Numerai is building the world's last hedge fund - autonomously run by a decentralized network of data, intelligence, and capital. 
https://numer.ai
https://youtu.be/dhJnt0N497c

Numerai

: offers the , a tool to help Data Scientists automate their weekly submission pipeline for the Numerai tournament. This CLI configures a in and automatically deploys your model to it. tl;dr Numerai numerai-cli Numerai Prediction Node Amazon Web Services (AWS) . 🚀 This guide describes how I set up my own weekly submission pipeline from scratch, using for free Microsoft Azure and python 🤖 Just give me the code: https://github.com/papaemman/azure-functions-with-python What is Numerai? is a quant hedge founded in San Francisco, CA, in 2015, built on thousands of crowdsourced machine learning models. Every week, Numerai gives away data so that Data Scientists worldwide have free, hedge-fund quality data to develop their Machine Learning (ML) / Deep Learning (DL) models — data they would otherwise not have access to. Numerai At the beginning of each round, competitors can stake (a cryptocurrency issued by Numerai) on the predictions they submit if they want to get paid (or "burnt") based on the predictive accuracy of their models. Staking on a model is fundamentally a type of "bet" competitors take towards the performance of their own model's predictions. NMR tokens Numerai Compute CLI Competing in Numerai is an ongoing, never-ending challenge because competitors need to download the new data every week and submit their predictions based on them. is a framework aiming to help participants automate their weekly submission workflow. Using the , participants can "provision" their cloud infrastructure in and deploy their pre-trained models as a Prediction Node that can Numerai Compute numerai-cli Amazon Web Services (AWS) be triggered by Numerai every weekend download new tournament data run the ML models and upload predictions to Numerai The configures a Numerai Prediction Node in Amazon Web Services (AWS). This solution is architected to cost less than on average, but actual costs may vary. numerai-cli $5/month You need 4 things to use Numerai CLI: , , , and . Docker Python Numerai API Keys AWS API Keys Why not just use Numerai Compute? 1. Cost Last week was tough for the crypto market. carried away the whole crypto market. Currently (May 2022), the NMR token has a price of around . Therefore, saving per month and investing them in buying more NMR to stake in your models is an appealing idea. The massive crash of LUNA 13$ 5$ 2. Learn something new When building something from scratch, you have the opportunity to understand how it works under the hood. Last year, I deep-dived into Microsoft Azure cloud technologies, and I wanted to test if my skills were enough to build a real-world solution. 4. Don't have an AWS account Many professionals prefer to work with other Cloud providers than AWS, such as or Google Cloud Platform. Setting up a new AWS account, adding billing information, and finding your way into the AWS console might seem like a lot of work. *, this guide is for you.* Microsoft Azure If you prefer to use Azure Goal Automate my weekly submission pipeline, for the Numerai Tournament, using Azure and python, with zero cost. Azure offers many services adequate to solve this task, but I decided to use Azure Functions after some research. is a cloud service available on-demand that provides all the continually updated infrastructure and resources needed to run your applications. You focus on the pieces of code that matter most to you, and Functions handles the rest. Functions provides serverless compute for Azure. Azure Functions Going for a is a great way to because you don't need to pay for the infrastructure you don't use. And if you already have the code you want to run, setting up an Azure function is . Also, Azure offers a which is more than enough for this project's scope. serverless architecture save money and time easy free tier, Thus my goal was to create an Azure Function that will: ⏲️ be triggered every Sunday 🔋interact with the Numerai API to download numerai_live_data and upload my predictions 💾 interact with Azure Storage to read and store and data pre-trained ML/DL models numerai_live_data numerai_predictions 📥 send a fun email notification after the submission 😊 allow me to enjoy my weekend MS Azure Architecture Design Technical Implementation Prerequisites Azure Account and VSCode Azure Extension Python 3.8 Azure Functions Core Tools Azure Storage Explorer Numerai API Keys Trained ML/DL models and inference python code Setup 1. Setup all prerequisites Download and install the required software Create your accounts in Azure and Numerai 2. Open VSCode and create a new local Azure function project While you don't have any workspace opened in VSCode, go to the extension, find and select _Create New Project…- S_elect , , and a to create a virtual environment- Select the template, give a name to the function, and set up the timer to run every [* 0 0 * * SUN]. You don't need to set up the function to run in short time intervals. Azure functions support " . Azure FUNCTIONS Directory Language (Python) python interpreter Time Trigger Sunday Execute Function now…" to trigger it manually within VSCode for testing purposes 3. Then, the VSCode will create a with a time-trigger function sample code. automatically Local project ## Azure function project codebase structure
.
├── .venv                     # Python Virtual environment
├── .vscode                   # Configuration options for VSCode
├── host.json                 # Configuration options that affect all functions in a function app instance
├── local.settings.json       # Maintains settings used when running functions locally.These settings aren't used when running in Azure
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├── AzureFunction_1
│ ├── function.json           # Azure Function Settings
│ ├── __init__.py             # Python Code
│ └── readme.md               # Documentation
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├── AzureFunction_2
│ ├── function.json           
│ ├── __init__.py             
│ └── readme.md               
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└── requirements.txt          # Package dependencies (Open VSCode and Press F5 or 4. Run the Function locally Run →Start Debugging) To debug, you must select a storage account for internal use by the Azure Functions runtime: Select a , create a , create a and select a for resources. Keep in mind that this is a . subscription new storage account new resource group, location temporary resource group Go to extension, find and go to . Find the function, right-click, and select Azure FUNCTIONS Local Project Execute Function now… Examine functions' logs Stop the function (ctrl+c) Delete the resource group Important note ⚠️ ”, update the extensionBundle “version”: “[2.*, 4.0.0)” If you get an error “connect ECONNREFUSED 127.0.0.1:9091 to version , in host.json file. 5. Publish the project to Azure Go to extension_,_ find and select Azure FUNCTIONS, Deploy to Function App… Select a and subscription create a new function App Select a and for the new resource runtime stack location This will create a new resource group with an App Service Plan, a Function App, a Storage Account, and an Application Insights service. 6. Run the Function in Azure Go to extension, find , open the related and find the function app. Azure FUNCTIONS subscription Open the function app, find the specific function and select This will run the function to the Azure environment. Execute Function now…. You can see the function logs in the Live metrics tab using the Application Insights resource. 7. Download the function app settings Open VSCode and download remote settings in the Local environment for the function App ( and select Azure Functions: Download Remote Settings) F1 This will update the file with info about App service Plan, Function App, Storage Account, and Application Insights. local.settings.json 8. Setup the Azure Storage account. Open the connect with your Azure account and find the related storage account inside the newly created resource group. Azure Storage Explorer, Create a new Blob container called and upload all your pre-trained ML models there. production-models Create a new Blob container called to store numerai_live_data for every round. live-data Create a new Blob container called to store numerai_predictions for every round. predictions-files 9. Purchase the Twilio SendGrid SaaS offering from Microsoft Azure Marketplace To send a notification email every time the function triggered and submitted predictions, I decided to use the Twilio SendGrid's Email API Purchase the Twilio SendGrid offer (free tier) from Microsoft Azure Marketplace and configure your account on their platform. Get API keys from Twilio SendGrid 10. Configure Function App Having the and hardcoded in the source code is a terrible practice. Azure Functions offers a dedicated Function's App configuration to store environmental variables and secrets. Numerai API Keys Twillio Seng Grid API keys Open the , find the and go to . The complete set of Environment Variables is stored in the Application settings by default. This helps developers define input/output bindings with other resources in Azure. Azure portal Function App service Configuration Create 2 new application settings and store Numeai API Keys and SendGridKeys NumeaiAPIKeys →secret_key=***;public_id=***” SendGridKeyss →*** Open VSCode and re-download remote settings to the local environment ( and ) to get the new Application Settings into the file. F1 Azure Functions: Download Remote Settings local.settings.json 11. Write the Python code Now it's time to write (or copy-paste) the python code responsible for reading the numerai live data & pre-trained ML/DL models and submitting predictions using Numerai API. The input and output configurations (configurations and paths to load and store files) are the only thing you have to modify in your previous python code (running locally) to run it as an Azure Function. Instead of local paths, you should use the newly created Storage Account. For this, you need to use the . Azure Storage Python SDK Go to the file and replace your python code. .py init 12. Run the Function locally and verify that it is working Run the locally ( F5 or Run → Start Debugging), go to A_zure extension,_ find the Function in , and select ` Function Local Project ExecutFunctionon now… Check Function's logs and the connection with the Azure Storage Account Stop the Function (ctrl+c) 13. Redeploy and verify the updated App Go to extension, find , and select Azure FUNCTIONS Deploy to function App… After the deployment completes, find the Function under the respective subscription in VSCode, right-click to function, and select Execute Function now… Check Function's logs in the Metrics tab for this Function. Conclusions Last Sunday, I woke up to this fun email from myself, letting me know that everything went as planned. The Azure Function was triggered on Sunday at 00:00:00 UTC, the Numerai submissions were successful, and I was able to enjoy my day! 🌞 Email Notification for successful submission PS. Check the awesome quote at the end. I added a random quote generator inside the function creating the email notification just to spark things up ✨ Get in touch I hope you liked this guide. If you have any questions or just want to introduce yourself, don't hesitate to reach out! Panagiotis Papaemmanouil 📧 papaemman.pan@gmail.com 💬 https://www.linkedin.com/in/panagiotis-papaemmanouil/ 🌐 https://papaemman.com/ 🔥 Check my under the name Numerai Models PAPAEMMAN https://numer.ai/papaemman1 Also Published here

Automate Submissions for the Numerai Tournament Using Azure Functions and Python

If you believe that action and practice are better than theory and have little to no experience in the field of and quantitative analysis (but a healthy interest) , this might be the post for you, for the rest it is a look into a probable future where one more thing could become decentralized and open sourced: . machine learning Wall Street There are 2 main ideas at play here, so I’ll try giving you a birds eye view of both : Within the world of finance, hedge funds are a group of investors that plunk money into a basket of financial instruments and investments with hopes of having a return, a $3 Trillion Dollar competitive industry. Within this industry is one of the first experiments in open sourcing at some level the management and decision of buying and selling investments. Numerai: Numerai Quantitative analysis in finance employs statistical & mathematical models to predict and evaluate the price and value of financial instruments, in a nutshell various numbers representing some feature of the instrument go in, and a prediction or score comes out, said prediction or valuation then informs the decision making on the funds capital (buying,selling, holding etc) Machine learning which is everywhere these days is but the latest tool in making sense of the numbers; to a researcher like me or someone more interested in different A.I. problems like pattern recognition this type of analysis is just a new, fun and probably profitable application. Quantitavie Analysis & Machine Learning: Numerai bridges both worlds and adds a crowdfunded twist by providing the raw data and asking you and me to provide a better estimate, these predictions are then used to control capital and the top performing ones get paid real money for their contribution, additionally there are incentives in the form of staking your own funds ( in the form of numerais own cryptocurrency) which in essence is a reward and punishment system for keeping your predictions novel and useful. This is a complex but interesting part which I will sidestep for now to focus on getting started. For the money, for the glory, and for the fun , but mostly for the money. In my experience it will take a novice data scientist, coder, or hobbyist a few weeks to get off the ground, at the end of this very short period you will have practical knowledge on applying various statistical and A.I. concepts to a real problem on a platform that you can then expand to your own interests, the competitive aspect and financial incentives can help you stay focused and motivated while learning although I should mention that making a dollar might take months. If you are not planning on participating but came for the whole open source wall street thing you can skip the following 2 sections. Hands on: This is but one path ( the one I took ) yours might be different depending on your starting point. Sign up to Numerai, read the documentation, get thoroughly intimidated by the documentation, the forums, the slack channel and the sinking feeling that even though you thought you were a smart cookie, this will require learning quite a few new things, take a deep breadth. Download the data set, it consists of multiple files which you will use, here’s what the latest one looks like: These files might make perfect sense if you’ve done some , if not I’ll try giving you a quick overview/introduction: data science Numerai is kind enough to provide 2 fully functioning and working examples, these are perfectly good springboards and working examples of a base implementation in R and Python, 2 very popular languages for big data analysis and machine learning, I went with Python since I had some experience with it, although I foolishly tried making a neural net on excel at first, my computer is still recovering. example_model.py & example_model.r : There are a few things to this file, so let’s start by looking at what’s inside: numerai_tournament_data.csv : Importantly there are various types of elements or categories ( id’s) divided into validation,live and test types, and each one has features, and some date information in the form of eras; the target column is what your model will predict in the form of a binary number (1 or 0). Numerai obfuscates the data, so even though we are assuming these are financial instruments (could be volume, open,close price, etc) there is nothing explicitly apparent, feature 11 on id 12546 could represent stock price at close date last week (era2) or something else, a dataset representing cancer rates or pixels in a dog recognition problem would look the same ! numerai_training_data.csv: An additional dataset, the training set is also provided, if you are familiar with it will be obvious what this dataset represents and how it is used. In a nutshell, you are given a problems definition and it’s solution, ( ), you a model to it and then generalize the solution to new data ( ) and make your predictions. It is the equivalent of learning to ride a bicycle with training wheels ( here you are the model ) and then trying your skill (predicting) by riding on a bicycle without them, if you fall you have a bad model. cross validation the training set fit the blank test and live rows on the tournament dataset This is an example of what you will be submitting, a column of id’s ( the same id’s from numerai_tournament_data.csv) and a probabilistic prediction that the id will be of class 1, so while the target of your model is a binary number, it needs to output probabilities on said target. example_predictions.csv : Once you have these files, you now need to set up your environment for making, predicting, keeping track of and submitting predictions. In my case it involved installing python plus setting up my editor ( I use Atom with script for running python) and creating a few spreadsheets to test,tweak and keep track of predictions, this is time consuming and will probably take a few weeks if not months to streamline. As mentioned, the example models provided by numerai are an excellent way to get started making predictions and working with models, the code does everything for you (read files, divide data, define a model, train,predict, write to file ) provided you have the right libraries installed, pandas and numpy for dealing with the data and sklearn for the statistical models for instance. Models : Numerai has a weekly tournament and after each one the clock resets and a new dataset is provided, you can have 3 models submitting predictions and are limited to uploading 25 per day (it’s usually plenty once you learn how to calculate logloss and other metrics on your own). They also provide an API if you choose to automate the download/upload. The Tournament : Once you have successfully submitted a model, you will be in the leaderboard and your predictions will be scored across various dimensions : At this point the algorithms for originality,concordance and consistency are not open source, but they are described as follows ( ) you also need to meet a specific treshold to qualify for the tournament and control capital Logloss across era, explanation forthcoming. ( ) Consistency: 75% an up Somehow controversial since it might favor early submitters; a check to see if your predictions/model were already submitted. ( a pass/ fail flag) Originality : A check to see if one model was applied to all data_types. ( a pass/fail flag) Concordance : Once your model is prequalified by these metrics, you are then scored by your logloss against live data in order to actually win money, a process that takes 4 weeks, the scoreboard is then not a reflection of the actual place your model will place in the tournament. Numerai is not telling the specifics, but they hint at a meta model based on the top predictions, those closests in logloss to their own model and their live data, the time period also plays into favoring models and predictions over a longer time span, since logloss is the main ranking metric, it’s important to know about it. is a graded statistical measure of confidence over your predictions, in real terms something like 0.50 would represent a random guess and 0.00 a perfectly confident prediction, it is graded because the more confident you are about a certain outcome ( I will not fall of the bicycle) the more it penalizes you if in fact you fall of the bicycle. In the case Numerai it could be understood as I am certain to this degree about my predictions. Logloss : ( or logarithmic loss ) And that’s it, once you have your setup ready you are welcome to try any of the multiple statistical models that are open source, and any technique and combination of parameters for analyzing this data, a good place to start is the sklearn library and all their models. I should also mention that ranking within the 100 or so models that are paid is difficult and requires a lot of experimenting, applying advanced techniques and in general getting to know the dataset, so using out of the box models will probably not get you very far. : Folks interested on the whole open source Wall Street thing continue reading here Beyond a great way to get into applied machine learning and quantitative analysis, Numerai might become something bigger and arguably better for the world of finance, it is early days and it is on the experimental side like many startups, but the upside potential is there. The hedge fund and banking industry have traditionally been elite,closed and secretive places with a bad reputation, in the same way that Hollywood and the TV networks were once the only source for movies and shows, Youtube has provided a new avenue for both consumers and content creators. A distributed and open source wall street would allow diverse talent and ideas to contribute, a self taught analyst in a developing country that does not like wearing a suit and the heavy handed bro atmosphere of wall street could make a living and contribute to a hedge fund. A researcher in academia might not want to abandon his full time job or research, but might be able to contribute part time. The consumers of the hedge fund industry as a whole would also benefit if the experiment proves successful, this bet hinges on the wisdom of the collective quant crowd vs the existing industry and the performance of these new types of funds. I am personally somehow optimistic, but I am also aware that consistent positive performance in the hedge fund industry (and other investments) usually favors an index approach vs stock picking and active management, so in the end a quant approach, even an open sourced one might not be all that profitable. Finally, I believe the hedge fund industry will probably have nothing to fear,but will coexist peacefully and even contribute if the experiment resolves favorably, established banks might even one day foster this type of funds, competition wise, I also think there is room for everyone since there is plenty of untapped investment capital facilitated by the new ecosystems (cryptocurrencies,globalization, the web) which numerai an other similar ventures might be better positioned to capture; if there is a point of concern for the hedge fund industry, is that of cost, the current fee structure ( 2 percent on assets and 20% on profits ) is pricey for 2 reasons, the cost of having an infrastructure (brokers, analysts, researchers, back office, etc) and the walled garden of finance that limits participation, it is in this space that a desentralized open source hedge fund can lower costs and compete, so as mentioned we could very well see a new more competitive type of hedge fund appear and the existing ones would need to adapt. 2 and 20 Parting Words I left a few things out for lack of space and because I wanted to focus this short piece as a both an alternative way of onboarding, which I struggled with and to discuss albeit briefly what this type of fund could herald. You can read about staking via this , and keep up with numerai at their . whitepaper blog I should also mention and other writings on , and here on medium as a way to start and further your learning (as well as mine). kaggle numerai machine learning quantitative analysis Small Update: (Oct 1st 2017)The dataset has been changed, there is now roughly twice the features and 10–20 K more rows, this means that if you are on basic hardware ( I am on a i3 4GB RAM HD windows machine) you will most likely experience memory errors on some models, the solution is to subsample. Also, I got my first NMR by ranking within the first 100 ! Medium sized Update: (Dec 25 2017 ) Numerai has now released the source for scoring predictions and streamlined the API for automatic submissions along with some other minor changes and a vision statement & road map, read more here : Most importantly, payouts have now changed in nature (more NMR, less usd). I personally have been doing great ranking in the top 50 – 60 more or less consistently. Numerai’s Master Plan. Early 2018 Update — Breaking changes: Payouts for the tournament have changed once more, the only way to win both NMR and USD . This means that in order to compete you need to have NMR to stake , and in order to have NMR you have to buy them or have been part of the original distribution, it is early to know how this will affect the project, but in my eyes the project has changed in nature, moving the bar higher and making it harder for newcomers, time will tell if this breaks the project. is through staking Mid 2018 Update — More changes: There are now 5 different tournaments and the payout structure has gone through a number of complex rule changes, consult the Numerai page for the current ones, there is also a new reputation system that rewards you with 0.1 NMR every time you beat benchmark, ( there was also a sweet reputation bonus for previously submitted performant models ); so now you have a path to stake NMR without buying them, I think it’s perhaps too small an amount, but understand there are issues in giving out more (like bots). I’ve been consistently ranking in the top 20 but earnings are now tied to how much you stake, your confidence level and that of other players,coupled with the current cryptocurrency slump, my earnings have been low ; there have also been problems with bots taking advantage of airdrops and players exploiting rules, these have all been progressively addressed by Numerai resulting in added complexity and rules. Disclaimer : I just took a short term contract position with Numerai (August 2018-Oct 2018) I will be helping them with Front End,UX and enything else I can. I won’t be competing during the duration… April 2019 Update : There’s been quite a few new things in the world of Numerai since I left and went back to being a competitor, there’s new data, new tournaments ( still only 5 though) and a new measuring metric (AUC) instead of logloss, it has also become much more competitive ( ), mostly pay to play and payouts per participant have gone down, ( although general prize pools are bigger now). I rank top 100-200 now Thanks ! Keno About the Author : Born Eugenio Noyola Leon (Keno) I am a Designer,Web Developer/programmer, Artist and Inventor, currently living in Mexico City, you can find me at www.k3no.com

Numerai walkthrough: Quantitative Analysis & Machine learning for fun and profit.

and are two of the most transformative forces shaping the modern world. AI has the potential to revolutionize industries by automating processes, enhancing decision-making, and unlocking new insights from vast amounts of data. Artificial Intelligence (AI) blockchain technology Blockchain, on the other hand, offers a decentralized and secure framework for digital transactions, enabling trust and transparency in an increasingly interconnected world. The convergence of AI and blockchain has given rise to a new breed of cryptocurrencies known as AI crypto coins. These digital assets leverage the power of AI to enhance various aspects of the blockchain ecosystem, from improving the efficiency of decentralized networks to enabling more intelligent decision-making in financial markets. In this article, we will explore the concept of AI crypto coins, their underlying mechanisms, and their potential applications. I will try to explain this topic based on my experience of working  with crypto and AI projects. In this article, we will explore the benefits and risks associated with AI crypto coins, as well as how to buy them and what to consider before investing. This article should not be regarded as financial advice. Always remember to DYOR! Understanding AI Crypto Coins Unlike traditional cryptocurrencies, AI crypto coins use machine learning algorithms and natural language processing to analyze market patterns, make predictions, and execute automated trades. This allows them to adapt to changing market conditions in real-time, providing investors with a more agile and responsive investment option. , which are automated computer programs, are a key component of AI cryptocurrencies. They can be used for automated trading strategies, amplifying trading profits by applying these strategies to multiple cryptocurrencies on one or diverse platforms. Some of the most known AI trading bots include Defiquant, Pionex, GunBot, Kryll, Zignaly, Dash 2 Trade, ArbitrageScanner, and Cryptohopper. AI trading bots These bots collect big data on crypto information in real-time and analyze it to derive useful and actionable metrics, with the target of future crypto prices. They can pull information from various social sources, crypto markets, news sites, and other sources to provide valuable insights for investors. AI trading bots are beneficial for modern investors, as they can be configured to run automatically, which is particularly useful for copy trading and building a bot from scratch. They can also be used for social trading, paper trading, backtesting, and strategy design. Benefits of AI Tokens Improved Decision-Making One of the key benefits of AI crypto coins is their ability to improve decision-making. By analyzing large volumes of data, AI algorithms can spot patterns and trends that human analysts might miss, helping traders make more informed decisions. This can lead to better risk management and improved returns on investment. For example, AI algorithms can analyze market trends, social media sentiment, and news articles to predict price movements. This can help traders make more accurate predictions and reduce the risk of losses. Enhanced Network Security AI crypto coins can also enhance network security. For example, some AI cryptocurrencies use facial recognition technology to verify transactions and prevent fraud. This can provide an additional layer of security, making it more difficult for hackers to gain access to sensitive information. AI algorithms can also detect and prevent cyber attacks in real-time, providing an added level of protection for investors. Automation of Functions Another advantage of AI crypto coins is their ability to automate numerous functions in the cryptocurrency market. By using smart contracts, AI algorithms can execute trades automatically, speeding up transactions and saving time. This can also reduce the risk of human error, which can be a significant factor in the fast-paced world of cryptocurrency trading.For example, AI algorithms can automatically execute trades based on pre-defined criteria, such as price movements or market conditions. This can help traders take advantage of opportunities more quickly and efficiently. Notable AI Crypto Tokens 1. SingularityNET (AGI) SingularityNET is a decentralized AI marketplace that enables users to access a wide range of AI services through the use of AGI tokens. By leveraging a decentralized network of AI algorithms, SingularityNET aims to democratize access to AI technology and foster innovation within the crypto AI landscape. As of March 2023, SingularityNET has over 50 AI services available on its platform and has partnered with major organizations such as IBM and the United Nations. 2. Fetch.AI (FET) Fetch.AI is an AI-powered decentralized network that enables the creation of intelligent autonomous agents. These agents can perform various tasks, such as optimizing supply chain logistics, facilitating peer-to-peer trading, and providing personalized recommendations. FET tokens are used to access the Fetch.AI network and incentivize participation. Fetch.AI has raised over $15 million in funding and has partnerships with organizations such as Bosch and Datarella. 3. Ocean Protocol (OCEAN) Ocean Protocol is a decentralized data exchange platform that enables users to securely share and monetize data through the use of OCEAN tokens. By leveraging AI capabilities, Ocean Protocol aims to create a more equitable and transparent data economy, ultimately benefiting data providers, consumers, and developers alike. Ocean Protocol has raised over $20 million in funding and has partnerships with organizations such as Aviva and Roche. 4. Numerai (NMR) Numerai is a decentralized hedge fund that leverages AI algorithms to make investment decisions. Contributors to the Numerai platform can submit their AI models, which are then used to inform the fund's trading strategies. In exchange for their contributions, users are rewarded with NMR tokens. Numerai has over 40,000 data scientists contributing to its platform and has over $1 billion in assets under management. 5. Cortex (CTXC) Cortex is a decentralized AI platform that enables the creation of intelligent smart contracts. By leveraging AI capabilities, Cortex aims to improve the performance, scalability, and security of smart contracts, ultimately leading to the development of more sophisticated and intelligent dApps. CTXC tokens are used to access the Cortex network and incentivize participation. Cortex has raised over $10 million in funding and has partnerships with organizations such as Binance and Bitmain. Risks Associated with AI Crypto Coins However, there are also risks associated with AI cryptocurrencies. The lack of regulatory oversight in the market can create an environment where scams and fraud are more likely to occur. AI cryptocurrencies are also subject to high levels of volatility, which can result in significant losses for investors. Technical issues, such as errors in the smart contract code, can also lead to loss of funds or security breaches. Ethical concerns, such as the potential for bias in decision-making or the impact on employment in the financial industry, should also be considered. How to Buy AI Crypto Coins To buy AI crypto coins, investors need to find a cryptocurrency exchange that supports the specific AI crypto coin they want to purchase. After creating an account and funding it with a payment method such as bank transfer, credit card, or cryptocurrency transfer, investors can use the exchange to buy the AI crypto coin of their choice. Once purchased, it is recommended to transfer the coin to a secure cryptocurrency wallet to ensure the safety of the investment. Ultimately, AI crypto coins have the potential to become a valuable addition to any investment portfolio. One key area where AI crypto tokens could make a significant impact is in the realm of decentralized finance (DeFi). It could lead to the creation of new investment opportunities and the democratization of financial services, making them more accessible to a broader audience. If you’re interested in the most innovative AI projects and research, you’re welcome to subscribe to my newsletter ‘ ’. It’s free! AI Hunters P.S. Check out some of my previous articles at HackerNoon: Google Introduces Gemma: Revolutionary Access to Pre-Trained AI Models Sora's AI Innovation Set to Disrupt Video Content Creation Breaking Down the Next Big Thing in NFTs: ERC-404 Spotlight Gemini 1.5 Unleashes Unprecedented Context for AI Applications The AI Revolution in Journalism: A New Era of Enhanced Reporting DeFi Community Building: A Step-by-Step Guide for Crypto Startups

AI Crypto Coins: When the Power of AI Meets the Blockchain

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Image credit: on Shubham Dhage Unsplash It's no secret that large language models (LLMs) like ChatGPT have transformed how we work today. The trading landscape is no different. Crypto With the introduction of these powerful and new technologies, Crypto traders have thought of various ways they can use these technologies to revolutionize the way traders approach market analysis, generate trading signals, automate trading processes, and even democratize decision-making. In this article, I will mention some ways LLMs can be used to change the way we trade and potentially reshape the trading landscape: Sentiment analysis based trading Strategy LLM models, which are deep learning algorithms, can perform various tasks. One of these tasks is natural language processing (NLP) tasks. This means they can process large volumes of textual data from news articles, social media, and other sources to provide a comprehensive market analysis that can be used for sentiment analysis-based trading. This can save time for traders as they no longer need to sift through large volumes of news sources. So, how does this work? A sentiment analysis-based trading strategy allows traders to gauge the market sentiment of some cryptocurrencies and identify market-moving events. Traders can use the result from this sentiment for market trends analysis, allowing traders to swiftly react to news and decide when to buy and sell. Zanyang talked a bit more about this in his , where he used algorithmic trading platforms like QuantConnect. Use of NLP-Powered sentiment analysis in trading strategy paper Customer support and communication Besides Traders, LLMs can benefit everyone in the crypto space, including cryptocurrency founders, project owners, community leaders, and trading platforms. For community and trading platforms, LLMs can be used for knowledge-based databases, Q&A bots, and customer support. This way, they can seamlessly help users navigate the platform, improve customer experience, answer project-related questions, resolve issues, and assist in technical support. The best thing about this system is that, unlike traditional conversational AI, LLM-powered bots can understand human language and respond better 24/7. Some AI Chatbots you can explore for this are and . AwesomeQA UltimateGPT Generate trading signals A trading signal is a trigger telling a trader when to buy or sell orders. Triggers are generated by simply analyzing indicators, patterns, or data points. These common technical indicators include MACD, RSI, KD, and Bollinger Bands. This talks a lot more about these indicators. article by BYDFi With LLMs, you can generate trading signals in various ways: Building trading algorithms that depend on pre-established rules. Identify trends through sentiment. Interprets news events that can cause market movements. Recognize patterns and interpret news events. With these signals, traders can use the insights to capitalize on anticipated price reactions and make more informed decision-making. The best part is that this signal generation can be automated, thus reducing any latency in trade execution. This exciting discussion on the . Numerai forum discusses using LLMs to create trading signals Build, Build, and Build with LLMs You’ve probably used LLMs in code generation or to assist you in generating codes; If not, I have got an [Analytics Vidhya’s article on How to Build LLMs for Code?](https://www.analyticsvidhya.com/blog/2023/09/llms-for-code/#:~:text=A%20Large%20Language%20Model%20(LLM,understand%20and%20generate%20computer%20code.) for you. Based on your prompt, LLM can be used to write code faster and much more efficiently. You can also use it to generate a whole project. Bringing this idea to the crypto space, you can use LLM models like , , and to generate codes, write smart contracts, and test. These models can also read, analyze and modify your codes or even improve your codebase. WizardCoder StarCoder Codeium All this can be done using simple prompts and knowing what you want. Democratizing decision-making and knowledge One of my favorite things about LLM models is that they make information accessible. You can get information about just about anything by writing a simple prompt. Imagine a model trained on real-world financial data that can understand financial concepts, formulas, and market data. This means you can ask this model about anything in this domain, and it will provide you with information and help you make better decisions. Such a custom LLM would benefit not just market leaders but novice traders and beginners in the crypto space. One of my favorite Crypto knowledge bases is , an AI-powered search engine. MinMax Wrap-up You can use LLMs in various ways, from generating codes to providing 24/7 customer support. While many of these models are still under development, they are becoming increasingly powerful and more accurate as the day passes. As this happens, you will likely use them for even more tasks in the crypto industry in the future.

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