Crypto Basics

Are NFTs Dead? - Complete Analysis for NFT Investors

Learn everything about NFTs with our comprehensive analysis and uncover if NFTs are truly dead.
S. Vishwa
7 Minutes
MIN

The world of digital art and collectibles has been revolutionized by the emergence of Non-Fungible Tokens (NFTs). These unique digital assets have captured the attention of artists, collectors, and investors alike.

However, as with any emerging trend, questions arise about its longevity and sustainability. In this article, we delve into the current state of NFTs, answering the burning question: Are NFTs dead?

The Rise and Fall of NFTs

The rise and fall of NFTs can be attributed to several key factors. Let us delve into a few significant factors that have had a considerable impact on the NFT industry.

The Rise of NFTs

The emergence of NFTs created a buzz in the art world and beyond. Artists saw an opportunity to showcase and sell their work in a new and exciting way. Digital creators and influencers started exploring the possibilities of monetizing their online presence through the creation and sale of NFTs.

Marketplaces dedicated to NFT trading, such as OpenSea and Rarible, experienced a surge in user activity, with transactions reaching significant values. The NFT frenzy became a hot topic in mainstream media, attracting both enthusiasts and skeptics.

NFTs in the Gaming Industry

NFTs have also made their way into the gaming industry, offering unique benefits to players and developers. In the realm of blockchain-based games, players can truly own and trade in-game assets, blurring the line between virtual and real-world ownership.

NFTs provide a solution to the issue of scarcity in digital gaming. Players can acquire rare items, skins, or characters as NFTs, giving them exclusive and tradable virtual possessions. This has led to the emergence of decentralized gaming economies and the potential for players to earn real-world value from their gaming activities.

Artistic Expression and NFTs

NFTs have opened up new avenues for artistic expression and creativity. Digital artists who previously struggled to monetize their work found an opportunity to showcase and sell their creations directly to collectors. This democratization of the art world challenged traditional gatekeepers and offered a more inclusive platform for artists.

Additionally, NFTs introduced the concept of "programmable art." Artists can embed dynamic features into their digital assets, allowing them to evolve and respond to external factors. This fusion of art and technology presents exciting possibilities for artistic exploration.

The Fall of NFTs

The decline of NFTs can be attributed to various crucial factors. Let's examine a few significant elements that have had a considerable impact on it.

Market Volatility

One of the key challenges for NFTs is market volatility. Prices of digital assets can fluctuate wildly, making it a risky investment for some. 

Critics argue that the NFT market resembles a speculative bubble, and caution against blindly jumping into the trend without thorough research and consideration.

Despite the volatility, there are success stories of NFT investments yielding substantial returns. As with any investment, understanding the market dynamics and carefully evaluating potential risks is crucial.

The Hype and Its Consequences

With the rise of NFTs came an undeniable wave of hype. Investors rushed to buy and trade digital assets, often resulting in exorbitant prices. Some high-profile sales, like Beeple's artwork selling for millions, contributed to the frenzy and inflated market valuations.

However, as with any hype-driven market, there were consequences. Speculators entered the scene, hoping to make quick profits, sometimes at the expense of genuine artists and collectors. This led to concerns about the long-term sustainability and intrinsic value of NFTs.

Environmental Concerns

As the popularity of NFTs soared, environmental concerns came into focus. The majority of NFT transactions occur on blockchain networks like Ethereum, which rely on a proof-of-work (Switched to Proof of Stake) consensus mechanism. This energy-intensive process requires significant computing power and contributes to carbon emissions.

Critics argue that the carbon footprint associated with NFTs is disproportionate to the value they provide. The debate over the environmental impact of NFTs has prompted discussions within the blockchain community about transitioning to more sustainable alternatives, such as proof-of-stake protocols.

The Role of Established Institutions

Established institutions, such as museums and galleries, have taken notice of the NFT phenomenon. Some have embraced it as a new way to engage with audiences and expand their digital presence. Others remain skeptical, questioning the value and longevity of digital art.

However, the involvement of established institutions brings credibility to the NFT space. Collaborations between renowned artists and museums have generated renewed interest and validated the potential of NFTs as a legitimate art form.

Challenges and Criticisms

Alongside the hype, NFTs face several challenges and criticisms. One concern is the lack of regulation and potential for fraudulent activities. The decentralized nature of blockchain technology makes it difficult to enforce copyright protection and prevent unauthorized reproductions.

Another criticism revolves around the carbon footprint and energy consumption, as mentioned earlier. The environmental impact of NFTs has led to debates about sustainability and the need for greener alternatives.

Furthermore, critics argue that the NFT market is saturated with low-quality and derivative works. This oversaturation can make it difficult for artists to stand out and for collectors to discern valuable assets from mere cash grabs.

Are NFTs Dead?

After exploring various aspects of the NFT landscape, we can now answer the burning question: Are NFTs dead? The answer is No. While the initial hype may have subsided, NFTs continue to evolve and adapt.

The market has experienced a correction after the initial frenzy, which was necessary to establish a more sustainable foundation. Although some projects and assets may lose value or fade into obscurity, the underlying technology and concept of NFTs remain relevant.

NFTs have proven their potential in revolutionizing various industries, including art, gaming, and collectibles. As the market matures, we can expect greater emphasis on quality, innovation, and long-term value.

Also Read - Is Bitcoin Dead? - Complete Analysis

NFTs and the Future

Although NFTs have gained recognition primarily for their association with expensive profile pictures, their potential extends far beyond that. 

Here are several compelling reasons why NFTs have the potential to regain traction and become increasingly popular in the future:

Advancements in Infrastructure: As the technology supporting NFTs continues to evolve and become more accessible, it has the potential to attract a larger number of investors and buyers to the market.

Exclusivity: NFTs possess a unique quality—they cannot be replicated, resulting in inherent scarcity. As the demand for specific NFTs grows, their prices may rise due to limited supply.

Portfolio Diversification: NFTs offer a distinct opportunity to invest in digital assets, which remains a relatively new and unexplored territory. In their pursuit of diversifying investment portfolios, investors may consider including NFTs as a part of their overall strategy.

Increasing Adoption: Although NFTs have garnered significant attention in recent years due to the surge in interest for digital art and collectibles, their potential in the gaming sector and other applications remains largely untapped. This untapped potential could provide a substantial boost to the popularity of NFTs in the future.

Growing Acceptance: Mainstream artists and renowned brands are progressively entering the NFT space, creating and selling their own NFTs. 

This increasing acceptance and participation from established entities could lead to more individuals buying and trading NFTs, consequently driving up their value.

While there are no guarantees regarding the future recovery of NFTs, the aforementioned factors indicate a promising potential for growth in the NFT market. 

However, it is crucial to acknowledge the inherent risks associated with investments, especially with nascent assets like NFTs. As of now, all we can do is wait and observe how the situation unfolds.

Also Read - NFT Rarity - What it Means and How to Calculate it?

The Bottom Line

In conclusion, while the initial hype surrounding NFTs has subsided, they are far from dead. NFTs have transformed the art world, opened new possibilities for creativity, and introduced innovative concepts to various industries like gaming.

However, challenges such as market volatility, environmental concerns, and the need for quality control remain. As the market matures and stakeholders address these challenges, NFTs will likely find their place as a significant aspect of the digital economy. 

Artists will continue to explore the potential of NFTs as a means of expression, and collectors will seek unique and valuable digital assets. The future of NFTs lies in balancing innovation, sustainability, and inclusivity.

Disclaimer

The information provided on this website does not constitute investment advice, financial advice, trading advice, or any other sort of advice and you should not treat any of the website's content as such.

Token Metrics does not recommend that any cryptocurrency should be bought, sold, or held by you. Do conduct your own due diligence and consult your financial advisor before making any investment decisions.

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Token Metrics Team

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Support and Resistance API: Auto-Calculate Smart Levels for Better Trades

Token Metrics Team
4
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Most traders still draw lines by hand in TradingView. The support and resistance API from Token Metrics auto-calculates clean support and resistance levels from one request, so your dashboard, bot, or alerts can react instantly. In minutes, you’ll call /v2/resistance-support, render actionable levels for any token, and wire them into stops, targets, or notifications. Start by grabbing your key on Get API Key, then Run Hello-TM and Clone a Template to ship a production-ready feature fast.

What You’ll Build in 2 Minutes

A minimal script that fetches Support/Resistance via /v2/resistance-support for a symbol (e.g., BTC, SOL).

  • A one-liner curl to smoke-test your key.
  • A UI pattern to display nearest support, nearest resistance, level strength, and last updated time.

Next Endpoints to add

  • /v2/trading-signals (entries/exits)
  • /v2/hourly-trading-signals (intraday updates)
  • /v2/tm-grade (single-score context)
  • /v2/quantmetrics (risk/return framing)

Why This Matters

Precision beats guesswork. Hand-drawn lines are subjective and slow. The support and resistance API standardizes levels across assets and timeframes, enabling deterministic stops and take-profits your users (and bots) can trust.

Production-ready by design. A simple REST shape, predictable latency, and clear semantics let you add levels to token pages, automate SL/TP alerts, and build rule-based execution with minimal glue code.

Where to Find

Need the Support and Resistance data? The cURL request for it is in the top right of the API Reference for quick access.

👉 Keep momentum: Get API Key • Run Hello-TM • Clone a Template

How It Works (Under the Hood)

The Support/Resistance endpoint analyzes recent price structure to produce discrete levels above and below current price, along with strength indicators you can use for priority and styling. Query /v2/resistance-support?symbol=<ASSET>&timeframe=<HORIZON> to receive arrays of level objects and timestamps.

Polling vs webhooks. For dashboards, short-TTL caching and batched fetches keep pages snappy. For bots and alerts, use queued jobs or webhooks (where applicable) to avoid noisy, bursty polling—especially around market opens and major events.

Production Checklist

  • Rate limits: Respect plan caps; add client-side throttling.
  • Retries/backoff: Exponential backoff with jitter for 429/5xx; log failures.
  • Idempotency: Make alerting and order logic idempotent to prevent duplicates.
  • Caching: Memory/Redis/KV with short TTLs; pre-warm top symbols.
  • Batching: Fetch multiple assets per cycle; parallelize within rate limits.
  • Threshold logic: Add %-of-price buffers (e.g., alert at 0.3–0.5% from level).
  • Error catalog: Map common 4xx/5xx to actionable user guidance; keep request IDs.
  • Observability: Track p95/p99; measure alert precision (touch vs approach).
  • Security: Store API keys in a secrets manager; rotate regularly.

Use Cases & Patterns

  • Bot Builder (Headless): Use nearest support for stop placement and nearest resistance for profit targets. Combine with /v2/trading-signals for entries/exits and size via Quantmetrics (volatility, drawdown).
  • Dashboard Builder (Product): Add a Levels widget to token pages; badge strength (e.g., High/Med/Low) and show last touch time. Color the price region (below support, between levels, above resistance) for instant context.
  • Screener Maker (Lightweight Tools): “Close to level” sort: highlight tokens within X% of a strong level. Toggle alerts for approach vs breakout events.
  • Risk Management: Create policy rules like “no new long if price is within 0.2% of strong resistance.” Export daily level snapshots for audit/compliance.

Next Steps

  • Get API Key — generate a key and start free.
  • Run Hello-TM — verify your first successful call.
  • Clone a Template — deploy a levels panel or alerts bot today.
  • Watch the demo: Compare plans: Scale confidently with API plans.

FAQs

1) What does the Support & Resistance API return?

A JSON payload with arrays of support and resistance levels for a symbol (and optional timeframe), each with a price and strength indicator, plus an update timestamp.

2) How timely are the levels? What are the latency/SLOs?

The endpoint targets predictable latency suitable for dashboards and alerts. Use short-TTL caching for UIs, and queued jobs or webhooks for alerting to smooth traffic.

3) How do I trigger alerts or trades from levels?

Common patterns: alert when price is within X% of a level, touches a level, or breaks beyond with confirmation. Always make downstream actions idempotent and respect rate limits.

4) Can I combine levels with other endpoints?

Yes—pair with /v2/trading-signals for timing, /v2/tm-grade for quality context, and /v2/quantmetrics for risk sizing. This yields a complete decide-plan-execute loop.

5) Which timeframe should I use?

Intraday bots prefer shorter horizons; swing/position dashboards use daily or higher-timeframe levels. Offer a timeframe toggle and cache results per setting.

6) Do you provide SDKs or examples?

Use the REST snippets above (JS/Python). The docs include quickstarts, Postman collections, and templates—start with Run Hello-TM.

7) Pricing, limits, and enterprise SLAs?

Begin free and scale as you grow. See API plans for rate limits and enterprise SLA options.

Disclaimer

This content is for educational purposes only and does not constitute financial advice. Always conduct your own research before making any trading decisions.

Research

Quantmetrics API: Measure Risk & Reward in One Call

Token Metrics Team
5
MIN

Most traders see price—quants see probabilities. The Quantmetrics API turns raw performance into risk-adjusted stats like Sharpe, Sortino, volatility, drawdown, and CAGR so you can compare tokens objectively and build smarter bots and dashboards. In minutes, you’ll query /v2/quantmetrics, render a clear performance snapshot, and ship a feature that customers trust. Start by grabbing your key at Get API Key, Run Hello-TM to verify your first call, then Clone a Template to go live fast.

What You’ll Build in 2 Minutes

  • A minimal script that fetches Quantmetrics for a token via /v2/quantmetrics (e.g., BTC, ETH, SOL).
  • A smoke-test curl you can paste into your terminal.
  • A UI pattern that displays Sharpe, Sortino, volatility, max drawdown, CAGR, and lookback window.

Next Endpoints to Add

  • /v2/tm-grade (one-score signal)
  • /v2/trading-signals
  • /v2/hourly-trading-signals (timing)
  • /v2/resistance-support (risk placement)
  • /v2/price-prediction (scenario planning)

Why This Matters

Risk-adjusted truth beats hype. Price alone hides tail risk and whipsaws. Quantmetrics compresses edge, risk, and consistency into metrics that travel across assets and timeframes—so you can rank universes, size positions, and communicate performance like a professional.

Built for dev speed

A clean REST schema, predictable latency, and easy auth mean you can plug Sharpe/Sortino into bots, dashboards, and screeners without maintaining your own analytics pipeline. Pair with caching and batching to serve fast pages at scale.

Where to Find

The Quant Metrics cURL request is located in the top right of the API Reference, allowing you to easily integrate it with your application.

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

How It Works (Under the Hood)

Quantmetrics computes risk-adjusted performance over a chosen lookback (e.g., 30d, 90d, 1y). You’ll receive a JSON snapshot with core statistics:

  • Sharpe ratio: excess return per unit of total volatility.
  • Sortino ratio: penalizes downside volatility more than upside.
  • Volatility: standard deviation of returns over the window.
  • Max drawdown: worst peak-to-trough decline.
  • CAGR / performance snapshot: geometric growth rate and best/worst periods.

Call /v2/quantmetrics?symbol=<ASSET>&window=<LOOKBACK> to fetch the current snapshot. For dashboards spanning many tokens, batch symbols and apply short-TTL caching. If you generate alerts (e.g., “Sharpe crossed 1.5”), run a scheduled job and queue notifications to avoid bursty polling.

Production Checklist

  • Rate limits: Understand your tier caps; add client-side throttling and queues.
  • Retries & backoff: Exponential backoff with jitter; treat 429/5xx as transient.
  • Idempotency: Prevent duplicate downstream actions on retried jobs.
  • Caching: Memory/Redis/KV with short TTLs; pre-warm popular symbols and windows.
  • Batching: Fetch multiple symbols per cycle; parallelize carefully within limits.
  • Error catalog: Map 4xx/5xx to clear remediation; log request IDs for tracing.
  • Observability: Track p95/p99 latency and error rates; alert on drift.
  • Security: Store API keys in secrets managers; rotate regularly.

Use Cases & Patterns

  • Bot Builder (Headless): Gate entries by Sharpe ≥ threshold and drawdown ≤ limit, then trigger with /v2/trading-signals; size by inverse volatility.
  • Dashboard Builder (Product): Add a Quantmetrics panel to token pages; allow switching lookbacks (30d/90d/1y) and export CSV.
  • Screener Maker (Lightweight Tools): Top-N by Sortino with filters for volatility and sector; add alert toggles when thresholds cross.
  • Allocator/PM Tools: Blend CAGR, Sharpe, drawdown into a composite score to rank reallocations; show methodology for trust.
  • Research/Reporting: Weekly digest of tokens with Sharpe ↑, drawdown ↓, and volatility ↓.

Next Steps

  • Get API Key — start free and generate a key in seconds.
  • Run Hello-TM — verify your first successful call.
  • Clone a Template — deploy a screener or dashboard today.
  • Watch the demo: VIDEO_URL_HERE
  • Compare plans: Scale with API plans.

FAQs

1) What does the Quantmetrics API return?

A JSON snapshot of risk-adjusted metrics (e.g., Sharpe, Sortino, volatility, max drawdown, CAGR) for a symbol and lookback window—ideal for ranking, sizing, and dashboards.

2) How fresh are the stats? What about latency/SLOs?

Responses are engineered for predictable latency. For heavy UI usage, add short-TTL caching and batch requests; for alerts, use scheduled jobs or webhooks where available.

3) Can I use Quantmetrics to size positions in a live bot?

Yes—many quants size inversely to volatility or require Sharpe ≥ X to trade. Always backtest and paper-trade before going live; past results are illustrative, not guarantees.

4) Which lookback window should I choose?

Short windows (30–90d) adapt faster but are noisier; longer windows (6–12m) are steadier but slower to react. Offer users a toggle and cache each window.

5) Do you provide SDKs or examples?

REST is straightforward (JS/Python above). Docs include quickstarts, Postman collections, and templates—start with Run Hello-TM.

6) Polling vs webhooks for quant alerts?

Dashboards usually use cached polling. For threshold alerts (e.g., Sharpe crosses 1.0), run scheduled jobs and queue notifications to keep usage smooth and idempotent.

7) Pricing, limits, and enterprise SLAs?

Begin free and scale up. See API plans for rate limits and enterprise SLA options.

Disclaimer

All information provided in this blog is for educational purposes only. It is not intended as financial advice. Users should perform their own research and consult with licensed professionals before making any investment or trading decisions.

Research

Crypto Trading Signals API: Put Bullish/Bearish Calls Right in Your App

Token Metrics Team
4
MIN

Timing makes or breaks every trade. The crypto trading signals API from Token Metrics lets you surface bullish and bearish calls directly in your product—no spreadsheet wrangling, no chart gymnastics. In this guide, you’ll hit the /v2/trading-signals endpoint, display actionable signals on a token (e.g., SOL, BTC, ETH), and ship a conversion-ready feature for bots, dashboards, or Discord. Start by creating a key on Get API Key, then Run Hello-TM and Clone a Template to go live fast.

What You’ll Build in 2 Minutes

  • A minimal script that fetches Trading Signals via /v2/trading-signals for one symbol (e.g., SOL).
  • A copy-paste curl to smoke-test your key.
  • A UI pattern to render signal, confidence/score, and timestamp in your dashboard or bot.

Endpoints to add next

  • /v2/hourly-trading-signals (intraday updates)
  • /v2/resistance-support (risk placement)
  • /v2/tm-grade (one-score view)
  • /v2/quantmetrics (risk/return context)

Why This Matters

Action over analysis paralysis. Traders don’t need more lines on a chart—they need an opinionated call they can automate. The trading signals API compresses technical momentum and regime reads into Bullish/Bearish events you can rank, alert on, and route into strategies.

Built for dev speed and reliability. A clean schema, predictable performance, and straightforward auth make it easy to wire signals into bots, dashboards, and community tools. Pair with short-TTL caching or webhooks to minimize polling and keep latency low.

Where to Find

You can find the cURL request for Crypto Trading Signals in the top right corner of the API Reference. Use it to access the latest signals!

Live Demo & Templates

  • Trading Bot Starter: Use Bullish/Bearish calls to trigger paper trades; add take-profit/stop rules with Support/Resistance.
  • Dashboard Signal Panel: Show the latest call, confidence, and last-updated time; add a history table for context.
  • Discord/Telegram Alerts: Post signal changes to a channel with a link back to your app.

How It Works (Under the Hood)

Trading Signals distill model evidence (e.g., momentum regimes and pattern detections) into Bullish or Bearish calls with metadata such as confidence/score and timestamp. You request /v2/trading-signals?symbol=<ASSET> and render the most recent event, or a small history, in your UI.

For intraday workflows, use /v2/hourly-trading-signals to update positions or alerts more frequently. Dashboards typically use short-TTL caching or batched fetches; headless bots lean on webhooks, queues, or short polling with backoff to avoid spiky API usage.

Production Checklist

  • Rate limits: Know your tier caps; add client-side throttling and queues.
  • Retries/backoff: Exponential backoff with jitter; treat 429/5xx as transient.
  • Idempotency: Guard downstream actions (don’t double-trade on retries).
  • Caching: Memory/Redis/KV with short TTLs for reads; pre-warm popular symbols.
  • Webhooks & jobs: Prefer webhooks or scheduled workers for signal change alerts.
  • Pagination/Bulk: Batch symbols; parallelize with care; respect limits.
  • Error catalog: Map common 4xx/5xx to clear fixes; log request IDs.
  • Observability: Track p95/p99 latency, error rate, and alert delivery success.
  • Security: Keep keys in a secrets manager; rotate regularly.

Use Cases & Patterns

  • Bot Builder (Headless): Route Bullish into candidate entries; confirm with /v2/resistance-support for risk and TM Grade for quality.
  • Dashboard Builder (Product): Add a “Signals” module per token; color-code state and show history for credibility.
  • Screener Maker (Lightweight Tools): Filter lists by Bullish state; sort by confidence/score; add alert toggles.
  • Community/Discord: Post signal changes with links to token pages; throttle to avoid noise.
  • Allocator/PM Tools: Track signal hit rates by sector/timeframe to inform position sizing (paper-trade first).

Next Steps

  1. Get API Key — create a key and start free.
  2. Run Hello-TM — confirm your first successful call.
  3. Clone a Template — deploy a bot, dashboard, or alerting tool today.

FAQs

1) What does the Trading Signals API return?

A JSON payload with the latest Bullish/Bearish call for a symbol, typically including a confidence/score and generated_at timestamp. You can render the latest call or a recent history for context.

2) Is it real-time? What about latency/SLOs?

Signals are designed for timely, programmatic use with predictable latency. For faster cycles, use /v2/hourly-trading-signals. Add caching and queues/webhooks to reduce round-trips.

3) Can I use the signals in a live trading bot?

Yes—many developers do. A common pattern is: Signals → candidate entry, Support/Resistance → stop/targets, Quantmetrics → risk sizing. Always backtest and paper-trade before going live.

4) How accurate are the signals?

Backtests are illustrative, not guarantees. Treat signals as one input in a broader framework with risk controls. Evaluate hit rates and drawdowns on your universe/timeframe.

5) Do you provide SDKs and examples?

You can integrate via REST using JavaScript and Python snippets above. The docs include quickstarts, Postman collections, and templates—start with Run Hello-TM.

6) Polling vs webhooks for alerts?

Dashboards often use cached polling. For bots/alerts, prefer webhooks or scheduled jobs and keep retries idempotent to avoid duplicate trades or messages.

7) Pricing, limits, and enterprise SLAs?

Begin free and scale as you grow. See API plans for allowances; enterprise SLAs and support are available.

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