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In this video you will be informed by our professor Grigory Vilkov what machine learning in finance means. Learn more: 🤍
QUANTT and QMIND came together to offer a unique experience for those interested in Financial Machine Learning (ML). Unifying these two clubs is Dr. Ernest Chan, an investor, researcher, and educator with an expertise in Quantitative Trading, Algorithmic Trading, and Financial Machine Learning.
Master the most in-demand skill-set of the world's top financial institutions with one of the most practical, comprehensive and affordable courses in Financial Machine Learning. ★ ★ Machine Learning in Finance ★ ★ Release Date: EST 1200 01-Dec Learn more and register your interest here: 🤍 Why I started an online Course? _ ★ The QuantPy Story ★ If you don't know me, my is Jonathon Emerick and I started a YouTube channel called QuantPy. I noticed a lack of video style quantitative finance resources online, and decided to embark on the journey of creating online tutorials and documenting my journey towards "becoming a Quant". The goal was to follow my interests in quantitative finance and attempt to help others along the way. ★ Collaboration with Hariom Tatsat ★ I never intended to make an online course, however at the end of 2021 I was introduced to Hariom Tatsat a co-author of the book "Machine Learning and Data Science Blueprints for Finance". Hariom has extensive market knowledge and experience in applying machine learning to many financial applications, having done so throughout his career working at large Investment Banks. Leaning on his years of experience and knowledge, he proposed creating a course that delivered a very complete and practical approach to the applications of machine learning in finance. ★ My Contribution ★ Along with my ability to explain complex financial topics simply and effectively, I have a strong interest in the potential applications of Machine Learning and AI within both my own industry, the energy industry, and the financial industry into the future. Hence I was happy to enbark on this collaboration, both for solidifying concepts I was introduced to while studying Financial Mathematics at University, as well as learning from the extensive experience Hariom has gained throughout his career in applying machine learning to real world problems. ★ Our Goal ★ We developed this course with an emphasis on providing value first and foremost. Therefore, we hope to have not only delivered a complete and practical course on Machine Learning in Finance, but most importantly an affordable course, having designed this program keeping students in mind. We know you'll appreciate the amount of value and insight provided by Hariom's experience and access to 15+ case studies with over 10,000+ lines of code applying machine learning to real world financial problems. For more details, please read the Machine Learning in Finance section of this website. ★ ★ QuantPy GitHub ★ ★ Collection of resources used on QuantPy YouTube channel. 🤍 ★ ★ Discord Community ★ ★ Join a small niche community of like-minded quants on discord. 🤍 ★ ★ Support our Patreon Community ★ ★ Get access to Jupyter Notebooks that can run in the browser without downloading python. 🤍 ★ ★ ThetaData API ★ ★ ThetaData's API provides both realtime and historical options data for end-of-day, and intraday trades and quotes. Use coupon 'QPY1' to receive 20% off on your first month. 🤍 ★ ★ Online Quant Tutorials ★ ★ WEBSITE: 🤍 ★ ★ Contact Us ★ ★ EMAIL: pythonforquants🤍gmail.com Disclaimer: All ideas, opinions, recommendations and/or forecasts, expressed or implied in this content, are for informational and educational purposes only and should not be construed as financial product advice or an inducement or instruction to invest, trade, and/or speculate in the markets. Any action or refraining from action; investments, trades, and/or speculations made in light of the ideas, opinions, and/or forecasts, expressed or implied in this content, are committed at your own risk an consequence, financial or otherwise. As an affiliate of ThetaData, QuantPy Pty Ltd is compensated for any purchases made through the link provided in this description.
Machine learning is going to change radically the decision making processes in financial institutions. Find out more about Imperial's new Executive Education programme 'From Data to Decisions - Machine Learning in Finance' 🤍
MIT 15.S08 FinTech: Shaping the Financial World, Spring 2020 Instructor: Prof. Gary Gensler View the complete course: 🤍 YouTube Playlist: 🤍 In this video, the class discusses the finance technology stack, AI and machine learning, public policy frameworks and how AI policy fits into that. License: Creative Commons BY-NC-SA More information at 🤍 More courses at 🤍 Support OCW at 🤍 We encourage constructive comments and discussion on OCW’s YouTube and other social media channels. Personal attacks, hate speech, trolling, and inappropriate comments are not allowed and may be removed. More details at 🤍
#Shorts #shorts Are you interested in machine learning but also a finance freak? Here are five machine learning project ideas that you can add to your resume. 1. Stock Price Prediction If trading in the stock market peaks your interest, then this is for you. You can simply use a Long Short Term Memory Neural Network, or a multi-layered perceptron which are great for time-series forecasting. 2. Credit Card Fraud Detection Credit card fraud deduction helps banks detect transaction anomalies. 3. Loan Approval Prediction Here's a project idea that will help you understand how your loan application is approved or rejected by your bank. 4. Customer Churn Prediction This is a very useful project idea, not just in finance, but in every business domain. you can use three based ensemble methods to predict whether a customer is likely to churn or not. 5. Credit Card Scoring Analysis The credit scoring system helps evaluate the. A person's credit worthiness and credit risk, So go ahead and start working on your machine learning project right now.
#artificialintelligence #machinelearning #financeresearch Using AI and Machine learning in asset pricing and asset management is in the midst of a boom. But are portfolios based on these richly parameterized models well understood? In this video, Bryan Kelly, Professor of Finance at Yale School of Management and Head of Machine Learning at AQR Capital Management, talks about the behavior of return prediction models in a high complexity regime and the ability of high complex models to predict recessions. Stay up to date on financial research! Sign-up to the Swedish House of Finance #newsletter to take part of events, listen to interviews with leading experts, and keep informed on relevant policy issues: 🤍
Today we will be exploring the financial data structures as discussed in Advances in Financial Machine Learning by Prof. Marcos Lopez de Prado [2018]. We will create four standard price and volume bars commonly used in financial machine learning within academic literature: time bars, tick bars, volume bars and dollar bars. In this tutorial we are learning how to construct these standard bars in Python from raw trade information on a given product, here we are using Commonwealth Bank of Australia (CBA) equities trading information from my CommSec brokerage. We also discuss the issues of using time bars for financial machine learning in terms of over and under sampling information of low and high-activity periods as well as undesirable statistical properties such as serial correlation, heteroscedasticity, and non-normality of returns. ★ ★ Code Available on GitHub ★ ★ GitHub: 🤍 Specific Tutorial Link: 🤍 ★ ★ QuantPy Patreon Community ★ ★ Get access to Jupyter Notebooks and join a small niche community of like-minded quants on discord. 🤍 ★ ★ ThetaData API ★ ★ ThetaData's API provides both realtime and historical options data for end-of-day, and intraday trades and quotes. Use coupon 'QuantPy' to receive 15% off on your first 2 months. You also receive an additional 50% your first month. 🤍 ★ ★ ONLINE TUTORIALS ★ ★ WEBSITE: 🤍 ★ ★ CONTACT US ★ ★ EMAIL: pythonforquants🤍gmail.com Disclaimer: All ideas, opinions, recommendations and/or forecasts, expressed or implied in this content, are for informational and educational purposes only and should not be construed as financial product advice or an inducement or instruction to invest, trade, and/or speculate in the markets. Any action or refraining from action; investments, trades, and/or speculations made in light of the ideas, opinions, and/or forecasts, expressed or implied in this content, are committed at your own risk an consequence, financial or otherwise. As an affiliate of ThetaData, QuantPy Pty Ltd is compensated for any purchases made through the link provided in this description.
Lex Fridman Podcast full episode: 🤍 Please support this podcast by checking out our sponsors: - Audible: 🤍 to get $9.95 a month for 6 months - Tryolabs: 🤍 - Blinkist: 🤍 and use code LEX to get 25% off premium - Athletic Greens: 🤍 and use code LEX to get 1 month of fish oil GUEST BIO: Richard Craib is the founder of Numerai, a crowd-sourced, AI-run stock trading system. PODCAST INFO: Podcast website: 🤍 Apple Podcasts: 🤍 Spotify: 🤍 RSS: 🤍 Full episodes playlist: 🤍 Clips playlist: 🤍 SOCIAL: - Twitter: 🤍 - LinkedIn: 🤍 - Facebook: 🤍 - Instagram: 🤍 - Medium: 🤍 - Reddit: 🤍 - Support on Patreon: 🤍
We are talking about Artificial Intelligence applications in finance. Let's review the current list of trends and what to expect from the industry. ▶ Contact Jelvix: hello🤍jelvix.com | jelvix.com We are a technology consulting and software development company eager to share our knowledge and experience. Subscribe for more tech tips and tutorials: 🤍 ▶ USEFUL LINKS: - Machine Learning vs. Deep Learning vs. Artificial Intelligence - 🤍 - Application of AI in Finance - 🤍 ▶ TIME CODES: ▶ Follow us: Facebook - 🤍 Twitter - 🤍 Instagram - 🤍 Linkedin - 🤍 Upwork - 🤍 ▶ About this video: How is AI applied in finance? 1. Automation It enables organizations to boost productivity and cut operational costs. Statistics say that AI helps companies save up to 70% of the costs associated with repetitive tasks. 2. Credit Decisions We once developed AI-based software that helps banks assess potential borrowers. It immediately analyzes countless factors thus saving costs and making the process much faster. Sounds exciting, doesn’t it? 3. Trading AI-driven trading systems can analyze massive amounts of data much quicker. You won’t believe it, but predictions made by AI algorithms are more accurate because they can analyze a lot of historical data. 4. Risk Management AI can handle risk management tasks much more efficiently and analyze various financial activities in real-time. 5. Fraud Prevention AI-driven fraud detection tools can analyze clients’ behavior, track their locations, and determine their purchasing habits. Therefore, they can quickly detect any unusual activities. 6. Personalized Banking AI-powered chatbots minimize the workload of the call centers. There are also many apps that offer personalized financial advice so that users can achieve their financial goals, track regular expenses, income, and purchasing habits.
artificialintelligence #machinelearning #financeresearch #AIapplications In this video, we take a look at implementations of AI and machine learning in the Swedish market as well as in the much larger US market. What are the similarities and the differences? Are they due to market size, liquidity, regulation or are there no significant differences? Pehr Wissén, Professor Emeritus of Finance at Swedish House of Finance, moderates a panel discussion with Sven Törnkvist (EQT), Kathryn M. Kaminski (AlphaSimplex), and Svante Bergström (Lynx Asset Management). Stay up to date on financial research! Sign-up to the Swedish House of Finance #newsletter to take part of events, listen to interviews with leading experts, and keep informed on relevant policy issues: 🤍
Today's book review is, "Advances in Financial Machine Learning" by Marco Lopez de Prado. I gave this book a 4/5 stars. The book does a good job at trying to teaching something complex in a short format without being too high level. The best part of the book is the discussion around setting up a team with the right structure. I have seen the same problems he has seen where teams back-test incorrectly or they over fit the model to the out of time sample by looping back. He covers why machine learning fails in practice (people don't implement it correctly) and I have seen similar issues with traditional statistics. The book is a good book if you want to see a few new ideas and how to structure finance problems. Buy the Book Here (affiliate link): 🤍 Quant t-shirts, mugs, and hoodies: 🤍 Connect with me: 🤍 🤍 ☕ Show Your Support and Buy Me a Coffee ☕ 🤍
In “The Analytics of Finance,” an installment of Wharton’s #Beyond Business Tarnopol Dean’s Lecture series, Finance Professor Michael Roberts explained his research into how data analytics and machine learning can guide financial decision-making for investors, banks, and non-financial firms. Learn more about the Beyond Business series, hosted by Dean Erika James and streaming live on the Wharton School’s LinkedIn page: 🤍 #dataanalytics #finance #machinelearning #financialanalysis - Founded in 1881 as the world’s first collegiate business school, the Wharton School of the University of Pennsylvania is shaping the future of business by incubating ideas, driving insights, and creating leaders who change the world. With a standing faculty of 241 renowned professors, Wharton has 5,000+ students across four degree programs: undergraduate, MBA, executive MBA, and doctoral. Each year 13,000+ professionals from around the world advance their careers through Wharton Executive Education’s individual, company-customized, and online programs – with 200,000+ others earning certificates from Wharton Online since 2015. More than 104,000 Wharton alumni form a powerful global network of leaders who transform business every day. Learn more about Wharton: 🤍 Subscribe to the Wharton YouTube channel: 🤍
I review the book, "Machine Learning & Data Science Blueprints for Finance" by Tatsat, Puri, and Lookabaugh. As machine learning and data science have become the hot topic in finance these days it is becoming more important to really understand the basics. Most of the basics come in the form of traditional statistics and the scientific method. Besides fitting lines to data, a full range of tests need to be conducted to really understand your data, the model structure, the output, and its usage. Machine Learning and Data Science Blueprints for Finance (my affiliate link) 🤍 Rating: 2/5 STARS Quant t-shirts, mugs, and hoodies: teespring.com/stores/fancy-quant Connect with me: 🤍 🤍 ☕ Show Your Support and Buy Me a Coffee ☕ 🤍
This talk, titled The 7 Reasons Most Machine Learning Funds Fail, looks at the particularly high rate of failure in financial machine learning. The few managers who succeed amass a large number of assets, deliver consistently exceptional performance to their investors. However, that is a rare outcome. This presentation will go over the 7 critical mistakes underlying most financial machine learning failures based off of Marcos López de Prado’s experiences and observations. To learn more about Quantopian, visit 🤍 The slides for this presentation can be found at 🤍 Bio of the Speaker: Dr. Marcos López de Prado is the chief executive officer at True Positive Technologies LP. He founded Guggenheim Partners’ Quantitative Investment Strategies (QIS) business, where he applied cutting-edge machine learning to the development of high-capacity strategies that delivered superior risk-adjusted returns. After managing up to $13 billion in assets, López de Prado acquired QIS and successfully spun out that business in 2018. López de Prado is a research fellow at Lawrence Berkeley National Laboratory (U.S. Department of Energy, Office of Science). A top 10-most-read author in finance based on SSRN's rankings, he has published dozens of scientific articles on machine learning and supercomputing and holds multiple international patent applications on algorithmic trading. Marcos earned a Ph.D. in Financial Economics (2003), a Ph.D. in Mathematical Finance (2011) from Universidad Complutense de Madrid, and is a recipient of Spain's National Award for Academic Excellence (1999). He completed his post-doctoral research at Harvard University and Cornell University. Disclaimer Quantopian provides this presentation to help people write trading algorithms - it is not intended to provide investment advice. More specifically, the material is provided for informational purposes only and does not constitute an offer to sell, a solicitation to buy, or a recommendation or endorsement for any security or strategy, nor does it constitute an offer to provide investment advisory or other services by Quantopian. In addition, the content neither constitutes investment advice nor offers any opinion with respect to the suitability of any security or any specific investment. Quantopian makes no guarantees as to accuracy or completeness of the views expressed in the website. The views are subject to change, and may have become unreliable for various reasons, including changes in market conditions or economic circumstances.
Students are eager to jump into the finance industry thinking they can apply the machine learning and artificial intelligence methods they have learned in school or the tech industry. The truth is, the finance industry has been making slow progress however the high analytical standard and regulations of finance make this progress much slower than the tech industry. In this video I will cover a variety of topics including CCAR, Equal Credit Opportunity Act (ECOA), and the Consumer Finance Protection Bureau (CFPB). SUPPORT THE CHANNEL Quant t-shirts, mugs, and hoodies: 🤍 Connect with me: 🤍 🤍 ☕ Show Your Support and Buy Me a Coffee ☕ 🤍
In the last six years, the financial sector has seen an increase in the use of machine learning models in financial, banking and insurance contexts. Data science and advanced analytics teams in the financial and insurance community are implementing these models regularly and have found a place for them in their toolbox. The success of machine learning, and in particular deep learning in image recognition and natural language processing applications, has created high expectations and their use has rapidly spread to many different areas. The financial sector is no exception and the last six years have seen an increase in these types of models in financial, banking and insurance contexts. Data science and advanced analytics teams in the financial and insurance community are implementing these models regularly and have found a place for them in their toolbox. 🤍
In this tutorial, we'll learn how to predict tomorrow's S&P 500 index price using historical data. We'll also learn how to avoid common issues that make most stock price models overfit in the real world. We'll start by downloading S&P 500 prices using a package called yfinance. Then, we'll clean up the data with pandas, and get it ready for machine learning. We'll train a random forest model and make predictions using backtesting. Then, we'll improve the model by adding predictors. We'll end with next steps you can use to improve the model on your own. You can find an overview of the project and the code here - 🤍 . If you enjoyed this tutorial, check out this link 🤍 for free courses that will help you master data skills. Chapters 00:00 - Introduction 01:28 - Downloading S&P 500 price data 03:30 - Cleaning and visualizing our stock market data 04:29 - Setting up our target for machine learning 08:19 - Training an initial machine learning model 17:01 - Building a backtesting system 23:05 - Adding additional predictors to our model 28:45 - Improving our model 33:37 - Summary and next steps with the model - Join 1M+ Dataquest learners today! Master data skills and change your life. Sign up for free: 🤍
Marcos delivered an inspiring keynote presentation on "Ten Financial Applications of Machine Learning" at EXDC2019. Marcos López de Prado is the CIO of True Positive Technologies (TPT), and professor of practice at Cornell University's School of Engineering. He has over 20 years of experience developing investment strategies with the help of machine learning algorithms and supercomputers. EXDC is the world’s first annual conference bringing together leading minds in finance, business, media and sustainability to discuss the real-world use cases for alternative data. The first edition of EXDC took place at the Times Center NYC on Nov. 5th and featured 30 speakers from Goldman Sachs, UBS, 500startups, The Wall Street Journal, Business Insider, Bank of America, Greenpeace, Alliance Bernstein, Adweek, Cornell University, USAFacts, NYU, The Pudding and more. Learn more 🤍 Topics include machine learning, ESG, labor markets, product & retail trends, competitive intelligence, future of news and open data. Want to join our next event? Stay tuned for EXDC 2020: 🤍 LinkedIn: 🤍 🤍 Facebook: 🤍 Twitter: 🤍 🤍
Get notified of the free Python course on the home page at 🤍 Sign up for the Full Stack course here and use YOUTUBE50 to get 50% off: 🤍 Hopefully you enjoyed this video. 💼 Find AWESOME ML Jobs: 🤍 Oh, and don't forget to connect with me! LinkedIn: 🤍 Facebook: 🤍 GitHub: 🤍 Patreon: 🤍 Join the Discussion on Discord: 🤍 Happy coding! Nick P.s. Let me know how you go and drop a comment if you need a hand! #machinelearning #python #datascience
Full episode with Michael Kearns (Nov 2019): 🤍 New clips channel (Lex Clips): 🤍 Once it reaches 20,000 subscribers, I'll start posting the clips there instead. (more links below) For now, new full episodes are released once or twice a week and 1-2 new clips or a new non-podcast video is released on all other days. Clip from full episode: 🤍 If you enjoy these clips, subscribe to the new clips channel (Lex Clips): 🤍 Once it reaches 20,000 subscribers, I'll start posting the clips there instead. For now, new full episodes are released once or twice a week and 1-2 new clips or a new non-podcast video is released on all other days. (more links below) Podcast full episodes playlist: 🤍 Podcasts clips playlist: 🤍 Podcast website: 🤍 Podcast on Apple Podcasts (iTunes): 🤍 Podcast on Spotify: 🤍 Podcast RSS: 🤍 Michael Kearns is a professor at University of Pennsylvania and a co-author of the new book Ethical Algorithm that is the focus of much of our conversation, including algorithmic fairness, bias, privacy, and ethics in general. But, that is just one of many fields that Michael is a world-class researcher in, some of which we touch on quickly including learning theory or theoretical foundations of machine learning, game theory, algorithmic trading, quantitative finance, computational social science, and more. Subscribe to this YouTube channel or connect on: - Twitter: 🤍 - LinkedIn: 🤍 - Facebook: 🤍 - Instagram: 🤍 - Medium: 🤍 - Support on Patreon: 🤍
#artificialintelligence #machinelearning #financeresearch Investors are inundated with more data than ever before. What does an influx of “high-dimensional data” mean for financial forecasting, asset pricing and investment? In this video, Stefan Nagel, Professor of Finance at Chicago Booth School of Business, talks about forecasting future cash flows and returns when asset characteristics and factors multiply. Stay up to date on financial research! Sign-up to the Swedish House of Finance #newsletter to take part of events, listen to interviews with leading experts, and keep informed on relevant policy issues: 🤍
#artificialintelligence #machinelearning #finance The Swedish House of Finance brought together researchers, practitioners and policymakers to talk about AI and Machine Learning in finance. Watch the presentations and learn more about topics such as algorithm aversion and evidence from Robo-Advice, algorithmic discrimination in credit markets, using AI and Machine Learning to improve investment decisions and stock picking in the era of Big Data.
READY TO LEARN HOW ARTIFICIAL INTELLIGENCE IS TRANSFORMING FINANCIAL SERVICES I.E. AI IN FINANCE? 👇 Learn from those who are building the future of finance in the biggest banks, tech companies and fast-growing startups: 🤍ion/aifinance (Worldwide) and 🤍 (Singapore) The course is developed by CFTE - Centre for Finance, Technology and Entrepreneurship - and Ngee Ann Polytechnic giving you the knowledge on how to get into Fintech. It is designed around 18 modules of video lectures, reading assignments and assessment quizzes. Learners can interact with other participants through an online forum, and receive weekly emails with additional content. Once enrolled in the course, participants join a global community of finance professionals, technologists and entrepreneurs interested in AI. The program is accredited in Singapore. In this video, senior leaders from banks, tech company, startup and regulator, share their views about the impact of Artificial Intelligence and Machine Learning in Finance. You might even learn how to build a bank! Understand how AI is transforming finance with the first online course led by 20+ experts who teach the applications in different sectors of finance, from banking to asset management and insurance. Hear the reviews from Head of Regulatory Affairs at Scor, Priscilla Cournede, about our course 🤍 For more information: 🤍ion/aifinance (Worldwide) and 🤍 (Singapore). For the press release of the launch of this course: 🤍ion/wp-content/uploads/2018/04/20180424_Launch-of-AI-in-Finance-1.pdf Appearances in the video: David Hardoon (MAS), Ayesha Khanna (ADDO.AI), Sopnendu Mohanty (MAS), Jean-Philippe Desbiolles (IBM), Martin Markiewicz (Silent Eight), Shameek Kundu (Standard Chartered) —————————————————————————————————— Related Materials How to launch a Fintech Startup? 🤍ion/2018/06/27/how-to-launch-a-fintech-startup/ is an article that gives us an overview of the current situation in the Fintech industry. Learn more about what you can expect now and where you can find courses equivalent to Oxford Fintech programme or MIT Fintech Courses. Learn more about what are the current in-demand Fintech skills from this article "Your Future in Fintech: 2019’s Most In-Demand Industry Skills" 🤍ion/2019/01/21/future-in-fintech-industry-skills-needed/ these skills include current knowledge to catch up on technologies such as Artificial Intelligence (AI) and Machine Learning (ML), programming skills and specific soft skills which will be mentioned in the article —————————————————————————————————— Subscribe to our channel to keep up to date with the latest FinTech content from CFTE and be part of our growing FinTech ecosystem! Learn more about our education project at 🤍cfte.education Visit our blog for latest CFTE news: 🤍cfte.education/blog
Learn how to perform algorithmic trading using Python in this complete course. Algorithmic trading means using computers to make investment decisions. Computer algorithms can make trades at a speed and frequency that is not possible by a human. After learning the basics of algorithmic trading, you will learn how to build three algorithmic trading projects. 💻 Code: 🤍 ✏️ Course developed by Nick McCullum. Learn more about Nick here: 🤍 ⭐️ Course Contents ⭐️ ⌨️ (0:00:00) Algorithmic Trading Fundamentals & API Basics ⌨️ (0:17:20) Building An Equal-Weight S&P 500 Index Fund ⌨️ (1:38:44) Building A Quantitative Momentum Investing Strategy ⌨️ (2:54:02) Building A Quantitative Value Investing Strategy Note that this course is meant for educational purposes only. The data and information presented in this video is not investment advice. One benefit of this course is that you get access to unlimited scrambled test data (rather than live production data), so that you can experiment as much as you want without risking any money or paying any fees. This course is original content created by freeCodeCamp. This content was created using data and a grant provided by IEX Cloud. You can learn more about IEX Cloud here: 🤍 Any opinions or assertions contained herein do not represent the opinions or beliefs of IEX Cloud, its third-party data providers, or any of its affiliates or employees.
Lex Fridman Podcast full episode: 🤍 Please support this podcast by checking out our sponsors: - Eight Sleep: 🤍 to get special savings - BetterHelp: 🤍 to get 10% off - Fundrise: 🤍 - Athletic Greens: 🤍 to get 1 month of fish oil GUEST BIO: Andrej Karpathy is a legendary AI researcher, engineer, and educator. He's the former director of AI at Tesla, a founding member of OpenAI, and an educator at Stanford. PODCAST INFO: Podcast website: 🤍 Apple Podcasts: 🤍 Spotify: 🤍 RSS: 🤍 Full episodes playlist: 🤍 Clips playlist: 🤍 SOCIAL: - Twitter: 🤍 - LinkedIn: 🤍 - Facebook: 🤍 - Instagram: 🤍 - Medium: 🤍 - Reddit: 🤍 - Support on Patreon: 🤍
Leading banks and financial services organisations are utilizing AI technology, such as machine learning, for a variety of reasons. Contact us. to learn more about how machine learning can assist your financial business. Know More 🤍 🤍 #MachineLearning #AITechnology #FinanceSector #ML Leave some feedback: Make sure you LIKE, SHARE and COMMENT on this video if you haven't done so already at ▶️ SUBSCRIBE On YouTube: 🤍 For More Information CLICK HERE 🌐 Website: 🤍 CLICK HERE 🌐 BLOG: 🤍 👉 BECOME A FOUNDER NOW: 🤍 ➡️➡️ Follow Us on Social Media ⬇️ Facebook: 🤍 LinkedIn: 🤍 Twitter: 🤍 Instagram: 🤍 ▶ SUBSCRIBE to ONPASSIVE on YouTube Channel ☑️ ▶️ 🤍 Please Provide Your Valuable ONPASSIVE Review ⭐️ on GOOGLE 🤍
Today we discuss the common misconceptions of retail traders regarding the algorithmic trading or more commonly termed ‘algo trading’. Here we dive into why finding alpha in today’s financial markets is non-trivial, and conflicts of interest present in financial markets. Later in the video we discuss 3 trading strategies that are used as the foundation of most financial companies that participate in the financial markets. These strategies consistently make companies money and are the bedrock of companies providing financial services, or products in general. Let’s aim to be pessimistic when people online promise that someone/anyone with a novice level of python experience can create an algo trading strategy that consistently makes money. I will endeavour to never give in to this fallacy and aim to point it out to my viewers on this channel. ★ ★ QuantPy GitHub ★ ★ Collection of resources used on QuantPy YouTube channel. 🤍 ★ ★ Discord Community ★ ★ Join a small niche community of like-minded quants on discord. 🤍 ★ ★ Support our Patreon Community ★ ★ Get access to Jupyter Notebooks that can run in the browser without downloading python. 🤍 ★ ★ ThetaData API ★ ★ ThetaData's API provides both realtime and historical options data for end-of-day, and intraday trades and quotes. Use coupon 'QPY1' to receive 20% off on your first month. 🤍 ★ ★ Online Quant Tutorials ★ ★ WEBSITE: 🤍 ★ ★ Contact Us ★ ★ EMAIL: pythonforquants🤍gmail.com Disclaimer: All ideas, opinions, recommendations and/or forecasts, expressed or implied in this content, are for informational and educational purposes only and should not be construed as financial product advice or an inducement or instruction to invest, trade, and/or speculate in the markets. Any action or refraining from action; investments, trades, and/or speculations made in light of the ideas, opinions, and/or forecasts, expressed or implied in this content, are committed at your own risk an consequence, financial or otherwise. As an affiliate of ThetaData, QuantPy Pty Ltd is compensated for any purchases made through the link provided in this description.
Today we learn how to build a financial AI assistant in Python. DISCLAIMER: This is not investing advice. I am not a professional who is qualified in giving any financial advice. This is a video purely about programming using financial data. ◾◾◾◾◾◾◾◾◾◾◾◾◾◾◾◾◾ 📚 Programming Books & Merch 📚 💻 The Algorithm Bible Book: 🤍 🐍 The Python Bible Book: 🤍 👕 Programming Merch: 🤍 🌐 Social Media & Contact 🌐 📱 Website: 🤍 🎙 Discord: 🤍 📷 Instagram: 🤍 🐦 Twitter: 🤍 🤵 LinkedIn: 🤍 📁 GitHub: 🤍 🎵 Outro Music From: 🤍 Timestamps: (0:00) Intro (0:16) Preview (1:24) Packages & Imports (3:37) Intents (7:44) Basic Structure (11:35) Portfolio Management (18:43) Portfolio Performance (24:22) Plot Stock Charts (28:50) Wrapping Up Assistant (32:32) Demonstration (36:22) Outro
Here is a video which provide you the detailed explanation about how we can apply the Machine Learning in Finance. You can buy my book at Packrt url : 🤍 Amazon url: 🤍
Dr. Thomas Starke Speaks on Machine Learning Trading with Deep Reinforcement Learning (DRL). In this captivating video, join Dr. Thomas Starke as he unravels the fascinating world of deep reinforcement learning (DRL) and its application to trading. Witness how DRL, the groundbreaking technology that conquered the world's hardest board game, GO, can revolutionize the way we approach the financial markets. Ready to embark on an exciting journey? Gain a comprehensive understanding of machine learning trading, refine your skills in implementing ML algorithms, and unlock the power of algo trading to achieve success. Discover what EPAT holds for you with our counselors 👉🤍 Discover the fundamental elements of reinforcement learning, delve into the concept of Markov Decision Process, and explore how these concepts can be harnessed to optimize trading strategies. Dr. Starke explains the challenges faced in trading and unveils how "gamification" can be leveraged to train robust trading systems. Throughout the video, you'll gain insights into essential topics such as retroactive labeling, the utilization of the Bellman Equation, and the implementation of deep reinforcement learning. Understand how to train the system effectively, design a suitable reward function, and determine the most relevant features for optimal performance. Witness the testing phase of reinforcement learning algorithms and explore the considerations for selecting the right neural network for trading applications. Dr. Starke presents testing results, highlighting the potential and challenges of employing deep reinforcement learning in the trading domain. ▶️ Machine Learning For Trading Tutorials: 🤍 ▶️ ChatGPT and Machine Learning in Trading: 🤍 🔔 Subscribe to our channel for more Algorithmic Trading tutorials and tips! 👍 Like this video and share it with your fellow traders. 💬 Drop your questions and comments below. We'd love to hear from you! - About Speaker: Dr Thomas Starke (CEO, AAAQuants) Dr Starke has a PhD in Physics and currently leads the quant-trading team in one of the leading prop-trading firms in Australia, AAAQuants, as its CEO. He has also held the senior research fellow position at Oxford University. Dr Starke has previously worked at the proprietary trading firm Vivienne Court, and at Memjet Australia, the world leader in high-speed printing. He has led strategic research projects for Rolls-Royce Plc (UK) and is also the co-founder of the microchip design company pSemi. - Chapters: 00:00 - Dr Starke Introduction 01:34 - What is Reinforcement Learning? 05:38 - Markov Decision Process 08:30 - Application to Trading 11:46- The Problem 16:21 - Retroactive Labelling 18:24 - How to use Bellman Equation 25:16 - Deep Reinforcement Learning 27:52 - Implementation 31:52 - What is Gamification 33:00 - How to train the System? 35:47 - Reward Function design 41:11 - What features to use? 44:59 - Testing the Reinforcement Learning 47:46 - Which Neural Network should I use? 49:28 - Testing Results 54:03 - Challenges 55:54 - Full Simulation 56:41 - Lessons Learned 59:15 - Conclusion 01:00:16 - Q&A #machinelearningtrading #deeplearning #MachineLearningTutorial
Confused about understanding machine learning models? Well, this video will help you grab the basics of each one of them. From what they are, to why they are used, and what purpose do they serve. All Major Software Architecture Patterns Explained in 7 Minutes | Meaning, Design, Models & Examples 🤍 7 Basic Machine Learning Concepts for Beginners 🤍 What is Deep Learning and How it Works | Deep Learning Explained 🤍 Machine Learning Model Deployment Explained 🤍 What is Neural Network and How it Works | Neural Network Explained 🤍 What is Data Science Project Life Cycle Explained Step by Step 🤍 After watching this video, you'll be able to answer, - How many machine learning models are there - Some common machine learning models explained - What is supervised learning - What is unsupervised learning - What is regression - Types of ml models - Common types of regression - Common types of classification - What is classification - What are popular ML models explained - What are the types of supervised learning - What are the types of unsupervised learning - Understanding the basics of machine learning models - Learn machine learning models from scratch - What are common machine learning models for beginners - Understand machine learning models overview - Whats are few ml models basics to grasp Obviously, there is a ton of complexity if you dive into any particular model, but this should give you a fundamental understanding of how each machine learning model works! Like my content? Be sure to smash that like button and hit Subscribe to get the latest updates! Let's get social! 🤍 🤍 🤍 #WhiteboardProgramming #MachineLearning #MLmodels
Stock Price Prediction Using Python & Machine Learning (LSTM). In this video you will learn how to create an artificial neural network called Long Short Term Memory to predict the future price of stock. Disclaimer: The material in this video is purely educational and should not be taken as professional investment advice. Invest at your own discretion. NOTE: Some errors in the video: (1) In the video to calculate the RMSE I put the following statement: rmse=np.sqrt(np.mean((predictions- y_test)2)) When in fact I meant to put: rmse=np.sqrt(np.mean(((predictions- y_test)2))) You can also use the following statements to calculate RMSE: 1. rmse =np.sqrt(np.mean(((predictions- y_test)2))) 2. rmse = np.sqrt(np.mean(np.power((np.array(y_test)-np.array(predictions)),2))) 3. rmse = np.sqrt(((predictions - y_test) 2).mean()) (2) The preprocessing of data using MinMaxScaler resulted in data leakage, leading to the creation of future bias. Despite this, the code can still be utilized to obtain a basic understanding of Neural Network implementation in Python for classification and prediction purposes. Please Subscribe ! ⭐Get the code here⭐: 🤍 ⭐Please Subscribe !⭐ ⭐Support the channel and/or get the code by becoming a supporter on Patreon: 🤍 ⭐Websites: ► 🤍 ⭐Helpful Programming Books ► Python (Hands-Machine-Learning-Scikit-Learn-TensorFlow): 🤍 ► Learning Python: 🤍 ►Head First Python: 🤍 ► C-Programming : 🤍 ► Head First Java: 🤍 ▀▀▀▀▀▀▀▀▀▀▀▀▀▀▀▀▀▀▀▀▀▀▀▀▀▀ 📚Helpful Financial Books📚 ▀▀▀▀▀▀▀▀▀▀▀▀▀▀▀▀▀▀▀▀▀▀▀▀▀▀ 🌟Stock Market Investing Books: ✔️The Bogleheads' Guide to Investing 🤍 ✔️The Intelligent Investor 🤍 ✔️A Random Walk Down Wall Street 🤍 🌟Money Mindset Books ✔️Rich Dad Poor Dad: 🤍 ✔️Get Good With Money: Ten Simple Steps To Becoming Financially Whole: 🤍 #StockPrediction #Python #MachineLearning
Join the Hudson and Thames Reading Group: 🤍 The first lecture from the Experimental Design and Common Pitfalls of Machine Learning in Finance series, focuses on the four horsemen that present a barrier to adopting the scientific approach to machine learning in finance. The most significant barrier to the adoption of machine learning in financial services is the perception that the technology is too complex and risky. This is despite the fact that there are many well-established machine learning techniques that have been proven to work in the financial markets. Another barrier to adoption is the lack of skilled personnel who understand both machine learning and finance. Additionally, there is a lack of understanding of how to effectively use machine learning within the context of financial markets, making it difficult to determine what techniques are most appropriate and how best to use them. In conclusion, the adoption of machine learning in the financial services industry is hindered by a lack of understanding.
in this 2 part series Andrew Ng explains how he would learn machine learning Follow me on tiktok: 🤍 My instagram: 🤍 #lexfridman #lexfridmanpodcast #datascience #machinelearning #deeplearning #study
#artificialintelligence #machinelearning #financeresearch Investors are inundated with more data than ever before. What does an influx of “high-dimensional data” mean for financial forecasting, asset pricing and investment? In this video, Thierry Foucault, Professor of Finance at HEC Paris, talks about the growth of alternative data. He presents evidence from his research about the effects of alternative data on the quality of securities analysts' forecasts and its implications for the real economy. Stay up to date on financial research! Sign-up to the Swedish House of Finance #newsletter to take part of events, listen to interviews with leading experts, and keep informed on relevant policy issues: 🤍
In this comprehensive course on algorithmic trading, you will learn about three cutting-edge trading strategies to enhance your financial toolkit. In the first module, you'll explore the Unsupervised Learning Trading Strategy, utilizing S&P 500 stocks data to master features, indicators, and portfolio optimization. Next, you'll leverage the power of social media with the Twitter Sentiment Investing Strategy, ranking NASDAQ stocks based on engagement and evaluating performance against the QQQ return. Lastly, the Intraday Strategy will introduce you to the GARCH model, combining it with technical indicators to capture both daily and intraday signals for potential lucrative positions. ✏️ Course developed by 🤍lachone_ 💻 Code and course resources: 🤍 🔗 Learn more about Lachezar and Quantitative Trading with Python here: 🤍 ⭐️ Contents ⭐️ 0:00:00 - Algorithmic Trading & Machine Learning Fundamentals 0:15:25 - Building An Unsupervised Learning Trading Strategy 2:05:08 - Building A Twitter Sentiment Investing Strategy 2:28:08 - Building An Intraday Strategy Using GARCH Model 🎉 Thanks to our Champion and Sponsor supporters: 👾 davthecoder 👾 jedi-or-sith 👾 南宮千影 👾 Agustín Kussrow 👾 Nattira Maneerat 👾 Heather Wcislo 👾 Serhiy Kalinets 👾 Justin Hual 👾 Otis Morgan 👾 Oscar Rahnama Learn to code for free and get a developer job: 🤍 Read hundreds of articles on programming: 🤍
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