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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

APIs Explained: How They Connect Software and Data

Token Metrics Team
5
MIN

APIs — application programming interfaces — are the invisible glue that lets software talk to software. Whether you're building a dashboard, feeding data into an AI model, or fetching market prices for analytics, understanding what an API is and how it works is essential to designing reliable systems. This guide explains APIs in plain language, shows how they’re used in crypto and AI, and outlines practical steps for safe, scalable integration.

What is an API? Core definition and common types

An API (application programming interface) is a defined set of rules and endpoints that lets one software program request and exchange data or functionality with another. Think of it as a contract: the provider defines what inputs it accepts and what output it returns, and the consumer follows that contract to integrate services reliably.

Common API types:

  • REST APIs: Use HTTP verbs (GET, POST, PUT, DELETE) and structured URLs. They are stateless and often return JSON.
  • GraphQL: Allows clients to request exactly the data they need via a single endpoint, improving efficiency for complex queries.
  • WebSocket / Streaming APIs: Provide persistent connections for real-time data flows, useful for live feeds like price updates or chat.
  • RPC & gRPC: Remote procedure calls optimized for low-latency, typed interactions, often used in microservices.

How APIs work: requests, endpoints, and authentication

At a technical level, using an API involves sending a request to an endpoint and interpreting the response. Key components include:

  • Endpoint: A URL representing a resource or action (e.g., /v1/prices/bitcoin).
  • Method: The HTTP verb that signals the intent (GET to read, POST to create, etc.).
  • Headers & Body: Metadata (like authentication tokens) and payloads for requests that change state.
  • Response codes: Numeric codes (200 OK, 404 Not Found, 429 Too Many Requests) that indicate success or error types.
  • Authentication: API keys, OAuth tokens, JWTs, or mutual TLS are common ways to authenticate and authorize consumers.

Understanding these elements helps teams design error handling, retry logic, and monitoring so integrations behave predictably in production.

APIs in crypto and AI: practical use cases

APIs enable many building blocks in crypto and AI ecosystems. Examples include:

  • Market data & price feeds: REST or websocket APIs provide real-time and historical prices, order book snapshots, and trade events.
  • On-chain data: Indexing services expose transactions, balances, and contract events via APIs for analytics and compliance workflows.
  • Model serving: AI inference APIs let applications call trained models to generate predictions, embeddings, or natural language outputs.
  • Wallet & transaction APIs: Abstract common wallet operations like address generation, signing, and broadcasting transactions.

When integrating APIs for data-driven systems, consider latency, data provenance, and consistency. For research and model inputs, services that combine price data with on-chain metrics and signals can reduce the time it takes to assemble reliable datasets. For teams exploring such aggregations, Token Metrics provides an example of an AI-driven analytics platform that synthesizes multiple data sources for research workflows.

Best practices and security considerations for API integration

Secure, maintainable APIs follow established practices that protect data and reduce operational risk:

  1. Authentication & least privilege: Use scoped API keys or OAuth to limit access, rotate credentials regularly, and avoid embedding secrets in client code.
  2. Rate limiting and retries: Respect provider rate limits, implement exponential backoff, and design idempotent operations to avoid duplication.
  3. Input validation and sanitization: Validate incoming data and sanitize outputs to prevent injection and misuse.
  4. Versioning: Use semantic versioning in endpoint paths (e.g., /v1/) and deprecate old versions with clear timelines.
  5. Monitoring and observability: Log requests, latency, errors, and usage patterns. Set alerts for anomalies and integrate telemetry into incident response playbooks.
  6. Data integrity and provenance: When using third-party feeds, capture timestamps, unique identifiers, and proof-of-origin where available so downstream analysis can trace sources.

Following these practices helps teams scale API usage without sacrificing reliability or security.

Build Smarter Crypto Apps & AI Agents with Token Metrics

Token Metrics provides real-time prices, trading signals, and on-chain insights all from one powerful API. Grab a Free API Key

What is an API and why is it useful?

An API is a set of rules that enables software components to interact. It’s useful because it abstracts complexity, standardizes data exchange, and enables modular development across systems and teams.

Which API type should I choose: REST, GraphQL, or streaming?

Choose based on access patterns: REST is simple and widely supported; GraphQL excels when clients need flexible queries and fewer round trips; streaming (WebSocket) is best for low-latency, continuous updates. Consider caching, complexity, and tooling support.

How do I secure API keys and credentials?

Store secrets in secure vaults or environment variables, avoid hardcoding them in source code, rotate keys periodically, and apply principle of least privilege to limit access scopes.

What are rate limits and how should I handle them?

Rate limits restrict how many requests a client can make in a time window. Handle them by respecting limits, implementing exponential backoff for retries, caching responses, and batching requests where possible.

How do I evaluate an API provider?

Assess documentation quality, uptime SLAs, authentication methods, data freshness, cost model, and community or support channels. Test with realistic workloads and review security practices and versioning policies.

Can APIs be used to power AI agents?

Yes. AI agents often call APIs for data ingestion, model inference, or action execution. Reliable APIs for feature data, model serving, and orchestration are key to building robust AI workflows.

Disclaimer

This article is for educational and informational purposes only. It does not constitute financial, investment, legal, or professional advice. Evaluate APIs and data sources independently and consider security and compliance requirements specific to your use case.

Research

APIs Explained: How Application Interfaces Work

Token Metrics Team
6
MIN

APIs power modern software by acting as intermediaries that let different programs communicate. Whether you use a weather app, sign in with a social account, or combine data sources for analysis, APIs are the plumbing behind those interactions. This guide breaks down what an API is, how it works, common types and use cases, plus practical steps to evaluate and use APIs responsibly.

What an API Is and Why It Matters

An application programming interface (API) is a contract between two software components. It specifies the methods, inputs, outputs, and error handling that allow one service to use another’s functionality or data without needing to know its internal implementation. Think of an API as a well-documented door: the requester knocks with a specific format, and the server replies according to agreed rules.

APIs matter because they:

  • Enable modular development and reuse of functionality across teams and products.
  • Abstract complexity so consumers focus on features rather than implementation details.
  • Drive ecosystems: public APIs can enable third-party innovation and integrations.

How APIs Work: Key Components

At a technical level, an API involves several elements that define reliable communication:

  • Endpoint: A URL or address where a service accepts requests.
  • Methods/Operations: Actions permitted by the API (e.g., read, create, update, delete).
  • Payload and Format: Data exchange format—JSON and XML are common—and schemas that describe expected fields.
  • Authentication & Authorization: Mechanisms like API keys, OAuth, or JWTs that control access.
  • Rate Limits and Quotas: Controls on request volume to protect stability and fairness.
  • Versioning: Strategies (URI versioning, header-based) for evolving an API without breaking clients.

Most web APIs use HTTP as a transport; RESTful APIs map CRUD operations to HTTP verbs, while alternatives like GraphQL let clients request exactly the data they need. The right style depends on use cases and performance trade-offs.

Common API Use Cases and Types

APIs appear across many layers of software and business models. Common categories include:

  • Public (Open) APIs: Exposed to external developers to grow an ecosystem—examples include mapping, social, and payment APIs.
  • Private/Internal APIs: Power internal systems and microservices within an organization for modularity.
  • Partner APIs: Shared with specific business partners under contract for integrated services.
  • Data APIs: Provide structured data feeds (market data, telemetry, or on-chain metrics) used by analytics and AI systems.

Practical examples: a mobile app calling a backend to fetch user profiles, an analytics pipeline ingesting a third-party data API, or a serverless function invoking a payment API to process transactions.

Design, Security, and Best Practices

Designing and consuming APIs effectively requires both technical and governance considerations:

  1. Design for clarity: Use consistent naming, clear error codes, and robust documentation to reduce friction for integrators.
  2. Plan for versioning: Avoid breaking changes by providing backward compatibility or clear migration paths.
  3. Secure your interfaces: Enforce authentication, use TLS, validate inputs, and implement least-privilege authorization.
  4. Observe and throttle: Monitor latency, error rates, and apply rate limits to protect availability.
  5. Test and simulate: Provide sandbox environments and thorough API tests for both functional and load scenarios.

When evaluating an API to integrate, consider documentation quality, SLAs, data freshness, error handling patterns, and cost model. For data-driven workflows and AI systems, consistency of schemas and latency characteristics are critical.

APIs for Data, AI, and Research Workflows

APIs are foundational for AI and data research because they provide structured, automatable access to data and models. Teams often combine multiple APIs—data feeds, enrichment services, feature stores—to assemble training datasets or live inference pipelines. Important considerations include freshness, normalization, rate limits, and licensing of data.

AI-driven research platforms can simplify integration by aggregating multiple sources and offering standardized endpoints. For example, Token Metrics provides AI-powered analysis that ingests diverse signals via APIs to support research workflows and model inputs.

Discover Crypto Gems with Token Metrics AI

Token Metrics uses AI-powered analysis to help you uncover profitable opportunities in the crypto market. Get Started For Free

What is an API? (FAQ)

1. What does API stand for and mean?

API stands for Application Programming Interface. It is a set of rules and definitions that lets software components communicate by exposing specific operations and data formats.

2. How is a web API different from a library or SDK?

A web API is accessed over a network (typically HTTP) and provides remote functionality or data. A library or SDK is code included directly in an application. APIs enable decoupled services and cross-platform access; libraries are local dependencies.

3. What are REST, GraphQL, and gRPC?

REST is an architectural style using HTTP verbs and resource URIs. GraphQL lets clients specify exactly which fields they need in a single query. gRPC is a high-performance RPC framework using protocol buffers and is suited for internal microservice communication with strict performance needs.

4. How do I authenticate to an API?

Common methods include API keys, OAuth 2.0 for delegated access, and JWTs for stateless tokens. Choose an approach that matches security requirements and user interaction patterns; always use TLS to protect credentials in transit.

5. What are typical failure modes and how should I handle them?

Failures include rate-limit rejections, transient network errors, schema changes, and authentication failures. Implement retries with exponential backoff for transient errors, validate responses, and monitor for schema or semantic changes.

6. Can APIs be used for real-time data?

Yes. Polling HTTP APIs at short intervals can approximate near-real-time, but push-based models (webhooks, streaming APIs, WebSockets, or event streams) are often more efficient and lower latency for real-time needs.

7. How do I choose an API provider?

Evaluate documentation, uptime history, data freshness, pricing, rate limits, privacy and licensing, and community support. For data or AI integrations, prioritize consistent schemas, sandbox access, and clear SLAs.

8. How can I learn to design APIs?

Start with principles like consistent resource naming, strong documentation (OpenAPI/Swagger), automated testing, and security by design. Study public APIs from major platforms and use tools that validate contracts and simulate client behavior.

Disclaimer

This article is for educational and informational purposes only. It does not constitute investment advice, financial recommendations, or endorsements. Readers should perform independent research and consult qualified professionals where appropriate.

Research

Understanding APIs: How They Power Modern Apps

Token Metrics Team
5
MIN

APIs — short for application programming interfaces — are the invisible connectors that let software systems communicate, share data, and build layered services. Whether you’re building a mobile app, integrating a payment gateway, or connecting an AI model to live data, understanding what an API does and how it behaves is essential for modern product and research teams.

What is an API? Core definition and types

An API is a defined set of rules, protocols, and tools that lets one software component request services or data from another. Conceptually, an API is an interface: it exposes specific functions and data structures while hiding internal implementation details. That separation supports modular design, reusability, and clearer contracts between teams or systems.

Common API categories include:

  • Web APIs: HTTP-based interfaces that deliver JSON, XML, or other payloads (e.g., REST, GraphQL).
  • Library or SDK APIs: Language-specific function calls bundled as libraries developers import into applications.
  • Operating system APIs: System calls that let applications interact with hardware or OS services.
  • Hardware APIs: Protocols that enable communication with devices and sensors.

How APIs work: a technical overview

At a high level, interaction with an API follows a request-response model. A client sends a request to an endpoint with a method (e.g., GET, POST), optional headers, and a payload. The server validates the request, performs logic or database operations, and returns a structured response. Key concepts include:

  • Endpoints: URLs or addresses where services are exposed.
  • Methods: Actions such as read, create, update, delete represented by verbs (HTTP methods or RPC calls).
  • Authentication: How the API verifies callers (API keys, OAuth tokens, mTLS).
  • Rate limits: Controls that restrict how frequently a client can call an API to protect availability.
  • Schemas and contracts: Data models (OpenAPI, JSON Schema) that document expected inputs/outputs.

Advanced setups add caching, pagination, versioning, and webhook callbacks for asynchronous events. GraphQL, in contrast to REST, enables clients to request exactly the fields they need, reducing over- and under-fetching in many scenarios.

Use cases across industries: from web apps to crypto and AI

APIs are foundational in nearly every digital industry. Example use cases include:

  • Fintech and payments: APIs connect merchant systems to payment processors and banking rails.
  • Enterprise integration: APIs link CRM, ERP, analytics, and custom services for automated workflows.
  • Healthcare: Secure APIs share clinical data while complying with privacy standards.
  • AI & ML: Models expose inference endpoints so apps can send inputs and receive predictions in real time.
  • Crypto & blockchain: Crypto APIs provide price feeds, on-chain data, wallet operations, and trading endpoints for dApps and analytics.

In AI and research workflows, APIs let teams feed models with curated live data, automate labeling pipelines, or orchestrate multi-step agent behavior. In crypto, programmatic access to market and on-chain signals enables analytics, monitoring, and application integration without manual data pulls.

Best practices and security considerations

Designing and consuming APIs requires intentional choices: clear documentation, predictable error handling, and explicit versioning reduce integration friction. Security measures should include:

  • Authentication & authorization: Use scoped tokens, OAuth flows, and least-privilege roles.
  • Transport security: Always use TLS/HTTPS to protect data in transit.
  • Input validation: Sanitize and validate data to prevent injection attacks.
  • Rate limiting & monitoring: Protect services from abuse and detect anomalies through logs and alerts.
  • Dependency management: Track third-party libraries and patch vulnerabilities promptly.

When integrating third-party APIs—especially for sensitive flows like payments or identity—run scenario analyses for failure modes, data consistency, and latency. For AI-driven systems, consider auditability and reproducibility of inputs and outputs to support troubleshooting and model governance.

Build Smarter Crypto Apps & AI Agents with Token Metrics

Token Metrics provides real-time prices, trading signals, and on-chain insights all from one powerful API. Grab a Free API Key

FAQ — What is an API?

Q: What is the simplest way to think about an API?

A: Think of an API as a waiter in a restaurant: it takes a client’s request, communicates with the kitchen (the server), and delivers a structured response. The waiter abstracts the kitchen’s complexity.

FAQ — What types of APIs exist?

Q: Which API styles should I consider for a new project?

A: Common choices are REST for broad compatibility, GraphQL for flexible queries, and gRPC for high-performance microservices. Selection depends on client needs, payload shape, and latency requirements.

FAQ — How do APIs handle authentication?

Q: What authentication methods are typical?

A: Typical methods include API keys for simple access, OAuth2 for delegated access, JWT tokens for stateless auth, and mutual TLS for high-security environments.

FAQ — What are common API security risks?

Q: What should teams monitor to reduce API risk?

A: Monitor for excessive request volumes, suspicious endpoints, unusual payloads, and repeated failed auth attempts. Regularly review access scopes and rotate credentials.

FAQ — How do APIs enable AI integration?

Q: How do AI systems typically use APIs?

A: AI systems use APIs to fetch data for training or inference, send model inputs to inference endpoints, and collect telemetry. Well-documented APIs support reproducible experiments and production deployment.

Disclaimer

This article is for educational and informational purposes only. It does not provide financial, legal, or professional advice. Evaluate third-party services carefully and consider security, compliance, and operational requirements before integration.

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Research

Bullish or Bearish? Interpreting AI Signals in Today’s Volatile Crypto Market

Token Metrics Team
5 min
MIN

Introduction

Crypto moves fast — and traders who can't read the signs get left behind. But in a market where emotions dominate, how do you distinguish between a real trend and a fakeout? That’s where AI-powered trading signals come in.

Token Metrics AI monitors over 6,000 tokens using 80+ data points, from technical momentum to on-chain activity and social sentiment. Its bullish and bearish signals aren’t just flashes of color — they’re actionable, data-driven insights that can guide decisions in chaotic markets.

In this post, we break down how to interpret bullish and bearish signals, what they’ve been saying recently, and how to react when market direction flips suddenly.

What Are Bullish and Bearish Signals?

Let’s start with the basics:

  • Bullish Signal (Green Dot): Indicates that a token is showing signs of an upward trend based on combined technical, sentiment, and on-chain analysis.
  • Bearish Signal (Red Dot): Suggests that a token is losing momentum, and price downside or stagnation is likely.

But these signals aren’t standalone — they come with contextual grades, like the Trader Grade, which ranks signal strength from 0 to 100. This allows you to not just know the direction, but the confidence behind it.

What Happened Recently? The May 30 Flip

On May 30, 2025, Token Metrics AI issued a broad bearish flip across much of the market. That included:

  • Ethereum
  • Bittensor
  • Launchcoin
  • Many Real World Asset and L2 tokens

The AI signal flipped red, and Trader Grades fell across the board. Why? Here's what the AI detected:

  • Slowing volume
  • Negative sentiment shift
  • Liquidity thinning on DEXs
  • On-chain accumulation stalling

This wasn’t panic-driven — it was a data-driven, proactive warning that the cycle had peaked. In a world where most traders rely on lagging indicators or Twitter sentiment, this was an edge.

How to Interpret a Bullish Signal

A bullish signal isn’t an instant “buy” — it's a call to investigate. Here's what to check when a green dot appears:

✅ 1. Trader Grade Above 80

This means high conviction. If it's between 60–79, the trend is forming, but may lack strength.

✅ 2. Volume Confirmation

Price up + volume up = good. Price up + volume flat = caution.

✅ 3. Narrative Alignment

If the token fits a hot theme (like RWAs or AI), that adds strength to the signal.

✅ 4. Recent Price Action

Did the signal appear after a breakout, or just before? Entry timing depends on whether you're catching the beginning or chasing the middle of the trend.

✅ 5. Compare to Peers

If 3–5 similar tokens are also turning bullish, that indicates sector-wide rotation — a better entry environment.

How to Interpret a Bearish Signal

Red doesn’t mean "dump immediately" — it means it's time to tighten your risk.

❗ 1. Trader Grade Below 50

This indicates deteriorating conviction — exit or reduce exposure.

❗ 2. Volume Divergence

If price is flat but volume is fading, that’s a warning of a potential breakdown.

❗ 3. Signal Timing

Did the bearish flip happen near local highs? That’s often the best exit point.

❗ 4. Check for Repeats

Was this the second red dot in a week? That could confirm a longer-term downtrend.

❗ 5. BTC/ETH Context

If Bitcoin or ETH also flip bearish, it may suggest macro pressure, not just token-specific weakness.

Real-Time Examples from the Webinar

During the June 5 Token Metrics webinar, we walked through examples of how these signals worked in real time:

🟢 Bullish (April) – Launchcoin

Strong signal, grade in the 80s. Resulted in a massive short-term run.

🔴 Bearish (May 30) – Ethereum

Signal turned red around $3,490. Traders who followed it avoided the 55% drawdown that followed.

🔴 Bearish (June) – Fartcoin

After a 700% run-up, the signal flipped bearish with a low Trader Grade of ~24. Result? A slow bleed lower as sentiment cooled.

What Makes AI Signals Different from Traditional TA?

Feature                                                    Token Metrics AI                     Traditional TA

Combines social + on-chain                    ✅                                       ❌

Updated in real time                                 ✅                                       ❌

Machine learning trained on past data   ✅                                       ❌

Outputs confidence grade                       ✅                                       ❌

Adapts to new narratives                         ✅                                       ❌

This isn’t about moving averages or MACD — it’s about combining the entire digital footprint of a token to anticipate what comes next.

How to React to a Signal Flip

What do you do when your favorite token suddenly flips from bullish to bearish?

  1. Reduce exposure immediately — even if you don’t sell everything, cut risk.
  2. Check the Grade — if it’s falling, momentum is likely over.
  3. Watch Peer Tokens — if similar projects are also turning red, it confirms sector rotation.
  4. Set New Alerts — if the signal flips back to green, be ready to re-enter.

Your job isn’t to predict the market. It’s to respond to what the data is saying.

How to Combine AI Signals with a Strategy

Here’s a basic framework:

Entry

  • Bullish signal + Trader Grade > 80 = enter with full size.
  • Grade 60–79 = enter small or wait for confirmation.

Exit

  • Bearish signal = scale out or exit.
  • Grade < 50 = no new positions unless for short trades.

Risk

  • Position size scales with grade.
  • Only trade tokens with high liquidity and volume confirmation.

This keeps your system simple, repeatable, and data-driven.

Conclusion

In volatile markets, conviction matters. Token Metrics AI doesn’t just point up or down — it tells you how strong the trend is, how likely it is to last, and when it’s time to pivot.

Don’t trade on emotions. Don’t chase hype. Use the signals — and trust the grade.

Because in a market that never sleeps, it pays to have an AI watching your back.

Research

Crypto Market Cools Off: What Is Token Metrics AI Saying Now

Token Metrics Team
5 min
MIN

Introduction

The euphoria of April and May in the crypto market has officially hit the brakes. While traders were riding high just weeks ago, the mood has shifted — and the data confirms it. Token Metrics’ proprietary AI signals flipped bearish on May 30, and since then, the market has been slowly but steadily declining.

In this post, we break down what’s happened since the bearish signal, how major altcoins and sectors are reacting, and what Token Metrics’ indicators are telling us about what might come next.

The Big Picture: Cooling Off After a Hot Q1 and Q2 Start

The platform’s AI signal turned bearish on May 30 when the total crypto market cap hit $3.34 trillion. Since then, the momentum that defined early 2025 has reversed.

This wasn’t a sudden crash — it’s a slow bleed. The signal shift didn’t come from headline-driven panic, but from data-level exhaustion: volume softening, sentiment stalling, and trend strength fading across most tokens.

Token Metrics AI recognized the shift — and issued the warning.

What the Bearish Signal Means

The AI model analyzes over 80 metrics across price, volume, sentiment, and on-chain data. When key trends across these data sets weaken, the system flips from bullish (green) to bearish (red).

On May 30:

  • Trader Grades across most tokens declined
  • Signal sentiment flipped bearish
  • Momentum and velocity cooled down

According to the model, these were signs of a broad de-risking cycle — not just isolated weakness.

Sectors Showing Declines

Even tokens that had been performing well throughout Q2 began to stall or roll over.

🚨 Launch Coin

Previously one of the top performers in April, Launch Coin saw its grades decrease and price action softened.It may even be rebranding — a typical signal that a project is pivoting after a hype cycle.

🏦 Real World Assets (RWAs)

RWAs were hot in March–May, but by early June, volume and signal quality had cooled off significantly.

🔐 ZK and L2s

Projects like Starknet and zkSync, once dominant in trader attention, have seen signal strength drop, with many now scoring below 70.

The cooling effect is broad, touching narratives, sectors, and high-performing individual tokens alike.

The Bull-Bear Indicator in Action

One of the key tools used by Token Metrics is the Bull vs. Bear Indicator, which aggregates bullish vs. bearish signals across all tokens tracked.

As of early June:

  • The percentage of tokens with bullish signals dropped to its lowest since January.
  • New projects launching with strong grades also saw a decline.
  • Even community-favorite tokens began receiving “exit” alerts.

This isn’t fear — it’s fatigue.

How Traders Are Reacting

During the webinar, we noted that many users who rely on Token Metrics signals began rotating into stables once the May 30 signal flipped. Others reduced leverage, paused entries, or shifted into defensive plays like ETH and BTC.

This reflects an important philosophy:

"When the data changes, we change our approach."

Instead of trying to fight the tape or chase rebounds, disciplined traders are using the bearish signal to protect gains and preserve capital.

What About Ethereum and Bitcoin?

Even ETH and BTC, the two bellwether assets, aren’t immune.

  • Ethereum: Lost momentum after a strong May push. Its Trader Grade is dropping, and the AI signals currently reflect neutral-to-bearish sentiment.
  • Bitcoin: While still holding structure better than altcoins, it has also declined since peaking above $72k. Volume weakening and sentiment falling suggest caution.

In previous cycles, ETH and BTC acted as shelters during altcoin corrections. But now, even the majors show weakness — another reason why the bearish flip matters.

What Could Reverse This?

Abdullah Sarwar, head of research at Token Metrics, mentioned that for the signals to flip back bullish, we would need to see:

  • Increased momentum across top tokens
  • New narratives (e.g., real-world utility, cross-chain demand)
  • Higher volume and liquidity inflows
  • Positive macro or ETF news

Until then, the system will remain in defensive mode — prioritizing safety over chasing trades.

How to Act During a Bearish Signal

The team offered several tips for traders during this cooling-off period:

  1. Reduce exposure
    Don’t hold full positions in assets with weak grades or bearish signals.

  2. Watch signal reversals
    Keep an eye on sudden bullish flips with high Trader Grades — they often mark trend reversals.

  3. Rebalance into safer assets
    BTC, ETH, or even stables allow you to sit on the sidelines while others take unnecessary risk.

  4. Use Token Metrics filters
    Use the platform to filter for:

    • Top tokens with >80 grades
    • Signals that flipped bullish in the last 3 days
    • Low market-cap tokens with strong on-chain activity

These tools help find exceptions in a weak market.

Conclusion: Bearish Doesn’t Mean Broken

Markets cycle — and AI sees it before headlines do.

Token Metrics' bearish signal wasn’t a call to panic. It was a calibrated, data-backed alert that the trend had shifted — and that it was time to switch from offense to defense.

If you’re navigating this new phase, listen to the data. Use the tools. And most importantly, avoid trading emotionally.

The bull market might return. When it does, Token Metrics AI will flip bullish again — and you’ll be ready.

Research

Backtesting Token Metrics AI: Can AI Grades Really Predict Altcoin Breakouts?

Token Metrics Team
5 min
MIN

To test the accuracy of Token Metrics' proprietary AI signals, we conducted a detailed six-month backtest across three different tokens — Fartcoin, Bittensor ($TAO), and Ethereum. Each represents a unique narrative: memecoins, AI infrastructure, and blue-chip Layer 1s. Our goal? To evaluate how well the AI’s bullish and bearish signals timed market trends and price action.

Fartcoin:

The green and red dots on the following Fartcoin price chart represent the bullish and bearish market signals, respectively. Since Nov 26, 2024, Token Metrics AI has given 4 trading signals for Fartcoin. Let’s analyze each signal separately.

The Fartcoin chart above displays green and red dots that mark bullish and bearish signals from the Token Metrics AI, respectively. Over the last six months — starting November 26, 2024 — our system produced four significant trade signals for Fartcoin. Let’s evaluate them one by one.

The first major signal was bullish on November 26, 2024, when Fartcoin was trading at $0.29. This signal preceded a massive run-up, with the price topping out at $2.49. That’s an astounding 758% gain — all captured within just under two months. It’s one of the most powerful validations of the AI model’s ability to anticipate momentum early.

Following that rally, a bearish signal was triggered on January 26, 2025, just before the market corrected. Fartcoin retraced sharply, plunging 74.76% from the highs. Traders who acted on this bearish alert could have avoided substantial drawdowns — or even profited through short-side exposure.

On March 25, 2025, the AI turned bullish again, as Fartcoin traded near $0.53. Over the next several weeks, the token surged to $1.58, a 198% rally. Again, the AI proved its ability to detect upward momentum early.

Most recently, on June 1, 2025, Token Metrics AI flipped bearish once again. The current Trader Grade of 24.34 reinforces this view. For now, the system warns of weakness in the memecoin market — a trend that appears to be playing out in real-time.

Across all four trades, the AI captured both the explosive upside and protected traders from steep corrections — a rare feat in the volatile world of meme tokens.

Bittensor

Next, we examine Bittensor, the native asset of the decentralized AI Layer 1 network. Over the last six months, Token Metrics AI produced five key signals — and the results were a mixed bag but still largely insightful.

In December 2024, the AI turned bearish around $510, which preceded a sharp decline to $314 by February — a 38.4% drawdown. This alert helped traders sidestep a brutal correction during a high-volatility period.

On February 21, 2025, the system flipped bullish, but this trade didn't play out as expected. The price dropped 25.4% after the signal. Interestingly, the AI reversed again with a bearish signal just five days later, showing how fast sentiment and momentum can shift in emerging narratives like AI tokens.

The third signal marked a solid win: Bittensor dropped from $327 to $182.9 following the bearish call — another 44% drop captured in advance.

In April 2025, momentum returned. The AI issued a bullish alert on April 19, with TAO at $281. By the end of May, the token had rallied to over $474, resulting in a 68.6% gain — one of the best performing bullish signals in the dataset.

On June 4, the latest red dot (bearish) appeared. The model anticipates another downward move — time will tell if it materializes, but the track record suggests caution is warranted.

Ethereum

Finally, we analyze the AI’s predictive power for Ethereum, the second-largest crypto by market cap. Over the six-month window, Token Metrics AI made three major calls — and each one captured critical pivots in ETH’s price.

On November 7, 2024, a green dot (bullish) appeared when ETH was priced at $2,880. The price then surged to $4,030 in less than 40 days, marking a 40% gain. For ETH, such a move is substantial and was well-timed.

By December 24, the AI flipped bearish with ETH trading at $3,490. This signal was perhaps the most important, as it came ahead of a major downturn. ETH eventually bottomed out near $1,540 in April 2025, avoiding a 55.8% drawdown for those who acted on the signal.

In May 2025, the AI signaled another bullish trend with ETH around $1,850. Since then, the asset rallied to $2,800, creating a 51% gain.

These three trades — two bullish and one bearish — show the AI’s potential in navigating large-cap assets during both hype cycles and corrections.Backtesting Token Metrics AI across memecoins, AI narratives, and Ethereum shows consistent results: early identification of breakouts, timely exit signals, and minimized risk exposure. While no model is perfect, the six-month history reveals a tool capable of delivering real value — especially when used alongside sound risk management.

Whether you’re a trader looking to time the next big altcoin rally or an investor managing downside in turbulent markets, Token Metrics AI signals — available via the fastest crypto API — offer a powerful edge.

Backtesting Token Metrics AI across memecoins, AI narratives, and Ethereum shows consistent results: early identification of breakouts, timely exit signals, and minimized risk exposure. While no model is perfect, the six-month history reveals a tool capable of delivering real value — especially when used alongside sound risk management.

Whether you’re a trader looking to time the next big altcoin rally or an investor managing downside in turbulent markets, Token Metrics AI signals — available via the fastest crypto API — offer a powerful edge.

Research

Token Metrics API vs. CoinGecko API: Which Crypto API Should You Choose in 2025?

Token Metrics Team
7 min
MIN

As the crypto ecosystem rapidly matures, developers, quant traders, and crypto-native startups are relying more than ever on high-quality APIs to build data-powered applications. Whether you're crafting a trading bot, developing a crypto research platform, or launching a GPT agent for market analysis, choosing the right API is critical.

Two names dominate the space in 2025: CoinGecko and Token Metrics. But while both offer access to market data, they serve fundamentally different purposes. CoinGecko is a trusted source for market-wide token listings and exchange metadata. Token Metrics, on the other hand, delivers AI-powered intelligence for predictive analytics and decision-making.

Let’s break down how they compare—and why the Token Metrics API is the superior choice for advanced, insight-driven builders.

🧠 AI Intelligence: Token Metrics Leads the Pack

At the core of Token Metrics is machine learning and natural language processing. It’s not just a data feed. It’s an AI that interprets the market.

Features exclusive to Token Metrics API:

  • Trader Grade (0–100) – Short-term momentum score based on volume, volatility, and technicals
  • Investor Grade (0–100) – Long-term asset quality score using fundamentals, community metrics, liquidity, and funding
  • Bullish/Bearish AI Signals – Real-time alerts based on over 80 weighted indicators
  • Sector-Based Smart Indices – Curated index sets grouped by theme (AI, DeFi, Gaming, RWA, etc.)
  • Sentiment Scores – Derived from social and news data using NLP
  • LLM-Friendly AI Reports – Structured, API-returned GPT summaries per token
  • Conversational Agent Access – GPT-based assistant that queries the API using natural language

In contrast, CoinGecko is primarily a token and exchange aggregator. It offers static data: price, volume, market cap, supply, etc. It’s incredibly useful for basic info—but it lacks context or predictive modeling.

Winner: Token Metrics — The only crypto API built for AI-native applications and intelligent automation.

🔍 Data Depth & Coverage

While CoinGecko covers more tokens and more exchanges, Token Metrics focuses on providing actionable insights rather than exhaustively listing everything.

Feature                                                   Token Metrics API                                    CoinGecko API

Real-time + historical OHLCV              ✅                                                          ✅

Trader/Investor Grades                        ✅ AI-powered                                     ❌

Exchange Aggregation                         ✅ (Used in indices, not exposed)     ✅

Sentiment & Social Scoring                  ✅ NLP-driven                                     ❌

AI Signals                                               ✅                                                         ❌

Token Fundamentals                            ✅ Summary via deepdive         ⚠️ Limited

                                                                        endpoint

NFT Market Data                                  ❌                                                          ✅

On-Chain Behavior                               ✅ Signals + Indices                   ⚠️ Pro-only (limited)

If you're building something analytics-heavy—especially trading or AI-driven—Token Metrics gives you depth, not just breadth.

Verdict: CoinGecko wins on broad metadata coverage. Token Metrics wins on intelligence and strategic utility.

🛠 Developer Experience

One of the biggest barriers in Web3 is getting devs from “idea” to “prototype” without friction. Token Metrics makes that easy.

Token Metrics API Includes:

  • SDKs for Python, Node.js, and Postman
  • Quick-start guides and GitHub sample projects
  • Integrated usage dashboard to track limits and history
  • Conversational agent to explore data interactively
  • Clear, logical endpoint structure across 21 data types

CoinGecko:

  • Simple REST API
  • JSON responses
  • Minimal docs
  • No SDKs
  • No built-in tooling (must build from scratch)

Winner: Token Metrics — Serious devs save hours with ready-to-go SDKs and utilities.

📊 Monitoring, Quotas & Support

CoinGecko Free Tier:

  • 10–30 requests/min
  • No API key needed
  • Public endpoints
  • No email support
  • Rate limiting enforced via IP

Token Metrics Free Tier:

  • 5,000 requests/month
  • 1 request/min
  • Full access to AI signals, grades, rankings
  • Telegram & email support
  • Upgrade paths to 20K–500K requests/month

While CoinGecko’s no-login access is beginner-friendly, Token Metrics offers far more power per call. With just a few queries, your app can determine which tokens are gaining momentum, which are losing steam, and how portfolios should be adjusted.

Winner: Token Metrics — Better for sustained usage, scaling, and production reliability.

💸 Pricing & Value

Plan Feature                CoinGecko Pro            Token Metrics API

Entry Price                  ~$150/month                $99/month

AI Grades & Signals            ❌                               ✅ 

Sentiment Analytics            ❌                               ✅

Sector Index Insights          ❌                               ✅

NLP Token Summaries       ❌                                ✅

Developer SDKs                  ❌                                ✅

Token-Based Discounts     ❌                                ✅ (up to 35% with $TMAI)

For what you pay, Token Metrics delivers quant models and intelligent signal streams — not just raw price.

Winner: Token Metrics — Cheaper entry, deeper value.

🧠 Use Cases Where Token Metrics API Shines

  • Trading Bots
    Use Trader Grade and Signal endpoints to enter/exit based on AI triggers.
  • GPT Agents
    Generate conversational answers for “What’s the best AI token this week?” using structured summaries.
  • Crypto Dashboards
    Power sortable, filtered token tables by grade, signal, or narrative.
  • Portfolio Rebalancers
    Track real-time signals for tokens held, flag risk zones, and show sector exposure.
  • LLM Plugins
    Build chat-based investment tools with explainability and score-based logic.

🧠 Final Verdict: CoinGecko for Info, Token Metrics for Intelligence

If you're building a crypto price tracker, NFT aggregator, or exchange overview site, CoinGecko is a solid foundation. It’s reliable, broad, and easy to get started.

But if your product needs to think, adapt, or help users make better decisions, then Token Metrics API is in another class entirely.

You're not just accessing data — you're integrating AI, machine learning, and predictive analytics into your app. That’s the difference between showing the market and understanding it.

🔗 Ready to Build Smarter?

  • ✅ 5,000 free API calls/month
  • 🤖 Trader & Investor Grades
  • 📊 Live Bull/Bear signals
  • 🧠 AI-powered summaries and GPT compatibility
  • ⚡ 21 endpoints + Python/JS SDKs

👉 Start with Token Metrics API

Research

Python Quick-Start with Token Metrics: The Ultimate Crypto Price API

Token Metrics Team
10 min
MIN

If you’re a Python developer looking to build smarter crypto apps, bots, or dashboards, you need two things: reliable data and AI-powered insights. The Token Metrics API gives you both. In this tutorial, we’ll show you how to quickly get started using Token Metrics as your Python crypto price API, including how to authenticate, install the SDK, and run your first request in minutes.

Whether you’re pulling live market data, integrating Trader Grades into your trading strategy, or backtesting with OHLCV data, this guide has you covered.

🚀 Quick Setup for Developers in a Hurry

Install the official Token Metrics Python SDK:

pip install tokenmetrics

Or if you prefer working with requests directly, no problem. We’ll show both methods below.

🔑 Step 1: Generate Your API Key

Before anything else, you’ll need a Token Metrics account.

  1. Go to app.tokenmetrics.com/en/api
  2. Log in and navigate to the API Keys Dashboard
  3. Click Generate API Key
  4. Name your key (e.g., “Development”, “Production”)
  5. Copy it immediately — keep it secret.

You can monitor usage, rate limits, and quotas right from the dashboard. Track each key’s status, last used date, and revoke access at any time.

📈 Step 2: Retrieve Crypto Prices in Python

Here’s a simple example to fetch the latest price data for Ethereum (ETH):

import requests

API_KEY = "YOUR_API_KEY"

headers = {"x-api-key": API_KEY}

url = "https://api.tokenmetrics.com/v2/daily-ohlcv?symbol=ETH&startDate=<YYYY-MM-DD>&endDate=<YYYY-MM-DD>"

response = requests.get(url, headers=headers)

data = response.json()

for candle in data['data']:

    print(f"Date: {candle['DATE']} | Close: ${candle['CLOSE']}")

You now have a working python crypto price API pipeline. Customize startDate or endDate to get specific range of historical data.

📊 Add AI-Powered Trader Grades

Token Metrics’ secret sauce is its AI-driven token ratings. Here’s how to access Trader Grades for ETH:

grade_url = "https://api.tokenmetrics.com/v2/trader-grades?symbol=ETH&limit=30d"

grades = requests.get(grade_url, headers=headers).json()['data']

for day in grades:

    print(f"{day['DATE']} — Trader Grade: {day['TA_GRADE']}")

Use this data to automate trading logic (e.g., enter trades when Grade > 85) or overlay on charts.

🔁 Combine Data for Backtesting

Want to test a strategy? Merge OHLCV and Trader Grades for any token:

import pandas as pd

ohlcv_df = pd.DataFrame(data['data'])

grades_df = pd.DataFrame(grades)

combined_df = pd.merge(ohlcv_df, grades_df, on="DATE")

print(combined_df.head())

Now you can run simulations, build analytics dashboards, or train your own models.

⚙️ Endpoint Coverage for Python Devs

  • /daily-ohlcv: Historical price data
  • /trader-grades: AI signal grades (0–100)
  • /trading-signals: Bullish/Bearish signals for short and long positions.
  • /sentiment: AI-modeled sentiment scores
  • /tmai: Ask questions in plain English

All endpoints return structured JSON and can be queried via requests, axios, or any modern client.

🧠 Developer Tips

  • Each request = 1 credit (tracked in real time)
  • Rate limits depend on your plan (Free = 1 req/min)
  • Use the API Usage Dashboard to monitor and optimize
  • Free plan = 5,000 calls/month — perfect for testing and building MVPs

💸 Bonus: Save 35% with $TMAI

You can reduce your API bill by up to 35% by staking and paying with Token Metrics’ native token, $TMAI. Available via the settings → payments page.

🌐 Final Thoughts

If you're searching for the best python crypto price API with more than just price data, Token Metrics is the ultimate choice. It combines market data with proprietary AI intelligence, trader/investor grades, sentiment scores, and backtest-ready endpoints—all in one platform.

✅ Real-time & historical data
✅ RESTful endpoints
✅ Python-ready SDKs and docs
✅ Free plan to start building today

Start building today → tokenmetrics.com/api

Looking for SDK docs? Explore the full Python Quick Start Guide

Research

Crypto API to Google Sheets in 5 Minutes: How to Use Token Metrics API with Apps Script

Token Metrics Team
6 min
MIN

If you're a trader, data analyst, or crypto enthusiast, chances are you've wanted to pull live crypto data directly into Google Sheets. Whether you're tracking prices, building custom dashboards, or backtesting strategies, having real-time data at your fingertips can give you an edge.

In this guide, we'll show you how to integrate the Token Metrics API — a powerful crypto API with free access to AI-powered signals — directly into Google Sheets in under 5 minutes using Google Apps Script.

📌 Why Use Google Sheets for Crypto Data?

Google Sheets is a flexible, cloud-based spreadsheet that:

  • Requires no coding to visualize data
  • Can be shared and updated in real time
  • Offers formulas, charts, and conditional formatting
  • Supports live API connections with Apps Script

When combined with the Token Metrics API, it becomes a powerful dashboard that updates live with Trader Grades, Bull/Bear Signals, historical OHLCV data, and more.

🚀 What Is Token Metrics API?

The Token Metrics API provides real-time and historical crypto data powered by AI. It includes:

  • Trader Grade: A score from 0 to 100 showing bullish/bearish potential
  • Bull/Bear Signal: A binary signal showing market direction
  • OHLCV: Open-High-Low-Close-Volume price history
  • Token Metadata: Symbol, name, category, market cap, and more

The best part? The free Basic Plan includes:

  • 5,000 API calls/month
  • Access to core endpoints
  • Hourly data refresh
  • No credit card required

👉 Sign up for free here

🛠️ What You’ll Need

  • A free Token Metrics API key
  • A Google account
  • Basic familiarity with Google Sheets

⚙️ How to Connect Token Metrics API to Google Sheets

Here’s how to get live AI-powered crypto data into Sheets using Google Apps Script.

🔑 Step 1: Generate Your API Key

  1. Visit: https://app.tokenmetrics.com/en/api
  2. Click “Generate API Key”
  3. Copy it — you’ll use this in the script

📄 Step 2: Create a New Google Sheet

  1. Go to Google Sheets
  2. Create a new spreadsheet
  3. Click Extensions > Apps Script

💻 Step 3: Paste This Apps Script

const TOKEN_METRICS_API_KEY = 'YOUR_API_KEY_HERE';

async function getTraderGrade(symbol) {

  const url = `https://api.tokenmetrics.com/v2/trader-grades?symbol=${symbol.toUpperCase()}`;

  const options = {

    method: 'GET',

    contentType: 'application/json',

    headers: {

      'accept': 'application/json',

      'x-api-key': TOKEN_METRICS_API_KEY,

    },

    muteHttpExceptions: true

  };

  

  const response = UrlFetchApp.fetch(url, options);

  const data = JSON.parse(response.getContentText() || "{}")

  

  if (data.success && data.data.length) {

    const coin = data.data[0];

    return [

      coin.TOKEN_NAME,

      coin.TOKEN_SYMBOL,

      coin.TA_GRADE,

      coin.DATE

    ];

  } else {

    return ['No data', '-', '-', '-'];

  }

}

async function getSheetData() {

  const sheet = SpreadsheetApp.getActiveSpreadsheet().getActiveSheet();

  const symbols = sheet.getRange('A2:A').getValues().flat().filter(Boolean);

  const results = [];

  results.push(['Name', 'Symbol', 'Trader Grade', 'Date']);

  for (const symbol of symbols) {

    if (symbol) {

      const row = await getTraderGrade(symbol);

      results.push(row);

    }

  }

  sheet.getRange(2, 2, results.length, results[0].length).setValues(results);

}

🧪 Step 4: Run the Script

  1. Replace 'YOUR_API_KEY_HERE' with your real API key.
  2. Save the project as TokenMetricsCryptoAPI.
  3. In your sheet, enter a list of symbols (e.g., BTC, ETH, SOL) in Column A.
  4. Go to the script editor and run getSheetData() from the dropdown menu.

Note: The first time, Google will ask for permission to access the script.

✅ Step 5: View Your Live Data

After the script runs, you’ll see:

  • Coin name and symbol
  • Trader Grade (0–100)
  • Timestamp

You can now:

  • Sort by Trader Grade
  • Add charts and pivot tables
  • Schedule automatic updates with triggers (e.g., every hour)

🧠 Why Token Metrics API Is Ideal for Google Sheets Users

Unlike basic price APIs, Token Metrics offers AI-driven metrics that help you:

  • Anticipate price action before it happens
  • Build signal-based dashboards or alerts
  • Validate strategies against historical signals
  • Keep your data fresh with hourly updates

And all of this starts for free.

🏗️ Next Steps: Expand Your Sheet

Here’s what else you can build:

  • A portfolio tracker that pulls your top coins’ grades
  • A sentiment dashboard using historical OHLCV
  • A custom screener that filters coins by Trader Grade > 80
  • A Telegram alert system triggered by Sheets + Apps Script + Webhooks

You can also upgrade to the Advanced Plan to unlock 21 endpoints including:

  • Investor Grades
  • Smart Indices
  • Sentiment Metrics
  • Quantitative AI reports
  • 60x API speed

🔐 Security Tip

Never share your API key in a public Google Sheet. Use script-level access and keep the sheet private unless required.

🧩 How-To Schema Markup (for SEO)

{

  "@context": "https://schema.org",

  "@type": "HowTo",

  "name": "Crypto API to Google Sheets in 5 Minutes",

  "description": "Learn how to connect the Token Metrics crypto API to Google Sheets using Google Apps Script and get real-time AI-powered signals and prices.",

  "totalTime": "PT5M",

  "supply": [

    {

      "@type": "HowToSupply",

      "name": "Google Sheets"

    },

    {

      "@type": "HowToSupply",

      "name": "Token Metrics API Key"

    }

  ],

  "tool": [

    {

      "@type": "HowToTool",

      "name": "Google Apps Script"

    }

  ],

  "step": [

    {

      "@type": "HowToStep",

      "name": "Get Your API Key",

      "text": "Sign up at Token Metrics and generate your API key from the API dashboard."

    },

    {

      "@type": "HowToStep",

      "name": "Create a New Google Sheet",

      "text": "Open a new sheet and list crypto symbols in column A."

    },

    {

      "@type": "HowToStep",

      "name": "Add Apps Script",

      "text": "Go to Extensions > Apps Script and paste the provided code, replacing your API key."

    },

    {

      "@type": "HowToStep",

      "name": "Run the Script",

      "text": "Execute the getSheetData function to pull data into the sheet."

    }

  ]

}

✍️ Final Thoughts

If you're serious about crypto trading or app development, integrating live market signals into your workflow can be a game-changer. With the Token Metrics API, you can get institutional-grade AI signals — right inside Google Sheets.

This setup is simple, fast, and completely free to start. Try it today and unlock a smarter way to trade and build in crypto.

👉 Get Your API Key & Start for Free

Announcements

🚀Put Your $TMAI to Work: Daily Rewards, No Locks, Up To 200% APR.

Token Metrics Team
5 min
MIN

Liquidity farming just got a major upgrade. Token Metrics AI ($TMAI) has launched its first liquidity incentive campaign on Merk — and it’s designed for yield hunters looking to earn fast, with no lockups, no gimmicks, and real rewards from Day 1.

📅 Campaign Details

  • Duration: June 5 – June 19, 2025
  • Rewards Begin: 17:00 UTC / 1:00 PM ET
  • Total TMAI Committed: 38 million+ $TMAI
  • No Lockups: Enter or exit at any time
  • APR Potential: Up to 200%

For two weeks, liquidity providers can earn high daily rewards across three different pools. All rewards are paid in $TMAI and distributed continuously — block by block — through the Merkl platform.

💧 Where to Earn – The Pools (as of June 5, 17:00 UTC)

Pool                                                    Starting APR %               Total Rewards (14 days)                Current TVL

Aerodrome WETH–TMAI        150%                                16.79M TMAI (~$11,000)                   $86,400

Uniswap v3 USDC–TMAI        200%                                14.92M TMAI (~$9,800)                    $19,900

Balancer 95/5 WETH–TMAI    200%                                5.60M TMAI (~$3,700)                       $9,500

These pools are live and actively paying rewards. APR rates aren’t displayed on Merkl until the first 24 hours of data are available — but early providers will already be earning.

🧠 Why This Campaign Stands Out

1. Turbo Rewards for a Short Time

This isn’t a slow-drip farm. The TMAI Merkl campaign is designed to reward action-takers. For the first few days, yields are especially high — thanks to low TVL and full daily reward distribution.

2. No Lockups or Waiting Periods

You can provide liquidity and withdraw it anytime — even the same day. There are no lockups, no vesting, and no delayed payout mechanics. All rewards accrue automatically and are claimable through Merkl.

3. Choose Your Risk Profile

You get to pick your exposure.

  • Want ETH upside? Stake in Aerodrome or Balancer.
  • Prefer stablecoin stability? Go with the Uniswap v3 USDC–TMAI pool.

4. Influence the Future of TMAI Yield Farming

This campaign isn’t just about yield — it’s a test. If enough users participate and volume grows, the Token Metrics Treasury will consider extending liquidity rewards into Q3 and beyond. That means more TMAI emissions, longer timelines, and consistent passive income opportunities for LPs.

5. Built for Transparency and Speed

Rewards are distributed via Merkl by Angle Labs, a transparent, gas-efficient platform for programmable liquidity mining. You can see the exact rewards, TVL, wallet counts, and pool analytics at any time.

🔧 How to Get Started

Getting started is simple. You only need a crypto wallet, some $TMAI, and a matching asset (either WETH or USDC, depending on the pool).

Step-by-step:

  1. Pick a pool:
    Choose from Aerodrome, Uniswap v3, or Balancer depending on your risk appetite and asset preference.

  2. Provide liquidity:
    Head to the Merkl link for your pool, deposit both assets, and your position is live immediately.

  3. Track your earnings:
    Watch TMAI accumulate daily in your Merkl dashboard. You can claim rewards at any time.

  4. Withdraw when you want:
    Since there are no lockups, you can remove your liquidity whenever you choose — rewards stop the moment liquidity is pulled.

🎯 Final Thoughts

This is a rare opportunity to earn serious rewards in a short amount of time. Whether you’re new to liquidity mining or a DeFi veteran, the TMAI Merkl campaign is built for speed, flexibility, and transparency.

You’re still early. The best yields happen in the first days, before TVL rises and APR stabilizes. Dive in now and maximize your returns while the turbo phase is still on.

👉 Join the Pools and Start Earning

Announcements

Token Metrics API Joins RapidAPI: The Fastest Way to Add AI-Grade Crypto Data to Your App

Token Metrics Team
5 min
MIN

The hunt for a dependable Crypto API normally ends in a graveyard of half-maintained GitHub repos, flaky RPC endpoints, and expensive enterprise feeds that hide the true cost behind a sales call. Developers waste days wiring those sources together, only to learn that one fails during a market spike or that data schemas never quite align. The result? Bots mis-fire, dashboards drift out of sync, and growth stalls while engineers chase yet another “price feed.”

That headache stops today. Token Metrics API, the same engine that powers more than 70 000 users on the Token Metrics analytics platform, is now live on RapidAPI—the largest marketplace of public APIs with more than four million developers. One search, one click, and you get an AI-grade Crypto API with institutional reliability and a 99.99 % uptime SLA.

Why RapidAPI + Token Metrics API Matters

  • Native developer workflow – No separate billing portal, OAuth flow, or SDK hunt. Click “Subscribe,” pick the Free plan, and RapidAPI instantly generates a key.

  • Single playground – Run test calls in-browser and copy snippets in cURL, Python, Node, Go, or Rust without leaving the listing.

  • Auto-scale billing – When usage grows, RapidAPI handles metering and invoices. You focus on product, not procurement.

What Makes the Token Metrics Crypto API Different?

  1. Twenty-one production endpoints

    Live & historical prices, hourly and daily OHLCV, proprietary Trader & Investor Grades, on-chain and social sentiment, AI-curated sector indices, plus deep-dive AI reports that summarise fundamentals, code health, and tokenomics.

  2. AI signals that win

    Over the last 24 months, more than 70 % of our bull/bear signals outperformed simple buy-and-hold. The API delivers that same alpha in flat JSON.

  3. Institutional reliability

    99.99 % uptime, public status page, and automatic caching for hot endpoints keep latency low even on volatile days.

Three-Step Quick Start

  1. Search “Token Metrics API” on RapidAPI and click Subscribe.
  2. Select the Free plan (5 000 calls / month, 20 request / min) and copy your key.
  3. Test:

bash

CopyEdit

curl -H "X-RapidAPI-Key: YOUR_KEY" \

     -H "X-RapidAPI-Host: tokenmetrics.p.rapidapi.com" \

     https://tokenmetrics.p.rapidapi.com/v2/trader-grades?symbol=BTC

The response returns Bitcoin’s live Trader Grade (0-100) and bull/bear flag. Swap BTC for any asset or explore /indices, /sentiment, and /ai-reports.

Real-World Use Cases

Use case

How developers apply the Token Metrics API

Automated trading bots

Rotate allocations when Trader Grade > 85 or sentiment flips bear.

Portfolio dashboards

Pull index weights, grades, and live prices in a single call for instant UI load.

Research terminals

Inject AI Reports into Notion/Airtable for analyst workflows.

No-code apps

Combine Zapier webhooks with RapidAPI to display live sentiment without code.

Early adopters report 30 % faster build times because they no longer reconcile five data feeds.

Pricing That Scales

  • Free – 5 000 calls, 30-day history.
  • Advanced – 20 000 calls, 3-month history.
  • Premium – 100 000 calls, 3-year history.
  • VIP – 500 000 calls, unlimited history.

Overages start at $0.005 per call.

Ready to Build?

• RapidAPI listing: https://rapidapi.com/tm-ai/api/token-metrics 

https://rapidapi.com/token-metrics-token-metrics-default/api/token-metrics-api1
• Developer docs: https://developers.tokenmetrics.com
• Support Slack: https://join.slack.com/t/tokenmetrics-devs/shared_invite/…

Spin up your key, ship your bot, and let us know what you create—top projects earn API credits and a Twitter shout-out.

Announcements

Crypto MCP Server: Token Metrics Brings One-Key Data to OpenAI, Claude, Cursor & Windsurf

Token Metrics Team
5 min
MIN

The modern crypto stack is a jungle of AI agents: IDE copilots that finish code, desktop assistants that summarise white-papers, CLI tools that back-test strategies, and slide generators that turn metrics into pitch decks. Each tool speaks a different protocol, so developers juggle multiple keys and mismatched JSON every time they query a Crypto API. That fragmentation slows innovation and creates silent data drift.

To fix it, we built the Token Metrics Crypto MCP Server—a lightweight gateway that unifies every tool around a single Multi-Client Crypto API. MCP (Multi-Client Protocol) sits in front of the Token Metrics API and translates requests into one canonical schema. Paste your key once, and a growing suite of clients speaks the same crypto language:

  • OpenAI Agents SDK – build ChatGPT-style agents with live grades
  • Claude Desktop – natural-language research powered by real-time metrics
  • Cursor / Windsurf IDE – in-editor instant queries
  • Raycast, Tome, VS Code, Cline and more

Why a Crypto MCP Server Beats Separate APIs

Consistency – Claude’s grade equals Windsurf’s grade.
One-time auth – store one key; clients handle headers automatically.
Faster prototyping – build in Cursor, test in Windsurf, present in Tome without rewriting queries.
Lower cost – shared quota plus $TMAI discount across all tools.

Getting Started

  1. Sign up for the Free plan (5 000 calls/month) and get your key: https://app.tokenmetrics.com/en/api
  2. Click the client you want to setup mcp for: smithery.ai/server/@token-metrics/mcp or https://modelcontextprotocol.io/clients

Your LLM assistant, IDE, CLI, and slide deck now share a single, reliable crypto brain. Copy your key, point to MCP, and start building the next generation of autonomous finance.

How Teams Use the Multi-Client Crypto API

  • Research to Execution – Analysts ask Claude for “Top 5 DeFi tokens with improving Trader Grades.” Cursor fetches code snippets; Windsurf trades the shortlist—all on identical data.
  • DevRel Demos – Share a single GitHub repo with instructions for Cursor, VS Code, and CLI; workshop attendees choose their favorite environment and still hit the same endpoints.
  • Compliance Dashboards – Tome auto-refreshes index allocations every morning, ensuring slide decks stay current without manual updates

Pricing, Rate Limits, and $TMAI

The Crypto MCP Server follows the core Token Metrics API plans: Free, Advanced, Premium, and VIP up to 500 000 calls/month and 600 req/min. Paying or staking $TMAI applies the familiar 10 % pay-in bonus plus up to 25 % staking rebate—35 % total savings. No new SKU, no hidden fee.

Build Once, Query Everywhere

The Token Metrics Crypto MCP Server turns seven scattered tools into one cohesive development environment. Your LLM assistant, IDE, CLI, and slideshow app now read from the same real-time ledger. Copy your key, point to MCP, and start building the next generation of autonomous finance.

• Github repo: https://github.com/token-metrics/mcp

👉 Ready to build? Grab your key from https://app.tokenmetrics.com/en/api

👉 Join Token Metrics API Telegram group  

Step-by-step client guides at smithery.ai/server/@token-metrics/mcp or https://modelcontextprotocol.io/clients — everything you need to wire Token Metrics MCP into Open AI, Claude, Cursor, Windsurf and more.

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