How to Conduct Crypto Market Sentiment Analysis: A Step-by-Step Guide

Crypto market sentiment analysis has become an essential tool for investors navigating the volatile cryptocurrency market. This approach combines fundamental analysis with social media trends and investor emotions to gauge market mood and predict potential price movements. By monitoring sentiment indicators, traders can gain valuable insights into market psychology, helping them make more informed decisions in an often unpredictable environment.

This guide will walk readers through the process of conducting crypto sentiment analysis, covering key aspects such as data sources, analysis techniques, and interpretation of sentiment signals. It will explore how to set up a real-time sentiment analysis workflow, discuss the challenges and limitations of this approach, and provide practical tips to incorporate sentiment data into a comprehensive trading strategy. By mastering these techniques, investors can enhance their understanding of market dynamics and potentially improve their trading outcomes in the fast-paced world of cryptocurrency.

Related Read: Bitcoin Whales Scoop Up 84,000 BTC in July, Largest since October 2014

Fundamentals of Crypto Market Sentiment

Defining Market Sentiment

Crypto market sentiment, also known as crypto investor sentiment, reflects the overall attitude and psychology of investors in the cryptocurrency space. It’s akin to reading the collective mind of cryptocurrency traders and investors, gauging whether the mood is bullish (expecting rising prices) or bearish (anticipating a drop). This sentiment considers traders’ emotional opinions and attitudes towards specific cryptocurrencies, examining the psychology of the crowd as it aligns with their market positions.

Market sentiment is fueled by a blend of emotions, news, and social media buzz. Positive news can spark excitement and buying sprees, while fear, uncertainty, and doubt (FUD) spread on social media can lead to panic selling. The crypto market’s social nature means that if more people tend to act on an event, thought, feeling, or idea, they can significantly impact price momentum.

Why Sentiment Matters in Crypto

Understanding market sentiment is crucial when navigating the cryptocurrency market for several reasons:

1. Price Influence: Sentiment can significantly influence the direction of cryptocurrency prices by affecting supply and demand dynamics. It’s a key driver, influencing trends, trading decisions, and even shaping market realities through investors’ expectations.
2. Market Dynamics: Unlike traditional financial markets, cryptocurrency is more influenced by social pressure and can be affected by a single tweet or a road map update. A prime example is when a single tweet from Elon Musk causes Dogecoin’s price to surge or drop accordingly.
3. Decision-Making Tool: Analyzing market sentiment can help investors decide whether the “fear of missing out” (FOMO) on a trade is based on solid reasoning or just a gut feeling. It assists in determining whether FOMO is justified or merely a result of herd mentality.
4. Comprehensive Analysis: When combined with fundamental and technical analysis, sentiment analysis offers a more holistic view of the cryptocurrency market. This combination provides a comprehensive picture – not just how the market feels, but why it feels that way.
5. Trading Strategy Enhancement: By incorporating sentiment analysis, traders can:
   • Gain a better understanding of short-term and medium-term price movements
   • Keep emotions in check while trading
   • Spot potential profitable price trends more effectively
6. Market Indicators: Sentiment analysis utilizes various indicators to measure attitudes, offering insights into the investor’s mindset. These include:
   • Technical indicators analyzing price movements and volumes
   • Social media tools assessing online discussion moods

Interpreting these indicators helps investors gauge the market’s direction, either towards optimism or pessimism, which is crucial for informed investment decisions.

In conclusion, while sentiment analysis is a powerful tool in understanding market mood, it’s most effective when used in conjunction with other analytical methods. Combining fundamental analysis, technical analysis, and sentiment analysis provides traders with a comprehensive toolkit to navigate the volatile cryptocurrency market more effectively.

Sources of Sentiment Data in Crypto Markets

Crypto market sentiment analysis relies on diverse data sources to gauge investor attitudes and market psychology. These sources provide valuable insights into the emotional landscape surrounding cryptocurrencies, helping traders make informed decisions.

Social Media Platforms

Social media has become a powerful tool for predicting crypto prices. A recent study by finance professors and a PhD student at Pennsylvania State University found that sentiment on social media significantly influences short-term crypto returns. This impact is evident in anecdotal cases, such as Dogecoin’s 10,000% rally following supportive tweets from Elon Musk and other celebrities in 2021.

Key social media platforms for sentiment analysis include:

1. Twitter: A primary source of real-time crypto discussions and sentiment.
2. Reddit: Hosts numerous cryptocurrency-focused subreddits where investors share opinions and analyses.
3. Telegram: Offers channels and groups dedicated to specific cryptocurrencies and trading discussions.
4. Discord: Provides platforms for direct engagement with project teams and community members.

These platforms generate vast amounts of sentiment data. Research shows that people produce more than 100 MB of data every minute, including insights into their feelings about various topics, including cryptocurrencies. Social media discussions have a significant impact on shaping sentiment, with emotions influencing how people perceive cryptocurrency value and, consequently, actual returns.

To leverage this data effectively, traders can:

• Use data collection software tools to track social mentions.
• Monitor hashtags, mentions, and user interactions to identify emerging trends.
• Join official forums and groups to communicate directly with project teams and community members.

News Outlets

While news media sentiment has been found to have less impact on crypto prices compared to social media, it remains a crucial source of information for traders. News can significantly influence cryptocurrency prices in several ways:

1. Positive News: Announcements of mainstream adoption, successful project developments, or regulatory clarity can trigger buying sprees, driving prices upwards.
2. Negative News: Reports of hacks, security breaches, or regulatory crackdowns can lead to panic selling, causing prices to plummet.

To stay informed, traders should:

• Keep up with the latest industry news by visiting media portals and blogs.
• Follow trusted cryptocurrency media platforms like CoinDesk and CoinTelegraph for expert opinions and analyses.
• Use Google Trends to gauge the level of interest in a cryptocurrency.

Trading Forums

Trading forums serve as valuable sources of sentiment data, offering insights into investor opinions and market trends. These platforms allow traders to engage in discussions, share analyses, and express their views on various cryptocurrencies.

Key aspects of trading forums include:

1. Community Engagement: Joining Telegram channels, Reddit discussions, and official project forums helps traders engage directly with the community and gain insights into market sentiment.
2. Technical Analysis: Many forum users publish technical analyses and thoughts on market trends, which can influence broader market sentiment.
3. Diverse Perspectives: Forums often host a mix of experienced traders and newcomers, providing a range of viewpoints on market conditions.

To effectively use trading forums for sentiment analysis, traders should:

• Regularly participate in discussions to gauge community sentiment.
• Look for patterns in user opinions and how they correlate with price movements.
• Be aware of potential biases and echo chambers that can form within these communities.

By leveraging these diverse sources of sentiment data, traders can gain a comprehensive understanding of market psychology and make more informed decisions in the volatile cryptocurrency market.

Sentiment Analysis Techniques for Crypto

Sentiment analysis has become a crucial tool for traders navigating the volatile cryptocurrency market. By leveraging advanced techniques, investors can gain valuable insights into market psychology and make more informed decisions. This section explores the key methods used in crypto sentiment analysis.

Natural Language Processing

Natural Language Processing (NLP) forms the foundation of sentiment analysis in the crypto space. NLP techniques enable the extraction and interpretation of sentiment from textual data sources such as news articles, social media posts, and forum discussions.

One popular NLP tool is the Natural Language Toolkit (NLTK), a Python library designed for working with human language data. NLTK includes a pre-trained sentiment analysis model that can be applied to analyze the sentiment of text data related to cryptocurrencies.

To implement NLTK for sentiment analysis, traders can follow these steps:

1. Install NLTK and download the pre-trained model:import nltk
   nltk.download(‘vader_lexicon’)
2. Use the SentimentIntensityAnalyzer to analyze text:from nltk.sentiment import SentimentIntensityAnalyzer
   sia = SentimentIntensityAnalyzer()
   text = “Bitcoin is the future of money and will revolutionize the way we do business.”
   sentiment = sia.polarity_scores(text)
   print(sentiment)

This process allows traders to quantify sentiment from various text sources, providing a numerical representation of market mood.

Machine Learning Algorithms

Machine learning algorithms have proven to be highly effective in crypto sentiment analysis. These algorithms can be trained to analyze vast amounts of data from social media and news articles to detect sentiment surrounding specific cryptocurrencies.

Deep learning architectures, such as Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), and Bidirectional Encoder Representations from Transformers (BERT), are extensively applied in sentiment analysis. Each architecture has its strengths:

• CNNs excel in feature extraction
• RNNs are effective for sequence modeling
• BERT specializes in capturing contextual information from language

These models are often fine-tuned with financial datasets to better understand the nuances of financial language, enhancing their effectiveness in crypto sentiment analysis.

Sentiment Scoring Models

Sentiment scoring models are essential for quantifying the overall market sentiment. These models typically classify online conversations as positive, negative, or neutral based on their automatic identification of emotion, tone, and urgency.

Key components of sentiment scoring models include:

1. Sentiment Lexicons: Curated dictionaries featuring words or phrases annotated with sentiment scores. Specialized lexicons for financial terminology enhance accuracy by accounting for domain-specific expressions.
2. Sentiment Indices: Numerical representations of market sentiment, typically ranging from negative to positive. These indices are calculated using algorithms that analyze text data and assign sentiment scores based on the presence of certain keywords and phrases.
3. Social Media Monitoring Tools: These programs track mentions of specific cryptocurrencies across social media platforms and assess the tone of those mentions, offering real-time insights into public opinion.
4. Algorithmic Analysis: Advanced algorithms examine market mood while evaluating the credibility and impact of sources, providing a more complex picture of sentiment.

By combining these techniques, traders can develop a comprehensive understanding of market sentiment. For example, a study utilizing NLTK for sentiment analysis of news articles found that Ethereum had a higher mean positive score (0.082) compared to Bitcoin (0.055), and a higher maximum compound score (0.883 vs. 0.878 for Bitcoin). Such insights can be valuable for traders in making informed decisions about their cryptocurrency investments.

Setting Up a Sentiment Analysis Workflow

Choosing Analysis Tools

Setting up a sentiment analysis workflow for cryptocurrencies requires careful selection of appropriate tools. Crypto analytical tools are specialized software solutions designed to provide insights into the complex and dynamic world of cryptocurrencies. These tools analyze various aspects of cryptocurrencies, including market trends, transaction volumes, wallet activities, and network health.

When choosing analysis tools, consider the following factors:

1. Data Sources: Look for tools that gather data from multiple sources like crypto exchanges, blockchain networks, and financial news outlets to present a comprehensive view of the cryptocurrency market.
2. Advanced Technologies: Select tools that employ advanced technologies such as data analytics, machine learning, and blockchain analytics to deliver accurate and timely information.
3. Features: Opt for tools offering real-time market monitoring, historical data analysis, predictive modeling, and sentiment analysis.
4. User-Friendly Interface: Choose tools with a friendly and easy-to-use user interface, especially if you’re new to sentiment analysis.
5. Integration Capabilities: Consider tools that can be easily integrated with your existing trading platforms or data analysis workflows.

Data Collection Methods

Effective data collection is crucial for accurate sentiment analysis. Here are some key methods to consider:

1. Social Media Monitoring: Utilize tools that track mentions of specific cryptocurrencies across social media platforms like Twitter, Reddit, and Telegram. These platforms are hotbeds of crypto discussions and provide valuable insights into public sentiment.
2. News Aggregation: Collect data from news websites and cryptocurrency forums to gain a broader perspective on market sentiment.
3. API Integration: Leverage Application Programming Interfaces (APIs) to pull data from various sources in real-time. APIs allow developers to integrate sentiment analysis tools with trading platforms, providing instant updates on market sentiment.
4. Web Scraping: Employ web scraping techniques to gather relevant data from websites, forums, and other online sources.
5. Survey and Review Analysis: Analyze feedback from surveys and product reviews to gain insights into customer sentiment towards specific cryptocurrencies or blockchain projects.

Processing Pipeline

Once data is collected, it needs to be processed through a well-structured pipeline. Here’s a step-by-step guide to setting up an effective processing pipeline:

1. Data Cleaning: Clean the collected data by removing irrelevant or redundant items. This process includes eliminating spam, duplicate entries, advertisements, and any other content that does not contribute to the analysis. Techniques such as natural language processing (NLP) can help in identifying and filtering out non-relevant data.
2. Data Labeling: Label the cleaned data based on its emotional tone. This can be done manually or through automated tools. Typically, content is categorized as negative, positive, or neutral. Automated tools often use sentiment analysis algorithms that can efficiently process large volumes of data.
3. Model Training: Use the labeled dataset to train your sentiment analysis model. Machine learning algorithms such as Naive Bayes, Support Vector Machines (SVM), or more advanced deep learning techniques like Recurrent Neural Networks (RNN) and Transformers can be employed.
4. Model Evaluation: After training your model, evaluate its performance using various metrics such as accuracy, precision, recall, and F1 score. This step involves testing the model with a separate validation dataset to ensure it can generalize well to new, unseen data.
5. Fine-tuning: Based on the evaluation results, fine-tune the model to improve its accuracy and reliability.
6. Real-time Analysis: Implement the trained model for real-time sentiment analysis of incoming data. This allows traders to stay ahead of market trends and react promptly to changes in sentiment.

By following this structured approach to setting up a sentiment analysis workflow, cryptocurrency traders and investors can gain valuable insights into market sentiment, helping them make more informed decisions in the volatile crypto market.

Conducting Real-Time Sentiment Analysis

Real-time sentiment analysis has become an essential tool for cryptocurrency traders and investors seeking to capitalize on market opportunities and mitigate risks. By leveraging advanced technologies and data analysis techniques, traders can gain valuable insights into market conditions and make informed decisions based on up-to-the-minute information.

Live Data Streaming

Live data streaming forms the backbone of real-time sentiment analysis in the cryptocurrency market. This process involves the continuous collection and analysis of data from various sources, providing traders with instant access to market conditions, price movements, and trading volumes. The ability to process this information in real-time allows traders to execute trading decisions based on the most current data available, potentially capitalizing on short-lived opportunities or addressing risks before they escalate.

To implement live data streaming for sentiment analysis, traders can follow these steps:

1. Generate API keys: Obtain access to platforms like Twitter/X and CoinGecko, which provide valuable data for sentiment analysis.
2. Set up the development environment: Use an Integrated Development Environment (IDE) or code editor, such as Jupyter Notebook, and install Python along with necessary packages.
3. Install required Python packages: Utilize packages like ReGex for data cleaning, Tweepy for accessing the Twitter/X API, and NLTK Vader Lexicon for sentiment analysis.
4. Extract and process data: Develop functions to retrieve tweets and cryptocurrency price data, then apply sentiment analysis techniques to generate polarity scores.
5. Visualize results: Create real-time visualizations that display the correlation between tweet sentiment and cryptocurrency price changes.

It’s important to note that the granularity of data available may vary depending on the API used. For instance, some free APIs may offer data at one-minute intervals, while others might provide more frequent updates.

Continuous Monitoring Techniques

Continuous monitoring is crucial for maintaining an up-to-date understanding of market sentiment. This approach enables traders to respond promptly to opportunities and threats, maximizing profitability and efficiency. To implement effective continuous monitoring techniques, consider the following strategies:

1. Automated trading platforms: Utilize platforms that incorporate real-time monitoring capabilities, allowing for instant recognition of market changes and improved decision-making.
2. Intelligent alerting systems: Implement systems that can notify traders of notable events or anomalies, even when they are not actively watching the markets.
3. Multi-platform monitoring: Engage with various social media platforms, including Telegram channels, Reddit discussions, Discord servers, and official project forums, to gain a comprehensive view of community sentiment.
4. Data preprocessing: Clean and prepare collected data by removing noise, standardizing text, and tokenizing content to ensure only relevant information is analyzed.
5. Sentiment analysis approaches: Choose appropriate methods such as lexicon-based analysis or machine learning models like Support Vector Machines, Naive Bayes, or deep learning models for analyzing sentiment.
6. Visualization techniques: Employ charts, graphs, and other visual tools to aid in the interpretation of sentiment analysis results and identify correlations between sentiment and market behavior.
7. Historical data integration: While focusing on real-time data, incorporate the ability to quickly reference and analyze historical data alongside current market conditions for added context in decision-making.

By implementing these continuous monitoring techniques, traders can develop a robust real-time sentiment analysis workflow that provides valuable insights into market dynamics and supports informed trading decisions in the volatile cryptocurrency market.

Interpreting Sentiment Signals for Trading

Interpreting sentiment signals has become a crucial aspect of cryptocurrency trading, offering valuable insights into market dynamics and potential price movements. Traders who understand and effectively utilize these signals can make more informed decisions in the volatile crypto market.

Sentiment Indicators

Sentiment indicators play a pivotal role in gauging the overall mood of the cryptocurrency market. These tools examine how bullish or bearish market participants are, attempting to forecast investors’ future behavior based on their thoughts and feelings. One widely used sentiment indicator is the Bitcoin Crypto Fear and Greed index, which measures the prevailing emotions in the Bitcoin market on a scale of 0 to 100.

The Bitcoin Crypto Fear and Greed index combines data from various sources to provide a single numerical representation of market sentiment. A score of 0 indicates extreme fear among market participants, while 100 signifies extreme greed. This index serves as a valuable tool for investors, offering insights into potential market trends and investor behavior.

During periods of rapid cryptocurrency price surges, the index often reflects increased greed among investors. This can trigger a “fear of missing out” (FOMO) among those watching from the sidelines, potentially leading to additional capital flowing into the market. Conversely, when cryptocurrency prices are plummeting, the index may indicate heightened fear, which could prompt investors to sell their holdings.

Correlation with Price Action

The relationship between sentiment and price action in the cryptocurrency market is complex and multifaceted. Research has shown that investor sentiments and expectations have a significant impact on cryptocurrency prices, similar to their effect on stock prices. Bitcoin’s price, for instance, has been known to rise and fall rapidly based on investor sentiments, economic circumstances, and regulatory actions.

A study using transaction-level buy and sell orders from the major digital currency exchange Coinbase formulated a measure for investor sentiment and provided new evidence on the sentiment-return relation for Bitcoin. The research revealed that rising sentiment is associated with positive price changes, while declining sentiment correlates with negative price changes. This relationship remains robust across various market conditions, from periods of extreme negative returns to periods of extreme positive returns.

Interestingly, the correlation between sentiment and price action in cryptocurrencies appears to be more intense compared to traditional markets. Cryptocurrencies that are more popular with investors tend to follow similar patterns to stocks, albeit with heightened volatility [53]. This suggests that Bitcoin and other major cryptocurrencies are increasingly viewed and treated like stocks by traders and investors.

To effectively interpret sentiment signals for trading, consider the following approaches:

1. Monitor social media: Track mentions and discussions on platforms like Twitter, Reddit, and Telegram to gauge community sentiment.
2. Utilize sentiment analysis tools: Employ data collection tools to track social media mentions and analyze overall market mood.
3. Stay informed: Keep up with industry news from trusted cryptocurrency media sources to understand factors influencing sentiment.
4. Watch whale activity: Monitor large cryptocurrency holders’ actions using tools like Whale Alert to identify potential market-moving transactions.
5. Assess search trends: Use Google Trends to evaluate the level of interest in specific cryptocurrencies, which can indicate shifts in market sentiment.

By combining these approaches with technical and fundamental analysis, traders can develop a more comprehensive understanding of market dynamics and make more informed decisions in the ever-changing cryptocurrency landscape.

Challenges and Limitations of Sentiment Analysis

Data Quality Issues

Sentiment analysis in the cryptocurrency market faces significant challenges related to data quality. One of the primary concerns is the accuracy of sentiment models when applied to the unique terminology of the crypto market. Models not specifically trained in crypto jargon may yield misleading results, potentially skewing trading decisions. This issue underscores the importance of developing domain-specific models that incorporate the nuanced language of cryptocurrency trading and discussions.

Another critical data quality issue stems from the prevalence of bot accounts in social media discussions about cryptocurrencies. Research estimates that nearly 15% of tweets regarding cryptocurrency originate from bot accounts, significantly distorting sentiment analysis results. Identifying these bot accounts presents a considerable challenge, especially when datasets are not manually labeled. However, recent advancements in neural networks have shown promise in this area, with some studies reporting up to 82% accuracy in bot detection.

Sentiment Manipulation

The cryptocurrency market is particularly susceptible to sentiment manipulation, which can have a substantial impact on market movements. Influencers with large followings on platforms like Twitter can sway market direction with a single post. A positive tweet about a cryptocurrency can attract an influx of buyers, driving up the price, while a negative comment can cause fear, leading to sell-offs.

This vulnerability to manipulation is exacerbated by the emotional nature of the cryptocurrency market. Fear and greed often drive significant price movements, with fear leading to rapid sell-offs and greed fueling unsustainable price rallies. These emotional reactions can create a feedback loop, where sentiment analysis results both reflect and amplify market volatility.

Interpretation Biases

Several cognitive biases can affect the interpretation of sentiment signals in cryptocurrency trading, potentially leading to suboptimal decision-making:

1. Recency Bias: Traders may give too much weight to recent events or information, ignoring past trends or data that could be crucial for success in the volatile crypto market.
2. Fear-Based Bias: While a certain level of caution is beneficial, excessive fear can lead traders to doubt themselves and avoid potentially profitable opportunities due to an aversion to risk.
3. Hindsight Bias: This occurs when traders believe they could have predicted past events, leading to overconfidence in their ability to forecast future market movements.
4. News-Based Bias: Over-reliance on news events to guide trading decisions can result in missed opportunities. Traders may prioritize news reports over analyzing price movements, potentially overlooking profitable trades.
5. Overconfidence Bias: Both experienced and novice traders can fall victim to overconfidence, leading to reckless choices and the overlooking of potential risks.

To mitigate these challenges and limitations, traders and analysts can employ several strategies:

1. Develop and adhere to a structured trading plan to mitigate emotional reactions to market movements.
2. Utilize objective sentiment indicators to provide a clearer market picture and aid in more rational decision-making.
3. Create a comprehensive analysis routine that incorporates sentiment indicators, social media trends, and technical analysis.
4. Integrate fundamental and technical analysis with sentiment analysis for a more balanced perspective on market conditions.

By acknowledging these challenges and implementing strategies to address them, traders can enhance the effectiveness of sentiment analysis in their cryptocurrency trading strategies.

Conclusion

Crypto market sentiment analysis has emerged as a valuable tool for investors navigating the volatile cryptocurrency landscape. By combining data from social media, news outlets, and trading forums with advanced techniques like natural language processing and machine learning, traders can gain deeper insights into market psychology and potential price movements. This approach, when used alongside technical and fundamental analysis, provides a more comprehensive view of market dynamics, helping investors make more informed decisions in an often unpredictable environment.

While sentiment analysis offers significant benefits, it’s crucial to be aware of its challenges, including data quality issues, sentiment manipulation, and interpretation biases. To make the most of this tool, traders should develop a structured approach, use objective indicators, and integrate multiple analysis methods. As the crypto market continues to evolve, staying informed and adapting strategies will be key to success. To deepen your understanding of these topics, explore the Education section of hashherald.com, which is regularly updated with useful articles.

FAQs

1. What are the steps involved in performing sentiment analysis for cryptocurrencies?
   To perform sentiment analysis in the cryptocurrency market, follow these steps:
   • Collect relevant data on cryptocurrencies.
   • Gather historical price fluctuations of cryptocurrencies.
   • Clean the collected dataset to ensure accuracy.
   • Label the dataset content based on emotional tone to prepare it for analysis.
   • Train a model using this labeled dataset.
   • Assess how well your model performs in sentiment analysis.
     
2. Can you describe the step-by-step process for conducting sentiment analysis?
   The process of sentiment analysis involves several key steps:
   • Cleaning: Start by removing any special characters and numbers from the text.
   • Tokenization: Break the text down into individual words or tokens.
   • Part-of-speech (POS) tagging: Assign a part of speech to each token, such as noun, verb, etc.
   • Removing stop words: Eliminate commonly used words that do not contribute to the sentiment analysis.
   • Stemming: Reduce words to their root form.
   • Final Analysis: Analyze the processed text to extract sentiment.
     
3. How is market sentiment analysis conducted?
   Market sentiment analysis often utilizes moving averages to understand price trends over specific periods, such as 50 days or 200 days. If the 50-day moving average exceeds the 200-day average, the sentiment is typically considered bullish, indicating an upward trend. Conversely, if the 50-day average is below the 200-day, the sentiment is bearish, suggesting a downward trend.
   
4. What are the methods to analyze the cryptocurrency market?
   To effectively analyze the cryptocurrency market, consider the following approaches:
   • Review the project’s white paper to understand its purpose and technology.
   • Research the team behind the cryptocurrency to assess their credibility and experience.
   • Learn about the leadership and their track record in the industry.
   • Engage with the community supporting the cryptocurrency to gauge their enthusiasm and trust.
   • Understand the underlying technology to evaluate its potential and scalability.
   • Comprehend the vision of the cryptocurrency to see if it aligns with market needs.
   • Examine the roadmap to understand the future plans and milestones of the project.