivatives pricing and forecasting the volatility.
Not only does electronic trading continue to make our financial markets more competitive, but it has brought numerous benefits to all investors This presentation seeks to provide an overview of the evolution of electronic trading, provide clear definitions of often misused terms, and demystify electronic trading strategies like high frequency trading.
Among the topics discussed in this presentation:
The modernization of our financial markets using electronic trading
Definitions of electronic trading, algorithmic trading and high frequency trading
The Securities and Exchange Commission and high frequency trading
The Commodity Futures Trading Commission and high frequency trading
Regulatory framework in place to safeguard investors who invest in markets where electronic trading is prevalent
Classification of quantitative trading strategies webinar pptQuantInsti
There exist thousands of academic research papers written on trading strategies. Learn what these academics found out and how we can use their knowledge in the trading world.
The webinar covers:
- Overview of research in a field of quantitative trading
- Taxonomy of quantitative trading strategies
- Where to look for unique alpha
- Examples of lesser-known trading strategies
- Common issues in quant research
Learn more about our EPAT™ course here: https://www.quantinsti.com/epat/
Most Useful links
Join EPAT – Executive Programme in Algorithmic Trading: https://goo.gl/3Oyf2B
Visit us at: https://www.quantinsti.com/
Like us on Facebook: https://www.facebook.com/quantinsti/
Follow us on Twitter: https://twitter.com/QuantInsti
Access the webinar recording here: http://ow.ly/1YwO30dz5FD
Know more about EPAT™ by QuantInsti™ at http://www.quantinsti.com/epat/
Support resistance trading strategies - a comparisonHimanshu Patil
Support/Resistance is one of the key techniques in Technical Analysis that performs very well if done properly. This webinar will focus on all the strategies based on Support/Resistance and give the pros and cons of each one with examples so the attendee can decide which one to use.
- Pivot Levels
- New High/New Low
- Fibonacci Retracements
- Support/Resistance (Manually drawn or Automatic like Auto-SR)
- Brief intro to factors identifying strong support/resistance.
- Best ways to use Auto-Support/Resistance
- Using the Risk/Reward Ratio
- Using Support/Resistance Zones for more accuracy
- Brief intro to Volume Breakout strategy
The focus of the webinar will be for all types of traders, intraday, short-term and long-term
"Deep Q-Learning for Trading" by Dr. Tucker Balch, Professor of Interactive C...Quantopian
Reinforcement Learning (RL) has been around for a long time, but it has not attracted much attention over the last decade. Until, that is, a group of Google researchers showed how RL can be used to train a computer to play video games at far above human capabilities.
Besides video games, the RL problem is also well aligned to solve trading problems as well (e.g., work by Dr. Michael Kearns). In this talk, Tucker will provide a gentle introduction to Q-Learning, one of the leading RL methods.
He will also show how Q-Learning can be integrated with artificial neural network learners and how such a system can be used to learn and execute a trading strategy. This is joint work with David Byrd at Georgia Tech.
EXANTE's lecture at Stockholm School of Economics in Riga.
– Objectives of algorithmic trading
– Various types of algorithms
– The process of creating one
– Testing and evaluation
– Understanding the possible pitfalls (and solutions)
By www.ProfitableTradingTips.com
Scalping in Day Trading
Traders who engage in rapid momentum trades are often scalping in day trading. These traders make their profit from the difference between bid and ask prices. Even in a flat market traders can profit from scalping in day trading. In order to successfully make a business out of scalping in day trading the trader needs to pay close attention to the market, always be aware of market fundamentals, and keep abreast of technical analysis. Despite the theoretical possibility of trading in an absolutely flat market the price of a stock constantly moves to some degree throughout the trading day. Thus when scalping in day trading one acts as a mini trend trader as well.
In and Out of Positions in a Hurry
There is a rhythm to scalping in day trading and it is fast. Traders seek to profit from the actions of traders to simply take the bid and ask prices of a stock. This strategy guarantees a profit if the trader acts quickly. It can result in losses if the stock price moves too quickly. As an example, Xyz Corporation has a bid price of $10.10 and ask price of $10.15. If the scalper can buy at the bid price and sell at the ask price he gains $0.05 per share, a small amount but a lot if repeated many times throughout the day. However, the market might move lower before he can complete his trade. Let’s say that the stock moves so that the bid price is now $9.90 and the ask price is $9.95. The trader who purchased for $10.10 now needs to sell at $9.95 if he wants to quickly exit his trade. The other choice is to continue the trade in hopes that the market will turn upward and not fall farther. This later course is anathema to scalping in day trading. When scalping a trader is never trying to outguess the market but simply helping to make the market and make repetitive small profits.
The Nature of Bid and Ask Prices
Bid and ask prices are available on markets across the world. By using this price system traders are able to execute trades immediately, so long as there are enough bid prices to match ask prices. The difference between bid and ask prices is called the spread. Gaining the spread on every trade is the goal when scalping in day trading. The ideal scalping trade would be instantaneous. Buy at the low price and sell at the high. Getting in and out in an instant would seem to be the ideal situation if dealing with absolutely static bid and ask prices. However, the market is never static so traders must look to market direction even when scalping in day trading. A successful scalper also engages in trend following in day trading.
Think of the Spread as a Bonus
Scalping in day trading takes advantage of market movement as well as the bid to ask spread. While trend traders use technical analysis to read market sentiment they attempt to ride out a trade to gain the maximum profit.
This presentation from FXstreet.com will help you design your own trading system from scratch with a proven and practical example.
Creating a trading system is the best way to manage risk, increase profitability and avoid emotions and subjective elements from affecting your judgement when trading forex.
"Trading Strategies That Are Designed Not Fitted" by Robert Carver, Independe...Quantopian
Engineers design stuff. Why do Quants prefer to fit? In this talk, Robert will explain what designing a trading system actually involves, explore why designing might be better than fitting, and introduce some of the tools you could use. He will also take you through the design process for an example trading strategy.
Finally, he will discuss how we can have the best of both worlds: strategies that are well designed and also fitted to the data.
This is part of the Education Series prepared by StockStream Financial Services. This session looks at developing trading strategies using Pivot Points.
Support/Resistance is one of the techniques that performs very well if done properly. This webinar will focus on all the strategies based on Support/Resistance and show the attendee some tips on using each one and which one gives the best results and the reasons why. It also introduces a new feature in Investar, namely, Risk/Reward Ratio.
- Pivot Levels
- Fibonacci Retracements
- Gap Up/Gap Down
- New High/New Low
- Support/Resistance (Manually drawn or Automatic like Auto-SR)
- Brief intro to factors identifying strong support/resistance.
- Best settings for Auto-Support/Resistance
- Using the Risk/Reward Ratio
- Using Support/Resistance Zones
Algorithmic trading, also called automated trading, black-box trading, or algo trading, is the use of electronic platforms for entering trading orders with an algorithm which executes pre-programmed trading instructions accounting for a variety of variables such as timing, price, and volume.
Statistics - The Missing Link Between Technical Analysis and Algorithmic Trad...Quantopian
Trading leveraged derivatives using only technical analysis or speculative analysis can lead to windfall losses for even the most disciplined trader and investor. Statistics are often an ignored area of work when it comes to derivatives trading. Our talk shall focus upon how volatility can be used for dynamically adjusting the stop losses. It will talk about how correlation is an essential method to diversify the class of derivatives being traded or hedged. It will focus on co-integration as a key method to distinguish a mean reverting time series to a non-mean reverting time series. It will touch upon other essential time series econometrics like OU process, VRT as well as statistical tools like PCA, ARCH, GARCH etc. which are essential for derivatives pricing and forecasting the volatility.
Not only does electronic trading continue to make our financial markets more competitive, but it has brought numerous benefits to all investors This presentation seeks to provide an overview of the evolution of electronic trading, provide clear definitions of often misused terms, and demystify electronic trading strategies like high frequency trading.
Among the topics discussed in this presentation:
The modernization of our financial markets using electronic trading
Definitions of electronic trading, algorithmic trading and high frequency trading
The Securities and Exchange Commission and high frequency trading
The Commodity Futures Trading Commission and high frequency trading
Regulatory framework in place to safeguard investors who invest in markets where electronic trading is prevalent
Today’s trading is complex and frequently involves little human intervention. Five years after the "Flash Crash," do you know how high frequency trading and dark pools work? Our new report separates fact from fiction.
Real time trade surveillance in financial marketsHortonworks
Who’s winning the deep forensic analysis ‘arms race’ for compliance? Real-time trade surveillance in global financial markets has created a data tsunami. With greater volumes of data comes greater compliance risk. CNBC reports U.S. Banks have been fined over $200B since the financial crisis. How are compliance teams fighting back to make more of the data and stay out of regulatory hot water? Rapid response to suspect trades means compliance teams need to access and visualize trade patterns, real time and historic data, to navigate the data in depth and flag possible violations. Join Hortonworks and Arcadia for this live webinar: we’ll cover the use case at a top 50 Global Bank who now has deep forensic analysis of trade activity. The result: interactive, ad hoc data visualization and access across multiple platforms – without limits on historic data – to detect irregularities as they happen. In-depth expert presentations by:
Shailesh Ambike, Executive Co-Chair of Compliance & Legal Section (CLS) Education Sub-Committee of the Investment Industry Regulatory Organization of Canada (IIROC)
Vamsi K Chemitiganti, GM – Financial Services at Hortonworks
Apidays Singapore 2024 - Modernizing Securities Finance by Madhu Subbuapidays
Modernizing Securities Finance: The cloud-native prime brokerage platform transforming capital markets.
Madhu Subbu, Managing Director, Head of Securities Finance Engineering
Apidays Singapore 2024: Connecting Customers, Business and Technology (April 17 & 18, 2024)
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https://apilandscape.apiscene.io/
Op Risk High Frequency Trading June 14 Finaltestytre
Presentation on High Frequency Trading risks delivered during OpRisk conference in London in June 2012. Content includes an overview of key risks affecting high frequency trading.
1. Failure to meet regulatory and exchange requirements.
2. Removal of human decision making once the algorithms are finished.
3. Extreme market behaviour: Flash Crash (2010).
4. Theft or loss of Intellectual Property.
5. Errors or problems suffered by clients using Direct Market Access and Algo/HFT.
6. Business impact of latency (system errors may increase delays).
7. Limited security controls at the infrastructure level.
8. Failure of hedges. 9. Incorrect/untested strategies.
David Ramirez
IT Audit Director
Slides from my presentation on the Augur decentralized prediction market for the Blockchain Smart Contracts - Seattle Working Group Meetup on 07/23. The slides provide an overview of prediction markets, the benefits and challenges of creating one which operates in a decentralized manner, Augur's different market stages and their functions, and the risks and incentives of the Augur system.
The presentation "Tick by Tick Market Data" offers a comprehensive overview of market data, specifically focusing on the intricacies and applications of tick-by-tick (TbT) and snapshot data. Designed for financial professionals, traders, analysts, and data scientists, this PowerPoint presentation is an essential resource for anyone interested in understanding the dynamics of financial data in trading.
Introduction to Market Data: The presentation starts with a foundational overview of market data. It differentiates between TbT data and snapshot data, establishing a base for the detailed exploration that follows.
Tick-by-Tick Data Users: A section dedicated to users of TbT data provides insights into its importance and applications in financial markets. This segment is crucial for understanding the role of TbT data in real-time trading and analysis.
Snapshot Data Users: Similarly, the presentation discusses the use of snapshot data in financial markets, offering a clear comparison to TbT data. This part is tailored to those interested in the broader applications and implications of snapshot data in market analysis.
Comparative Analysis: A unique feature of this presentation is its direct comparison of TbT and snapshot data. This comparison is invaluable for professionals who need to understand the strengths, limitations, and appropriate applications of each data type.
Challenges in Offering TbT Data: Addressing the practical aspects of market data, this section delves into the challenges of providing TbT data. It's particularly insightful for understanding the technical and operational complexities involved in handling such detailed market data.
Expert Insights on TbT vs. Snapshot Data: Featuring insights from Shrini Viswanath, a prominent figure in the field and co-founder of Upstox, this part of the presentation adds depth to the discussion. Viswanath's experience in low-latency, high-frequency algorithmic trading enriches the presentation with practical insights.
Snapshot Data in Trading Charts: The final section of the presentation illustrates how snapshot data is used in trading charts, providing a visual representation and understanding of how this data type is applied in real-world trading scenarios.
Overall, "Tick by Tick Market Data" is a valuable and accessible resource for anyone involved in financial markets, offering a clear, detailed, and practical exploration of market data types. Its targeted content, expert insights, and comparative analysis make it a crucial tool for professionals seeking to enhance their understanding of financial data and its applications in modern trading environments.
Real-time, high-frequency trading (HFT) is placing increasing pressure on regulatory compliance teams to keep up with and monitor the industry's widening pools of structured and unstructured data. Emerging technologies can help capital markets firms use big-data analytics to collect, classify and analyze high volumes of data to formulate strategies for better surveillance, compliance and spot abuse.
2020/11/19 PRIMA2020: Implementation of Real Data for Financial Market Simula...Masanori HIRANO
Masanori HIRANO, Hiroyasu MATSUSHIMA, Kiyoshi IZUMI, and Hiroki SAKAJI,
"Implementation of Real Data for Financial Market Simulation using Clustering, Deep Learning, and Artificial Financial Market,"
The 23rd International Conference on Principles and Practice of Multi-Agent Systems (PRIMA 2020), Aichi, Nagoya, Japan, Nov. 18-20th, 2020. (Online)
Introduction: This workshop will provide a hands-on introduction to Machine Learning (ML) with an overview of Deep Learning (DL).
Format: An introductory lecture on several supervised and unsupervised ML techniques followed by light introduction to DL and short discussion what is current state-of-the-art. Several python code samples using the scikit-learn library will be introduced that users will be able to run in the Cloudera Data Science Workbench (CDSW).
Objective: To provide a quick and short hands-on introduction to ML with python’s scikit-learn library. The environment in CDSW is interactive and the step-by-step guide will walk you through setting up your environment, to exploring datasets, training and evaluating models on popular datasets. By the end of the crash course, attendees will have a high-level understanding of popular ML algorithms and the current state of DL, what problems they can solve, and walk away with basic hands-on experience training and evaluating ML models.
Prerequisites: For the hands-on portion, registrants must bring a laptop with a Chrome or Firefox web browser. These labs will be done in the cloud, no installation needed. Everyone will be able to register and start using CDSW after the introductory lecture concludes (about 1hr in). Basic knowledge of python highly recommended.
Floating on a RAFT: HBase Durability with Apache RatisDataWorks Summit
In a world with a myriad of distributed storage systems to choose from, the majority of Apache HBase clusters still rely on Apache HDFS. Theoretically, any distributed file system could be used by HBase. One major reason HDFS is predominantly used are the specific durability requirements of HBase's write-ahead log (WAL) and HDFS providing that guarantee correctly. However, HBase's use of HDFS for WALs can be replaced with sufficient effort.
This talk will cover the design of a "Log Service" which can be embedded inside of HBase that provides a sufficient level of durability that HBase requires for WALs. Apache Ratis (incubating) is a library-implementation of the RAFT consensus protocol in Java and is used to build this Log Service. We will cover the design choices of the Ratis Log Service, comparing and contrasting it to other log-based systems that exist today. Next, we'll cover how the Log Service "fits" into HBase and the necessary changes to HBase which enable this. Finally, we'll discuss how the Log Service can simplify the operational burden of HBase.
Tracking Crime as It Occurs with Apache Phoenix, Apache HBase and Apache NiFiDataWorks Summit
Utilizing Apache NiFi we read various open data REST APIs and camera feeds to ingest crime and related data real-time streaming it into HBase and Phoenix tables. HBase makes an excellent storage option for our real-time time series data sources. We can immediately query our data utilizing Apache Zeppelin against Phoenix tables as well as Hive external tables to HBase.
Apache Phoenix tables also make a great option since we can easily put microservices on top of them for application usage. I have an example Spring Boot application that reads from our Philadelphia crime table for front-end web applications as well as RESTful APIs.
Apache NiFi makes it easy to push records with schemas to HBase and insert into Phoenix SQL tables.
Resources:
https://community.hortonworks.com/articles/54947/reading-opendata-json-and-storing-into-phoenix-tab.html
https://community.hortonworks.com/articles/56642/creating-a-spring-boot-java-8-microservice-to-read.html
https://community.hortonworks.com/articles/64122/incrementally-streaming-rdbms-data-to-your-hadoop.html
HBase Tales From the Trenches - Short stories about most common HBase operati...DataWorks Summit
Whilst HBase is the most logical answer for use cases requiring random, realtime read/write access to Big Data, it may not be so trivial to design applications that make most of its use, neither the most simple to operate. As it depends/integrates with other components from Hadoop ecosystem (Zookeeper, HDFS, Spark, Hive, etc) or external systems ( Kerberos, LDAP), and its distributed nature requires a "Swiss clockwork" infrastructure, many variables are to be considered when observing anomalies or even outages. Adding to the equation there's also the fact that HBase is still an evolving product, with different release versions being used currently, some of those can carry genuine software bugs. On this presentation, we'll go through the most common HBase issues faced by different organisations, describing identified cause and resolution action over my last 5 years supporting HBase to our heterogeneous customer base.
Optimizing Geospatial Operations with Server-side Programming in HBase and Ac...DataWorks Summit
LocationTech GeoMesa enables spatial and spatiotemporal indexing and queries for HBase and Accumulo. In this talk, after an overview of GeoMesa’s capabilities in the Cloudera ecosystem, we will dive into how GeoMesa leverages Accumulo’s Iterator interface and HBase’s Filter and Coprocessor interfaces. The goal will be to discuss both what spatial operations can be pushed down into the distributed database and also how the GeoMesa codebase is organized to allow for consistent use across the two database systems.
OCLC has been using HBase since 2012 to enable single-search-box access to over a billion items from your library and the world’s library collection. This talk will provide an overview of how HBase is structured to provide this information and some of the challenges they have encountered to scale to support the world catalog and how they have overcome them.
Many individuals/organizations have a desire to utilize NoSQL technology, but often lack an understanding of how the underlying functional bits can be utilized to enable their use case. This situation can result in drastic increases in the desire to put the SQL back in NoSQL.
Since the initial commit, Apache Accumulo has provided a number of examples to help jumpstart comprehension of how some of these bits function as well as potentially help tease out an understanding of how they might be applied to a NoSQL friendly use case. One very relatable example demonstrates how Accumulo could be used to emulate a filesystem (dirlist).
In this session we will walk through the dirlist implementation. Attendees should come away with an understanding of the supporting table designs, a simple text search supporting a single wildcard (on file/directory names), and how the dirlist elements work together to accomplish its feature set. Attendees should (hopefully) also come away with a justification for sometimes keeping the SQL out of NoSQL.
HBase Global Indexing to support large-scale data ingestion at UberDataWorks Summit
Data serves as the platform for decision-making at Uber. To facilitate data driven decisions, many datasets at Uber are ingested in a Hadoop Data Lake and exposed to querying via Hive. Analytical queries joining various datasets are run to better understand business data at Uber.
Data ingestion, at its most basic form, is about organizing data to balance efficient reading and writing of newer data. Data organization for efficient reading involves factoring in query patterns to partition data to ensure read amplification is low. Data organization for efficient writing involves factoring the nature of input data - whether it is append only or updatable.
At Uber we ingest terabytes of many critical tables such as trips that are updatable. These tables are fundamental part of Uber's data-driven solutions, and act as the source-of-truth for all the analytical use-cases across the entire company. Datasets such as trips constantly receive updates to the data apart from inserts. To ingest such datasets we need a critical component that is responsible for bookkeeping information of the data layout, and annotates each incoming change with the location in HDFS where this data should be written. This component is called as Global Indexing. Without this component, all records get treated as inserts and get re-written to HDFS instead of being updated. This leads to duplication of data, breaking data correctness and user queries. This component is key to scaling our jobs where we are now handling greater than 500 billion writes a day in our current ingestion systems. This component will need to have strong consistency and provide large throughputs for index writes and reads.
At Uber, we have chosen HBase to be the backing store for the Global Indexing component and is a critical component in allowing us to scaling our jobs where we are now handling greater than 500 billion writes a day in our current ingestion systems. In this talk, we will discuss data@Uber and expound more on why we built the global index using Apache Hbase and how this helps to scale out our cluster usage. We’ll give details on why we chose HBase over other storage systems, how and why we came up with a creative solution to automatically load Hfiles directly to the backend circumventing the normal write path when bootstrapping our ingestion tables to avoid QPS constraints, as well as other learnings we had bringing this system up in production at the scale of data that Uber encounters daily.
Scaling Cloud-Scale Translytics Workloads with Omid and PhoenixDataWorks Summit
Recently, Apache Phoenix has been integrated with Apache (incubator) Omid transaction processing service, to provide ultra-high system throughput with ultra-low latency overhead. Phoenix has been shown to scale beyond 0.5M transactions per second with sub-5ms latency for short transactions on industry-standard hardware. On the other hand, Omid has been extended to support secondary indexes, multi-snapshot SQL queries, and massive-write transactions.
These innovative features make Phoenix an excellent choice for translytics applications, which allow converged transaction processing and analytics. We share the story of building the next-gen data tier for advertising platforms at Verizon Media that exploits Phoenix and Omid to support multi-feed real-time ingestion and AI pipelines in one place, and discuss the lessons learned.
Building the High Speed Cybersecurity Data Pipeline Using Apache NiFiDataWorks Summit
Cybersecurity requires an organization to collect data, analyze it, and alert on cyber anomalies in near real-time. This is a challenging endeavor when considering the variety of data sources which need to be collected and analyzed. Everything from application logs, network events, authentications systems, IOT devices, business events, cloud service logs, and more need to be taken into consideration. In addition, multiple data formats need to be transformed and conformed to be understood by both humans and ML/AI algorithms.
To solve this problem, the Aetna Global Security team developed the Unified Data Platform based on Apache NiFi, which allows them to remain agile and adapt to new security threats and the onboarding of new technologies in the Aetna environment. The platform currently has over 60 different data flows with 95% doing real-time ETL and handles over 20 billion events per day. In this session learn from Aetna’s experience building an edge to AI high-speed data pipeline with Apache NiFi.
In the healthcare sector, data security, governance, and quality are crucial for maintaining patient privacy and ensuring the highest standards of care. At Florida Blue, the leading health insurer of Florida serving over five million members, there is a multifaceted network of care providers, business users, sales agents, and other divisions relying on the same datasets to derive critical information for multiple applications across the enterprise. However, maintaining consistent data governance and security for protected health information and other extended data attributes has always been a complex challenge that did not easily accommodate the wide range of needs for Florida Blue’s many business units. Using Apache Ranger, we developed a federated Identity & Access Management (IAM) approach that allows each tenant to have their own IAM mechanism. All user groups and roles are propagated across the federation in order to determine users’ data entitlement and access authorization; this applies to all stages of the system, from the broadest tenant levels down to specific data rows and columns. We also enabled audit attributes to ensure data quality by documenting data sources, reasons for data collection, date and time of data collection, and more. In this discussion, we will outline our implementation approach, review the results, and highlight our “lessons learned.”
Presto: Optimizing Performance of SQL-on-Anything EngineDataWorks Summit
Presto, an open source distributed SQL engine, is widely recognized for its low-latency queries, high concurrency, and native ability to query multiple data sources. Proven at scale in a variety of use cases at Airbnb, Bloomberg, Comcast, Facebook, FINRA, LinkedIn, Lyft, Netflix, Twitter, and Uber, in the last few years Presto experienced an unprecedented growth in popularity in both on-premises and cloud deployments over Object Stores, HDFS, NoSQL and RDBMS data stores.
With the ever-growing list of connectors to new data sources such as Azure Blob Storage, Elasticsearch, Netflix Iceberg, Apache Kudu, and Apache Pulsar, recently introduced Cost-Based Optimizer in Presto must account for heterogeneous inputs with differing and often incomplete data statistics. This talk will explore this topic in detail as well as discuss best use cases for Presto across several industries. In addition, we will present recent Presto advancements such as Geospatial analytics at scale and the project roadmap going forward.
Introducing MlFlow: An Open Source Platform for the Machine Learning Lifecycl...DataWorks Summit
Specialized tools for machine learning development and model governance are becoming essential. MlFlow is an open source platform for managing the machine learning lifecycle. Just by adding a few lines of code in the function or script that trains their model, data scientists can log parameters, metrics, artifacts (plots, miscellaneous files, etc.) and a deployable packaging of the ML model. Every time that function or script is run, the results will be logged automatically as a byproduct of those lines of code being added, even if the party doing the training run makes no special effort to record the results. MLflow application programming interfaces (APIs) are available for the Python, R and Java programming languages, and MLflow sports a language-agnostic REST API as well. Over a relatively short time period, MLflow has garnered more than 3,300 stars on GitHub , almost 500,000 monthly downloads and 80 contributors from more than 40 companies. Most significantly, more than 200 companies are now using MLflow. We will demo MlFlow Tracking , Project and Model components with Azure Machine Learning (AML) Services and show you how easy it is to get started with MlFlow on-prem or in the cloud.
Extending Twitter's Data Platform to Google CloudDataWorks Summit
Twitter's Data Platform is built using multiple complex open source and in house projects to support Data Analytics on hundreds of petabytes of data. Our platform support storage, compute, data ingestion, discovery and management and various tools and libraries to help users for both batch and realtime analytics. Our DataPlatform operates on multiple clusters across different data centers to help thousands of users discover valuable insights. As we were scaling our Data Platform to multiple clusters, we also evaluated various cloud vendors to support use cases outside of our data centers. In this talk we share our architecture and how we extend our data platform to use cloud as another datacenter. We walk through our evaluation process, challenges we faced supporting data analytics at Twitter scale on cloud and present our current solution. Extending Twitter's Data platform to cloud was complex task which we deep dive in this presentation.
Event-Driven Messaging and Actions using Apache Flink and Apache NiFiDataWorks Summit
At Comcast, our team has been architecting a customer experience platform which is able to react to near-real-time events and interactions and deliver appropriate and timely communications to customers. By combining the low latency capabilities of Apache Flink and the dataflow capabilities of Apache NiFi we are able to process events at high volume to trigger, enrich, filter, and act/communicate to enhance customer experiences. Apache Flink and Apache NiFi complement each other with their strengths in event streaming and correlation, state management, command-and-control, parallelism, development methodology, and interoperability with surrounding technologies. We will trace our journey from starting with Apache NiFi over three years ago and our more recent introduction of Apache Flink into our platform stack to handle more complex scenarios. In this presentation we will compare and contrast which business and technical use cases are best suited to which platform and explore different ways to integrate the two platforms into a single solution.
Securing Data in Hybrid on-premise and Cloud Environments using Apache RangerDataWorks Summit
Companies are increasingly moving to the cloud to store and process data. One of the challenges companies have is in securing data across hybrid environments with easy way to centrally manage policies. In this session, we will talk through how companies can use Apache Ranger to protect access to data both in on-premise as well as in cloud environments. We will go into details into the challenges of hybrid environment and how Ranger can solve it. We will also talk through how companies can further enhance the security by leveraging Ranger to anonymize or tokenize data while moving into the cloud and de-anonymize dynamically using Apache Hive, Apache Spark or when accessing data from cloud storage systems. We will also deep dive into the Ranger’s integration with AWS S3, AWS Redshift and other cloud native systems. We will wrap it up with an end to end demo showing how policies can be created in Ranger and used to manage access to data in different systems, anonymize or de-anonymize data and track where data is flowing.
Big Data Meets NVM: Accelerating Big Data Processing with Non-Volatile Memory...DataWorks Summit
Advanced Big Data Processing frameworks have been proposed to harness the fast data transmission capability of Remote Direct Memory Access (RDMA) over high-speed networks such as InfiniBand, RoCEv1, RoCEv2, iWARP, and OmniPath. However, with the introduction of the Non-Volatile Memory (NVM) and NVM express (NVMe) based SSD, these designs along with the default Big Data processing models need to be re-assessed to discover the possibilities of further enhanced performance. In this talk, we will present, NRCIO, a high-performance communication runtime for non-volatile memory over modern network interconnects that can be leveraged by existing Big Data processing middleware. We will show the performance of non-volatile memory-aware RDMA communication protocols using our proposed runtime and demonstrate its benefits by incorporating it into a high-performance in-memory key-value store, Apache Hadoop, Tez, Spark, and TensorFlow. Evaluation results illustrate that NRCIO can achieve up to 3.65x performance improvement for representative Big Data processing workloads on modern data centers.
Background: Some early applications of Computer Vision in Retail arose from e-commerce use cases - but increasingly, it is being used in physical stores in a variety of new and exciting ways, such as:
● Optimizing merchandising execution, in-stocks and sell-thru
● Enhancing operational efficiencies, enable real-time customer engagement
● Enhancing loss prevention capabilities, response time
● Creating frictionless experiences for shoppers
Abstract: This talk will cover the use of Computer Vision in Retail, the implications to the broader Consumer Goods industry and share business drivers, use cases and benefits that are unfolding as an integral component in the remaking of an age-old industry.
We will also take a ‘peek under the hood’ of Computer Vision and Deep Learning, sharing technology design principles and skill set profiles to consider before starting your CV journey.
Deep learning has matured considerably in the past few years to produce human or superhuman abilities in a variety of computer vision paradigms. We will discuss ways to recognize these paradigms in retail settings, collect and organize data to create actionable outcomes with the new insights and applications that deep learning enables.
We will cover the basics of object detection, then move into the advanced processing of images describing the possible ways that a retail store of the near future could operate. Identifying various storefront situations by having a deep learning system attached to a camera stream. Such things as; identifying item stocks on shelves, a shelf in need of organization, or perhaps a wandering customer in need of assistance.
We will also cover how to use a computer vision system to automatically track customer purchases to enable a streamlined checkout process, and how deep learning can power plausible wardrobe suggestions based on what a customer is currently wearing or purchasing.
Finally, we will cover the various technologies that are powering these applications today. Deep learning tools for research and development. Production tools to distribute that intelligence to an entire inventory of all the cameras situation around a retail location. Tools for exploring and understanding the new data streams produced by the computer vision systems.
By the end of this talk, attendees should understand the impact Computer Vision and Deep Learning are having in the Consumer Goods industry, key use cases, techniques and key considerations leaders are exploring and implementing today.
Big Data Genomics: Clustering Billions of DNA Sequences with Apache SparkDataWorks Summit
Whole genome shotgun based next generation transcriptomics and metagenomics studies often generate 100 to 1000 gigabytes (GB) sequence data derived from tens of thousands of different genes or microbial species. De novo assembling these data requires an ideal solution that both scales with data size and optimizes for individual gene or genomes. Here we developed an Apache Spark-based scalable sequence clustering application, SparkReadClust (SpaRC), that partitions the reads based on their molecule of origin to enable downstream assembly optimization. SpaRC produces high clustering performance on transcriptomics and metagenomics test datasets from both short read and long read sequencing technologies. It achieved a near linear scalability with respect to input data size and number of compute nodes. SpaRC can run on different cloud computing environments without modifications while delivering similar performance. In summary, our results suggest SpaRC provides a scalable solution for clustering billions of reads from the next-generation sequencing experiments, and Apache Spark represents a cost-effective solution with rapid development/deployment cycles for similar big data genomics problems.
Encryption in Microsoft 365 - ExpertsLive Netherlands 2024Albert Hoitingh
In this session I delve into the encryption technology used in Microsoft 365 and Microsoft Purview. Including the concepts of Customer Key and Double Key Encryption.
Transcript: Selling digital books in 2024: Insights from industry leaders - T...BookNet Canada
The publishing industry has been selling digital audiobooks and ebooks for over a decade and has found its groove. What’s changed? What has stayed the same? Where do we go from here? Join a group of leading sales peers from across the industry for a conversation about the lessons learned since the popularization of digital books, best practices, digital book supply chain management, and more.
Link to video recording: https://bnctechforum.ca/sessions/selling-digital-books-in-2024-insights-from-industry-leaders/
Presented by BookNet Canada on May 28, 2024, with support from the Department of Canadian Heritage.
Essentials of Automations: Optimizing FME Workflows with ParametersSafe Software
Are you looking to streamline your workflows and boost your projects’ efficiency? Do you find yourself searching for ways to add flexibility and control over your FME workflows? If so, you’re in the right place.
Join us for an insightful dive into the world of FME parameters, a critical element in optimizing workflow efficiency. This webinar marks the beginning of our three-part “Essentials of Automation” series. This first webinar is designed to equip you with the knowledge and skills to utilize parameters effectively: enhancing the flexibility, maintainability, and user control of your FME projects.
Here’s what you’ll gain:
- Essentials of FME Parameters: Understand the pivotal role of parameters, including Reader/Writer, Transformer, User, and FME Flow categories. Discover how they are the key to unlocking automation and optimization within your workflows.
- Practical Applications in FME Form: Delve into key user parameter types including choice, connections, and file URLs. Allow users to control how a workflow runs, making your workflows more reusable. Learn to import values and deliver the best user experience for your workflows while enhancing accuracy.
- Optimization Strategies in FME Flow: Explore the creation and strategic deployment of parameters in FME Flow, including the use of deployment and geometry parameters, to maximize workflow efficiency.
- Pro Tips for Success: Gain insights on parameterizing connections and leveraging new features like Conditional Visibility for clarity and simplicity.
We’ll wrap up with a glimpse into future webinars, followed by a Q&A session to address your specific questions surrounding this topic.
Don’t miss this opportunity to elevate your FME expertise and drive your projects to new heights of efficiency.
Neuro-symbolic is not enough, we need neuro-*semantic*Frank van Harmelen
Neuro-symbolic (NeSy) AI is on the rise. However, simply machine learning on just any symbolic structure is not sufficient to really harvest the gains of NeSy. These will only be gained when the symbolic structures have an actual semantics. I give an operational definition of semantics as “predictable inference”.
All of this illustrated with link prediction over knowledge graphs, but the argument is general.
Securing your Kubernetes cluster_ a step-by-step guide to success !KatiaHIMEUR1
Today, after several years of existence, an extremely active community and an ultra-dynamic ecosystem, Kubernetes has established itself as the de facto standard in container orchestration. Thanks to a wide range of managed services, it has never been so easy to set up a ready-to-use Kubernetes cluster.
However, this ease of use means that the subject of security in Kubernetes is often left for later, or even neglected. This exposes companies to significant risks.
In this talk, I'll show you step-by-step how to secure your Kubernetes cluster for greater peace of mind and reliability.
GDG Cloud Southlake #33: Boule & Rebala: Effective AppSec in SDLC using Deplo...James Anderson
Effective Application Security in Software Delivery lifecycle using Deployment Firewall and DBOM
The modern software delivery process (or the CI/CD process) includes many tools, distributed teams, open-source code, and cloud platforms. Constant focus on speed to release software to market, along with the traditional slow and manual security checks has caused gaps in continuous security as an important piece in the software supply chain. Today organizations feel more susceptible to external and internal cyber threats due to the vast attack surface in their applications supply chain and the lack of end-to-end governance and risk management.
The software team must secure its software delivery process to avoid vulnerability and security breaches. This needs to be achieved with existing tool chains and without extensive rework of the delivery processes. This talk will present strategies and techniques for providing visibility into the true risk of the existing vulnerabilities, preventing the introduction of security issues in the software, resolving vulnerabilities in production environments quickly, and capturing the deployment bill of materials (DBOM).
Speakers:
Bob Boule
Robert Boule is a technology enthusiast with PASSION for technology and making things work along with a knack for helping others understand how things work. He comes with around 20 years of solution engineering experience in application security, software continuous delivery, and SaaS platforms. He is known for his dynamic presentations in CI/CD and application security integrated in software delivery lifecycle.
Gopinath Rebala
Gopinath Rebala is the CTO of OpsMx, where he has overall responsibility for the machine learning and data processing architectures for Secure Software Delivery. Gopi also has a strong connection with our customers, leading design and architecture for strategic implementations. Gopi is a frequent speaker and well-known leader in continuous delivery and integrating security into software delivery.
Key Trends Shaping the Future of Infrastructure.pdfCheryl Hung
Keynote at DIGIT West Expo, Glasgow on 29 May 2024.
Cheryl Hung, ochery.com
Sr Director, Infrastructure Ecosystem, Arm.
The key trends across hardware, cloud and open-source; exploring how these areas are likely to mature and develop over the short and long-term, and then considering how organisations can position themselves to adapt and thrive.
JMeter webinar - integration with InfluxDB and GrafanaRTTS
Watch this recorded webinar about real-time monitoring of application performance. See how to integrate Apache JMeter, the open-source leader in performance testing, with InfluxDB, the open-source time-series database, and Grafana, the open-source analytics and visualization application.
In this webinar, we will review the benefits of leveraging InfluxDB and Grafana when executing load tests and demonstrate how these tools are used to visualize performance metrics.
Length: 30 minutes
Session Overview
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During this webinar, we will cover the following topics while demonstrating the integrations of JMeter, InfluxDB and Grafana:
- What out-of-the-box solutions are available for real-time monitoring JMeter tests?
- What are the benefits of integrating InfluxDB and Grafana into the load testing stack?
- Which features are provided by Grafana?
- Demonstration of InfluxDB and Grafana using a practice web application
To view the webinar recording, go to:
https://www.rttsweb.com/jmeter-integration-webinar
Elevating Tactical DDD Patterns Through Object CalisthenicsDorra BARTAGUIZ
After immersing yourself in the blue book and its red counterpart, attending DDD-focused conferences, and applying tactical patterns, you're left with a crucial question: How do I ensure my design is effective? Tactical patterns within Domain-Driven Design (DDD) serve as guiding principles for creating clear and manageable domain models. However, achieving success with these patterns requires additional guidance. Interestingly, we've observed that a set of constraints initially designed for training purposes remarkably aligns with effective pattern implementation, offering a more ‘mechanical’ approach. Let's explore together how Object Calisthenics can elevate the design of your tactical DDD patterns, offering concrete help for those venturing into DDD for the first time!
Generating a custom Ruby SDK for your web service or Rails API using Smithyg2nightmarescribd
Have you ever wanted a Ruby client API to communicate with your web service? Smithy is a protocol-agnostic language for defining services and SDKs. Smithy Ruby is an implementation of Smithy that generates a Ruby SDK using a Smithy model. In this talk, we will explore Smithy and Smithy Ruby to learn how to generate custom feature-rich SDKs that can communicate with any web service, such as a Rails JSON API.
State of ICS and IoT Cyber Threat Landscape Report 2024 previewPrayukth K V
The IoT and OT threat landscape report has been prepared by the Threat Research Team at Sectrio using data from Sectrio, cyber threat intelligence farming facilities spread across over 85 cities around the world. In addition, Sectrio also runs AI-based advanced threat and payload engagement facilities that serve as sinks to attract and engage sophisticated threat actors, and newer malware including new variants and latent threats that are at an earlier stage of development.
The latest edition of the OT/ICS and IoT security Threat Landscape Report 2024 also covers:
State of global ICS asset and network exposure
Sectoral targets and attacks as well as the cost of ransom
Global APT activity, AI usage, actor and tactic profiles, and implications
Rise in volumes of AI-powered cyberattacks
Major cyber events in 2024
Malware and malicious payload trends
Cyberattack types and targets
Vulnerability exploit attempts on CVEs
Attacks on counties – USA
Expansion of bot farms – how, where, and why
In-depth analysis of the cyber threat landscape across North America, South America, Europe, APAC, and the Middle East
Why are attacks on smart factories rising?
Cyber risk predictions
Axis of attacks – Europe
Systemic attacks in the Middle East
Download the full report from here:
https://sectrio.com/resources/ot-threat-landscape-reports/sectrio-releases-ot-ics-and-iot-security-threat-landscape-report-2024/