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Stop Guessing, Start Trading: The Token Metrics API Advantage

Announcements

Big news: We’re cranking up the heat on AI-driven crypto analytics with the launch of the Token Metrics API and our official SDK (Software Development Kit). This isn’t just an upgrade – it's a quantum leap, giving traders, hedge funds, developers, and institutions direct access to cutting-edge market intelligence, trading signals, and predictive analytics.

Crypto markets move fast, and having real-time, AI-powered insights can be the difference between catching the next big trend or getting left behind. Until now, traders and quants have been wrestling with scattered data, delayed reporting, and a lack of truly predictive analytics. Not anymore.

The Token Metrics API delivers 32+ high-performance endpoints packed with powerful AI-driven insights right into your lap, including:

  • Trading Signals: AI-driven buy/sell recommendations based on real-time market conditions.
  • Investor & Trader Grades: Our proprietary risk-adjusted scoring for assessing crypto assets.
  • Price Predictions: Machine learning-powered forecasts for multiple time frames.
  • Sentiment Analysis: Aggregated insights from social media, news, and market data.
  • Market Indicators: Advanced metrics, including correlation analysis, volatility trends, and macro-level market insights.

Getting started with the Token Metrics API is simple:

  1. Sign up at www.tokenmetrics.com/api
  2. Generate an API key and explore sample requests.
  3. Choose a tier–start with 50 free API calls/month, or stake TMAI tokens for premium access.
  4. Optionally–download the SDK, install it for your preferred programming language, and follow the provided setup guide.

At Token Metrics, we believe data should be decentralized, predictive, and actionable. 

The Token Metrics API & SDK bring next-gen AI-powered crypto intelligence to anyone looking to trade smarter, build better, and stay ahead of the curve. With our official SDK, developers can plug these insights into their own trading bots, dashboards, and research tools – no need to reinvent the wheel.

Research

What Are The Risks of AI Controlling DAOs? Critical Risks Every Crypto Investor Must Know

Talha Ahmad
5 min
MIN

The convergence of artificial intelligence and decentralized autonomous organizations (DAOs) marks a groundbreaking moment in blockchain technology. This fusion promises to revolutionize governance by automating decision making and enhancing efficiency through AI-driven systems. However, while integrating AI technologies into DAOs offers exciting opportunities, it also introduces a complex array of risks that could fundamentally undermine the democratic ideals upon which decentralized autonomous organizations were founded. Understanding what are the risks of AI controlling DAOs is essential for anyone involved in decentralized finance, governance, or the broader crypto ecosystem.

Introduction to AI-Controlled DAOs: Definition and Overview

AI-Controlled DAOs are decentralized autonomous organizations that leverage artificial intelligence to manage and govern their operations with minimal human intervention. By integrating advanced AI models and algorithms into the core of DAO governance, these entities can autonomously execute decision making processes, optimize asset management, and adapt to changing environments in real time. Artificial intelligence AI enables DAOs to analyze complex data sets, identify patterns, and make informed decisions without relying on centralized authorities or manual oversight. This fusion of AI and DAOs is reshaping the landscape of decentralized governance, offering the potential for more efficient, scalable, and self-sustaining organizations. As AI development continues to advance, the role of AI models in decentralized autonomous organizations is set to expand, fundamentally transforming how decisions are made and assets are managed across the crypto ecosystem.

The Rise of AI-Driven DAOs: A Double-Edged Innovation

AI-driven DAOs represent a new paradigm in the DAO space, where artificial intelligence tools and advanced AI models are entrusted with governance responsibilities traditionally held by human token holders. These AI agents can propose changes, vote on governance issues, and even execute decisions autonomously via smart contracts. This shift from human-centric governance to algorithm-driven decision making promises increased scalability and productivity, potentially unlocking new revenue streams and optimizing asset management.

However, this evolution also introduces unique challenges. The autonomous nature of AI acting within DAOs raises critical questions about ethical concerns, security vulnerabilities, and the balance of power between AI systems and human intervention. Unlike traditional DAOs, where risks often stem from voter apathy or central authority influence, AI DAOs face the threat of model misalignment—where AI algorithms optimize for objectives that deviate from human intentions. This misalignment is not merely theoretical; it is a practical issue that can disrupt consensus mechanisms and jeopardize the strategic direction of decentralized autonomous organizations.

In essence, while AI technologies can propel DAOs into a new era of efficiency and data-driven insights, they also potentially lead to scenarios where AI systems act in ways that conflict with the foundational principles of decentralization and democratic governance, potentially leading to significant risks or harm if not properly managed.

But what if AI ownership within DAOs shifts the balance of power entirely, allowing autonomous agents to make decisions without meaningful human oversight? But what happens when collective intelligence is governed by algorithms rather than people, and how might this reshape the future of decentralized organizations?

The Benefits of AI-Controlled DAOs: Promise and Potential

The integration of AI and DAOs brings a host of compelling benefits that are driving innovation in decentralized governance. AI-Controlled DAOs can automate decision making processes, enabling faster and more consistent responses to governance challenges. By harnessing the analytical power of AI daos, these organizations can process vast amounts of data, uncover actionable insights, and make data-driven decisions that enhance overall performance. This automation not only streamlines operations but also opens up new revenue streams and business models, as AI-driven DAOs can identify and capitalize on emerging opportunities more efficiently than traditional structures. Improved asset management is another key advantage, with AI systems optimizing resource allocation and risk management. Ultimately, the synergy between AI and DAOs empowers organizations to become more resilient, adaptive, and innovative, paving the way for a new era of decentralized, autonomous governance.

Critical Risk Categories in AI-Controlled DAOs

1. Model Misalignment and Goal Deviation

At the heart of AI risks in DAO governance lies the problem of model misalignment. AI systems, especially those powered by machine learning models and large language models, operate by optimizing specific metrics defined during training. Training AI models in isolated environments or silos can increase the risk of misalignment and loss of control, as these models may not be exposed to the diverse perspectives and values necessary for safe and ethical outcomes. However, these metrics might not capture the full spectrum of human values or community goals. As a result, an AI system could pursue strategies that technically fulfill its programmed objectives but harm the DAO’s long-term interests.

For example, an AI agent managing financial assets within a decentralized autonomous organization might prioritize maximizing short-term yield without considering the increased exposure to security risks or market volatility. The Freysa contest highlighted how malicious actors exploited an AI agent’s misunderstanding of its core function, tricking it into transferring $47,000. This incident underscores how AI models, if not properly aligned and monitored, can be manipulated or confused, leading to catastrophic outcomes.

2. Algorithmic Bias and Democratic Erosion

AI systems inherit biases from their training data and design, which can erode the democratic ethos of DAO governance. While DAO governance AI tools are designed to enhance proposal management and moderation, they can also inadvertently reinforce biases if not properly monitored. Biases embedded in AI algorithms may result in unfair decision making, favoring certain proposals, contributors, or viewpoints disproportionately. These biases manifest in several ways:

  • Selection Bias: AI models might consistently prioritize certain types of proposals or token holders, marginalizing others.
  • Historical Bias: Training data reflecting past inequalities can perpetuate systemic discrimination within DAO governance.
  • Confirmation Bias: AI-driven decision-making could reinforce existing organizational tendencies, stifling innovation and diverse perspectives.

Such biases threaten to undermine the promise of decentralized networks by creating invisible barriers to participation, effectively centralizing power despite the decentralized structure.

3. Security Vulnerabilities and Attack Vectors

Integrating AI into DAOs introduces new security risks that extend beyond traditional smart contract vulnerabilities. AI systems depend heavily on training data and algorithms, both of which can be targeted by malicious actors seeking to manipulate governance outcomes.

Key security concerns include:

  • Unauthorized Access and Model Tampering: Without robust access controls, attackers can alter AI model outputs, compromising trust in decision making. Improper or overly broad ai permission settings can allow attackers to exploit AI authority within DAOs, making it essential to enforce strict permission controls to limit unauthorized actions.
  • Data Poisoning: Corrupting training data can mislead AI systems, causing them to make flawed or harmful decisions.
  • Adversarial Attacks: Malicious inputs crafted specifically to deceive AI models can lead to unpredictable and damaging behavior.
  • Prompt Injection: Attackers can exploit AI assistants or natural language processing capabilities to inject deceptive instructions, as seen in the Freysa incident.
  • Model Extraction: Competitors or attackers may reverse-engineer proprietary AI algorithms, exposing intellectual property and governance strategies.

These vulnerabilities underscore the necessity for decentralized autonomous organizations to implement robust security protocols that safeguard both AI systems and the underlying smart contracts.

4. Centralization Through Technical Complexity

One of the most subtle yet profound risks of AI in DAOs is the potential for centralization of power among a small group of technical experts or "AI wizards." The complexity of AI development and maintenance creates a knowledge barrier that limits meaningful participation to those with specialized skills. This technical gatekeeping can result in governance control shifting from the broader community to a few individuals who understand and can manipulate AI systems.

Such centralization contradicts the decentralized ethos of DAOs and risks creating new oligarchies defined by AI expertise rather than token ownership or community contribution. Over time, this dynamic could erode trust and reduce the legitimacy of AI-driven DAO governance.

5. Regulatory and Compliance Nightmares

AI-controlled DAOs operate in a regulatory gray area, facing challenges that traditional organizations do not. The autonomous nature of AI acting within decentralized networks complicates accountability and legal responsibility. Key regulatory concerns include:

  • Accountability: When AI systems make autonomous decisions, determining liability for errors or malicious actions becomes complex.
  • Cross-Jurisdictional Compliance: DAOs operate globally, but AI governance regulations vary widely by jurisdiction, creating compliance challenges.
  • Evolving Standards: Rapid advances in AI development and shifting regulatory landscapes mean DAOs risk falling out of compliance as standards change.

These factors introduce legal uncertainties that could expose AI DAOs to sanctions, fines, or operational restrictions, complicating their long-term viability.

Real-World Manifestations of AI DAO Risks

The risks associated with AI-driven DAOs are not merely theoretical. In 2025 alone, smart contract security flaws led to over $90 million in losses due to hacks and exploits within DAO structures. When AI systems are layered onto these vulnerabilities, the potential for cascading failures grows exponentially.

Incidents have already demonstrated how attackers exploit governance mechanisms, manipulating voting and decision-making processes. AI’s speed and efficiency can be weaponized to identify arbitrage opportunities that disadvantage the DAO itself. Moreover, AI systems processing community input may be vulnerable to sophisticated social engineering and disinformation campaigns, further destabilizing governance. The use of ai agent comments in forum discussions and governance decisions can amplify manipulation by allowing AI to influence outcomes directly. There is also a risk that AI-powered moderation tools could inadvertently generate or spread hate speech, making it essential to implement safeguards to prevent toxic content. Additionally, the proliferation of ai generated content, such as misinformation or abusive material, poses dangers by misleading users and undermining the stability of governance.

These real-world examples highlight the urgent need for comprehensive risk management strategies in AI DAO integration.

The Data Quality Crisis

A frequently overlooked risk in AI-controlled DAOs is the quality and integrity of data used to train and operate AI models. Since AI systems rely heavily on training data, any flaws or manipulation in this data can compromise the entire governance process.

In decentralized autonomous organizations, this risk manifests through:

  • Incomplete Information: DAOs often lack extensive historical data, limiting the reliability of AI-driven predictions and decisions.
  • Gaming Risks: Members may intentionally manipulate input data to influence AI decision making for personal gain.
  • External Dependencies: Reliance on third-party data sources introduces vulnerabilities to external tampering or failures.

Ensuring the accuracy, completeness, and security of training data is therefore paramount to maintaining AI safety and trustworthy DAO governance.

Asset Management in AI-Controlled DAOs: New Frontiers, New Risks

Asset management is at the heart of many AI-Controlled DAOs, as these organizations are tasked with overseeing and optimizing a wide range of financial assets and digital resources. By deploying advanced AI models, including machine learning models and natural language processing tools, AI-Controlled DAOs can analyze market data, forecast trends, and make strategic investment decisions with unprecedented speed and accuracy. However, this reliance on AI systems introduces new security vulnerabilities and risks. Malicious actors may attempt to exploit weaknesses in AI algorithms, manipulate training data, or launch sophisticated attacks targeting the DAO’s asset management protocols. To address these challenges, AI-Controlled DAOs must implement robust security protocols, ensure the integrity and quality of their training data, and establish transparent governance structures that can respond to emerging threats. By proactively managing these risks, AI-Controlled DAOs can unlock new opportunities for growth while safeguarding their financial assets and maintaining trust within their communities.

Navigating the Risks: Lessons from Token Metrics

Despite these challenges, responsible integration of artificial intelligence in DAO governance is achievable. Platforms like Token Metrics exemplify how AI tools can enhance decision making without sacrificing transparency or human oversight.

Token Metrics is an AI-powered crypto analytics platform that leverages advanced AI models and predictive analytics to identify promising tokens and provide real-time buy and sell signals. By anticipating future trends through AI-driven predictions, Token Metrics helps DAOs and investors improve investment strategies and resource management. Some of the advanced AI models used by Token Metrics are based on large language model technology, which underpins content analysis and decision support for more effective DAO operations. Their approach balances AI-driven insights with human judgment, embodying best practices for AI and DAOs:

  • Transparency: The platform openly explains its AI recommendations, mitigating the black box problem common in AI systems.
  • Human-AI Collaboration: Rather than replacing humans, Token Metrics augments human analysis with AI-generated data-driven insights.
  • Risk Management: Multiple data sources and validation mechanisms reduce single points of failure and improve reliability.
  • Continuous Learning: Their AI models are regularly updated based on market feedback and user input to maintain alignment with investor goals.

With a track record of 8,000% returns from AI-selected crypto baskets, Token Metrics demonstrates that artificial intelligence tools, when implemented with robust safeguards and human oversight, can unlock new revenue streams and improve DAO productivity without compromising security or ethical standards.

AI Model Ownership: Legal and Technical Implications

As AI models become central to the operation of AI-Controlled DAOs, questions around ownership and intellectual property take on new significance. Determining who owns the rights to an AI model—whether it’s the developers, the DAO itself, or the broader community—can have far-reaching legal and technical implications. Issues of liability and accountability also arise, especially when AI-driven decisions lead to unintended consequences or disputes. To navigate these complexities, AI-Controlled DAOs need to establish clear policies regarding AI model ownership, including licensing agreements and governance frameworks that protect the interests of all stakeholders. Addressing these challenges is essential for ensuring transparency, safeguarding intellectual property, and fostering innovation in the rapidly evolving landscape of AI and decentralized autonomous organizations.

Mitigation Strategies and Best Practices

Although AI-controlled DAOs face significant risks, these challenges are not insurmountable. Proactive strategies can help organizations safely integrate AI technologies into their governance structures. It is especially important to establish clear rules and safeguards for scenarios where AI owns assets or treasuries within DAOs, as this fundamentally changes traditional notions of ownership and financial authority.

  • Implement robust access controls and multi-signature wallets to prevent unauthorized actions by AI agents.
  • Regularly audit smart contracts and AI code to identify and patch vulnerabilities before they can be exploited.
  • Use platforms like Ocean Protocol to enable decentralized data sharing and AI integration, which can enhance security, empower data owners, and automate governance processes.
  • Foster collaboration between DAOs, such as the Amazon rainforest DAO, where AI-powered metagovernance and swarm intelligence facilitate joint decision-making and address environmental challenges like deforestation and climate change.
  • Consider the environmental impact of AI deployment, particularly the significant energy consumption of data centers that power large-scale AI systems, and prioritize sustainable infrastructure to minimize harm.

1. Hybrid Governance Models

Combining AI automation with human oversight is critical. DAOs should reserve high-impact decisions for human token holders or expert councils, ensuring AI-driven decisions are subject to review and intervention when necessary. This hybrid approach preserves the benefits of AI while maintaining democratic participation.

2. Continuous Alignment Monitoring

Alignment between AI algorithms and community values must be an ongoing process. Regular audits and testing of AI decision-making against expected outcomes help detect and correct goal deviations early. Treating alignment as a continuous operational expense is essential for AI safety.

3. Distributed Technical Expertise

Investing in community education and skill-building democratizes AI stewardship. By broadening technical expertise among members, DAOs can prevent governance capture by a small group of AI experts and foster a more decentralized technical ecosystem.

4. Robust Security Frameworks

Implementing comprehensive security protocols is vital. Measures include:

  • Multi-signature requirements for AI-initiated transactions
  • Anomaly detection systems to flag unusual AI behavior
  • Regular security audits of both AI systems and smart contracts
  • Incident response plans tailored to AI-specific threats

These steps help safeguard DAO governance against malicious AI and external attacks.

The Path Forward: Balancing Innovation and Risk

The future of AI-controlled DAOs hinges on striking the right balance between leveraging AI’s capabilities and preserving meaningful human intervention. As AI development and decentralized networks continue to evolve, more sophisticated governance models will emerge that integrate AI-driven decision making with community oversight.

Organizations exploring AI in DAO governance should:

  1. Start with limited AI roles in non-critical functions to minimize risk.
  2. Maintain transparency about AI system operations to build trust.
  3. Preserve mechanisms for human override to ensure control remains with the community.
  4. Conduct regular audits to monitor AI performance and compliance.
  5. Educate members about AI risks and benefits to foster informed participation.

By adopting these practices, DAOs can harness the advantages of artificial intelligence while mitigating its inherent risks.

Conclusion: Preparing for an AI-Governed Future

Integrating artificial intelligence into decentralized autonomous organizations offers transformative potential but also brings significant challenges. While AI can enhance efficiency, reduce certain human biases, and enable more responsive governance, it simultaneously introduces new security risks, ethical concerns, and governance complexities that could threaten the democratic foundations of DAOs.

Success in this evolving landscape depends on thoughtful AI development, robust risk management, and transparent human-AI collaboration. Platforms like Token Metrics illustrate how AI products can deliver powerful, data-driven insights and automation while maintaining accountability and community trust.

As we stand at this technological crossroads, understanding what are the risks of AI controlling DAOs is essential. By acknowledging these risks and implementing appropriate safeguards, the crypto community can work towards a future where AI enhances rather than replaces human agency in decentralized governance—preserving the revolutionary promise of decentralized autonomous organizations.

For investors and participants in the crypto ecosystem, staying informed about these emerging technologies and choosing platforms with proven responsible AI implementation will be crucial for navigating the complex and rapidly evolving DAO space.

Research

Can AI and Blockchain Be Combined for IoT? The Revolutionary Convergence Transforming Industries in 2025

Talha Ahmad
5 min
MIN

In the rapidly evolving digital landscape of 2025, a groundbreaking convergence is taking place among three transformative technologies: Artificial Intelligence (AI), Blockchain, and the Internet of Things (IoT). This powerful combination is not merely a theoretical possibility—it is actively reshaping industries by redefining how connected devices communicate, how data is managed, and how decisions are made autonomously. Understanding can AI and blockchain be combined for IoT applications is essential for businesses, investors, and technologists aiming to harness the full potential of this technological revolution.

At the forefront, IoT devices generate vast amounts of data from sensors embedded in everything from smart cities to healthcare systems. AI algorithms analyze this real-time data to derive actionable insights, while blockchain technology ensures data integrity and security through decentralized, tamper-proof transaction records. Together, these technologies enable smarter, more secure, and autonomous IoT ecosystems that are transforming how industries operate.

The Foundation: Understanding the Technological Trinity

To appreciate the synergy between AI, blockchain, and IoT, it is important to understand each technology’s role.

Artificial Intelligence refers to computer systems capable of human-like cognition, including reasoning, learning, and decision-making. In 2025, AI systems leverage advanced machine learning and neural networks to process massive datasets generated by IoT sensors in real time. These AI models empower IoT devices to evolve from simple data collectors into autonomous systems capable of predictive maintenance, anomaly detection, and optimized resource allocation.

Blockchain technology acts as a decentralized ledger that records digital transactions securely and transparently without intermediaries. By storing data across distributed blockchain networks, it enhances security features and guarantees data provenance and integrity. Blockchain protocols enable smart contracts—self-executing agreements that automate and secure interactions between IoT devices, ensuring trustworthy digital transactions.

Internet of Things (IoT) encompasses the vast network of connected devices embedded with sensors and software that collect and exchange data. IoT systems span smart grids, smart cities, healthcare devices, and industrial automation. With projections estimating around 30 billion IoT devices worldwide by 2030, the volume of data generated demands robust AI and blockchain integration to optimize data management and security.

The Market Reality: Explosive Growth and Convergence

The convergence of AI, blockchain, and IoT is no longer a futuristic concept but a tangible market phenomenon with significant economic impact. The combined market capitalization of these technologies exceeded $1.362 trillion in 2024 and is expected to grow exponentially as their integration deepens.

The IoT market alone, valued at $300 billion in 2021, is projected to surpass $650 billion by 2026, with estimates reaching $3.3 trillion by 2030. This growth is fueled by the increasing demand for secure, intelligent IoT networks that can handle the massive data flows generated by connected devices.

This convergence addresses practical challenges faced by traditional cloud-based data processing, such as latency, high costs, and vulnerability to cyber threats. Integrating AI and blockchain within IoT ecosystems optimizes data analysis and enhances security protocols, making it an indispensable strategy for modern enterprises.

How the Integration Works: The Technical Symphony

AI as the Intelligence Layer

AI forms the cognitive backbone of IoT systems by transforming raw data collected from IoT sensors into meaningful insights. Through machine learning and neural networks, AI analyzes data generated by connected devices to detect anomalies, predict equipment failures, and optimize energy management in real time.

For example, AI algorithms embedded in smart grids can forecast electricity demand and adjust distribution accordingly, reducing waste and improving sustainability. Similarly, in manufacturing, AI-driven predictive maintenance minimizes downtime by identifying potential faults before they escalate.

By integrating AI processes with IoT data flows, enterprises can automate decision-making and enhance operational efficiency, turning IoT devices into autonomous systems capable of adapting dynamically to changing environments.

Blockchain as the Trust Infrastructure

While AI provides intelligence, blockchain technology offers the trust and security layer vital for IoT networks. Blockchain enhances security by decentralizing data storage and transaction records, making it resistant to tampering and cyber breaches.

Key applications of blockchain in IoT include:

  • Device Authentication: Each IoT device receives a unique digital identity secured cryptographically on the blockchain, ensuring only authorized devices participate in the network. This prevents unauthorized access and exploits of vulnerabilities.
  • Data Provenance and Integrity: Blockchain records the origin and history of data generated by IoT sensors, guaranteeing its authenticity. For instance, blockchain can verify that temperature readings in a cold chain logistics system were not altered during transit.
  • Smart Contracts for Automated Transactions: Blockchain-enabled smart contracts facilitate secure, automated transactions between devices without intermediaries. This capability supports autonomous financial transactions such as toll payments by connected vehicles or peer-to-peer energy trading in smart grids.

By integrating blockchain systems with IoT, enterprises can achieve enhanced security features and maintain data privacy while enabling transparent and tamper-proof data management.

The Convergence in Action

The fusion of AI, blockchain, and IoT technologies creates a new digital operating system where blockchain serves as the secure trust layer, IoT devices provide continuous streams of real-time data, and AI models analyze and act on this data autonomously. This convergence is revolutionizing industries by enabling decentralized AI models to operate securely across blockchain networks, optimizing IoT ecosystems with enhanced data security and operational intelligence.

Real-World Applications Transforming Industries

Smart Supply Chains and Logistics

Supply chains are becoming increasingly complex, requiring flexible, transparent, and adaptive solutions. AI algorithms analyze IoT data from sensors monitoring shipment conditions and locations, predicting delays and optimizing delivery routes. Blockchain technology ensures the authenticity and traceability of goods by securely recording transaction records and device authentication events.

This integration improves inventory management by providing end-to-end visibility, reducing fraud, and preventing data breaches, ultimately enhancing customer trust and operational efficiency.

Autonomous Financial Transactions

The combination of AI and blockchain enables connected devices like autonomous vehicles and drones to conduct financial transactions independently. Smart contracts automate payments for services such as EV charging, tolls, or retail purchases, reducing friction and improving user experience.

For example, an electric vehicle can automatically pay for charging at a smart grid station using blockchain transactions secured by AI-enhanced security protocols. This autonomous system streamlines commerce within the IoT ecosystem.

Energy Management and Sustainability

In smart grids, AI optimizes energy distribution by analyzing real-time data from IoT sensors, forecasting demand, and adjusting supply dynamically. Blockchain facilitates peer-to-peer energy trading between participants, ensuring secure and transparent transactions.

This integration supports sustainability goals by optimizing resource allocation, reducing energy waste, and enabling decentralized energy markets that empower consumers and producers alike.

Healthcare and Medical Devices

IoT medical devices continuously collect sensitive patient data, which AI systems analyze to detect early signs of diseases and personalize treatment plans. Blockchain technology ensures the secure management and privacy of patient data by decentralizing storage and controlling access through smart contracts.

This convergence enhances healthcare system efficiency, enabling seamless and secure sharing of medical records across providers while protecting against data breaches.

The Role of Advanced Analytics: Token Metrics Leading the Way

Navigating the complex intersection of AI, blockchain, and IoT requires sophisticated analytics platforms. Token Metrics, a premier crypto trading and analytics platform, leverages AI technologies to help investors identify promising AI-blockchain-IoT projects early.

Token Metrics integrates AI-driven data analytics, sentiment analysis, and real-time market data across thousands of tokens. Its AI models assign Trader Grades and Investor Grades to tokens, guiding users in making informed decisions within this rapidly evolving market.

By consolidating research, portfolio management, and trading tools, Token Metrics empowers investors to capitalize on the AI blockchain’s role in transforming IoT ecosystems and digital transactions.

Current Challenges and Solutions

Scalability and Data Management

The enormous volume of data generated by IoT devices demands scalable AI processing and blockchain storage solutions. Edge computing addresses latency and bandwidth constraints by processing data closer to the source. Layer-2 blockchain protocols improve transaction throughput, making blockchain operations more efficient and cost-effective.

Security and Privacy

While blockchain enhances security, integrating AI models and IoT networks introduces new vulnerabilities. Enterprises must implement robust security features, including advanced encryption and privacy-preserving AI techniques, to protect sensitive data and comply with data privacy regulations.

Interoperability

Diverse blockchain networks, AI frameworks, and IoT protocols present challenges for seamless integration. Standardized interfaces and cross-platform compatibility solutions are essential to enable smooth data flows and cohesive system operation.

Future Outlook: The 2030 Vision

Looking ahead, the integration of AI, blockchain, and IoT is poised to create an adaptable, interconnected digital ecosystem. By 2030, AI-enhanced blockchain networks combined with 5G connectivity will enable unprecedented real-time data analysis and autonomous decision-making across industries.

Digital wallets, empowered by blockchain protocols, will expand beyond cryptocurrencies to support seamless device authentication and smart contract interactions. The in-car payment market alone is expected to reach $530 billion, with vehicles conducting secure, autonomous transactions via blockchain-linked SIM cards.

This complete ecosystem integration will power smart cities, smart grids, healthcare systems, and autonomous systems, unlocking new efficiencies and innovations.

Strategic Implications for Businesses

In 2025, companies that fail to embrace the convergence of AI, blockchain, and IoT risk falling behind. To remain competitive, organizations must:

  • Develop integrated technology infrastructures that unify AI systems, blockchain networks, and IoT devices.
  • Implement data strategies that leverage AI for data analysis while using blockchain to ensure data integrity and secure management.
  • Establish comprehensive security protocols addressing the unique challenges of interconnected AI-blockchain-IoT environments.
  • Invest in talent capable of navigating the intersection of these technologies rather than isolated specializations.

Conclusion: The Inevitable Future

The question is no longer can AI and blockchain be combined for IoT—the answer is a resounding yes. The real challenge lies in how swiftly organizations can adapt to this convergence that is fundamentally transforming digital ecosystems.

By harnessing AI intelligence, blockchain trust, and IoT connectivity, businesses can create autonomous systems that think, transact, and optimize in real time while maintaining the highest standards of data privacy and security. Platforms like Token Metrics provide the tools to navigate this revolution, identifying opportunities and mitigating risks in the evolving digital economy.

The convergence of AI, blockchain, and IoT is here, the market is responding, and transformation is accelerating. The future belongs to those ready to embrace this revolutionary synergy and lead the next wave of innovation. Are you ready to be part of this transformative journey?

Research

What Are Real-World Assets in Crypto? A Complete Guide

Talha Ahmad
5 min
MIN

The cryptocurrency landscape is undergoing a revolutionary transformation as traditional finance converges with blockchain technology. At the forefront of this evolution are Real-World Assets (RWAs) – a groundbreaking innovation bridging the gap between physical assets and digital finance. As we navigate through 2025, understanding what are real world assets in crypto has become essential for investors, institutions, and anyone looking to capitalize on this trillion-dollar opportunity.

Understanding Real-World Assets (RWAs)

Real-World Assets in crypto refer to digital tokens that represent ownership or economic rights to tangible, physical assets or traditional financial instruments. Unlike conventional cryptocurrencies, which derive their value primarily from market speculation or utility within a digital ecosystem, RWA tokens are backed by concrete, verifiable assets existing in the real world. This intrinsic backing provides a level of stability and investor confidence that pure digital assets may lack.

In essence, RWAs are crypto tokens that represent physical assets such as bonds, real estate properties, commodities, machinery, and more. They serve as a bridge bringing traditional financial assets into the realm of decentralized finance (DeFi). Through the tokenization process, these often illiquid assets are transformed into tradeable digital tokens, enabling features like fractional ownership and global access. This innovation opens new doors for asset management, making high-value and previously inaccessible assets available to a broader range of investors.

Types of Real-World Assets Being Tokenized

The scope of real world asset tokenization is vast and continues to expand across various asset classes:

Financial Instruments

Government bonds, treasury bills, and corporate debt are increasingly being tokenized, merging traditional fixed-income investing with blockchain technology. For example, BlackRock’s BUIDL fund, launched in March 2024, commands a dominant 44% market share in tokenized treasuries. These financial assets offer stable yields and introduce a new dimension of financial innovation by making traditional assets accessible via digital tokens.

Real Estate

The real estate industry is one of the most active sectors embracing tokenization. Platforms like Propy and RealT facilitate tokenized real estate investment, allowing investors to acquire fractional ownership in commercial and residential properties. This approach democratizes access to valuable assets that were traditionally out of reach for retail investors, enabling them to benefit from rental income and property appreciation through onchain finance.

Commodities

Commodity-backed tokens, particularly those linked to precious metals like gold, constitute a significant portion of the RWA market. Tokens such as Tether Gold (XAUT) and PAX Gold (PAXG) represent approximately 84% of this sector, which has seen growth of 67.8% since 2024, reaching a market capitalization of $1.9 billion. These tokens provide a secure and liquid means of investing in tangible assets without the need for physical storage.

Art and Collectibles

High-value artworks, vintage wines, and collectibles are being tokenized to increase accessibility and liquidity. Blockchain verification ensures authenticity and provenance, allowing investors to own fractions of unique and intellectual property assets that were once difficult to trade.

Private Credit

Private credit protocols are emerging as a bridge between traditional lending and DeFi, providing loans to real-world businesses, especially in emerging markets. This sector leverages crypto capital to support business growth, offering new investment opportunities and enhancing financial inclusion.

The Technology Behind RWA Tokenization

The process of asset tokenization involves multiple critical steps to ensure security, compliance, and seamless integration with the digital ecosystem:

Asset Selection and Verification

Before tokenization, asset owners and asset issuers conduct thorough evaluations of the underlying asset. This includes legal due diligence to comply with local laws and regulatory compliance, valuation assessments, and verification of ownership. This rigorous process ensures that the tokenized assets represent valuable and legitimate physical or financial assets.

Smart Contract Development

Once the asset is selected, developers create smart contracts that define the token’s specifications. These contracts determine whether the token is fungible or non-fungible, select appropriate token standards (such as ERC20 or ERC721), and choose the blockchain platform best suited for the token’s purpose. Smart contracts automate ownership transfers, dividend distributions, and other functions, streamlining asset management and reducing counterparty risk.

Oracle Integration

A crucial component of RWA tokenization is integrating real world data with blockchain networks. Oracles like Chainlink securely bridge off-chain information to on-chain environments, ensuring that the digital tokens accurately reflect the status and value of the underlying assets. This integration allows tokenized real world assets to function effectively within decentralized finance ecosystems.

Market Growth and Institutional Adoption

The RWA sector has witnessed explosive growth, with overall market capitalization reaching unprecedented levels. By September 2025, top Real World Assets coins collectively boast a market cap of approximately $38.46 billion, driven largely by strong interest from institutional investors and financial institutions.

Forecasts suggest that by the end of 2025, the total value of RWAs on-chain (excluding stablecoins) could surpass $500 billion, with over $50 billion already tokenized. Prominent financial players are spearheading this movement:

  • BlackRock introduced the BUIDL tokenized money market fund, pioneering tokenized treasury investments.
  • Goldman Sachs and BNP Paribas have launched pilot projects on the Canton Network to explore tokenized asset offerings.
  • Fidelity and VanEck announced plans for tokenized funds slated for 2025.
  • HSBC debuted cross-border tokenized deposit products, enhancing global access.

This institutional adoption is a testament to the growing confidence in the digital asset ecosystem and the potential for RWAs to transform traditional finance.

Benefits of RWA Tokenization

The tokenization of real-world assets brings numerous advantages that revolutionize how investors interact with traditional and physical assets:

Enhanced Liquidity

Many traditionally illiquid assets such as real estate and private equity become tradeable 24/7 through tokenization. The ability to buy and sell fractions of assets on secondary markets eliminates lengthy settlement processes and unlocks liquidity pools that were previously inaccessible.

Fractional Ownership

Tokenization enables fractional ownership, allowing investors to purchase small portions of high-value assets. This lowers entry barriers and democratizes access to markets like real estate investment and government bonds, which have traditionally required substantial capital.

Global Accessibility

By leveraging blockchain technology, tokenized assets transcend geographic and regulatory boundaries. Investors worldwide gain global access to diverse asset classes, promoting financial inclusion and broadening the investor base.

Transparency and Security

Blockchain offers immutable records of ownership and transaction history, enhancing transparency in asset management. Coupled with robust security measures, this reduces fraud and increases investor confidence.

Cost Efficiency

Automated processes via smart contracts eliminate intermediaries, reducing transaction fees and administrative overhead. This streamlining leads to faster settlements and improved cost efficiency for both asset managers and investors.

Leveraging Token Metrics for RWA Trading Success

As the RWA market grows, navigating its complexities requires sophisticated tools and analytics. Token Metrics stands out as a premier crypto trading and analytics platform, empowering investors to make informed decisions in the evolving RWA landscape.

Token Metrics uses advanced AI-powered analytics to analyze market trends, token performance, and emerging investment opportunities across thousands of tokens, including those representing RWAs. Its features include:

  • Real-time AI buy and sell signals that help spot winning tokens early.
  • Deep-dive research tools that assess tokenomics, underlying asset quality, and growth potential.
  • AI-curated portfolios optimized for short and long-term gains.
  • Seamless trading integration with API connectivity, enabling users to execute trades based on AI insights.

For investors seeking to capitalize on the growth of RWAs, Token Metrics offers a comprehensive toolkit to enhance decision-making and portfolio management.

Current Challenges and Future Outlook

Despite the promising potential, real world assets rwas face several challenges that must be addressed for widespread adoption:

Regulatory Uncertainty

The legal status of tokenized RWAs remains ambiguous in many jurisdictions, as these tokens do not fit neatly into existing categories like commodities or securities. This regulatory uncertainty complicates compliance and investor protection efforts.

Standardization Issues

The lack of universal protocols for RWA tokens hampers interoperability across different blockchain platforms and ecosystems. Standardization is critical for seamless asset transfer and integration with traditional financial systems.

Security Risks

While blockchain offers enhanced security, the systems remain vulnerable to exploits and hacker attacks. Protecting digital tokens and ensuring the integrity of private blockchain networks are ongoing priorities.

Nevertheless, the outlook is optimistic. Regulatory clarity is expected to improve, with influential appointments such as Paul Atkins as SEC chair signaling a more defined legal framework. Continued innovation in digital technology and onchain finance will further streamline real world asset tokenization, making it more accessible and secure.

Leading RWA Platforms and Projects

Several pioneering projects are shaping the RWA ecosystem by providing infrastructure and innovative solutions:

  • Ondo Finance (ONDO) specializes in tokenizing U.S. Treasuries and traditional financial products, offering institutional clients new avenues for fixed-income investing.
  • Centrifuge (CFG) builds infrastructure that connects real-world assets to DeFi protocols, enabling asset owners to tokenize and finance their holdings.
  • Maple Finance focuses on undercollateralized lending to institutions, bridging traditional finance and crypto capital.
  • TrueFi provides uncollateralized lending protocols aimed at real-world businesses, supporting growth through decentralized credit.

Projects like Ondo, VeChain, and Chromia are recognized as some of the best RWA tokens for diversified portfolios. VeChain, in particular, leverages its experience with mainstream firms to enhance data management and tokenization processes, reinforcing the ecosystem’s robustness.

Conclusion

Real-World Assets represent a paradigm shift in ownership, investment, and financial markets. By converting tangible assets and traditional assets into digital tokens, the financial industry is moving towards a more inclusive, efficient, and transparent system that benefits both retail and institutional investors.

The era of real world asset tokenization is no longer a distant vision but a present reality, with 2025 marking a pivotal year in narrowing the divide between crypto-native communities and traditional finance. For investors eager to harness this revolutionary trend, platforms like Token Metrics provide the essential tools and insights needed to navigate the complex RWA landscape successfully.

Ultimately, the convergence of blockchain technology and traditional financial assets is not just changing how we invest—it is redefining the very nature of asset ownership and participation in the digital economy. Embracing this digital transformation unlocks unparalleled opportunities for financial inclusion, cost efficiency, and enhanced liquidity in the global financial system.

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Crypto Basics

What Indicators Should I Use for Technical Crypto Analysis?

Token Metrics Team
8 min
MIN

If you’re serious about trading cryptocurrency, technical analysis (TA) is an essential skill. It helps you understand price movements, predict trends, and identify high-probability entry and exit points.

But with hundreds of technical indicators available, one common question is: “Which indicators should I use for technical analysis?”

In this guide, we’ll break down the most effective indicators for crypto trading, explain how they work, and show you how Token Metrics combines them with AI-driven insights to help you trade smarter.

Why Use Technical Indicators in Crypto?

Unlike traditional stocks, cryptocurrency markets trade 24/7, are more volatile, and are largely driven by sentiment and speculation.

Technical indicators help you:

  • Identify trends (bullish or bearish).

  • Pinpoint support and resistance levels.

  • Detect overbought or oversold conditions.

  • Find entry and exit points with better timing.

The key is not using one indicator in isolation but combining multiple tools for confirmation—which is exactly what Token Metrics does with its AI-driven trading signals.

The Most Important Indicators for Technical Analysis

Here are the must-know indicators for crypto traders:

1. Moving Averages (MA & EMA)

What they do:
Moving averages smooth out price data to help you identify overall market direction.

  • Simple Moving Average (SMA): Calculates the average closing price over a set period (e.g., 50-day, 200-day).

  • Exponential Moving Average (EMA): Gives more weight to recent prices, making it more responsive.

How to use them:

  • Golden Cross: When the 50-day MA crosses above the 200-day MA → bullish signal.

  • Death Cross: When the 50-day MA crosses below the 200-day MA → bearish signal.

Best for:
Spotting long-term trends and momentum.

2. Relative Strength Index (RSI)

What it does:
RSI measures price momentum and identifies overbought (70+) or oversold (30-) conditions.

How to use it:

  • Above 70: Asset may be overbought → possible pullback.

  • Below 30: Asset may be oversold → potential bounce.

Best for:
Finding reversal points and confirming trend strength.

3. Moving Average Convergence Divergence (MACD)

What it does:
MACD measures the relationship between two EMAs (usually 12-day and 26-day) and generates buy/sell signals based on crossovers.

How to use it:

  • Bullish crossover: MACD line crosses above the signal line.

  • Bearish crossover: MACD line crosses below the signal line.

Best for:
Spotting trend changes early.

4. Bollinger Bands

What they do:
Bollinger Bands create a price channel around an asset using a moving average plus/minus two standard deviations.

How to use them:

  • Price near upper band: Potential overbought condition.

  • Price near lower band: Potential oversold condition.

  • Band squeeze: Indicates upcoming volatility.

Best for:
Predicting volatility and identifying breakout opportunities.

5. Volume Indicators (OBV & VWAP)

What they do:
Volume indicators confirm price movements and help spot trend strength.

  • On-Balance Volume (OBV): Tracks buying/selling pressure.

  • VWAP (Volume-Weighted Average Price): Shows average price relative to volume.

Best for:
Confirming whether a trend is supported by strong trading volume.

6. Fibonacci Retracement

What it does:
Identifies key support and resistance levels based on Fibonacci ratios (23.6%, 38.2%, 50%, 61.8%, etc.).

How to use it:

  • Place retracement levels between swing highs and lows to find potential pullback or breakout zones.

Best for:
Setting targets and identifying price zones for entries/exits.

7. Stochastic Oscillator

What it does:
Measures price momentum by comparing closing prices to recent price ranges.

How to use it:

  • Above 80: Overbought.

  • Below 20: Oversold.

  • Use crossovers for potential buy/sell signals.

Best for:
Short-term traders looking for momentum shifts.

8. Ichimoku Cloud

What it does:
Provides a complete view of trend, momentum, and support/resistance levels in one indicator.

How to use it:

  • Price above cloud: Bullish.

  • Price below cloud: Bearish.

  • Cloud crossovers: Signal trend reversals.

Best for:
Swing traders who need multi-factor confirmation in one tool.

How Token Metrics Combines Indicators with AI

Instead of manually tracking dozens of indicators, Token Metrics uses AI to analyze 80+ technical, fundamental, and sentiment-based data points for each asset—giving you actionable insights without the guesswork.

Here’s how:

1. AI-Powered Bullish & Bearish Signals

Our system combines RSI, MACD, MAs, and more to generate real-time buy/sell signals.

2. Trader & Investor Grades

  • Trader Grade: Helps short-term traders focus on cryptos with strong technical setups.

  • Investor Grade: Identifies long-term investment opportunities with strong fundamentals.

3. Narrative Detection

Token Metrics tracks emerging narratives (AI tokens, DeFi, etc.) so you can spot trends before they explode.

4. AI-Managed Indices

Don’t want to analyze charts? Our AI-driven indices automatically rebalance portfolios using technical indicators and market conditions.

How to Combine Indicators Effectively

The most successful traders don’t rely on one indicator. Instead, they combine them for confirmation.

Example:

  • Use RSI to spot oversold conditions.

  • Confirm with MACD bullish crossover.

  • Check volume to ensure strong buying pressure.

When multiple indicators align, your trade has a higher probability of success—and Token Metrics does this automatically.

Advanced Tips for Using Indicators

  1. Don’t Overload: Use 3–5 indicators for clarity.

  2. Adjust for Volatility: Crypto is more volatile than stocks—shorten timeframes for faster signals.

  3. Combine With Fundamentals: Use Token Metrics Investor Grades to pair TA with project fundamentals.

  4. Practice Risk Management: Even the best indicators fail—always use stop-loss orders.

Final Thoughts

So, what indicators should you use for technical analysis?

Start with moving averages, RSI, MACD, Bollinger Bands, and Fibonacci levels—then add volume indicators and advanced tools like the Ichimoku Cloud as you gain experience.

But here’s the truth: indicators are only as good as the trader using them. That’s why Token Metrics simplifies the process by combining dozens of technical indicators with AI-powered analysis, giving you clear, actionable insights for smarter trades.

Whether you’re a day trader or a long-term investor, Token Metrics helps you use technical indicators strategically—not emotionally.

Research

Understanding Cryptocurrency: A Comprehensive Guide

Token Metrics Team
4
MIN

What is Cryptocurrency?

Cryptocurrency is a form of digital or virtual currency that uses cryptography for security, making it difficult to counterfeit or double-spend. Unlike traditional currencies issued by governments and central banks, cryptocurrencies operate on decentralized networks based on blockchain technology. This decentralization allows for peer-to-peer transactions without intermediaries such as banks.

The concept emerged with the introduction of Bitcoin in 2009, which paved the way for numerous other cryptocurrencies designed to serve various purposes within digital ecosystems.

Blockchain Technology Basics

At the core of cryptocurrencies lies blockchain, a distributed ledger that records all transactions across a network of computers (nodes). Each transaction is grouped into a block, which is then cryptographically linked to the previous block, forming a chain.

  • Decentralization: No single entity controls the blockchain; this distributes trust among participants and enhances security.
  • Transparency: All transactions are visible to participants, ensuring accountability.
  • Immutability: Once recorded, transactions cannot be altered, ensuring data integrity.

This technology underpins various cryptocurrencies and offers applications beyond currency, including smart contracts and decentralized applications (dApps).

How Cryptocurrency Works

When a user initiates a cryptocurrency transaction, it is broadcast to the network of nodes. These nodes verify the transaction’s validity through consensus mechanisms such as Proof of Work (PoW) or Proof of Stake (PoS).

  • Verification: Nodes confirm that the sender has sufficient funds and that the transaction follows protocol rules.
  • Consensus: The network agrees on the transaction's legitimacy via consensus algorithms.
  • Recording: Verified transactions are added to the blockchain as blocks.

Users store cryptocurrencies in digital wallets protected by private keys, which are critical for accessing and authorizing transactions.

Types of Cryptocurrencies

The cryptocurrency landscape is diverse, including:

  • Bitcoin (BTC): The first and most widely recognized cryptocurrency, mainly used as digital gold.
  • Altcoins: Alternative cryptocurrencies such as Ethereum (ETH), which supports smart contracts and decentralized applications.
  • Stablecoins: Cryptocurrencies pegged to stable assets like fiat currencies to reduce volatility.
  • Tokens: Digital assets created on existing blockchains representing various utilities or assets.

Understanding these distinctions is essential for analyzing different crypto projects and their use cases.

Researching Cryptocurrency

Due to the complexity and rapid evolution of the crypto space, thorough research is essential. Key approaches include:

  • Fundamental Analysis: Assess project goals, team credibility, technology, and market applicability.
  • Technical Analysis: Study price charts and market trends, while acknowledging the volatility inherent in crypto markets.
  • Security Considerations: Evaluate the blockchain’s robustness and the security of wallets and exchanges.
  • Utilizing Analytical Tools: Platforms like Token Metrics leverage AI to offer ratings and insights, aiding users in making informed evaluations.

Benefits and Challenges of Cryptocurrency

Benefits:

  • Decentralization: Reduces reliance on centralized institutions.
  • Financial Inclusion: Provides access to financial services globally, especially to unbanked populations.
  • Transparency and Security: Blockchain's design increases trust and reduces fraud.

Challenges:

  • Volatility: Cryptocurrency prices can be highly volatile.
  • Regulatory Uncertainty: Evolving regulations vary by jurisdiction, affecting use and development.
  • Scalability: Some networks face issues processing large volumes of transactions rapidly.
  • Security Risks: Risks such as theft of private keys and vulnerabilities in smart contracts.

How AI and Analytical Tools Support Cryptocurrency Analysis

The introduction of AI-driven research tools has transformed how individuals dissect the vast and complex cryptocurrency markets. Platforms like Token Metrics employ machine learning and natural language processing techniques to analyze various data streams including market sentiment, on-chain metrics, and project fundamentals.

These tools provide:

  • Quantitative Ratings: Assessing the strength and potential of different cryptocurrencies.
  • Scenario Modelling: Projecting possible market developments under various conditions.
  • Risk Analysis: Identifying vulnerabilities and risk factors in crypto projects.

Users can complement traditional research methods with AI insights to foster a balanced understanding.

Conclusion and Disclaimer

Cryptocurrency represents a revolutionary shift in digital finance, enabled by blockchain technology and supported increasingly by AI analytics. Understanding its fundamentals, technology, and associated risks is essential for meaningful engagement.

This article is for educational purposes only and does not constitute financial advice. Readers should conduct independent research and consult professionals before making decisions related to cryptocurrencies.

Crypto Basics

How Do Taxes Work for Crypto Trading?

Token Metrics Team
5 min
MIN

Cryptocurrency trading can be exciting and profitable, but one thing many traders overlook is taxation. Whether you’re buying Bitcoin, trading altcoins, or earning staking rewards, your activities can create taxable events—and failure to report them properly can lead to hefty penalties.

If you’ve been wondering, “How do taxes work for crypto trading?”, this guide breaks it all down. We’ll explain how crypto taxes work, the most common taxable events, and how Token Metrics can help you make smarter, tax-conscious trading decisions.

Are Cryptocurrencies Taxed?

Yes. In most countries, cryptocurrencies are treated as property, not currency, for tax purposes. This means:

  • Buying crypto with fiat (e.g., USD → BTC) is not taxable by itself.

  • Selling, swapping, or spending crypto is a taxable event.

  • Earning crypto (through staking, mining, or airdrops) is taxable income.

While tax laws vary by country, the principle is the same: any gain or income from crypto is subject to taxation.

Common Taxable Events in Crypto

To manage your taxes effectively, you need to know what counts as a taxable event.

1. Selling Crypto for Fiat

If you sell Bitcoin, Ethereum, or any other crypto for fiat currency, you must report capital gains or losses.

Example:

  • You bought 1 BTC for $20,000 and sold it for $40,000.

  • Your capital gain is $20,000—which is taxable.

2. Trading One Crypto for Another

Swapping one crypto for another (e.g., BTC → ETH) is also a taxable event.

Why?

  • You’re considered to have “sold” BTC at its fair market value and “bought” ETH.

3. Spending Crypto on Goods or Services

Using crypto to buy a car, laptop, or coffee? That’s a taxable event too.

Example:

  • You bought 1 ETH at $1,000.

  • You used it to buy goods when ETH was worth $2,000.

  • The $1,000 gain is taxable.

4. Earning Crypto

  • Staking & Mining Rewards: Counted as ordinary income at the time you receive them.

  • Airdrops & Hard Forks: The value at the time you receive them is taxable income.

5. DeFi & NFT Transactions

DeFi activities like yield farming, lending, or NFT trading may also trigger complex taxable events depending on jurisdiction.

Capital Gains: Short-Term vs. Long-Term

When you sell crypto, your profit is classified as a capital gain—either short-term or long-term.

  • Short-Term Gains:


    • Assets held less than 12 months.

    • Taxed at your ordinary income tax rate (higher).

  • Long-Term Gains:


    • Assets held more than 12 months.

    • Taxed at reduced capital gains rates (in many jurisdictions).

Pro Tip: Holding crypto for at least a year can significantly lower your tax burden.

How to Calculate Your Crypto Taxes

Step 1: Track Every Transaction

Record:

  • Date & time of the trade.

  • Type of transaction (buy, sell, swap).

  • Amount & value in fiat currency.

  • Fees paid.

Step 2: Determine Cost Basis

Your cost basis is how much you paid for the asset (including fees).

Step 3: Calculate Gains/Losses

Subtract your cost basis from the selling price:

  • Profit = Taxable gain

  • Loss = Tax-deductible (in some countries).

Step 4: Report Income

Report staking, mining, and airdrop rewards as ordinary income at their market value when received.

How Token Metrics Helps with Tax-Efficient Trading

Staying compliant doesn’t just mean reporting correctly—it means trading smartly to reduce your tax burden. That’s where Token Metrics can help.

1. AI-Powered Portfolio Tracking

Track all your crypto holdings across exchanges and wallets, making tax reporting easier.

2. Long-Term vs. Short-Term Insights

Use Investor Grades to identify cryptos worth holding long-term, reducing your tax liability on gains.

3. Strategic Trade Timing

Get AI-powered trading signals to make smarter decisions on when to sell—balancing profits and taxes.

4. Narrative Tracking

Spot emerging narratives (AI tokens, DeFi trends) early, allowing you to plan tax-friendly entries and exits.

Strategies to Minimize Crypto Taxes

  1. Hold for Over a Year: Qualify for long-term capital gains rates.

  2. Harvest Losses: Sell underperforming assets to offset taxable gains.

  3. Use Tax-Friendly Accounts: Some countries allow crypto investments in tax-advantaged accounts.

  4. Gift Crypto: In certain jurisdictions, gifting crypto can reduce tax exposure.

  5. Use AI for Smart Exits: Token Metrics can help you plan trades strategically to minimize taxable events.

Common Mistakes to Avoid

  • Not tracking transactions: Without records, you can’t calculate accurate gains/losses.

  • Ignoring swaps: Trading crypto-to-crypto is still taxable.

  • Forgetting airdrops/staking rewards: These are considered income at receipt.

  • Panic-selling without strategy: Leads to high taxes and missed opportunities.

Global Variations in Crypto Taxes

Tax treatment varies by country:

  • U.S.: IRS treats crypto as property.

  • UK: HMRC applies Capital Gains Tax on disposals.

  • Australia: ATO taxes crypto as property.

  • EU: Policies vary by member state but generally treat crypto as assets.

Always consult a crypto tax professional for advice in your jurisdiction.

Practical Example

Let’s say you:

  • Bought 2 ETH at $1,500 each ($3,000 total).

  • Sold 1 ETH at $3,000.

  • Your gain: $1,500 (minus fees).

  • If you held for over a year, you pay long-term capital gains tax. If not, it’s short-term at a higher rate.

With Token Metrics, you could track purchase dates and AI signals to time your exit strategically—potentially saving on taxes.

Final Thoughts

So, how do taxes work for crypto trading?

  • Selling, swapping, and spending crypto are taxable events.

  • Staking, mining, and airdrops are taxed as income.

  • Holding for over a year often reduces your tax bill.

The key to staying compliant and minimizing taxes is tracking transactions, planning trades strategically, and using the right tools.

That’s where Token Metrics helps. With AI-driven portfolio tracking, tax-efficient trading insights, and market analytics, Token Metrics empowers you to trade smarter, reduce taxable events, and keep more of your profits.

Don’t just trade crypto—trade tax-smart.

Research

What Are Limit Orders vs. Market Orders?

Token Metrics Team
8 min
MIN

When you trade cryptocurrency, you’ll come across different types of orders—market orders and limit orders being the two most common. Understanding how they work is essential for controlling your entry and exit points, minimizing slippage, and making better trading decisions.

If you’ve ever wondered, “What’s the difference between limit orders and market orders?”, you’re in the right place. This guide will break it down in detail, explain when to use each, and show how Token Metrics helps you execute smarter trades with AI-powered insights.

What Is a Market Order?

A market order is the simplest type of trade. It tells an exchange:

“Buy or sell this cryptocurrency at the best available price right now.”

How It Works:

  • When you place a market order to buy, the system instantly matches you with sellers at the lowest available price.

  • When you place a market order to sell, it instantly matches you with buyers at the highest available price.

Advantages of Market Orders:

  1. Instant Execution: Your trade goes through immediately.

  2. Simplicity: Perfect for beginners who want fast transactions.

  3. Useful in High-Volatility Conditions: If you need to enter or exit a trade quickly, market orders are your best bet.

Disadvantages of Market Orders:

  1. Slippage: If liquidity is low or the market moves quickly, you might pay more (or receive less) than expected.

  2. No Price Control: You accept whatever price the market offers at that moment.

Example:
If Bitcoin is trading at $40,000 and you place a market order for 1 BTC, you may end up paying $40,050 if the price moves up in the milliseconds it takes to execute your trade.

What Is a Limit Order?

A limit order gives you more control. It tells an exchange:

“Buy or sell this cryptocurrency at this specific price (or better).”

How It Works:

  • When you place a buy limit order, you set the maximum price you’re willing to pay.

  • When you place a sell limit order, you set the minimum price you’re willing to accept.

  • The order stays open until the market reaches your price—or until you cancel it.

Advantages of Limit Orders:

  1. Price Control: You won’t pay more (or sell for less) than your chosen price.

  2. Better for Planning: Ideal for setting up trades without constantly watching the market.

  3. Lower Fees: Some exchanges offer lower “maker” fees for limit orders since they add liquidity.

Disadvantages of Limit Orders:

  1. No Guarantee of Execution: If the market doesn’t reach your price, your order won’t go through.

  2. Slower Execution: Not suitable for urgent trades in fast-moving markets.

Example:
If Bitcoin is trading at $40,000, you could set a buy limit order at $39,500. If the price dips to that level, your order fills. If it doesn’t, you won’t buy.

Limit Orders vs. Market Orders: Key Differences

When Should You Use Market Orders?

Market orders are best when:

  • You need speed: Entering or exiting a trade quickly.

  • The market is highly liquid: For large-cap coins like Bitcoin and Ethereum, slippage is minimal.

  • News-based trading: When reacting to breaking news or sudden price surges.

When Should You Use Limit Orders?

Limit orders are best when:

  • You want price control: Setting entries at discount levels or selling at specific profit targets.

  • You’re trading low-liquidity coins: Prevents overpaying due to slippage.

  • You’re building a strategy: Setting multiple entries and exits without active monitoring.

How Token Metrics Helps You Use Market & Limit Orders Effectively

Placing orders is one thing—placing them at the right time is what makes you profitable. That’s where Token Metrics helps.

1. AI-Powered Trading Signals

Get real-time bullish and bearish alerts based on 80+ technical indicators, helping you know when to execute market or limit orders for maximum efficiency.

2. Trader & Investor Grades

Use Trader Grades for short-term trading opportunities (perfect for market orders) and Investor Grades for long-term accumulation (ideal for limit orders).

3. Narrative Tracking

Our AI identifies emerging narratives—like AI tokens or DeFi plays—so you can strategically place limit orders before trends take off.

4. AI-Managed Indices

Don’t want to place manual orders? Token Metrics’ AI-managed portfolios automatically rebalance based on market conditions, helping you avoid constant order management.

Practical Example: Combining Both Orders

Imagine you’re trading Ethereum (ETH):

  • Step 1: Token Metrics issues a bullish signal at $2,400, projecting a potential breakout.

  • Step 2: You place a market order to enter quickly at $2,400 to catch the momentum.

  • Step 3: You set a limit sell order at $2,700 to automatically secure profits without monitoring the chart.

This combination allows you to enter fast and exit smart—with less emotional stress.

Advanced Tips for Using Limit & Market Orders

  1. Use Stop-Limit Orders: Combine stop-loss and limit functions to protect against big losses.

  2. Layer Your Limit Orders: Place multiple buy/sell limits at different price levels for better cost averaging.

  3. Avoid Market Orders on Illiquid Coins: Slippage can cost you significantly.

  4. Use AI for Timing: Token Metrics helps you time entries and exits—so you’re not just placing random orders.

Final Thoughts

So, what’s the difference between limit orders and market orders?

  • Market orders are for speed—you get the trade done instantly at the best available price.

  • Limit orders are for control—you set the exact price you want to buy or sell.

The key is knowing when to use each—and using data-driven insights to make those decisions.

That’s where Token Metrics gives you an edge. With AI-powered trading signals, narrative tracking, and automated portfolio tools, Token Metrics helps you decide when to use market vs. limit orders, so you can trade confidently and profitably.

In crypto trading, timing is everything—and Token Metrics makes timing smarter.

Research

How Do I Manage Risk in Crypto Trading?

Token Metrics Team
8 min
MIN

Cryptocurrency trading can be highly profitable—but it’s also one of the riskiest markets in the world. Prices can swing wildly in seconds, projects can collapse overnight, and emotional decisions often lead to costly mistakes.

If you want to survive (and thrive) in crypto trading, you must learn how to manage risk effectively. In this guide, we’ll break down why risk management is critical, the top strategies to protect your capital, and how Token Metrics can help you trade smarter, safer, and with more confidence using AI-driven insights.

Why Risk Management Is Non-Negotiable in Crypto

In traditional markets, a 5% price movement is big news. In crypto? Daily moves of 10–20% are normal.

  • High volatility: Crypto is more speculative than stocks or forex.
  • Unregulated space: Scams and market manipulation are more common.
  • Emotional trading: Fear and FOMO lead to poor decisions.

Without a solid risk management plan, even experienced traders can lose everything.

Top Risk Management Strategies for Crypto Trading

1. Only Invest What You Can Afford to Lose

This is rule number one.

  • Never invest rent, emergency savings, or money you can’t afford to lose.
  • Treat crypto like a high-risk asset class—because it is.

2. Diversify Your Portfolio

Don’t put all your funds into one token.

  • Spread across different coins & sectors: Bitcoin, Ethereum, AI tokens, DeFi, etc.
  • Balance risk levels: Mix large-cap coins with small-cap moonshots.

How Token Metrics Helps:
Use AI-driven indices to build a diversified, auto-rebalanced portfolio aligned with your risk tolerance.

3. Use Stop-Loss Orders

Set stop-loss levels to automatically sell if prices fall below a certain threshold.

  • Prevents small losses from becoming catastrophic.
  • Helps maintain discipline during market dips.

Pro Tip: Place stops at strategic support levels, not random round numbers.

4. Size Your Positions Properly

Don’t go “all-in” on one trade.

  • Position sizing ensures no single trade wipes out your portfolio.
  • A common rule: risk 1–2% of your capital per trade.

5. Avoid Overleveraging

Leverage amplifies gains—but also losses.

  • Start with low or no leverage until you’re experienced.
  • High leverage trading can lead to instant liquidation in volatile markets.

6. Keep Emotions in Check

Fear, greed, and FOMO (fear of missing out) destroy portfolios.

  • Stick to your strategy.
  • Don’t chase pumps or panic-sell during dips.

How Token Metrics Helps:
Our AI-powered bullish and bearish signals take emotions out of trading by giving you data-backed entry and exit points.

7. Stay Informed

Markets move on news—regulations, partnerships, or even tweets.

  • Follow credible sources for updates.
  • Use narrative tracking to spot market-shifting trends early.

How Token Metrics Helps:
Our AI tracks emerging narratives (e.g., AI tokens, DeFi, RWAs), so you can adjust positions before the crowd reacts.

8. Use Risk/Reward Ratios

Before entering a trade, ask:

  • Is the potential reward worth the risk?
  • Aim for at least a 2:1 or 3:1 reward-to-risk ratio.

9. Practice Secure Asset Storage

Risk management isn’t just about trades—it’s also about keeping your crypto safe.

  • Use hardware wallets for long-term holdings.
  • Enable 2FA on exchanges.

10. Review & Adjust Your Strategy

Markets evolve—your plan should too.

  • Track your wins and losses.
  • Optimize your strategy using performance data.

How Token Metrics Helps You Manage Risk

Token Metrics isn’t just a research platform—it’s an AI-powered risk management assistant.

1. AI-Powered Trade Insights

Get real-time bullish and bearish signals to time entries and exits more effectively—reducing impulsive trades.

2. Trader & Investor Grades

Quickly assess tokens for short-term trading potential (Trader Grade) or long-term viability (Investor Grade)—helping you avoid high-risk projects.

3. AI-Managed Indices

Use auto-rebalanced AI indices to maintain diversification and reduce risk exposure.

4. Moonshot Finder

Interested in high-risk, high-reward tokens? Our Moonshot Finder filters low-cap projects using AI, helping you avoid scams and rug pulls.

5. Narrative Detection

Our AI tracks emerging narratives so you can anticipate market movements early—reducing exposure to collapsing trends.

Example: Risk Management Using Token Metrics

Imagine you want to invest $5,000 in crypto:

  • Without Token Metrics: You randomly choose 3 coins based on hype and YouTube videos. Two collapse, and your portfolio drops 50%.

  • With Token Metrics: You build a diversified AI-managed index of large-cap and promising small-cap tokens, use stop-loss levels, and set alerts for bullish/bearish signals. Your portfolio drops only 10% in a downturn—saving thousands.

This is how data-driven trading transforms risk into opportunity.

Practical Risk Management Checklist

  1. Never invest more than you can afford to lose.

  2. Diversify across coins and sectors.

  3. Use stop-loss orders to cap losses.

  4. Avoid overleveraging at all costs.

  5. Set realistic reward-to-risk ratios (2:1 or better).

  6. Use AI-powered tools like Token Metrics for smarter trades.

  7. Stay informed about news and emerging narratives.

  8. Secure your assets with hardware wallets and 2FA.

  9. Track and adjust your trading strategy regularly.

  10. Trade with logic, not emotions.

Final Thoughts

So, how do you manage risk in crypto trading? It’s about balancing your portfolio, using smart tools, and staying disciplined.

While volatility is unavoidable, you can control how much you’re exposed to it. With Token Metrics, you gain AI-powered insights, trade signals, and portfolio tools that help you reduce risks while maximizing opportunities.

In crypto, it’s not just about making profits—it’s about keeping them.

Research

What’s the Best Time to Trade Cryptocurrency?

Token Metrics Team
8 min
MIN

Cryptocurrency trading is a 24/7 market—unlike stocks, there’s no opening or closing bell. This leads many traders to wonder: “When is the best time to trade cryptocurrency?”

The truth is, timing your trades can make a huge difference in profitability. Trading during periods of high liquidity or increased volatility can provide better opportunities, while poorly timed trades can lead to slippage and losses.

In this guide, we’ll explore the best times to trade crypto, how different time zones affect price movements, and how Token Metrics can help you identify the right moments to enter or exit trades using AI-powered insights.

Why Timing Matters in Crypto Trading

Even though the crypto market never sleeps, trading activity is not equal throughout the day.

  • High liquidity: When more traders are active, spreads are tighter, making it easier to enter and exit trades at good prices.
  • Volatility opportunities: Price swings create profit potential—but also risk.
  • Market overlaps: When multiple regions are active, volume surges.

Knowing when the market is most active can help you reduce trading fees, minimize slippage, and maximize your gains.

The Best Times to Trade Cryptocurrency

1. Overlap Between U.S. and European Markets (1:00 PM – 5:00 PM UTC)

The busiest period for crypto trading is when U.S. and European sessions overlap.

  • Why?

    • The U.S. is home to major crypto exchanges and institutional traders.
    • Europe contributes significant trading volume and liquidity.

  • Benefits:

    • Higher volatility for short-term traders.
    • Tighter spreads for efficient execution.

This overlap often sees the biggest price movements of the day—perfect for day traders and scalpers.

2. Early U.S. Trading Hours (12:00 PM – 3:00 PM UTC)

The start of the U.S. session brings increased activity from institutional investors and retail traders waking up to market news.

  • Best for:

    • News-based trades (after U.S. economic announcements).
    • Entering/exiting positions with high liquidity.

3. Weekend Volatility (Saturday & Sunday)

Crypto is unique because weekend trading can be extremely volatile due to:

  • Lower liquidity (fewer institutional players).
  • Retail-driven price movements.

  • Who benefits:

    • Swing traders looking for sharp moves.
    • Those willing to tolerate higher risk for bigger gains.

4. After Major News & Events

The “best time” isn’t always about the clock—it’s about market catalysts.

  • Regulatory updates.
  • Exchange announcements.
  • Macro events (interest rate changes, economic data).

These events can cause instant surges or drops—great for traders who react quickly.

Does the Day of the Week Matter?

Yes. Research shows:

  • Mondays & Fridays: Often more volatile as traders adjust positions before/after the weekend.
  • Midweek (Tuesday–Thursday): Tends to have steady, higher trading volumes.

For swing traders, entering positions midweek may reduce erratic weekend exposure.

How Token Metrics Helps You Trade at the Right Time

Timing trades isn’t just about watching charts—it’s about analyzing multiple data points in real time. That’s where Token Metrics gives you a powerful edge.

1. AI-Powered Trading Signals

Get real-time bullish and bearish alerts based on 80+ data points, helping you know when to enter or exit trades confidently.

2. Narrative Tracking

Our AI monitors emerging crypto narratives (AI tokens, DeFi, RWA, etc.) so you can act before the crowd—crucial for timing market moves.

3. Trader & Investor Grades

Use Trader Grades for short-term opportunities and Investor Grades for long-term positions, aligning your trades with market momentum.

4. AI-Driven Indices

Don’t want to time trades manually? Token Metrics offers auto-managed crypto indices that rebalance based on AI-identified market conditions.

Tips for Timing Crypto Trades

  1. Trade During High Liquidity Windows: Overlaps between U.S. and European sessions are best.
  2. Watch for News: Major announcements can create rapid price moves—be ready.
  3. Avoid Low-Volume Hours: Late-night trading (UTC) often has higher slippage.
  4. Use AI for Precision: Let Token Metrics guide entry and exit points with data-backed insights.
  5. Set Alerts: Use Token Metrics’ customizable alerts for price, grades, and signals so you never miss an opportunity.

Example: Using Token Metrics for Timing

Imagine you’re planning to trade Ethereum. Instead of guessing when to enter:

  • You use Token Metrics Trader Grades to spot when ETH has strong short-term potential.
  • You set AI-powered alerts for price changes and bullish signals.
  • You execute your trade during the U.S.-EU overlap when liquidity is highest—reducing slippage and maximizing efficiency.

This data-driven approach takes the guesswork out of timing.

Final Thoughts

So, what’s the best time to trade cryptocurrency?

  • Weekdays during the U.S.-EU overlap (1:00–5:00 PM UTC) offer the best liquidity and volatility.
  • Early U.S. hours and weekends can also present opportunities—but with higher risks.
  • Major news events often create the best entry/exit windows.

But the truth is, there’s no universal “perfect time.” The real key is using data-driven insights to trade at your best time—when probabilities are in your favor.

That’s why Token Metrics is essential for traders. With AI-powered trading signals, narrative tracking, and portfolio management tools, Token Metrics helps you time the market intelligently, not emotionally.

Whether you’re a day trader chasing volatility or a long-term investor, Token Metrics puts the right data at your fingertips—so you can trade with confidence, anytime.

Research

How Do I Keep My Cryptocurrency Safe?

Token Metrics Team
8 min
MIN

The cryptocurrency market has created life-changing wealth for millions, but it has also attracted hackers, scammers, and bad actors. From exchange hacks to phishing scams and rug pulls, billions of dollars in crypto have been lost due to poor security practices.

If you’re serious about trading or investing in crypto, one of your top priorities should be keeping your cryptocurrency safe. In this guide, we’ll walk through the biggest risks to your crypto security, best practices to protect your assets, and how Token Metrics can help you make informed, safe decisions when navigating the crypto market.

Why Is Cryptocurrency Security So Important?

Unlike a traditional bank account, cryptocurrency transactions are irreversible. If your funds are stolen, there’s usually no way to recover them.

  • No centralized authority: There’s no bank to call for refunds.
  • Pseudonymous transactions: It’s nearly impossible to trace funds once they’re moved.
  • High-value targets: Hackers actively target crypto investors due to the potential for large payoffs.

This means you are your own bank—and you’re responsible for protecting your funds.

Common Threats to Your Cryptocurrency

Before learning how to protect your crypto, you need to know what you’re up against:

1. Exchange Hacks

Crypto exchanges are frequent targets for hackers. Even top-tier exchanges like Mt. Gox and FTX have experienced devastating losses.

2. Phishing Attacks

Scammers use fake websites, emails, or social media accounts to trick you into revealing your private keys or login credentials.

3. Malware & Keyloggers

Cybercriminals install malicious software on your devices to steal passwords and seed phrases.

4. Rug Pulls & Scams

Fraudulent projects lure investors with promises of high returns, then disappear with the funds.

5. Human Error

Lost passwords, mishandled private keys, or accidental transfers can lead to permanent loss of funds.

How to Keep Your Cryptocurrency Safe

Here’s a step-by-step approach to safeguarding your crypto:

1. Use a Secure Wallet

Hardware Wallets:

  • The safest option for long-term storage.
  • Devices like Ledger or Trezor keep your private keys offline, out of reach of hackers.

Software Wallets:

  • Convenient for active trading but more vulnerable to attacks.
  • Use reputable wallets like MetaMask or Trust Wallet and enable strong security features.

Pro Tip: Use a hardware wallet for long-term holdings and a software wallet for small, active trading balances.

2. Enable Two-Factor Authentication (2FA)

Always enable 2FA on your exchange accounts, wallets, and email addresses.

  • Use Google Authenticator or Authy, not SMS-based 2FA, which is susceptible to SIM swaps.

3. Beware of Phishing & Fake Websites

  • Double-check URLs before logging into exchanges or wallets.
  • Never click links from suspicious emails or DMs.
  • Bookmark the official websites of your exchanges and platforms.

4. Keep Your Seed Phrase Offline

Your wallet’s seed phrase is the master key to your funds.

  • Write it down on paper and store it securely (safe, vault).
  • Avoid storing it in digital form (cloud storage, photos).
  • Consider splitting the phrase into parts and storing them in separate locations.

5. Research Projects Before Investing

Many scams and rug pulls target uninformed investors chasing hype.

How Token Metrics Helps:

  • Use AI-powered Investor Grades to assess projects for long-term viability.
  • Leverage Moonshot Finder to discover vetted early-stage tokens with real growth potential—not scams.

6. Diversify Your Storage

Don’t keep all your crypto in one place.

  • Use multiple wallets for different purposes (trading vs. holding).
  • Spread funds across hardware, software, and exchange wallets.

7. Stay Updated on Threats

The crypto landscape changes fast.

  • Follow credible crypto news platforms.
  • Monitor regulatory updates and security alerts.
  • Use Token Metrics to stay ahead of market narratives and avoid projects flagged as high-risk.

How Token Metrics Helps You Stay Secure

While Token Metrics isn’t a wallet or exchange, it’s a powerful AI-powered platform designed to help you make safer investment decisions.

1. AI-Powered Risk Analysis

Token Metrics uses AI-driven evaluations to score projects, helping you avoid high-risk tokens and scam projects.

2. Investor & Trader Grades

Our proprietary grading system helps you quickly identify safe, high-quality assets for both short-term trading and long-term holding.

3. Moonshot Finder

Interested in low-cap tokens? The Moonshot Finder helps you spot legitimate early-stage projects before they pump—without falling into scams.

4. Narrative & Market Tracking

Stay ahead of emerging narratives and security concerns with Token Metrics’ real-time AI-driven analysis, helping you dodge risky trends.

Practical Security Checklist for Every Crypto Holder

  1. Use a hardware wallet for long-term holdings.
  2. Enable 2FA on all accounts—never rely on SMS-based security.
  3. Store seed phrases offline and securely.
  4. Avoid suspicious links and verify all URLs.
  5. Research every project using platforms like Token Metrics.
  6. Diversify storage across multiple wallets.
  7. Stay informed about new threats and market risks.

Final Thoughts

So, how do you keep your cryptocurrency safe? It comes down to strong storage practices, personal vigilance, and making informed investment decisions.

While hardware wallets and 2FA secure your assets, knowing where to invest is just as important. That’s where Token Metrics comes in—using AI-powered analysis to help you avoid scams, choose safe projects, and trade with confidence.

In crypto, you are your own bank—so take your security seriously. And with Token Metrics guiding your decisions, you can navigate this high-risk market safely and profitably.

Research

What Are Trading Fees and How Do They Work?

Token Metrics Team
8 min
MIN

If you’ve ever bought or sold cryptocurrency, you’ve likely noticed that exchanges charge trading fees. While these fees may seem small at first glance, they can add up significantly over time, especially for frequent traders.

Understanding how trading fees work is essential for maximizing your profits and choosing the right platform and strategy. In this guide, we’ll explain what trading fees are, how they’re calculated, the different types of fees, and how Token Metrics can help you minimize costs while optimizing your trades.

What Are Trading Fees?

Trading fees are charges imposed by cryptocurrency exchanges whenever you execute a trade—whether you’re buying, selling, or swapping digital assets.

  • Purpose:

    • They compensate exchanges for providing liquidity, security, and order-matching services.
    • They discourage spam orders that could overload the exchange.

Example:
If you buy $1,000 worth of Bitcoin with a 0.1% trading fee, you’ll pay $1 in fees for that transaction.

How Are Trading Fees Calculated?

Most crypto exchanges calculate fees based on:

  1. Trade Value (Percentage):

    • The most common model.
    • Fees are a percentage of the total trade amount (e.g., 0.1% per trade).

  2. Flat Fees:

    • Some platforms charge a fixed fee per trade, regardless of trade size.

  3. Tiered Fee Structures:

    • Frequent traders often benefit from lower fees when they reach certain monthly trading volumes.

Maker vs. Taker Fees

Many exchanges use a maker-taker model to incentivize liquidity:

  • Maker Fees:

    • You pay a lower fee (or sometimes nothing) when you add liquidity by placing a limit order that isn’t immediately matched.

  • Taker Fees:

    • You pay a higher fee when you remove liquidity by placing a market order that is filled instantly.

Example:
On Binance, makers might pay 0.08%, while takers pay 0.1%.

Types of Crypto Trading Fees

When trading crypto, you’ll encounter more than just trading fees:

1. Trading (Transaction) Fees

  • Charged when buying or selling crypto.
  • Often range from 0.05%–0.5% depending on the exchange.

2. Deposit Fees

  • Some exchanges charge when funding accounts via credit card, PayPal, or bank transfers.
  • Crypto deposits are typically free.

3. Withdrawal Fees

  • Charged when moving funds off the exchange.
  • Often fixed per withdrawal (e.g., 0.0005 BTC).

4. Network (Gas) Fees

  • Separate from exchange fees, paid to blockchain validators.
  • Ethereum and other networks can have high gas fees during peak demand.

Why Do Trading Fees Matter?

Even small fees can significantly impact your profits, especially if you trade often.

Example:

  • A day trader making 50 trades/month with a 0.1% fee per trade on $1,000 positions will pay $50/month in fees.
  • Over a year, that’s $600 lost to fees—before considering withdrawal or gas costs.

This is why reducing fees is crucial for active traders.

How to Minimize Trading Fees

1. Use Low-Fee Exchanges

Compare platforms—Binance, Bybit, and Kraken often offer competitive rates for high-volume traders.

2. Increase Your Trading Volume

Many exchanges lower fees as your monthly trading volume increases.

3. Use Limit Orders

Save on fees by acting as a maker instead of a taker whenever possible.

4. Pay with Exchange Tokens

Some exchanges offer discounts when fees are paid with their native token (e.g., BNB on Binance).

5. Optimize Your Trading Strategy with AI

Instead of making dozens of small trades, use data-driven insights from Token Metrics to make high-probability trades—reducing overall fee impact.

How Token Metrics Helps You Save on Fees

Token Metrics isn’t an exchange—it’s an AI-powered crypto research and trading platform designed to maximize your profits.

Here’s how it helps with trading fees:

1. AI-Powered Trade Optimization

Token Metrics provides bullish/bearish trading signals based on 80+ technical indicators, helping you enter fewer but higher-quality trades—minimizing unnecessary fees.

2. Trader & Investor Grades

Our proprietary grading system helps you identify top-performing cryptos, reducing guesswork and avoiding costly trial-and-error trading.

3. AI-Managed Indices

Instead of manually rebalancing portfolios (and paying multiple trading fees), use Token Metrics AI-driven crypto indices, which rebalance intelligently while keeping transaction costs low.

4. Narrative Tracking

Our AI detects emerging crypto narratives early, helping you position yourself before the market gets crowded—reducing the need for excessive short-term trading.

Practical Example: Using Token Metrics to Reduce Fees

Imagine you’re trading altcoins manually and make 20 trades/month.

  • At 0.2% per trade on $1,000 positions, you pay $40/month in fees.
  • With Token Metrics AI signals, you cut this down to 8 high-probability trades per month—reducing your fees by 60% while increasing win rates.

This is how data-driven trading saves both money and stress.

Final Thoughts

So, what are trading fees and how do they work? They’re the charges exchanges take for facilitating trades, withdrawals, and transactions—and while they may seem small, they can eat into your profits quickly.

The good news? You can minimize trading fees with the right strategies—and Token Metrics can help.

By providing AI-powered trading signals, long-term Investor Grades, and automated crypto indices, Token Metrics helps you reduce unnecessary trades, make smarter decisions, and maximize your returns—all while minimizing the impact of trading fees.

Whether you’re a beginner or advanced trader, using Token Metrics means trading smarter, not harder.

Research

Should I Use Leverage When Trading Crypto?

Token Metrics Team
8 min
MIN

Leverage trading has become a hot topic in the crypto world. Many traders are attracted by the promise of amplified profits—but fail to realize it also magnifies losses. If you’ve been wondering, “Should I use leverage when trading crypto?” the short answer is: only if you fully understand the risks and have a solid strategy.

In this guide, we’ll break down what leverage trading is, how it works, its pros and cons, and how AI-powered tools like Token Metrics can help you make smarter decisions when trading with leverage.

What Is Leverage Trading in Crypto?

Leverage trading lets you borrow funds from an exchange to increase your position size, allowing you to control more capital than you actually own.

For example:

  • If you have $1,000 and use 10x leverage, you’re trading with $10,000.
  • A 10% price increase can give you a 100% profit on your original investment.
  • But a 10% price drop could wipe out your position completely.

Popular platforms like Binance, Bybit, and OKX offer leverage levels from 2x to 125x for certain assets.

Pros of Leverage Trading

Leverage isn’t all bad. When used responsibly, it offers several benefits:

1. Higher Profit Potential

Even small price movements can generate significant returns when using leverage.

2. Portfolio Efficiency

Traders can free up capital for other positions while maintaining large exposure in a leveraged trade.

3. Opportunities in Bear Markets

Leverage allows for short selling, enabling you to profit when prices go down.

Cons of Leverage Trading

The same factors that make leverage attractive also make it high-risk:

1. Amplified Losses

Just as leverage magnifies profits, it magnifies losses. A minor price drop could liquidate your entire position.

2. Liquidation Risk

When your collateral falls below a certain threshold, the exchange automatically closes your position, locking in your losses.

3. Emotional Stress

Leverage trading is fast-paced and stressful, often leading to panic selling or revenge trading after losses.

Should Beginners Use Leverage?

No.
If you’re new to trading, avoid leverage until you have:

  • A proven strategy with consistent results in spot trading.
  • Strong risk management skills (e.g., using stop-loss orders).
  • The ability to stay disciplined under pressure.

For beginners, leveraged trading can be a fast track to losing your capital.

When Does It Make Sense to Use Leverage?

For experienced traders, leverage can make sense when:

  • You’re hedging your portfolio against downside risk.
  • You have high conviction in a trade and want to maximize gains.
  • You’re trading high-liquidity assets like BTC or ETH with well-defined stop-losses.

How Token Metrics Helps You Trade Smarter with Leverage

Leverage trading requires precision, and that’s where Token Metrics gives you an edge.

1. AI-Powered Bullish & Bearish Signals

Identify high-probability trades with real-time AI signals based on 80+ technical indicators. This helps you avoid entering positions blindly.

2. Trader & Investor Grades

Use Trader Grades for short-term trading opportunities and Investor Grades for long-term positions—helping you decide when to scale up or reduce leverage.

3. Risk Assessment Tools

Token Metrics evaluates crypto projects and market trends to reduce exposure to high-risk assets—a must for leveraged positions.

4. AI-Driven Indices

Don’t want to trade manually? Use our AI-managed crypto indices to gain diversified exposure without taking unnecessary leveraged risks.

5. Narrative Detection

Our AI identifies emerging narratives before they hit mainstream attention, giving you a strategic edge in timing trades—crucial when using leverage.

Best Practices for Using Leverage in Crypto

If you decide to use leverage, follow these rules:

  1. Start Small: Use low leverage (2x–3x) instead of going all in at 20x or 50x.
  2. Set Stop-Loss Orders: Always predetermine your maximum loss tolerance.
  3. Trade Liquid Assets: Stick to high-volume coins like BTC and ETH to avoid slippage.
  4. Don’t Overtrade: Only use leverage on trades with strong conviction and proper analysis.
  5. Use AI Tools: Let Token Metrics guide your entries and exits with data-driven insights.

Why Overleveraging Is Dangerous

Many traders lose money by overleveraging, which leads to:

  • Margin calls and forced liquidations.
  • Revenge trading, where losses push traders into reckless decisions.
  • Complete portfolio wipeouts from one bad trade.

Pro Tip: Only trade with leverage on funds you can afford to lose.

Final Thoughts

So, should you use leverage when trading crypto?

For beginners, the answer is no—focus on mastering spot trading first. For experienced traders, leverage can be a powerful tool when used with strict risk management and data-driven insights.

That’s where Token Metrics makes a difference. With AI-powered trading signals, real-time risk analysis, and narrative tracking, Token Metrics helps you trade smarter, reduce emotional mistakes, and optimize your leverage strategy.

Leverage can make you or break you—so don’t go in blind. Use Token Metrics to guide your trades and stay ahead of the market.

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