The expansion of Artificial Intelligence (AI) is predicted to have a significant influence on the growth of cryptocurrency markets. Over the last few years, the crypto industry has experienced compelling growth, gaining many users globally.
Why Does FLUID Use AI?
Over the last few years, the crypto industry has experienced compelling growth, gaining a large number of users globally
At present, AI is widely employed in the cryptocurrency market, but many experts predict that this is only the beginning and that AI usage in the crypto market will skyrocket in the next few years
FLUID’s patented hybrid cryptocurrency prediction models consist of various powerful Deep Learning techniques like Artificial Neural Networks (ANN) and Support Vector Machines (SVM)
The FLUID team is constantly working on improving the models and testing them against different market conditions to build a model that could adapt to any market conditions
The role of AI in cryptocurrency
The expansion of Artificial Intelligence (AI) is predicted to have a significant influence on the growth of cryptocurrency markets. Over the last few years, the crypto industry has experienced compelling growth, gaining many users globally. The fact that it is easy for crypto beginners to get started is one of the reasons why the market is extremely popular, and the advancement of AI may make it even easier for users to begin trading cryptocurrency.
In an earlier blogpost, FLUID explained the impact of AI on the finance industry and crypto market. To read, click here.
Cryptocurrency markets are complex and nonlinear by nature. It takes time and expertise to comprehend the unpredictable cryptocurrency market and carry out investments. As more tech-driven solutions become available, asset managers are increasingly turning to AI, Machine Learning (ML), and Natural Language Processing (NLP) to make sense of managing crypto assets in fund portfolios. These models provide a multi-feature architecture leveraging multiple indicators such as quantitative models, sentiment analysis, commodities markets, the CPI index, and futures and options markets.
Today, AI is widely employed in the cryptocurrency market. Still, many experts predict that this is only the beginning and that AI usage in the crypto market will skyrocket in the next few years.
What Can AI Provide in Crypto?
More Accurate Crypto Market Predictions: Market Volatility is one of the main challenges in cryptocurrency trading, and manual research, extraction, and analysis methods are ineffective at discovering investments and buy/sell signals. Large volumes of unstructured data may be gathered, cleaned, processed, and analyzed to help investors better identify and forecast essential developments in the cryptocurrency market.
Additionally, combining blockchain with AI increases their combined strength. Blockchain records everything of value, including transactions, digitally. Since blockchain permits the safe storage and sharing of data, AI may be used to evaluate and provide insights from historical and real-time blockchain data. Blockchain transactions can also provide behavioral trends that can be used to understand the motivations behind the cryptocurrency market.
Crypto Market Sentiment Analysis: Sentiment analysis for the cryptocurrency marketuses AI and natural language processing to examine how people feel or think about a particular subject. An overall favorable mood toward a digital currency might forecast a price increase, while a general negative sentiment could forecast a price decline.
News, blogs, articles, forums, social media postings, stock message boards, and even comments must be gathered, processed, and evaluated to determine if they are negative, neutral, or positive to gauge the sentiment of the cryptocurrency market. Unusual behaviors in sentiment can be used to identify market manipulation warning signs.
Automated Crypto Trading Strategies: Due to AI’s ability to mimic human intellect, analysts and investors frequently employ AI in high-frequency trading techniques. Traders who can swiftly complete deals on bitcoin exchanges are more successful. Investment and hedge funds utilize high-frequency trading, an algorithmic trading practice in which a computer quickly executes many orders. The markets may be analyzed in a matter of seconds, and cryptocurrencies can be bought or sold thanks to algorithms based on data from mathematical computing, predictive analytics, and prediction techniques.
Making Money with Crypto Insights: Signals are very important to cryptocurrency traders. However, it might be extremely difficult to manually produce precise signals given the dominance of unstructured data in the digital world. Data scientists and developers may create models that traders can utilize to get accurate and relevant data. Data may be categorized, and entities can be extracted using AI’s NLP algorithms based on various factors, such as the currency’s name, document type, currency founder, and more. Through a simple dashboard or interface, data scientists may provide precise trading insights that are intelligible to non-technical traders or investors. Using the newfound knowledge, traders and investors may then improve their profits.
Why Does FLUID Use AI?
Because of their linear nature and inability to understand the different factors of large amounts of data, research has shown that statistical quant models have limited predicted precision. This is where Al comes in. By utilizing machine learning and deep learning methodologies, FLUID can grasp the macroeconomic and microeconomic aspects required to estimate the price of an aggregated order book accurately.
Deep Learning, a subset of machine learning, is based on unique artificial neural networks that let us use learning sequences in the data to solve prediction and classification issues.
FLUID’s patented hybrid cryptocurrency prediction models consist of various powerful Deep Learning techniques like Artificial Neural Networks (ANN) and Support Vector Machines (SVM). FLUID’s prediction model develops data-driven computer programs that can execute complicated tasks like predicting values without explicit instructions, or in other words, autonomously, and is supported by linear and nonlinear statistical models, among other techniques.
Cryptocurrency marketplaces have complex dynamics because of the many interconnected parts and multiple nonlinear features. This complexity, brought on by the high volatility and massive data points, makes it challenging to make predictions using standard methodologies. To improve the forecasting of cryptocurrency order book prices in a time series with its complexity and nonlinear nature, FLUID leverages ML, a subset of Al. In addition to being more flexible than traditional methods, it can also reveal hidden structures in data sets by identifying relations and predictions between important variables.
FLUID’s Competitive Edge
FLUID’s competitive edge is that it uses AI quant-based methodologies to provide a high throughput service to its clients, in contrast to other systems that only offer quant-based solutions. Al from FLUID uses a hybrid prediction model for cryptocurrencies that combines machine learning and deep learning to forecast real-time order book values accurately. Preprocessing and feature selection impact the model’s performance while collecting real-time data since bitcoin values are dynamic and highly volatile with massive amounts of data. The use of deep learning, which automates the feature extraction portion of the process and eliminates human interaction while enabling the consumption of enormous real-time datasets, is one of the significant characteristics that sets apart FLUID’s hybrid cryptocurrency forecasting model.
This may also be seen as scalable machine learning that classifies and locates relationships between datasets to estimate prices effectively. Together with a group of specialists in AI led by Dr. Lawrence Henesey, a professor at the Blekinge Institute of Technology in Karlskrona, Sweden, Fluid’s team has created models that have been positively stress-tested on live and historical data.
The FLUID team is constantly working on improving the models and testing them against different market conditions to build a model that could adapt to market conditions.