title
Stock Price Prediction | AI in Finance

description
Can AI be used in the financial sector? Of course! In fact, finance was one of the pioneering industries that started using AI in the early 80s for market prediction. Since then, major financial firms and hedge funds have adopted AI technologies for everything from portfolio optimization, to credit lending, to stock betting. In this video, we'll go over all the different ways AI can be used in applied finance, then build a stock price prediction algorithm in python using Keras and Tensorflow. Code for this video: https://github.com/llSourcell/AI_in_Finance Please Subscribe! And like. And comment. That's what keeps me going. Want more education? Connect with me here: Twitter: https://twitter.com/sirajraval Facebook: https://www.facebook.com/sirajology instagram: https://www.instagram.com/sirajraval More learning resources: https://hackernoon.com/unsupervised-machine-learning-for-fun-profit-with-basket-clusters-17a1161e7aa1 https://www.datacamp.com/community/tutorials/finance-python-trading http://www.cuelogic.com/blog/python-in-finance-analytics-artificial-intelligence/ https://www.udacity.com/course/machine-learning-for-trading--ud501 https://www.oreilly.com/learning/algorithmic-trading-in-less-than-100-lines-of-python-code Join us in the Wizards Slack channel: http://wizards.herokuapp.com/ Sign up for the next course at The School of AI: https://www.theschool.ai And please support me on Patreon: https://www.patreon.com/user?u=3191693 Signup for my newsletter for exciting updates in the field of AI: https://goo.gl/FZzJ5w Hit the Join button above to sign up to become a member of my channel for access to exclusive content! Join my AI community: http://chatgptschool.io/ Sign up for my AI Sports betting Bot, WagerGPT! (500 spots available): https://www.wagergpt.co

detail
{'title': 'Stock Price Prediction | AI in Finance', 'heatmap': [{'end': 700.347, 'start': 661.649, 'weight': 0.851}, {'end': 1227.261, 'start': 1195.334, 'weight': 0.767}, {'end': 1302.913, 'start': 1239.192, 'weight': 0.724}], 'summary': 'Explores ai applications in finance, including stock price prediction, hedge fund allocation optimization, fintech opportunities, robo-advisors, predictive modeling, and trading strategies, highlighting competitive demonstrations, fraud detection, algorithmic trading, and the use of machine learning algorithms like lstm networks and interactive brokers api.', 'chapters': [{'end': 120.518, 'segs': [{'end': 31.771, 'src': 'embed', 'start': 0.209, 'weight': 0, 'content': [{'end': 1.05, 'text': 'hello world.', 'start': 0.209, 'duration': 0.841}, {'end': 3.992, 'text': "it's siraj and stock price prediction.", 'start': 1.05, 'duration': 2.942}, {'end': 7.755, 'text': 'how can we use ai to predict stock prices?', 'start': 3.992, 'duration': 3.763}, {'end': 15.361, 'text': "this is a part of the ai for business series that i'm doing, and what you're seeing behind me is a demo of using ai to predict stock prices.", 'start': 7.755, 'duration': 7.606}, {'end': 17.063, 'text': 'so this is actually a game.', 'start': 15.361, 'duration': 1.702}, {'end': 22.686, 'text': "What I'm doing is I'm competing against an AI to see who can make better buys and sells.", 'start': 17.563, 'duration': 5.123}, {'end': 24.247, 'text': 'And this is a simulated market.', 'start': 22.766, 'duration': 1.481}, {'end': 27.009, 'text': "So I'm going to say I'm going to buy some stock right here.", 'start': 24.587, 'duration': 2.422}, {'end': 28.95, 'text': 'Okay, so you see this dark blue.', 'start': 27.069, 'duration': 1.881}, {'end': 31.771, 'text': 'The AI already beat me so I can start over again.', 'start': 28.97, 'duration': 2.801}], 'summary': 'Using ai to predict stock prices in a simulated market.', 'duration': 31.562, 'max_score': 0.209, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/7vunJlqLZok/pics/7vunJlqLZok209.jpg'}, {'end': 134.324, 'src': 'embed', 'start': 100.55, 'weight': 1, 'content': [{'end': 103.031, 'text': 'So how do we start here?', 'start': 100.55, 'duration': 2.481}, {'end': 104.271, 'text': 'What do we talk about first?', 'start': 103.051, 'duration': 1.22}, {'end': 110.734, 'text': 'Well, we know that the finance industry in general has been an early pioneer of AI technologies.', 'start': 104.692, 'duration': 6.042}, {'end': 116.956, 'text': 'Since the 70s, Wall Street has been using predictive models to try to predict the prices of the market right?', 'start': 111.154, 'duration': 5.802}, {'end': 120.518, 'text': 'how is what trend, what direction is the market moving in?', 'start': 117.336, 'duration': 3.182}, {'end': 127.761, 'text': 'how can these hedge funds best allocate their funds such that they are optimizing how much money they are earning at any given point?', 'start': 120.518, 'duration': 7.243}, {'end': 134.324, 'text': "right, there's so many different data points out there on the web and these techniques are very closely guarded as secrets.", 'start': 127.761, 'duration': 6.563}], 'summary': 'Finance industry has used ai since the 70s to predict market prices and optimize fund allocation.', 'duration': 33.774, 'max_score': 100.55, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/7vunJlqLZok/pics/7vunJlqLZok100550.jpg'}], 'start': 0.209, 'title': 'Ai for stock price prediction', 'summary': 'Discusses using ai to predict stock prices through a simulated environment, featuring a competitive demonstration between a human and ai, and delves into the application of ai in finance.', 'chapters': [{'end': 120.518, 'start': 0.209, 'title': 'Ai for stock price prediction', 'summary': 'Discusses using ai to predict stock prices through a simulated environment, featuring a competitive demonstration between a human and ai, and delves into the application of ai in finance.', 'duration': 120.309, 'highlights': ['The chapter features a demo of using AI to predict stock prices through a simulated environment where a human competes against an AI in making buy-sell orders.', 'The AI for business series explores various AI applications in finance beyond stock prediction, highlighting the early adoption of AI in the finance industry since the 70s.']}], 'duration': 120.309, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/7vunJlqLZok/pics/7vunJlqLZok209.jpg', 'highlights': ['The chapter features a demo of using AI to predict stock prices through a simulated environment where a human competes against an AI in making buy-sell orders.', 'The AI for business series explores various AI applications in finance beyond stock prediction, highlighting the early adoption of AI in the finance industry since the 70s.']}, {'end': 554.751, 'segs': [{'end': 143.448, 'src': 'embed', 'start': 120.518, 'weight': 0, 'content': [{'end': 127.761, 'text': 'how can these hedge funds best allocate their funds such that they are optimizing how much money they are earning at any given point?', 'start': 120.518, 'duration': 7.243}, {'end': 134.324, 'text': "right, there's so many different data points out there on the web and these techniques are very closely guarded as secrets.", 'start': 127.761, 'duration': 6.563}, {'end': 136.946, 'text': "they don't want us to know how they're doing things right.", 'start': 134.324, 'duration': 2.622}, {'end': 138.626, 'text': "that's their trade secret.", 'start': 136.946, 'duration': 1.68}, {'end': 140.107, 'text': 'why would they open source that?', 'start': 138.626, 'duration': 1.481}, {'end': 143.448, 'text': "that's, that's their value, that's, that's how they make money.", 'start': 140.107, 'duration': 3.341}], 'summary': 'Hedge funds aim to optimize earnings by closely guarding their trade secrets and not open-sourcing their techniques.', 'duration': 22.93, 'max_score': 120.518, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/7vunJlqLZok/pics/7vunJlqLZok120518.jpg'}, {'end': 254.455, 'src': 'embed', 'start': 211.67, 'weight': 4, 'content': [{'end': 216.193, 'text': 'It could be a classification problem, it could be a regression problem, there are different models we can use.', 'start': 211.67, 'duration': 4.523}, {'end': 223.558, 'text': "We can use a neural network, we can use a support vector machine, we can use linear regression, so we'll go into that later on.", 'start': 216.213, 'duration': 7.345}, {'end': 233.123, 'text': 'Citibank or Citigroup estimates that the biggest banks have doubled the number of people that they employ to handle compliance and regulation.', 'start': 225.639, 'duration': 7.484}, {'end': 237.565, 'text': 'And this has cost the banking industry billions of dollars, lots in loss of money.', 'start': 233.183, 'duration': 4.382}, {'end': 240.907, 'text': 'So this was a very interesting survey that I found,', 'start': 237.985, 'duration': 2.922}, {'end': 247.75, 'text': 'where a bunch of banks were asked are you considering deploying an AI solution in the next 18 months?', 'start': 240.907, 'duration': 6.843}, {'end': 250.832, 'text': 'And the majority said that they have it on their roadmap.', 'start': 248.291, 'duration': 2.541}, {'end': 254.455, 'text': 'the majority of the responses within the next 18 months.', 'start': 251.132, 'duration': 3.323}], 'summary': 'Banks are doubling compliance staff; majority planning ai deployment in 18 months.', 'duration': 42.785, 'max_score': 211.67, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/7vunJlqLZok/pics/7vunJlqLZok211670.jpg'}, {'end': 379.785, 'src': 'embed', 'start': 353.55, 'weight': 3, 'content': [{'end': 365.097, 'text': 'But to really drive my point home, the market for AI in finance is expected to grow from 1.3 billion last year to 7.4 billion in 2022.', 'start': 353.55, 'duration': 11.547}, {'end': 366.679, 'text': "That's four years from now.", 'start': 365.097, 'duration': 1.582}, {'end': 368.019, 'text': "That's a lot.", 'start': 367.319, 'duration': 0.7}, {'end': 369.54, 'text': 'So the market will grow.', 'start': 368.38, 'duration': 1.16}, {'end': 373.982, 'text': 'but the problem is that there are problems with integrating.', 'start': 370.981, 'duration': 3.001}, {'end': 375.523, 'text': "ai right, they've got these.", 'start': 373.982, 'duration': 1.541}, {'end': 378.204, 'text': 'these companies have these legacy technology environments.', 'start': 375.523, 'duration': 2.681}, {'end': 379.785, 'text': "it's hard to upgrade.", 'start': 378.204, 'duration': 1.581}], 'summary': 'Ai in finance market set to grow from 1.3b to 7.4b by 2022, facing integration challenges.', 'duration': 26.235, 'max_score': 353.55, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/7vunJlqLZok/pics/7vunJlqLZok353550.jpg'}, {'end': 446.351, 'src': 'embed', 'start': 421.01, 'weight': 6, 'content': [{'end': 426.517, 'text': "So here's a little map for you to see all the different ways that AI can be used in the FinTech market,", 'start': 421.01, 'duration': 5.507}, {'end': 430.182, 'text': 'from credit scoring to personal finance assistance for millennials.', 'start': 426.517, 'duration': 3.665}, {'end': 433.307, 'text': "So there's also opportunity in the consumer space as well.", 'start': 430.443, 'duration': 2.864}, {'end': 440.629, 'text': "young millennials prefer or not prefer, but they're more willing to listen to financial advice from not a human but from an ai.", 'start': 433.707, 'duration': 6.922}, {'end': 441.67, 'text': 'right a chat bot.', 'start': 440.629, 'duration': 1.041}, {'end': 444.071, 'text': 'hey, i see you have this goal for your budget to be.', 'start': 441.67, 'duration': 2.401}, {'end': 446.351, 'text': 'you know, save this much amount in this month.', 'start': 444.071, 'duration': 2.28}], 'summary': 'Ai in fintech includes credit scoring and personal finance assistance for millennials, with increased willingness to receive financial advice from ai chatbots.', 'duration': 25.341, 'max_score': 421.01, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/7vunJlqLZok/pics/7vunJlqLZok421010.jpg'}, {'end': 530.212, 'src': 'embed', 'start': 501.672, 'weight': 5, 'content': [{'end': 503.173, 'text': "that's the whole idea here.", 'start': 501.672, 'duration': 1.501}, {'end': 505.855, 'text': 'save these companies money and you will make money.', 'start': 503.173, 'duration': 2.682}, {'end': 512.099, 'text': "so, um, when it comes to improving security, mastercard implements what's called decision intelligence.", 'start': 505.855, 'duration': 6.244}, {'end': 517.182, 'text': "so this is just one example of a fintech company that's using ai to for security.", 'start': 512.099, 'duration': 5.083}, {'end': 519.443, 'text': "in this case it's fraud detection.", 'start': 517.182, 'duration': 2.261}, {'end': 521.085, 'text': "what they're doing is anomaly detection.", 'start': 519.443, 'duration': 1.642}, {'end': 522.926, 'text': 'Obviously, this is closed source,', 'start': 521.465, 'duration': 1.461}, {'end': 530.212, 'text': "But I'm gonna guess that they're using some deep auto encoder Where they are trying to detect the anomaly in a transaction data set.", 'start': 522.926, 'duration': 7.286}], 'summary': "Fintech companies use ai, like mastercard's decision intelligence, for fraud detection to save money and improve security.", 'duration': 28.54, 'max_score': 501.672, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/7vunJlqLZok/pics/7vunJlqLZok501672.jpg'}], 'start': 120.518, 'title': 'Hedge fund allocation optimization and ai in fintech opportunities', 'summary': 'Delves into optimizing hedge fund allocations, revealing closely guarded techniques used by hedge funds, and discusses the potential for growth in ai applications in the finance industry, emphasizing increased efficiency, security, and consumer engagement.', 'chapters': [{'end': 158.913, 'start': 120.518, 'title': 'Optimizing hedge fund allocations', 'summary': "Explores the optimization of hedge fund allocations to maximize earnings, emphasizing the abundance of data points and closely guarded techniques used by hedge funds, while offering insights into their optimization strategies and the speaker's personal financial success.", 'duration': 38.395, 'highlights': ['The techniques used by hedge funds are closely guarded as trade secrets to optimize earnings, with an emphasis on the abundance of data points available on the web.', 'The speaker, uninterested in the secretive nature of hedge fund techniques due to their own financial success, aims to share insights on optimizing earnings with the audience.', 'The speaker, financially successful through YouTube ads and partnerships, aims to guide the audience on optimizing hedge fund earnings.']}, {'end': 554.751, 'start': 158.913, 'title': 'Ai in fintech opportunities', 'summary': "Discusses the various applications of ai in the finance industry, emphasizing the potential for growth in the market, the challenges faced by companies, and the opportunities for startups in niche fields, with a focus on ai's role in increasing efficiency, security, and consumer engagement.", 'duration': 395.838, 'highlights': ['The market for AI in finance is expected to grow from 1.3 billion last year to 7.4 billion in 2022, indicating significant potential for expansion. The forecasted growth of the AI market in finance from 1.3 billion to 7.4 billion by 2022 highlights the substantial opportunity for AI integration in the finance industry.', "Citibank estimates that the biggest banks have doubled the number of people employed to handle compliance and regulation, resulting in significant costs for the banking industry. The revelation by Citibank regarding the doubling of personnel for compliance and regulation in major banks, leading to substantial financial costs, emphasizes the industry's need for efficient solutions such as AI.", 'The majority of banks are considering deploying AI solutions in the next 18 months, presenting a significant opportunity for startups in the fintech space to provide innovative AI solutions. The indication that the majority of banks are planning to implement AI solutions within the next 18 months underscores the potential for startups to offer impactful AI solutions in the fintech sector.', "AI's role in increasing security is crucial, as it can help reduce the rate of false positives in fraud detection, leading to substantial cost savings for companies and providing significant business opportunities. The significance of AI in enhancing security, particularly in minimizing false positives in fraud detection, is highlighted as a key opportunity for cost savings and business growth.", 'The chapter also emphasizes the potential for AI to improve consumer engagement and offers insights into the preferences of young millennials for AI-driven financial advice, indicating an emerging opportunity in the consumer space. The potential of AI to enhance consumer engagement, particularly among young millennials, and the preference for AI-driven financial advice signifies an emerging opportunity within the consumer market.']}], 'duration': 434.233, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/7vunJlqLZok/pics/7vunJlqLZok120518.jpg', 'highlights': ['The techniques used by hedge funds are closely guarded as trade secrets to optimize earnings, with an emphasis on the abundance of data points available on the web.', 'The speaker, financially successful through YouTube ads and partnerships, aims to guide the audience on optimizing hedge fund earnings.', 'The speaker, uninterested in the secretive nature of hedge fund techniques due to their own financial success, aims to share insights on optimizing earnings with the audience.', 'The market for AI in finance is expected to grow from 1.3 billion last year to 7.4 billion in 2022, indicating significant potential for expansion.', 'The majority of banks are considering deploying AI solutions in the next 18 months, presenting a significant opportunity for startups in the fintech space to provide innovative AI solutions.', "AI's role in increasing security is crucial, as it can help reduce the rate of false positives in fraud detection, leading to substantial cost savings for companies and providing significant business opportunities.", 'The chapter also emphasizes the potential for AI to improve consumer engagement and offers insights into the preferences of young millennials for AI-driven financial advice, indicating an emerging opportunity in the consumer space.', 'Citibank estimates that the biggest banks have doubled the number of people employed to handle compliance and regulation, resulting in significant costs for the banking industry.']}, {'end': 714.111, 'segs': [{'end': 599.158, 'src': 'embed', 'start': 555.171, 'weight': 1, 'content': [{'end': 562.457, 'text': 'And when it comes to fraudulent transactions, you can use a network, a neural network or any type of model really that will perform anomaly detection.', 'start': 555.171, 'duration': 7.286}, {'end': 564.898, 'text': 'An auto encoder is a great example of that.', 'start': 562.537, 'duration': 2.361}, {'end': 569.022, 'text': 'Google or YouTube auto encoder Suraj for a great video on that.', 'start': 565.259, 'duration': 3.763}, {'end': 575.927, 'text': "Sift Science is another startup that is focusing on this, but they're collecting data from over 6,", 'start': 570.385, 'duration': 5.542}, {'end': 579.128, 'text': '000 websites and then using that in their fraud detection solution.', 'start': 575.927, 'duration': 3.201}, {'end': 584.43, 'text': 'Another great example of AI in fintech is reducing processing times right?', 'start': 579.648, 'duration': 4.782}, {'end': 588.511, 'text': 'So one example would be receipt, like processing receipts right?', 'start': 584.47, 'duration': 4.041}, {'end': 591.432, 'text': "So one startup that's focused on this is called Parascript.", 'start': 588.551, 'duration': 2.881}, {'end': 599.158, 'text': "And what they're doing is they're using OCR, that is, object optical character recognition technology, to read in receipts,", 'start': 591.872, 'duration': 7.286}], 'summary': 'Ai in fintech includes using neural networks for fraudulent transaction detection and ocr to reduce processing times.', 'duration': 43.987, 'max_score': 555.171, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/7vunJlqLZok/pics/7vunJlqLZok555171.jpg'}, {'end': 661.309, 'src': 'embed', 'start': 640.462, 'weight': 0, 'content': [{'end': 653.266, 'text': 'And what Numerai does is they have built an open sourced hedge fund for data scientists where you can submit a model based on some data that they provide and they will award the best data scientists with some cryptocurrency.', 'start': 640.462, 'duration': 12.804}, {'end': 656.366, 'text': 'So if your model outperforms the others, you will win.', 'start': 653.306, 'duration': 3.06}, {'end': 661.309, 'text': "So that's a great way of imagining what a good hedge fund looks like.", 'start': 656.486, 'duration': 4.823}], 'summary': 'Numerai has built an open-sourced hedge fund for data scientists, awarding the best with cryptocurrency for outperforming others.', 'duration': 20.847, 'max_score': 640.462, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/7vunJlqLZok/pics/7vunJlqLZok640462.jpg'}, {'end': 700.347, 'src': 'heatmap', 'start': 661.649, 'weight': 0.851, 'content': [{'end': 672.035, 'text': "Obviously there's a lot of room for improvement here and they're just one company and you can definitely make a competitor to Numerai find a pain point that they don't really focus on and focus on that.", 'start': 661.649, 'duration': 10.386}, {'end': 674.196, 'text': 'Sentient Technologies is another one.', 'start': 672.455, 'duration': 1.741}, {'end': 679.539, 'text': "What they've done is they've used AI to create a high frequency trading bot that is,", 'start': 674.536, 'duration': 5.003}, {'end': 683.582, 'text': 'running trillions of simulated trading scenarios using public data.', 'start': 679.539, 'duration': 4.043}, {'end': 687.226, 'text': 'So they can squeeze 1, 800 days of trading into a few minutes.', 'start': 683.982, 'duration': 3.244}, {'end': 691.051, 'text': "That's incredible, and that's something that only a machine, not a human, could do.", 'start': 687.647, 'duration': 3.404}, {'end': 700.347, 'text': 'Credit lending, right? If you think about deciding whether or not to give a person some insurance or a loan.', 'start': 692.934, 'duration': 7.413}], 'summary': 'Companies like numerai and sentient technologies are using ai for high frequency trading and credit lending, demonstrating the potential for innovation in these areas.', 'duration': 38.698, 'max_score': 661.649, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/7vunJlqLZok/pics/7vunJlqLZok661649.jpg'}, {'end': 723.734, 'src': 'embed', 'start': 700.647, 'weight': 3, 'content': [{'end': 707.909, 'text': "What you are doing is you're making a prediction, right, based on their past data, where they're from, how much money they make, their marital status.", 'start': 700.647, 'duration': 7.262}, {'end': 708.91, 'text': 'These are features.', 'start': 708.19, 'duration': 0.72}, {'end': 711.651, 'text': 'This is the perfect application for machine learning.', 'start': 709.21, 'duration': 2.441}, {'end': 714.111, 'text': "And that's what we're going to start to see over time.", 'start': 712.031, 'duration': 2.08}, {'end': 723.734, 'text': "Zest Finance is one startup that does this, but there's a lot of room for competition in the space, approved borrowers that other lenders are missing.", 'start': 714.531, 'duration': 9.203}], 'summary': 'Machine learning predicts borrower creditworthiness; zest finance leads, room for competition.', 'duration': 23.087, 'max_score': 700.647, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/7vunJlqLZok/pics/7vunJlqLZok700647.jpg'}], 'start': 555.171, 'title': 'Ai in fintech', 'summary': 'Explores ai in fintech, covering fraud detection using neural networks, anomaly detection models, ai-driven receipt processing by parascript, and algorithmic trading by hedge funds like numerai and sentient technologies.', 'chapters': [{'end': 714.111, 'start': 555.171, 'title': 'Ai in fintech: fraud detection & algorithmic trading', 'summary': 'Discusses the application of ai in fintech, including examples such as fraud detection using neural networks and anomaly detection models, ai-driven receipt processing by parascript, and algorithmic trading by hedge funds like numerai and sentient technologies.', 'duration': 158.94, 'highlights': ['Algorithmic trading by hedge funds like Numerai and Sentient Technologies Hedge funds like Numerai and Sentient Technologies are using AI for algorithmic trading, with Numerai rewarding data scientists with cryptocurrency for outperforming others.', 'Fraud detection using neural networks and anomaly detection models AI is utilized in fraud detection through neural networks and anomaly detection models, with Sift Science collecting data from over 6,000 websites for this purpose.', 'AI-driven receipt processing by Parascript Parascript employs OCR technology to automate receipt processing, replacing manual data input with automation, thus improving processing times.', 'Credit lending prediction using machine learning Machine learning is applied to predict credit lending decisions based on past data and individual features, making it a perfect application for AI in fintech.']}], 'duration': 158.94, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/7vunJlqLZok/pics/7vunJlqLZok555171.jpg', 'highlights': ['Algorithmic trading by hedge funds like Numerai and Sentient Technologies, rewarding data scientists with cryptocurrency for outperforming others.', 'Fraud detection using neural networks and anomaly detection models, with Sift Science collecting data from over 6,000 websites for this purpose.', 'AI-driven receipt processing by Parascript employing OCR technology to automate receipt processing, improving processing times.', 'Credit lending prediction using machine learning to predict credit lending decisions based on past data and individual features.']}, {'end': 962.186, 'segs': [{'end': 768.344, 'src': 'embed', 'start': 714.531, 'weight': 0, 'content': [{'end': 723.734, 'text': "Zest Finance is one startup that does this, but there's a lot of room for competition in the space, approved borrowers that other lenders are missing.", 'start': 714.531, 'duration': 9.203}, {'end': 725.535, 'text': 'So check out that link as well.', 'start': 724.314, 'duration': 1.221}, {'end': 727.526, 'text': 'Portfolio management.', 'start': 726.405, 'duration': 1.121}, {'end': 733.368, 'text': 'This goes back to what I was talking about, about helping millennials keep track of their finances.', 'start': 727.726, 'duration': 5.642}, {'end': 739.491, 'text': "If they have a human financial advisor, that's expensive, but not everybody has the money for that.", 'start': 733.508, 'duration': 5.983}, {'end': 741.652, 'text': "Ideally, they don't need that.", 'start': 739.991, 'duration': 1.661}, {'end': 742.692, 'text': 'They can use a machine.', 'start': 741.732, 'duration': 0.96}, {'end': 743.932, 'text': 'This could be an app.', 'start': 742.712, 'duration': 1.22}, {'end': 746.754, 'text': 'This could be some kind of Chrome extension.', 'start': 744.193, 'duration': 2.561}, {'end': 749.715, 'text': "What it does, it's a robo-advisor.", 'start': 747.454, 'duration': 2.261}, {'end': 758.479, 'text': "We could call it a robo-advisor that will spread investments across asset classes and financial instruments in order to reach the user's goals.", 'start': 749.915, 'duration': 8.564}, {'end': 761.141, 'text': 'Like, I want to save X amount in the next years.', 'start': 758.84, 'duration': 2.301}, {'end': 763.142, 'text': "Okay, let's see how much money you have.", 'start': 761.481, 'duration': 1.661}, {'end': 764.903, 'text': "Let's see what fields you're interested in.", 'start': 763.462, 'duration': 1.441}, {'end': 768.344, 'text': 'Let me invest in this, this, this, and this, and then track returns over time.', 'start': 765.123, 'duration': 3.221}], 'summary': 'Zest finance is a startup offering a robo-advisor app for portfolio management, catering to underserved borrowers and helping users track their finances and investments.', 'duration': 53.813, 'max_score': 714.531, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/7vunJlqLZok/pics/7vunJlqLZok714531.jpg'}, {'end': 818.301, 'src': 'embed', 'start': 788.922, 'weight': 3, 'content': [{'end': 794.786, 'text': 'So how do we do this using the tools that we have at our availability? We have the Python programming language.', 'start': 788.922, 'duration': 5.864}, {'end': 795.686, 'text': 'We have TensorFlow.', 'start': 794.806, 'duration': 0.88}, {'end': 796.527, 'text': 'We have Keras.', 'start': 795.726, 'duration': 0.801}, {'end': 797.848, 'text': 'We have scikit-learn.', 'start': 796.847, 'duration': 1.001}, {'end': 799.829, 'text': 'These are open source machine learning models.', 'start': 798.108, 'duration': 1.721}, {'end': 805.693, 'text': 'We have data sets available online from Quantopia, from Google Finance, from Kaggle.', 'start': 799.869, 'duration': 5.824}, {'end': 810.036, 'text': "There's a bunch of public transaction data sets online, stock data sets online.", 'start': 805.713, 'duration': 4.323}, {'end': 811.177, 'text': 'And we have Twitter.', 'start': 810.396, 'duration': 0.781}, {'end': 818.301, 'text': 'We can scrape Twitter, we can scrape Reddit, we can scrape CNN a bunch of news headlines.', 'start': 811.577, 'duration': 6.724}], 'summary': 'Utilize python, tensorflow, keras, scikit-learn for ml with public datasets from quantopia, google finance, and kaggle, and use twitter, reddit, and cnn for scraping news.', 'duration': 29.379, 'max_score': 788.922, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/7vunJlqLZok/pics/7vunJlqLZok788922.jpg'}, {'end': 893.797, 'src': 'embed', 'start': 870.034, 'weight': 4, 'content': [{'end': 877.939, 'text': 'Not just prices, but all these different financial data points that would be inside of a financial report, for example.', 'start': 870.034, 'duration': 7.905}, {'end': 879.961, 'text': 'So returns, dividends, et cetera, things like that.', 'start': 878.099, 'duration': 1.862}, {'end': 882.512, 'text': "So that's one way to think about it.", 'start': 881.371, 'duration': 1.141}, {'end': 884.873, 'text': "Given the past data, what's the next point?", 'start': 882.552, 'duration': 2.321}, {'end': 888.694, 'text': 'Another way to think about this is as a classification problem, right?', 'start': 885.273, 'duration': 3.421}, {'end': 891.576, 'text': 'So binary yes, no buy or sell right?', 'start': 888.714, 'duration': 2.862}, {'end': 893.797, 'text': 'The price will go up or it will go down.', 'start': 892.016, 'duration': 1.781}], 'summary': 'Analyzing financial data for predicting returns, dividends, and price changes.', 'duration': 23.763, 'max_score': 870.034, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/7vunJlqLZok/pics/7vunJlqLZok870034.jpg'}, {'end': 942.735, 'src': 'embed', 'start': 913.226, 'weight': 5, 'content': [{'end': 915.988, 'text': "One great one would be a neural network that's pre-trained.", 'start': 913.226, 'duration': 2.762}, {'end': 921.671, 'text': "so we can use a pre-trained neural network that's been trained on different types of text labeled data sets,", 'start': 915.988, 'duration': 5.683}, {'end': 925.013, 'text': 'so it knows if some text is generally positive or negative.', 'start': 921.671, 'duration': 3.342}, {'end': 934.666, 'text': 'Some great libraries that do this right out of the box are Text Blob is one, Text Blob is one, but there are others as well.', 'start': 925.273, 'duration': 9.393}, {'end': 938.09, 'text': "But I'm going to get back to you on what some of those good libraries are.", 'start': 935.126, 'duration': 2.964}, {'end': 942.735, 'text': 'There we go.', 'start': 939.111, 'duration': 3.624}], 'summary': 'Utilize a pre-trained neural network for text sentiment analysis, e.g., textblob library.', 'duration': 29.509, 'max_score': 913.226, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/7vunJlqLZok/pics/7vunJlqLZok913226.jpg'}], 'start': 714.531, 'title': 'Robo-advisors and predictive modeling', 'summary': 'Discusses the potential competition in providing robo-advisors for millennials, citing startups like zest finance and responsive.ai. it also explores predictive modeling with python, tensorflow, and scikit-learn, covering regression, classification, sentiment analysis, and stock price prediction.', 'chapters': [{'end': 788.101, 'start': 714.531, 'title': 'Robo-advisors for millennials', 'summary': 'Discusses the potential for competition in the space of providing robo-advisors for millennials to help them manage their finances, with examples of startups like zest finance and responsive.ai already doing so.', 'duration': 73.57, 'highlights': ['Zest Finance and Responsive.ai are examples of startups providing robo-advisors to help millennials manage their finances, indicating a potential for competition in this space.', 'The chapter emphasizes the need for robo-advisors to help millennials keep track of their finances, especially for those who cannot afford human financial advisors.', "The concept of robo-advisors involves spreading investments across asset classes and financial instruments to help users reach their financial goals, with potential for personalized investment strategies based on user's interests and returns over time."]}, {'end': 962.186, 'start': 788.922, 'title': 'Predictive modeling with machine learning', 'summary': 'Discusses using python, tensorflow, keras, and scikit-learn for predictive modeling, exploring regression and classification problems using numerical and textual data obtained from various sources, and employing sentiment analysis for stock price prediction.', 'duration': 173.264, 'highlights': ['Regression and classification approaches using Python, TensorFlow, Keras, and scikit-learn for predictive modeling', 'Utilizing numerical data for regression models to predict future stock prices and considering additional financial data points for enhanced analysis', 'Applying sentiment analysis to textual data from Twitter and Reddit using pre-trained neural networks and libraries like TextBlob and NLTK']}], 'duration': 247.655, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/7vunJlqLZok/pics/7vunJlqLZok714531.jpg', 'highlights': ['Zest Finance and Responsive.ai are examples of startups providing robo-advisors for millennials, indicating potential competition in this space.', 'The need for robo-advisors to help millennials manage their finances, especially for those who cannot afford human financial advisors, is emphasized.', "Robo-advisors involve spreading investments across asset classes and financial instruments to help users reach their financial goals, with potential for personalized investment strategies based on user's interests and returns over time.", 'Regression and classification approaches using Python, TensorFlow, Keras, and scikit-learn for predictive modeling are discussed.', 'Utilizing numerical data for regression models to predict future stock prices and considering additional financial data points for enhanced analysis is covered.', 'Applying sentiment analysis to textual data from Twitter and Reddit using pre-trained neural networks and libraries like TextBlob and NLTK is explored.']}, {'end': 1458.349, 'segs': [{'end': 1029.916, 'src': 'embed', 'start': 1001.423, 'weight': 0, 'content': [{'end': 1004.485, 'text': "Then what we could say is, well, we've already run sentiment analysis.", 'start': 1001.423, 'duration': 3.062}, {'end': 1008.608, 'text': "Let's take that 0 or 1 and add it to our numerical data set.", 'start': 1004.585, 'duration': 4.023}, {'end': 1012.071, 'text': 'And then we could classify it as a regression problem.', 'start': 1009.009, 'duration': 3.062}, {'end': 1014.453, 'text': "So we've already run sentiment analysis.", 'start': 1012.111, 'duration': 2.342}, {'end': 1022.178, 'text': 'We could take that sentiment data, add it as a row, a single row in our numerical data set, and there we go.', 'start': 1014.793, 'duration': 7.385}, {'end': 1023.659, 'text': 'Now we have a regression problem.', 'start': 1022.278, 'duration': 1.381}, {'end': 1026.055, 'text': "So that's how we would do that.", 'start': 1025.034, 'duration': 1.021}, {'end': 1029.916, 'text': "So let's think about this.", 'start': 1029.116, 'duration': 0.8}], 'summary': 'Sentiment analysis results added to numerical data for classification and regression.', 'duration': 28.493, 'max_score': 1001.423, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/7vunJlqLZok/pics/7vunJlqLZok1001423.jpg'}, {'end': 1187.988, 'src': 'embed', 'start': 1163.851, 'weight': 1, 'content': [{'end': 1172.357, 'text': 'however, when it comes to neural networks, the one that has outperformed most others when it comes to stock price analysis is the lstm network.', 'start': 1163.851, 'duration': 8.506}, {'end': 1174.759, 'text': 'this is the long, short-term memory network.', 'start': 1172.357, 'duration': 2.402}, {'end': 1179.062, 'text': 'so neural networks can predict prices that are short-term, but a long,', 'start': 1174.759, 'duration': 4.303}, {'end': 1183.405, 'text': 'short-term memory network can make predictions about sequences far in the future,', 'start': 1179.062, 'duration': 4.343}, {'end': 1187.988, 'text': 'and these have been applied to textual character recognition modules.', 'start': 1183.405, 'duration': 4.583}], 'summary': 'Lstm neural network outperforms others in stock price analysis, predicting sequences far in the future.', 'duration': 24.137, 'max_score': 1163.851, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/7vunJlqLZok/pics/7vunJlqLZok1163851.jpg'}, {'end': 1227.261, 'src': 'heatmap', 'start': 1195.334, 'weight': 0.767, 'content': [{'end': 1202.218, 'text': "what we can do is we can use the keros deep learning library to do this, which is built on tensorflow, and here's an example of that.", 'start': 1195.334, 'duration': 6.884}, {'end': 1207.963, 'text': "we use the same scikit-learn library to load the input data, and we're using keros to build a simple model.", 'start': 1202.218, 'duration': 5.745}, {'end': 1213.449, 'text': "Now there's one more type of learning I want to talk about, and that's reinforcement learning.", 'start': 1209.105, 'duration': 4.344}, {'end': 1216.452, 'text': 'So reinforcement learning is all about learning from trial and error.', 'start': 1213.589, 'duration': 2.863}, {'end': 1219.615, 'text': 'And I think that in our case,', 'start': 1216.812, 'duration': 2.803}, {'end': 1227.261, 'text': 'the best way to really learn from our input data is to use reinforcement learning combined with a supervised learning model.', 'start': 1219.615, 'duration': 7.646}], 'summary': 'Using keros for deep learning with tensorflow, scikit-learn for input data, and combining reinforcement learning with supervised learning.', 'duration': 31.927, 'max_score': 1195.334, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/7vunJlqLZok/pics/7vunJlqLZok1195334.jpg'}, {'end': 1302.913, 'src': 'heatmap', 'start': 1239.192, 'weight': 0.724, 'content': [{'end': 1245.579, 'text': "There's a difference, right? So what we can do is we can say inside of a simulated environment, let's run some market.", 'start': 1239.192, 'duration': 6.387}, {'end': 1251.604, 'text': "But outside of that, we have an agent that's learning from multiple simulations what to do and what not to do.", 'start': 1245.999, 'duration': 5.605}, {'end': 1259.135, 'text': 'So reinforcement learning is all about trial and error an agent in an environment will take, will make an action receive a reward,', 'start': 1251.805, 'duration': 7.33}, {'end': 1264.302, 'text': 'update itself and repeat to try to optimize for that action to receive that reward right.', 'start': 1259.135, 'duration': 5.167}, {'end': 1270.676, 'text': 'so The best way to do this, or the easiest way, is to use the OpenAI Gym Environment,', 'start': 1264.302, 'duration': 6.374}, {'end': 1274.798, 'text': 'which makes making reinforcement learning agents very simple with just a few lines of code.', 'start': 1270.676, 'duration': 4.122}, {'end': 1279.399, 'text': 'And what we can do is use the SIREN OpenAI Reinforcement Learning Environment.', 'start': 1275.238, 'duration': 4.161}, {'end': 1289.463, 'text': 'This is pulling data directly from the Interactive Brokers API to create an interface for off-the-shelf ML algorithms to trade on real live financial markets.', 'start': 1279.68, 'duration': 9.783}, {'end': 1290.944, 'text': 'Very exciting stuff.', 'start': 1289.804, 'duration': 1.14}, {'end': 1293.005, 'text': 'So I definitely recommend checking that out.', 'start': 1291.284, 'duration': 1.721}, {'end': 1296.328, 'text': "When it comes to our demo, now it's time for our demo.", 'start': 1293.485, 'duration': 2.843}, {'end': 1302.913, 'text': "When it comes to our demo, what we're really doing is we're just using a simple linear regression model for the sake of this demo.", 'start': 1296.888, 'duration': 6.025}], 'summary': 'Reinforcement learning with openai gym and siren for trading on live financial markets; using linear regression for demo.', 'duration': 63.721, 'max_score': 1239.192, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/7vunJlqLZok/pics/7vunJlqLZok1239192.jpg'}, {'end': 1302.913, 'src': 'embed', 'start': 1279.68, 'weight': 2, 'content': [{'end': 1289.463, 'text': 'This is pulling data directly from the Interactive Brokers API to create an interface for off-the-shelf ML algorithms to trade on real live financial markets.', 'start': 1279.68, 'duration': 9.783}, {'end': 1290.944, 'text': 'Very exciting stuff.', 'start': 1289.804, 'duration': 1.14}, {'end': 1293.005, 'text': 'So I definitely recommend checking that out.', 'start': 1291.284, 'duration': 1.721}, {'end': 1296.328, 'text': "When it comes to our demo, now it's time for our demo.", 'start': 1293.485, 'duration': 2.843}, {'end': 1302.913, 'text': "When it comes to our demo, what we're really doing is we're just using a simple linear regression model for the sake of this demo.", 'start': 1296.888, 'duration': 6.025}], 'summary': 'Using interactive brokers api for ml algorithm trading demo.', 'duration': 23.233, 'max_score': 1279.68, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/7vunJlqLZok/pics/7vunJlqLZok1279680.jpg'}], 'start': 962.547, 'title': 'Stock prediction and trading strategies', 'summary': 'Covers sentiment analysis for stock classification, adding to a numerical dataset for regression analysis, and utilizing various models like linear regression, support vector machines, and neural networks. additionally, it explores the use of lstm networks for predicting long-term stock prices and the application of interactive brokers api to create a web app for trading with off-the-shelf ml algorithms.', 'chapters': [{'end': 1163.851, 'start': 962.547, 'title': 'Stock sentiment analysis and regression models', 'summary': 'Discusses using sentiment analysis to classify stock sentiment as good or bad, then adding it to a numerical dataset for regression analysis. it also explores using linear regression, support vector machines, and neural networks for predicting stock prices.', 'duration': 201.304, 'highlights': ['Using sentiment analysis to classify stock sentiment and adding it to a numerical dataset for regression analysis. The majority sentiment from Reddit posts, tweets, blog posts, and comments is classified as the overall sentiment for the stock for a given day, turning it into a numerical data point (0 or 1).', 'Exploring the use of linear regression model for predicting stock prices based on a single data point. Building a model around a single data point of price and date to predict the next price for the next day, using the simple equation y equals mx plus b.', 'Discussing the use of support vector machines for regression analysis on stock prices. Replacing the linear regression model with a support vector machine for regression analysis, leveraging the scikit-learn library for the implementation.', 'Introducing the concept of using neural networks for predicting stock prices. Explaining the simple equation for a neural network, which involves input data, multiplication by weight matrix, addition of bias value, application of non-linearity or activation function, and obtaining the output for predicting stock prices.']}, {'end': 1279.399, 'start': 1163.851, 'title': 'Lstm network for stock price analysis', 'summary': 'Discusses the use of lstm networks for stock price analysis, highlighting its capability to predict long-term sequences and its application in reinforcement learning for optimizing future outcomes.', 'duration': 115.548, 'highlights': ['The LSTM network has outperformed most others in stock price analysis, with the ability to make predictions about sequences far in the future.', 'Reinforcement learning combined with supervised learning models is recommended for learning from input data and optimizing future outcomes.', 'The OpenAI Gym Environment simplifies the process of creating reinforcement learning agents with just a few lines of code.']}, {'end': 1458.349, 'start': 1279.68, 'title': 'Interactive brokers api demo', 'summary': 'Discusses using interactive brokers api to create a web app for trading with off-the-shelf ml algorithms, focusing on a simple linear regression model for stock price prediction and the potential to integrate sentiment analysis from social media platforms.', 'duration': 178.669, 'highlights': ['Using Interactive Brokers API to create web app for trading with off-the-shelf ML algorithms The chapter emphasizes the use of Interactive Brokers API to develop a web app for trading with pre-built machine learning algorithms.', 'Focusing on a simple linear regression model for stock price prediction The demo centers on the use of a simple linear regression model for stock price prediction, utilizing past stock prices as input data.', 'Potential to integrate sentiment analysis from social media platforms as additional feature for stock prediction The chapter suggests the potential to enhance stock prediction by integrating sentiment analysis from social media platforms as an additional feature, such as sentiment analysis from Twitter API or Reddit posts.']}], 'duration': 495.802, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/7vunJlqLZok/pics/7vunJlqLZok962547.jpg', 'highlights': ['Using sentiment analysis to classify stock sentiment and adding it to a numerical dataset for regression analysis.', 'The LSTM network has outperformed most others in stock price analysis, with the ability to make predictions about sequences far in the future.', 'Using Interactive Brokers API to create web app for trading with off-the-shelf ML algorithms.']}], 'highlights': ['The LSTM network has outperformed most others in stock price analysis, with the ability to make predictions about sequences far in the future.', 'Using sentiment analysis to classify stock sentiment and adding it to a numerical dataset for regression analysis.', 'The chapter features a demo of using AI to predict stock prices through a simulated environment where a human competes against an AI in making buy-sell orders.', 'The techniques used by hedge funds are closely guarded as trade secrets to optimize earnings, with an emphasis on the abundance of data points available on the web.', 'The market for AI in finance is expected to grow from 1.3 billion last year to 7.4 billion in 2022, indicating significant potential for expansion.', 'The majority of banks are considering deploying AI solutions in the next 18 months, presenting a significant opportunity for startups in the fintech space to provide innovative AI solutions.', "AI's role in increasing security is crucial, as it can help reduce the rate of false positives in fraud detection, leading to substantial cost savings for companies and providing significant business opportunities.", 'The chapter also emphasizes the potential for AI to improve consumer engagement and offers insights into the preferences of young millennials for AI-driven financial advice, indicating an emerging opportunity in the consumer space.', 'Algorithmic trading by hedge funds like Numerai and Sentient Technologies, rewarding data scientists with cryptocurrency for outperforming others.', 'Fraud detection using neural networks and anomaly detection models, with Sift Science collecting data from over 6,000 websites for this purpose.', 'AI-driven receipt processing by Parascript employing OCR technology to automate receipt processing, improving processing times.', 'Credit lending prediction using machine learning to predict credit lending decisions based on past data and individual features.', 'Zest Finance and Responsive.ai are examples of startups providing robo-advisors for millennials, indicating potential competition in this space.', 'The need for robo-advisors to help millennials manage their finances, especially for those who cannot afford human financial advisors, is emphasized.', "Robo-advisors involve spreading investments across asset classes and financial instruments to help users reach their financial goals, with potential for personalized investment strategies based on user's interests and returns over time.", 'Regression and classification approaches using Python, TensorFlow, Keras, and scikit-learn for predictive modeling are discussed.', 'Utilizing numerical data for regression models to predict future stock prices and considering additional financial data points for enhanced analysis is covered.', 'Applying sentiment analysis to textual data from Twitter and Reddit using pre-trained neural networks and libraries like TextBlob and NLTK is explored.', 'Using Interactive Brokers API to create web app for trading with off-the-shelf ML algorithms.']}