Crypto Basics

Cryptocurrency Analysis - How to Analyze Cryptocurrencies

Learn everything about crypto analysis and how to analyze cryptocurrencies, in this descriptive guide.
Token Metrics Team
9 minutes
MIN

Crypto has indeed gained significant attention and has been a popular investment option in recent times. However, analyzing a cryptocurrency before investing is crucial to make informed decisions and minimize risks. In this article, we will help you understand the key factors that impact a cryptocurrency's price and demand. By doing so, we aim to make it easier for you to embark on your crypto journey with confidence.

But why is it essential to analyze a cryptocurrency before investing in the first place? The answer lies in the volatile nature of cryptocurrencies. The value of a cryptocurrency can fluctuate significantly within a short time, making it a high-risk investment option. By analyzing a cryptocurrency's underlying factors, you can gain insights into its potential risks and rewards, which can help you make more informed investment decisions. With that in mind, let's dive into the key factors that affect a cryptocurrency's value.

How to Analyze Cryptocurrencies?

There are two popular ways (or types) to analyze a cryptocurrency before investing in it.

  1. Fundamental analysis
  2. Technical analysis 

Fundamental Analysis of Cryptocurrency

Fundamental analysis is the starting point of understanding the history of any crypto asset you want to invest in. 

It involves getting to the Adam and Eve of an asset, the team behind the project, the price history, the white paper, the tokenomics of the crypto asset - the whole deal.

The sole purpose of performing this analysis is to understand if the asset is overvalued or undervalued. Having said that, fundamental analysis needs the support of technical analysis to predict the crypto asset completely. 

The most important factors to consider when performing the fundamental analysis are:

1. Reviewing the white paper

Every crypto asset is backed by a white paper demonstrating the objectives and other technical details. While some details might seem quite difficult to understand for casual investors, it’s crucial to learn about the vision and mission of the project. 

The white paper should clearly explain the goals of the project, how they plan to achieve these goals using their technology, and how the cryptocurrency will function in the market. 

One red flag is a white paper full of generic promises with no proper detailing. 

2. Know the Team behind the project

To estimate the project's future performance, try to get the details of the professional experience that the team holds. 

For this, you can scroll through the LinkedIn profiles of the technical or leadership staff. You can also check the crypto asset's "About Us" page to get the details of who is heading the project. 

If you don't find the identity of the founders and the team anywhere on the website or the internet, consider it a red flag in most cases. 

3. Learn about the leadership

Find the executive team that handles the financials, operations, and vision. Learning about who is leading the project can help you understand the success probability of the crypto asset. 

So, here are a few questions to consider when researching a crypto company’s leadership:

  • Do they have a history of successful leadership positions?
  • Are they reputable/well-known leaders?
  • Is their crypto history good or bad?
  • Are they publicly identified or anonymous?

4. Get to know the community

Crypto is a community-driven space. Knowing whether the project is pure hype and speculation or is genuinely trusted by a community can help you decide the success of a crypto asset. 

Join the community's Discord or Telegram channel to understand it better. No discussion means no growth. 

5. Understand the vision and mission of the project

Crypto assets should have a clear vision and mission if they are genuinely intending to grow.

They should have a direction and a road map and be well-organized in everything they do.

6. Reviewing the roadmap

Similar to traditional companies, cryptocurrencies should have a well-defined roadmap with specific goals and timelines. It is crucial to assess the roadmap before investing in a cryptocurrency to ensure that the asset has a clear path to success. Understanding how the cryptocurrency plans to achieve its goals is essential in making informed investment decisions.

If the goals outlined in the roadmap are too generic and lack specificity, it can be a red flag. Additionally, if the timelines are not well mapped out or appear unrealistic, it could indicate poor planning or execution, which can impact the cryptocurrency's long-term success.

7. Learn about the tokenomics of the project

Tokenomics refers to the economic principles that govern the supply, distribution, and circulation of a cryptocurrency. These principles significantly impact the price and long-term viability of a crypto asset. Understanding the supply and demand dynamics is crucial for investors looking to make informed decisions.

Similar to the stock market, the crypto market's price is influenced by the intersection of demand and supply. When demand exceeds supply, the price of a cryptocurrency tends to increase, and vice versa. Therefore, it is essential to consider the following three data points when evaluating a cryptocurrency investment opportunity:

  1. Circulating supply: This is the amount of cryptocurrency currently in circulation and available in the market. It is a critical factor in determining the current market value of a cryptocurrency.
  2. Total supply: This is the maximum amount of cryptocurrency that will ever be available, including tokens that are not yet in circulation. It is important to consider the total supply when evaluating the potential long-term value of a cryptocurrency.
  3. Maximum supply: Many cryptocurrencies have a mechanism to mint new tokens, and the maximum supply represents the upper limit of the number of tokens that will ever be in existence. Understanding the maximum supply is crucial in assessing the potential dilution of a cryptocurrency's value over time.

In summary, evaluating a cryptocurrency's supply dynamics is an essential aspect of making informed investment decisions. Understanding the circulating, total, and maximum supply of a cryptocurrency can provide valuable insights into its potential value and long-term viability.

8. Review the price history

Cryptocurrencies are known for their high volatility, but there are various factors that impact their price. Understanding these factors, such as token supply and marketing efforts, is crucial in determining the dependability of a cryptocurrency's price. Additionally, reviewing a cryptocurrency's price history can provide valuable insights into its growth or decline over time.

Analyzing price fluctuations in a given time period can help you determine whether a cryptocurrency is thriving or dying. For instance, if a cryptocurrency experienced a significant price spike but failed to recover over subsequent months or years, it could be a warning sign for investors. Similarly, market capitalization and trading volume are crucial indicators of a cryptocurrency's potential. If a cryptocurrency's market cap continues to decline over time, or if there is little trading volume, it may not be a viable investment option.

Technical Analysis of Cryptocurrency

Technical analysis of a crypto asset involves analyzing historical price and volume data to identify patterns and trends that can provide insight into future price movements.

Here are 5 factors to consider in the technical analysis of a crypto asset:

  1. Price charts and patterns: Technical analysis typically involves using price charts to identify trends and patterns. Common patterns include support and resistance levels, trend lines, and chart formations such as triangles and head-and-shoulders patterns.
  2. Volume: Volume refers to the number of cryptocurrency shares or units traded during a given period. High trading volume can indicate a strong trend, while low trading volume may suggest that a market is consolidating or lacks momentum.
  3. Moving averages: Moving averages are widely used in technical analysis. They are used to smooth out price fluctuations and identify trends. Popular moving averages include the simple moving average (SMA) and the exponential moving average (EMA).
  4. Oscillators: Oscillators are indicators that oscillate between two extremes, such as overbought and oversold conditions. Common oscillators used in technical analysis include the relative strength index (RSI) and the stochastic oscillator.
  5. Market sentiment: Market sentiment refers to investors' overall mood or attitude towards a particular cryptocurrency. Sentiment can be measured using various methods, such as social media sentiment analysis or surveys of market participants. Considering market sentiment when analyzing a crypto asset is important because it can impact price movements. 

Technical analysis can provide valuable insights into a cryptocurrency's price movements, but it requires expertise in the field. The term "technical" refers to the complex algorithms and charts used to analyze market trends and predict future price movements.

Therefore, it may be too complicated for casual investors who do not have a strong understanding of the underlying principles. Here at Token Metrics, we automated Technical Analysis for thousands of cryptocurrencies with the help of AI and Machine Learning.

Just visit the page of a crypto you want to analyze, click on "Trader" from the left menu, and then click on the "Resistance and Support Levels" tab to start analyzing.

The Bottom Line

A cryptocurrency analysis involves examining and verifying a lot of information about a digital asset.

To get the deepest understanding of an asset, you can start with a fundamental analysis followed by a technical analysis.

We, at Token Metrics, work hard to make all of these tools available to you with a click of a button, automated and powered by AI to help you analyze thousands of cryptocurrencies fast.

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Token Metrics Team
Token Metrics Team

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API Management Essentials for Teams

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APIs are the connective tissue of modern software. As organizations expose more endpoints to partners, internal teams and third-party developers, effective api management becomes a competitive and operational imperative. This article breaks down practical frameworks, governance guardrails, and monitoring strategies that help teams scale APIs securely and reliably without sacrificing developer velocity.

Overview: What API management solves

API management is the set of practices, tools and processes that enable teams to design, publish, secure, monitor and monetize application programming interfaces. At its core it addresses three recurring challenges: consistent access control, predictable performance, and discoverability for developers. Well-managed APIs reduce friction for consumers, decrease operational incidents, and support governance priorities such as compliance and data protection.

Think of api management as a lifecycle discipline: from design and documentation to runtime enforcement and iterative refinement. Organizations that treat APIs as products—measuring adoption, latency, error rates, and business outcomes—are better positioned to scale integrations without accumulating technical debt.

Governance & Security: Policies that scale

Security and governance are non-negotiable for production APIs. Implement a layered approach:

  • Access control: Use token-based authentication (OAuth 2.0, JWT) and centralize identity validation at the gateway to avoid duplicating logic across services.
  • Rate limiting & quotas: Protect backend services and control cost by enforcing per-key or per-tenant limits. Different tiers can align with SLAs for partners.
  • Input validation & schema contracts: Define explicit contracts using OpenAPI/JSON Schema and validate at the edge to reduce injection and integration errors.
  • Audit & compliance: Log authentication events, data access, and configuration changes. Retain logs in a way that maps to regulatory obligations.

Combining automated policy enforcement at an API gateway with a governance framework (ownerable APIs, review gates, and versioning rules) ensures changes are controlled without slowing legitimate feature delivery.

Developer experience & the API product model

Developer experience (DX) determines adoption. Treat APIs as products by providing clear documentation, SDKs and a self-service developer portal. Key practices include:

  • Interactive docs: Publish OpenAPI-driven docs that allow developers to try endpoints in a sandbox.
  • Onboarding flows: Provide quick start guides, sample payloads and error explanations to reduce time-to-first-call.
  • Versioning strategy: Use semantic versioning and deprecation notices to minimize breaking changes.
  • Feedback loops: Instrument usage and surface developer issues to product owners so APIs evolve with consumer needs.

Metrics to track DX include signups, first successful call time, and repeat usage per key. These are leading indicators of whether an API is fulfilling its product intent.

Monitoring, observability & reliability

Operational visibility is essential for api management. Implement monitoring at multiple layers—gateway, service, and database—to triangulate causes when issues occur. Core telemetry includes:

  • Traffic metrics: requests per second, latency percentiles (p50/p95/p99), and throughput.
  • Error rates: HTTP 4xx/5xx breakdowns, client-specific failure patterns, and circuit-breaker triggers.
  • Business KPIs: API calls tied to revenue, conversions, or key workflows to prioritize fixes that have impact.

Observability practices—distributed tracing, structured logs, and context propagation—help teams move from alert fatigue to actionable incident response. Build runbooks that map common alerts to remediation steps and owners.

Implementation roadmap & tooling choices

Adopt an incremental roadmap rather than a big-bang rollout. A pragmatic sequence looks like:

  1. Inventory existing endpoints and annotate owners.
  2. Standardize contracts with OpenAPI and publish baseline docs.
  3. Introduce an API gateway for auth, rate limiting, and basic WAF rules.
  4. Instrument telemetry, set SLAs, and define retention for logs and traces.
  5. Launch a developer portal and iterate based on usage signals.

Choose tools that match team maturity: managed API platforms accelerate setup for companies lacking infra resources, while open-source gateways provide control for those with specialized needs. Evaluate vendors on extensibility, observability integrations, and policy-as-code support to avoid lock-in.

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What is API management and why does it matter?

API management encompasses the processes and tools required to publish, secure, monitor, and monetize APIs. It matters because it enables predictable, governed access to services while maintaining developer productivity and operational reliability.

Which components make up an API management stack?

Common components include an API gateway (auth, routing, rate limiting), developer portal (docs, keys), analytics and monitoring systems (metrics, traces), and lifecycle tooling (design, versioning, CI/CD integrations).

How should teams approach API security?

Implement defense-in-depth: centralized authentication, token validation, input schema checks, rate limits, and continuous auditing. Shift security left by validating contracts and scanning specs before deployment.

What metrics are most useful for API health?

Track latency percentiles, error rates, traffic patterns, and consumer-specific usage. Pair operational metrics with business KPIs (e.g., API-driven signups) to prioritize work that affects outcomes.

How do teams manage breaking changes?

Use explicit versioning, deprecation windows, and dual-running strategies where consumers migrate incrementally. Communicate changes via the developer portal and automated notifications tied to API keys.

When should an organization introduce an API gateway?

Introduce a gateway early when multiple consumers, partners, or internal teams rely on APIs. A gateway centralizes cross-cutting concerns and reduces duplicated security and routing logic.

Disclaimer

This article is for educational and informational purposes only. It provides neutral, analytical information about api management practices and tools and does not constitute professional or investment advice.

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How Modern Web APIs Power Connected Apps

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APIs are the connective tissue of modern software: they expose functionality, move data, and enable integrations across services, devices, and platforms. A well-designed web API shapes developer experience, system resilience, and operational cost. This article breaks down core concepts, common architectures, security and observability patterns, and practical steps to build and maintain reliable web APIs without assuming a specific platform or vendor.

What is a Web API and why it matters

A web API (Application Programming Interface) is an HTTP-accessible interface that lets clients interact with server-side functionality. APIs can return JSON, XML, or other formats and typically define a contract of endpoints, parameters, authentication requirements, and expected responses. They matter because they enable modularity: front-ends, mobile apps, third-party integrations, and automation tools can all reuse the same backend logic.

When evaluating or designing an API, consider the consumer experience: predictable endpoints, clear error messages, consistent versioning, and comprehensive documentation reduce onboarding friction for integrators. Think of an API as a public product: its usability directly impacts adoption and maintenance burden.

Design patterns and architectures

There are several architectural approaches to web APIs. RESTful (resource-based) design emphasizes nouns and predictable HTTP verbs. GraphQL centralizes query flexibility into a single endpoint and lets clients request only the fields they need. gRPC is used for low-latency, binary RPC between services.

Key design practices:

  • Model your resources to reflect domain concepts; avoid ad-hoc endpoints that duplicate behavior.
  • Keep contracts stable and use semantic versioning or evolving schema techniques (e.g., deprecation headers, feature flags) to handle changes.
  • Document thoroughly using OpenAPI/Swagger, GraphQL schemas, or similar—machine-readable specs enable client generation and automated testing.

Choose the pattern that aligns with your performance, flexibility, and developer ergonomics goals, and make that decision explicit in onboarding docs.

Security, authentication, and rate limiting

Security must be built into an API from day one. Common controls include TLS for transport, OAuth 2.0 / OpenID Connect for delegated authorization, API keys for service-to-service access, and fine-grained scopes for least-privilege access. Input validation, output encoding, and strict CORS policies guard against common injection and cross-origin attacks.

Operational protections such as rate limiting, quotas, and circuit breakers help preserve availability if a client misbehaves or a downstream dependency degrades. Design your error responses to be informative to developers but avoid leaking internal implementation details. Centralized authentication and centralized secrets management (vaults, KMS) reduce duplication and surface area for compromise.

Performance, monitoring, and testing

Performance considerations span latency, throughput, and resource efficiency. Use caching (HTTP cache headers, CDN, or in-memory caches) to reduce load on origin services. Employ pagination, partial responses, and batch endpoints to avoid overfetching. Instrumentation is essential: traces, metrics, and logs help correlate symptoms, identify bottlenecks, and measure SLAs.

Testing should be layered: unit tests for business logic, contract tests against API schemas, integration tests for end-to-end behavior, and load tests that emulate real-world usage. Observability tools and APMs provide continuous insight; AI-driven analytics platforms such as Token Metrics can help surface unusual usage patterns and prioritize performance fixes based on impact.

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What is the difference between REST and GraphQL?

REST exposes multiple endpoints that represent resources and rely on HTTP verbs for operations. It is simple and maps well to HTTP semantics. GraphQL exposes a single endpoint where clients request precisely the fields they need, which reduces overfetching and can simplify mobile consumption. GraphQL adds complexity in query planning and caching; choose based on client needs and team expertise.

How should I approach API versioning?

Prefer backward-compatible changes over breaking changes. Use semantic versioning for major releases, and consider header-based versioning or URI version prefixes when breaking changes are unavoidable. Maintain deprecation schedules and communicate timelines in documentation and response headers so clients can migrate predictably.

Which authentication method is best for my API?

OAuth 2.0 and OpenID Connect are standard for delegated access and single-sign-on. For machine-to-machine communication, use short-lived tokens issued by a trusted authorization server. API keys can be simple to implement but should be scoped, rotated regularly, and never embedded in public clients without additional protections.

How do I test and monitor an API in production?

Implement synthetic monitoring for critical endpoints, collect real-user metrics (latency percentiles, error rates), and instrument distributed tracing to follow requests across services. Run scheduled contract tests against staging and production-like environments, and correlate incidents with deployment timelines and dependency health.

How do I design for backward compatibility?

Make additive, non-breaking changes where possible: add new fields rather than changing existing ones, and preserve default behaviors. Document deprecated fields and provide feature flags to gate new behavior. Maintain versioned client libraries to give consumers time to upgrade.

Disclaimer

This article is educational and technical in nature. It does not provide legal, financial, or investment advice. Implementations should be evaluated with respect to security policies, compliance requirements, and operational constraints specific to your organization.

Research

API Endpoint Essentials: Design, Security & Tips

Token Metrics Team
5
MIN

APIs power modern software by exposing discrete access points called endpoints. Whether you re integrating a third-party data feed, building a microservice architecture, or wiring a WebSocket stream, understanding what an api endpoint is and how to design, secure, and monitor one is essential for robust systems.

What is an API endpoint and how it works

An api endpoint is a network-accessible URL or address that accepts requests and returns responses according to a protocol (usually HTTP/HTTPS or WebSocket). Conceptually, an endpoint maps a client intent to a server capability: retrieve a resource, submit data, or subscribe to updates. In a RESTful API, endpoints often follow noun-based paths (e.g., /users/123) combined with HTTP verbs (GET, POST, PUT, DELETE) to indicate the operation.

Key technical elements of an endpoint include:

  • URI pattern (path and optional query parameters)
  • Supported methods (verbs) and expected payloads
  • Authentication and authorization requirements
  • Response format and status codes
  • Rate limiting and throttling rules

Endpoints can be public (open to third parties) or private (internal to a service mesh). For crypto-focused data integrations, api endpoints may also expose streaming interfaces (WebSockets) or webhook callbacks for asynchronous events. For example, Token Metrics is an example of an analytics provider that exposes APIs for research workflows.

Types of endpoints and common protocols

Different application needs favor different endpoint types and protocols:

  • REST endpoints (HTTP/HTTPS): Simple, stateless, and cache-friendly, ideal for resource CRUD operations and broad compatibility.
  • GraphQL endpoints: A single endpoint that accepts queries allowing clients to request exactly the fields they need; reduces overfetching but requires careful schema design and complexity control.
  • WebSocket endpoints: Bidirectional, low-latency channels for streaming updates (market data, notifications). Useful when real-time throughput matters.
  • Webhook endpoints: Server-to-server callbacks where your service exposes a publicly accessible endpoint to receive event notifications from another system.

Choosing a protocol depends on consistency requirements, latency tolerance, and client diversity. Hybrid architectures often combine REST for configuration and GraphQL/WebSocket for dynamic data.

Design best practices for robust API endpoints

Good endpoint design improves developer experience and system resilience. Follow these practical practices:

  1. Clear and consistent naming: Use predictable URI patterns and resource-oriented paths. Avoid action-based endpoints like /getUserData in favor of /users/{id}.
  2. Versioning: Expose versioned endpoints (e.g., /v1/users) to avoid breaking changes for consumers.
  3. Input validation: Validate payloads early and return explicit error codes and messages to guide client correction.
  4. Pagination and filtering: For list-heavy endpoints, require pagination tokens or limits to protect backend resources.
  5. Documentation and examples: Provide schema samples, curl examples, and expected response bodies to accelerate integration.

API schema tools (OpenAPI/Swagger, AsyncAPI) let you define endpoints, types, and contracts programmatically, enabling automated client generation, testing, and mock servers during development.

Security, rate limits, and monitoring

Endpoints are primary attack surfaces. Security and observability are critical:

  • Authentication & Authorization: Prefer token-based schemes (OAuth2, JWT) with granular scopes. Enforce least privilege for each endpoint.
  • Transport security: Enforce TLS, HSTS, and secure ciphers to protect data in transit.
  • Rate limiting & quotas: Apply per-key and per-IP limits to mitigate abuse and preserve quality of service.
  • Input sanitization: Prevent injection attacks by whitelisting allowed fields and escaping inputs.
  • Observability: Emit structured logs, traces, and metrics per endpoint. Monitor latency percentiles, error rates, and traffic patterns to detect regressions early.

Operational tooling such as API gateways, service meshes, and managed API platforms provide built-in policy enforcement for security and rate limiting, reducing custom code complexity.

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What is the difference between an api endpoint and an API?

An API is the overall contract and set of capabilities a service exposes; an api endpoint is a specific network address (URI) where one of those capabilities is accessible. Think of the API as the menu and endpoints as the individual dishes.

How should I secure a public api endpoint?

Use HTTPS only, require authenticated tokens with appropriate scopes, implement rate limits and IP reputation checks, and validate all input. Employ monitoring to detect anomalous traffic patterns and rotate credentials periodically.

When should I version my endpoints?

Introduce explicit versioning when you plan to make breaking changes to request/response formats or behavior. Semantic versioning in the path (e.g., /v1/) is common and avoids forcing clients to adapt unexpectedly.

What are effective rate-limiting strategies?

Combine per-key quotas, sliding-window or token-bucket algorithms, and burst allowances. Communicate limits via response headers and provide clear error codes and retry-after values so clients can back off gracefully.

Which metrics should I monitor for endpoints?

Track request rate (RPS), error rate (4xx/5xx), latency percentiles (p50, p95, p99), and active connections for streaming endpoints. Correlate with upstream/downstream service metrics to identify root causes.

When is GraphQL preferable to REST for endpoints?

Choose GraphQL when clients require flexible field selection and you want to reduce overfetching. Prefer REST for simple resource CRUD patterns and when caching intermediaries are important. Consider team familiarity and tooling ecosystem as well.

Disclaimer

The information in this article is technical and educational in nature. It is not financial, legal, or investment advice. Implementations should be validated in your environment and reviewed for security and compliance obligations specific to your organization.

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