Crypto Basics

Types of Stablecoins: A Complete Guide for 2025

Discover the types of stablecoins—fiat-backed, crypto-backed, algorithmic, and commodity-based. Learn how they work, their pros and cons, and which is best for your crypto strategy in 2025.
Token Metrics Team
8 min
MIN

The cryptocurrency market is famous for volatility—Bitcoin can swing by thousands of dollars in a day, and altcoins can rise or crash in hours. But what if you need price stability? That’s where stablecoins come in.

Stablecoins are digital currencies pegged to stable assets like the US dollar, gold, or even algorithmically maintained price levels. They bridge the gap between the speed and efficiency of blockchain technology and the reliability of traditional money.

As of 2025, stablecoins account for over $140 billion in circulating supply, making them essential for traders, investors, and decentralized finance (DeFi) users. But not all stablecoins are the same—different types use different mechanisms to maintain their stability, each with unique pros and cons.

In this guide, we’ll break down the types of stablecoins, how they work, their risks, and which might be best for your needs.

What Are Stablecoins?

A stablecoin is a cryptocurrency designed to maintain a stable value by pegging its price to a reference asset. Most stablecoins are pegged to fiat currencies like the U.S. dollar (e.g., 1 USDT ≈ $1), but some track commodities (like gold) or are algorithmically balanced to hold value.

They are widely used for:

  • Trading: Moving quickly in and out of volatile assets.
  • DeFi: Providing liquidity, borrowing, and earning yields.
  • Remittances: Sending low-cost, fast cross-border payments.
  • Hedging: Protecting against crypto market volatility.

Types of Stablecoins

There are four main types of stablecoins:

  1. Fiat‑Collateralized Stablecoins
  2. Crypto‑Collateralized Stablecoins
  3. Algorithmic (Non‑Collateralized) Stablecoins
  4. Commodity‑Backed Stablecoins

Each has a different method of maintaining its peg. Let’s break them down.

1. Fiat‑Collateralized Stablecoins

Definition:
These are backed 1:1 by traditional currencies like the U.S. dollar, euro, or yen. For every stablecoin issued, an equivalent amount of fiat is held in reserve by a trusted custodian.

How They Work:
If you buy 1 USDC, Circle (its issuer) holds $1 in a regulated bank account or short-term U.S. Treasury securities. When you redeem that stablecoin, the issuer sends you the equivalent amount in fiat and burns the coin.

Examples:

  • Tether (USDT) – Largest stablecoin by market cap; widely used on exchanges.
  • USD Coin (USDC) – Issued by Circle, fully regulated and audited.
  • TrueUSD (TUSD) – Offers real-time attestation of reserves.
  • PayPal USD (PYUSD) – Launched by PayPal for payments and DeFi.

Pros:

  • High stability – Pegged directly to fiat.
  • Transparent – Many provide audits and attestations.
  • Easy adoption – Ideal for traders, merchants, and payment platforms.

Cons:

  • Centralization – Custodians control reserves and can freeze accounts.
  • Regulatory risks – Subject to government oversight.
  • Less transparency for some issuers – (e.g., Tether faced scrutiny over its reserves).

2. Crypto‑Collateralized Stablecoins

Definition:
These are backed by cryptocurrencies like Ethereum or Bitcoin instead of fiat. Because crypto is volatile, these stablecoins are overcollateralized (e.g., $150 in ETH backs $100 in stablecoins).

How They Work:
When you mint a crypto-backed stablecoin like DAI, you deposit collateral (e.g., ETH) into a smart contract. If the collateral value drops too much, the contract automatically liquidates some assets to maintain the peg.

Examples:

  • DAI – Issued by MakerDAO, backed by ETH, USDC, and other assets.


  • sUSD – A synthetic USD issued by Synthetix.
  • MIM (Magic Internet Money) – Collateralized by multiple crypto assets.

Pros:

  • Decentralized – No single company controls reserves.
  • Transparent – All collateral balances are viewable on-chain.
  • Resistant to censorship – Issuers can’t freeze accounts.

Cons:

  • Volatility risk – Collateral can lose value quickly, requiring liquidations.
  • Overcollateralization – Ties up more capital than fiat-backed options.
  • Complexity – Requires understanding of DeFi mechanics.

3. Algorithmic (Non‑Collateralized) Stablecoins

Definition:
These don’t use physical reserves. Instead, they maintain their peg via algorithmic supply adjustments—minting or burning tokens to balance price around $1.

How They Work:
If demand increases and the price rises above $1, the protocol mints more coins. If it falls below $1, the protocol burns coins or incentivizes users to buy them back.

Examples:

  • Ampleforth (AMPL) – Elastic supply adjusts daily.
  • UST (TerraUSD) – Infamously collapsed in 2022 after its peg broke.
  • Frax (FRAX) – Uses a hybrid model: partly collateralized, partly algorithmic.

Pros:

  • Highly capital-efficient – Doesn’t require large reserves.
  • Decentralized – Often governed by smart contracts and DAOs.

Cons:

  • Peg instability – Prone to “death spirals” when market confidence drops.
  • Complex mechanisms – Harder for average users to understand.
  • History of failures – UST/LUNA crash eroded trust in algorithmic coins.

4. Commodity‑Backed Stablecoins

Definition:
These stablecoins are pegged to commodities like gold, silver, or oil, giving them intrinsic value beyond fiat.

How They Work:
For every coin issued, an equivalent amount of the commodity is held in a secure vault. For example, owning 1 PAX Gold (PAXG) means you own 1 troy ounce of physical gold stored by the issuer.

Examples:

  • PAX Gold (PAXG) – Backed by physical gold.


  • Tether Gold (XAUT) – Gold-backed token from Tether.
  • Digix Gold (DGX) – Pegged to gold bars in Singapore vaults.

Pros:

  • Hedge against inflation – Commodities like gold retain value during economic uncertainty.
  • Diversification – Offers exposure to both crypto and commodities.

Cons:

  • Low liquidity – Less widely used than fiat-backed stablecoins.
  • Storage & audit challenges – Requires trust in the issuer to maintain reserves.

Why Do Stablecoins Matter?

Stablecoins are the backbone of DeFi and crypto trading.

  • Trading & arbitrage: They provide a quick way to exit volatile positions without converting back to fiat.
  • Cross-border payments: Cheaper and faster than SWIFT or remittance services.
  • DeFi participation: Essential for lending, borrowing, yield farming, and liquidity pools.
  • On-ramp/off-ramp: They simplify converting between traditional finance and blockchain.

Which Type of Stablecoin Should You Use?

If you want simplicity & security: Go with fiat-backed coins like USDC or PYUSD.

If you value decentralization: Choose crypto-collateralized options like DAI.

If you’re comfortable with high risk: Explore algorithmic models like FRAX (but beware of peg risks).

If you want inflation protection: Consider commodity-backed coins like PAXG.

Key Risks of Stablecoins

  • Regulation: Governments are increasing oversight of fiat-backed coins.
  • Centralization: Some issuers can freeze funds or blacklist wallets.
  • Smart contract risks: DeFi-based stablecoins can suffer from bugs or exploits.
  • Peg breaks: Algorithmic models are especially vulnerable to confidence loss.

The Future of Stablecoins

In 2025, stablecoins are evolving to meet regulatory and market demands:

  • Central Bank Digital Currencies (CBDCs): Governments are issuing digital dollars and euros, potentially competing with stablecoins.
  • Regulated issuers: Projects like USDC are working closely with regulators to ensure compliance.
  • Hybrid models: Combining fiat and algorithmic elements (e.g., Frax) to enhance stability.

As DeFi expands and global adoption grows, stablecoins will remain at the heart of crypto finance.

Final Thoughts

Stablecoins are more than just “digital dollars.” They’re a critical bridge between traditional finance and blockchain innovation.

  • Fiat-backed stablecoins bring stability and ease of use.
  • Crypto-collateralized ones offer transparency and decentralization.
  • Algorithmic models push innovation but carry higher risks.
  • Commodity-backed coins provide a hedge against inflation and diversify exposure.

Choosing the right type depends on your risk tolerance, use case, and trust level. Whether for trading, saving, or participating in DeFi, understanding the types of stablecoins can help you navigate the crypto ecosystem with confidence.

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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.

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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.

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  • 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.

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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.
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  • Error catalog: Map common 4xx/5xx to clear fixes; log request IDs.
  • Observability: Track p95/p99 latency, error rate, and alert delivery success.
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Use Cases & Patterns

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  • 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.
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  • 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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What You’ll Build in 2 Minutes

A minimal script to fetch Fundamental Grade from /v2/fundamental-grade for any symbol (e.g., BTC).

  • Optional curl to smoke-test your key in seconds.
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Endpoints to consider next

  • /v2/tm-grade (technical/sentiment/momentum)
  • /v2/price-prediction (scenario planning)
  • /v2/resistance-support (risk levels)
  • /v2/quantmetrics (risk/return stats)

Why This Matters

Beyond price, toward quality. Markets are noisy—hype rises and fades. Fundamental Grade consolidates hard-to-track signals (community growth, token distribution, liquidity venues, investor quality, DeFi integrations) into a clear, comparable score. You get a fast “is this worth time and capital?” answer for screening, allocation, and monitoring.

Build trust into your product. Whether you run an investor terminal, exchange research tab, or a portfolio tool, Token Metrics discovery helps users justify positions. Pair it with TM Grade or Quantmetrics for a balanced picture: what to buy (fundamentals) and when to act (signals/levels).

Where to Find

The Fundamental Grade is easily accessible in the top right of the API Reference. Grab the cURL request for seamless access!

Ready to build?

  • Get API Key — generate a key and start free.
  • Run Hello-TM — verify your first successful call.
  • Clone a Template — deploy a screener or token page today.

Watch the demo: VIDEO_URL_HERE. Compare plans: Scale confidently with API plans.

FAQs

1) What does the Fundamental Grade API return?

A JSON payload with the overall score/grade plus component scores (e.g., community, tokenomics, exchange presence, VC backing, DeFi health) and timestamps. Use the overall grade for ranking and component scores for explanations.

2) How fast is the endpoint? Do you publish SLOs?

The API is engineered for predictable latency. For high-traffic dashboards, add short-TTL caching and batch requests; for alerts, use jobs/webhooks to minimize round-trips.

3) Can I combine Fundamental Grade with TM Grade or signals?

Yes. A common pattern is Fundamental Grade for quality filter + TM Grade for technical/sentiment context + Trading Signals for timing and Support/Resistance for risk placement.

4) How “accurate” is the grade?

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5) Do you offer SDKs and examples?

You can use REST directly (see JS/Python above). The docs include quickstarts, Postman, and ready-to-clone templates—start with Run Hello-TM.

6) Polling vs webhooks for fundamentals updates?

For UI pages, cached polling works well. For event-style notifications (upgrades/downgrades), prefer webhooks or scheduled jobs to avoid spiky traffic.

7) What about pricing, limits, and enterprise SLAs?

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

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