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Electronic trading surges with traders eyeing the impact of machine learning - CityAM

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Professional traders are anticipating artificial intelligence and machine learning to be the most influential technology over the next three years. JP Morgan's flagship survey reveals more than half of professional and institutional traders anticipate machine learning to lead technology. Currently a third of client traders predict mobile trading applications to be the most influential this year. Certainly the Reddit Gamestop rally powered by low cost trading platform is already testament to just how quickly the environment has changed. Read more: 'Robots will take our jobs': Eigen boss Lewis Liu on the future of the City worker Electronic trading picked up last year and all surveyed expect to increase electronic volumes this year.


Artificial intelligence and the Gamestonk blowback

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Surrounded by rallies of "power to the people," a rag-tag group of scrappy underdogs recently managed to bring Wall Street to its knees through a dazzling display of disobedient investing that saw Gamestop stocks rocket Moonward. This unprecedented seizure of power by the proletariat has been lauded far and wide as a smack in the mouth for the establishment. Some say it's a warning shot to the financial kings and queens of the Earth. The "Gamestonk" legend will be told for years to come – Hollywood's already making sure of that. But the story is far from done.


Automating Trading and Market Making With Artificial Intelligence

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The goal is to capture information in a market's order books and use that information to predict market movement/direction. That prediction can enable repricing of orders and more efficient market making. Such an approach allows the market maker to provide liquidity whilst making profits at the same time. Market makers are essential to modern markets. They provide the markets with necessary liquidity and make sure the bid/ask spread is reasonably narrow to allow efficient purchasing.


Deploying and Hosting a Machine Learning Model with FastAPI and Heroku

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This tutorial looks at how to deploy a machine learning model, for predicting stock prices, into production on Heroku as a RESTful API using FastAPI.


How blockchain can make AI tech not just safer, but better - TechHQ

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With the increased pressures placed on the business over the past year or so, perhaps it should come as little surprise that many enterprises chose to delay or are lagging behind in their digital transformation pushes. But simultaneously, organizations are acknowledging that a digital overhaul is now a prerequisite for continued growth, with artificial intelligence (AI) and blockchain technologies establishing themselves as two powerful DX trends in recent years. Long the stuff of science fiction fodder, AI has often been depicted as running amok and attempting hostile takeovers of electronic systems in media. But recent Google research indicates AI can become overly aggressive in the right circumstances, or is that the wrong circumstances? Either way, the intrinsic structural elements of another major enterprise trend, blockchain tech, could be the solution to combating AI aggression.


Automating Data Collection For AI At Morningstar

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One of the biggest challenges in making AI projects a success is dealing with the requirements for data needed by machine learning systems. Machine learning systems work by generalizing learnings from data, so if that data is insufficient in quantity or poor in quality, then the machine learning project will fail. Nothing is more true for artificial intelligence than the tech adage, "garbage in is garbage out". Shariq Ahmad, head of technology in the data collection group at financial services data firm Morningstar knows this very well. As part of his role at Morningstar, he is responsible for building a pipeline and methodology for dealing with large quantities of data in a wide variety of formats, qualities, and levels of completeness and accuracy to support big data projects, including those that support their machine learning efforts.


2021-2026 Enterprise Artificial Intelligence Market: Analysis by Business Growth, Development Factors, Applications, and Future Prospects – The Bisouv Network

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Global "Enterprise Artificial Intelligence Market" report provides qualitative and quantitative information covering market size breakdown, revenue, and growth rate by important segments. The Enterprise Artificial Intelligence market report provides a competitive landscape of major players with the current industry scenario, market concentration status. The report study explores the information on production, consumption, export, and import of Enterprise Artificial Intelligence market in each region. The Enterprise Artificial Intelligence Market is fairly fragmented. The Enterprise Artificial Intelligence Market report profiles some of the key market players while reviewing significant market developments and strategies adopted by them.


Creating an Algorithmic Trading Strategy Using Python and Logistic Regression

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Obtaining historical data on the stocks that we want to observe is a two-step process. The library get-all-tickers allows us to compile a list of stock tickers by filtering companies on aspects like market cap or exchange. For this example, I am looking at companies that have a market cap between $150,000 and $10,000,000 (in millions). You will notice that I also included a line of code to print the number of tickers we are using. You will need to be sure that you are not targeting more than 2,000 tickers, because the Yfinance API has a 2,000 API calls per hour limit.


When Should You Use AI to Solve Problems?

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They didn't get to be division heads and CEOs by robotically following some leadership checklist. Of course, intuition and instinct can be important leadership tools, but not if they're indiscriminately applied. The rise of artificial intelligence has exposed flaws in traits we have long valued in executive decision makers. Algorithms have revealed actions once considered prescient to be lucky, decision principles previously considered sacred to be unproven, and unwavering conviction to be myopic. Look no further than the performance of actively managed investment funds to see the shortcomings of time-honored human decision-making approaches.


Global Artificial Intelligence in IoT Market : IBM, Microsoft, Google, PTC, AWS, Oracle, GE, Salesforce, SAP, Hitachi, etc. – The Bisouv Network

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Gauging through Scope: Global Artificial Intelligence in IoT Market, 2020-26 A new report defining the global Artificial Intelligence in IoT market offers readers with vivid details on current and most recent industry developments along with futuristic predictions that allow players to recognize exact vendor initiatives, end-user preferences and purchase decisions along with profitability. The report delivers pertinent details on strategic planning and tactical business decisions that influence and stabilize growth prognosis in global Artificial Intelligence in IoT market. The report in its opening section introduces the global Artificial Intelligence in IoT market, featuring market definitions, overview, classification, segmentation, inclusive of market type and applications followed by product specifications, manufacturing initiatives,pricing structures, raw material sourcing and the like. Following this, the report also focuses and analyzes the main regional market conditions followed by a global assessment. Vendor Landscape The report draws references of an extensive analysis of the Artificial Intelligence in IoT market, entailing crucial details about key market players, complete with a broad overview of expansion probability and expansion strategies.