On all major stock markets across the world, it is believed that between 60 and 75 percent of trade is algorithmic. High frequency trading, bots, algo trading, algo portfolio management, and increasingly, the application of Artificial Intelligence (AI) in algorithmic trading and finance, dominate this market.
While it is difficult to estimate the exact percentage of algorithmic trading that uses AI, Machine Learning (ML) has recently taken the lead in the market by employing huge data taught on learning networks to statistically risk adjust everything from single trades to whole portfolios.
While the majority of this technology’s use is currently in the hands of market experts known as “quant traders” or “quants,” it is now becoming increasingly accessible to “retail traders,” and solutions are beginning to emerge for crypto trading.
With the aid of Large Language Models (LLMs), generative text models that are well-suited for a range of specialized tasks and natural language creation, ChatGPT has ushered in the next phase of heuristic technology. These models are distinguished by their vastness and are made possible by AI accelerators that handle enormous volumes of data by data-scraping from the Internet.
LLMs like ChatGPT assist in interpreting charts, trends, and financial data and presenting the results in a clear manner. Traders who want to better understand market dynamics, risk considerations, and investment opportunities can benefit from the analysis.
Crypto exchange Coinbase Global now employs ChatGPT for risk analysis and uses it to screen all new digital asset uploaded to its marketplace. Omni, a crypto bot for the Solana blockchain, claims to be a “expert” on DeFi and may enable passive income schemes like as crypto staking. 3SingularityNET provides a variety of AI bots for market and data analysis.
If you want, you can even use ChatGPT to build your own crypto trading bot. The application of AI and language learning models developing in the market is driving transformation in cryptocurrency trading.
The machine learning tool developed by GNY.io is intended to predict the volatility of the top 12 cryptocurrencies utilizing a variety of data points and sophisticated algorithms. To better comprehend time-series financial data, GNY’s Range Report makes use of ChatGPT and Meta’s Large Language Model Meta AI 2 (LLaMa 2) and Long Short-Term Memory (LSTM) models.
The models give support for both technical and market analysis, and they may identify patterns and indicators in price (chart) data to provide trading signals and forecast price movements. The models are also capable of predicting asset volatility and price movements, such as those for stocks, commodities, and cryptocurrencies.
GNY.io has created a model to learn from three years of trading patterns in data across 25 charts and indicators. It uses this model to anticipate the volatility of cryptocurrencies over the next seven days and claims the algo has an average accuracy of over 95%.
Many professional traders derive a significant portion of their income from volatility, and unpredictable markets and assets are a significant draw for traders. In the last several years, there have been astronomical booms and busts for cryptocurrencies, with prices rising to record highs and then dropping. Most cryptocurrencies are viewed as speculative by many traders, and momentum-driven volatility presents several possibilities for trading.
With major coins like bitcoin and ethereum up 50 and 30 percent, respectively, year to date, and trading volumes remaining historically low, cryptocurrency volatility is currently lower than it was. The volume of bitcoin exchanged last month was almost five years low.
CCDATA’s Alissa Ostrove reports that volatility for Bitcoin and Ethereum has reached an all-time low, despite the biggest liquidation since FTX on August 17.
In the cryptocurrency market, bull runs frequently follow periods of low trading volume. As prices decline, traders typically stop trading, which causes trading volumes to decline. After a great start to the year, many believe that traders are seeking for an excuse to get back into the market.
Low volatility deprives investors of short-term opportunities, but it also encourages stockpiling of fundamentally sound assets in preparation for the next market cycle, according to Ostrove.
The upcoming approval of bitcoin ETF applications from prominent asset managers such as Blockrock, Fidelity, WisdomTree, Invesco Galaxy, and others is expected to propel the next cryptocurrency bull market, according to market analysts. Grayscale’s recent triumph in a countersuit to convert its Grayscale Bitcoin Trust (GBTC) into a listed bitcoin ETF had previously been denied by the SEC, and market observers estimate that bitcoin ETF approvals are still months away.
Human Emotion Can Be Overcome by Robots
If they had access to AI and machine learning technologies for trading, 95% of study respondents would trade more than the $5,000 monthly minimum floor they presently trade. According to the study, traders would up their trading by 16 percent on average if they trusted AI tools to spot trading patterns and forecast price changes. In order to identify patterns and forecast price changes, nearly three out of four traders think that adopting AI and machine trading tools would be beneficial.
The research focused on the availability and quality of trading data as the main problem. With cryptocurrency platform and exchange news services being the most popular, only 29% of traders ranked their existing data sources as outstanding. Since there is a lot of noise and information to sort through, most traders are not impressed with the data sources they currently have access to.
Any trader seeking to succeed in the cryptocurrency markets must be able to read the market, recognize trends, and tune out the cacophony of competing data. However, it can be challenging to maintain emotional control when trading. This is one of the main explanations for why AI is becoming more prevalent in cryptocurrency trading as well as other financial services in general.
Although experts believe that AI will inevitably play a part in trading, Cosmas Wong, CEO of GNY, asserts that AI can never fully replace a human’s judgement and oversight. As a result of their extensive experience working with various models, they are aware of both their advantages and disadvantages. Because people are skilled in some areas but machines are not, AI technologies will need to be closely watched. They encourage cooperation to get the finest results.
The influence of emotion, prevailing attitude, and bias in trading is a crucial aspect of cryptocurrency trading that AI and machine learning address. Nine out of ten retail traders, according to the study, acknowledge that emotion has some bearing on their cryptocurrency trading, with a quarter of them admitting that it is the primary motivator.
Emotion costs traders money. According to the study, on average, traders believe that 20% of their losing transactions, or one in five, were made due to emotion rather than logical judgements based on data analysis.
60 percent of traders feel social media material has a significant impact on cryptocurrency values, while more than half of traders say breaking news about companies active in the industry, such as the demise of FTX or Genesis, is the largest factor driving trading. When asked, 75% of traders said they prefer to trade when there is more volatility and liquidity, such as when the U.S. and U.K. markets open.
If the programme has been evaluated for performance and a predicted conclusion, emotion can be removed when traders transition to automated “programmatic” trading. An AI-driven algo trading bot is able to execute trades using a predetermined programme approach and respond swiftly to market movements.
In the 1970s, the venerable Richard Dennis made the bold claim that his Turtle Trading System could transform anyone with no prior financial or trading experience into a top-tier commodities trader in just two weeks, provided they could adhere to the program’s guidelines. Successful turtle traders followed the rules; unsuccessful ones allowed prejudice, sentiment, and emotions to get in the way.
However, it’s crucial to keep in mind that beta-versions of apps like ChatGPT are still being evaluated, thus questions about their long-term performance remain unanswered. It can produce erroneous results because its training data only extends through September 2021.
Additionally, there is a risk involved in depending solely on an AI programme to perform all tasks in the cryptocurrency market because these programmes have limitations on the amount of prediction they are capable of. There are always possible data security and privacy risks with AI, as seen with the FTX collapse, which had nothing to do with cryptocurrency prices or volatility.
Although AI provides a number of tools that are becoming more and more significant, human traders are still the ones that design “programme strategies,” which require research and back testing. Traders will always play a crucial role in the decision-making process, but they must expand their capabilities by utilizing the quantitative power of AI to produce consistent risk-adjusted strategies and returns for both humans and machines.
Legislation to govern the AI business is a top priority, and both the industry and legislators agree that it needs to be done quickly, as U.S. technology heavyweight CEOs meet senators in Washington this week. Elon Musk, the CEO of Tesla, and Mark Zuckerberg, the CEO of Facebook, are among the top tech executives. They were gathered by Senator Chuck Schumer (D-NY), together with the CEOs of Google, Microsoft, and IBM.
While there will be a lot of dogmatic debates about “Open AI,” which is “open” to manipulation and hacking by state-sponsored terrorists seeking to undermine democratic capitalism, versus the “closed” development of AI by corporations and NGOs, one thing is for certain: the AI trading revolution in financial services is just getting started, and retail crypto traders are its latest beneficiary.
