Research

REST API Explained: Design, Security & Best Practices

Learn REST API fundamentals, secure design patterns, observability, and tooling. Practical guidance for building scalable, maintainable REST APIs and integrating with AI and services today for teams.
Token Metrics Team
4
MIN

REST APIs are the connective tissue of modern web and mobile applications. Whether you're integrating services, building microservices, or exposing data for AI agents, a clear grasp of REST API principles helps you design interfaces that are maintainable, performant, and secure. This guide walks through the core concepts, practical design patterns, authentication and security considerations, and tooling that make REST APIs reliable in production.

What is a REST API and core principles

REST (Representational State Transfer) is an architectural style that uses standard HTTP verbs and status codes to manipulate resources. Key tenets include:

  • Statelessness: Each request contains all information needed to process it; servers don’t maintain client session state.
  • Resources and representations: Resources are identified by URIs; responses return representations (JSON, XML) describing resource state.
  • Uniform interface: Use predictable HTTP methods (GET, POST, PUT, DELETE, PATCH) and status codes for consistent client-server interaction.
  • Layered system: Clients need not be aware of whether they communicate with the origin server or an intermediary.

Understanding these principles helps when choosing between REST, GraphQL, or RPC for a given use case. REST is well-suited for CRUD-style operations, caching, and wide compatibility with HTTP tooling.

Design patterns: resources, versioning, and idempotency

Good API design starts with modeling resources and their relationships. Practical patterns include:

  • Resource naming: Use plural nouns and hierarchical paths (e.g., /users/{userId}/orders).
  • Versioning: Use URL or header-based versioning (e.g., /v1/ or Accept header) to avoid breaking clients.
  • Idempotency: Ensure methods like PUT and DELETE can be retried safely; supply idempotency keys for POST when necessary.
  • Pagination and filtering: Provide cursor-based or offset-based pagination, with clear metadata for total counts and next cursors.

Design with backward compatibility in mind: deprecate endpoints with clear timelines, and prefer additive changes over breaking ones.

Authentication, authorization, and security considerations

Security is non-negotiable. Common, interoperable mechanisms include:

  • API keys: Simple and useful for identifying applications, but pair with TLS and usage restrictions.
  • OAuth 2.0: Industry-standard for delegated authorization in user-centric flows; combine with short-lived tokens and refresh tokens.
  • JWTs: JSON Web Tokens are compact bearer tokens useful for stateless auth; validate signatures and expiration, and avoid storing sensitive data in payloads.
  • Transport security: Enforce TLS (HTTPS) everywhere and use HSTS policies; mitigate mixed-content risks.
  • Rate limiting & throttling: Protect backends from abuse and accidental spikes; return clear headers that expose remaining quota and reset times.

Also consider CORS policies, input validation, and strict output encoding to reduce injection risks. Implement principle of least privilege for every endpoint and role.

Performance, observability, and tooling

Operational maturity requires monitoring and testing across the lifecycle. Focus on these areas:

  • Caching: Use HTTP cache headers (Cache-Control, ETag) and CDN fronting for public resources to reduce latency and load.
  • Instrumentation: Emit structured logs, request traces (OpenTelemetry), and metrics (latency, error rate, throughput) to diagnose issues quickly.
  • API specifications: Define schemas with OpenAPI/Swagger to enable client generation, validation, and interactive docs.
  • Testing: Automate contract tests, integration tests, and fuzzing for edge cases; run load tests to establish scaling limits.
  • Developer experience: Provide SDKs, clear examples, and consistent error messages to accelerate integration and reduce support overhead.

Tooling choices—Postman, Insomnia, Swagger UI, or automated CI checks—help maintain quality as the API evolves. For AI-driven integrations, exposing well-documented JSON schemas and stable endpoints is critical.

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What is REST and when should I choose it?

REST is ideal for resource-oriented services where standard HTTP semantics are beneficial. Choose REST when caching, simplicity, wide client compatibility, and predictable CRUD semantics are priorities. For highly dynamic queries, consider GraphQL as a complement rather than a replacement.

How do I manage breaking changes?

Version endpoints, use feature flags, and publish changelogs with migration guides. Prefer additive changes (new fields, new endpoints) and give clients time to migrate before removing legacy behavior.

What authentication method should I implement?

Match the method to the use case: API keys for server-to-server integrations, OAuth 2.0 for delegated user access, and JWTs for stateless session claims. Always layer these with TLS and short token lifetimes.

How should I handle rate limits and abuse?

Enforce per-key and per-IP limits, surface quota headers, and provide graceful 429 responses with a Retry-After header. Use adaptive throttling to protect critical downstream systems.

Which tools help maintain a healthy API lifecycle?

Adopt OpenAPI for specs, use Postman or Swagger UI for exploratory testing, integrate contract tests into CI, and deploy observability stacks (Prometheus, Grafana, OpenTelemetry) to monitor behavior in production.

Disclaimer

This article is for educational and technical guidance only. It does not constitute legal, security, or operational advice. Evaluate risks and compliance requirements against your own environment before implementing changes.

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Token Metrics Team
Token Metrics Team

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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

Choose from Platinum, Gold, and Silver packages
Reach with 25–30% open rates and 0.5–1% CTR
Craft your own custom ad—from banners to tailored copy
Perfect for Crypto Exchanges, SaaS Tools, DeFi, and AI Products