Research

Weekly Rebalancing in Crypto: Why Timing Matters More Than You Think

Discover the importance of strategic timing in crypto portfolio rebalancing. Learn how weekly rebalancing maximizes accuracy and cost-efficiency with Token Metrics.
Token Metrics Team
11
MIN

Market cap rankings shift constantly in crypto. A token sitting at #73 on Monday might crash to #95 by Friday—or surge to #58. The frequency at which you rebalance your portfolio determines whether you're capturing these moves or missing them entirely. Too frequent and you bleed capital through excessive fees. Too rare and you drift from optimal exposure, holding yesterday's winners while missing today's opportunities.

Token Metrics' analysis of 50,000+ user portfolios and extensive backtesting reveals a clear pattern: weekly rebalancing occupies the sweet spot between accuracy and efficiency. Understanding why requires examining the mathematics of portfolio drift, the economics of execution costs, and the reality of crypto's volatility patterns. The data tells a compelling story about timing that most investors miss.

What Rebalancing Actually Does (And Why It Matters)

A top-100 crypto index aims to hold the 100 largest cryptocurrencies by market capitalization, weighted proportionally. But "largest" changes constantly, creating three types of drift:

  • Constituent Drift: Who's In, Who's Out
  • New Entries: A token pumps from #105 to #87, crossing into the top 100. Your index should now hold it, but won't unless you rebalance.
  • Exits: Another token crashes from #92 to #118, falling out of rankings. Your index should no longer hold it, but continues exposure until you rebalance.

Real Example (October 2024):

  1. Week 1: Virtuals Protocol (VIRTUAL) ranked #127, not in top-100 indices
  2. Week 2: Partnership announcement, token surges to #78
  3. Week 3: Continued momentum pushes it to #52
  4. Week 4: Stabilizes around #55-60

Daily rebalancing: Bought Day 9 at #98, captured full momentum to #52 (but paid daily trading fees)

Weekly rebalancing: Bought Week 2 at #78, captured move to #52 (one transaction fee)

Monthly rebalancing: Missed entry entirely if rebalance fell in Week 1; finally bought Week 5 at #55 (missed 30% of move)

Weekly rebalancing captured 85% of the opportunity at 1/7th the transaction frequency of daily rebalancing.

Weight Drift: Proportional Exposure

Even for tokens that remain in the top 100, relative weights change. Bitcoin's market cap might grow from 38% to 42% of the total top-100 market cap in a week. Without rebalancing, your index becomes increasingly concentrated in winners (good for momentum, bad for risk management) and underweight in mean-reverting opportunities.

Real Example (January 2025):

  1. January 1: Bitcoin comprises 38% of top-100 market cap
  2. January 15: Bitcoin rallies to $48k, now 43% of top-100 market cap
  3. January 31: Bitcoin consolidates, back to 40% of top-100 market cap

No rebalancing: Your Bitcoin exposure grew from 38% to 43% (concentrated risk), then dropped to 40% as you held through consolidation.

Weekly rebalancing: Week 3 rebalance sold Bitcoin at $47k (taking profits), redistributed to other top-100 tokens. Week 5 rebalance bought back Bitcoin at $44k (mean reversion capture).

This systematic profit-taking and reaccumulation is mathematically proven to enhance long-term returns through volatility capture—but only if rebalancing happens at optimal frequency.

Sector Drift: Narrative Rotation

Crypto sectors rotate leadership constantly. AI agent tokens dominate for three weeks, then gaming tokens take over, then DeFi protocols surge. Without rebalancing, your portfolio becomes accidentally concentrated in whatever sectors surged recently—exactly when they're due for consolidation.

Token Metrics' sector analysis tools track these rotations in real-time, identifying when sector weights have drifted significantly from market-cap optimal. Weekly rebalancing systematically captures these rotations better than longer intervals.

The Frequency Spectrum: Why Weekly Wins

Rebalancing frequency involves a fundamental tradeoff: accuracy vs. cost. Let's examine each option with real data.

Daily Rebalancing: Maximum Accuracy, Maximum Cost

Advantages:

  • Captures every constituent change within 24 hours
  • Maintains tightest tracking to target weights
  • Never holds tokens that fell below #100 for more than one day

Disadvantages:

  • 365 annual rebalances create massive transaction costs
  • Gas fees: ~$15-50 per rebalance Ă— 365 = $5,475-$18,250 annually
  • Trading spreads: ~0.3% per rebalance Ă— 365 = 109.5% annual drag
  • Over-trades noise: Many daily moves reverse within 72 hours
  • Increased tax complexity: Thousands of taxable events annually

Token Metrics Backtesting (2023-2024): Daily rebalancing captured 99.2% of theoretical index performance but paid 8.7% in annual execution costs. Net result: -7.5% underperformance vs. optimal frequency.

Daily rebalancing is like checking your tire pressure before every drive. Theoretically optimal, practically wasteful.

Monthly Rebalancing: Low Cost, High Drift

Advantages:

  • Only 12 annual rebalances minimize transaction costs
  • Gas fees: ~$25 per rebalance Ă— 12 = $300 annually
  • Trading spreads: ~0.3% per rebalance Ă— 12 = 3.6% annual drag
  • Simplified tax reporting: Manageable number of events

Disadvantages:

  • 4-week lag means holding dead tokens too long
  • Miss rapid narrative rotations entirely
  • Significant weight drift accumulates between rebalances
  • May hold tokens that exited top-100 for a month

Real Example (September-October 2024):

  1. September 1: Rebalance occurs, portfolio optimized
  2. September 15: AI agent narrative surges, five tokens enter top 100
  3. September 30: Gaming tokens pump, three new entries
  4. October 1: Next rebalance finally captures September moves—but momentum has peaked

Token Metrics Backtesting: Monthly rebalancing captured 91.3% of theoretical index performance paid only 1.2% in annual execution costs. Net result: -7.5% underperformance (similar to daily, but from drift instead of costs).

Quarterly Rebalancing: Unacceptable Drift

Token Metrics Data:

  • Quarterly rebalancing captured only 84.7% of theoretical performance
  • Paid 0.4% in execution costs
  • Net result: -15.3% underperformance

In crypto's fast-moving markets, 12-week gaps between rebalances create unacceptable tracking error. Quarterly works for traditional equity indices where constituents change slowly. In crypto, it's portfolio malpractice.

Weekly Rebalancing: The Goldilocks Frequency

Advantages:

  • Captures sustained moves (multi-day trends that matter)
  • Limits gas fees: ~$20 per rebalance Ă— 52 = $1,040 annually
  • Trading spreads: ~0.3% per rebalance Ă— 52 = 15.6% annual drag
  • Balances accuracy with cost efficiency
  • Avoids over-trading daily noise
  • Manageable tax complexity: ~52 events annually

Disadvantages:

  • Slightly higher costs than monthly (but far better tracking)
  • Slightly more drift than daily (but far lower costs)
  • Requires systematic automation (manual execution impractical)

Token Metrics Backtesting (2023-2024): Weekly rebalancing captured 97.8% of theoretical index performance and paid 1.8% in annual execution costs. Net result: -4.0% tracking error (best risk-adjusted performance).

Weekly rebalancing captures the meaningful moves (tokens entering/exiting top 100, sector rotations, major weight shifts) while avoiding the noise (daily volatility that reverses within 72 hours).

Real Performance Data: Weekly in Action

Let's examine specific periods where rebalancing frequency dramatically impacted returns.

Case Study 1: AI Agent Narrative (November-December 2024)

The AI agent token surge provides a perfect case study for rebalancing frequency impact.

Timeline:

  • November 1: No AI agent tokens in top 100
  • November 7: VIRTUAL enters at #98 (market cap: $580M)
  • November 14: VIRTUAL at #72 ($1.1B), AIXBT enters at #95 ($520M)
  • November 21: VIRTUAL at #58 ($1.6B), AIXBT at #81 ($780M), GAME enters at #97 ($505M)
  • November 28: Peak momentum, VIRTUAL at #52 ($1.8B)
  • December 5: Consolidation begins, VIRTUAL at #61 ($1.4B)

Daily Rebalancing Results:

Bought VIRTUAL on November 7 at $580M, captured full move. Added AIXBT November 14, GAME November 21. Sold VIRTUAL December 3 at $1.7B (near peak). Transaction count: 28 trades across three tokens. Execution costs: ~$420 in gas + $850 in spreads = $1,270. Gross gain: $12,400 on $5,000 position. Net gain after costs: $11,130 (224% return).

Weekly Rebalancing Results:

Bought VIRTUAL on November 11 rebalance at $820M (missed first 41% but captured 120%). Added AIXBT November 18, GAME November 25. Sold VIRTUAL December 2 rebalance at $1.65B. Transaction count: 4 trades. Costs: ~$80 in gas + $120 in spreads = $200. Gross gain: $10,100. Net after costs: $9,900 (198% return).

Monthly Rebalancing Results:

Bought VIRTUAL on December 1 rebalance at $1.5B (missed entire run-up). Next rebalance: January 1, likely selling at a loss. Result: Net loss of -$670 (-13%).

Verdict: Weekly captured 89% of daily's gross gains at 16% of transaction costs. Monthly missed the move entirely and bought at the worst time.

Case Study 2: Mean Reversion Capture (February 2024)

Rebalancing isn't just about capturing pumps—it's about systematically taking profits and reaccumulating during dips.

February 2024 Bitcoin Rally:

  • February 1: BTC at $43k, 38% of top-100 market cap
  • February 15: BTC at $52k (+21%), 44% of top-100
  • February 29: BTC at $61k (+42%), 46% of top-100

No Rebalancing: Your BTC position grew from 38% to 46%. When BTC corrected to $56k, your overweight position amplified losses. Weekly rebalancing: Rebalanced from 39% to 38%, selling $1k at $44k, then from 42% to 38%, selling $4k at $49k, and so on, systematically capturing profits during the rally.

This approach reduces downside risk and allows more capital to stay allocated to outperforming assets during consolidation.

Token Metrics: The intelligence behind optimal timing. Automated weekly rebalancing reduces emotional bias, captures sustained moves, and maintains disciplined risk management.

Choosing weekly rebalancing is one thing. Executing it systematically is another. Token Metrics has built the infrastructure to make weekly rebalancing effortless for TM Global 100 Index holders.

Automated Rebalance Execution

Every Monday at 00:00 UTC, Token Metrics' rebalancing engine:

  • Queries current market caps for all cryptocurrencies
  • Determines top-100 ranking using Token Metrics' proprietary data feeds
  • Calculates optimal weights based on market-cap proportions
  • Identifies required trades (buys, sells, weight adjustments)
  • Executes transactions via optimized smart contract batching
  • Updates holdings in real-time treemap and table views
  • Logs all transactions with timestamps, quantities, and fees

Users wake up Monday morning to updated portfolios—no action required.

Smart Execution Optimization

Token Metrics doesn't just rebalance mechanically. The platform's AI-powered execution algorithms optimize:

  • Slippage Minimization: Orders split across multiple liquidity sources (DEXs, aggregators) to minimize price impact
  • Gas Optimization: Transactions batched into single operations where possible, reducing network fees by 40-60%
  • Timing Within Window: Rebalances execute during optimal liquidity windows (avoiding thin overnight Asian hours)
  • Tax Efficiency: Where regulations permit, holding period awareness minimizes short-term capital gains

This sophisticated execution infrastructure—developed by Token Metrics as the leading crypto analytics platform—ensures that weekly rebalancing delivers theoretical benefits in practice, not just on paper.

Regime Switching + Weekly Rebalancing

TM Global 100 combines two mechanisms:

  • Weekly Rebalancing: Updates constituents and weights every Monday, maintaining optimal top-100 exposure
  • Regime Switching: Moves entire portfolio between crypto and stablecoins based on Token Metrics' market signals (happens as needed, not on schedule)

These work together seamlessly. During bullish regimes, weekly rebalancing optimizes exposure. When signals turn bearish, the entire portfolio exits to stablecoins—no more rebalancing until bullish signals return.

Example Flow: Weeks 1-8: Bullish regime, weekly rebalancing maintains top-100; Week 9: Market signals turn bearish, full exit to stablecoins; Weeks 10-14: Bearish regime, no rebalancing; Week 15: Bullish signals return, re-enter top-100. This dual approach provides both optimization and protection.

The Transparency & Cost Advantage

Token Metrics built TM Global 100 with radical transparency around rebalancing:

  • Pre-Rebalance Notification: Alerts 12 hours before Monday rebalances
  • Transaction Logs: Fully documented execution details
  • Holdings Updates: Treemap and table update in real-time
  • Strategy Explanation: Methodology page details reasons for changes

This transparency lets users verify that rebalancing follows stated rules—critical for trust in automated systems. Traditional index providers show "current holdings" but rarely document what changed and why. Token Metrics exposes everything.

Cost Preview & Efficiency

Projected rebalancing costs for TM Global 100:

  • Annual Platform Fee: 1.5-2.0% (pro-rated daily)
  • Weekly Gas Fees: ~$20 Ă— 52 = $1,040 annually
  • Trading Spreads: ~0.3% per rebalance Ă— 52 = 15.6% (actual ~8-12%) due to optimized execution
  • Total Annual Cost: ~10-14% in worst-case scenario, typically 6-9%

This is competitive compared to manual weekly, daily, or monthly rebalancing approaches which often incur higher costs or worse performance drift. Weekly systematic rebalancing via Token Metrics ensures consistent results with institutional-grade execution.

Decision Framework: Is Weekly Right For You?

Weekly rebalancing makes sense if:

  • You want systematic exposure to top-100 crypto
  • You value optimization without micromanagement
  • You understand that execution costs are an investment in accuracy
  • You trust data-driven timing over emotional decisions
  • You lack the time/infrastructure for manual weekly rebalancing

Consider alternatives if:

  • You hold fewer than 15 positions (manual rebalance manageable)
  • You have multidecade horizons where short-term drift is irrelevant
  • You prefer concentrated bets over diversification
  • You have institutional infrastructure with lower costs
  • You enjoy active management as a hobby

For most investors seeking broad crypto exposure, systematic weekly rebalancing offers an optimal balance of precision, cost-efficiency, and operational simplicity.

Conclusion: Discipline Over Frequency

The best rebalancing frequency isn't about minimizing costs or maximizing accuracy in isolation—it's about finding the optimal tradeoff and sticking to it. Daily rebalancing captures more but costs too much; monthly rebalancing saves costs but drifts too far; quarterly is too slow for crypto markets. Weekly rebalancing hits the "sweet spot": it captures sustained moves that truly matter, avoids daily noise, and remains feasible through automation. Token Metrics' TM Global 100 implements this optimal schedule with institutional-grade execution and transparency, making portfolio discipline automatic, regardless of market sentiment. In fast-moving crypto markets, timing matters more than you think. Weekly rebalancing proves that you don’t need perfect daily precision—you just need consistent discipline.

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AI Agents in Minutes, Not Months
Real-time prices, trading signals, and on-chain insights all from one powerful API.
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Token Metrics Team
Token Metrics Team

Recent Posts

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.

Research

Fundamental Grade Crypto API: Real Crypto Fundamentals in One Score

Token Metrics Team
3
MIN

Most traders chase price action; Fundamental Grade Crypto API helps you see the business behind the token—community traction, tokenomics design, exchange presence, VC signals, and DeFi health—consolidated into one score you can query in code. In a few minutes, you’ll fetch Fundamental Grade, render it in your product, and ship a due-diligence UX that drives trust. Start by grabbing your key at the Get API Key page, 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 to fetch Fundamental Grade from /v2/fundamental-grade for any symbol (e.g., BTC).

  • Optional curl to smoke-test your key in seconds.
  • A drop-in pattern to display the grade + key drivers in dashboards, screeners, and research tools.

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?

It’s an opinionated synthesis of multiple inputs—not financial advice. Historical studies can inform usage, but past performance doesn’t guarantee future results. Always layer risk management and testing.

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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