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Artificial intelligence and machine learning are two phrases that everyone nowadays seems to use. Lately, it’s on the hype across most industries where technological advancements are crucial to survival and growth, e.g., agriculture, healthcare, and manufacturing. The AI and machine learning fields have seen tremendous growth that warrants some hype, as they’re becoming critical to improving the industries that most affect us. But it’s important to know exactly how AI/machine learning is transforming the industries and their role in our technological advancements. To start, let’s briefly define both terms. is the simulation of human intelligence processes by machines, mainly computer systems. It’s a wide-ranging branch of computer science that entails training computers to do tasks usually done by humans. Artificial intelligence is a subset of artificial intelligence that deals with building computer systems to learn and adapt automatically without human input, by using algorithms to analyze and draw conclusions from data. Machine learning AI and machine learning are the foundation for using computers to automate ordinary human tasks, so we can focus on other important tasks that only humans typically perform. They’re helping to make things easier across many industries in the following ways: Manufacturing Automating mundane tasks is the first and foremost way that AI is helping transform the manufacturing industry. Thanks to AI improvements over the past decade, robots can now perform many manufacturing tasks that humans typically perform, e.g., assembling final products, moving heavy tools across large manufacturing spaces, welding, drilling, and quality control checks. AI is also adopted during product development to simulate products in virtual environments to know how they’ll perform in the real world. For example, automobile designs are run through aerodynamics simulation software to measure performance before manufacturing starts in real life. Though such simulation software already exists, the simulation process is far from perfect because it requires many hours of calculations and estimations. Computer systems are learning to simulate real-world scenarios with more accuracy and less time with machine learning. Also, AI helps with the predictive maintenance of complex machines, giving a clue of when parts are at their breaking point and need care. Healthcare AI is helping transform the healthcare industry in ways including: Drug development Many biotech and pharmaceutical companies now use AI to aid drug discovery. Drug discovery involves complex processes at all steps, such as predicting chemical properties, identifying molecules, and image analysis. For example, drug discovery research usually requires hundreds of laboratory hours to analyze microscopic images and observe how diseases unfold and how drug candidates affect them. This task requires experts to spend valuable time studying the images and taking down their findings, but they could spend that valuable time on other crucial tasks that computers typically can’t perform. Pharmaceutical companies now adopt machine learning to help analyze microscopic images and free valuable time for biochemists to work on other things. Medical Imaging (Testing) Medical imaging is another area where AI and machine learning are making major healthcare improvements. For example, when radiologists perform x-ray scans on patients and send the results to doctors for analysis, doctors typically try their best to infer the medical problems affecting a patient so they can begin effective treatment. However, doctors don’t have infinite time and often miss subtle problems like cancers and blood vessel blockages. With machine learning, computers can be trained on a dataset of millions of images and use that experience to point out medical conditions from x-ray images. Already, hospitals to develop artificial intelligence software that reads x-rays and other medical scans with hopes that it’ll spot symptoms that doctors typically miss. are spending millions of dollars Cybersecurity AI is creeping into the cybersecurity field with very positive results. Many companies have propped up in the niche of developing cybersecurity software that uses artificial intelligence and machine learning to analyze networks and fish out cyber threats, e.g., CrowdStrike, SentinelOne, and Cylance. AI works better when analyzing cyber threats because machine learning systems can learn much more over time than humans, whose time is often limited. Also, hackers could launch countless types of attacks on a network, and AI techniques can detect them better than typical antivirus software when enormous data is involved. Cyber security is one of the critical fields which benefits from AI and machine learning advancements. With AI applied in cybersecurity, it’s easier for IT personnel working at large enterprises to take note of the typical backdoors and attack vendors hackers take advantage of and work to patch them up. AI in the cyber field is just at the beginning stages and still has much potential ahead. Retail AI brings many benefits to the retail industry by automating mundane tasks usually performed by humans, e.g., cashiers. Many big retailers now adopt AI-based checkout processes, e.g., Amazon Go, because they benefit from it. For example, a company named Caper AI makes smart shopping carts and cashier-less checkout systems for small convenience stores. AI’s impact on retail doesn’t stop at the consumer side but also affects the back-end operations such as warehouse management and logistics. AI has given birth to robots that move goods within warehouses with ease and autonomous truck driving that’ll help move goods from one place to another at lower costs. Transportation Autonomous driving accomplished with AI will make a big dent in the transportation industry. Many transportation companies, especially trucking and public transport, have judged autonomous driving to be the industry’s future and are spending big to make it happen. Automobile companies alike are also investing billions of dollars in developing autonomous driving software for their customers. If all goes as planned, we’ll have suitable autonomous driving within the next 100 years that’ll revamp the transportation industry as we know it. Finance and Investments Yes, AI is also transforming the finance industry. Hedge funds, mutual funds, venture capital firms, high-frequency trading firms, and the likes are increasingly adopting AI and machine learning to help make investment decisions. To make profitable investments in the securities market, investors analyze large swaths of data to predict which securities will perform well in the future. This analysis is time-consuming and exhaustive, making it difficult for humans to do. It’s no surprise that financial firms have adopted machine learning and artificial intelligence to help with their analysis. For example, Stripes, a venture capital firm, employs a virtual robot that trawls for information around the web on over 13 million private companies and looks out for potential investments for the VC firm. The robot has helped Stripes’ 0% net of fees, . according to Bloomberg Investors also employ AI and machine learning for automated trading to a good point. Examples of software they use include the , , and . There are countless other industries that AI is transforming by the day, such as agriculture, education, construction, cloud computing, etc. Bloomberg Terminal MetaTrader 5 Alphasense Challenges of AI in the Enterprise Though AI brings about much-needed advancements across industries, there are various challenges in implementing them to satisfaction. They include: Ethics Automation stemming from AI can lead to significant job losses without viable alternatives in the short term. AI is gradually replacing jobs such as truck driving, customer service, receptionists, and book-keeping, which are jobs held by many people. There’ll be adverse economic consequences if we don’t create alternative employment for these people. Cost Developing and implementing AI and machine learning systems requires a large number of computing resources that translate to high costs. High cost is a barrier for small businesses to partake in the benefits of AI advancements. Training Obtaining the vast data set needed to develop machine learning and AI techniques can be difficult for enterprises. Also, the data obtained may be corrupt or falsified and distort an AI system’s workability. All in all, the rise of AI and machine learning is a net benefit for our world. It’s bound to improve the economy and the utility of many industries. McKinsey, a leading management consulting firm, estimates that AI may deliver an . It’s evident at this point that AI is here to stay, and it’s only up to us to adapt to it or stay behind. additional economic output of $13 trillion by 2030

How AI and Machine Learning Changes Modern-day Industries

If you missed the first AI industry analysis on the Insurance vertical , check that out here . I. Financial Innovation: Lots of Talk, Little Action? TThe financial sector is historically one of the most resistant to change you might think of. It is then inevitable that big banks from one hand and startups from the other hand are creating a huge break in the financial industry and I believe this is happening not because of the use of a specific technology but rather because of their intrinsic cultural differences, diverse structural rigidity, and alternative cost-effective business models. In other words, banks do not innovate either because they are too big to quickly adapt and follow external incentives or because they don’t know how (and want to) truly change. This is not simply true in the industry but also in academia, where until the mid-nineties there were no relevant contributions to financial innovation at all (Frame and White, 2002). In fact, in few survey articles (Cohen and Levin, 1989; Cohen, 1995) with more than 600 different articles and books quoted, . none of them was related to financial innovation subjects Of course, things changed over the last five years, but my opinion is that was really out of necessity rather than a voluntary push-approach from the banking sector. Financial innovation is, therefore, something which seems to be usually rather than , and often more characterized by a rather than a one (although this might be a controversial opinion, I guess). Given the new technological paradigm (which is tightening the inner strong causal relationship between innovation and growth) it seems natural to wonder whether a better innovation model can be therefore imported by a different (and more successful) sector. imported internally generated product-innovation process I found that there is a very specific and interesting case of a sector which had to ‘ ’ rather than ‘ ’: the (Baker, 2003; Gans and Stern, 2004; Fuchs and Krauss, 2003; Lichtenthaler, 2008). innovate-to-survive innovate-to-grow biopharma industry Innovation drivers map. The biopharma companies feel more the urgency to innovate and are also more committed to that . The graph I built plots 25 major banks (blue) and 25 major pharmaceutical (red) companies based on their Innovation Impulse and Commitment. The Impulse variable has been built using the number of patents a company filed (a proxy for the external pressure to innovate) and the number of recorded shareholders (a proxy for the internal pressure to innovate). The Commitment shows instead the R&D intensity (net sales) while the size of the bubbles the net income for each company. The data points were obtained by Medtrack, Osiris, and Zephyr in 2014. II. Innovation Transfer: the Biopharma Industry The biopharma industry is not a single sector but actually two different ones: , populated by smaller companies that drive the research and exploration phase, and the , big giants that through the last century became huge go-to-market and sales enterprises. the biotech space pharmaceutical companies Hence, there is from one hand and from the other…Is it something that we have already seen somewhere, didn’t we? pure (risky) innovation pure commercialization skills The biopharma industry and the financial sector suffer from a strong polarized innovation What characterizes the industry is that the risky activity lies in the initial development process rather than in the market phase. The problem is not to match customer demand or find a market for your product, but it is actually developing the molecule in the first place. The probability of success is extremely low and the timeline very long (10–15 years) and the 20-years patents give you only a temporary advantage. More importantly, it looks that only 3 out of 10 of the drugs produced are able to repay the development costs (Meyer, 2002) and that most of the companies operate at loss while the top 3% companies alone generate almost 80% of the entire industry profits (Li and Halal, 2002). A tough business, isn’t it? The biopharma industry is then no longer simply a but also a . Innovation is not ancillary but it is the quintessential driver to survive. And this is also why they had to identify a range of different methods to foster their growth-by-innovation: R&D, competitive collaboration schemes, venture funding, co-venture creation, built-to-buy deals, limited partnership agreements, etc. human-intensive business capital-demanding one It should be clear by now where I am heading to: as the biopharma sector and it is and make it profitable. the financial industry doesn’t strongly feel the need to innovate not experimenting and pushing to create new models that might spread their innovation risk The financial industry doesn’t strongly feel the need to innovate III. Introducing AI, Your Personal Financial Disruptor Image Credit: Haru1/Shutterstock By now you might object “All good man, but FS and biopharma are still sooo different, so why should I import innovation models from a sector which is completely different from mine?”. Well, that’s the catch: I don’t think they are. And the reason why they are becoming a lot more similar is precisely Artificial Intelligence. and has a which are somehow similar to the biopharmaceutical one: it requires a long time to be created, implemented and correctly deployed (with respect to the financial industry standards, of course); it is highly technical and requires highly specialized talents; it is highly uncertain, because you need to experiment a lot before finding something that works; it is expensive, both in terms of time as well as monetary investments (talents, hardware, and data are really expensive); it is risky and the risk lies in the initial development phase, with a very high-payout but a high likelihood to fail as well. AI is creating a strong pressure to innovate for the financial sector development cycle and characteristics AI is creating a strong pressure to innovate for the financial sector But AI is also introducing a completely new speed and degree of trust in the financial industry, which lowers the tolerable mistakes at the same level of the biopharma sector. If your algorithms point out to the wrong product to sell or the wrong book to be recommended, it is not a big deal. If your system misinterprets some signals in the market or while developing a drug though, you end up losing millions in seconds or even losing human lives. It is then not only stretching out issues that intrinsically belong to the financial sector such as or , but it is also bringing new problems such as to the picture (specifically in consumer applications). regulation accountability or the biased data lack of transparency And last but not least, AI is making the question mark on the matter bigger than even in FS, the same as it was in the biopharma industry back in the nineties and that culminated in the current biotech-pharmaceutical dichotomy (if you are wondering anyway, this choice is all focused around on your , , and with respect to your competitors — do you have enough data to train an ANI? Can your team/project scale? Is the ANI unique or something your peers are doing or need to do as well?). “build vs buy” data capacity team and project scalability uniqueness of the project AI is revolutionizing a centuries-old industry innovation flow from the ground This is why I believe AI in financial services to be extremely important — not much for the specific innovation or product it is introducing but rather because . it is revolutionizing a centuries-old industry innovation flow from the ground IV. Segmentation of AI in Fintech Image Credit: TZIDO/Shutterstock Artificial Intelligence is using structured and unstructured data in financial services to improve the customer experience and engagement, to detect outliers and anomalies, to increase revenues, reduce costs, find predictability in patterns and increase forecasts reliability…but it is not so in any other industry? We all know this story, right? So what is really peculiar about AI in financial services? First of all, FS is an industry full of data. You might expect this data to be concentrated in big financial institutions’ hands, but most of them are actually and thanks to the new to smaller players as well. AI can then be easily developed and applied because the barriers to entry are lower with respect to other sectors. public EU payment directive (PSD2) larger datasets are available Second, many of the underlying processes can be relatively while many others can be improved by either brute force computation or speed. And historically is one of the sectors that needed this type of innovation the most, is incredibly competitive and is always looking for some new source of ROI. easier to be automatized Bottom line: the marginal impact of AI is greater than in other sectors. Third, the makes the field really fertile for AI. AI needs (a lot of) innovative data and above all feedback to improve, and millennials are not only happy to use AI as well as providing feedback, but apparently even less concerned about privacy and giving away their data. transfer of wealth across different generations There are also, of course, a series of in financial sector that limit a smooth and rapid implementation: legacy systems that do not talk to each other; data silos; poor data quality control; lack of expertise; lack of management vision; lack of cultural mindset to adopt this technology. specific challenges for AI So what is missing now is only having an overview of the AI fintech landscape. There are also plenty of maps and classification of AI fintech startups out there (probably the best ones are the one provided by , in particular and ), so I am not introducing anything new here but rather simply giving you my personal framework: CB Insights this this : this category is about making the end-client life better and easier and it includes ; ; automated financial advisors and planners that assist the users in making financial decisions ( check ); smart wallets that coach users differently based on their habits and needs. Financial Wellness personalized financial services credit scoring robo-advisor, virtual assistants, and chatbots — CB Insights map if you like Examples include [robo-advisors and conversational interfaces] Kasisto ; Trim ; Penny ; Cleo ; Acorns ; Fingenius ; Wealthfront ; SigFig ; Betterment ; LearnVest ; Jemstep ; [credit scoring] Aire ; TypeScore ; CreditVidya ; ZestFinance ; Applied Data Finance ; Wecash ; : I think that, given the importance of this instrument, it deserves a separate category regardless of the specific application is being used for (which may be payments, compliance, trading, etc.). Blockchain Examples include: Euklid ; Paxos ; Ripple ; Digital Asset ; : this can be divided into (payment security and physical identification — ) and (looking for fraudulent and abnormal financial behavior — ). Financial Security identification biometrics and KYC detection AML and fraud detection Examples include, respectively: EyeVerify ; Bionym ; FaceFirst ; Onfido ; and Feedzai ; Kount , APEX Analytics ; this category includes payments, peer-to-peer lending, and debt collection. Money Transfer: Examples include: TrueAccord ; LendUp ; Kabbage ; LendingClub ; : this is a big section, and I tend to divide it into five main subsections: Capital Markets (either algotrading or trading/exchange platforms). i) Trading Examples include: Euclidean ; Quantestein ; Renaissance Technologies , Walnut Algorithms ; EmmaAI; Aidyia ; Binatix ; Kimerick Technologies ; Pit.ai ; Sentient Technologies ; Tickermachine ; Walnut Algorithm ; Clone Algo ; Algoriz ; Alpaca ; Portfolio123 ; Sigopt ; (either crowdsource funds or home-trading). ii) Do-It-Yourself Funds Examples include: Sentifi ; Numerai ; Quantopian ; Quantiacs ; QuantConnect ; Inovance ; (information extraction or insights generation). iii) Markets Intelligence Examples include: Indico Data Solutions ; Acuity Trading ; Lucena Research ; Dataminr ; Alphasense ; Kensho Technologies ; Aylien ; I Know First ; Alpha Modus ; ArtQuant ; (most of the alternative data applications are in capital markets rather than broader financial sector so it makes sense to put it here). iv) Alternative Data Examples include: Cape Analytics ; Metabiota ; Eagle Alpha ; v) (this section is more a residual subcategory because most of the time startups in this group fall within other groups as well). Examples include: Risk Management Ablemarkets ; Financial Network Analysis . Conclusions I am arguing since the beginning of the article that AI is making financial services and biopharma much more alike, and that the FS industry might learn something from how the other industry innovates. The reality is that the financial industry has also very specific traits and challenges it needs to overcome. The biggest difference I currently see in that is the effect AI is having on the physical products market: while in almost any sector AI is used with the final goal of creating or improving new products (and this is true also for drug development, for example) in the financial ecosystem is having exactly the opposite effect. . Its final goal will be to create the (frictionless) bank of the future: no branches, no credit cards, no frauds, no menial reporting activities. A with modular components that increases our financial literacy and has no physical products or spaces. AI is making the industry more digitalized than ever before bank-as-a-platform It would definitely be a great world to live in. Can’t wait for it. Reference Baker, A. (2003). “Biotechnology’s Growth-Innovation Paradox and the New Model for Success”. 9 (4): 286–88. Journal of Commercial Biotechnology Cohen, W. (1995). “Empirical Studies of Innovative Activity”, in , edited by Paul Stoneman. Cambridge, Mass.: Blackwell. Ch. 6, 182–264. Handbook of the Economics of Innovation and Technological Change Cohen, W., Levin, R. (1989). “Empirical Studies of Innovation and Market Structure”, in , Vol. 2, edited by Richard Schmalensee and Robert Willig. Amsterdam: North-Holland. Ch. 18, 1059–1107. Handbook of Industrial Organization Frame, W. S., White, L. J. (2002). “Empirical studies of financial innovation: lots of talk, little action?”. Working Paper, Federal Reserve Bank of Atlanta, N. 2002–12. Fuchs, G., Krauss, G. (2003). “Biotechnology in Comparative Perspective”. In: . G. Fuchs (ed.). New York: Routledge, 1–13. Biotechnology in Comparative Perspective Gans, J., Stern, S. (2003). “Managing Ideas: Commercialization Strategies for Biotechnology”. 01/03: 1–24. Intellectual Property Research Institute of Australia Working Paper Li, J., Halal, W. E. (2002). “Reinventing the biotech manager”. , 20 Suppl (6): 61–3. Nature Biotechnology Lichtenthaler, U. (2008). “Open Innovation in Practice: An Analysis of Strategic Approaches to Technology Transactions”. , 55 (1): 148–157. IEEE Transactions on Engineering Management Meyer, F. J. (2002). “Business Models That Biotech Companies Employ”. November 25, 2002 Enterprise Development KFBS Biotech Speakers Series — — Follow me on Medium Look at my other articles on AI and Machine Learning: Unsupervised Investments (I): A Guide to AI Investors Unsupervised Investments (II): A Guide to AI Accelerators and Incubators Who is best positioned to invest in Artificial Intelligence?

Artificial Intelligence Verticals (II): Fintech

Chiliz's Game-Changing Year: Blockchain Touchdowns and the Rise of SportFi in 2023 As the world of blockchain and sports converges in a dance of innovation and fan interaction, Chiliz reflects on a monumental 2023 – a year that saw SportFi, operating at the intersection of real-world sports and web3, defy industry challenges and soar to new heights of success. Let's dive into the highlights of Chiliz's blockbuster year and its impact on the sports and entertainment industry. Chiliz and Socios.com: A Symbiotic Journey Redefining SportFi Established in 2018, Chiliz has positioned itself as a pivotal player in the blockchain space, tailoring its services specifically for the sports and entertainment sector. The company is unwavering in its commitment to developing robust and secure blockchain solutions aimed at elevating fan experiences through the incorporation of digital assets. At the core of the Chiliz ecosystem lies CHZ, its native digital token, which currently drives platforms like Socios.com, ChilizX, and the Chiliz Chain. Socios.com, fueled by the digital currency Chiliz ($CHZ), serves as a standout fan engagement and rewards application, empowering fans to connect with their favorite teams and clubs through digital assets called Fan Tokens. Furthermore, Socios Collectibles, utilizing the Chiliz Chain, addresses counterfeiting concerns by embedding each item with an NFC chip and storing its data on the blockchain. This forward-thinking strategy enables owners to effortlessly verify the authenticity and ownership of their collectibles using a smartphone. The integration of blockchain technology underscores Chiliz's dedication to transforming fan engagement and asset ownership in the dynamic realm of sports and entertainment. Chiliz Chain: A New Era Unveiled The curtain rose on 2023 with the celebration of Chiliz's fifth anniversary and the grand unveiling of its native Layer 1 blockchain – Chiliz Chain. The genesis block, validated on February 8, marked the birth of a platform that has since become a playground for developers crafting SportFi applications. Chiliz Chain, in synergy with the Socios.com app, has become a cornerstone for global sporting organizations seeking to amplify fan engagement in the ever-evolving landscape of web3. Chiliz Chain's launch set the stage for an extraordinary year for SportFi, with global sporting organizations embracing the potential of web3 for fan incentivization and community building. The platform's versatility supports various SportFi use cases, from NFT minting and Fan Tokens to DeFi products, innovative games, and web3-ready ticketing. The result- A year of unprecedented growth and exploration at the intersection of sports and blockchain. Chiliz Labs: Nurturing Innovation in Sports Blockchain by Strategic Partnerships and Acquisitions The year also witnessed Chiliz's commitment to fostering innovation through the launch of a $50M Chiliz Labs incubator and accelerator program. This initiative aims to nurture early-stage blockchain projects in the sports sector, propelling the industry forward with cutting-edge technologies and ideas. Chiliz's journey in 2023 was marked by strategic collaborations, including a 20% stake acquisition in MatchWornShirt, a move amplifying the role of blockchain in authenticating sports collectibles. Over 25 key partnerships with projects like Blockasset, FanFest, LiveLike, Football at AlphaVerse, TopGoal Footballcraft, and Lillius further expanded the SportFi ecosystem, creating a diverse playground for fans and enthusiasts alike. Socios.com Milestones and Chiliz Chain's Transaction Triumph The Socios.com app, a hub for fan engagement and rewards, witnessed remarkable growth in 2023. With a 30% increase in users across 160 countries, major clubs like Tottenham Hotspur joining the platform, and innovative features like Locker Room allowing fans to secure unique digital collectibles, Socios.com became a digital haven for sports enthusiasts. In 2023, Chiliz Chain set a record with over 3 million transactions, contributing to a cumulative revenue of $437 million generated for the sports industry to date. The blockchain's success becomes the driving force for Chiliz as they aim to build upon these achievements in 2024, intensifying efforts to mainstream SportFi for the mutual benefit of clubs and fans alike. Conclusion: A Pioneering Path into 2024 As we bid farewell to a groundbreaking 2023 for Chiliz and SportFi, the stage is set for an even more thrilling act in 2024. Chiliz's commitment to innovation, strategic partnerships, and the exploration of web3 possibilities solidify its role as a pioneer in the intersection of blockchain and sports. The journey continues, promising fans and enthusiasts an exhilarating ride into the future of sports engagement – a journey where innovation knows no bounds, and the passion of fans fuels the flames of progress. Get ready for a year where the lines between the virtual and the real blur, as Chiliz leads the way into a new era of sports entertainment. Don’t forget to like and share the story! Vested Interest Disclosure: This author is an independent contributor publishing via our . Be it through direct compensation, media partnerships, or networking, the author has a vested interest in the company/ies mentioned in this story. HackerNoon has reviewed the report for quality, but the claims herein belong to the author. #DYOR brand-as-author program

Chiliz Revolutionizes Sports Engagement: A Year in Review and a Glimpse into 2024

You won't believe how many jerks a person could meet in one day that just being nice is already a marketing advantage for yourself and your product. You see people mistreating each other every day. They cut the line to the register, tail-gate you on the road, self-promote themselves online, and send mass emails without bothering to get your name correct. I'm not saying people are naturally worse today than the past. Maybe it is a causal chain of negative medias that are removing the sense of self-worth in people and making them less considerate and patient to each other, or maybe it isn't entirely something else. All I'm saying is most of them aren't shy about mistreating each other. Remember the times when jerks were considered alphas and went on to achieve great things?  Those days kind of ended with . Right now, there are so many bad actors out there that being one is just uncool and ignorable. Travis Kalanick Being genuine differentiates you. When I started working on , I really placed a bet on the genuine bits in myself and others. After all, I was going to launch a scrappy service, and there was no use disguising as an Uber-equivalent. I decided there was no need to launch a flashy landing page or employ any kind of fancy email marketing template to communicate to the users. Alphaseek I felt that it was just a flat lie to them and worst of all, to myself. I mean, who am I to boast? I might as well spend the rest of the time left from building just talking to people instead of fixing my website CSS to perfection. That's why for each of the user who have signed up and the prospects, I'd tried my best to reach out and connect in-person either through a scheduled phone call or direct chat. I ask for a phone number in the because I want to make sure I can directly connect with my early users, not just email. signup form I'd tried to take time really knowing them and what they were doing and make sure I was genuinely interested in what they were doing. I made sure I the conversation, because I'll bet they would feel it too. It's a relationship, not a transaction. enjoyed Email: the enemy of connection. I've been using emails just for important communications like announcing a release or sending credentials to my users. When it comes to genuine communication, emails suck. Again, this isn't because email is inherently bad, but just because people are flooded with emails nowadays than ever, especially the non-personal ones. Emails had become synonymous to spamming (most laymen associate marketing emails and even those from services they're subscribed to as spams). Email is one-sided, and what I dislike about it is you can never be sure how the recipient feels about your what you write. Of course, you could track if your email has been opened, but that says very little about how the opener feels. It is also a bit creepy. People want to be listened. The reason why this will work is because we all long to be listened, not solicited. Hearing people out is a value anyone can offer, yet so rare. When was the last time you engage with someone who contacted you to sell something? Selling is good only if you're selling something of value to someone, and that's difficult to do for everyone. Listen to people and engaging with them is as good for anyone. There's just no reason not to do it. At the very least, listening makes you feel good. Being a jerk is taxing. If you aren't a psychopath, mistreating people will eventually take a toll on you and affect your long-term motivation and resilience. You need constant drives to get up every morning and make it through the day, and there is no easier way to earn them than treating people decently. The positive karmas are just very powerful. It feels almost like . When you make someone happy, that energy bounces back at you. It may be the result of knowing that that person will also share that to the next person, I don't know. At least that's how I feel. Newton's Third Law of Reactive Force

#Don'tBeAJerk as A Viable Marketing Strategy

Three months ago in the Bay Area, I was working on another project, had an ex-Google tech co-founder, and a chat bot nobody cared about. Now I'm on a New Year vacation in Thailand, I've just launched , a toolkit for hackers to create referral marketing campaign to 100+ beta customers, and one co-founder short. Alphaseek SDK . And I couldn't be happier The Spinoff I had no background in marketing at all. I was just hopeless after my last failed chat bot project, and the referral was part of what I've built from scratch for its signup page. I realized all referral marketing products are made for marketers and had the following things I didn't like: They charge from $50 to $800+ per month Bundle up bloated features to justify pricing Pricing does not reflect your campaign's traffic Cater to companies with a marketing budget Higher learning curve for makers So I tried building on top of what I already had. I took longer than I thought to generalize it. I knew with a full-time job and a family, I didn't have a lot of time. I became extra skeptical about what I work on, making sure it would move the needles. A few things I decided early on NOT spend time on: (I'm using Notion) Landing page (I'm using Typeform) Signup form Graphic design (I'm using FreePiks and Unsplash) Overkill technical deployment (I'm using GCloud) Now I'm quite happy to be focusing on what I like doing while having time to spend with families and friends. I would love to get people to try out and give me feedback and bug reports. Pick the Right Guy for the Wrong Job My mistake early on was to work with someone who was seemingly talented. My last co-founder was an ex-Google and ex-Microsoft. I wanted him for his experience, not who he was. I didn't listen to my gut feelings, however small they were. It turned out that he didn't put in a lot. His grandeur experience in the big tech also didn't translate that well when it came to learning and doing new things in a startup way. But most of all, it was clearly not what he was passionate about. Next time, if I couldn't tell myself I really want to spend every day with a person and go out for a drink with him, I'm gonna pass. Doing Ugly Things I'm working for a startup, albeit a pretty successful one. I used to think a lot of things we did there was clunky. Many things could have been more optimized. People could have done things differently. We could've used better tools, etc. Turned out when I'm building something, I realized those were engineering traps. Builders and founders don't think like that. They think in terms of "good enough". The priority is to get something out the door quickly and get users' feedback before we implode. Optimization means nothing when something is still in flux. Being Immune to BS Admit it, there's just too many BS's out there in this world--online and offline. They are there to distract us from very few things that matter for us. Every one of them has an agenda, and almost always it is not in your favor. I learned to shut off all the noises, whether they were nonsense or legit. Even some things that are relevant can be noises at times. It really depends on the context and how you feel internally. The better thing to do is whenever I have to get into the forest, always have a goal. Going with one is just like going shopping with your stomach empty. You gobble up . everything This new year, I have no resolutions. I only hope to do things a bit better each day. Because I know that there's no leaps in life. Only baby steps and falls.

I Launched on New Year's Eve and This is What I've Learned

Photo by on Adrian Curiel Unsplash In the last 5–10 years algorithmic trading, or , has gained popularity with the individual investor. The rise in popularity has been accompanied by a proliferation of tools and services, to both test and trade with algorithms. I’ve put together a list of 9 tools you should consider using for your algo trading process. algo trading Web Services: The following are managed-services that you can use through web browsers, and don’t require much setup from the user. As someone who’s recently started in this field, I found it easy for new algo traders to try out. (1) : Quantopian A Boston-based crowd-sourced hedge fund, Quantopian provides an online IDE to backtest algorithms. Their platform is built with python, and all algorithms are implemented in Python. When testing algorithms, users have the option of a quick backtest, or a larger full backtest, and are provided the visual of portfolio performance. Live-trading was discontinued in September 2017, but still provide a large range of historical data. They also have a serious community of developers, and post an ongoing DAILY contest with 10 winners awarded each day for a total of $5000 per month in prize money (*updated from the previously written as “periodically hold contests”). Quantopian provides capital to the winning algorithm. _Algorithm writers win thousands of dollars each month in this quant finance contest. Quantopian provides the education…_www.quantopian.com Quantopian Contest (2) : QuantConnect QuantConnect, is another platform that provides an IDE to both backtest and live-trade algorithmically. Their platform was built using C#, and users have the options to test algorithms in multiple languages, including both C# and Python. QuantConnect also embraces a great community from all over the world, and provides access to equities, futures, forex and . They offer live-trading integration with various names such as InteractiveBrokers, OANDA, and GDAX. crypto trading (3) QuantRocket: QuantRocket is a platform that offers both backtesting and live trading with InteractiveBrokers, with live trading capabilities on forex as well as US equities. It’s specifically designed for trading with InteractiveBrokers, and sets itself apart with its flexibility. QuantRocket supports multiple engines — its own Moonshot, as well as third party engines chosen by the user. While QuantRocket doesn’t have a traditional IDE, it is integrated well with Jupyter to produce something similar. One thing to keep in mind is that QuantRocket . Pricing plans start at 19.99/month USD, with annual options. is not free Local Backtesting/LiveTrading Engines: In today’s software world, you have lots more freedom if you make some effort outside of those managed-services. If you are comfortable this way, I recommend backtesting locally with these tools: (4) : Zipline/Zipline-Live _zipline - Zipline, a Pythonic Algorithmic Trading Library_github.com quantopian/zipline Quantopian’s IDE is built on the back of Zipline, an open source backtesting engine for trading algorithms. Zipline runs locally, and can be configured to run in virtual environments and Docker containers as well. Zipline comes with all of Quantopian’s functions, but not all of its data. To balance that, users can write custom data to backtest on. Zipline also provides raw data from backtests, allowing for versatile uses of visualization. Zipline discontinued live trading in 2017, but there is an open source project that works with Interactive Brokers. It has many of the same features Zipline does, and provides live trading. Zipline-live (5) : BackTrader _from datetime import datetime import backtrader as bt class SmaCross(bt.SignalStrategy): params = (('pfast', 10)…_www.backtrader.com backtrader - Backtesting / Trading Backtrader is currently one of the most popular backtesting engines available. It was built using python, and has a clean, simple, and efficient interface that runs locally (no Web Interface). One thing to keep in mind, backtrader doesn’t come with any data, but you can hook up your own market data in csv and other formats pretty easily. Starting with release 1.5.0, BackTrader has live-trading capabilities. It’s been a popular choice with algo traders, especially after Zipline discontinued live trading. (6) : IBPy _IbPy - Python API for the Interactive Brokers on-line trading system._github.com blampe/IbPy IBPy is an unaffiliated third party python wrapper for InteractiveBroker’s Trade Workstation API. Before IB started providing their official API library for python, this was the only way to connect to TWS for algorithms written in python. IB has released an official python SDK, and this library is heading towards begin obsolete(while still being relevant for python2 users). But there still remain a significant number of live trading engines/tools that still use this library, and it’s good learning material for whoever wants to learn about implementing API’s. While it’s good to learn about this library since it’s ubiquitous, if you are starting fresh, we recommend IB’s official python SDK. is even much simpler to use! Alpaca Trade API Python SDK Analytical Tools: Back testing will output a significant amount of raw data. Some IDE’s will provide basic visualization and analysis, usually algorithm performance. If you’re looking for deeper evaluation, I recommend these tools: (7) : Pyfolio _pyfolio - Portfolio and risk analytics in Python_github.com quantopian/pyfolio Pyfolio is another open source tool developed by Quantopian that focuses on evaluating a portfolio. What sets Pyfolio apart, is its ability to introduce degrees of uncertainty to a static set of data points, and evaluate Bayesian metrics from the user’s portfolio. The Pyfolio API offers a number of visualizations, which can be found on their GitHub repository. (8) : Alphalens _alphalens - Performance analysis of predictive (alpha) stock factors_github.com quantopian/alphalens Alphalens is also an analysis tool from Quantopian. Unlike Pyfolio, Alphalens works well with the raw data output from Zipline, and rather than evaluate the portfolio, is performance analysis of predictive stock factors. Alphalens has its own range of visualizations found on their GitHub repository. Median Daily Returns by Factor Quantile — one of the visualizations that alphalens offers (9) : TradingView TradingView is a visualization tool with a vibrant open-source community. It’s entirely web-based, and allows users to visualize data, whether the data is the result of paper trading or algorithmic back-testing. Like Quantopian, TradingView allows users to share their results and visualizations with others in the community, and receive feedback. (Bonus) Execution Platforms aka Broker-Dealers: (10)InteractiveBrokers: InteractiveBrokers is an online broker-dealer for active traders in general. They have been in the market since 1978. Algo trading isn’t IB’s focus, but multiple engines offer live trading through integration with their Trader Workstation. We’ve mentioned IB several times in this article — they’re just that good! (11)Alpaca: Finally, Alpaca! Alpaca was founded in 2015, and is an up and coming commission-free, broker-dealer designed specifically for algo trading. Alpaca also has a trade api, along with multiple open-source tools, which include a database optimized for time-series financial data known as the . MarketStore The brokerage is scheduled to be publicly available this September (you can play around with the MarketStore right now), but if you can’t wait, head over to our website and for a chance at early access! jump on the waitlist _Lower your cost, Maximize your profits. Algo trading commision free._alpaca.markets Alpaca | Algo Trading Commission Free with REST API Miscellaneous Tools to Take a Look At: — another simplistic python backtesting engine qtpylib — proprietary trading platform for forex and equities Multicharts — desktop tool which allows C# backtesting, with live trading exclusive to Fidelity WealthLab — for crypto trading Enygma Catalyst — backtesting/livetrading desktop app, de-fact in forex MetaTrader I hope this quick primer on tools available right now was useful. If you liked it, please leave a clap (or two, I don’t mind). If you think there are tools that I missed, leave a comment below! I always appreciate any, and all feedback. by Rao Vinnakota Please follow Alpaca and Automation Generation for fresh posts on Financial Market, Algorithmic Trading, Technology. You can find us @AlpacaHQ , if you use twitter. If you’re a hacker and can create something cool that works in the financial market, please check out our project “ Commission Free Stock Trading API ” where we provide simple REST Trading API and real-time market data for free. Brokerage services are provided by Alpaca Securities LLC ( ), member FINRA/SIPC. Alpaca Securities LLC is a wholly-owned subsidiary of AlpacaDB, Inc. alpaca.markets

9 Great Tools for Algo Trading

My code-free experience learning how to clone, test and trade eight extremely profitable quant finance algos on the Quantopian platform. Last week, Boston company released its first crowdsourced from the Quantopian community in amounts ranging from $100K to $3M. The algorithms were submitted by an open community of authors from all over the world. Capital allocations from the were made to authors in eight countries, including Australia, Canada, China, Colombia, India, Spain, and the United States, and are now trading live, with . Quite amazing! Quantopian Multi-million dollar capital allocations to algorithms Q fund third-party capital that Quantopian manages Understandably, Quantopian hasn’t released publicly to which specific algorithms it has allocated the capital, nor how much. This gave me a wacky idea. Brainwashed by Hippies I had been lurking around , and a couple of their hackathons. Quantopian Community Forums I distinctly recall realizing quite early that I had stumbled onto something huge, but I try to remain suspect of my own biases. Years ago I moved West for a spell and was brainwashed by hippies into always questioning my own worldview and assumptions. Coincidentally, this starting point seems to accord with the quant teachings of some of my favorite math snobs, like and his , including this one from QuantCon last year: . Delaney Granizo-Mackenzie Quantopian lectures and seminars You don’t know how wrong you are Recognizing and challenging our own biases is sometimes half the battle. Confirmation Bias Thus began my interest in Quantopian, which seemed to consistently reinforce my personal bias in favour of democratized quant finance. My internal ‘confirmation bias’ detector tingled a little stronger last July when Hedgefund-superstar pledged . And then, just last week, mega-tron fired every one of its going forward. Stephen A. Cohen a cool quarter $Billion to be managed by Quantopian BlackRock Capital stock-picking hedge fund managers in NYC, in favour of exclusively quantitative algorithmic trading Hacking the Quants And so, with only wannabe coding skills, and just a simple background in philosophy, but (hopefully) a knack for calling myself on my own bullshit, I set out to crib from the best Algos being discussed in Quantopian forums, run the backtests, understand the strengths and weaknesses of each, learn all the required ‘Quant’ concepts (Sortino, Sharpe, Alpha, Beta, MaxDD etc.), and ideally set each Algo alive into ‘paper trading’ (ie. pretend money) all with plenty of time before (April 28–30 in NYC), where Quantopian plans to release their much anticipated , to be used by anyone that joins their open quant finance community. QuantCon 2017 Futures API Once you get used to the Quantopian platform, Algos begin to flow pretty freely. My Process I researched, cloned and backtested eight separate Algos from the open-source . (There are some really powerful ideas being discussed there!). A great starting point is . Another place to start is this library, which has some overlap. Quantopian Community Forums this Post linking to the 10 most cloned Algos and Notebooks on Quantopian Q Algorithm Research and Ideas When choosing these Algos (delineated below), I tried to use the same . Then I set a few of these Algos loose (in paper trading). Five of the Algos are live paper trading today! (If you sign up below, I’ll keep you posted of the results of each Algo on a weekly basis). allocation criteria that Quantopian uses For the initial Eight Algos, I chose 4 from the (which appeal to my ‘legal and regulatory’ background). For the remaining 4 Algos, I dug a little deeper into the substantive . There many familiar (and unfamiliar) quant strategies, concepts and variants of Algos are cloned, debated and improved by minds much more seasoned and insightful than mine. It’s quite a sharp learning curve, fascinating and rewarding! Quantpedia ‘Earnings Announcements’ series Quantopian Community Forums provide a great starting point to analyze our Algos Quantopian’s Allocation Criteria I like each of the Eight Algos that were chosen for different reasons, with a few consistent themes: Each Algo 1) demonstrates consistently profitable backtests, 2) uses a wide universe of stocks and broadly distributes capital, independent of any particular stock or sector, 3) remains market agnostic, and 4) meets the criteria prescribed by the Quantopian team. I describe each briefly below, and have created deeper Medium and Quantopian Posts for each separate Algo (including some definitions of those funny words above), just click! Several of the Algos chosen are quite profitable already! Eight Cloned Algos: 1. Zack’s Long-Short PEAD with News Sentiment and the Street’s Consensus (LIVE TRADING) A fun algo that surfs the Post Earnings Announcement Drift , using several data sources, taking long and short positions as appropriate. 2. Zack’s Long PEAD with News Sentiment The Post Earnings Announcement Drift strategy describe above, taking long positions only. Profitable in various backtesting scenarios. 3. Are Earnings Predictable with Buyback Announcements? (LIVE TRADING) Leverages Regulatory Filings (Schedule 13-D, Share Buyback Announcements) to predict momentum during Earnings Announcements. Quite profitable in backtesting! 4. Reversals During Earnings Announcements (LIVE TRADING) Another Earnings Announcement variant, riding the Earnings Announcements post-overreaction reversals. 5. Clenow Momentum Strategy (as Amended) Andreas Clenow , author of a several classic books on ‘Momentum Trading,’ has published his own version of the underlying Momentum Algo he promotes, which had already seen many prior attempts and iterations by the Community. Let’s check out both variants. 6. VIX Robinhood Momentum Strategy (LIVE TRADING) An ‘implied volatility’ strategy much discussed and iterated upon in Quantopian forums, originated by Charles Witt in 2016. Originally termed “Robinhood Extreme Vetting.” 7. JAVoIS: Just Another Volatility Strategy (LIVE TRADING) Originally submitted by Peter Bakker in June 2016, with much input and iteration from the Community. 8. 101 Alphas Project: Alpha# 41 Using an academic paper as a starting point, the 101 Alphas Project aspires to create 101 profitable Formulaic Quant Trading Strategy. This is Alpha #41. More to come.. With just a little exposure, I am consistently having more and more ideas for variants of these Algos and others, some of which I hope to publish soon (or maybe submit at the ). One logical next step would be to create a meta-Algo that smartly allocates capital among each of the other Algos, perhaps even borrowing from . QuantCon 2017 Hackathon these starting points My goal here was to simply leverage (i.e. clone, test and trade) several Algos from the open source quant community that comprises Quantopian. (As Quantopian itself has done in its own initial capital allocations). Some of the community’s ideas are way cooler than mine anyway ;) But I’ve got a couple in mind for the in NYC on April 30, 2017 (some using Quantopian’s shiny new and !). QuantCon Hackathon Futures API data Alpha is Truth. And Truth, Alpha The Search for Truth In his awesome 2007 book , author describes the ‘Truth’ or ‘Alpha’ (at p. 8) as: The Quants Scott Patterson Revealed in the study of obscure patterns in the market, the Truth [is] the key to unlocking billions in profits…It [is] about money, of course, but it [is] also about proof. Each added dollar was another tiny step toward proving they had fulfilled their academic promise and uncovered the Truth. The Truth [is] a universal secret about the way the market works that [can] only be discovered through mathematics. There is a new era upon us. A democratized algorithmic era, where we can all code our own Truth(s), backtest our algorithms, and employ our strategies in our present day scenario and marketplace, testing at first, and eventually with real dollars and risk (and ideally build in our own escape hatches, in the case of market hiccups, crashes and other events). Dare I say that the ‘Truth’ is knowable by anyone, with a little discipline, insight and code. Acknowledgements I take no credit for any of these Algos — they were created by the Quantopian open quant finance community, and iterated upon therein. I just read everything I could, found these fascinating threads (and many others), and cloned the latest versions, ran some more backtests, and started paper trading. I try to ascribe appropriate credit where possible — and may be in error, or have forgotten you. If so, please advise. I encourage all feedback (negative and positive) and discourse! I feel indebted to Quantopian, and grateful for the fascinating discourse happening with thousands of diverse brainiacs on the Quantopian Community Forums. I have become quite impassioned, and have already found the next 5 Algos I want to try. Caveat This is not investment advice. I am not affiliated nor associated with Quantopian in any way. Also, I have not mentioned herein some of latest features and functionality on the Quantopian platform, including and , which I hope to use and discuss in subsequent posts. Alphalens Optimize API Keep you posted? If you would like me to keep you posted on the results, please sign up below.

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