Flowtrace

flowtrace

11 Ways Your Competitors Can Hit You With Negative SEO Attacks And How To Bolster Your Defenses

STRAIT OF MAGELLAN.—CLIMATE OF THE SOUTHERN COASTS

ON THE ELEVATION OF THE EASTERN COAST OF SOUTH AMERICA

Value indicators are one of the cornerstones of quantitative(quant) trading. Even today, in the mist of the golden era of quant investing, most strategies were based on variations of three fundamental indicators: value, size and momentum. In the case of crypto-assets, value indicators are entirely clear as there is no established methodology to evaluate the value of this new asset class. What it is clear, however, is that establishing some notion of value indicators is foundational to assessing the potential of crypto assets. Years ago, Wall Street legend James O’Shaughnessy . Today, I would like to revisit some of those ideas in the context of crypto assets. proposed one of the most famous quant value strategies of all time The traditional notion of value indicator relies on metrics like price-to-earnings ratio, free cash flow or debt-to-equity ratio that have no obvious equivalent in the crypto space. However, that’s not necessarily a bad thing and just another sign that crypto requires a new set of value indicators powered by the elements that make crypto a unique asset class. Just like momentum, value indicators are essential establishing any meaningful quantitative strategy in the crypto space. To evaluate the potential of this type of strategies, let’s look at one of its maximum expressions. O’Shaughnessy Trending Value Investment Strategy In the fourth edition of it’s famous book , James O’Shaughnessy proposed a new strategy that he called: The trending value investment strategy was based on two fundamental principles: What Works on Wall Street: The Classic Guide to the Best-Performing Investment Strategies of All Time “the top stock-market strategy of the past 50 years”. Pick the most undervalued companiesWith the highest stock price increase over the past six months Instead of focusing on a single value ration like most strategies, the value investment method used a combination of six value growth metrics: Price to book valuePrice to salesEarnings before interest, taxes, depreciation and amortization to Enterprise value (EBITDA / EV)Price to cash flowPrice to earningsShareholder Yield (Dividend yield + Percentage of Shares Repurchased) If a company’s price-to-book ratio is in the lowest 1% of the dataset, it gets a score of 1. For some ratios it’s the other way around, for instance EBITDA/EV. If a company belongs to the highest 1%, it gets a score of 1. If a value is missing, it gets a score of 50. The same calculation is repeated for each of the ratios and then sum up these values. Companies are again divided into 100 groups based on this score. This final result is called value composite. A value composite of 1 means that the company belongs to the 1% cheapest companies according to these factors. After that, the trending value strategy selects the top 10% ranked according to this value composite score. Then he filters these stocks by a momentum factor,. The result is an extremely cheap group of stocks but with a foundational value and also enough momentum to continue raising. Strategies like the trending value investment method are definitely applicable to the crypto asset market with the small caveat that they will require new value indicators. There is been some interesting work done in this area already but I would like to present some new ideas that might be worth exploring. Four New Value Indicators for Crypto Assets The notion of quantifying value for crypto assets have been one of permanent challenges for analysts in the space. There is been some creative work in the space like the famous that have proven to be somewhat effective under specific market conditions. At , we have been working on some models that produce signals that have an intrinsic representation of value. Some variations of those signals could be adapted to produce new groups of meaningful value indicators. Here are a few ideas: “Network Value To Transactions Ratio” IntoTheBlock Price to In-The-Money Ratio captures a statistical distribution of the groups of investors realizing gains or losses relative to the current price. IntoTheBlock’s In/Out of the Money Using this notion, we can develop a simple ratio that evaluates the price relative to a representation of the number of investors “in the money”. A high ratio could mean that the price of a crypto-asset of overvalued relative to the population of investors that have realized gains. Price to Active Market Cap Ratio IntoTheBlock’s Active Addresses analysis measures the segment of a crypto network that has experienced regular activity. Evaluating the positions of those addresses we can estimate the notion of “active market cap” or a segment of a market cap of a crypto asset that have regularly influence in it. The price to active market cap ratio could provide a more objective view about the current value of a crypto-asset. A high ration might indicate an overvalued crypto asset relative to its active market cap while a low ratio might indicate some signs of potential future growth. Price to Large Transactions Ratio Large transactions are a strong indicator of the vitality of a crypto asset and the commitment of investors. IntoTheBlock’s large transaction signal tracks the number of large transactions on a daily basics. The price to large transaction ratio could be an indicator that illustrates the value of a crypto assets relative to the amount of funds processed by large investors. A high ratio could indicate an overvalued asset that could vulnerable to a price drop by a large order. Price to Exchange Inflow Ratio Crypto exchanges move a significant percentage of the daily volume of most crypto assets. IntoTheBlock has been working on a new series of signals that evaluate the flow of funds processed by crypto exchanges. The price to exchange inflow ratio measures the current value of a crypto asset relative to the regular flow of funds into centralized exchanges. A low ratio could indicate an undervalue asset that could be pose for growth based on future activity in crypto exchanges. These are just some ideas about new value indicators for crypto assets. If you noticed that I was being very generic on the formulas, it was on purpose as we are still working out the details. In future posts we will deep dive into the specific indicators. ( : ) Disclosure The Author is the CTO at Into The Block

Adapting a Famous Quant Value Strategy to Crypto Assets: Some New Ideas About Value Indicators for C

With its new approach to modeling the changes in its data center, Alibaba is showing how sandbox simulations can better predict problems and bring facilities closer to unattended operation. As the size of Alibaba’s data center (DC) grows with the expansion of its business footprint, the increasing frequency of everyday operations like drills and optimization introduces exponential levels of complexity into the system. This makes it increasingly difficult for DC experts to forecast the failures a change to the system could cause, while the business impact of DC failures continues to rise beyond estimation. To counter such problems, DC operators need a reliable, standardized verification system that can indicate whether changes will affect DC safety and whether more suitable options are available. Now, following a year of dedicated efforts, the collaborative Alibaba-NTU Singapore Joint Research Institute has implemented and launched a sandbox system based on online Computational Fluid Dynamics (CFD) for high-precision, real-time monitoring. This article looks at the development of this original sandbox in detail, from its origins in research to testing and verification on off-the-shelf CDF software. Origins of the Sandbox: Limitations of Previous Solutions Compared to changes of power topology, the more intangible thermal and airflow organization changes involved in heating, ventilation, and air conditioning (HVAC) adjustments are relatively difficult to simulate in the real world. To this end, Alibaba’s IDC operation optimization team has conducted extensive research on how CFD can effectively achieve production standards for DC room simulations. CFD offers a general DC room simulation solution for checking the thermodynamics of various changes in the DC room. CFD modeling can be used to calculate airflow distribution and temperatures inside a compartment by constructing a physical model and loading specific thermodynamic settings, such as heat source volumes and cold supply airflow volumes. As such it is a relatively mature technology that is widely used in aerodynamics- and thermodynamics-related fields. For data centers, CFD simulation applications range from the compartment level down to the chip level but are generally used only for pre-design and planning purposes due to their precision limitations. A visualization of streamline data for data center compartments For the purposes of building a sandbox system that will serve as a digital testing environment, CFD presents a number of challenges. First, commercially available CFD software obtains heat and airflow pattern data based on compartment simulation, with incomplete information at the design stage leading to low accuracy. As such, rough simulation results can diverge significantly from real scenarios. In temperature prediction, for example, simulations can miscalculate by three degrees Celsius or more, while the sandbox system demands higher precision. Second, existing CFD software is designed for human-computer interactions, rather than automated operations. It thus cannot meet the requirements of automatic data acquiring and results exportation, making it difficult to establish as an important block of the DC operation pipeline. Lastly, critical heat source and air conditioning configuration data needed for simulation can only be obtained at runtime. This data can only ensure accurate modeling if it has been accurately verified. Alternatively, the modeling process can infer the difference between design and implementation and can promote the standardization of storage for DC configurations and operational data. Reaching a Solution Alibaba’s successful launch of a high-precision CFD-based sandbox with real-time monitoring reflects almost a year of research, development, and tests in collaboration with Professor Wen Yonggang of the School of Computer Science and Engineering at Nanyang Technical University, Singapore. This effort designated an Alibaba data center room as the pilot test-bed, with an emphasis on physical modeling, model calibration, and project implementation for the target data center. Physical modeling The physical modeling process primarily defines the physical structures of DC room spaces, and thus provides the basis for simulation. To achieve the highest possible accuracy in reproduction, the project team implemented several modeling operations. With structural modeling, the room structure, walls, vents, ceilings, and pipe layout for the center were set-up; with IT deployment modeling, rows of cabinets and server slot positions were set-up; with environmental modeling, the air conditioning equipment and sensors were configured in the model; with device modeling, the servers were installed into the model according to vendor type. Model calibration Model calibration aims to achieve a true reproduction of three key aspects of the DC room. First, it needs to reproduce the amount of heat generated and supplied cooling source in the DC rooms. Second, it needs to reproduce all causes of change in airflow in the DC room and to confirm that the resulting airflow model is consistent with actual conditions. Finally, it needs to ensure that the model’s predicted temperature for the room is consistent with actual conditions. To ensure the model can reach industrial-grade precision, the project team carried out extensive data auditing and model adjustment work. Through these efforts, all relevant information and settings for the entire DC room were comprehensively combed and verified, and a complete set of standardized calibration documents was accumulated, laying a foundation for modeling and promotion. These efforts toward calibration can be viewed in terms of two categories: data auditing, and model adjustment. Data auditing can again be divided into two categories: server auditing (such as server position conflict cleaning and server power consumption calibration) and sensor auditing (including air supply temperature, ACU fan speed calibration, and the positions and readings of sensors in the hot and cold aisles). Model adjustments include adjusting hot air leak settings (as hot air leaks will cause cold channel temperatures to rise), adjusting the simulation mode to meet the accuracy requirements, and adjusting server air volume settings. The above calibration operation enabled the final model to reach a level of precision never before achieved by a design-phase CFD model. This precision owes to the team’s accurate reproduction of hardware layouts, data auditing at the level of each operation, and fine-grained calculation of server flowrates. Project implementation The above flow chart illustrates the implementation of the CFD-based sandbox. After meeting the predefined target for model accuracy, the team made a further step toward CFD simulation automation; by accessing Alibaba’s self-developed data center infrastructure management system (DCIM), the team obtained data for real-time server power consumption and air conditioning settings, and wrote this data into the CFD model with a data exchange layer to enable the model to simulate real DC conditions in real time. This means that once a simulation is completed, simulation results (including images and data sheets) are automatically exported and transferred to the DCIM system. With these steps, the team realized the integration of the sandbox system into the DCIM system, such that the entire process could be automated. This laid a solid foundation for further application and generalization of the sandbox in the future. Results In terms of model accuracy, the team used real monitoring data as the input for the sandbox system to compute the mean absolute error (MAE) between the predicted and the real monitoring data for designated sensors in the cold and hot aisles. With tests over a sufficiently long time period, its accuracy met the requirements of Alibaba’s DC standards. Theoretically, the sandbox system can replace the sensors in the DC room to monitor operating states. In terms of successful implementation, at present the model has successfully accessed the DCIM and can automatically retrieve data from DCIM and return results. The current simulation time is around one hour, and this is expected to speed up to just 10 minutes. With this live CFD simulation system, Alibaba’s DCIM system has become the only data center cloud management system worldwide to provide high-precision, real-time CFD simulation modules. Key practical advantages The CFD-based sandbox system offers key advantages in DC visualization, fault discovery, design verification, design optimization, HVAC control recommendation, and server dispatch communication. Regarding DC visualization, the model upgrades the display mode from a 2D digital visualization to a 3D digital and graphical visualization covering 3D layout, thermal status, and airflow patterns. With it, data center management experts gain a better assessment of the state of the DC room. With the sandbox system, operators can quickly identify heat and ventilation issues occurring in the room. Regarding fault discovery, the simulation results are of centimeter-level granularity, enabling detection of fine-grained temperature rises (or local hot spots). This supports faster, stronger risk identification capabilities to prevent the emergence of temperature rises. For design verification purposes, the physical setup information needed for the modeling process is usually determined at the design phase. The error feedback information obtained during the modeling process can directly verify differences between design and implementation. With design optimization, the model can guide changes and simulate the operation of different data center designs. It can thus be used as an platform for design optimization and data center changes. a priori For HVAC control recommendations, the model allows users to apply different air conditioning settings to determine the best cooling control approach from a perspective of lowest energy consumption, thus achieving reliable, smart control of DC cooling. Finally, regarding service dispatch recommendation, the model can provide reference to the service dispatch system with the detailed temperature distribution in the DC room. The service distribution can achieve more uniform temperature distribution to reduce cooling energy consumption and improve server health. Looking Forward One future direction for the model is to develop it into an industry-level application with the goal of making it an industry standard. The sandbox system can be applied to a greater number of DC rooms in the Alibaba data center to further validate DC design and optimize operational controls. In the future, the team hopes to extend the sandbox system to the entire HVAC system, including elements such as refrigeration equipment located outside the DC room. This can realize simulation for the full cooling chain and achieve design verification and control optimization for the entire HVAC system. In summary, the sandbox system will significantly promote automation levels in areas ranging from data center design to operation and maintenance, and will provide support for more stable and efficient data center management. This reflects an effort to move “from zero to one”, as it marks the first completion of a real-time, high-precision sandbox system to help data center operators validate whether a change will cause issues, thus reducing the chance of outages. Further, Alibaba can apply the sandbox system to building operation recommendations and even to achieving automatic DC reconfiguration, among other operations. In this way, Alibaba is approaching its goal of unattended DC management. (The original article is written by Chen Li陈丽) Alibaba Tech First hand and in-depth information about Alibaba’s latest technology → Facebook: . Twitter: . “Alibaba Tech” “AlibabaTech”

Stories, News & Mentions about FLOWTRACE

Flowtrace’s Stories on HackerNoon