Research

How Do I Prioritize Features When Building a Web3 MVP?

Building a Web3 minimum viable product presents unique challenges that traditional software development doesn't face. You're not just creating an application—you're architecting an ecosystem where technology, economics, and community coordination intersect.
Talha Ahmad
5 min
MIN

Building a Web3 minimum viable product presents unique challenges that traditional software development doesn't face. You're not just creating an application—you're architecting an ecosystem where technology, economics, and community coordination intersect. With blockchain's immutable nature and the complexity of decentralized systems, knowing which features to prioritize can mean the difference between launching a successful project and wasting months building the wrong things.

In 2025, Web3 MVP development requires more than technical skills. It demands speed, clarity, and the ability to learn from real users fast. This comprehensive guide will walk you through proven prioritization strategies specifically designed for Web3 projects, helping you make informed decisions that maximize your chances of achieving product-market fit.

Understanding the Web3 MVP Challenge

A minimum viable product is a simplified version of your product that includes only the core features necessary to meet the needs of early adopters. In Web3, an MVP allows startups to explore decentralized applications and blockchain functionalities without committing extensive resources upfront.

The stakes are uniquely high in Web3. Smart contract immutability means you can't easily patch bugs after deployment. Scope creep poses existential dangers since adding features often requires complete system overhauls rather than simple updates. Security vulnerabilities aren't just reputational problems—they're existential threats that can drain millions in seconds.

Before diving into feature prioritization, understand what makes Web3 MVP development different from traditional software. You're simultaneously solving technical problems, fostering communities, creating economic systems through tokenomics, and building foundational infrastructure for a decentralized internet.

The Foundation: Identifying Core Value

Before applying any prioritization framework, you must answer one fundamental question: What specific problem does your Web3 project solve, and for whom?

Conduct thorough market research to identify your target audience, assess viability, gauge problem-solving potential, and devise strategies to align your product with customer requirements. In crypto, understanding the optimal approach for delivering value and effectively communicating benefits is paramount.

Define precise, unambiguous metrics that will quantitatively evaluate the efficacy of your launch. For Web3 applications, this might include the number of wallet connections, transaction volume, total value locked, or active community members.

Successful projects like Uniswap demonstrate this principle perfectly. Starting in 2018 with under $50,000, no CEO, and no marketing blitz—just a smart contract on Ethereum and a vision for trustless token swaps—Uniswap focused exclusively on solving one problem: enabling decentralized token exchanges. By 2025, it has surpassed $3 trillion in total trading volume by maintaining laser focus on core functionality before expanding.

The MoSCoW Prioritization Framework for Web3

The MoSCoW method creates a hierarchy for your feature requests based on their importance. Developed by Dai Clegg while working at Oracle in 1994, this framework divides features into four unambiguous categories, particularly useful in conjunction with fixed timeframes.

Must Have (M): These are non-negotiable requirements to launch your product. An easy way to identify Must Have features is asking: "What happens if this requirement isn't met?" If the answer is "cancel the project," it's a Must Have.

For a Web3 DEX, Must Haves might include wallet connection, token swapping functionality, basic liquidity pool creation, and essential smart contract security audits. These are the features that define your product's core value proposition.

Should Have (S): These features are important but not immediately critical. They significantly enhance user experience and can be included in the first release if resources permit, but the product can launch without them.

For our DEX example, Should Haves might include advanced trading features like limit orders, portfolio tracking, or multi-chain support for additional networks beyond your primary blockchain.

Could Have (C): These are desirable enhancements that would improve user satisfaction but aren't necessary for launch. Often called "nice-to-haves," they're the first to be removed if timelines or resources become constrained.

Could Have features might include advanced analytics dashboards, social features, gamification elements, or integrations with other DeFi protocols.

Won't Have (W): These features are explicitly excluded from the current iteration. They may be reconsidered for future releases, but are intentionally deferred to maintain focus and prevent scope creep.

The MoSCoW framework ensures you build a genuine minimum viable product by prioritizing Must Have features while creating contingency within requirements. This approach is particularly effective in Web3 where resource constraints and technical complexity demand ruthless prioritization.

Value vs. Complexity Analysis

Another powerful prioritization technique for Web3 MVPs involves plotting features on a two-dimensional matrix: Value against Complexity (or Effort).

Value represents the benefit your customers and business receive from a feature. Does it alleviate customer pain points? Will it drive user adoption? Does it strengthen your competitive position? In Web3, value might also include community building potential, network effects, or tokenomic alignment.

Complexity encompasses what it takes for your organization to deliver the feature: development time, required expertise, infrastructure costs, security audit requirements, gas optimization needs, and ongoing maintenance burden.

Plot each potential feature on a 2x2 grid:

High Value, Low Complexity: These are your quick wins. Prioritize these features first—they deliver maximum impact with minimal investment. Examples might include integrating a widely-used wallet provider or implementing standard ERC-20 token support.

High Value, High Complexity: These are strategic initiatives that define your competitive advantage. Plan these carefully, break them into smaller deliverables, and build them after quick wins demonstrate traction. Examples might include novel AMM algorithms or cross-chain bridging infrastructure.

Low Value, Low Complexity: These are fill-in tasks suitable when waiting for dependencies or during low-activity periods. Don't let these distract from higher priorities.

Low Value, High Complexity: Avoid these entirely—they're resource drains that won't move the needle on user adoption or business success.

Community-Driven Prioritization in Web3

Unlike traditional software, Web3 projects succeed by building active communities from day one. Your community becomes both your testing ground and marketing engine, making community-driven prioritization essential.

Create dedicated Discord channels for testing feedback. Run community polls to validate feature prioritization decisions. Use governance forums to gather input on economic parameter changes and roadmap decisions.

This community involvement serves multiple purposes beyond feature validation. It provides extensive testing coverage no internal team could match, builds community investment in your project's success, and creates feedback loops that traditional QA processes miss.

However, maintain roadmap discipline. Active communities generate constant feature requests and suggestions. While input is valuable, allowing every suggestion to influence your roadmap leads to scope creep and delayed launches. Communicate priorities and reasoning regularly, and use governance forums to discuss potential changes transparently.

Security and Audit Prioritization

In Web3, security isn't just another feature—it's the foundation everything else builds upon. Hacks are existential threats, not merely reputational problems.

Design smart contracts as modular systems from the beginning. Use proxy patterns that allow upgrades while maintaining security. Plan clear upgrade paths for adding functionality without compromising existing security guarantees.

Prioritize comprehensive security audits for all smart contracts before mainnet deployment. Budget 15-25% of development resources for security reviews, formal verification where appropriate, and bug bounty programs. This isn't optional—it's the price of admission in Web3.

Progressive Decentralization Strategy

Don't try to build fully autonomous organizations from the start. Launch with appropriate centralized control and create clear roadmaps for progressive decentralization.

Your initial MVP should prioritize functionality and security over complete decentralization. Many successful Web3 projects launched with admin keys and centralized control, then gradually transferred governance to the community as the system matured and edge cases were addressed.

Premature decentralization often leads to governance paralysis, inability to respond to emergencies, and security vulnerabilities. Plan your decentralization roadmap as carefully as your feature roadmap.

Real-World Data: Learning from Token Metrics

Token Metrics exemplifies smart feature prioritization in Web3 analytics platforms. Rather than trying to build every possible crypto analysis tool simultaneously, Token Metrics focused on core value propositions first: AI-powered token ratings, smart contract audits, and comprehensive market analytics.

As the premier crypto trading and analytics platform, Token Metrics demonstrates how strategic feature prioritization creates competitive advantages. The platform started with essential Must Have features—reliable data feeds, AI rating algorithms, and intuitive interfaces—before expanding to Should Have capabilities like automated trading indices and cross-chain analytics.

In March 2025, Token Metrics launched integrated on-chain trading, transforming from an analytics platform into an end-to-end solution. This strategic expansion came only after establishing market leadership in analytics, demonstrating smart sequencing of high-value features.

Token Metrics' approach illustrates several key prioritization principles for Web3 builders:

Start with data quality and reliability as non-negotiable Must Haves. Without accurate, real-time blockchain data, no analytics features matter.

Build AI-powered insights as differentiators once core data infrastructure is solid. Token Metrics' Trader Grades (0-100) and Investor Grades provide unique value that competing platforms lack.

Layer on convenience features like integrated trading only after achieving product-market fit with core analytics. This sequencing prevents premature complexity while building toward a comprehensive platform.

Continuously gather user feedback to validate feature priorities. Token Metrics evolved from pure analytics to trading integration based on customer requests for a direct path from research to execution.

For Web3 builders, Token Metrics' journey offers valuable lessons. Prioritize features that deliver immediate, measurable value to early adopters. Build trust through reliability and security before expanding functionality. Listen to your community but maintain roadmap discipline. Sequence features strategically, ensuring each addition builds on proven foundations.

AI-Assisted Prioritization in 2025

Web3 development in 2025 increasingly leverages AI tools for smarter feature prioritization. AI-driven user analytics can predict which features will drive adoption, while AI-generated feature scoring helps teams make data-driven decisions faster.

Use AI to analyze competitor offerings, identify gaps in the market, and predict user behavior patterns. These tools shave weeks off development timelines and reduce human bias in prioritization decisions.

However, AI should inform human judgment, not replace it. Your understanding of community needs, technical constraints, and strategic vision remains irreplaceable.

Practical Implementation Timeline

A realistic Web3 MVP development timeline might look like:

Week 1-2: Problem interviews and hypothesis development. Validate that your identified problem is real and your proposed solution resonates with potential users.

Week 3-4: No-code or low-code prototype to validate user flow. Collect 20-50 user responses before writing a single line of smart contract code.

Week 5-8: Build lean MVP prototype focusing exclusively on Must Have features. Instrument analytics and set clear KPIs for measuring success.

Week 9-12: Iterate based on user data. Deploy to testnet and expand testing to broader community. Begin security audit processes for smart contracts.

Week 13-16: Security hardening, audit remediation, and mainnet deployment preparation.

This aggressive timeline requires ruthless feature prioritization. Every feature not classified as Must Have gets deferred—no exceptions.

Common Prioritization Mistakes to Avoid

Overloading Must Haves: Teams often classify too many features as Must Haves, leading to extended timelines and resource depletion. Be ruthless—if launching without it won't invalidate your core value proposition, it's not truly a Must Have.

Ignoring technical debt: Choosing quick implementations that create maintenance nightmares undermines long-term success. Balance speed with code quality, especially for immutable smart contracts.

Feature parity battles: Trying to match every competitor feature leads to unfocused products that don't excel at anything. Identify your unique value proposition and double down.

Neglecting tokenomics: In Web3, your economic model isn't just a fundraising mechanism—it's a core product feature requiring the same design rigor as smart contracts. Poor tokenomics kills promising projects.

Building in isolation: Launching without community building leads to products nobody uses. Start community engagement on day one, before you have a product to show.

Conclusion

Prioritizing features for a Web3 MVP requires balancing technical constraints, community needs, security requirements, and business objectives. The most successful projects use structured frameworks like MoSCoW alongside value-complexity analysis, while maintaining flexibility to adapt as they learn from real users.

Platforms like Token Metrics demonstrate how strategic feature prioritization creates sustainable competitive advantages. By focusing relentlessly on core value propositions—accurate data, AI-powered insights, and user-friendly interfaces—before expanding to integrated trading, Token Metrics built the premier crypto analytics platform through disciplined execution.

Your Web3 MVP journey begins with identifying the single most important problem you're solving and the minimum feature set required to solve it. Apply proven prioritization frameworks, engage your community early, prioritize security above all else, and plan for progressive decentralization. Most importantly, launch quickly to start the learning cycle—market feedback is the only true validation of your priorities.

The future belongs to Web3 builders who recognize they're not just creating products, but architecting ecosystems. Master feature prioritization, and you'll dramatically increase your odds of building something users actually want in the decentralized internet of tomorrow.

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Token Metrics API

Price Prediction API: Model Moon/Base/Bear Scenarios in Minutes

Sam Monac
5 min
MIN

Every trader wonders: how high could this token really go? The price prediction API from Token Metrics lets you explore Moon, Base, and Bear scenarios for any asset—grounded in market-cap assumptions like $2T, $8T, $16T and beyond. In this guide, you’ll call /v2/price-prediction, render scenario bands (with editable caps), and ship a planning feature your users will bookmark. Start by creating a key at Get API Key, then Run Hello-TM and Clone a Template to go live fast.

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What You’ll Build in 2 Minutes

  • A minimal script that fetches Price Predictions via /v2/price-prediction for any symbol (e.g., BTC, SUI).

  • A simple UI pattern showing Moon / Base / Bear ranges and underlying market-cap scenarios.

  • Optional one-liner curl to smoke-test your API key.

  • Endpoints to add next: /v2/tm-grade (quality context), /v2/trading-signals / /v2/hourly-trading-signals (timing), /v2/resistance-support (stop/target placement), /v2/quantmetrics (risk/return framing).

Why This Matters

Scenario planning beats guessing. Prices move, narratives change, but market-cap scenarios provide a common yardstick for upside/downside. With the price prediction API, you can give users transparent, parameterized ranges (Moon/Base/Bear) and the assumptions behind them—perfect for research, allocation, and position sizing.

Build investor trust. Pair scenario ranges with TM Grade (quality) and Quantmetrics (risk-adjusted performance) so users see both potential and risk. Add optional alerts when price approaches a scenario level to turn curiosity into action—without promising outcomes.

Where to Find 

Find the cURL request for Price Predictions in the top right corner of the API Reference. Use it to easily pull up predictions for your project.

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

Live Demo & Templates

  • Scenario Planner (Dashboard): Select a token, choose caps (e.g., $2T / $8T / $16T), and display Moon/Base/Bear ranges with tooltips.

  • Portfolio Allocator: Pair scenario bands with Quantmetrics and TM Grade to justify position sizes and rebalances.

  • Alert Bot (Discord/Telegram): Ping when price approaches a scenario level; link to the dashboard for context.

Fork a scenario planner or alerting template, plug in your key, and deploy. Confirm your environment by Running Hello-TM, and when you’re scaling users or need higher limits, review API plans.

How It Works (Under the Hood)

The Price Prediction endpoint maps market-cap scenarios to implied token prices, then categorizes them into Bear, Base, and Moon bands for readability. Your inputs can include a symbol and optional market caps; the response returns a scenario array you can plot or tabulate.

A common UX path is: Token selector → Scenario caps input → Prediction bands + context. For deeper insight, link to TM Grade (quality), Trading Signals (timing), and Support/Resistance (execution levels). This creates a complete plan–decide–act loop without overpromising outcomes.

Polling vs webhooks. Predictions don’t require sub-second updates; short-TTL caching and batched fetches work well for dashboards. If you build alerts (“price within 2% of Base scenario”), use a scheduled job and make notifications idempotent to avoid duplicates.

Production Checklist

  • Rate limits: Understand your tier caps; add client throttling and worker queues.

  • Retries & backoff: Exponential backoff with jitter for 429/5xx; capture request IDs.

  • Idempotency: De-dup alerts and downstream actions (e.g., avoid repeat pings).

  • Caching: Memory/Redis/KV with short TTLs; pre-warm popular symbols.

  • Batching: Fetch multiple symbols per cycle; parallelize within rate limits.

  • User controls: Expose caps (e.g., $2T/$8T/$16T) and save presets per user.

  • Display clarity: Label Bear/Base/Moon and show the implied market cap next to each price.

  • Compliance copy: Add a reminder that scenarios are not financial advice; historical outcomes don’t guarantee future results.

  • Observability: Track p95/p99 latency and error rate; log alert outcomes.

  • Security: Store API keys in secrets managers; rotate regularly.

Use Cases & Patterns

  • Bot Builder (Headless): Size positions relative to scenario distance (smaller size near Moon, larger near Bear) while confirming timing with /v2/trading-signals.

  • Dashboard Builder (Product): Add a Predictions tab on token pages; let users tweak caps and export a CSV of bands.

  • Screener Maker (Lightweight Tools): Rank tokens by upside to Base or distance to Bear; add alert toggles for approach thresholds.

  • PM/Allocator: Create policy rules like “increase weight when upside-to-Base > X% and TM Grade ≥ threshold.”

  • Education/Content: Blog widgets showing scenario bands for featured tokens; link to your app’s detailed page.

Next Steps

  • Get API Key — generate a key and start free.

  • Run Hello-TM — verify your first successful call.

  • Clone a Template — deploy a scenario planner or alerts bot today.

  • Watch the demo: VIDEO_URL_HERE

  • Compare plans: Scale confidently with API plans.

FAQs

1) What does the Price Prediction API return?
A JSON array of scenario objects for a symbol—each with a market cap and implied price, typically labeled Bear, Base, and Moon for clarity.

2) Can I set my own scenarios?
Yes, you can pass custom market caps (e.g., $2T, $8T, $16T) to reflect your thesis. Store presets per user or strategy for repeatability.

3) Is this financial advice? How accurate are these predictions?
No. These are scenario estimates based on your assumptions. They’re for planning and research, not guarantees. Always test, diversify, and manage risk.

4) How often should I refresh predictions?
Scenario bands typically don’t need real-time updates. Refresh on page load or at a reasonable cadence (e.g., hourly/daily), and cache results for speed.

5) Do you offer SDKs and examples?
REST is straightforward—see the JavaScript and Python snippets above. The docs include quickstarts, Postman collections, and templates—start with Run Hello-TM.

6) How do I integrate predictions with execution?
Pair predictions with TM Grade (quality), Trading Signals (timing), and Support/Resistance (SL/TP). Alert when price nears a scenario and route to your broker logic (paper-trade first).

7) Pricing, limits, and SLAs?
Start free and scale up as you grow. See API plans for rate limits and enterprise SLA options.

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Token Metrics API

Moonshots API: Discover Breakout Tokens Before the Crowd

Sam Monac
5 min
MIN

The biggest gains in crypto rarely come from the majors. They come from Moonshots—fast-moving tokens with breakout potential. The Moonshots API surfaces these candidates programmatically so you can rank, alert, and act inside your product. In this guide, you’ll call /v2/moonshots, display a high-signal list with TM Grade and Bullish tags, and wire it into bots, dashboards, or screeners in minutes. Start by grabbing your key at Get API Key, then Run Hello-TM and Clone a Template to ship fast.

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What You’ll Build in 2 Minutes

  • A minimal script that fetches Moonshots via /v2/moonshots (optionally filter by grade/signal/limit).

  • A UI pattern to render symbol, TM Grade, signal, reason/tags, and timestamp—plus a link to token details.

  • Optional one-liner curl to smoke-test your key.

  • Endpoints to add next: /v2/tm-grade (one-score ranking), /v2/trading-signals / /v2/hourly-trading-signals (timing), /v2/resistance-support (stops/targets), /v2/quantmetrics (risk sizing), /v2/price-prediction (scenario ranges).

Why This Matters

Discovery that converts. Users want more than price tickers—they want a curated, explainable list of high-potential tokens. The moonshots API encapsulates multiple signals into a short list designed for exploration, alerts, and watchlists you can monetize.

Built for builders. The endpoint returns a consistent schema with grade, signal, and context so you can immediately sort, badge, and trigger workflows. With predictable latency and clear filters, you can scale to dashboards, mobile apps, and headless bots without reinventing the discovery pipeline.

Where to Find 

The Moonshots API cURL request is right there in the top right of the API Reference. Grab it and start tapping into the potential!

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

Live Demo & Templates

  • Moonshots Screener (Dashboard): A discover tab that ranks tokens by TM Grade and shows the latest Bullish tags and reasons.

  • Alert Bot (Discord/Telegram): DM when a new token enters the Moonshots list or when the signal flips; include S/R levels for SL/TP.

  • Watchlist Widget (Product): One-click “Follow” on Moonshots; show Quantmetrics for risk and a Price Prediction range for scenario planning.

Fork a screener or alerting template, plug your key, and deploy. Validate your environment with Hello-TM. When you scale users or need higher limits, compare API plans.

How It Works (Under the Hood)

The Moonshots endpoint aggregates a set of evidence—often combining TM Grade, signal state, and momentum/volume context—into a shortlist of breakout candidates. Each row includes a symbol, grade, signal, and timestamp, plus optional reason tags for transparency.

For UX, a common pattern is: headline list → token detail where you render TM Grade (quality), Trading Signals (timing), Support/Resistance (risk placement), Quantmetrics (risk-adjusted performance), and Price Prediction scenarios. This lets users understand why a token was flagged and how to act with risk controls.

Polling vs webhooks. Dashboards typically poll with short-TTL caching. Alerting flows use scheduled jobs or webhooks (where available) to smooth traffic and avoid duplicates. Always make notifications idempotent.

Production Checklist

  • Rate limits: Respect plan caps; batch and throttle in clients/workers.

  • Retries & backoff: Exponential backoff with jitter on 429/5xx; capture request IDs.

  • Idempotency: De-dup alerts and downstream actions (e.g., don’t re-DM on retries).

  • Caching: Memory/Redis/KV with short TTLs; pre-warm during peak hours.

  • Batching: Fetch in pages (e.g., limit + offset if supported); parallelize within limits.

  • Sorting & tags: Sort primarily by tm_grade or composite; surface reason tags to build trust.

  • Observability: Track p95/p99, error rates, and alert delivery success; log variant versions.

  • Security: Store keys in a secrets manager; rotate regularly.

Use Cases & Patterns

  • Bot Builder (Headless):


    • Universe filter: trade only tokens appearing in Moonshots with tm_grade ≥ X.

    • Timing: confirm entry with /v2/trading-signals; place stops/targets with /v2/resistance-support; size via Quantmetrics.

  • Dashboard Builder (Product):


    • Moonshots tab with Badges (Bullish, Grade 80+, Momentum).

    • Token detail page integrating TM Grade, Signals, S/R, and Predictions for a complete decision loop.

  • Screener Maker (Lightweight Tools):


    • Top-N list with Follow/alert toggles; export CSV.

    • “New this week” and “Graduated” sections for churn/entry dynamics.

  • Community/Content:


    • Weekly digest: new entrants, upgrades, and notable exits—link back to your product pages.

Next Steps

  • Get API Key — generate a key and start free.

  • Run Hello-TM — verify your first successful call.

  • Clone a Template — deploy a screener or alerts bot today.

  • Watch the demo: VIDEO_URL_HERE

  • Compare plans: Scale confidently with API plans.

FAQs

1) What does the Moonshots API return?
A list of breakout candidates with fields such as symbol, tm_grade, signal (often Bullish/Bearish), optional reason tags, and updated_at. Use it to drive discover tabs, alerts, and watchlists.

2) How fresh is the list? What about latency/SLOs?
The endpoint targets predictable latency and timely updates for dashboards and alerts. Use short-TTL caching and queued jobs/webhooks to avoid bursty polling.

3) How do I use Moonshots in a trading workflow?
Common stack: Moonshots for discovery, Trading Signals for timing, Support/Resistance for SL/TP, Quantmetrics for sizing, and Price Prediction for scenario context. Always backtest and paper-trade first.

4) I saw results like “+241%” and a “7.5% average return.” Are these guaranteed?
No. Any historical results are illustrative and not guarantees of future performance. Markets are risky; use risk management and testing.

5) Can I filter the Moonshots list?
Yes—pass parameters like min_grade, signal, and limit (as supported) to tailor to your audience and keep pages fast.

6) Do you provide SDKs or examples?
REST works with JavaScript and Python snippets above. Docs include quickstarts, Postman collections, and templates—start with Run Hello-TM.

7) Pricing, limits, and enterprise SLAs?
Begin free and scale up. See API plans for rate limits and enterprise options.

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Token Metrics API

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

Sam Monac
5 min
MIN

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.

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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.

  • Endpoints to add next: /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

Live Demo & Templates

  • SL/TP Alerts Bot (Telegram/Discord): Ping when price approaches or touches a level; include buffer %, link back to your app.

  • Token Page Levels Panel (Dashboard): Show nearest support/resistance with strength badges; color the latest candle by zone.

  • TradingView Overlay Companion: Use levels to annotate charts and label potential entries/exits driven by Trading Signals.

Kick off with our quickstarts—fork a bot or dashboard template, plug your key, and deploy. Confirm your environment by Running Hello-TM. When you’re scaling or need webhooks/limits, review API plans.

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: VIDEO_URL_HERE

  • 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.

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