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Understanding Proprietary Market Signals: How Crypto Indices Detect Bull and Bear Markets

Learn how crypto indices and proprietary market signals, including AI-powered insights from Token Metrics, help detect bull and bear markets for risk-conscious investors.
Token Metrics Team
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In the fast-paced and often unpredictable world of cryptocurrency, understanding how crypto indices detect bull and bear markets is crucial for investors aiming to maximize profits and minimize losses. Unlike traditional financial markets, where a simple 20% price drop might signify a bear market, the crypto market is far more volatile, with frequent sharp price fluctuations that do not necessarily indicate a shift in market regime. This volatility demands more sophisticated tools that analyze a wide range of data points, enabling investors and traders to distinguish genuine bull and bear markets from short-term noise.

Traditional indicators commonly used in traditional finance often fall short when applied to digital assets. To address this challenge, proprietary market signals have emerged as a useful tool that leverages advanced analytics, including artificial intelligence and on-chain data, to detect market regime shifts with greater accuracy. This article explores the limitations of legacy indicators, the architecture behind proprietary signals, and how platforms like Token Metrics are revolutionizing the way market phases are identified and acted upon.

The Inadequacy of Traditional Indicators

Many newcomers to crypto rely heavily on legacy technical indicators such as the Relative Strength Index (RSI), Moving Average Convergence Divergence (MACD), and simple moving averages. These tools have a long history in traditional markets, but they exhibit critical weaknesses when applied to the crypto market cycle.

The Lagging Problem

Traditional indicators are inherently backward-looking. For example, RSI may only indicate that Bitcoin is "overbought" after a significant rally has already occurred, while MACD crossover signals often lag behind actual trend changes by days. This delay means that by the time traders receive warnings, institutional investors and experienced traders have often already adjusted their positions. The widely cited 200-day moving average (MA) is a classic example: it requires 200 days of data to calculate, making it slow to react to rapid market shifts. When Bitcoin’s price dropped below the 200-day MA in March 2025, many declared a bear market, but this signal came too late to capture the onset of the downturn effectively.

The False Signal Epidemic

Crypto's notorious volatility generates frequent false alarms with traditional metrics. A 20% price drop—enough to define a bear market in stocks—can be a routine correction or consolidation in crypto, occurring without a genuine regime change. Bitcoin, for instance, can experience significant price trends downward during an overall bull market and then resume its upward momentum. Relying on these traditional signals often results in premature selling during temporary bear phases or missed opportunities during early bull phases.

Single-Factor Blindness

Legacy indicators typically focus on a single dimension, usually price action. They overlook critical factors like on-chain activity, wallet flows, network fundamentals, trading volume, market sentiment, liquidity, and broader macroeconomic influences such as interest rates or the wider economy. This narrow view produces an incomplete and sometimes misleading picture of the market’s health.

The Architecture of Proprietary Market Signals

Proprietary market signals represent a significant evolution beyond traditional tools by incorporating three key innovations: multi-factor analysis, machine learning adaptation, and signal confluence validation.

Multi-Factor Analysis

Advanced systems evaluate dozens of independent data sources spanning multiple categories to build a nuanced understanding of market conditions:

By integrating these diverse data points, proprietary systems develop a holistic view of the crypto market, enabling more accurate detection of bull or bear market regimes.

Machine Learning Adaptation

Unlike static technical indicators, the most sophisticated proprietary systems employ machine learning models that continuously learn from historical outcomes. These AI-powered models analyze thousands of past market setups to identify which combinations of factors preceded genuine breakouts or downturns. This adaptability is critical because the crypto market cycle evolves rapidly; patterns effective in 2020 may not hold in 2025 as the market matures and institutional participation increases.

By constantly refining their algorithms based on new data, these systems maintain their effectiveness across different market phases, including bull markets, bear markets, and sideways consolidations.

Signal Confluence

A defining feature of proprietary signals is the requirement that multiple independent indicators align before a signal is triggered. Instead of reacting to any single metric crossing a threshold, these systems wait for a confluence of price action, volume patterns, on-chain metrics, and sentiment indicators to confirm a setup.

This multi-factor confirmation dramatically reduces false positives. When several data points simultaneously indicate bullish conditions, the probability of a true bull market increases significantly compared to relying on one indicator alone. The same applies to detecting bear markets, where combined signals of selling pressure, declining volume, and negative sentiment provide stronger evidence of a downturn.

Token Metrics: Setting the Standard for Market Intelligence

Token Metrics has emerged as a leader in applying proprietary signal technology to the crypto space, offering what many consider the industry's most sophisticated market detection system. As a cryptocurrency investment research firm leveraging AI and machine learning, Token Metrics analyzes over 6,000 projects, processing more data points and generating more accurate signals than most competitors.

The Token Metrics Signal Architecture

Token Metrics’ Bullish and Bearish Signals operate through a multi-layered system that integrates over 80 data points per token. These include price momentum shifts across various timeframes, higher trading volumes and anomalies, interactions with support and resistance levels, on-chain activity strength, whale wallet flow patterns, social sentiment analysis, and liquidity and order book depth.

Importantly, signals are only triggered when multiple critical factors confirm the setup, following the signal confluence approach that eliminates many false alarms common in traditional methods.

AI-Powered Learning

What sets Token Metrics apart is its continuous machine learning-driven refinement. The platform’s AI models train on outcomes, not just patterns, learning which factor combinations consistently lead to real breakouts or downturns. This outcome-based training ensures the system remains responsive to evolving market dynamics, such as increased institutional adoption and the rise of tokenized real-world assets.

Rigorous backtesting against extensive historical data ensures signals maintain accuracy across bull and bear cycles, avoiding deterioration that plagues static indicators.

Real-World Performance

Token Metrics regularly showcases its signal effectiveness with documented examples. For instance, a Bullish Signal on a lesser-known token, $DEGEN, preceded a +35% breakout despite minimal social media attention. Conversely, a Bearish Signal on $ENA helped users avoid a -22% correction. These examples illustrate how proprietary intelligence offers insights that can assist in managing market risks and opportunities.

How Regime Switching Works in Practice

The practical application of proprietary signals is evident in automated index strategies like Token Metrics’ TM Global 100 Index, which demonstrates systematic regime switching based on real-time market intelligence.

The Detection Process

Token Metrics continuously monitors its proprietary indicators, synthesizing price action, volume, on-chain metrics, sentiment data, and macroeconomic factors into comprehensive assessments of market regimes. When multiple signals align bullishly, the system classifies the environment as a bull market and maintains full exposure to the top 100 cryptocurrencies by market capitalization, capturing upside potential across the ecosystem.

When bearish signals dominate, indicating elevated risk and selling pressure, the index automatically shifts holdings into stablecoins like USDC, preserving capital during downturns often referred to as crypto winter.

The Advantage Over Manual Trading

Manual market timing is challenging for individual investors who must juggle numerous indicators, overcome emotional biases like fear and greed, and execute complex portfolio adjustments efficiently. Proprietary systems remove these obstacles by making emotionless, data-driven decisions automatically. This disciplined approach helps avoid panic sells during bottoms and impulsive buys at tops, common pitfalls for many traders.

Transparency Without Complexity

While the underlying AI and multi-factor models are complex, Token Metrics maintains transparency by providing users with a Market Gauge showing current regime assessments, interactive portfolio visualizations, detailed transaction logs, and performance analytics. This openness fosters trust while safeguarding the proprietary nature of the algorithms.

Beyond Binary: Understanding Market Nuance

Sophisticated proprietary systems recognize that markets are not simply in a binary bull or bear state but operate along a spectrum with varying conviction levels. Token Metrics’ signals can indicate conditions ranging from "strong bullish" to "strong bearish," allowing for nuanced position sizing and risk management.

During strong bullish phases, indices maintain maximum exposure to volatile assets and emerging projects with strong demand. In neutral or uncertain periods, exposure is moderated, and in strong bearish environments, defensive allocations to stablecoins are preferred. This approach helps to optimize returns and manage risk effectively.

The Competitive Moat of Proprietary Intelligence

The significance of proprietary signals lies in the information advantage they offer. Investors who effectively detect regime shifts early can capitalize on opportunities before the broader market recognizes them. By the time mainstream media or lagging indicators declare a bear or bull market, those relying on shared signals may have missed the optimal entry or exit points.

Token Metrics invests heavily in AI infrastructure, data science, and comprehensive data gathering to sustain its competitive edge. Its ability to analyze billions of data points and adapt models swiftly provides insights difficult to replicate manually, democratizing sophisticated market detection for individual investors.

Practical Application for Investors

Understanding how crypto indices detect bull and bear markets is vital, but the key lies in how investors apply this knowledge:

The Future of Market Intelligence

As the crypto ecosystem matures, with innovations in DeFi, AI, and tokenized assets, the need for advanced analytics will intensify. Token Metrics plans to expand its proprietary signal technology with sector-specific indicators, enhanced natural language processing, integration with automated trading, and deeper on-chain health metrics to keep users at the forefront of market intelligence.

Conclusion: Intelligence as Competitive Advantage

In today’s complex financial landscape, accurately identifying market regimes is essential for systemic risk management and opportunity capture. Proprietary signals, powered by AI and multi-factor analysis, offer a comprehensive and adaptive approach that surpasses traditional indicators. Platforms like Token Metrics provide invaluable insights that help investors navigate volatile markets effectively. Recognizing regime shifts early and acting on them systematically can be the difference between sustained success and missed opportunities. Equip yourself with cutting-edge tools to stay ahead in the evolving crypto world.

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What are crypto indices?

Crypto indices are financial instruments that track the performance of a basket of digital assets, typically weighted by market capitalization or other criteria. These indices provide a broad overview of the cryptocurrency market, making it easier to monitor overall trends and benchmark returns without focusing on individual tokens.

How do proprietary signals differ from traditional indicators?

Proprietary signals integrate multiple data sources—including price action, on-chain data, sentiment, and macroeconomic factors—while leveraging machine learning and advanced analytics. Traditional indicators often rely on single-factor price analysis. By synthesizing diverse data, proprietary signals are better equipped to detect genuine market regime shifts and reduce false signals.

How does Token Metrics generate its market signals?

Token Metrics employs a multi-factor, AI-driven system that analyzes over 80 data points per token, including price dynamics, volume, on-chain activity, liquidity, and sentiment. Signals are only triggered when several independent metrics confirm the same market regime, ensuring robustness and reducing the risk of false alerts.

Can individual investors use proprietary market signals?

Yes. Platforms like Token Metrics provide access to proprietary signals, making advanced analytics available to retail investors. This democratizes the sophisticated tools once reserved for institutions and enables individuals to make more informed decisions and manage risk effectively.

How do crypto indices handle market volatility?

Advanced crypto indices employ real-time monitoring and regime-switching algorithms, allowing them to shift allocations—from full exposure to stablecoins—based on current market conditions. This adaptability helps manage risk and reduce the impact of extreme volatility commonly observed in the crypto market.

Disclaimer

This content is for informational and educational purposes only and should not be interpreted as financial advice, investment recommendations, or an offer to buy or sell any asset. The information provided is accurate to the best of our knowledge at the time of publication. Always conduct your own research and consult with a professional advisor before making investment decisions. Cryptocurrency markets are volatile, and past performance is not indicative of future results.

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About Token Metrics
Token Metrics: AI-powered crypto research and ratings platform. We help investors make smarter decisions with unbiased Token Metrics Ratings, on-chain analytics, and editor-curated “Top 10” guides. Our platform distills thousands of data points into clear scores, trends, and alerts you can act on.
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Recent Posts

Research

Regime Switching Explained: Why Smart Crypto Indices Move to Stablecoins

Token Metrics Team
6

The best trade in crypto isn't always a trade—sometimes it's knowing when to step aside. While traditional indices force you to ride every crash from peak to bottom, regime-switching indices take a smarter approach: participate when conditions warrant, preserve capital when they don't. This systematic method of moving between crypto exposure and stablecoins has become the defining feature of next-generation index products, with Token Metrics leading the implementation through data-driven market signals.

What Is Regime Switching?

Regime switching is a systematic investment approach that classifies market conditions into distinct states—typically "bullish" and "bearish"—then adjusts portfolio positioning accordingly. Unlike static indices that maintain constant exposure regardless of conditions, regime-switching strategies dynamically allocate between risk assets and defensive positions based on quantifiable signals.

In practice for crypto indices:

This isn't emotional market timing or gut-feel trading. It's rules-based risk management following consistent, transparent criteria. The decision to switch regimes comes from systematic signals, not fear or greed.

The Traditional Index Problem

Standard crypto indices like market-cap weighted baskets operate on a simple premise: buy the universe, hold forever, rebalance periodically. This works well in prolonged bull markets but fails catastrophically during extended bear cycles.

Real performance data illustrates the problem:

Net result: Investors who bought January 2021 and held through December 2022 saw minimal net gains despite experiencing a massive bull run. The issue isn't the bull market capture—traditional indices do fine when prices rise. The problem is forced participation during catastrophic drawdowns that destroy years of gains in months. A 75% drawdown requires a 300% gain just to break even.

Research across historical crypto cycles shows that systematic regime-switching approaches have historically reduced maximum drawdowns by 40-60% while capturing 70-85% of bull market upside—a compelling risk-adjusted return profile that buy-and-hold cannot match.

Why Stablecoins, Not Cash?

Regime-switching crypto indices face a unique constraint: they operate on-chain and must maintain 24/7 liquidity for instant redeployment. This makes stablecoins the optimal defensive asset for several critical reasons:

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The Signal Behind the Switch

The most critical component of regime-switching isn't the mechanism—it's the signal that triggers the switch. Token Metrics has built its reputation as a leading crypto analytics platform by developing sophisticated, data-driven market intelligence relied upon by over 50,000 traders and investors daily.

Token Metrics' Market Regime Detection employs a proprietary multi-factor model analyzing:

This synthesis results in a probabilistic assessment: whether the environment is bullish enough to risk capital or bearish enough to prioritize preservation.

Transparency is maintained by displaying current regime signals in real-time via visual market gauges, while proprietary model parameters and thresholds remain confidential to prevent strategic front-running.

Real Performance: When Regime Switching Matters Most

Regime-switching strategies excel during major bear markets that erode traditional portfolios:

Starting with $100,000 in November 2021, buy-and-hold would result in approximately $89,000 after recovering from -73%. The regime approach, with smaller drawdowns and better upside capture, could have grown the portfolio to around $152,000, emphasizing how avoiding large losses compounds benefits over time.

Implementation: How TM Global 100 Executes Switches

The TM Global 100 index automates regime switching with simplicity and transparency:

User Experience

Behind the Scenes

When signals turn bearish, the index:

Reversal when signals turn bullish follows the same systematic process.

The Cost of Switching

Every regime transition incurs costs:

Token Metrics estimates costs at approximately 0.8-1.5% per full switch, which becomes cost-effective when signals reliably avoid large drawdowns. Frequent whipsaws are mitigated by the model's stability factors, and projected costs are shown upfront for transparency.

Decision Framework: Is Regime Switching Right for You?

Consider regime switching if you:

Alternatively, if you have long-term horizons, believe markets are fully efficient, or prefer a buy-and-hold strategy, it might not suit you. Both approaches have their merits, but regime switching offers a balanced risk-adjusted profile for volatile crypto markets.

Where Research Meets Execution

Token Metrics has established itself as a leading crypto analytics platform by providing:

However, research alone isn't enough. The TM Global 100 index closes the gap between signal generation and automated execution—applying sophisticated regime logic seamlessly, enabling users to act on data-driven insights instantly with transparency and confidence.

Conclusion: Discipline Over Emotion

Regime switching removes emotional decision-making—often the hardest part of crypto investing. It systematically guides investors to participate during bull runs, protect capital during downturns, and avoid knee-jerk reactions to market volatility. This disciplined approach helps to sustain long-term growth while minimizing the pain of large drawdowns, demonstrating the evolution of systematic crypto investment strategies.

Click here to get early access to Token Metrics indices.

Research

Why Manual Crypto Portfolio Management Is Costing You Money (And Time)

Token Metrics Team
6

You're tracking 50+ tokens across three exchanges, updating your rebalancing spreadsheet every weekend, and second-guessing every exit decision at 2 AM. Sound familiar? Manual crypto portfolio management isn't just exhausting—it's expensive. Between missed rebalances, execution drag, and behavioral mistakes during volatility, DIY portfolio management quietly erodes returns before you see any market gains.

The data tells the story: investors who manually manage diversified crypto portfolios typically underperform comparable automated strategies by 12-18% annually, with 60% of that gap coming from operational inefficiency rather than market timing. If you're spending 10+ hours weekly maintaining positions, those hours have a cost—and it's higher than you think.

The Hidden Costs Destroying Your Returns

Time Drain: The 500-Hour Tax

Managing a diversified crypto portfolio demands constant vigilance. For investors holding 20+ positions, the weekly time investment breaks down to approximately:

  • Market monitoring: 5-8 hours tracking prices, news, and on-chain metrics
  • Rebalancing calculations: 2-3 hours determining optimal weights and required trades
  • Order execution: 3-5 hours placing trades across multiple platforms
  • Record keeping: 1-2 hours logging transactions for tax reporting
  • Research updates: 3-5 hours staying current on project developments

That's 14-23 hours weekly, or 728-1,196 hours annually. At a conservative $50/hour opportunity cost, you're spending $36,400-$59,800 in time value maintaining your portfolio. Even if you value your time at minimum wage, that's still $10,000+ in annual "sweat equity" that automated solutions eliminate.

Execution Drag: Death by a Thousand Trades

Small trades erode portfolios through accumulated friction. Every manual rebalance across a 50-token portfolio requires dozens of individual transactions, each incurring:

  • Trading fees: 0.1-0.5% per trade (average 0.25%)
  • Bid-ask spreads: 0.2-0.8% depending on liquidity
  • Slippage: 0.3-1.2% on smaller cap tokens
  • Gas fees: $2-50 per transaction depending on network congestion

For a $100,000 portfolio rebalanced monthly with 40 trades per rebalance, the costs add up:

  • Average cost per trade: ~$100
  • Monthly execution drag: $4,000
  • Annual execution drag: $48,000 (48% of portfolio value)

The smaller your individual trades, the worse the ratio becomes. A $500 rebalancing trade on a low-liquidity altcoin might pay $25 in fees—a 5% instant loss before any price movement.

Automated indices solve this. TM Global 100, Token Metrics' rules-based index, consolidates 100 individual positions into a single transaction at purchase, with weekly rebalances executed through optimized smart contract batching. Users typically save 3-7% annually in execution costs alone compared to manual approaches.

Behavioral Mistakes: Your Worst Enemy Is in the Mirror

Market psychology research shows that manual portfolio managers tend to make predictable, costly mistakes:

  • Panic selling during drawdowns: When Bitcoin drops 25% in a week, can you stick to your exit rules? Many override their plans during high volatility, often selling near local bottoms.
  • FOMO buying at peaks: Tokens up 300% in a week attract chase behavior, with managers entering after the movement is mostly over.
  • Rebalancing procrastination: Putting off rebalancing leads to drift, holding too much of past winners and missing new opportunities.

Token Metrics' systematic approach removes emotion from the equation. The TM Global 100 Index follows a transparent ruleset: hold the top 100 tokens by market cap during bullish phases, shift to stablecoins during bearish cycles, and rebalance weekly—eliminating emotional override and procrastination.

Missed Rebalances: Drifting Out of Position

Market cap rankings shift constantly. A token ranked #73 on Monday might hit #95 by Friday, or surge to #58. Without systematic rebalancing, your portfolio becomes a collection of recent winners or dumpers.

In Q3 2024, Solana ecosystem tokens surged while Ethereum DeFi tokens consolidated. Manual managers who missed weekly rebalances held too much ETH and insufficient SOL exposure. The result: 15-20% underperformance compared to systematically rebalanced portfolios. Data from Token Metrics shows that weekly rebalancing outperforms monthly or quarterly approaches by 8-12% annually.

Tax Reporting Nightmares

Every trade creates a taxable event. Manual managers executing over 200 trades yearly face:

  • Hours spent compiling transaction logs
  • Reconciliation across multiple exchanges
  • Cost-basis tracking for numerous lots
  • High professional accounting fees ($500-2,000+)

Automated solutions like Token Metrics provide transparent transaction logs for each rebalance, simplifying tax reporting and reducing accounting costs.

The Token Metrics Advantage: Research Meets Execution

Token Metrics has established itself as a leading crypto analytics platform, supporting over 50,000 users with AI-powered token ratings, market regime detection, portfolio optimization tools, and trading signals. But analysis alone isn't enough—implementation is crucial.

TM Global 100 Index bridges this gap. It turns research into actionable, tradeable products by automating rebalancing based on Token Metrics' signals and methodology. One click replaces hours of manual work, following a validated systematic approach.

Automation Without Compromise

The best automation is transparent. TM Global 100 offers:

  • Rules-Based Discipline: Bull markets—hold top 100 tokens; bear markets—move to stablecoins
  • Weekly rebalancing every Monday
  • Full methodology disclosure
  • One-Click execution via embedded self-custodial wallet
  • Real-time market insights and holdings visualization
  • Transaction logs with fees and timestamps

This streamlined process allows users to rapidly execute disciplined rebalancing, saving countless hours and increasing operational efficiency while maintaining asset control.

Decision Framework: When to Automate

Automation suits investors who:

  • Hold 15+ tokens and find rebalancing burdensome
  • Miss optimal rebalancing windows due to time constraints
  • Have experienced emotional trading decisions during volatility
  • Spend over 5 hours a week on portfolio management
  • Want broad exposure without manual tracking

Manual management may be suitable for those with fewer positions, active trading infrastructure, or tactical strategies. For most diversified portfolios, automation enhances efficiency and reduces operational errors.

The Compound Effect of Efficiency

Small inefficiencies compound over time. Over five years, a $50,000 portfolio managed manually with a 12% annual return minus 4-2-1% losses yields roughly a 5% net return, ending at about $63,814. A systematic approach with optimizer integration, zero behavioral errors, and regular rebalancing can attain a 13% net return, reaching approximately $92,246—an increase of over $28,000, not counting time saved.

Conclusion: Time Back, Returns Up

Manual crypto portfolio management made sense when portfolios were small and concentrated. Today’s diversified sets require operational discipline to prevent erosion of returns due to execution drag, missed rebalances, and emotional mistakes. Token Metrics built TM Global 100 to turn research into automated, transparent execution, reclaim your time, and boost portfolio discipline—without sacrificing control.

Research

Moonshots API: Discover Breakout Tokens Before the Crowd

Token Metrics Team
5

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.

What You’ll Build in 2 Minutes

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

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

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 enables users to 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 to smooth traffic and avoid duplicates. Always make notifications idempotent.

Production Checklist

Use Cases & Patterns

Next Steps

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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