Artificial Intelligence in Crypto Trading – All You Need To Know

Artificial Intelligence (AI) In Crypto Trading – Introduction

Cryptocurrencies and blockchain are often classified as one of the best disruptive technologies to come out of the 21st Century. However, the nascent industry is joined by Artificial Intelligence as another trailblazing technology. Interestingly, developers and crypto investors often merge both these pioneering tech for AI-powered crypto trading.

What is AI?

A.I. is an acronym for Artificial Intelligence. AI is a partially or wholly automated system that can emulate human cognition. AI can perform digitized tasks such as running a search query or creating a response for a given prompt with computational efficiency while generating outputs based on enhanced data analysis and statistical examination.

It means that AI can look for correlations and detect patterns concerning a particular query to generate appropriate and efficient outputs. At the same time, AI is better than regular computational units such as personal computers on account of its compatibility with digitized forms of visual perception, cognition, speech recognition, translation, and decision-making abilities.

Origin of Artificial Intelligence

 The term was coined by John McCarthy in 1956 for the first time and organized an AI conference as well. However, philosophers presented the idea of mechanization of human cognition in the 1700s. The concept of machines eventually impacting and even mimicking the human intelligence and thinking process made way for the creation of programmable digital computers such as Atanasoff Berry Computer (ABC) in the 1940s.

Mathematician Alan Turing also set up a test to measure the cognitive prowess of autonomous computation devices famously known today as the Turing test. Even before the 1700s mathematicians, philosophers, and logicians continued to present concepts and ideas about sentient machines.

One of the earliest depictions of an anthropomorphic bot is found in Greek mythology about an automaton named Talos.  During 384-322 BCE Greek philosopher, Aristotle also presented his despotism theory concerning automation.

Types of A.I.

AI is divided into two main types and these two types can be further divided into various other subsets.

1) Capability-based AI

2) Functionality-based AI

• Capability-based AI

The first way to classify AI tech is based on their performance and operational abilities. Depending on their capabilities AI can be divided into three further types:

• Artificial Narrow Intelligence
• Artificial General Intelligence
• Artificial Superintelligence

1) Artificial Narrow Intelligence

Artificial Narrow Intelligence or ANI is colloquially known as narrow or weak AI. This type of AI is often present in the form of tools that are used for predetermined commands or processing recurring inputs. The cognitive ability of ANI is often one-tracked and it cannot undertake learning. Examples of ANI are self-driving cars and AI virtual assistance bots like Siri and Google Assistant.

2) Artificial General Intelligence

The second type of AI in this category is Artificial General Intelligence or AGI. This type of AI can think, learn, and undertake a wide array of actions. The AI that falls into this category often mimics human-like functions such as writing prompts and working on creative prompts. Examples of AGIs are supercomputers, generative AI models such as ChatGPT, quantum hardware, etc.

3) Artificial Superintelligence

Artificial Superintelligence or ASI mostly persists in the theoretical form. ASIs are said to be fully sentient and they are often depicted in science fiction tropes and stories such as I Robot, Terminator, and Ex Machina, etc. At present, there are no real-life renditions of ASIs.

• Functionality-based AI

Functionality-based AI units are classified on account of their data processing ability, reaction to external stimuli, and interactions with their environment. Here are its sub-types with a few examples:

• Reactive Machines
• Limited Memory
• Theory of Mind
• Self-Awareness

1) Reactive Machines

Reactive machines in the AI realm are the ones that can generate prompts when input is fed to them. However, they are unable to store any data from their processing memory or apply learned throughput to the new results. Some examples of this type of AI are the Deep Blue program created by IBM and Stockfish created by Marco Costa, Tord Romstad, and Joona Kiiski.

2) Limited Memory

This type of AI functionality is all about knowledge storage. It means that AI with a limited memory function can refine its future operating results based on the memory from past operations. Search engines such as Google and Image Net have used this AI function to improve their algorithms for generating new results based on past queries run by the users.

3) Theory of Mind

The next step of evolution in terms of AI functionality is a theory of mind. AI with limited memory can improve its future throughputs based on the processing memory stored from past processes.

However, a theory of mind AI can go even further than that and can sense changes in emotional responses and environmental variations. There are currently some experimental AI projects that have this ability but it is mostly in the testing phase. An example of this type of AI is the Sophia project that is created by Henson Robotics.

4) Self-Awareness

Self-Aware AIs are the ones that are bound to be fully sentient and individual beings that can have a unique identity much like a human being. When an AI bot reaches this stage it is called the point of singularity.

A computer scientist at Google recently left the company after making claims that their AI model Lambda has become sentient. However, these interjections were not confirmed and to this day self-aware robots remain a theoretical and fictional concept.

Role of Artificial Intelligence in Crypto Trading

Professional financial analysts and investors have been leveraging AI technology for years to improve their traditional investment strategies. Therefore, it is only natural that cryptocurrency investors have also started to leverage AI tech to do the same.

Also Read: How Is Crypto Related To Artificial Intelligence?

There are a number to implement AI tech in crypto trading. Here are some of the most common ways that AI is often implemented and integrated with the crypto trading process:

AI Cryptocurrencies

AI Crypto Trading Bots

Generative AI Prompts

DeFi Trading Bots

Risk Management with AI

Social Media AI Bots

• A.I. Cryptocurrencies

Some blockchain developers have started to integrate AI technology for issuing cryptocurrencies. Good examples of AI cryptocurrencies are SingularityNET and The Graph. Deep learning AI models can process a greater amount of data while recognizing patterns and making predictions more efficiently.

Therefore, AI-based blockchains may enjoy greater decentralization, transparency, censorship resistance, and immutable data storage. AI-based blockchains can be implemented to upgrade sectors such as supply chain, cyber security, authenticity verification, data analytics, and financial services. Here are the top 5 AI cryptocurrency projects in 2023:

1) The Graph (GRT)

The Graph is used for indexing and data querying operations that are collected from the blockchain sources. It also can organize on-chain data in smaller subsets that are known as subgraphs. The project is hosted on the Ethereum blockchain and its native cryptocurrency GRT. The current market cap of The Graph is $907 million. 

2) SingularityNET (AGIX)

SingularityNET allows users to build, share, and monetize AI-based services. The project has also ingrained an internal marketplace for AI product sales and purchases. The buyers and merchants on this marketplace can conduct trades using its native currency named AGIX.

Developers put up unique AI solutions for sale here without having to develop a dedicated application. Meanwhile, other programmers can purchase these AI solutions to add functionality or solve problems in their existing applications. The market cap of SingularityNET is $268 million.

3) Fetch.ai (FET)

Fetch.ai offers both AI and Machine Learning services on its blockchain. It automates business and trading-related tasks for the users such as data processing, pattern recognition, and trading. Users can avail of the services of Fetch.ai by dealing in its native cryptocurrency called FET. The current market cap of Fetch.ai is $192 million as per CoinGecko.

4) Ocean Protocol (OCEAN)

Ocean Protocol is another AI project that is based on the Ethereum blockchain. The project enables data monetization and offers other data-related services. It can mean mining operations for researchers or companies by offering monetary compensation to the data contributors. The market cap for Ocean Protocol is around $199 million.

5) iExec RLC (RLC)

iExecRLC can monetize the computational power of its users by renting out cloud-computing services. Th]e native currency of this project is $91.03 million. 

• AI Crypto Trading Bots

Another way that AI is becoming increasingly ingrained in the cryptocurrency sector is crypto trading AIxx bots. As mentioned before AI offers deep learning capabilities that allow engines and algorithms to generate better outputs, more accurate predictions, and enhances their pattern recognition capabilities. Therefore, many investors have started to use AI bots as an aid or tool for improving their crypto trading strategies. Here are two ways that anyone can access crypto trading bots:

• Make It Yourself

Technology has continued to evolve and it has made it possible for crypto investors with programming backgrounds to generate their crypto trading bots. Here is a step-by-step guide to setup a personalized AI crypto trading bot:

Step-1: Set up the scope of your project. Define the required output and functionality that you wish to add to your AI crypto trading bot.

Step-2: Pick Programming Language and Tech Stack. Select a suitable private server or go with cloud computing services such as AWS for reliable data storage. Furthermore, pick a suitable programming language for your AI crypto bot for both the front and back end.

Step-3: Set up a suitable budget that accounts for all the development, research, hosting, hardware, software, and human capital costs.

Step-4: Recruit a development team for both back and front ends. Full-stack developers may be able to use their skills and experience to put together a working bot on their own. They may hire consultants to work out any bugs and conduct the technical audit for the best results. 

Step-5: Once the AI bot is created, it is time to feed it tons of raw data so that it can train on the material. The more data the bot practices the better its chances of generating better outputs granted that it has limited memory.

Step-6: Devs can add upgraded functionalities by increasing data quality, arranging in more appropriate data formats, identifying outliers in data sets, analyzing variables, and organizing training data for input.

Step-7: It is the best practice to document everything along the way that helps others to understand the product and works as a reference for any future upgrades.

• Crypto Trading Bots Marketplaces

There are many places where aspiring or experienced cryptocurrency investors can purchase crypto trading bots. Here is a list of the best crypto trading bots and marketplaces in 2023:

3Commas

3Commas is the most popular choice among shoppers of AI crypto trading bots. It is a trading bot for hire that generates exclusive trading strategies for its buyers keeping in view their personalized goals and needs. Buyers can create an account and set up their 3Commas trading bot to perform automated DCA trades as well as delve intoto crypto futures trading strategies.

Pionex

Pionex is a crypto bot trading platform. It means that several different trading bots are listed on this forum. The most common types of bots that are listed on this platform can be divided into grid trading bots, DCA trading method bots, and rebalancing strategy bots.

CryptoHopper

CryptoHopper is often listed among the most popular AI crypto trading engines. This platform aggregates various trading functions into a singular bot namely copy trading, hub functions, social trading, and investment portfolio management.

This bot specializes in 75 cryptocurrencies including Bitcoin, Ethereum, and Litecoin. CryptoHopper is a fairly popular artificial intelligence Bitcoin trading bot.  At the same time, it can integrate with the trading accounts at 9 different exchanges such as Binance, Huobi, Kraken, Coinbase Pro, Bitfinex, etc.

• Generative AI Prompts

These days the internet is overtaken by a host of generative AI projects such as ChatGPT by OpenAI, Bard by Google, Midjourney, DALL-E, and Deep Mind, etc. Many of these AI models are free of cost and can offer search results in simplified and conversational output.

Many users submit their everyday queries to the generative AI models to get the best answers and solutions such as fixing code. Crypto analysts and journalists have set a new trend of running price analysis queries through generative AI models. There are also instances, where developers have used these AI engines to fix or create new AI trading bots. However, there is always a caveat concerning the accuracy of the final results.

• DeFi Trading Bots

Another way that AI is venturing into the crypto trading arena is through DeFi bots. Since DeFi mostly depends on smart contracts, therefore crypto investors can leverage AI bots such as ChatGPT to create a new crypto trading bot.

AI can edit, upgrade, amend, and fix code for new smart contracts, AI trading bots, and smart contract-based AI trading bots. At present, these bots are not able to automatically manage a DeFi trading portfolio but they may issue detailed trading strategies for carrying out predefined functions such as staking, trading, swapping, etc.

• Risk Management with AI

Professional investors who are working in TradeFi usually spent years in the trading market to improve their risk assessment and management skills. One ideal tool to improve the process faster is AI.

Some commercial enterprises such as Coinbase Global have integrated AI programs such as ChatGPT to improve their risk analysis. For the average crypto investor with no trading training and zero coding knowledge, it is possible to run a risk assessment query concerning a cryptocurrency by using a simple prompt in ChatGPT.

• Social Media AI Bots

Social media bots have made an indirect foray into the world of crypto trading. There are many blockchains and protocols such as XDC Networks that allow anyone to create a new cryptocurrency without any coding background.

However, these newly minted tokens can only gain traction among users if they are known in the crypto community. Therefore, many token minters may hire or create social media bots to post updates about their new cryptocurrencies to add traction to their projects. However, they may expose potential investors to scams and fraudulent cryptocurrencies.

Risks involved with Crypto Trading Bots

As noticed above, AI can improve crypto trading results in various ways. However, there are also many setbacks involved when using AI to trade crypto that every investor must take into consideration.

Hallucinations

Lack of Accuracy

Biased Results

• Hallucinations

Hallucination is a technical term associated with generative AI models. It takes place when AI programs such as ChatGPT or Bard may include input, information, or solution that is based on fabricated facts.

There have been many instances where generative models were displayed that were entirely made up and not based on real facts. Therefore, investors who planning to use generative AI models to create a trading strategy or write code for new AI Trading bots must consider potential hallucinations.

• Lack of Accuracy

As per Roman Yampolskiy, the director of the Cyber Security Lab at Louisville University pointed out that AI systems are prone to errors that are rooted in their performance, lifecycle phase, and systematic integrity.

Therefore, the final output from an AI crypto trading bot may or may not be completely accurate. The crypto investors who are depending on these trading bots may have limited resources to confirm the accuracy of these outputs and can suffer from losses by adopting them without checking.

• Biased Results

There are also some instances where AI trading bots have created biased results. The partiality of these AI models may stem from the poor quality of data input that is used to train them. There are other instances, where a bias present in the data reserve feed for AI trading bots can lead to skewed results.

On the other hand, AI models depend on an appropriate configuration to generate the most effective results. Therefore, the users may only employ the output provided by AI trading bots or use them to conduct automated trades if they have an accurate understanding of their inner workings and financial market fundamentals.

Conclusion

Technology continues to evolve. Cryptocurrencies have revolutionized the financial sector. However, AI is now transforming the blockchain sector by bringing more functionality, increasing efficiency, and creating ease.

Nevertheless, there are caveats involved when using AI for crypto trading. Investors can get the best results by learning more about the inner workings of AI and getting familiar with crypto trading fundamentals.